research topics in health science

77 interesting medical research topics for 2024

Last updated

25 November 2023

Reviewed by

Brittany Ferri, PhD, OTR/L

Medical research is the gateway to improved patient care and expanding our available treatment options. However, finding a relevant and compelling research topic can be challenging.

Use this article as a jumping-off point to select an interesting medical research topic for your next paper or clinical study.

• How to choose a medical research topic

When choosing a research topic , it’s essential to consider a couple of things. What topics interest you? What unanswered questions do you want to address? 

During the decision-making and brainstorming process, here are a few helpful tips to help you pick the right medical research topic:

Focus on a particular field of study

The best medical research is specific to a particular area. Generalized studies are often too broad to produce meaningful results, so we advise picking a specific niche early in the process. 

Maybe a certain topic interests you, or your industry knowledge reveals areas of need.

Look into commonly researched topics

Once you’ve chosen your research field, do some preliminary research. What have other academics done in their papers and projects? 

From this list, you can focus on specific topics that interest you without accidentally creating a copycat project. This groundwork will also help you uncover any literature gaps—those may be beneficial areas for research.

Get curious and ask questions

Now you can get curious. Ask questions that start with why, how, or what. These questions are the starting point of your project design and will act as your guiding light throughout the process. 

For example: 

What impact does pollution have on children’s lung function in inner-city neighborhoods? 

Why is pollution-based asthma on the rise? 

How can we address pollution-induced asthma in young children? 

• 77 medical research topics worth exploring in 2023

Need some research inspiration for your upcoming paper or clinical study? We’ve compiled a list of 77 topical and in-demand medical research ideas. Let’s take a look. 

• Exciting new medical research topics

If you want to study cutting-edge topics, here are some exciting options:

COVID-19 and long COVID symptoms

Since 2020, COVID-19 has been a hot-button topic in medicine, along with the long-term symptoms in those with a history of COVID-19. 

Examples of COVID-19-related research topics worth exploring include:

The long-term impact of COVID-19 on cardiac and respiratory health

COVID-19 vaccination rates

The evolution of COVID-19 symptoms over time

New variants and strains of the COVID-19 virus

Changes in social behavior and public health regulations amid COVID-19

Vaccinations

Finding ways to cure or reduce the disease burden of chronic infectious diseases is a crucial research area. Vaccination is a powerful option and a great topic to research. 

Examples of vaccination-related research topics include:

mRNA vaccines for viral infections

Biomaterial vaccination capabilities

Vaccination rates based on location, ethnicity, or age

Public opinion about vaccination safety 

Artificial tissues fabrication

With the need for donor organs increasing, finding ways to fabricate artificial bioactive tissues (and possibly organs) is a popular research area. 

Examples of artificial tissue-related research topics you can study include:

The viability of artificially printed tissues

Tissue substrate and building block material studies

The ethics and efficacy of artificial tissue creation

• Medical research topics for medical students

For many medical students, research is a big driver for entering healthcare. If you’re a medical student looking for a research topic, here are some great ideas to work from:

Sleep disorders

Poor sleep quality is a growing problem, and it can significantly impact a person’s overall health. 

Examples of sleep disorder-related research topics include:

How stress affects sleep quality

The prevalence and impact of insomnia on patients with mental health conditions

Possible triggers for sleep disorder development

The impact of poor sleep quality on psychological and physical health

How melatonin supplements impact sleep quality

Alzheimer’s and dementia 

Cognitive conditions like dementia and Alzheimer’s disease are on the rise worldwide. They currently have no cure. As a result, research about these topics is in high demand. 

Examples of dementia-related research topics you could explore include:

The prevalence of Alzheimer’s disease in a chosen population

Early onset symptoms of dementia

Possible triggers or causes of cognitive decline with age

Treatment options for dementia-like conditions

The mental and physical burden of caregiving for patients with dementia

• Lifestyle habits and public health

Modern lifestyles have profoundly impacted the average person’s daily habits, and plenty of interesting topics explore its effects. 

Examples of lifestyle and public health-related research topics include:

The nutritional intake of college students

The impact of chronic work stress on overall health

The rise of upper back and neck pain from laptop use

Prevalence and cause of repetitive strain injuries (RSI)

• Controversial medical research paper topics

Medical research is a hotbed of controversial topics, content, and areas of study. 

If you want to explore a more niche (and attention-grabbing) concept, here are some controversial medical research topics worth looking into:

The benefits and risks of medical cannabis

Depending on where you live, the legalization and use of cannabis for medical conditions is controversial for the general public and healthcare providers.

Examples of medical cannabis-related research topics that might grab your attention include:

The legalization process of medical cannabis

The impact of cannabis use on developmental milestones in youth users

Cannabis and mental health diagnoses

CBD’s impact on chronic pain

Prevalence of cannabis use in young people

The impact of maternal cannabis use on fetal development 

Understanding how THC impacts cognitive function

Human genetics

The Human Genome Project identified, mapped, and sequenced all human DNA genes. Its completion in 2003 opened up a world of exciting and controversial studies in human genetics.

Examples of human genetics-related research topics worth delving into include:

Medical genetics and the incidence of genetic-based health disorders

Behavioral genetics differences between identical twins

Genetic risk factors for neurodegenerative disorders

Machine learning technologies for genetic research

Sexual health studies

Human sexuality and sexual health are important (yet often stigmatized) medical topics that need new research and analysis.

As a diverse field ranging from sexual orientation studies to sexual pathophysiology, examples of sexual health-related research topics include:

The incidence of sexually transmitted infections within a chosen population

Mental health conditions within the LGBTQIA+ community

The impact of untreated sexually transmitted infections

Access to safe sex resources (condoms, dental dams, etc.) in rural areas

• Health and wellness research topics

Human wellness and health are trendy topics in modern medicine as more people are interested in finding natural ways to live healthier lifestyles. 

If this field of study interests you, here are some big topics in the wellness space:

Gluten sensitivity

Gluten allergies and intolerances have risen over the past few decades. If you’re interested in exploring this topic, your options range in severity from mild gastrointestinal symptoms to full-blown anaphylaxis. 

Some examples of gluten sensitivity-related research topics include:

The pathophysiology and incidence of Celiac disease

Early onset symptoms of gluten intolerance

The prevalence of gluten allergies within a set population

Gluten allergies and the incidence of other gastrointestinal health conditions

Pollution and lung health

Living in large urban cities means regular exposure to high levels of pollutants. 

As more people become interested in protecting their lung health, examples of impactful lung health and pollution-related research topics include:

The extent of pollution in densely packed urban areas

The prevalence of pollution-based asthma in a set population

Lung capacity and function in young people

The benefits and risks of steroid therapy for asthma

Pollution risks based on geographical location

Plant-based diets

Plant-based diets like vegan and paleo diets are emerging trends in healthcare due to their limited supporting research. 

If you’re interested in learning more about the potential benefits or risks of holistic, diet-based medicine, examples of plant-based diet research topics to explore include:

Vegan and plant-based diets as part of disease management

Potential risks and benefits of specific plant-based diets

Plant-based diets and their impact on body mass index

The effect of diet and lifestyle on chronic disease management

Health supplements

Supplements are a multi-billion dollar industry. Many health-conscious people take supplements, including vitamins, minerals, herbal medicine, and more. 

Examples of health supplement-related research topics worth investigating include:

Omega-3 fish oil safety and efficacy for cardiac patients

The benefits and risks of regular vitamin D supplementation

Health supplementation regulation and product quality

The impact of social influencer marketing on consumer supplement practices

Analyzing added ingredients in protein powders

• Healthcare research topics

Working within the healthcare industry means you have insider knowledge and opportunity. Maybe you’d like to research the overall system, administration, and inherent biases that disrupt access to quality care. 

While these topics are essential to explore, it is important to note that these studies usually require approval and oversight from an Institutional Review Board (IRB). This ensures the study is ethical and does not harm any subjects. 

For this reason, the IRB sets protocols that require additional planning, so consider this when mapping out your study’s timeline. 

Here are some examples of trending healthcare research areas worth pursuing:

The pros and cons of electronic health records

The rise of electronic healthcare charting and records has forever changed how medical professionals and patients interact with their health data. 

Examples of electronic health record-related research topics include:

The number of medication errors reported during a software switch

Nurse sentiment analysis of electronic charting practices

Ethical and legal studies into encrypting and storing personal health data

Inequities within healthcare access

Many barriers inhibit people from accessing the quality medical care they need. These issues result in health disparities and injustices. 

Examples of research topics about health inequities include:

The impact of social determinants of health in a set population

Early and late-stage cancer stage diagnosis in urban vs. rural populations

Affordability of life-saving medications

Health insurance limitations and their impact on overall health

Diagnostic and treatment rates across ethnicities

People who belong to an ethnic minority are more likely to experience barriers and restrictions when trying to receive quality medical care. This is due to systemic healthcare racism and bias. 

