health data research futures

The future of healthcare data science: trends and predictions 

In recent years, the healthcare industry has witnessed a significant transformation with the integration of data science into its core. No way should we forget that healthcare is intended for people, not equipment, technologies, or places. To that end, data science in healthcare has not only revolutionized the way information is processed but has also paved the way for personalized and efficient patient care. 

Some interesting figures : According to Statista, as of the end of 2022 there were about 50,000 mHealth applications on the Apple App Store and 54,000 – on the Google Play Store . At the same time, research by McKinsey & Company says technology-driven innovation is likely to make $350 billion–$410 billion in annual value in the healthcare industry by 2025. 

As we look ahead, healthcare data science promises even more groundbreaking advancements. From the digitization of patient records to the advent of precision medicine, data science has become a driving force in shaping the future of healthcare. This article is about what to anticipate and what is popular when it comes to healthcare data science. 

• What are healthcare data science trends today? 

Let’s start with what data science is. It is an interdisciplinary field that utilizes data collection, cleaning, preparation, integration , visualization, big data analytics, deep learning, Natural Language Processing (NLP), and generally applies Machine Learning (ML) and Artificial Intelligence (AI) in scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data.

In simple terms, data science is like a detective work for information in the digital age. It involves collecting, cleaning, and analyzing data to uncover patterns and insights that can help solve problems or make better decisions. Data scientists use various technologies, tools, and techniques to make sense of large amounts of information, helping businesses make informed choices based on evidence and facts. It’s all about turning raw data into meaningful knowledge. 

In healthcare, data science is applied to various aspects, including clinical decision-making, patient care, drug discovery , public health, etc.. After we have made clear what data science is like, it’s high time to see some data-driven trends in healthcare. 

• Big data & precision medicine

One of the prominent trends in healthcare data science is the utilization of big data to drive precision medicine. Big data analytics allows healthcare institutions to analyze vast datasets, including genetic information, clinical records, and lifestyle data, to tailor treatments and interventions to individual patients. This approach is a departure from the traditional one-size-fits-all model, enabling more accurate diagnoses and targeted therapies.

Precision medicine, fuelled with big data, not only considers the genetic makeup of patients but also takes into account lifestyle factors, and environmental influences. This interplay between big data and precision medicine ensures that treatments are customized to the unique characteristics of each patient, leading to improved outcomes and reduced side effects. 

• Healthcare data visualization

Data visualization in healthcare is another pivotal trend that is gaining traction. As the volume and complexity of healthcare data continue to grow, effective data visualization tools become essential for healthcare professionals to derive meaningful insights. Advanced visualization techniques, such as interactive dashboards and 3D imaging, empower clinicians to interpret complex data more intuitively, aiding in diagnosis, treatment planning, and communication with patients. 

Visualization tools also play a crucial role in patient engagement. By presenting health information in a visually accessible format, patients can better understand their conditions, treatment options, and progress. This transparency fosters a collaborative relationship between healthcare providers and patients, ultimately contributing to better healthcare results. 

• Personalized healthcare through predictive analytics 

The shift towards personalized healthcare is a paradigm shift that data science has catalyzed. Personalized medicine involves customizing medical decisions, practices, and recommendations to individual patients. With the integration of data science, healthcare providers can leverage patient-specific data to predict disease risks, optimize treatment plans, and prescribe medications with greater precision.

The use of predictive analytics enables early identification of potential health issues, allowing for proactive interventions. For example, by analyzing a patient’s historical health data, algorithms can identify patterns indicative of certain conditions, prompting healthcare professionals to initiate preventive measures or recommend lifestyle changes. This proactive approach not only improves patient outcomes but also contributes to the overall efficiency of healthcare systems.  

• Some more promising applications of data science for healthcare
• AI in healthcare

Further to predictive analytics, AI takes up the baton since it excels there as well. AI is a game-changer in healthcare data science generally. AI applications, such as robotics, image recognition, or virtual health assistants, are transforming various aspects of healthcare delivery. For example, robot arms help in pharmacies by selecting, counting, packaging, or other AI-powered robot devices facilitate even inventing medications, assisting in surgeries, physical rehabilitation by adapting to patients’ movements and providing personalized and targeted therapy for improved recovery.

AI-driven diagnostic tools are enhancing the accuracy and speed of medical diagnoses. To sum up, AI is streamlining administrative tasks, automating routine processes, and reducing the burden on healthcare staff, allowing them to focus more on patient care. 

• Machine learning in healthcare 

Machine learning algorithms are increasingly being employed to analyze healthcare data and extract valuable insights. These algorithms can predict disease outcomes, identify high-risk patient populations, and optimize treatment plans. Being trained on vast datasets, they can recognize patterns in medical images, aiding in the early detection of diseases such as cancer. In addition to diagnostic applications, machine learning is also contributing to the development of personalized treatment strategies by considering individual patient characteristics, treatment responses, and genetic profiles. 

NLP, a subset of machine learning, is influencing how healthcare experts interact with and extract information from unstructured clinical notes, research papers, and patient records. By understanding and interpreting human language, NLP algorithms facilitate more efficient data retrieval and analysis, improving the overall decision-making process in healthcare. 

We, at GreenM, believe that AI and ML will be exponentially applied everywhere, in particular in the healthcare industry. 

• And what about digital health innovations?
• Future of telemedicine

The future of healthcare is undoubtedly intertwined with the evolution of telemedicine. Telemedicine, driven by digital health innovations, allows patients to receive medical consultations and treatment remotely, breaking down geographical barriers and improving access to healthcare services. 

The continued development of telemedicine technologies is expected to enhance the virtual patient-doctor interaction. Innovations such as augmented reality (AR) and virtual reality (VR) may enable more immersive and realistic telehealth experiences. These technologies have the potential to revolutionize medical education, training, and even surgical procedures performed remotely. 

• Future of telehealth 

Telehealth, a broader concept that encompasses telemedicine, is expanding its scope beyond virtual consultations. The future of telehealth includes remote patient monitoring, wearable devices that continuously collect and transmit health data, and smart home technologies that create a connected healthcare ecosystem. 

The integration of telehealth solutions into chronic disease management is a promising avenue. Patients with chronic conditions can benefit from continuous monitoring and real-time feedback, leading to prompt relief and treatment. The combination of telehealth and data science is pointing the way for a new era of patient-centered care, where individuals actively participate in managing their health with the support of digital technologies. 

• Conclusion: data science matters in healthcare 

The bottom line: The convergence of big data, precision medicine, AI, and digital health technologies is reshaping how healthcare is delivered, making it more personalized, efficient, and accessible. And it cannot be overstated how important data science in healthcare is. It holds the key to unlocking valuable insights from the vast amounts of healthcare data generated daily. By harnessing the power of data, healthcare professionals can make informed decisions, predict and prevent diseases, and provide individualized care that takes into account the unique characteristics of each patient. 

As we navigate the future of healthcare data science, it is crucial to prioritize ethical considerations, data security, data governance , and patient privacy. Balancing technological advancements with ethical practices ensures that data science continues to be a force for positive change in the healthcare industry. 

In the coming years, we can expect even more exciting developments as data science continues to push the boundaries of what is possible in healthcare. By staying at the forefront of these trends and embracing the potential of data science, the healthcare industry is poised to deliver better outcomes for patients worldwide. The future of healthcare is data-driven, and the possibilities are limitless. If you have no idea how to implement this all into your business, just contact us and we will be glad to answer any of your questions. See you soon! 

Serhii Tereshchenko

• Data Engineering
• Effective Operations

How to use a local ChatGPT in healthcare securely? 

• Better Services
• Cost Effective
• Smart Decisions

Hacks for reducing software development costs 

How to validate your product idea? 

Addressing data cleansing challenges with professional services 

Agile methodology pros and cons for software development 

8 best free data visualization tools and how to use them

Your turning point is so close.

Just contact our team, and we'll help your business get the most out of data and technology

We inform you that this site uses own, technical and third parties cookies to make sure our web page is user-friendly and to guarantee a high functionality of the webpage. By continuing to browse this website, you declare to accept the use of cookies.

Copyright © 2024 GreenM, Inc. All rights reserved.

As the national institute for health data science, HDR UK want to ensure that everyone who wants to build a career in this rapidly advancing field has access to high quality learning and training opportunities, whatever their background and location.

Our virtual learning platform Health Data Research Futures is a free and flexible learning platform offering expert led Continuing Professional Development (CPD) accredited courses designed for you.

How HDR Futures works

• Sign up for free
• Browse available CPD accredited courses
• Follow learning pathways at your own pace
• Create bite-size video playlists
• Gain certificates and badges
• View training offered by HDR UK’s partners

Find out more by visiting the Futures website or sign up and get started straight away.

• Lean on the go via easily digestible bitesize videos
• Gain hands on skills via facilitated workshops
• Learn direct from experts via online webinars
• Gain insights from real-world case studies
• Hear about upcoming events
• Discover resources to support your career

Sign up to the Futures newsletter

For more about HDR UK training and learning sign up to the monthly e-newsletter .

Get in touch

Contact the Capacity Building Team at [email protected] .

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• TECHNOLOGY FEATURE
• 03 May 2022

Unlocking the potential of health data to help research and treatments

• Jyoti Madhusoodanan 0

Jyoti Madhusoodanan is a science writer in Portland, Oregon.

You can also search for this author in PubMed   Google Scholar

Health-care workers check a patient’s electronic health records on a COVID-19 ward in Cremona, Italy. Credit: Marco Mantovani/Getty

For the gastrointestinal condition known as ulcerative colitis, some physicians recommend using a particular drug twice a day, others, three times. But which protocol is the best way to help people with the condition to avoid surgery? Instead of launching a clinical trial, Peter Higgins, a gastroenterologist at the University of Michigan at Ann Arbor, examined the data.

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

Nature 605 , 182-183 (2022)

doi: https://doi.org/10.1038/d41586-022-01205-0

Related Articles

• Medical research
• Research data

People who lack the immune protein TNF can still fight infection

News & Views 28 AUG 24

Fate induction in CD8 CAR T cells through asymmetric cell division

Article 28 AUG 24

Urgently clarify how AI can be used in medicine under new EU law

Correspondence 27 AUG 24

Molecular architecture of coronavirus double-membrane vesicle pore complex

Article 14 AUG 24

Long COVID lung damage linked to immune system response

News 18 JUL 24

Vaccines save lives: how can uptake be increased?

Editorial 09 JUL 24

A guide to the Nature Index

Nature Index 21 AUG 24

Has your paper been used to train an AI model? Almost certainly

News 14 AUG 24

Estonians gave their DNA to science — now they’re learning their genetic secrets

News 26 JUN 24

Postdoctoral Associate- Neuromodulation and Computational Psychiatry

Houston, Texas (US)

Baylor College of Medicine (BCM)

Postdoctoral Fellow

Modeling Autism Spectrum Disorders using genetically modified human stem cell-derived brain organoids and mouse models.

New York City, New York (US)

Weill Cornell Medical College

Scientific Visualization Developer

APPLICATION CLOSING DATE: September 29th, 2024   About the institute Human Technopole (HT) is an interdisciplinary life science research institute,...

Human Technopole

Faculty Positions Open, ShanghaiTech University

6 major schools are now hiring faculty members.

Shanghai, China

ShanghaiTech University

Faculty Positions in Neurobiology, Westlake University

We seek exceptional candidates to lead vigorous independent research programs working in any area of neurobiology.

Hangzhou, Zhejiang, China

School of Life Sciences, Westlake University

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Medical, health, and genomics

GigaPath: Whole-Slide Foundation Model for Digital Pathology

Research Forum 2 | Panel Discussion: Transforming the Natural Sciences with AI

Scaling early detection of esophageal cancer with AI

Microsoft Health Futures

Showing 1 – 10 of 1814 results

A Methodology for Using Large Language Models to Create User-Friendly Applications for Medicaid Redetermination and Other Social Services  

Sumanth Ratna, Bill Weeks , Juan M. Lavista Ferres , Aneesh Chopra, Mayana Pereira

International Journal of Public Health | August 2024

Large-scale pathology foundation models show promise on a variety of cancer-related tasks  

August 8, 2024  |  Kristen Severson , Philip Rosenfield

Microsoft researchers collaborated to release new pathology foundation models. Their report shows models benefit from diverse data, increased model size, and specialized algorithms to enhance the accuracy and applicability of cancer diagnosis and treatment.

Decoding the Human Brain – A Neurosurgeon’s Experience  

August 1, 2024  |  Pascal Zinn, Ivan Tashev

Dr. Pascal O. Zinn, MD, PhD, FAANS Dr. Zinn will discuss his experience leveraging state-of-the-art techniques for decoding the human brain during awake surgeries and complex brain tumor removals. Utilizing electrical brain stimulation, electrocorticography (ECoG)…

Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology  

Eric Zimmermann, Eugene Vorontsov, Julian Viret, Adam Casson, Michal Zelechowski, George Shaikovski, Neil Tenenholtz , Jimmy Hall , Thomas J. Fuchs, Nicolo Fusi , Siqi Liu, Kristen Severson

Maternal Obesity and Risk of Sudden Unexpected Infant Death  

Darren Tanner , Jan-Marino Ramirez, Bill Weeks , Juan M. Lavista Ferres , Edwin A. Mitchell

Jama Pediatrics | July 2024

Applied Data Scientist – Dragon Ambient eXperience (DAX)  

Posted : July 18, 2024 Location : France; Germany; Netherlands; Remote Research Area(s) : Artificial intelligence, Data platforms and analytics, Medical, health and genomics

Our team has an exciting opportunity for an Applied Data Scientist to expand our Dragon Ambient eXperience (DAX) product to international markets. DAX listens to doctor/patient conversations and writes highly accurate clinical summaries of the…

Jacaranda PROMPTS  

An AI enabled digital health solution to address delays in maternal care seeking While being amongst the most developed African countries, Kenya still has one of the world’s highest maternal mortality rates (51x that of the…

Research Focus: Week of June 24, 2024  

June 26, 2024  |  Xenofon Foukas , Bozidar Radunovic , Francis Y. Yan , Payod Panda , Lev Tankelevitch , Sean Rintel , Sudha Rao , Chris Brockett , Bill Dolan , Shruthi Bannur , Kenza Bouzid , Daniel Coelho de Castro , Anton Schwaighofer , Sam Bond-Taylor, Max Ilse , Fernando Pérez García , Valentina Salvatelli , Harshita Sharma , Julia Gong , Anja Thieme , Matthew Lungren , Javier Alvarez-Valle , Stephanie Hyland

In this issue: RENC makes 5G vRAN servers more energy efficient; CoExplorer uses AI to keep video meetings on track; Automatic bug detection in LLM-powered text-based games; MAIRA-2: Grounded radiology report generation.

Democratizing Protein Language Models with Parameter-Efficient Fine-Tuning  

Samuel Sledzieski, Meghana Kshirsagar , Minkyung Baek, Bonnie Berger, Rahul Dodhia , Juan M. Lavista Ferres

PNAS | June 2024

MAIRA-2: Grounded Radiology Report Generation  

Shruthi Bannur , Kenza Bouzid , Daniel Coelho de Castro , Anton Schwaighofer , Sam Bond-Taylor, Maximilian Ilse , Fernando Pérez-García , Valentina Salvatelli , Harshita Sharma , Felix Meissen, Mercy Ranjit, Shaury Srivastav, Julia Gong , Fabian Flack, Ozan Oktay, Anja Thieme , Matthew P Lungren , Maria Teodora Wetscherek, Javier Alvarez-Valle , Stephanie Hyland

MSR-TR-2024-18 | June 2024

Published by Microsoft

• Follow on X
• Like on Facebook
• Follow on LinkedIn
• Subscribe on Youtube
• Follow on Instagram
• Subscribe to our RSS feed

Share this page:

• Share on Facebook
• Share on LinkedIn
• Share on Reddit

Future of Health and Data Science

Advanced topics in genetics and genomics.

Technologies like CRISPR and stem cell therapies, and research such as those in the fields of epigenetics and biotechnology, are changing how we understand and develop solutions for medicine, biology, and agriculture. Dive into genetics and genomics with the cutting-edge research and technologies revolutionizing the health and medicine industries.

Applications of Machine Learning in Medicine

Embracing innovation is not just a trend - it's essential. By understanding how technologies like AI are impacting patient care and research, you can feel confident leading your work, your team, and your organization into the future of healthcare.

Artificial Intelligence in Healthcare

Explore the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. 

Clinical Trials: Design, Strategy, and Analysis

Whether you’re in a patient-facing role looking to implement research into your professional career or seeking to better communicate with colleagues in the lab, this course gives you the tools you need to find answers for your health and medicine-related inquiries. 

Digital Health Product Development

From mobile health apps and wearable devices, to telehealth, digital health records, and personalized medicine, digital technology is playing an increasingly large role in healthcare. Learn to identify unmet clinical needs, develop ideas into products, and bring those products to market.

Foundations of Genetics and Genomics

Gain the fundamental knowledge needed to understand how we can progress in our work targeting human health and disease, and prepare you to explore more advanced topics.

Machine Learning Projects in Healthcare

Solve real-world healthcare challenges using machine learning. Explore methods for using large-scale electronic medical records data for machine learning, applying text mining to medical records, and for using ontologies for the annotation and indexing of unstructured content as well as for intelligent feature engineering.

Medical Statistics Program

By studying medical statistics, you will gain the statistical literacy needed to remain adept and adaptable in our ever-changing health industries. While the examples and applications are within the context of health and medicine, the statistical foundations you will gain can be applied to any industry.

Telehealth: Strategy, Implementation, and Optimization

Remote healthcare has seen an explosion in implementation with a 3,000 percent increase in usage during the height of the COVID-19 pandemic. Examine the theoretical foundations as well as the practical, technical, and business aspects of telemedicine and how to best utilize technology to enhance equitable access to healthcare.

Understanding the Future of Medicine: Generative AI Seminar Series

Learn how to evaluate the risks, opportunities, and applicability of generative AI for your organization in this collection of videos from Stanford faculty and industry experts. 

Health Data Analytics: Current Perspectives, Challenges, and Future Directions

• First Online: 13 August 2020

Cite this chapter

• Kavi Kumar Khedo 4 ,
• Shakuntala Baichoo 4 ,
• Soulakshmee Devi Nagowah 5 ,
• Leckraj Nagowah 5 ,
• Zahra Mungloo-Dilmohamud 4 ,
• Zarine Cadersaib 5 &
• Sudha Cheerkoot-Jalim 6  

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

753 Accesses

Over the last decade, there has been a tremendous growth in the amount and diversity of electronic health-related data, such as patient records, drug information, drug–disease associations, medical resource allocations, and clinical experiments’ results, altogether referred to as medical big data. Health data analytics refers to the proper exploitation of medical big data in view of getting better understandings that can drive health research, which may ultimately accelerate advancements in biomedicine, enhance patient outcomes, and reduce overall healthcare costs. This chapter provides an extensive review of the application areas that can benefit from health data analytics, namely drug–disease association, disease outbreak detection and surveillance, pharmacovigilance, healthcare management, clinical research, and clinical practice. A variety of tools and platforms have been developed to support health data analytics, each dealing with different application areas and diverse data types. These tools are analyzed. The challenges and future directions of health data analytics are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

The Era of Big Data: From Data-Driven Research to Data-Driven Clinical Care

Axes of a revolution: challenges and promises of big data in healthcare

Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness

E. AbuKhousa, N. Mohamed, J. Al-Jaroodi, e-Health cloud: Opportunities and challenges. Future Internet 4 (4), 621–645 (2012)

Google Scholar  

A. Alyass, M. Turcotte, D. Meyre, From big data analysis to personalized medicine for all: Challenges and opportunities. BMC Med. Genomics 8 (1), 33 (2015)

R. Amarasingham, R.E. Patzer, M. Huesch, N.Q. Nguyen, B. Xie, Implementing electronic health care predictive analytics: Considerations and challenges. Health Aff. (Project Hope) 33 (7), 1148–1154 (2014)

J. Annese, The importance of combining MRI and large-scale digital histology in neuroimaging studies of brain connectivity and disease. Front. Neuroinform. 6 , 13 (2012)

M.A. Badgeley, K. Shameer, B.S. Glicksberg, M.S. Tomlinson, M.A. Levin, P.J. McCormick, A. Kasarskis, D.L. Reich, J.T. Dudley, EHDViz: Clinical dashboard development using open-source technologies. BMJ Open 6 (3), e010579 (2016)

I. Bardhan, J. Oh, Z. Zheng, K. Kirksey, Predictive analytics for readmission of patients with congestive heart failure. Inf. Syst. Res. 26 (1), 19–39 (2015). https://doi.org/10.1287/isre.2014.0553

Article   Google Scholar  

D.W. Bates, S. Saria, L. Ohno-Machado, A. Shah, G. Escobar, Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Aff. (Project Hope) 33 (7), 1123–1131 (2014)

M. Bayati, M. Braverman, M. Gillam, K.M. Mack, G. Ruiz, M.S. Smith, E. Horvitz, Data-driven decisions for reducing readmissions for heart failure: General methodology and case study. PLoS One 9 (10), e109264 (2014)

A. Belle, R. Thiagarajan, S.M.R. Soroushmehr, F. Navidi, D.A. Beard, K. Najarian, Big data analytics in healthcare. Biomed. Res. Int. 2015 , 370194 (2015)

H.M. Berman, T.N. Bhat, P.E. Bourne, Z. Feng, G. Gilliland, H. Weissig, J. Westbrook, The Protein Data Bank and the challenge of structural genomics. Nat. Struct. Biol. 7 (Suppl), 957–959 (2000)

J. Billings, J. Dixon, T. Mijanovich, D. Wennberg, Case finding for patients at risk of readmission to hospital: Development of algorithm to identify high risk patients. BMJ 333 (7563), 327 (2006)

H. Brown, J. Terrence, P. Vasquez, D.W. Bates, E. Zimlichman, Continuous monitoring in an inpatient medical-surgical unit: A controlled clinical trial. Am. J. Med. 127 (3), 226–232 (2014)

A.J. Campbell, J.A. Cook, G. Adey, B.H. Cuthbertson, Predicting death and readmission after intensive care discharge. Br. J. Anaesth. 100 (5), 656–662 (2008)

K.C.C. Chan, Big data analytics for drug discovery, in 2013 IEEE International Conference on Bioinformatics and Biomedicine , (IEEE, Piscataway, 2013), pp. 1–1

E.S. Chen, G. Hripcsak, H. Xu, M. Markatou, C. Friedman, Automated acquisition of disease drug knowledge from biomedical and clinical documents: An initial study. J. Am. Med. Inform. Assoc. 15 (1), 87–98 (2008)

A.P. Chiang, A.J. Butte, Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin. Pharmacol. Ther. 86 (5), 507–510 (2009)

I.G. Cohen, R. Amarasingham, A. Shah, B. Xie, B. Lo, The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff. (Project Hope) 33 (7), 1139–1147 (2014)

A. Culotta, Towards detecting influenza epidemics by analyzing Twitter messages, in Proceedings of the First Workshop on Social Media Analytics – SOMA ’10 , (ACM Press, New York, 2010), pp. 115–122

Dell EMC Data Analytic Services for Healthcare [Online]. Available from: https://www.ciosummits.com/Online_Assets_Dell_EMC_Service_Overview_-_Chicago.pdf . Accessed 5 Sept 2019

Y. Demchenko, P. Grosso, C. de Laat, P. Membrey, Addressing big data issues in Scientific Data Infrastructure, in 2013 International Conference on Collaboration Technologies and Systems (CTS) , (IEEE, San Diego, California, 2013), pp. 48–55

D.V. Dimitrov, Medical internet of things and big data in healthcare. Healthc. Inform. Res. 22 (3), 156–163 (2016)

J.R. Edwards, D.A. Pollock, B.A. Kupronis, W. Li, J.S. Tolson, K.D. Peterson, R.B. Mincey, T.C. Horan, Making use of electronic data: The National Healthcare Safety Network eSurveillance Initiative. Am. J. Infect. Control 36 (3 Suppl), S21–S26 (2008)

EMC Healthcare Analytics Solution [Online]. Available at: https://www.emc.com/collateral/solution-overview/h11357-healthcare-analytics-so.pdf . Accessed 5 Sept 2019

EMIF, European Medical Information Framework [Online]. Available at: http://www.emif.eu/ . Accessed 5 Sept 2019

F. Estella, B.L. Delgado-Marquez, P. Rojas, O. Valenzuela, B.S. Roman, I. Rojas, Advanced system for automously classify brain MRI in neurodegenerative disease, in 2012 International Conference on Multimedia Computing and Systems , (IEEE, Piscataway, 2012), pp. 250–255

M. Flores, G. Glusman, K. Brogaard, N.D. Price, L. Hood, P4 medicine: How systems medicine will transform the healthcare sector and society. Pers. Med. 10 (6), 565–576 (2013)

R. Frijters, M. van Vugt, R. Smeets, R. van Schaik, J. de Vlieg, W. Alkema, Literature mining for the discovery of hidden connections between drugs, genes and diseases. PLoS Comput. Biol. 6 (9), e1000943 (2010)

V. Gligorijević, N. Malod-Dognin, N. Pržulj, Integrative methods for analyzing big data in precision medicine. Proteomics 16 (5), 741–758 (2016)

D. Gotz, H. Stavropoulos, J. Sun, F. Wang, ICDA: A platform for intelligent care delivery analytics. AMIA Annu. Symp. Proc. 2012 , 264–273 (2012)

T. Greenhalgh, J. Howick, N. Maskrey, Evidence Based Medicine Renaissance Group, Evidence based medicine: A movement in crisis? BMJ 348 , g3725 (2014)

E. Gultepe et al., From vital signs to clinical outcomes for patients with sepsis: A machine learning basis for a clinical decision support system. J. Am. Med. Inform. Assoc. 21 (2), 315–325 (2013)

T. Haferlach, A. Kohlmann, L. Wieczorek, G. Basso, G.T. Kronnie, M.-C. Béné, J. De Vos, J.M. Hernández, W.-K. Hofmann, K.I. Mills, A. Gilkes, S. Chiaretti, S.A. Shurtleff, T.J. Kipps, L.Z. Rassenti, A.E. Yeoh, P.R. Papenhausen, W.-M. Liu, P.M. Williams, R. Foà, Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: Report from the International Microarray Innovations in Leukemia Study Group. J. Clin. Oncol. 28 (15), 2529–2537 (2010)

R. Harpaz, W. DuMouchel, M. Schuemie, O. Bodenreider, C. Friedman, E. Horvitz, A. Ripple, A. Sorbello, R.W. White, R. Winnenburg, N.H. Shah, Toward multimodal signal detection of adverse drug reactions. J. Biomed. Inform. 76 , 41–49 (2017)

M. Hassanalieragh, A. Page, T. Soyata, G. Sharma, M. Aktas, G. Mateos, B. Kantarci, S. Andreescu, Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: Opportunities and challenges, in 2015 IEEE International Conference on Services Computing , (IEEE, New York, 2015), pp. 285–292

Healthcare Analytics & Business Intelligence | Sisense [Online]. Available at: https://www.sisense.com/solutions/healthcare/ . Accessed 10 Sept 2019

Healthcare Predictive Analytics – Big Data Analytics in Medicine and Genomics | Hortonworks [Online]. Available at: https://hortonworks.com/solutions/healthcare/ . Accessed 10 Sept 2019

HealthSuite Ecosystem | Philips Healthcare [Online]. Available at: https://www.usa.philips.com/healthcare/innovation/about-health-suite . Accessed 10 Sept 2019

M. Herland, T.M. Khoshgoftaar, R. Wald, A review of data mining using big data in health informatics. J. Big Data 1 (1), 2 (2014)

S. Hiremath, G. Yang, K. Mankodiya, Wearable Internet of Things: Concept, architectural components and promises for person-centered healthcare, in 2014 4th International Conference on Wireless Mobile Communication and Healthcare – Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH) , (IEEE, Piscataway, 2014), pp. 304–307

L. Hood, S.H. Friend, Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat. Rev. Clin. Oncol. 8 (3), 184–187 (2011)

P.L. Ingrassia, L. Carenzo, F.L. Barra, D. Colombo, L. Ragazzoni, M. Tengattini, F. Prato, A. Geddo, F. Della Corte, Data collection in a live mass casualty incident simulation: Automated RFID technology versus manually recorded system. Eur J Emerg Med 19 (1), 35–39 (2012)

E. Jovanov, Preliminary analysis of the use of smartwatches for longitudinal health monitoring, in Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2015 , (IEEE, Piscataway, 2015), pp. 865–868

D. Kansagara, H. Englander, A. Salanitro, D. Kagen, C. Theobald, M. Freeman, S. Kripalani, Risk prediction models for hospital readmission: A systematic review. J. Am. Med. Assoc. 306 (15), 1688–1698 (2011)

H. Khazaei, N. Mench-Bressan, C. McGregor, J.E. Pugh, Health informatics for neonatal intensive care units: An analytical modeling perspective. IEEE J. Transl. Eng. Health Med. 3 , 3000109 (2015)

E. Khurana, Y. Fu, V. Colonna, X.J. Mu, H.M. Kang, T. Lappalainen, A. Sboner, L. Lochovsky, J. Chen, A. Harmanci, J. Das, A. Abyzov, S. Balasubramanian, K. Beal, D. Chakravarty, D. Challis, Y. Chen, D. Clarke, L. Clarke, F. Cunningham, M. Gerstein, Integrative annotation of variants from 1092 humans: Application to cancer genomics. Science 342 (6154), 1235587 (2013)

H.C. Koh, G. Tan, Data mining applications in healthcare. J. Healthc. Inf. Manage. 19 (2), 64–72 (2005)

E.I. Konstantinidis, A.S. Billis, L. Plotegher, G. Conti, P.D. Bamidis, Indoor location IoT analytics “in the wild”: Active and healthy ageing cases, in XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings , ed. by E. Kyriacou, S. Christofides, C. S. Pattichis, (Springer International Publishing, Cham, 2016), pp. 1231–1236

C.S. Kruse, R. Goswamy, Y. Raval, S. Marawi, Challenges and opportunities of big data in health care: A systematic review. JMIR Med. Inform. 4 (4), e38 (2016)

V. Kumar, H. Park, R. Basole, M. Braunstein, M. Kahng, D. Chau, A. Tamersoy, D. Hirsh, N. Serban, J. Bost, B. Lesnick, B. Schissel, M. Thompson, Exploring clinical care processes using visual and data analytics: Challenges and opportunities, in 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshop on Data Science for Social Good, 2014

LexisNexis, Why data quality is the greatest challenge and opportunity for health care (2016). Available at: https://www.lexisnexis.com/risk/downloads/whitepaper/Data-Quality-POV.pdf