As a result, diagnostic and treatment rates in minority populations are a hot-button field of research. Examples of ethnicity-based research topics include:

Cancer biopsy rates in BIPOC women

The prevalence of diabetes in Indigenous communities

Access inequalities in women’s health preventative screenings

The prevalence of undiagnosed hypertension in Black populations

• Pharmaceutical research topics

Large pharmaceutical companies are incredibly interested in investing in research to learn more about potential cures and treatments for diseases. 

If you’re interested in building a career in pharmaceutical research, here are a few examples of in-demand research topics:

Cancer treatment options

Clinical research is in high demand as pharmaceutical companies explore novel cancer treatment options outside of chemotherapy and radiation. 

Examples of cancer treatment-related research topics include:

Stem cell therapy for cancer

Oncogenic gene dysregulation and its impact on disease

Cancer-causing viral agents and their risks

Treatment efficacy based on early vs. late-stage cancer diagnosis

Cancer vaccines and targeted therapies

Immunotherapy for cancer

Pain medication alternatives

Historically, opioid medications were the primary treatment for short- and long-term pain. But, with the opioid epidemic getting worse, the need for alternative pain medications has never been more urgent. 

Examples of pain medication-related research topics include:

Opioid withdrawal symptoms and risks

Early signs of pain medication misuse

Anti-inflammatory medications for pain control

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Research Topics & Ideas: Public Health

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring public health and/or epidemiology-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of research ideas , including examples from recent studies in public health and epidemiology.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Public Health-Related Research Topics

• Evaluating the impact of community-based obesity prevention programs in urban areas.
• Analyzing the effectiveness of public smoking bans on respiratory health outcomes.
• Investigating the role of health education in reducing the prevalence of HIV/AIDS in sub-Saharan Africa.
• The impact of air pollution on asthma rates in industrial cities.
• Evaluating the effectiveness of school nutrition programs on childhood obesity rates.
• The role of public health policies in addressing mental health stigma.
• Analyzing the impact of clean water access on infectious disease rates in rural communities.
• The effectiveness of needle exchange programs in reducing the spread of hepatitis C.
• Investigating the impact of social determinants on maternal and child health in low-income neighborhoods.
• The role of digital health interventions in managing chronic diseases.
• Analyzing the effectiveness of workplace wellness programs on employee health and productivity.
• The impact of urban green spaces on community mental health.
• Evaluating the effectiveness of vaccination campaigns in preventing outbreaks of infectious diseases.
• The role of public health initiatives in reducing alcohol-related harm.
• Analyzing the impact of aging populations on healthcare systems.
• Analyzing the impact of urbanization on mental health disorders in metropolitan areas.
• The effectiveness of telemedicine services in improving healthcare access in remote regions.
• Investigating the health impacts of electronic waste recycling practices.
• The role of health literacy in managing non-communicable diseases in aging populations.
• Evaluating the public health response to opioid addiction in rural communities.
• Analyzing the relationship between housing quality and respiratory illnesses.
• The effectiveness of community engagement in improving reproductive health services.
• Investigating the health effects of long-term exposure to low-level environmental radiation.
• The role of public health campaigns in reducing the prevalence of tobacco use among teenagers.
• Analyzing the impact of food deserts on nutritional outcomes in urban communities.

Epidemiology Research Ideas (Continued)

• Investigating the epidemiology of antibiotic-resistant infections in hospital settings.
• The impact of climate change on the spread of vector-borne diseases.
• Evaluating the factors contributing to the rise in type 2 diabetes prevalence.
• Analyzing the epidemiology of mental health disorders in conflict zones.
• The role of epidemiological surveillance in pandemic preparedness and response.
• Investigating the link between environmental exposures and the incidence of childhood cancers.
• The impact of dietary patterns on the prevalence of cardiovascular diseases.
• Evaluating the effectiveness of intervention strategies in controlling obesity epidemics.
• Analyzing the spread and control of zoonotic diseases in rural communities.
• The role of genetic factors in the epidemiology of autoimmune diseases.
• Investigating the socio-economic disparities in cancer incidence and outcomes.
• The impact of urbanization on the epidemiology of infectious diseases.
• Evaluating the public health consequences of occupational exposures to hazardous substances.
• Analyzing the trends and determinants of mental health disorders among adolescents.
• The role of lifestyle factors in the epidemiology of neurodegenerative diseases.
• Investigating the patterns of mental health service utilization during economic recessions.
• The epidemiology of sports-related concussions in youth athletics.
• Evaluating the effectiveness of public health interventions in reducing the spread of tuberculosis in high-risk populations.
• Analyzing the geographic distribution of Lyme disease in relation to climate change.
• The role of international travel in the spread of emerging infectious diseases.
• Investigating the demographic predictors of chronic kidney disease in population-based studies.
• The epidemiological impact of air pollution on asthma and other respiratory conditions.
• Evaluating the long-term health effects of exposure to endocrine-disrupting chemicals.
• Analyzing the incidence and risk factors of post-traumatic stress disorder in first responders.
• The role of socioeconomic status in the prevalence and management of diabetes.

Recent Studies: Public Health & Epidemiology

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the public health and epidemiology space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Tutorials in population neuroimaging: Using epidemiology in neuroimaging research (Godina et al., 2022)
• Application of Big Data in Digital Epidemiology (Naaz & Siddiqui, 2022)
• Response to comment on: Incidence of ocular and systemic disease affecting visual function among state bus drivers (Kohli et al., 2022)
• Why epidemiology is incomplete without qualitative and mixed methods (Lane-Fall, 2023)
• Teaching epidemiology: An overview of strategies and considerations (Hossain, 2022)
• Social Epidemiology: Past, Present, and Future (Roux, 2022)
• Population health assessment project: An innovative strategy for teaching principles of epidemiology (Keen et al., 2022)
• The functions of veterinary epidemiology in public health (Shaffi, 2023)
• Readying the Applied Epidemiology Workforce for Emerging Areas of
• Public Health Practice (Daly et al., 2022)
• Some Social Epidemiologic Lessons from the COVID-19 Pandemic (Schnake-Mahl & Bilal, 2023)
• The Filth Disease: Typhoid Fever and the Practices of Epidemiology in Victorian England by Jacob Steere-Williams (review) (Steere-Williams et al., 2022)
• Epidemiology of Adult Obesity, Measurements, Global Prevalence and Risk Factors (Orukwowu, 2022).
• Which disciplines form digital public health, and how do they relate to each other? (Pan, 2022)
• Information Flow and Data Gaps in COVID-19 Recording and Reporting at National and Provincial Levels in Indonesia (Barsasella et al., 2022). Epidemiology Blog of Neal D. Goldstein, PhD, MBI (Goldstein, 2023)
• Sensitivity analysis of SEIR epidemic model of Covid 19 spread in Indonesia (Rangkuti et al., 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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2 Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany

Najia Ahmadi

3 Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany

Sophia Grummt

Kilian-ludwig heine, dagmar krefting.

4 Department of Medical Informatics, University Medical Center, Goettingen, Germany

Andreas Kühn

Ines reinecke, julia scheel.

5 Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

Tobias Schmidt

6 Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany

Paul Schmücker

Christina schüttler.

7 Central Biobank Erlangen, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

Dagmar Waltemath

8 Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany

Michele Zoch

Martin sedlmayr.

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

Introduction

Digital health care information, as opposed to analog information, empowers clinicians, researchers, and patients with a wealth of information aiming to improve diagnosis, therapy outcome, and clinical care in general. According to Wyatt and Liu [ 1 ], medical informatics is the study and application of methods to improve the management of patient data, clinical knowledge, population data, and other information relevant to patient care and community health. Medical informatics can be seen as the subset of health informatics that is focused on clinical care, while the latter encompasses a wider range of applications. However, knowing, integrating, and using current computational technologies bears numerous pitfalls, limitations, and questions [ 2 ]. To shed light on current standards, applications, and underlying technologies, we present 10 topics to get started in the field of medical informatics research. Our key objective here was to improve interdisciplinary communication among stakeholders (eg, clinicians, experimental researchers, computer scientists, students, patient representatives), thereby bringing everyone on the same page of state-of-the-art medical informatics practices. In particular, improved interdisciplinary communication is essential in real-world problems and can be motivated by the following aspects:

• Advancing open research: Open collaboration between parties from different disciplines can lead to new research questions, innovative approaches, and novel discoveries [ 3 ].
• Bridging knowledge domains: Interdisciplinary communication can stimulate novel solutions, allowing researchers to gain a more comprehensive understanding of a specific problem or phenomenon [ 4 ], or can improve clinical decision-making [ 5 ].
• Addressing complex problems: Complex problems, such as the latest disease outbreak, require input from multiple domains to be comprehensively understood. Here, interdisciplinary communication is one key aspect to pinpoint the root causes and develop effective solutions [ 6 ].
• Promoting scientific inclusivity and diversity: Interdisciplinary communication was recently shown to foster diversity and inclusivity in science, by bringing together researchers from different backgrounds, cultures, and perspectives [ 7 , 8 ].