J. Li, S. Zheng, B. Chen, A.J. Butte, S.J. Swamidass, Z. Lu, A survey of current trends in computational drug repositioning. Brief. Bioinform. 17 (1), 2–12 (2016)

D. Lopez, M. Gunasekaran, B.S. Murugan, H. Kaur, K.M. Abbas, Spatial big data analytics of influenza epidemic in Vellore, India, in IEEE International Conference on Big Data 2014 , (IEEE, Washington, DC, USA, 2014), pp. 19–24

D. Luna, J.C. Mayan, M.J. García, A.A. Almerares, M. Househ, Challenges and potential solutions for big data implementations in developing countries. Yearb. Med. Inform. 9 , 36–41 (2014)

G. Luo, B.L. Stone, F. Sakaguchi, X. Sheng, M.A. Murtaugh, Using computational approaches to improve risk-stratified patient management: Rationale and methods. JMIR Res. Protoc. 4 (4), e128 (2015)

Z. Lv, J. Chirivella, P. Gagliardo, Bigdata oriented multimedia mobile health applications. J. Med. Syst. 40 (5), 120 (2016)

S.E. Madnick, R.Y. Wang, Y.W. Lee, H. Zhu, Overview and framework for data and information quality research. J. Data Inf. Qual. 1 (1), 1–22 (2009)

P. Mahindrakar, D. Hanumanthappa, Data mining in healthcare: A survey of techniques and algorithms with its limitations and challenges. Int. J. Eng. Res. Appl. 3 (6), 937–941 (2013)

MEDLINE Fact Sheet [Online]. Available at: https://www.nlm.nih.gov/pubs/factsheets/medline.html . Accessed 10 Sept 2019

R. Mirnezami, J. Nicholson, A. Darzi, Preparing for precision medicine. N. Engl. J. Med. 366 (6), 489–491 (2012)

B. Moore, The potential use of radio frequency identification devices for active monitoring of blood glucose levels. J. Diabetes Sci. Technol. 3 (1), 180–183 (2009)

NIH HIPAA Privacy Rule and Its Impacts on Research [Online]. Available at: https://privacyruleandresearch.nih.gov/pr_07.asp . Accessed 10 Sept 2019

A. Nikfarjam, A. Sarker, K. O’Connor, R. Ginn, G. Gonzalez, Pharmacovigilance from social media: Mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J. Am. Med. Inform. Assoc. 22 (3), 671–681 (2015)

I. Ouanes, C. Schwebel, A. Français, C. Bruel, F. Philippart, A. Vesin, L. Soufir, C. Adrie, M. Garrouste-Orgeas, J.-F. Timsit, B. Misset, Outcomerea Study Group, A model to predict short-term death or readmission after intensive care unit discharge. J. Crit. Care 27 (4), 422.e1–422.e9 (2012)

H. Pan, Y. Jiang, M. Boi, F. Tabbò, D. Redmond, K. Nie, M. Ladetto, A. Chiappella, L. Cerchietti, R. Shaknovich, A.M. Melnick, G.G. Inghirami, W. Tam, O. Elemento, Epigenomic evolution in diffuse large B-cell lymphomas. Nat. Commun. 6 , 6921 (2015)

R.B. Parikh, M. Kakad, D.W. Bates, Integrating predictive analytics into high-value care: The dawn of precision delivery. J. Am. Med. Assoc. 315 (7), 651–652 (2016)

G. Perna, Shannon Medical Center. Leaders at one Texas-based hospital are not taking hand hygiene for granted. Healthc. Inform. 30 (1), 28–30 (2013)

A.E. Powell, H.T.O. Davies, R.G. Thomson, Using routine comparative data to assess the quality of health care: Understanding and avoiding common pitfalls. Qual. Saf. Health Care 12 (2), 122–128 (2003)

K. Priyanka, N. Kulennavar, A survey on big data analytics in health care. Int. J. Comput. Sci. Inf. Technol. 5 (4), 5865–5868 (2014)

W. Raghupathi, V. Raghupathi, Big data analytics in healthcare: Promise and potential. Health Inf. Sci. Syst. 2 , 3 (2014)

D.C. Ranasinghe, R.L. Shinmoto Torres, A.P. Sample, J.R. Smith, K. Hill, R. Visvanathan, Towards falls prevention: A wearable wireless and battery-less sensing and automatic identification tag for real time monitoring of human movements, in Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2012 , (IEEE, Piscataway, 2012), pp. 6402–6405

S.M. Riazul Islam, D. Kwak, M. Humaun Kabir, M. Hossain, K.-S. Kwak, The internet of things for health care: A comprehensive survey. IEEE Access 3 , 678–708 (2015)

Sagitec HealHub [Online]. Available at: http://www.sagitec.com/healhub . Accessed 10 Sept 2019

R. Salazar, P. Roepman, G. Capella, V. Moreno, I. Simon, C. Dreezen, A. Lopez-Doriga, C. Santos, C. Marijnen, J. Westerga, S. Bruin, D. Kerr, P. Kuppen, C. van de Velde, H. Morreau, L. Van Velthuysen, A.M. Glas, L.J. Van’t Veer, R. Tollenaar, Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J. Clin. Oncol. 29 (1), 17–24 (2011)

A. Sarker, R. Ginn, A. Nikfarjam, K. O’Connor, K. Smith, S. Jayaraman, T. Upadhaya, G. Gonzalez, Utilizing social media data for pharmacovigilance: A review. J. Biomed. Inform. 54 , 202–212 (2015)

E.G. Schulz, C.L. Neumann, Interventional decentralized telemonitoring: Bridging the gap between patient’s device and physician’s needs in well selected indications. Kidney Blood Press. Res. 40 (2), 130–140 (2015)

I. Shams, S. Ajorlou, K. Yang, A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD. Health Care Manag. Sci. 18 (1), 19–34 (2015)

Y. Sun, B.H. Heng, S.Y. Tay, E. Seow, Predicting hospital admissions at emergency department triage using routine administrative data. Acad. Emerg. Med. 18 (8), 844–850 (2011)

K.B. Sundharakumar, S. Dhivya, S. Mohanavalli, R.V. Chander, Cloud based fuzzy healthcare system. Procedia Comput. Sci. 50 , 143–148 (2015)

A. Sunyaev, D. Chornyi, C. Mauro, H. Krcmar, Evaluation framework for personal health records: Microsoft HealthVault vs. Google Health, in 2010 43rd Hawaii International Conference on System Sciences , (IEEE, Piscataway, 2010), pp. 1–10

R.K. Thomas, Health Services Marketing: A Practitioner’s Guide (Springer Science & Business Media, New York, 2007)

J.E. Tym, C. Mitsopoulos, E.A. Coker, P. Razaz, A.C. Schierz, A.A. Antolin, B. Al-Lazikani, canSAR: An updated cancer research and drug discovery knowledgebase. Nucleic Acids Res. 44 (D1), D938–D943 (2016)

J. Vanschoren, J.N. van Rijn, B. Bischl, L. Torgo, OpenML: networked science in machine learning. ACM SIGKDD Explor. Newsl. 15 (2), 49–60 (2014)

D. Wang, E. Rundensteiner, R. Ellison, H. Wang, Probabilistic inference of object identifications for event stream analytics, in Proceedings of the 16th International Conference on Extending Database Technology – EDBT ’13 , (ACM Press, New York, 2013), p. 513

M.J. Ward, K.A. Marsolo, C.M. Froehle, Applications of business analytics in healthcare. Bus. Horiz. 57 (5), 571–582 (2014)

R.W. White, R. Harpaz, N.H. Shah, W. DuMouchel, E. Horvitz, Toward enhanced pharmacovigilance using patient-generated data on the internet. Clin. Pharmacol. Ther. 96 (2), 239–246 (2014)

R. Xu, Q. Wang, Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing. BMC Bioinformatics 14 , 181 (2013)

H. Yoshida, A. Kawaguchi, K. Tsuruya, Radial basis function-sparse partial least squares for application to brain imaging data. Comput. Math. Methods Med. 2013 , 591032 (2013)

MathSciNet   MATH   Google Scholar  

X. Yu, A. Ganz, Scalable patients tracking framework for mass casualty incidents, in Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011 , (IEEE, Piscataway, 2011), pp. 860–863

Q. Zhang, C. Pang, S. Mcbride, D. Hansen, C. Cheung, M. Steyn, Towards health data stream analytics, in IEEE/ICME International Conference on Complex Medical Engineering , (IEEE, Piscataway, 2010), pp. 282–287

K. Zolfaghar, N. Verbiest, J. Agarwal, N. Meadem, S.-C. Chin, S.B. Roy, A. Teredesai, D. Hazel, P. Amoroso, L. Reed, Predicting risk-of-readmission for congestive heart failure patients: A multi-layer approach (2013). Available at: https://arxiv.org/abs/1306.2094

Download references

This manuscript is part of the work of a funded project by the Mauritius Research Council, Reference HPCRIG-A03.

Author information

Authors and affiliations.

Department of Digital Technologies, FoICDT, University of Mauritius, Réduit, Mauritius

Kavi Kumar Khedo, Shakuntala Baichoo & Zahra Mungloo-Dilmohamud

Department of Software and Information Systems, FoICDT, University of Mauritius, Réduit, Mauritius

Soulakshmee Devi Nagowah, Leckraj Nagowah & Zarine Cadersaib

Department of Information and Communication Technologies, FoICDT, University of Mauritius, Réduit, Mauritius

Sudha Cheerkoot-Jalim

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Soulakshmee Devi Nagowah .

Editor information

Editors and affiliations.

Electronics and Communication Engineering, Vaagdevi College of Engineering, Warangal, Telangana, India

Nishu Gupta

Technology and Management School, Inst Politécnico de Viana do Castelo, Viana do Castelo, Portugal

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Khedo, K.K. et al. (2020). Health Data Analytics: Current Perspectives, Challenges, and Future Directions. In: Gupta, N., Paiva, S. (eds) IoT and ICT for Healthcare Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-42934-8_8

Download citation

DOI : https://doi.org/10.1007/978-3-030-42934-8_8

Published : 13 August 2020

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-42933-1

Online ISBN : 978-3-030-42934-8

eBook Packages : Engineering Engineering (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Public perspectives on increased data sharing in health research in the context of the 2023 National Institutes of Health Data Sharing Policy

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Columbia University School of Nursing, Columbia University Irving Medical Center, New York, New York, United States of America

Roles Conceptualization, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing

Roles Formal analysis, Investigation, Methodology, Software, Writing – review & editing

Roles Data curation, Formal analysis, Writing – review & editing

Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliation Biobehavioral Nursing & Health Informatics, University of Washington School of Nursing, Seattle, Washington, United States of America

Roles Supervision, Writing – original draft, Writing – review & editing

Affiliation Center for Clinical Medical Ethics, Columbia University Vagelos College of Physicians & Surgeons, New York, New York, United States of America

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

• Stephanie Niño de Rivera, 
• Ruth Masterson Creber, 
• Yihong Zhao, 
• Sarah Eslami, 
• Sabrina Mangal, 
• Lydia S. Dugdale, 
• Meghan Reading Turchioe

• Published: August 28, 2024
• https://doi.org/10.1371/journal.pone.0309161
• Reader Comments

The National Institutes of Health (NIH) is the largest public research funder in the world. In an effort to make publicly funded data more accessible, the NIH established a new Data Management and Sharing (DMS) Policy effective January 2023. Though the new policy was available for public comment, the patient perspective and the potential unintended consequences of the policy on patients’ willingness to participate in research have been underexplored. This study aimed to determine: (1) participant preferences about the types of data they are willing to share with external entities, and (2) participant perspectives regarding the updated 2023 NIH DMS policy. A cross-sectional, nationally representative online survey was conducted among 610 English-speaking US adults in March 2023 using Prolific. Overall, 50% of the sample identified as women, 13% as Black or African American, and 7% as Hispanic or Latino, with a mean age of 46 years. The majority of respondents (65%) agreed with the NIH policy, but racial differences were noted with a higher percentage (28%) of Black participants indicating a decrease in willingness to participate in research studies with the updated policy in place. Participants were more willing to share research data with healthcare providers, yet their preferences for data sharing varied depending on the type of data to be shared and the recipients. Participants were less willing to share sexual health and fertility data with health technology companies (41%) and public repositories (37%) compared to their healthcare providers (75%). The findings highlight the importance of adopting a transparent approach to data sharing that balances protecting patient autonomy with more open data sharing.

Citation: Niño de Rivera S, Masterson Creber R, Zhao Y, Eslami S, Mangal S, Dugdale LS, et al. (2024) Public perspectives on increased data sharing in health research in the context of the 2023 National Institutes of Health Data Sharing Policy. PLoS ONE 19(8): e0309161. https://doi.org/10.1371/journal.pone.0309161

Editor: Sylvester Chidi Chima, University of KwaZulu-Natal College of Health Sciences, SOUTH AFRICA

Received: December 11, 2023; Accepted: August 7, 2024; Published: August 28, 2024

Copyright: © 2024 de Rivera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The dataset underlying this study cannot be publicly shared in a repository due to current restrictions imposed by the Columbia University Institutional Review Board (IRB) and the participants did not consent to the public sharing of their data. However, the data are available upon request from the Columbia University IRB (email: [email protected] ) for researchers who meet the criteria for accessing confidential information.

Funding: National Institute of Neurological Disorders and Stroke, R01NS123639-03S1, Ms. Stephanie Niño de Rivera NHLBI Division of Intramural Research, R01HL161458, Dr. Ruth Masterson Creber National Institute of Neurological Disorders and Stroke, R01NS123639, Dr. Ruth Masterson Creber NHLBI Division of Intramural Research, R01HL152021, Dr. Ruth Masterson Creber National Institute of Nursing Research, R00NR019124, Dr. Meghan Reading Turchioe National Institute of Nursing Research, T32NR01691, Dr. Sabrina Mangal.

Competing interests: MRT: Boston Scientific (consulting), Iris OB Health (equity). The remaining authors have no conflicts of interest to disclose. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

In January 2023, the National Institutes of Health (NIH) updated its Data Management and Sharing (DMS) policy to increase data sharing practices for the purposes of transparency and reproducibility [ 1 ]. The NIH provides the largest amount of public funding for research studies in health of any US federal agency, and the updated policy mandates NIH-funded researchers to make their datasets available in data repositories [ 2 , 3 ]. Data repositories serve as platforms for researchers to access research data for secondary purposes [ 4 ] and include data collected in clinical trials, except for data covered by privacy law or excluded in initial participant consent forms.

Many of the datasets contain data from electronic health records (EHRs), claims, biobanks, and patient-reported outcomes, and include sensitive information, such as sexual health, fertility, and mental health data. Private companies may be among those accessing these datasets as they may have commercial value [ 5 ]. Repositories exhibit varying degrees of accessibility; some are open to the general public without oversight from ethics review boards while others enforce more stringent access policies [ 6 , 7 ].

As a mechanism to facilitate increased data sharing, researchers can use broad consent for the secondary uses of research data [ 8 ]. At the time of consent, the future use of the data, including the industries that might have access to it, are not explicitly disclosed, as the potential future use is unknown [ 9 ]. The immediate and future consequences of the uncertainty around future access and use of data can compound mistrust in communities that are already more reluctant to participate in research [ 10 – 13 ]. Participants have previously expressed apprehension around data sharing in research, raising concerns about transparency and trust in research practices even before recent policy updates [ 10 , 11 ]. For instance, in a narrative review of 27 papers exploring participant views towards research data sharing practices, Kalkman et al. found that participants are concerned about breaches of confidentiality and potential abuses of research data by external entities [ 13 ]. Therefore, it is imperative to ensure that research practices do not lead to unintended consequences, such as decreased trust which is already present in racial and ethnic minority communities that are underrepresented in research and less trustful of the research community due to historical abuse [ 14 ].