Here, we describe in detail how the initial topics have been selected from the literature and what design principles and structure each topic follows. A brief outline of the utilized methods for topic dissemination and an exemplary embedding into an educational training program are also presented.

Topic Selection

The initial topics were defined based on current developments in the health informatics field and an increasing number of published manuscripts between 2000 and 2021 (based on title-abstract-keyword screening in Scopus using the keywords “Health” AND “Informatics” AND “domain”) in the respective subdomains ( Figure 1 A). After a first definition of the specific topics, these were critically revised by internal and external domain experts, as well as scientists previously not familiar with medical informatics research.

Schematic summary and representation of the presented topics: (A) brief literature screening (title-abstract-keywords) for published manuscripts between 2000 and 2021, and the y-axis gap provides improved visibility of the less-occurring keywords; (B) most common topic terminologies, keywords (color-coded sections), and potential connections (grey) among topics in the medical informatics research domain. CDSS: clinical decision support system; CIS: clinical information system; EHR: electronic health record; ETL: extract, transform, and load; FAIR: findable, accessible, interoperable, reusable; FHIR: Fast Healthcare Interoperability Resources; GDPR: General Data Protection Regulation; i2b2: Informatics for Integrating Biology and the Bedside; OMOP: Observational Medical Outcomes Partnership.

Topic Design

The initial number of important topics and keywords exceeded the anticipated number of 10 topics, which found inspiration from the “Ten Simple Rules” collection in PLOS Computational Biology [ 9 ]. This is why the authors merged the most matching terms topic wise into groups. These groups finally produced topics that represent the broad range of the medical informatics domain in 3 main concepts, namely “Regulations and concepts,” “Harmonization and processing,” and “Evaluation, visualization, and dissemination” ( Figure 1 B). Figure 1 B also shows the initial keywords for each individual topic, as well as potential cross references between topics, which are highlighted in grey. The following sections provide important “do's and don'ts,” practical hints, and best practice guidelines. Further in-depth resources and practical tutorials will provide basic introductions to the referred domains. Kohane et al [ 10 ] already showed the importance of such clarifying introductions. This work extends the initial study and, in addition, provides detailed examples from the German national Medical Informatics Initiative (MII) [ 11 ].

All topics were divided into 3 parts to improve comprehension by the readers:

• Introduction: Background definitions for the specific context that motivated the topic
• Insight: Practical context to get started, including how to avoid pitfalls, state current limitations, and address current challenges
• Impact: Take home message and useful resources and best practices to deepen knowledge about the topic

Topic Utilization, Extension, and Embedding

Since it is of the utmost importance to keep the content current and as versatile as possible, we initiated an online resource at GitHub, in which contributions are highly emphasized [ 12 ]. Here, keywords and the corresponding literature are collected to allow for swift extension of the currently presented literature body in this article. In addition, the introduction of novel important topics that are not covered in this article might be included. To additionally demonstrate the practicability and adaptability of our proposed topic content, we exemplarily present how these can be embedded in higher education training and share external, introductory hands-on material ( Table 1 ).

Summary of tutorials and hands-on material about medical informatics standards and applications.

a SNOMED CT: Systematized Nomenclature of Medicine and Clinical Terms.

b ETL: extract, transform, and load.

c OMOP: Observational Medical Outcomes Partnership.

d CDM: common data model.

e FHIR: Fast Healthcare Interoperability Resources.

f OHDSI: Observational Health Data Sciences and Informatics.

g PLP: patient-level prediction.

h ODI: Open Data Institute.

Regulations and Concepts

Topic 1: privacy and ethics—“data privacy and ethics are the most important assets in the clinical domain.”.

Health information is sensitive and hence needs to be highly protected and should not be generously shared. Sharing regulations and data privacy matters are defined in the European General Data Protection Regulation (GDPR) [ 13 ]. The implementation of the GDPR is an ongoing process as the quickly evolving technology, data, and scientific practices demand continuous improvement, which include periodic adaptations of the technical and legal aspects [ 14 , 15 ]. In terms of ethics and with the rise of novel technologies, like artificial intelligence (AI), the possible re-identification of data, such as images and genomic information, is a major concern [ 16 , 17 ].

Anonymization is one important way to keep data private. It can also be achieved for high-dimensional data by changing patient-specific identifiers through removal, substitution, distortion, generalization, or aggregation [ 18 ]. In contrast, data pseudonymization is another de-identification procedure by which personally identifiable information fields within a data record are replaced by one or more artificial identifiers or pseudonyms [ 19 ]. To overcome the paucity of annotated medical data in real-world settings and (fully) save the patients’ anonymity, synthetic data generation is used to increase the diversity in data sets and to enhance the robustness and adaptability of AI models [ 20 ]. To conform with ethical regulations in a research context, medical data are only available in a highly controlled manner and according to strict procedures. New concepts, such as “systemic oversight” [ 21 ] or “embedded ethics” [ 22 ], might be needed to tackle the new data-driven developments around “medical big data” and AI in health care. To engage with the adoption of broad consent, systemic oversight was suggested as an approach, in which mechanisms like auditing mechanisms, expert advice, and public engagement initiatives (among others) should be adapted as additional layers to the newly arising ecosystem of health data [ 21 ]. Recently, embedded ethics was jointly suggested by ethicists and developers to address ethical issues via an iterative and continuous process from the outset of development, which could be an effective means of integrating robust ethical considerations into practical development [ 22 ]. A digital representation of information encoded in signed consent forms is needed to facilitate common data use and sharing, as already implemented in an MII informed consent template [ 23 ].

As a researcher in medical informatics, it is inevitable to be informed and knowledgeable about the fact that patients own their medical records and any use of those data requires great care. In Germany, health care providers can only use the data for first medical use. Secondary use, like research, needs to be approved by either broad or individual consent, which can be made available via the electronic health record (EHR). In addition to digitization efforts, it is still a considerable hurdle to convince patients to make their data available for medical research because personal skepticism commonly makes the entire data acquisition process more difficult [ 24 ]. Here, well-received external communication, transparency, and increased awareness are necessary for substantial improvements. In general, it is a balance between privacy, patient needs, and the use of data for the common good versus economic interests [ 25 ]. In particular, one should be aware of the specific legal regulations that apply within the country and additionally get in touch with the relevant data protection departments. Following this, a plan for infrastructure that meets these regulations and that contains, for example, a trustee for the electronic recording of patient consent and anonymization or direct pseudonymization processes to collect the data needs to be developed. Risk assessments for potential data leakage, approvals by ethics committee, as well as consultation with a data protection officer are essential considerations to further assure data security.

Topic 2: EHR and Clinical Information Systems—“Get to Know Your Clinical Information System to Understand the Required Data.”

Hospitals run clinical information systems (CIS) to collect, store, and alter clinical data about patients. A CIS, independent of the specialization and specific vendor, covers many clinical subdomains and integrates patient-related data to support doctors in their daily routine. Without a doubt, medical data are only useful if meaningful information can be derived from them. This requires high-quality data sets, seamless communication across IT systems, and standard data formats that can be processed by humans and machines [ 2 ]. Typical challenges in clinical IT implementations, especially for patient recruitment systems, were recently evaluated by Fitzer et al [ 26 ] for 10 German university hospitals, including requirements for data, infrastructure, and workflow integration. The implementation of an EHR, including an individual's medical data in a bundled form, into the CIS is a key aspect to prevent low reliability and poor user-friendliness of EHRs, which has recently been shown to affect time pressure among medical staff [ 27 ]. For example, in Scandinavia, the United States, and the United Kingdom, the Open Notes initiative [ 28 ] facilitates patients’ access to EHRs and health data sharing via “PatientsKnowBest ” to give health care professionals and families direct access to medical information [ 29 ].

An EHR is used primarily for the purposes of setting objectives and planning patient care, documenting the delivery of care, and assessing the outcomes of care [ 30 ]. EHRs have so far consisted of unstructured, narrative text as well as structured, coded data. Thus, it will be necessary to implement more systematic terminologies and codes so that the data contained in these records can be reused in clinical research, health care management, health services planning, and government reporting in an improved manner [ 31 , 32 ]. Since the domain of medical informatics is rather new, there are many possibilities for software solutions to improve EHR-related issues [ 33 ]. Exemplary for the EHR domain, the Systematized Nomenclature of Medicine and Clinical Terms (SNOMED CT) is utilized to develop comprehensive high-quality clinical content [ 34 ]. It provides a standardized way to represent clinical phrases captured by the clinician and enables automatic interpretation of these, which is showcased in a “five-step briefing” [ 35 ]. Interestingly, the number of annual publications on this subject has decreased since 2012. However, the need for a formal semantic representation of free text in health care remains, and automatic encoding into a compositional ontology could be a solution [ 36 ]. In terms of usability and user acceptance, evaluations and improvements of EHRs and clinical decision support systems (CDSS) are currently ongoing [ 37 ], for which already well-received examples can be attributed to CeoSYS [ 38 ] or the IPSS-M Risk Calculator [ 39 ]. Moreover, the actions of patients directly contributing to their own EHR records are also being evaluated. The study by Klein et al [ 40 ] indicates that such an approach facilitates the development of individual solutions for each patient, which in turn requires a flexible EHR during the course of a treatment process. Additionally, it was argued that data incorporation via different devices can also facilitate the convenient utilization of the application and, hence, may increase secondary use.