Although the public plays a crucial role as both contributors and consumers of health data, their perspectives regarding data sharing preferences for different types of data and attitudes toward recent NIH policy developments have not been well explored. Thus, this study aims to provide insight into public perspectives on those developments.

Ethics statement

The Institutional Review Board at Columbia University approved this study. Participants were administered an information sheet about the study and provided informed consent by checking a box on the online survey.

Study design

In March 2023, a U.S. representative sample of 610 adults was recruited using Prolific, an online survey recruitment platform [ 14 ]. The recruitment platform consists of verified users willing to participate in research studies and facilitates a matching process between researchers and users for rapid recruitment. Prolific uses U.S. Census Bureau data to divide the sample into subgroups by age, gender, and race with the same proportions as the national population. A representative sample option was selected for the survey on the platform. Prolific stratifies age using seven brackets: 18–24, 25–34, 35–44, 45–54, 55–64, 65–74 and 74+. ‘Sex’ is stratified into male and female, while race adheres to the five categories outlined by the UK Office of National Statistics: White, Mixed, Asian, Black, and Other. Also, participants must reside in the country being surveyed and demonstrate fluency in its primary language. The sampling frame for our study encompassed all 50 U.S. states. Prolific sent out invitations to potential participants that met eligibility criteria to participate in the survey. The data were collected and stored using Qualtrics, a HIPAA-compliant survey development tool. Participants were compensated $15 per hour and were prorated according to time of completion.

The questions for this online survey were developed through a literature review and expert input from physicians, bioethicists, and nurse-scientists. The survey was pilot tested with 10 members of the general public using Prolific for clarity and length and then revised accordingly. This cross-sectional survey was conducted in English and collected sociodemographic characteristics and attitudes toward data sharing across four domains: (1a) recipients of identifiable research data, (1b) recipients of de-identified research data, (1c) specific data types, and (2) reactions to the NIH DMS policy. It collected primarily closed-ended quantitative items but also included a small number of open-ended qualitative items. The survey had different blocks so that participants would only see some questions at a time and have the relevant definitions needed for the questions that followed. For example, the definition of the NIH DMS policy was presented with only the questions that were related to the policy (e.g., “Do you agree or disagree with the NIH’s new efforts to make research data collected about you more accessible to the scientific community and public?”). The survey questions can be found in S1 File .

Data collection

After logging in, participants first saw an overview of the study and were then asked to provide informed consent. Once they agreed to participate, they were asked about the groups (chosen family, chosen friends, doctors and nurses, or other healthcare providers) with whom they would be willing to share identifiable research data. Identifiable data were a separate category to clarify to participants that this data could be traced back and could be clinically meaningful for them (e.g., specific research results could be used by their healthcare team to inform their care). Second, they were asked about the external groups (health technology companies, public health organizations, health policy institutions, private foundations, or public platforms) with whom they would share de-identified or aggregated data. To ensure clarity, we provided examples of de-identified and aggregate data within the survey. Third, participants were informed about the potential for secondary uses of research data. They were then asked to specify their responses to sharing seven specific types of data that aligned with the NIH DMS policy (sexual health and fertility, mental health, genetic, imaging, biological, clinical, and consumer-generated data). Finally, participants were given a summary of the updated NIH DMS policy using lay terms and asked to express their opinions about the policy, with an option for open-text feedback.

Statistical analysis

Descriptive statistics on sociodemographic variables and closed-ended survey responses were generated for the overall sample. Differences in responses by self-reported race were assessed with Pearson’s chi-squared test, and Fisher’s exact test was used in the cases of small sample cells. A secondary analysis examined differences by race.

Qualitative analysis

We conducted a general thematic analysis of the open-ended survey responses about the NIH DMS policy. Two members of the research team concurrently reviewed a subset of responses and generated a preliminary list of themes. One researcher coded the remaining responses independently. The second researcher reviewed the final list of themes and illustrative quotes and discussed them with the second researcher until they reached a consensus.

Sample characteristics

Participant sociodemographic characteristics are summarized in Table 1 . Among 610 participants, 50% were female, with an average age of 46 years (standard deviation 16). Overall, 79% of participants self-identified as White, and 7% self-identified as Hispanic or Latinx. This closely matched with the US census data with respect to race [ 15 ]. The sample included participants from 46 US states and the District of Columbia ( S1 Table ). Over half of the participants completed a college degree or higher, and 31% of participants reported financial instability, indicated by the answer “not enough” financial resources. The average time of completion for the survey was 13 min and 55 seconds. There were two survey responses that took less than 5 minutes to complete, but they were not dropped after checking the quality of their responses.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0309161.t001

Quantitative results

Data sharing preferences varied based on the recipient involved. The majority of participants (95%) were willing to share identifiable research data with doctors and nurses, but fewer (48%) would share it with chosen friends ( Fig 1 ). Most participants (71–78%) were willing to share de-identified data with most external groups, including health technology companies, but fewer (53%) would share it with private foundations.

https://doi.org/10.1371/journal.pone.0309161.g001

Participants’ preferences varied based on the type of data. Across all types of data, participants were most willing to share their data with doctors and nurses ( Fig 2 ). The smallest proportion of participants were willing to share their sexual health and fertility data across all the external recipients (with the exception of doctors and nurses): chosen family member (32%), chosen friends (14%), health policy institution (41%), health technology companies (32%), public platform (37%). Similarly, less than 50% of participants indicated a willingness to share mental health data with external recipients outside of their healthcare team. Many participants were more willing to share genetic data with family members (68%) and healthcare professionals (81%), but less willing to share with private foundations (31%) and health technology companies (44%). Furthermore, of participants who decided to share consumer-generated data, 70% chose doctors and nurses, 51% health technology companies, 54% health policy institutions, and 48% public platforms. The entire analysis on the different types of data can be found in Fig 2 .

https://doi.org/10.1371/journal.pone.0309161.g002

Regarding perspectives on the 2023 NIH DMS Policy ( Table 2 ), more than half (65%) of participants either agreed or strongly agreed with it, but 17% disagreed or strongly disagreed. The policy would not change more than half (61%) of participant’s willingness to participate in research. There were significant differences in perspectives by race; a higher percentage of Black/African American participants (23%) disagreed or strongly disagreed with the policy compared to all other racial groups. Additionally, a higher percentage of Black participants (28%) indicated a decrease or strong decrease in their willingness to participate in research studies in response to the updated policy compared to most other racial groups.

https://doi.org/10.1371/journal.pone.0309161.t002

Qualitative analysis results

In response to the question regarding the new NIH Data Management and Sharing Policy, 302 (49%) participants provided written comments. The responses were categorized into one of the following themes: supportive of policy (33%), supported limiting access to data (37%), prioritized anonymity (17%), prioritized autonomy (6%), prioritized transparency (22%). Eight percent of responses were not categorized due to a theme not being applicable or identified. Some participants supported the policy, appreciating its potential benefits, such as transparency and the acceleration of research. However, many revealed concerns about the policy relating to a loss of autonomy, fear of misusing research data, lack of transparency, and lack of anonymity. Regarding concerns with loss of autonomy, participants felt that they were the rightful custodians of their data and should have control over designating data recipients, with one asserting, “It is my information, and I should be in charge of where it goes and who can access it.” (White female, 29) Other participants emphasized the importance of transparency in future uses of the data, as one mentioned, “While I agree that it can help to make positive changes, I would 100% want to know which outside companies they’re talking about before I would be willing…it does decrease my willingness [to participate].” (White female, 54) Finally, many participants were aware of how difficult it is to truly anonymize data, which also generated significant concerns: “By combining information from multiple sources, it is possible to de-anonymize data. If you know my age, zip code, and income, you are a long way towards knowing who I am.” (White Male, 66) These key themes are outlined with exemplary quotes in Table 3 .

https://doi.org/10.1371/journal.pone.0309161.t003

Participants’ willingness to share research data depends on the recipient group and the type of data involved. Variations in the types of data participants are willing to share with external entities highlights the need to evaluate broad data sharing practices in research. Furthermore, our findings reflect public perspectives considering the current research landscape, which is moving towards increased, broad data sharing practices.

Our present study reinforces participant concerns previously described prior to the 2023 NIH Data Sharing Policy. In alignment with previous literature, participant concerns such as the commercial use of research data, re-identification of de-identified data, and trust in research practices also emerged [ 10 – 13 , 16 ]. Consequently, the research community must find solutions to develop data sharing policies that comprehensively address participants’ concerns to reinforce transparency and mitigate apprehension about participating in research studies. These findings have important implications for researchers who collect a broad range of datasets in their work, many of whom receive NIH funding and need to comply with the updated policy.

We found that participants were more willing to share all types of data with healthcare providers compared to other groups such as health technology companies or private foundations. Perceptions of how that data will be used beyond immediate patient care or research purposes may influence participants’ reluctance to share data with external recipients beyond the healthcare entities. Notably, we found that consumer-generated data were less likely to be shared with health technology companies compared to healthcare providers. This may be due to concerns previously raised about potential profit generation from health data used to develop new technologies [ 17 , 18 ]. Our qualitative findings align with these sentiments, emphasizing concerns about others potentially profiting from participants’ health data. Previous studies focused on the collection of consumer-generated data, such as data from health apps, also shared these concerns regarding possible commercial agendas by external entities [ 13 , 19 , 20 ]. Mangal et al. also describes participants’ concerns about data commercialization and highlights how countries like Canada are addressing these issues through initiatives such as digital service taxation, which provides revenue back to communities [ 11 ].

One potential reason for reticence to share data in certain contexts, especially sensitive data such as sexual health and fertility data, could be due to concerns about re-identification. For instance, Chikwetu and colleagues highlight that despite efforts to anonymize data, various reidentification methods pose significant risks to privacy, especially in the context of wearable devices that individuals commonly use to track their health (e.g., reproductive health tracking, etc.) [ 21 ]. Furthermore, in our study, we found that, sexual health and fertility data are least likely to be shared with external entities beyond healthcare professionals, such as health technology companies and health policy institutions. This reluctance can be attributed to the growing sensitivity surrounding reproductive health issues in the United States, especially after the overturn of Roe v . Wade [ 22 , 23 ]. These concerns center around limited reproductive rights and fears of data collection and sharing from fertility tracking applications, with apprehensions that such data could be used to prosecute participants for crimes and to track miscarriages or abortions [ 23 ]. Consequently, the exposure of these types of data through public repositories poses significant risks, especially for individuals subject to evolving laws, potentially making both the patients and their healthcare professionals criminally liable [ 24 ]. A similar pattern was observed for mental health data, with fewer participants willing to share it with external entities not directly involved in their care, primarily due to the stigma associated with mental health conditions [ 25 , 26 ].

As the NIH and human sciences research moves toward enhancing research data accessibility, it is crucial to maintain a balance that also safeguards patient privacy and autonomy. While many participants displayed optimism toward the 2023 NIH DMS policy, individuals from underrepresented racial backgrounds expressed apprehension, which may be impacted by their knowledge of the history of unethical research studies and their experiences with ongoing systemic inequities in terms of access to healthcare and research today [ 27 ].

Previous research has highlighted the significant influence of data sharing practices on participants’ trust in the medical research community [ 28 ]. Trust in research can erode when participants feel they have limited control over how and with whom their personal health data are shared [ 10 – 12 ]. The unintended exposure of sensitive data through public repositories can inadvertently deter patients from participating in medical research or even seeking care at academic medical centers, where secondary use of medical records for research purposes may subject those records to the NIH data sharing policies [ 22 ]. Consequently, further research is warranted to address concerns related to broadly consenting to data sharing, participant autonomy in choosing where data are shared, and transparency regarding intentions of data use.

Our findings demonstrate that participants have varying levels of comfort regarding the sharing of specific types of data with certain groups. This supports previous findings of researchers moving towards implementing a participant-centered approach that offers customization options, allowing individuals to specify which data can be shared and with which industries and organizations [ 29 ]. The informed consent process presents an avenue through which willingness to participate in future research studies can be enhanced, ensuring that participant autonomy is upheld. Aside from participants having a greater influence on how their data is shared, the research community must also identify and promote best practices that facilitate increased regulation over the secondary uses of data. This can help participants feel comfortable to opt-in to share their data, specifically in the era of big data and AI-based algorithms and data analyses. AI by definition requires large amounts of data for training and testing, and the complexities of participant data-sharing preferences and the ethics of their data being used in these contexts are nuanced. Future work can examine if patient preferences differ when data are shared for building AI models. Moreover, it is imperative for researchers to prioritize the development of strategies that foster trust in data sharing practices within research. This requires researchers to ensure that participants have a comprehensive understanding of current practices to provide more details regarding the protection of data, especially among individuals who will be participating in a research study for their first time. By doing so, participants are empowered to make informed decisions in collaboration with researchers and researchers strengthen transparency of current data sharing practices.

Limitations

Study limitations include the online and English-only survey that was conducted through the Prolific survey platform. There are limitations with the information we can access regarding the survey’s administration. Prolific does not provide information on the response rate of the individuals that invitations were sent out to participate in our study. In addition, to use Prolific, respondents must have familiarity with technology, access to the Internet, and English proficiency. These participants are also likely to have higher trust in researchers since they are actively seeking to participate in survey research studies. A limitation in our survey questions could include participants misinterpreting certain terms, such as private foundation, or having differing baseline levels in understanding privacy and security research measures around data sharing. Our race-related results are also constrained by relatively small subsamples of non-white participants. Due to sample size limitations, we merged six race categories into four. Thus, our sample size did not permit in-depth examination of racial and ethnic minority groups. Another limitation includes that Prolific does not consider ethnicity for representative samples; therefore, our sample was not representative based on ethnicity, only race. To mitigate bias, future health informatics recruitment efforts should prioritize diversity through approaches like community engagement and improved technology accessibility.

Concerns expressed by research participants, especially participants from underrepresented racial groups, underscore the necessity of addressing participant perspectives to ensure that data sharing practices align with patient preferences. Prioritizing participant preferences for data sharing will prevent unintended barriers to recruitment and participation in research. Recognizing the varying levels of comfort participants have in sharing different types of sensitive data with external entities, it is vital to explore alternatives to broad consent, to ensure greater comfort and autonomy in deciding how data are shared.

Supporting information

S1 checklist. standards for reporting qualitative research (srqr)*..

http://www.equator-network.org/reporting-guidelines/srqr/ .

https://doi.org/10.1371/journal.pone.0309161.s001

S1 File. Supplementary methods.

https://doi.org/10.1371/journal.pone.0309161.s002

S1 Table. Prolific sample recruited by state in the US.

https://doi.org/10.1371/journal.pone.0309161.s003

• 1. NOT-OD-21-013: Final NIH Policy for Data Management and Sharing. [cited 25 Sep 2023]. Available: https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html .
• 2. Selecting a Data Repository. [cited 25 Sep 2023]. Available: https://sharing.nih.gov/data-management-and-sharing-policy/sharing-scientific-data/selecting-a-data-repository .
• View Article
• Google Scholar
• PubMed/NCBI
• 7. Types MD. Data Repository Guidance. Springer Nature; [cited 25 Sep 2023]. Available: https://www.nature.com/sdata/policies/repositories .
• 15. United States Census Bureau > Communications Directorate—Center for New Media. QuickFacts: United States. Available: https://www.census.gov/quickfacts/fact/table/US/RHI825222 .