Modern CIS support the interaction by doctors and patients with the recorded patient data (eg, using the EHR or patient portals, eHealth platforms). It is important to understand the basic architecture, especially challenges [ 26 ], of the hospital IT infrastructure to know where data are located and how they can be retrieved and integrated. Major improvements can be made when supporting international standards for data exchange. Beyond standard EHR, this includes interoperability standards like Fast Healthcare Interoperability Resources (FHIR; see Topic 6) and standard data models like the Observational Medical Outcomes Partnership (OMOP; see Topic 7). These criteria should be considered with every new order of clinical systems.

Topic 3: Data Provenance—“Trace Your Data, Even Within Large-scale Efforts.”

Meaningful and standardized metadata facilitate the interpretation of, retrieval of, and access to data [ 41 ]. When explainable data are processed with interoperable tools, scientists can create automated and reusable workflows and provide access to reproducible research outcomes and data analysis pipelines [ 42 ].

Data provenance describes the history of digital objects, where they came from, how they came to be in their present state, and who or what acted upon them [ 43 ]. In health care, provenance maintains the integrity of digital objects (eg, the results of data analyses engender greater trust if their provenance shows how they were obtained). In addition, it can be used to deliver auditability and transparency, specifically, in learning health systems, and it is applicable across a range of applications [ 44 ]. Inau et al [ 45 ] argued that the lessons learned from “FAIRification” processes in other domains will also support evidence-based clinical practice and research transparency in the era of big medical data and open research. Further work demonstrated that a findable, accessible, interoperable, reusable (FAIR) research data management plan can provide a data infrastructure in the hospital for machine-actionable digital objects [ 46 ]. Recently, the openEHR approach was also suggested for creating FAIR-compliant clinical data repositories as an alternative representation [ 47 ].

Key data management requirements are defined by the FAIR guiding principles [ 48 ]. Since data protection laws led to additional requirements for data privacy and data security, the FAIR-Health principles focused on defining additional requirements for information on the sample material used from biobanks, for provenance information, and incentive schemes [ 49 ]. Further work is needed to establish provenance frameworks in health research infrastructures [ 50 ].

Topic 4: Data Sharing—“If Data Won’t Come to the Model, the Model Must Go to the Data.”

Cross-sectional medical data-sharing is critical in modern clinical practice and medical research, in which the challenge of privacy-preserving transfer and utility needs to be addressed [ 51 ]. In order to facilitate high reuse of the data, a decentralized computational scheme that treats the available data as part of a federated (virtual) database, avoiding centralized data collection, processing, and raw data exchanges, is still needed in many countries to analyze large and widespread clinical data [ 52 ].

One possible solution for this federated learning approach is DataSHIELD [ 53 ]. In particular, orchestrating privacy-protected analyses of “medical big data'' from different resources is applicable within R and DataSHIELD [ 54 ]. Here, the developed computerized models represent mathematical concepts or trained machine learning (ML)–based approaches to solve a specific task. In this sense, the model is applied to distributed data sets of the protected (clinical) server infrastructure, and the user only sees the model results but does not retrieve any medical records. Moreover, implementations in other programming languages (eg, Python, Julia) have been introduced in the genomic domain and beyond [ 55 ]. Further concepts, such as Personal Health Train, specifically follow the FAIR principles during distributed analyses [ 56 ]. Secure multiparty computation (SMPC) is also a viable technology for solving clinical use cases that require cross-institution data exchange and collaboration [ 57 ]. Current limitations are thought to be addressed in a stepwise manner [ 58 ] or as blockchain [ 59 ].

By using approaches for distributed analyses, researchers are able to train, test, and validate their models on large-scale real-world clinical data. In combination with standardized data formats, these 2 concepts facilitate the use of those models in clinical routine, potentially in the form of a CDSS. This provides a basis for secondary use of observational data in the context of clinical trials, which show particular potential for identifying data characteristics in small cohorts (eg, identification of the individual patient risk for rare diseases or comorbidities).

Harmonization and Processing

Topic 5: extract, transform, and load (etl)—“ etl processes are computational approaches for data harmonization and data unification.”.

Data handling in medical informatics remains a major challenge. Even though most data in medicine are available electronically, the data often lack interoperability [ 60 ]. As a first step to actually use the data, processes to extract, transform, and load (ETL) are needed to obtain harmonized data from different data systems or clinical entities. One important example, among many others, reflects the uniform representation of the date and time in a common format (eg, Year-Month-Date, not Date-Month-Year). The ETL process is therefore a crucial, individual step toward data unification in large clinical systems, which must be secure, safe, and accurate [ 61 ].

The design of an ETL process faces several challenges, including the following: (1) The ETL process should be able to process huge amounts of data at once [ 62 ]; (2) the ETL process should be repeatable—if the source data change, the ETL process needs to be rerun to process the source data (Observational Health Data Sciences and Informatics [OHDSI]) [ 63 ]; (3) expert-level anonymization methodologies might be integrated into ETL workflows whenever possible [ 61 ]; and (4) there is a need to check for loss of data and compromised data integrity. The latter was highlighted in a recent study, in which inaccurate cohort identification took place because erroneous vocabulary mappings of a common data model were used (eg, ETL programming bugs and errors not captured during the quality assurance stages) [ 64 ]. Common solutions to implement ETL processes are code-based (eg, FHIR-to-OMOP [ 65 ]) or via Pentaho Data Integration, which is one of many ETL tools. Further subsequent processing may also include loading data into research data repositories, like OMOP (see Topic 7), tranSMART, and Talend Open Studio, which is a central component of the Integrated Data Repository Toolkit [ 66 ].

Since ETL processes are at the core of data handling, all risks associated with the ETL process need to be thoroughly checked, identified, and assessed, and contingency plans to mitigate these risks should be in place [ 67 ]. Once the ETL processes are executed, the resulting data will be trusted by researchers, who heavily rely on comprehensively checked data integrity to be able to conduct their research on this basis.

Topic 6: FHIR—“Set FHIR to Gain a Communication Standard for Real-time Applications at the Device-to-Device Level.”

Interoperability levels can be divided into technical, syntactic, semantic, and organizational interoperability [ 2 ]. Semantic and syntactic interoperability can be ensured by communication exchange standards, such as the FHIR [ 68 ] standard of Health Level 7 (HL7) and medical terminologies. A suitable starting point for the basic procedures is offered by FHIR drills [ 69 ] or fire.ly [ 70 ].

FHIR is one of many communication standards but will likely change the domain of clinical IT significantly [ 71 , 72 ]. As a communication standard, FHIR harmonizes data formats coming from different CIS and enables data exchange between institutions via a RESTful approach [ 73 ]. Moreover, FHIR is used to connect devices with each other, which means, in particular, that the Integrating the Healthcare Enterprise (IHE) [ 74 ] standard has been revised to support HL7 messaging as well. In turn, IHE has been developing an open-source device tool set for home and hospital use that recently enabled device control capabilities, a capability accelerated during the COVID-19 pandemic to allow nurses and physicians to operate ventilators and infusion devices outside the contaminated patient room [ 75 ].

Utilizing FHIR in multiple applications already shows its versatile and flexible use (eg, in mobile health applications [ 76 ], electrocardiogram monitoring [ 77 ], or wearable devices and precision medicine in digital health [ 72 ]). In particular, the SMART-on-FHIR technology enables third-party app development for health care applications [ 78 ] and encompasses feasible, secure, and time- and resource-efficient solutions [ 79 , 80 ].

Topic 7: OMOP—“Use Common Data Models as Well-defined Representations of Large-scale Research Projects.”

Data harmonization enables research teams to run real-world observational studies based on heterogeneous data across country borders. Thus, harmonized data embedded in a common data model (CDM), which is an agreement about the utilization of standardized terminologies for data representation, is crucial to exchange data and results on a large scale. To foster reliability and trust in the results of observational research on real-world data, it is essential to utilize CDMs whenever possible to ensure a high degree of data analysis reproducibility.

Several CDMs exist for that purpose; the OMOP CDM from the OHDSI community is one of the most promising and established approaches. In comparison with other CDMs, such as the Sentinel CDM or Informatics for Integrating Biology and the Bedside (i2b2), the OMOP CDM has broader terminology coverage [ 81 ]. The importance of the OMOP CDM increased a lot over the last years [ 82 ], not least since the European Medicines Agency initiated the Data Analysis and Real World Interrogation Network (DARWIN) [ 83 ] project to establish a research network in Europe to gain real-world evidence based on OMOP. Moreover, representations of genomic data [ 84 ], oncology [ 85 ], and imaging projects [ 86 ] are also suitable. In addition, the common representation of the data in OMOP semantic interoperability is ensured by utilizing international terminologies and vocabularies, such as SNOMED-CT, the International Statistical Classification of Diseases and Related Health Problems (ICD), the Logical Observation Identifiers Names and Codes (LOINC), and RxNorm to represent every clinical fact in OMOP. Additionally, the open-source OHDSI software stack provides standardized methodology and libraries for data analyses (Athenahene, Atlas, HADES) and training (EHDEN Academy) [ 87 ], as well as a framework to assess and improve data quality to foster reliability and trust in research results [ 88 ].

The OMOP CDM is one possibility to represent and analyze clinical data on a research scale. Definition of new cohorts within OMOP enables researchers to quickly investigate questions spanning multiple research entities. Collectively, both FHIR and OMOP can define the structure and relations of the clinical data corpus, and the individual EHRs provide content to these standardized data reservoirs. In comparison, OMOP is commonly used for static large-scale data analysis of research data, and FHIR is more suitable for rapid data integration scenarios (ie, for real-time applications and analysis). In summary, it is important to know and utilize newly established standards to participate in broader clinical networks for research. This way, all information within the EHR is comparable across different clinical sites and research settings.

Evaluation, Visualization, and Dissemination

Topic 8: data quality—“guarantee high quality and then publish the data.”.

What is meant to be appropriate data quality for health informatics research? In this domain, data quality depends on the quality of single data elements, data completeness, data conformance, and data plausibility aspects that may considerably determine the validity and veracity of analysis results [ 89 , 90 ]. Moreover, data quality across different institutional entities and even health sectors requires additional efforts concerning the different personnel, instruments, and more [ 91 ]. High-quality data at hand is one fundamental requirement that is often difficult or impossible to achieve, which is why the generation of synthetic data can be an alternative that satisfies privacy problems as well as research needs when data are expensive, scarce, or unavailable by augmentation [ 92 ].

First, a major problem is that clinical data have to be electronically recorded, accessed, and standardized in order to run quality assessment processes [ 26 ]. In addition, it would be important to design and use the same data quality tool, standard operating procedures, or ETL mapping rules in all involved institutions. However, in real-life scenarios, there is a lack of both centrally coordinated data quality indicators and formalization of plausibility rules, as well as a repository for automatic querying of the rules, especially in ETL processes [ 93 ]. Although numerous data quality evaluation frameworks exist, no clear and widespread approach has been adopted so far [ 67 , 94 - 96 ]. Even after a well-chosen data quality procedure is properly implemented, clinical data as such cannot be published along with the performed study. As an alternative, synthetic data generation models function in the following 2 different ways: (1) The model is trained, for example, using real-world data and, once trained, will not require any data in the future (model-based approaches), and (2) the model is constantly fed with data to generate synthetic data (data-driven approaches). There are 3 different categories of algorithms used in the generation of synthetic data: probabilistic models, such as Bayesian networks [ 97 ] and Copulas [ 98 ]; ML, such as Classification and Regression Trees (CART); and deep learning methods, such as a generative adversarial network (GAN) [ 99 - 101 ] and variational autoencoder (VAE) [ 102 ].

A combination of appropriate data quality evaluation and synthetic data generation highly facilitates the development of accurate AI models, which are essential in medical studies [ 103 ]. Thus, a corpus of high-quality synthetic data with many patients can be reused by other AI experts for model development and benchmarking. Moreover, it is essential to create an infrastructure that is used across a large community of hospitals; maps the entire treatment process electronically; and only generates interoperable, structured data based on FHIR (Topic 6) and OMOP (Topic 7) in accordance with the FAIR principles (Topic 3). Afterward, one can finally run quality assessment processes.

Topic 9: Clinical Decision Support Systems—“Bring Insights, Not Additional Work, Back to the Clinics via a CDSS and Other User-Centric Applications.”

CDSS are computer systems designed to assist the medical staff with decision-making tasks about individual patients and based on clinical data [ 104 ]. The decision-making process is still, and will remain, on the shoulders of the physician [ 105 ]. The categories of CDSS include knowledge-based systems that make use of clinical rules, nonknowledge-based systems (eg, AI-based systems), and hybrid CDSS that likewise utilize clinical models and knowledge in combination with AI.

The use of a CDSS in a well-implemented clinical workflow has many positive aspects. It may lead to fewer error rates [ 106 ], accelerate rare disease diagnosis [ 107 ], increase radiologists’ job satisfaction [ 108 ], offer personalized cancer treatment [ 109 ], or help with real-time cardiovascular risk assessment [ 110 ]. Interestingly, computerized alerting systems, which are one of the most disseminated CDSS, can decrease drug-drug interactions significantly [ 111 ]. On the other hand, if done improperly, a CDSS can cause alert fatigue by creating too many alerts. If a system is not context-sensitive, alerts can even be inappropriate [ 112 ]. According to Olakotan et al [ 112 ], influencing factors of a well-designed CDSS need to include aspects about the (1) technology (eg, usability, alert presentation, workload, and data entry), (2) human (eg, training, knowledge, skills, attitude, and behavior), (3) organization (eg, rules and regulations, privacy, and security), and (4) and process (eg, waste, delay, tuning, and optimization). To avoid a lack of transparency and facilitate acceptance by physicians, especially with nonknowledge-based systems, current CDSS seek to use explainable AI approaches; however, the selection of methods used to present explanations in an informative and efficient ( clinically useful ) manner remains challenging [ 113 ]. Of note, a CDSS may also have a negative influence on the performance of physicians, especially if inadequate suggestions occur more often, which cannot be compensated with explanations [ 114 ]. However, one among many other prominent approaches to obtain such explanations via ML-based feature selection and ranking can be found in the work from Wolfien et al [ 115 ]. In terms of an OMOP-based implementation in research, there is patient-level prediction (PLP), which is designed to foster the clinical decision-making process concerning diagnoses or treatment pathways based on the EHR of the patient and the current clinical guideline. It is used to answer questions, such as identifying patients among a larger population at higher risk of a certain outcome (eg, occurrence of cancer, severe side effects, or death) by using data in standardized formats (eg, as previously described via OMOP CDM). Once the model is designed, the covariates will be extracted from the respective CDM of the target person within the cohort, and the respective outcome will be predicted (eg, via PLP [ 116 , 117 ] or other customized prediction algorithms). Importantly, the results from model prediction should first be internally validated with previously unseen data and afterward compared with established scoring systems (eg, Framingham Risk Score [ 118 ], SCORE2 [ 119 ]) to connect with already known domain-specific contexts and to prove its benefit in clinical practice. An additional validation with external data, as part of a multicenter study, can be seen as highly beneficial, in which the already presented topics of federated learning (Topic 4) and OMOP (Topic 7) could significantly foster such an essential scenario [ 120 ].

Collectively, a CDSS increases patient safety, assists in clinical management, and can be cost-effective [ 104 ]. In general, findings of even erroneous CDSS can be used to guide the design of new CDSS alerts. However, the existing risks cannot be solved solely on a technical basis and require an interdisciplinary effort. In particular, continuous, clear communication between IT professionals (developers) and health professionals (end users) during the design process is key. Only a profound understanding of the needs and requirements of either of the involved parties can lead to well-designed systems that are actually able to support and relieve physicians in doing their job.

Topic 10: Visualizations—“Improved Dissemination of Local and External Data From Computational Models by Well-defined Interactive Visualizations.”

Large volumes of data collected from patient registries, health centers, genomic databases, and public records can potentially improve the efficiency and quality of health care via enhancing the interoperability of medical systems, assisting in clinical decision-making, and delivering feedback on effective procedures [ 121 ]. However, each and every raw data point must go through different analytical processes until they become useful and interpretable at the point of care.