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Yearb Med Inform
• v.31(1); 2022 Aug

Transforming Health Data to Actionable Information: Recent Progress and Future Opportunities in Health Information Exchange

Indra neil sarkar.

1 Brown University, Providence, RI, USA

2 Rhode Island Quality Institute, Providence, RI, USA

Objectives : Provide a systematic review of literature pertaining to health information exchange (HIE) since 2018. Summarize HIE-associated literature for most frequently occurring topics, as well as within the context of the COVID-19 pandemic and health equity. Finally, provide recommendations for how HIE can advance the vision of a digital healthcare ecosystem.

Methods : A computer program was developed to mediate a literature search of primary literature indexed in MEDLINE that was: (1) indexed with “Health Information Exchange” MeSH descriptor as a major topic; and (2) published between January 2018 and December 2021. Frequency of MeSH descriptors was then used to identify and to rank topics associated with the retrieved literature. COVID-19 literature was identified using the general COVID-19 PubMed Clinical Query filter. Health equity literature was identified using additional MeSH descriptor-based searches. The retrieved literature was then reviewed and summarized.

Results : A total of 256 articles were retrieved and reviewed for this survey. The major thematic areas summarized were: (1) Information Dissemination; (2) Delivery of Health Care; (3) Hospitals; (4) Hospital Emergency Service; (5) COVID-19; (6) Health Disparities; and (7) Computer Security and Confidentiality. A common theme across all areas examined for this survey was the maturity of HIE to support data-driven healthcare delivery. Recommendations were developed based on opportunities identified across the reviewed literature.

Conclusions : HIE is an essential advance in next generation healthcare delivery. The review of the recent literature (2018-2021) indicates that successful HIE improves healthcare delivery, often resulting in improved health outcomes. There remain major opportunities for expanded use of HIE, including the active engagement of clinical and patient stakeholders. The maturity of HIE reflects the maturity of the biomedical informatics and health data science fields.

1 Introduction

Fundamental to effective healthcare delivery is the transmission and availability of data to support information needs of clinicians, patients, and payers. For clinicians, reliable access to accurate and comprehensive health information is foundational to clinical decision making. For patients, health information is the basis for engagement in health care. For payers, health information forms the basis for supporting reimbursement models and ensuring care coordination. Collectively, health information is needed to support efficient, effective, and high-quality healthcare delivery across the entirety of the healthcare ecosystem. Systematic approaches to support the generation, transmission, and receiving of health information are a major motivation for the use of commonly templated medical charts [ 1 , 2 ]. Structured electronic medical charts, or “Electronic Health Records” (EHRs), enable health data access across “islands” of healthcare delivery [ 1 ]. This promise has increasingly led to the deployment and availability of EHRs globally, through a range of national programs across the Organisation for Economic Co-operation and Development nations as well as global health initiatives for lower and middle income countries [ 3 4 5 ]. The increased availability of EHRs presents the opportunity to leverage digital technologies and communications infrastructure for ensuring the highest quality of care by enabling access to needed information to “the right person at the right time.” An enabling feature of this tenet is an EHR’s ability to share information – and thus be “interoperable” – with other electronic health systems. In health information technology vernacular, this ability is commonly referred to as “Health Information Exchange” (HIE).

As a concept, HIE is either a verb (the act of health information transmission) or a noun (an entity that supports the transmission of health information, oftentimes referred to as a “Regional Health Information Organization” or a “Health Information Organization”). HIE is the basis for health and healthcare data interoperability, canonically organized into four levels [ 6 , 7 ]: (1) Foundational – the technical connection between health data sharing partners; (2) Structural – the defined format and syntax for transmission of health data; (3) Semantic – the representation of the transmitted health data into interpretable and meaningful structures for either human or machine use; and (4) Organizational – the sociolegal and policy frameworks to enable the use of the transmitted health data for use in treatment, payment, or operational decision making. Most of the prior reviews have outlined the major facets of HIE generally as well as their application in different contexts, focusing largely on aspects at these four levels.

HIE has increasingly become a major topic reported in biomedical literature, following a similar trend as for EHRs. The increased availability and usage of EHRs has increased the potential for HIE as well as establishment of government or industry endorsed health information organizations that advance the vision to enable the availability of crucial health information data wherever and whenever needed. The Medical Subject Heading (MeSH) descriptor “Health Information Exchange” was created in 2015 with the scope of being an “Organizational framework for the dissemination of electronic healthcare information or clinical data, across health-related institutions and systems. Its overall purpose is to enhance patient care” [ 8 ]. Of the 26 systematic reviews indexed in MEDLINE with HIE as a major topic to date [ 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 ] (retrieved using the search “health information exchange”[majr] AND systematic[sab]), some used “Health Information Exchange” as a search term; however, none explicitly used the MeSH descriptor in their search strategy, as determined from a structured search query ((“Health Information Exchange”[majr] or “Health Information Exchange”[mh]) NOT Editorial[pt] NOT Letter[pt]).

This review of the HIE literature presents the results from the first direct analysis of biomedical literature indexed in MEDLINE with the HIE MeSH descriptor. The search strategy did not have any inclusion/exclusion criteria pertaining to country of focus; however, most articles reviewed for this survey focused on HIE in the United States of America. In addition to presenting a summary of the top five topics discussed in the literature since 2018, a summary is provided on HIE studies done within the context of COVID-19 and the 2022 IMIA Yearbook theme (“Inclusive Digital Health: Addressing Equity, Literacy, and Bias for Resilient Health Systems”).

2 Objectives

The main objectives of this survey are to:

• Provide a systematic survey of HIE-relevant literature published since 2018;
• Identify and summarize the top five categories of HIE studies done since 2018;
• Summarize HIE studies done of relevance to COVID-19 to date;
• Summarize HIE studies done of relevance to the 2022 IMIA Yearbook thematic area; and,
• Provide recommendations on how HIE can advance the vision of an integrated digital healthcare ecosystem.

A computer program written in Julia (v1.7) [ 35 ] was developed and used to search MEDLINE using the Entrez programming utilities. The searches were restricted to those articles written in English (using the English[language] tag) that were published between January 1, 2018 and December 1, 2021. The search strategy explicitly excluded reviews, editorials, and letters. The LitCGeneral PubMed Clinical Query filter was used to identify COVID-19-related articles. The primary search used the following query: (((“health information exchange”[mh]) AND English[language] NOT Editorial[pt] NOT Letter[pt]) NOT LitCGeneral[filter] NOT (Systematic[sb] OR Review[pt]) AND (2018/01/01:2021/12/01[pdat])). The MeSH descriptors were tabulated for the articles retrieved from the primary search, excluding the following MeSH descriptors: Humans; Female; Male; Adult; Middle Aged; United States; Young Adult; Aged, 80 and Over; Adolescent; Medical Informatics; Japan; Aged; Health Information Exchange; Internet; Surveys and Questionnaires; Qualitative Research; Interviews as a Topic; Retrospective Studies; Cross-Sectional Studies; Medical Record Systems; Computerized; Reproducibility of Results; and Child. The top five occurring MeSH descriptors were used to retrieve (using the [mh:noexp] PubMed search tag) articles by combining them individually with the primary search. The COVID-19 specific search was done by toggling the LitCGeneral PubMed Clinical Query filter: (((“health information exchange”[majr]) AND English[language] NOT Editorial[pt] NOT Letter[pt]) AND LitCGeneral[filter] NOT (Systematic[sb] OR Review[pt]) AND (2018/01/01:2021/12/01[pdat])). The following query was used to identify relevant articles that included concepts pertaining to health knowledge and health disparities: (((“health information exchange”[majr]) AND English[language] NOT Editorial[pt] NOT Letter[pt]) NOT LitCGeneral[filter] NOT (Systematic[sb] OR Review[pt]) AND (2018/01/01:2021/12/01[pdat])) AND (“Health Knowledge, Attitudes, Practice”[mh] or health disparities[sb]). The articles for each of the top five HIE categories were manually reviewed and summarized, as well as for COVID-19 and health disparities. The source code for the computer program used for mediating the searches and MEDLINE record retrieval is available on GitHub ( https://github.com/INSARKAR/imiayb_hie_2022 ).

4 Findings and Analysis

The primary search yielded 235 articles indexed in MEDLINE with the “Health Information Exchange” MeSH descriptor as a major index term. Most of these articles focused on HIE within the United States of America (U.S.), which reflects differences in EHR deployment strategies globally. Specifically, in 2009 legislation was passed in the U.S. to promote and encourage the implementation of EHRs [ 36 ]. Subsequent legislation in 2016 aimed to further improve the flow and exchange of electronic health information across the U.S. [ 37 ]. Nearly all the articles reflect public policy implications either to encourage HIE or be guided by the benefits of HIE globally [ 3 4 5 ]. A total of 15 MeSH descriptors were found to occur across nine or more articles, which were used to identify the top ten MeSH descriptors for this review (shown in Table 1 ). Articles associated with the six MeSH descriptors that reflected the five most common MeSH descriptors in the retrieved article set (accounting for one tie) formed the basis of the summaries presented here. Additionally, summaries were done for HIE articles retrieved that pertained to COVID-19 (11 articles) or the 2022 IMIA Yearbook theme (10 articles). The presentation of the summaries is ordered from general to specific topical areas, followed by those topics that are cross-cutting.

Top Ten Ranked MeSH Descriptors. Grey-highlighted rows are the top five MeSH descriptors (including one tie) that formed the basis for this survey.

4.1 Information Dissemination

At the core of HIE is the development and use of technology to support the transmission of health information for healthcare treatment, management, and coordination. The second most frequent MeSH descriptor associated with the primary search was “Information Dissemination,” which is defined as the “circulation or wide dispersal of information” [ 38 ]. Characteristics of HIE have been captured using national surveys, which provide consistent evidence of nationwide desires to develop national HIE networks that span clinical and political boundaries [ 39 , 40 ]. However, in the U.S. there remain major concerns about “information blocking,” based on federal government regulations enumerating requirements for data sharing and exchange where data may not be shared due to non-care delivery reasons (e.g., business or political) [ 41 , 42 ]. Similarly, there is a need for health information to be shared with non-clinical members of a healthcare team [ 43 ]. Ultimately, the effectiveness of HIE will depend on community understanding of the role of HIE and overcoming barriers to support sharing of health data for enabling effective healthcare delivery [ 3 , 4 , 39 , 44 ].

HIE has been shown to improve care, through the availability of health information at critical times of need [ 45 , 46 ]. HIE enables critical information to be disseminated, supporting smooth transitions of care from acute events, such as stroke [ 47 ]. Health payment reform also depends on HIEs to enable the potential impacts of bundled payment models [ 48 ]. Major challenges with the acceptance and use of HIEs are linked to sociotechnical issues that can be addressed [ 49 , 50 ]. The sharing of information through HIEs enables improvement in care efficiencies that are based on effective means for disseminating relevant information to all members of a healthcare team [ 51 52 53 ]. Successful information dissemination across care sites improves the patient experience [ 54 ] and improves the potential to measure the quality of care and ensure patient safety [ 46 ]. HIEs support information dissemination for providers and payers, effectively serving as the underpinning healthcare data highway needed to facilitate the vision of a continuously improving healthcare system.

While not strictly clinical HIE, consumer HIE is an important aspect to support patients or their caregivers being informed members of the healthcare team. Consumer-facing resources are increasingly noted as an important complement to clinical data to inform healthcare delivery decisions [ 55 ]. This might include sharing of medically relevant videos [ 56 , 57 ], and may require clear guidelines to define the veracity of information being shared [ 56 ]. The sharing of information about complex health conditions, such as schizophrenia, may be done through social media (e.g., Twitter [ 58 ]). The development of HIE-integrated consumer-facing tools has been shown to improve nationwide HIE initiatives that may have stalled due to lack of community interest in HIE (e.g., in France [ 59 ]). Such engagement is essential to address patient concerns about HIE (largely pertaining to potential security or confidentiality issues) and explicitly demonstrate the clinical benefits [ 60 , 61 ]. The use of contemporary privacy preserving protocols (e.g., blockchain [ 62 ]) may therefore be essential for ubiquitous acceptance of HIEs in their use for ongoing monitoring applications.

4.2 Delivery of Health Care

Amidst the global interest in digital health and HIE, there remain notable challenges in leveraging HIE to support healthcare delivery. The fourth most occurring MeSH descriptor in the retrieved article set was “Delivery of Health Care,” which is defined as “The concept concerned with all aspects of providing and distributing health services to a patient population” [ 63 ]. With respect to HIE, it is essential to understand the barriers and enablers for clinician use of HIE systems [ 64 ]. Challenges can be linked to how healthcare systems are configured and how respective policy frameworks structure sharing of health information [ 65 ]. An underpinning key to the success of HIE is the availability of interoperable-ready EHRs. EHR adoption may be increased with country-specific incentives [ 66 ] or by linking with population-level payment models that are focused on care of individuals (“bundled payment”) [ 48 ]. Similarly, effective HIE is built around a common set of standards, such that they can be enforced across care environments using common vendor systems [ 67 ]. Alternatively, contemporary technologies like blockchain can support performant HIE across healthcare systems when implementation considers the architecture of the data being exchanged [ 68 ].

For health data made available by HIE to be rendered useful, the data must be clinically useful and interpretable. Effective HIE is positioned to support nurses, administrators, and researchers by providing otherwise challenging to locate clinical data that can impact clinical decisions, understanding of costs, or guide research inquiries [ 69 ]. HIE can also support availability of more complete information, such as medications [ 70 ]. Clinical decisions can also benefit from the availability of social care information as a component of HIE [ 71 ]. HIE enables the development of early detection systems, which can be highly impactful for conditions such as depression [ 72 ]. Enabling population analyses can be done through the use of graph-based query languages in combination with the growing adoption of the Fast Healthcare Interoperability Resources (FHIR) standard [ 73 ]. Timeliness in clinical interpretation of complex data available in HIE can be supported through improved visualizations, which can be impactful in emergency settings [ 74 ].

Patient engagement remains a major challenge in supporting effective delivery of care [ 75 ]. In contrast to concerns often reflected by providers or developers, patients themselves have limited concerns about HIE [ 76 ]. HIEs can support common patient tasks, such as appointment scheduling [ 77 ], which has been shown to drive HIE adoption more generally [ 59 , 78 ]. Improvements in clinical data entry interfaces improve patient access to their health data, and thus improve overall patient engagement [ 79 , 80 ]. The studies included in this survey demonstrate how HIE enables a healthcare ecosystem that fosters meaningful connections between patients and their healthcare team.

4.3 Hospitals

Historically, providers (including hospitals, health systems, and their clinicians) have been a major potential beneficiary of HIE services [ 65 ]. The most frequent MeSH descriptor in the primary article set was “Hospitals,” which are defined as “Institutions with an organized medical staff which provide medical care to patients” [ 81 ]. There have been limited studies to date that have directly aimed to assess the impact on hospitals. Recent studies provide an important insight to how HIE provides many benefits to hospitals, including improvement in hospital efficiency [ 82 ], as well as overall positive impacts on healthcare outcomes [ 40 , 45 , 51 , 83 84 85 ]. Of note, these benefits were shown regardless of which paradigm of HIE is used (i.e., query-based versus direct-access HIE) [ 52 ]. Query-based HIE is a federated approach of healthcare data sharing partners that agree to provide health data for a given patient as needed. Direct-access HIE is a centralized approach where healthcare data sharing partners provide health data as they are available into a commonly accessible system. Query-based HIE provides immediate access to timely health information and requires less centralized infrastructure. By contrast, direct-access HIE enables the development of longitudinal histories for patients. Hospitals that engaged in HIE were shown to have reduced rates of re-admission [ 67 ], reduction in information loss during care transitions from outpatient [ 86 ] or specialty (e.g., psychiatric [ 87 ]) settings to acute care hospitals. Ultimately, these studies demonstrate how increased availability and use of HIE in hospital settings have had a markedly positive impact on improving healthcare delivery.