R and Python are 2 versatile open-source programming languages that have gained popularity for different purposes, such as preprocessing (eg, tidyverse), statistical tests (eg, dplyr), ML and deep learning (eg, mlr package, caret), visualization (eg, ggplot), and writing reports directly using knitr and R markdown (RStudio education [ 122 ]). Like R, Python offers different libraries for data science tasks (eg, open mined [ 123 ]) in addition to a library specifically for health predictive models, namely PyHealth [ 124 ]. Another versatile visualization functionality is offered for both languages via R Shiny [ 125 ] and Plotly Dash [ 126 ]. These 2 platforms enable data scientists to create interactive web applications directly from a script. The applications can be extended using embedded CSS themes, HTML widgets, and Javascript actions. There is already evidence that implementing clinical dashboards or CDSS for immediate access to current patient information can improve processes and patient outcomes [ 127 ], especially if the data sets are further evaluated and refined [ 128 ]. Similar to FHIR, OHDSI provides tools for analyzing data in the OMOP CDM, which are written in R and use Shiny for the visualization. As a plus, data already stored in the OMOP CDM format can be used in systematic studies, patient-level analysis, and population-based estimations from scratch. The cBioPortal is one prime example of a web resource for exploring, visualizing, and analyzing multidimensional data, which reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events [ 129 ]. It was recently demonstrated how cBioPortal can be extended and integrated with other tools to a comprehensive and easily deployable software solution that supports the work of a molecular tumor board [ 130 ] and even deliver meaningful scientific insights [ 131 ]. Another translational research platform for the construction and integration of modern clinical research charts is Informatics for i2b2, which is also at the heart of clinical research [ 132 , 133 ].

Computational approaches and data analyses are tightly connected with medical research; the visualization of such complex data for clinicians in a routine setting especially plays a larger role. The current developments of translational research platforms, such as cBioPortal and i2b2, enable swift translation of research results into the clinic, if adequately adopted and enough trained people supervise the process.

The need for qualified IT specialists in medical informatics has increased continuously in recent years and will continue to grow in the future. On the other hand, medical informatics in Germany faces problems with the ​​promotion of young researchers. These current developments mean that vacancies in IT in hospitals and the health care industry can often not be filled or only after very long vacancies. In addition, these positions often have to be filled with nonspecialist staff due to a lack of applications. To keep track of these recent developments and provide a basis for interdisciplinary communication, we provide our list of 10 topics that could be used by different stakeholders individually ( Figure 2 ). With a particular focus in medicine, improved interdisciplinary communication has already been shown to positively impact patient outcomes and enhance employee engagement [ 134 ].

Exemplary outcome visualization of the underlying study, in which the color coding reflects the initial colors of the proposed sections; it starts with an individual perception of the term medical informatics (MI) based on the individual’s background and ends with acquisition of common domain knowledge for current important topics. CDSS: clinical decision support system; EHR: electronic health record; ETL; extract, transform, and load; FAIR; findable, accessible, interoperable, reusable; FHIR; Fast Healthcare Interoperability Resources; OMOP: Observational Medical Outcomes Partnership.

Furthermore, medical informatics has developed rapidly in recent years. This applies, for example, to new methods, techniques, tools, framework conditions, and organizational structures, especially in the field of medical data science. In particular, definitions of standards and a national digitized data corpus, namely the German Core Dataset [ 135 ], were agreed upon. The actual assessment and collection of digitized data in local university hospitals are utilized in so-called data integration centers. These interoperable research data infrastructures enable rapid multisite research, for example, with complex COVID-19 research data sets (German Corona Consensus Dataset [GECCO]) [ 136 ] including clinical data and data on biosamples from all German university hospitals in pseudonymized form (CODEX) [ 137 , 138 ] or the COVID-19 Data Portal [ 139 ]. The subsequent formation of the Network University Medicine (NUM) strengthens the existing interaction between research and patient care, stabilizes existing structures, and creates new structures that ensure more effective feedback and close cooperation between the clinics. The presented examples of NUM and CODEX, among others [ 140 ], attempt a central approach to bundle and harmonize necessary resources like broad consent or the elektronische Patientenakte (ePa), which is the implementation of EHR as a national entity to ultimately facilitate an interconnected health care system.

Finally, all those involved in medical informatics are called upon to engage in lifelong learning and continuously acquire further qualifications.

Exemplary Implementation of the Addressed Topics in the German Medical Informatics in Research and Care in University Medicine Consortium

This article offers newcomers to medical informatics a first introduction and a wealthy overview of current IT-related topics in research and patient care. Nevertheless, there is also a need for further qualification of employees through new, innovative offers for training, further education, and further training. As part of the MII [ 11 ], all consortia were asked to develop and set up appropriate offers and formats. The Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium [ 141 ] has reacted and set up the part-time training and further education program “Biomedical Informatics and Data Science” [ 142 ] and introduced it at the Mannheim University of Applied Sciences in October 2020. The program includes a time-flexible and individually adaptable part-time online master’s course, as well as certificate courses and programs for further scientific education. In addition to the establishment and continuous further development of a cloud-based learning platform, many new digital and target group–oriented learning resources and application-oriented learning environments were developed and introduced for the master's program.

All 10 topics listed in this article are reflected in the curriculum of the master’s degree and have been offered and dealt with in-depth in the individual courses for more than 2 years. The demand for the master’s program and certificate courses is high, and the evaluation has shown that these topic-specific foci correspond to the training and further education needs of the target groups. One particular aspect that was not covered in the final topics refers to the underlying infrastructure needed to provide the data storage and processing backbone. This aspect would have been too technical for a more broadly set, introductory article, such as this article. A starting point for more in-depth information about this aspect can be obtained from further literature [ 143 , 144 ]. However, to offer a practical start to the 10 topics, we provide links to well-known tutorials and hands-on materials ( Table 1 ).

We suggest a set of 10 topics to ease the start for researchers and clinicians to become engaged with basic concepts in health informatics research. We provide current review articles for more in-depth reading about the specific topic and present practical hands-on material. The presented topics likewise serve as a broad overview of the medical informatics research domain but also guide individuals and their specific interests. For example, a computer scientist familiar with CDSS development could more easily connect with important aspects, such as data privacy, FHIR, and specific EHRs that are highly relevant for daily work. In contrast, medical experts can obtain an overview of behind-the-scenes technologies, like ETL processes and underlying data quality approaches that are finally visualized as a summarizing clinical dashboard. For readers, we provided a first step toward an improved understanding of a lively and quickly expanding field, but more novel technologies and practical knowledge are ahead. Suggestions and contributions to improve the current topics can be made at GitHub, which will likewise enable content and readers to stay current [ 12 ].

Acknowledgments

This work was supported by the Federal Ministry of Health (BMG) and the German Federal Ministry of Education and Research (BMBF) within the Medical Informatics Initiative Medical Informatics in Research and Care in University Medicine (MIRACUM) Consortium (FKZ: 01ZZ180L [Dresden]; FZK: 01ZZ180A [Erlangen]; FKZ: 01ZZ1801M [Greifswald]). The article processing charge was funded by the joint publication funds of the Technische Universität (TU) Dresden, including the Carl Gustav Carus Faculty of Medicine; Saxon State and University Library (SLUB) Dresden; and the Open Access Publication Funding of the German Research Foundation (DFG).

The funding sources had no involvement in the conduct of the research and preparation of the article.

Abbreviations

Authors' Contributions: MW conceptualized the study, curated the data, and wrote the original manuscript draft. MW also defined the initial topics 1 and 2; MZ defined the initial topics 3 and 4; YP defined the initial topics 5 and 6; IR defined the initial topics 7 and 8; and NA defined the initial topics 8, 9, and 10. MS provided the resources and supervised the study. The topics were revised and extended by KF, AK, SG, DK, KLH, ICJ, CS, JS, TS, PS, and DW. MW, NA, YP, MZ, IR, and MS performed the formal analysis, and MW, NA, and MS created the visualizations. NA, YP, MZ, IR, and MS wrote, reviewed, and edited the manuscript, and all authors read and agreed to the final version of the manuscript.

Conflicts of Interest: None declared.

151+ Public Health Research Topics [Updated 2024]

The important area of public health research is essential to forming laws, influencing medical procedures, and eventually enhancing community well-being. As we delve into the vast landscape of public health research topics, it’s essential to understand the profound impact they have on society.

This blog aims to provide a comprehensive guide to selecting and understanding the diverse array of public health research topics.

Overview of Public Health Research Topics

Table of Contents

Public health research encompasses a wide range of subjects, reflecting the interdisciplinary nature of the field. From epidemiology and health policy to environmental health and infectious diseases, researchers navigate through various dimensions to address complex health challenges.

Each category holds its own significance, contributing to the overall understanding of public health dynamics.

Key Considerations in Selecting Public Health Research Topics

• Current Relevance: Assess the timeliness of potential topics by considering recent health trends, emerging issues, and societal concerns.
• Impact on Public Health: Evaluate the potential impact of the research on improving health outcomes, addressing disparities, or influencing policy and interventions.
• Feasibility and Resources: Gauge the practicality of conducting research on a particular topic, considering available resources, data accessibility, and research infrastructure.
• Ethical Considerations: Scrutinize the ethical implications of the research, ensuring it aligns with ethical standards and guidelines, especially when dealing with vulnerable populations or sensitive topics.

Top 151+ Public Health Research Topics

Epidemiology.

• The Impact of Social Determinants on Disease Outcomes
• Patterns and Trends in Emerging Infectious Diseases
• Investigating Health Disparities among Different Ethnic Groups
• Childhood Obesity and its Long-Term Health Consequences
• Assessing the Effectiveness of Contact Tracing in Disease Control

Health Policy

• Universal Healthcare: Comparative Analysis of Global Models
• The Role of Telemedicine in Improving Healthcare Access
• Evaluating Mental Health Policies and Their Impact on Communities
• Assessing the Impact of Affordable Care Act on Public Health
• Vaccine Policies and Public Perception: A Comprehensive Study

Environmental Health

• Climate Change and Health: Adapting to the Challenges
• Air Quality and Respiratory Health in Urban Environments
• Waterborne Diseases and Strategies for Safe Water Supply
• Occupational Health Hazards: A Comprehensive Workplace Analysis
• The Impact of Green Spaces on Mental Health in Urban Areas

Infectious Diseases

• Antimicrobial Resistance: Strategies for Mitigation
• Vaccination Strategies and Herd Immunity
• Global Health Security: Preparedness for Pandemics
• The Impact of Vector-Borne Diseases on Public Health
• Emerging Trends in Antibiotic-Resistant Infections

Chronic Diseases

• Lifestyle Interventions for Preventing Cardiovascular Diseases
• Genetic Factors in the Development of Cancer: A Comprehensive Study
• Aging and Health: Addressing the Healthcare Needs of the Elderly
• Diabetes Prevention Programs: Efficacy and Implementation
• Mental Health in Chronic Disease Patients: Bridging the Gap

Maternal and Child Health

• Maternal Mortality: Understanding Causes and Prevention
• The Impact of Breastfeeding on Infant Health and Development
• Childhood Immunization: Barriers and Strategies for Improvement
• Teenage Pregnancy and Its Long-Term Health Consequences
• Mental Health Support for Postpartum Women: Current Gaps and Solutions

Health Behavior and Promotion

• Smoking Cessation Programs: Effectiveness and Challenges
• Physical Activity Promotion in Schools: Strategies for Success
• Nutrition Education and Its Impact on Healthy Eating Habits
• Mental Health Awareness Campaigns: Assessing Public Perceptions
• The Role of Social Media in Health Promotion

Global Health

• Assessing the Impact of International Aid on Global Health
• Water, Sanitation, and Hygiene (WASH) Programs in Developing Countries
• The Role of Non-Governmental Organizations in Global Health
• Communicable Disease Control in Refugee Populations
• Global Access to Essential Medicines: Challenges and Solutions

Community Health

• Community-Based Participatory Research: Best Practices and Challenges
• The Impact of Community Health Workers on Health Outcomes
• Health Literacy and its Relationship to Health Disparities
• Assessing the Effectiveness of Mobile Health (mHealth) Interventions
• Community Resilience in the Face of Public Health Crises

Healthcare Quality and Patient Safety

• Hospital-Acquired Infections: Strategies for Prevention
• Patient Safety Culture in Healthcare Organizations
• Quality Improvement Initiatives in Primary Care Settings
• Healthcare Accreditation: Impact on Patient Outcomes
• Implementing Electronic Health Records: Challenges and Benefits

Mental Health

• Stigma Reduction Programs for Mental Health Disorders
• Integrating Mental Health into Primary Care Settings
• The Impact of COVID-19 on Mental Health: Long-Term Implications
• Mental Health in the Workplace: Strategies for Employee Well-being
• Suicide Prevention Programs: Effectiveness and Outreach

Health Disparities

• Racial Disparities in Healthcare: Addressing Systemic Inequities
• LGBTQ+ Health Disparities and Inclusive Healthcare Practices
• Socioeconomic Status and Access to Healthcare Services
• Geographical Disparities in Health: Rural vs. Urban
• The Impact of Gender on Health Outcomes and Access to Care

Public Health Education

• Evaluation of Public Health Education Programs
• Innovative Approaches to Teaching Public Health Concepts
• Online Health Education Platforms: Opportunities and Challenges
• Interdisciplinary Training in Public Health: Bridging Gaps
• Continuing Education for Public Health Professionals: Current Landscape

Digital Health

• The Role of Wearable Devices in Health Monitoring
• Telehealth Adoption: Barriers and Opportunities
• Health Apps for Chronic Disease Management: User Perspectives
• Blockchain Technology in Healthcare: Privacy and Security Implications
• Artificial Intelligence in Disease Diagnosis and Prediction

Health Economics

• Cost-Effectiveness of Preventive Health Interventions
• The Impact of Healthcare Financing Models on Access to Care
• Pharmaceutical Pricing and Access to Essential Medicines
• Economic Evaluation of Health Promotion Programs
• Health Insurance Coverage and Health Outcomes: A Global Perspective

Innovations in Public Health

• 3D Printing in Healthcare: Applications and Future Prospects
• Gene Editing Technologies and their Ethical Implications
• Smart Cities and Public Health: Integrating Technology for Well-being
• Nanotechnology in Medicine: Potential for Disease Treatment
• The Role of Drones in Public Health: Surveillance and Intervention

Food Safety and Nutrition

• Foodborne Illness Outbreaks: Investigating Causes and Prevention
• Sustainable Food Systems: Implications for Public Health
• Nutritional Interventions for Malnutrition in Developing Countries
• Food Labeling and Consumer Understanding: A Critical Review
• The Impact of Fast Food Consumption on Public Health

Substance Abuse

• Opioid Epidemic: Strategies for Prevention and Treatment
• Harm Reduction Approaches in Substance Abuse Programs
• Alcohol Consumption Patterns and Public Health Outcomes
• Smoking and Mental Health: Exploring the Connection
• Novel Psychoactive Substances: Emerging Threats and Strategies

Occupational Health

• Workplace Stress and Mental Health: Intervention Strategies
• Occupational Hazards in Healthcare Professions: A Comparative Analysis
• Ergonomics in the Workplace: Improving Worker Health and Productivity
• Night Shift Work and Health Consequences: Addressing Challenges
• Occupational Health and Safety Regulations: A Global Overview

Disaster Preparedness and Response

• Pandemic Preparedness and Lessons from COVID-19
• Natural Disasters and Mental Health: Post-Traumatic Stress
• Emergency Response Systems: Improving Timeliness and Efficiency
• Communicating Health Risks During Emergencies: Public Perception
• Collaborative Approaches to Disaster Response in Global Health

Cancer Research

• Precision Medicine in Cancer Treatment: Current Advancements
• Cancer Screening Programs: Efficacy and Challenges
• Environmental Factors and Cancer Risk: Exploring Connections
• Survivorship Care Plans: Enhancing Quality of Life after Cancer
• Integrative Therapies in Cancer Care: Complementary Approaches

Sexual and Reproductive Health

• Access to Contraception in Developing Countries: Challenges and Solutions
• Comprehensive Sex Education Programs: Impact on Teen Pregnancy
• Reproductive Health Rights: Global Perspectives and Challenges
• Infertility Treatment: Ethical Considerations and Societal Impact
• Maternal and Child Health in Conflict Zones: Addressing Challenges

Cardiovascular Health

• Hypertension Prevention Programs: Strategies and Effectiveness
• Cardiovascular Disease in Women: Gender-Specific Risk Factors
• Innovations in Cardiac Rehabilitation Programs
• Artificial Heart Technology: Advancements and Ethical Implications
• Impact of Air Pollution on Cardiovascular Health: A Global Concern

Social Determinants of Health

• Educational Attainment and Health Outcomes: Exploring Links
• Income Inequality and its Impact on Population Health
• Social Support Networks and Mental Health: A Comprehensive Study
• Neighborhood Environments and Health Disparities
• Employment and Health: The Interplay of Work and Well-being

Genomics and Public Health

• Population Genomics and its Implications for Public Health
• Genetic Counseling and Education: Empowering Individuals and Families
• Ethical Issues in Genetic Research: Privacy and Informed Consent
• Pharmacogenomics: Tailoring Drug Therapies to Individual Genotypes
• Gene-Environment Interactions in Disease Risk: Unraveling Complexities

Public Health Ethics

• Informed Consent in Public Health Research: Current Practices
• Ethical Challenges in Global Health Research: Balancing Priorities
• Confidentiality in Public Health Reporting: Striking the Right Balance
• Research with Vulnerable Populations: Ethical Considerations
• Ethical Implications of Emerging Technologies in Healthcare

Health Communication

• The Role of Media in Shaping Public Health Perceptions
• Health Literacy Interventions: Improving Understanding of Health Information
• Social Media Campaigns for Public Health Promotion: Best Practices
• Tailoring Health Messages for Diverse Audiences: Cultural Competency
• Risk Communication in Public Health Emergencies: Lessons Learned

Nutrigenomics

• Personalized Nutrition Plans based on Genetic Makeup
• Impact of Nutrigenomics on Chronic Disease Prevention
• Ethical Considerations in Nutrigenomics Research
• Public Perceptions of Nutrigenomic Testing: A Qualitative Study
• Integrating Nutrigenomics into Public Health Policies

Public Health and Artificial Intelligence

• Predictive Analytics in Disease Surveillance: Harnessing AI for Early Detection
• Ethical Considerations in AI-Driven Health Decision Support Systems
• Machine Learning in Epidemiology: Predicting Disease Outbreaks
• Natural Language Processing in Public Health: Text Mining for Insights
• Bias in AI Algorithms: Implications for Health Equity

Health Disparities in Aging

• Geriatric Health Disparities: Bridging the Gap in Elderly Care
• Ageism in Healthcare: Addressing Stereotypes and Discrimination
• Social Isolation and Health Consequences in Aging Populations
• Access to Palliative Care for Older Adults: A Global Perspective
• Alzheimer’s Disease and Ethnic Disparities in Diagnosis and Treatment
• Loneliness and Mental Health in the Elderly: Interventions and Support

Research Methodologies in Public Health

Public health research employs various methodologies, including quantitative, qualitative, and mixed-methods approaches. Each method brings its own strengths to the research process, allowing researchers to gain a comprehensive understanding of the complex issues they investigate. 