Hospital types can range from specialty focused to general acute care centers to community hospitals, often necessitating the transition of patients across hospital settings. HIE has been shown to be a catalyst to encourage patients to be shared across multiple clinical sites; however, sharing of patient populations may lead to concerns of potential clinical competition between hospitals [ 54 , 88 ]. Functional HIE enables access to critical decision-driving data, such as laboratory findings and test results [ 89 ]. Acknowledging the breadth of hospital types and clinical catchment area demographics, studies have shown that the type of hospital can impact the quality of HIE [ 53 , 90 ]. Specifically, hospitals that have the resources to invest in health information technology to support HIE are more efficient than those that do not. One study examined the potential of a game-theoretic approach (aiming to achieve Nash equilibrium) to predict the potential benefits of HIE in a range of hospital types [ 91 ]. Through this approach, it was found that hospitals with fewer resources may be less inclined to participate in HIE, due to market pressures regardless of any financial incentives. Thus, while successful implementation of HIE may improve healthcare delivery across multiple care sites, it is imperative to consider the financial implications for hospitals that may be consequential to increased market competition.

Alongside enabling their use in healthcare delivery, HIE can unleash the analytic potential of electronic health data for biomedical research, epidemiological, or surveillance uses. To support the use of electronic health data for advanced analytical modeling, such as for studies in critical care medicine [ 92 ], requires adherence to policy and legal requirements. Exchange of comprehensive health data sets can enable population-level patient monitoring, disease surveillance, or adverse event detection [ 93 94 95 ]. HIE data can also be used to examine the potential impact of alternative payment models, which accommodate care across multiple care sites [ 48 , 96 ].

Health data are only actionable if they consist of the right data that are made available in appropriate clinical workflows in the right format and at the right time [ 97 ]. Successful HIE is predicated on the use of healthcare team members who motivate both the use and improvement of electronic health data to support clinical decision making. HIE improvements and implementation can be driven by general practitioners to improve care transitions between ambulatory and hospital settings [ 98 ]. Nurses and primary care providers can furthermore motivate the use and adoption of HIEs across care settings [ 99 , 100 ]. The perceived benefits of HIE systems will depend on usability studies, which take into account planned actions relative to clinical decision making [ 101 ].

4.4 Hospital Emergency Service

Commonly referred to as the “Emergency Department” (ED), this hospital department is a major beneficiary of, and health data generator for, HIEs. The fifth most common MeSH descriptor associated with the retrieved article set was “Emergency Service, Hospital,” which is defined as “Hospital department responsible for the administration and provision of immediate medical or surgical care to the emergency patient” [ 102 ]. There are strong desires to connect HIEs into ED EHR systems and clinical workflows. However, there are challenges globally with this integration in a way that can be clinically actionable, largely due to limited consideration of ED workflows [ 44 , 74 , 99 ].

HIEs provide a comprehensive view to the use of healthcare services. With respect to ED utilization, HIEs can be a major source for studying utilization [ 103 ], causes for return visits [ 104 ], and the impact of social determinants of health on ED visits [ 105 ]. HIE-based interventions can be used to also identify causes for repeat-ED visits and provide approaches for their reduction [ 106 , 107 ]. Clinical trials can also be constructed to examine the value of HIEs across population-specific (e.g., veteran versus civilian hospitals) care settings [ 108 ]. The holistic view provided by HIEs for patients in ED settings poses opportunities to enable the study of disorders that involve multiple clinical sites (e.g., as associated with substance use [ 109 ]).

The value of HIEs in hospital emergency service contexts is dependent on the availability of necessary health data. There are some notable missing data types (e.g., imaging [ 110 ]) that can be critical for decision making. However, success has been demonstrated with exchange of poison information [ 111 ], as well as medication information [ 112 ]. The exchange of information between EDs and other care settings (e.g., nursing homes) can also have a major impact on better coordination of care [ 113 ].

4.5 COVID-19

The emergence and spread of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) resulted in the COVID-19 pandemic, which has challenged healthcare systems globally since the beginning of 2020. Ten articles were retrieved using the LitCGeneral PubMed Clinical Query with HIE as a major MeSH descriptor. The COVID-19 pandemic served as a focal point for several discussions around the relevance and need for digital health strategies [ 114 , 115 ]. Predictive modeling approaches have shown merit in the use of HIE-based data to enable prediction of healthcare resource utilization [ 116 ]. HIE has been leveraged to support population-level analyses, including those that may be correlated with sociodemographic, behavioral, or clinical data [ 117 ]. Harnessing HIE data for research studies also has underscored the importance of ensuring privacy of health data while meeting short-term information needs for research and healthcare delivery [ 118 ].

The COVID-19 pandemic has exposed numerous challenges in the healthcare infrastructure, including those pertaining to HIE. The lack of robust and uniform HIE has resulted in the need to develop ad hoc solutions to meet public health data needs [ 119 , 120 ]. Where there was no robust HIE system for supporting public health updates globally, social media has been leveraged as a mechanism to share real-time public health updates [ 121 ]. Some of the challenges are rooted in challenges with EHR interfaces, which necessitated reversion to paper records and devising systems for digital conversion of handwriting and markings [ 122 ]. Finally, the lack of digital HIE systems between nursing homes and acute care facilities required the development of new digital approaches for electronic document exchange [ 123 ]. Collectively, there is an increased acknowledgement of the need for digital approaches for HIE that will be an essential component of next generation public health infrastructures that will be informed by these studies.

4.6 Health Disparities

The overall health of populations is predicated on equal access to healthcare delivery and overall community literacy about health concepts. HIE provides the opportunity for unbiased exchange of health data and knowledge to support population health. For this survey, ten articles were retrieved pertaining to health literacy and equity within the context of HIE. A core tenet of impactful health care is engagement of patients. Patient engagement through online systems, such as patient portals, has been shown to improve overall healthcare outcomes [ 124 , 125 ]. It is important to also understand information needs of patients or their caregivers, who may rely on general consumer search engines (e.g., Google [ 126 ]). Health literacy is an essential facet of patient engagement, which can take the form of either an online forum [ 127 ] or public health knowledge campaigns [ 128 ]. In addition to digital systems, the use of community members has been shown as an effective peer-to-peer approach to improve health literacy [ 129 ]. In the context of patient engagement, HIE is more focused on the dissemination of knowledge in culturally congruent ways.

The use of HIE for exchange of clinical data among healthcare providers and public health agencies has been shown to improve overall population health [ 130 ]. HIE-based analysis of population trends (e.g., ED utilization) has been shown to be more accurate than using administrative data [ 109 ]. However, engagement in HIE can be challenged by differences in perception of the benefit across racial groups [ 131 ] or technical barriers found in rural settings [ 132 ]. The promise of HIE as a tangible benefit for populations will only be realized when these major challenges are addressed. The challenges faced in the implementation and use of HIE across populations are reflective of the challenges faced by biomedical informatics and health data science more generally.

4.7 Computer Security & Confidentiality

As with all health information technology, HIE requires clear principles to ensure security in the transmission of protected health information between trusted parties. Tied for the third most common MeSH descriptor in the retrieved article set for this survey were “Computer Security” and “Confidentiality”. “Computer Security” is defined as “Protective measures against unauthorized access to or interference with computer operating systems, telecommunications, or accompanying data; especially the modification, deletion, destruction, or release of data in computers. It includes methods of forestalling interference by computer viruses or computer hackers aiming to compromise stored data” [ 133 ]. “Confidentiality” is defined as “The privacy of information and its protection against unauthorized disclosure” [ 134 ]. The underpinning principle in HIE is that data are shared securely, which serves as a foundation for supporting the development of interoperable systems that serve communities [ 135 136 137 138 ]. Attention to security in HIE is especially important in sensitive clinical contexts, such as sharing information associated with organ donors [ 139 ] or supporting monitoring of conditions like diabetes mellitus [ 140 ]. Secure data sharing must account for public concerns for privacy [ 76 , 141 , 142 ], preservation of anonymity [ 143 ], and be trusted by the patient community [ 125 ]. In the U.S., the 21 st Century Cures Act explicitly addresses these concerns through the use of contemporary HIE technologies, namely FHIR and SMART-on-FHIR [ 144 ]. The transmission of protected health information (PHI) through HIE requires confidence that confidentiality will be ensured. There is a need for patient understanding of their control of PHI [ 145 ], which accounts for the balancing of public concerns about privacy, security, and confidentiality, while still providing the benefits of HIE in healthcare delivery [ 141 , 146 , 147 ]. Oftentimes, these concerns must consider political boundaries or legal issues [ 148 , 149 ]

HIE within and between healthcare delivery sites can occur in multiple ways. There is a need to acknowledge the respective benefits of multiple approaches to HIE, which together can provide the most robust and secure approach to support healthcare delivery [ 150 ]. HIE can support secure messaging protocols, which require consideration of secure and reliable transmission of PHI [ 151 ]. Medical images also have very specific security requirements that must be considered when transmitted [ 93 , 152 ]. A variety of approaches have been examined for supporting secure exchange of medical record data across systems, including cryptographic approaches [ 153 ], use of secure keys [ 154 , 155 ], multi-factorial authentication [ 156 ], and use of blockchain techniques [ 62 , 68 , 157 158 159 160 ].

Challenges in ensuring confidentiality can be especially difficult when considering large volumes of complex data, such as medical images [ 152 ], as well as clinical or research contexts [ 92 , 139 , 161 ]. The consideration of confidentiality in HIE requires the consideration of racial or ethnic biases [ 162 , 163 ], which also necessitates the need to be culturally sensitive [ 131 ].

HIEs can leverage a range of technical approaches to ensure confidentiality. These approaches can include the use of authentication keys [ 154 , 155 ], cryptography, and privacy preserving algorithms [ 137 , 153 ]. Contemporary techniques, such as blockchain, also show promise in supporting confidentiality without impacting usability of PHI across HIE [ 157 , 159 ]. Simpler techniques, like three-factor authentication, have also shown promise [ 156 ]. The choice of technique or algorithm used to ensure confidentiality across HIE requires consideration of efficiency [ 143 ]. The choice of approach needs to be made known to the public to allay concerns about potential privacy breaches with HIE. Gaining public trust is essential for the adoption and ultimate success of HIE [ 142 ].

5 Recommendations

The complexity of healthcare delivery requires a reliable and robust healthcare data infrastructure, such as enabled by HIE. The landscape of digital health technologies is rapidly expanding and presents a panoply of opportunities that will usher in a new era of data-driven health care. The importance of HIE in enabling this vision cannot be understated. As the first survey of literature indexed in MEDLINE with the “Health Information Exchange” MeSH descriptor for HIE, this review presents a positive outlook for HIE and describes the challenges in the successful use of HIE to improve care. Considering the topics examined here, three recommendations are offered based on common themes that emerged. These recommendations move beyond the benefit of EHRs in isolated healthcare delivery settings to HIE ecosystems of EHR-based data. It is important to emphasize that these recommendations are not novel, but instead further underscore fundamentals about HIE that have been discussed previously [ 25 , 164 165 166 167 168 ]. The full impact of HIE will depend on national public policies that support the availability and use of electronic health data across multiple healthcare settings [ 169 170 171 ]. HIE is not uniform across the globe and its implementation is hindered by notable barriers, such as costs and market share concerns that impact the potential for sustainability [ 172 , 173 ]. In the U.S., the recently (2022) announced Trusted Exchange Framework and Common Agreement (TEFCA [ 174 ]) aims to provide a foundational step towards universal interoperability for one nation by providing a common minimum set of infrastructural and technical standards across the variety of networks associated with healthcare data interchange across the country [ 39 ]. The recommendations presented here also therefore form the basis for national public policies (e.g., TEFCA) to support HIE.

5.1 Recommendation 1: Get the Basics Right

HIE endeavors often aim to collect, exchange, and transport all available health and healthcare information with equal importance. This can be challenging from a technical perspective and may result in limited benefit to stakeholders [ 169 , 175 , 176 ]. The need for trustworthy and secure technology and standards for HIE are well documented and provide a foundation for enabling robust sharing of health information [ 175 , 177 178 179 180 181 ]. Policies should support the expansion of organizations that enable HIE to be considered a component of public infrastructure, much like electricity or water delivery, to support healthcare delivery. Prioritization of data and formats should thus adhere to meet use cases that have clinical or public health impact [ 3 , 4 , 39 , 44 , 182 , 183 ]. National standards for interoperability should be prioritized by government designated entities. In the U.S., TEFCA identifies the United States Core Data for Interoperability (USCDI) as a standardized set of health data classes and constituent data elements for nationwide, interoperable HIE updated and maintained through the Interoperability Standard Advisory process from the Office of the National Coordinator for Health Information Technology [ 39 ]. In cases where national standards do not exist, stakeholder groups should generate accepted sets of data types to meet specific clinical or public health use cases. The choice of standards should first be driven by clinical needs (e.g., the problem list, allergies, medications, and immunizations) and patient specific aspects (e.g., social determinants of health). Policies should be explicit about the core data types and acceptable standards that form the core of HIE. This core needs not replicate the full content of an EHR, but should include those data that are essential during the transitions of care across healthcare delivery sites and home.

5.2 Recommendation 2: Focus on Complementing, not Competing

Digital health technologies continue to emerge and fulfill many clinical and public health needs. HIE endeavors should be seen as a major partner in these endeavors, supporting their use and adoption [ 172 , 182 , 184 ]. HIE activities should provide clear demonstration of value to patients, healthcare providers, governments, and public health agencies [ 64 , 182 , 185 ]. There are many gaps in health data that need to be addressed. Partnerships between HIE initiatives will be crucial for addressing these gaps in meaningful and sustainable ways [ 48 , 66 , 69 ]. Healthcare delivery depends on reliable, robust, and trustworthy infrastructure, which is predicated on successful HIE working in concert with healthcare teams [ 66 , 67 , 70 ]. National policies should be developed that expand beyond large or medium sized healthcare delivery systems and provide clear incentives for smaller clinical sites that also provide safeguards from loss of clinical market share.

5.3 Recommendation 3: Respect Patients and Providers

Health care is comprised of a menagerie of stakeholders that have a range of often conflicting needs. Effective HIE is where patient and provider needs are met effortlessly [ 21 , 169 , 170 , 186 ]. Attention needs to be given to how health data are delivered, and not be redundant or overwhelming. Acknowledging clinical workflow is paramount to identify what data are presented and how [ 79 , 80 , 187 , 188 ]. Supporting patients and their caregivers with tools that enable their engagement and membership in healthcare teams can be catalyzed through HIE [ 75 , 109 , 124 , 125 ]. HIE alone is not a panacea for health care, but its adoption by patients and providers is essential for effective clinical decision making [ 76 , 77 , 130 , 186 , 189 190 191 ]. Research, often on a local basis, is needed to understand stakeholder needs and identify what types of data are needed as part of HIE. National policies should include clear benchmarks for success that include patient (e.g., satisfaction) and provider (e.g., reduction of burnout) metrics alongside overall healthcare improvement outcomes.

6 Limitations

As the first systematic survey using the MeSH descriptor for HIE, there are some limitations of note. The use of MeSH descriptors enabled the design of a systematic approach that could be encoded into a computer program for supporting reproducibility; however, this did limit the potential to identify additional relevant articles that may have been identified through a hand search. It is important to also acknowledge that the indexing of biomedical literature with a given MeSH descriptor does not necessarily include the full universe of relevant articles that may have been found through a scoping review. Additionally, because MeSH descriptors are applied as an artifact of the MEDLINE-indexing process, MeSH descriptors may not necessarily reflect the original intention of the authors for a given article. The identification of topics for this survey were based on frequency of MeSH descriptors, not necessarily importance or quality. Future reviews may consider a citation-based approach to identify articles describing topics as a proxy for importance. The choice of frequency of co-occurring MeSH descriptors also may have limited detailed examination of known reoccurring topics of interest in HIE (e.g., technical architecture or governance [ 149 , 170 , 192 ]). Another major limitation of this review is that most articles focused on HIE in the U.S. This is likely an artifact of TEFCA and related discussions in the U.S. in recent years.

7 Conclusion

Healthcare delivery relies on the availability of necessary data for supporting clinical and public health decision making. HIE provides the foundation for making these data available to meet information needs for the multiple stakeholders in health care. The thematic areas examined for this survey reveal the major advances in HIE as well as opportunities for future enhancements. The importance of HIE in the future of healthcare delivery can be expected to increase and serves as a guiding example for how biomedical informatics and health data science positively impact patient care. The future of health care will undeniably depend on effective HIE.

Financing Global Health 2023: The Future of Health Financing in the Post-Pandemic Era

Published May 14, 2024

Explore by health focus area

Funding for HIV/AIDS reached an all-time high of $14.1 billion in 2023.

Development assistance for malaria amounted to $3.3 billion in 2023.

Non-communicable diseases

Development assistance for NCDs dropped by 34.4% between 2020 and 2023.

Maternal and child health

Over the last decade, development assistance for reproductive, maternal, newborn, and child health has grown by 18.2%.

Pandemic preparedness

Development assistance for health for pandemic preparedness and response peaked in 2020 at over $1 billion.

Tuberculosis

Development assistance for tuberculosis dropped by 3.3% between 2017 and 2021, but rose again in 2022 to $2.6 billion.

What's new in FGH 2023?

IHME has been publishing Financing Global Health since 2009. Here is what is new in this year’s report:

• New estimates of development assistance for health IHME provides estimates of development assistance for health through 2023, showing how trends have changed since the height of the COVID-19 public health emergency in 2020 and 2021.
• A look at countries’ debt following the COVID-19 pandemic During the pandemic, many countries’ debt increased as they borrowed money to help their people weather economic hardship and get vaccinated against COVID-19.
• Improved forecasts of health spending through 2030 IHME has updated its forecasts of development assistance for health, government health spending, and total health spending based on factors including countries’ debt and changing priorities. 

Institute for Health Metrics and Evaluation (IHME).  Financing Global Health 2023: The Future of Health Financing in the Post-Pandemic Era. Seattle, WA: IHME, 2024.

• Angela Apeagyei (Micah) ,
• Joseph Dieleman ,
• Katherine Leach-Kemon,
• Enis Barış ,
• Ian Cogswell,
• Hans Elliott,
• Brendan Lidral-Porter,
• Christopher J.L. Murray ,
• Nishali Patel,
• Carolyn Shyong,
• Juan Solorio,
• Golsum Tsakalos,
• Wesley Warriner,
• Asrat Wolde,
• Dereje Yohannis Yada,
• Yingxi Zhao,
• Bianca Zlavog

Supporting documents

All our datasets are housed in our data catalog, the Global Health Data Exchange (GHDx). Visit the GHDx to download data from this article.

Development Assistance for Health Database 1990-2022

Development assistance for health database 1990-2023, development assistance for health on covid-19 database 2020-2022, development assistance for health on covid-19 database 2020-2023, global expected health spending 2022-2050, global health spending 1995-2021, gross domestic product per capita 1960-2050 - fgh 2023, view trends in health financing.

Use interactive bar charts, maps, and line graphs to explore patterns of global health financing flows from 1990 to 2050.

Health financing

Why pepfar matters, with dr. anthony fauci, financing global health 2021: global health priorities in a time of change, u.s. dementia care spending by state: 2010–2019, subscribe to our newsletter.

Cookies on Our Future Health

We use small data files to make the website work, known as essential cookies.

We'd like to use analytics cookies so we can improve our site's experience. We use Matomo as it does not require us to share analytics data with any third parties.

You can read more in our privacy notice and cookie policy before you choose.

• Log in to researcher account
• Create an account

A world-leading resource for health research

Become a registered researcher at Our Future Health and apply for access to health data from the UK’s largest health research programme.

Our growing data set

Included in the june 2024 release:.

989,132 participants with completed baseline health questionnaires

330,069 participants with genotype array data

644,119 participants with linked health record data

Find out more about the data and cohort.

About the Our Future Health research programme

We’re bringing together data from up to 5 million adults in the UK. Registered researchers can request access to this data and analyse it to discover new ways to prevent, detect and treat diseases.

The aims of the programme

The programme aims to provide two main resources for health research:

• an observational dataset for basic science, epidemiological discovery and aetiological research
• a cohort of participants who can be contacted for translational research

Our research resources

Registered researchers at Our Future Health can:

• request access to our baseline health questionnaire, our genotype array data and NHS England linked health records data. In the future, we will make more data types available. Learn more about our data types .
• apply to access the highly secure Our Future Health Trusted Research Environment (TRE) to conduct analysis and collaborate with your colleagues
• use the wide range of data analysis tools available in the TRE

Apply to access the data

Data Collections, Research Projects, and Funding Opportunities

Discover data collections, research projects, and funding opportunities related to nutrition, food insecurity, and physical inactivity in tribal communities. 

Data Collections

Centers for disease control and prevention (cdc): adult physical inactivity outside of work interactive maps.

CDC developed  interactive maps  showing differences in physical activity among U.S. adults by race and ethnicity and location in 2022. 27 states had a physical inactivity prevalence of 30 percent or higher among non-Hispanic AI/AN adults.

National Cancer Institute (NCI): Cancer Resources 

For information on AI/AN specific cancer surveillance and tumor registries, as well as the programs that support these efforts, visit this National Cancer Institute (NCI) webpage . 

Learn about AI colorectal screening programs and review cancer literature searches specific for Native American populations on NCI’s Native American Health webpage .

National Institutes of Health (NIH) Office of Aids Research (OAR): Data Hub

To learn about funding opportunities and ongoing research on HIV/AIDS within AI/AN populations, visit NIH’s  Office of Aids Research (OAR) Data Hub . 

Research Projects and Resources

Centers for diabetes translation research (cdtr).

The   Centers for American Indian and Alaska Native Diabetes Translation Research , funded under the Centers for Diabetes Translation Research (CDTR) program , has a mission to translate research of proven efficacy into practice in both clinical and community settings, with the goal of improving the diabetes-related health of Native people.  Learn more about this grant award .

CDC: Keys to Success Tip Sheet: Enrolling and Retaining American Indian Participants in the National Diabetes Prevention Program Lifestyle Change Program

Including traditional foods in type 2 diabetes prevention programs serving AI/AN communities can help program participants achieve their goals. This tip sheet provides lessons learned and insights on how staff can include traditional foods and make their program more culturally relevant for AI/AN participants. 

NIH: ADVANCE: Advancing Prevention Research for Health Equity

NIH provides funding support for a variety of research, training, infrastructure development, and outreach and information dissemination projects. The NIH Office of Disease Prevention is coordinating the NIH-wide research effort, ADVANCE: Advancing Prevention Research for Health Equity . As part of this initiative, this Notice of Special Interest (NOSI) focuses specifically on preventive interventions to address cardiometabolic risk factors in populations that experience health disparities, including AI/AN people. In the United States, AI/AN children experience disproportionate health disparities, including high rates of diabetes, obesity, and dental caries when compared to all other groups. Youth from AI/AN populations also face socio-cultural barriers in school and community settings that undermine the importance of their Native identity.

NIH: Exploring Food Insecurity as a Social Determinant of Health Among American Indian and Alaska Native Adolescents at Risk for Gestational Diabetes

Exploring Food Insecurity as a Social Determinant of Health Among American Indian and Alaska Native Adolescents at Risk for Gestational Diabetes   aims to explore how food insecurity impacts AI/AN females prior to pregnancy and will identify solutions to decrease food insecurity and diabetes health disparities in AI/AN communities. 

NIH: Native Collective Research Effort to Enhance Wellness (N CREW) 

The NIH supported program   Native Collective Research Effort to Enhance Wellness (N CREW) supports Tribes and Native American Serving Organizations (T/NASOs). T/NASOs participating in the program conduct research to address overdose, substance use, and pain, including related factors such as mental health and wellness. Phase I completed in November 2023 with the goal to support T/NASOs to plan, develop, pilot, and implement research and data improvement projects. Projects have received an initial review, and the process to award the projects has been initiated.

NIH: Osage Community Supported Agriculture Study (OCSA) 

The Osage Community Supported Agriculture Study (OCSA)   will test the efficacy of a CSA program combined with culturally tailored nutrition and cooking education among Osage adults, evaluate its cost-effectiveness, and develop a multimedia toolkit for disseminating findings .

NIH: Promoting Linguistic and Cultural Identity through Bilingual Children’s Stories to Address Nutrition and Health in Indigenous Communities

The Promoting Linguistic and Cultural Identity through Bilingual Children’s Stories to Address Nutrition and Health in Indigenous Communities project will develop a platform for the creation, distribution, and consumption of Native-authored, bilingual resources. The resources, tailored to AI/AN families, will relate to health, nutrition, and traditional foods. The goal is to create a library of dynamic, bilingual children’s eBooks in AI/AN languages and English, with accompanying interactive activities to promote parent-child dialogue and co-reading.

NIH/NHLBI: Strong Heart Study (SHS) 

The  Strong Heart Study (SHS) is a study of cardiovascular disease and its risk factors among American Indian men and women, and is one of the largest epidemiological studies of American Indians ever undertaken. 

NIH/National Institute of Environmental Health Sciences (NIEHS): Building Food Sovereignty, Sustainability, and Better Health in Environmentally impacted Native Americans

This National Institute of Environmental Health Sciences (NIEHS) project   will identify and implement safe and nutritious farming practices and restore food sovereignty through development of a farming system program supported by the Turtle Clan-founded Munsee Three Sisters Medicinal Farm. This innovative study will integrate a culturally centered, environmental road map created from community input for food sovereignty and sustainability that can be shared and disseminated to other environmentally impacted Nations. 

NIH/NIEHS: Native American Health and the Environment

NIH supports research to determine how environmental agents cause or exacerbate human diseases, including research to improve the environmental health of American Indians and Alaska Natives (AI/AN).  Learn more about these NIEHS-founded initiatives .

Research Funding Opportunities

Nih: intervention research to improve native american health (irinah) program.

The  Intervention Research to Improve Native American Health (IRINAH) Program supports research on interventions that aim to improve the health and well-being of Native American populations, including traditional nutrition and sports. IRINAH supports: 

• Etiologic research that will directly inform intervention development or adaptations
• Research that develops, adapts, or tests interventions for health promotion, prevention, treatment, or recovery
• Research on dissemination and implementation that develops and tests strategies to overcome barriers to the adoption, integration, scale-up, and sustainability of effective interventions

The IRINAH program includes 3 different funding opportunities:  R01 - PAR-23-298 ,  R21 - PAR-23-299 , and  R34 - PAR-23-285 .

NIH: Native American Research Centers for Health (NARCH) Program

The  Native American Research Centers for Health (NARCH) Program funds federally recognized American Indian/Alaska Native (AI/AN) Tribes and organizations for health research, research career enhancement, and research infrastructure enhancement activities. The NARCH program aims to support research directly linked to health concerns specifically identified, selected, and prioritized by tribal communities. 

The NARCH program includes 2 funding opportunities:   S06– PAR-23-166 and   R34 – PAR-24-041 . 

NIH: Notices of Special Interest

The  Determining the Tri-directional Relationship Among Oral History, Nutrition, and Comprehensive Health Notice of Special Interest Funding Opportunity supports research on the interplay of nutrition/food insecurity, oral diseases, and comprehensive health across the lifespan.

The  Stimulating Research to Understand and Address Hunger, Food and Nutrition Insecurity Notice of Special Interest Funding Opportunity encourages research on the efficacy of interventions and development of new measures for nutrition security and the mechanisms of food insecurity on a variety of health outcomes.

The Office of Disease Prevention and Health Promotion (ODPHP) cannot attest to the accuracy of a non-federal website.

Linking to a non-federal website does not constitute an endorsement by ODPHP or any of its employees of the sponsors or the information and products presented on the website.

You will be subject to the destination website's privacy policy when you follow the link.

• U.S. Department of Health & Human Services
• National Institutes of Health
• Division of Program Coordination, Planning, and Strategic Initiatives (DPCPSI)
• ODSS Intranet (NIH Staff)

Announcing the 2024 DataWorks! Prize – Showcase Your Innovative Data Reuse Projects!

Monday, August 5, 2024

We're excited to announce the launch of the 2024 DataWorks! Prize, an initiative between the Federation of American Societies for Experimental Biology (FASEB) and the National Institutes of Health (NIH) recognizing research teams for innovative secondary analysis and data reuse that advance human health. This two-phase challenge encourages participants to propose and execute impactful projects utilizing data from the NIH-funded Generalist Repositories Ecosystem Initiative (GREI).

• Challenge Launch: August 5, 2024
• Phase One Submissions: August 14 – October 23, 2024
• Phase Two Submissions: January 15 – June 14, 2025

Successful teams will receive cash prizes and the opportunity to showcase their work through future DataWorks! Program events. Don't miss this chance to contribute to groundbreaking research and share your findings with the scientific community! For more information, visit herox.com/dataworks .

This page last reviewed on August 19, 2024

This page last reviewed on May 2, 2022

Research Trend Report: New Health Database Transforms Future AD Research

By Clare Maloney

Published On: Aug 28, 2024

Last Updated On: Aug 28, 2024

Eczema research continues to advance rapidly with new treatments and groundbreaking studies underway. Part of our mission at the National Eczema Association (NEA) is to support and contribute to these critical advances in how we understand and treat this complex condition. 

To inform our Research Trend Report, we asked members of our research team what recent developments in the broader eczema world they’re most excited about, helping everyone stay up to date on the latest news in eczema research. Here’s what they had to say:

1. New database called the All of Us Research Program from NIH

Exciting new research: Recently, the National Institutes of Health (NIH) released a new database called the All of Us Research Program . The All of Us Research Program collects health data from over 800,000 people across the United States, with an aim to reach one million. This data includes electronic health records (the digital records kept by doctors), biosamples like bloodwork and urine tests, genomic data, surveys and data from wearable devices, like FitBit. 

Why this matters: With over 80% of participants coming from groups traditionally underrepresented in biomedical research, the All of Us project ensures a diverse and representative sample that promises to unveil new and impactful connections in health research.

Large-scale databases like these help researchers to consider different angles, uncover exciting new connections and answer broader questions for patient communities. “Currently, there are over 10,000 All of Us participants with atopic dermatitis (AD). This is going to lead to an exciting new wave of discoveries and insights!” said Allison Loiselle, PhD, senior manager of data science and research at NEA.

2. Atopic dermatitis and related conditions in All of Us Research Program data

Exciting new research: Three recent articles utilizing the All of Us Research Program found associations between AD and contact dermatitis, hypertension and hyperlipidemia (high cholesterol) and inflammatory bowel disease. 1–3   

“One of the most exciting ways that databases like these can be used is to find out if different health conditions are related to each other,” said Loiselle. Researchers do this “cross-sectionally,” or by looking at data from a population at a specific point in time. “For example, they can see which conditions seem to occur more frequently in people with AD than those without AD. Typically, they use the large amount of supporting data to ‘control’ for any other factors that might influence whether or not someone gets the condition in question (like sex, race or smoking),” said Loiselle. 

What they found: Researchers found that people with AD had over four times the odds of having contact dermatitis than people without AD. This is important because the relationship between these two conditions is poorly understood. There are theories that AD patients might have an increased contact sensitivity or that the immune dysregulation and skin barrier disruption of AD makes them more susceptible to contact dermatitis.

People with AD were also found to be around twice as likely to have hypertension and hyperlipidemia than people without AD. Previous studies on these associations have shown conflicting results. The study authors propose that systemic inflammation and sleep disturbances may contribute to the development of these two conditions.

Another study demonstrated that individuals with AD have an approximately twofold increase in odds of having inflammatory bowel disease, and were also more likely to have Crohn’s disease or ulcerative colitis. The authors proposed that stress , inflammation and certain genes identified in both conditions may be the cause of the association.

Why this matters: Identifying associations with other health conditions demonstrates the multifaceted burdens that patients with AD face, and helps to inform a holistic treatment approach. By understanding these relationships, researchers can start to understand more about what causes or contributes to AD, and to potentially identify targets for future therapies. 

“Since AD patients may only be seeing a dermatologist regularly and not a primary care doctor, there may be a need for appropriate screening of certain conditions in AD care settings to improve clinical outcomes overall,” said Loiselle. 

3. Understanding factors contributing to negative quality of life impact in All of Us Research Program data

Exciting new research: Several studies have identified disparities in medical care based on race, ethnicity and sexual and gender status for various chronic inflammatory skin diseases, including AD. However, the impact of these factors on health-related quality of life has not been well-studied. 

Using the All of Us database, one paper looked at the quality of life impact from both financial (cost-related) barriers and non-financial (non-cost related) barriers to receiving care across multiple chronic inflammatory skin diseases (acne, AD, hidradenitis suppurativa, psoriasis and rosacea). 4   

The study looked at factors that participants reported as ‘cost barriers,’ such as delaying physician care (i.e., not filling a prescription due to not being able to afford the cost). It also studied ‘non-cost’ barriers, which included transportation issues, not being able to take time off from work for care visits, delaying care because a provider does not share the same race, religion or language or not always being treated with respect by providers. 

What they found: Overall they found that patients who experienced one or more of these barriers had twice the likelihood of poorer health-related quality of life across all the diseases examined. 

Why this matters: Understanding what contributes to negative health-related quality of life impact is important to address health disparities. “Healthcare providers, and in particular dermatologists who see many patients affected by chronic inflammatory skin diseases should be aware of these real-world cost and non-cost factors that affect care and outcomes,” said Wendy Smith Begolka, MBS, chief strategy officer of research, medical and community affairs at NEA.

Your role in research

If you have eczema or love someone who does, don’t miss your chance to take part in ongoing eczema research and help inform what happens next. Learn more about opportunities to get involved in eczema research.

References:

1. Sandler M, Chen LC, Yu J. The Association Between Atopic Dermatitis and Allergic Contact Dermatitis: A Cross-Sectional Analysis Using the All of Us Research Program. Dermatitis . 2024. doi:10.1089/derm.2024.0033

2. Craver AE, Chen GF, Cohen JM. Association between atopic dermatitis and hypertension and hyperlipidemia: A cross-sectional study in the All of Us Research Program. J Am Acad Dermatol . 2024;90(4):819-821. doi:10.1016/j.jaad.2023.11.026

3. Marina Z Joel, William Damsky, Jeffrey M Cohen, Mitchel Wride, Association between atopic dermatitis and inflammatory bowel disease among US adults in the All of Us Research Program, Clinical and Experimental Dermatology , 2024;49(4):390–392. doi:10.1093/ced/llad397

4. Nock, M.R., Barbieri, J.S. & Cohen, J.M. Barriers to care and health-related quality of life among US adults with several common chronic inflammatory skin diseases: a cross-sectional analysis of the NIH All of Us Research Program. Arch Dermatol Res. 2024;316(201). doi:10.1007/s00403-024-02954-w  

Related Posts

Under the microscope: high-sodium diets and eczema.

National Eczema Association-supported researcher aims to discover whether dietary sodium triggers eczema outbreaks.

5 Questions with NEA’s New CEO Kristin Belleson

Learn more about the National Eczema Association’s new president and CEO.

Do Air Purifiers Help with Allergies and Eczema…

This household appliance might be able to help minimize triggers.

Eczema Expo 2024: A Treat for All

The National Eczema Association’s Eczema Expo was a treat for all who attended this year in Denver, Colorado.

Get the latest eczema news delivered to your inbox.

• Advances in research and treatments
• Lifestyle tips and hacks
• Stories from the community

NEA Newsletter Signup with Segmentation

• Email (required) *
• zip (required) * ZIP / Postal Code
• What is your primary relationship to eczema? * Select one (required) I am an adult with eczema (18 and over) I am a teen with eczema (13 - 17) I am the parent of a child with eczema I am a caregiver/partner/spouse of someone with eczema I am a supporter of someone with eczema I am an eczema healthcare professional Corporate interest Other
• I want to receive the latest eczema news in my inbox!
• Hidden SF Lead Source *
• If user exists, mark true to remove opt-out check
• Name This field is for validation purposes and should be left unchanged.

The latest in eczema news, delivered

Evidence-based articles, expert-sourced lifestyle tips and stories from your community.

Search terms

2024 Future Health Index insights reveal how cardiology leaders are tackling the rising tide of chronic heart disease

Virtual care, automation, and ai are critical to help meet growing demand for acute cardiac care and chronic heart disease management.

Aug 29, 2024 | 3 minute read

Global increases in lifestyle and age-related diseases, such as type 2 diabetes, cardiovascular disease, stroke, and dementia, continue to put an unsustainable burden on healthcare systems and societies worldwide. Cardiology leaders are highly aware of the situation. In 2020, an estimated 523 million people had some form of cardiovascular disease (CVD), and approximately 19 million deaths were attributable to CVD; this represents approximately 32% of all global deaths, an increase of 18.7% from 2010 (1,2).

By clicking on the link, you will be leaving the official Royal Philips ("Philips") website. Any links to third-party websites that may appear on this site are provided only for your convenience and in no way represent any affiliation or endorsement of the information provided on those linked websites. Philips makes no representations or warranties of any kind with regard to any third-party websites or the information contained therein.

You are about to visit a Philips global content page

Get our latest news by e-mail

Share on social media.

• https://www.philips.com/a-w/about/news/archive/standard/news/articles/2024/2024-future-health-index-insights-reveal-how-cardiology-leaders-are-tackling-the-rising-tide-of-chronic-heart-disease.html Link copied

Related news

Press release | August 28, 2024

Philips showcases AI-enabled integrated solutions at #ESCCongress to deliver better cardiac care to more people

Press release | June 11, 2024

Philips aims to transform diagnostic cardiology with introduction of new Cardiac Workstation

Press release | June 04, 2024

AI in cardiovascular ultrasound to diagnose more patients

Press release | May 24, 2024

Philips expands global access to its ground-breaking 3D intracardiac echocardiography to Hong Kong

Press release | May 22, 2024

Philips ePatch and AI analytics platform rolled out to 14 hospitals across Spain to monitor heart patients

Press release | May 17, 2024

Philips presents study results at Heart Rhythm Annual Meeting demonstrating benefits of its AI-powered cardiac monitoring solutions

News & Insights

• Cardiology leader insights from 2024 Future Health Index
• Consumer products
• Product registration
• Healthcare professionals
• Specialties
• Other business solutions
• Hearing solutions
• Display solutions
• Dictation Solutions
• News & Insights
• Investor Relations
• Environmental, Social and Governance
• Contact & support
• Customer support
• Professional healthcare support
• Company contacts
• Stay up-to-date Stay up-to-date Subscribe to our news

Select country

• Privacy Notice
• Terms of use
• Cookie notice

Our site can best be viewed with the latest version of Microsoft Edge, Google Chrome or Firefox.

Shaping the future of child oral health

Southwest Health Equity Research Collaborative study hopes to diminish early childhood caries among Native Hawaiian and other Pacific Islander families

Getting children to brush their teeth daily—let alone properly flossing and avoiding sugary snacks—is difficult for all caregivers.

To assist families in developing lifelong good dental hygiene in their preschoolers, researchers from the University of Hawai`i at Hilo and the Northern Arizona University are examining the oral health knowledge, attitudes, and behaviors among Native Hawaiian and other Pacific Islander families living in Hilo, Hawai`i—an area where 33.9% of the population is Native Hawaiian, which is the highest in the country.

From the findings, Misty Pacheco , associate professor in the Department of Kinesiology and Exercise Sciences at the University of Hawai`i at Hilo ; Viacheslav Fofanov , associate professor and associate director of Research and Graduate Programs for NAU School of Informatics, Computing and Cyber Systems; and NAU Regents’ Professor Julie A. Baldwin , director of the Center for Health Equity Research (CHER) are creating an oral health intervention for high-risk preschoolers and their caregivers in the coastal community.

This project, “ Disparities in Early Childhood Caries Among Native Hawaiian and Other Pacific Islander Preschool-Aged Children ,” is being conducted through a diversity supplement awarded to Pacheco and is building upon data from two other Southwest Health Equity Research Collaborative (SHERC) projects: “ Defining Microbiological Drivers of Early Childhood Caries in Preschoolers in Southern Arizona ” and “ Defining Microbiological Drivers of Early Childhood Caries in Preschoolers of Native Hawaiian and Other Pacific Islander Descent .”

In the original study, Fofanov and other NAU researchers studied tooth decay in 350 preschoolers in northern Arizona to see if they are affected by the acidic bacteria in their oral “microbiome.” In the two earlier SHERC studies, researchers expanded to include children both in southern Arizona (Yuma County) and on the Big Island of Hawai`i who are Native Hawaiian or Pacific Islander. The Big Island has similar social determinants of health as rural populations in the continental United States.

The researchers are working to prove that biological components, combined with socioeconomic factors which include poverty and access to dental care, increase tooth decay. Fofanov and his colleagues have found that the bacteria strains S. mutans and S. sobrinus are the bacteria that most frequently contribute to childhood tooth decay, according to the results of their research.

Using data to develop a targeted oral health intervention

Through Pacheco’s diversity supplement, the researchers are creating their early childhood caries intervention program using data they gathered from about 30 caregivers of Native Hawaiian/Pacific Islander preschoolers who participated in their earlier Hawai`i study.

“In the original study in Hawai`i, the east Hawai`i preschools were amazing collaborators, as well as the [preschool students’] parents,” Pacheco said. During the current study, though all children are considered high risk, half of the caretakers will be informed that their preschoolers are at high risk and the other half will not know their sample results, Pacheco said.

The researchers will give the families basic oral health education, and they will survey the families on their hygiene behaviors. Through the study, each family will get dental supplies, including toothbrushes, toothpaste, floss and a timer.

The researchers will reconnect with the caregivers two months later and take one final swab sample, then give them the same survey they took at the beginning of the project.

“We want to assess if the education [program] or knowing your sample results [or both] affect oral health behavior,” Pacheco said.

Investing in early-stage faculty through a diversity supplement

In addition to designing a targeted intervention and education program, Baldwin and Fofanov have been working with Pacheco on career development, mentoring and support through the Hawai`i studies.

“Drs. Baldwin and Fofanov are a wealth of knowledge when it comes to research methods, technical skills, and engaging with the community,” Pacheco said. “Their work with Native families in Arizona is a great foundation and example for working with Indigenous families in my community.”

The young associate professor is the only female and Native Hawaiian/Pacific Islander tenured faculty member at the University of Hawai`i in Hilo’s Department of Kinesiology and Exercise Science.

“I first met Dr. Pacheco through another research program at the University of Washington called the Indigenous HIV/AIDS Research Training (IHART) program, where I served as one of her mentors. We have been collaborating ever since,” Baldwin said. “It is such a joy for me to work with Misty – she is a wonderful person, an outstanding researcher, and so committed to making a difference in her community. I know we will find ways to continue to work together for some time to come.”

In her role at UH-Hilo, Pacheco oversees the department’s health promotion track. She said her passion for public health ignited when she served as a public health educator in the Peace Corps in Kenya after graduating with her bachelor’s in chemistry from California State University, Sacramento, and her Master of Health Administration from the University of Southern California. Her Peace Corps volunteer work focused on sexual and reproductive health.

“I was amazed at what we could do [in Kenya] and what we were charged to do in an area with little to no resources,” Pacheco said. “It was there I realized my desire to serve the most vulnerable populations and to focus on those upstream factors of health was my calling.”

After returning to the U.S. from Kenya, she then earned a Doctor of Public Health from the University of Hawai`i at Mānoa.

Pacheco said that she provides her east Hawai’i community with healthcare education and accurate information, as well as other resources, to ensure they receive equitable access to healthcare.

Developing lifelong mentorships

Pacheco said that career development, support, and mentorship are essential to faculty and students.

“I can attest to the benefits great mentorship has had on me when it came to my education and career,” Pacheco said. “Having someone to learn from, guide you and support you in different ways is the key to growth and success.”

Pacheco said that collaborating with Baldwin and Fofanov marks the first time she has partnered with another university as a faculty member and researcher.

“Hawaiʻi is so isolated and it is often difficult to forge these relationships and partnerships, and it can be daunting,” she said. “This opportunity has been so positive and is working out better than I could have imagined. It is giving me the confidence to continue to seek out these partnerships when appropriate.” Although she has a bachelor’s degree in chemistry, Pacheco said she has not worked in the biological sciences recently, and that her renewed and newly acquired technical skills will impact her future career.

“Dr. Fofanovʻs mentorship, when it came to explaining and teaching me about the microbiological aspect of this study (analysis of samples), has been new and interesting,” Pacheco said. “Dr. Baldwin’s work with her Native community and her success when it comes to grant writing has been hugely beneficial.” Pacheco said that another longtime mentor, Keaweʻaimoku Kaholokula , associate professor and chair in the Department of Native Hawaiian Health in the John A. Burns School of Medicine at the University of Hawai`i at Mānoa, is also assisting her with the study’s focus.

“Because I am focusing on Native Hawaiian/Pacific Islander preschoolers, he continues to be a crucial mentor for me,” she said.

She added, “A family member of mine, Leilani Kupahu-Kahoʻāno , who is also a Native Hawaiian cultural expert and registered nurse, founded a nonprofit that focuses on children as well. I have consulted with her many times during this study.”

Through the research project, Pacheco is also including her undergraduate students and funding them.

“Because of this experience, one of my students decided to get an MPH degree,” the associate professor said. “[Participating in the project] solidified her decision to do research.” This research is supported by a NIMHD center grant to the Southwest Health Equity Research Collaborative at Northern Arizona University (U54MD012388).

• Julie Baldwin
• Misty Pacheco
• SHERC supplement
• Southwest Health Equity Research Collaborative
• Viacheslav Fofanov