Community-based participatory research is another valuable approach, emphasizing collaboration with communities to address their specific health concerns.

Challenges and Opportunities in Public Health Research

While public health research is immensely rewarding, it comes with its own set of challenges. Funding constraints, ethical dilemmas, the need for interdisciplinary collaboration, and the integration of technology pose both obstacles and opportunities. 

Researchers must navigate these challenges to ensure their work has a meaningful impact on public health.

In conclusion, public health research topics are diverse and dynamic, reflecting the complex nature of the field. As researchers embark on their journeys, they must carefully consider the relevance, impact, and ethical implications of their chosen topics. 

The collaborative and interdisciplinary nature of public health research positions it as a powerful tool in addressing the health challenges of our time. By exploring the depths of these topics, researchers contribute to the collective effort to build healthier and more equitable communities. 

As we move forward, a continued exploration of relevant public health research topics is essential for shaping the future of healthcare and improving the well-being of populations worldwide.

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Americans overwhelmingly say access to ivf is a good thing.

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Half of Americans or more say they are extremely or very comfortable talking about their mental health with a close friend, an immediate family member or a mental health therapist.

9 facts about Americans and marijuana

88% of Americans say marijuana should be legal for medical or recreational use. Just 11% say the drug should not be legal in any form.

Most Americans Favor Legalizing Marijuana for Medical, Recreational Use

Americans largely favor legalization of the drug, including 57% who say it should be legal for both medical and recreational use.

As obesity rates rise in the U.S. and worldwide, new weight-loss drugs surge in popularity

Last year, Ozempic, Rybelsus and Wegovy had combined sales of about $21.1 billion globally – up 89% since 2022.

How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

Just 20% of the public views the coronavirus as a major threat to the health of the U.S. population and only 10% are very concerned about getting a serious case themselves. In addition, a relatively small share of U.S. adults (28%) say they’ve received an updated COVID-19 vaccine since last fall.

How Americans View Weight-Loss Drugs and Their Potential Impact on Obesity in the U.S.

About three-quarters of Americans say they have heard a lot or a little about Ozempic, Wegovy and other similar drugs that are being used for weight loss. Among those familiar with these drugs, 53% think they are good options to lose weight for people with obesity or a weight-related health condition.

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The Digital Variome: Understanding the Implications of Digital Tools on Health

CERSI Collaborators: University of California at San Francisco (UCSF): Andrew Auerbach MD MPH (UCSF, Variome and DOVeS); Benjamin Rosner MD PhD (UCSF, Variome and DOVeS co-PI); Stanford Univerisity (Stanford): Matthew Horridge PhD (Stanford, DOVeS)

FDA Collaborators: Center for Devices and Radiological Health(CDRH): Bakul Patel, MS, MBA (Formerly of CDRH); Vinay Pai, PhD; Catherine Bahr; Leeda Rashid, MD, MPH, ABFM; Arti Tandon, PhD; Charlie Yongpravat, PhD; Anindita Saha, PhD

CERSI Subcontractors: Flying Buttress Associates- Jeph Herrin, PhD

CERSI In-Kind Collaborators: Stanford Univerisity (Stanford): Mark Musen, PhD (Stanford, DOVeS)

Non-Federal Entity Collaborators: Johnson and Johnson- Karla Childers, MSJ, Paul Coplan, ScD, MBA, Stephen Johnston, MSc

Project Start Date: October 12, 2021 Project End Date: February 28, 2022-->

Regulatory Science Framework

Charge I “Modernize development and evaluation of FDA-regulated project” and Focus Area “C. Analytical and computational Methods.”

Regulatory Science Challenge

There is a consistent need to research and develop the methods used to ensure the quality and safety of FDA-regulated products. Research and development in this area helps FDA employ scientifically valid approaches for combining patient input and data from multiple sources. These 'real world' data insights are key to informing regulatory decision-making both for traditionally regulated products (e.g. drugs and devices) as well as for new and emerging products such as digital health tools. Furthermore, as the FDA considers new products for regulatory approval, they may examine whether these new products are “substantially equivalent” to previously approved products. The wave of new digital health products creates regulatory decision challenges that need to be informed by real world data and data that help identify the degree to which products may be similar.

Project Description and Goals

The Digital Variome project extends work that is ongoing as part of our overarching CERSI project Developing Frameworks and Tools for Integration of Digital Health Tools into Clinical Practice , a national network of leading academic medical centers, researchers, and innovators working to identify how real world measures and data can be used across types of software used in health, and the eventual data sources required to carry out real world performance measurement and post-market surveillance of digital health tools (DHTs). ADviCE identified several challenges to DHT adoption: (1) Variable definitions of which DHTs are relevant to clinical care delivery; (2) Lack of consistent, common terms to describe DHTs during selection, (3) Wide variability in how health systems integrate DHTs into practice and, (4) Lack of a framework and tools to evaluate DHTs’ real-world performance through post-market surveillance.

The ADviCE project in turn framed the goals of the Variome proposal, which focused on identifying data sources and potential partnerships needed to create a learning health collaboration that might leverage tools such as NEST or resources (e.g., PCORnet, or payor data) to provide data needed to carry out post-market surveillance of DHTs. Few of these data networks or partnerships could gather information needed for DHT post-market surveillance, so investigators turned their attention to tools which would both facilitate efficient specification of DHT characteristics while also being flexibly able to accommodate measures that might vary between DHTs even though applicable to similar patients or health systems.

With this realization, the research team extended their Priority Measurement framework and expanded it to represent a range of potential metrics applicable to real world performance. Investigators built on their consensus work from Developing Frameworks and Tools for Integration of Digital Health Tools into Clinical Practice to identify specific domains and measures relevant to each broad domain. For example, within the area of Product Performance  Cybersecurity, investigators developed subdomains where metric identification was recognized as a key next step. Not surprisingly, a wide range of potential measures were identified. For example, each of the Measure Concepts for Real World Health might have dozens or even hundreds of patient- or population-specific metrics that are supported by evidence, are broadly used, or both.

This realization led to development of the Digital medicine Outcomes Value Set (DOVeS), as a powerful and flexible approach to classifying digital health tools according to key features and important clinical outcomes identified by our work to this point.

DOVeS was blueprinted using Protégé software using input from research collaborators and professional ontologists so that it permits flexible expansion as outcomes or population definitions change and technology advances. DOVeS was then tested and validated against real DHT and company characteristics to yield a working prototype that facilitates search and display of data using the overall ADviCE/Variome approach. DOVeS has been published on BioPortal and is publicly available for broad use.

DOVeS has the potential to be scaled up to include a broader and more representative sample of real-world digital health tools, accommodate new technologies (e.g. large language lodels (LLMs)), while also being tested for usability and feasibility as a practical framework for use by health systems, vendors, and regulators use.

Research Outcomes/Results

There are several outcomes to date associated with the development of the Variome project and DOVeS Ontology. The first is that the DOVeS ontology has been expanded substantially over the course of this support, informed by real world digital health outcomes gleaned from industry and academic experts. The second is that DOVeS has been made publicly available on the BioPortal website so that a community of digital health experts may continue to contribute to it over time. The third is that a prototype user interface overlying DOVeS has been created (only a non-functional wireframe was originally proposed) leading to functional demonstrations that show the power and value of DOVeS in identifying tools based on common outcomes. Fourth, several public presentations of DOVeS have been made. Finally, a peer reviewed publication on the development of DOVeS is forthcoming and will help disseminate awareness of the ontology and its value. In the future, investigators hope to convert the prototype front end user interface into a robust platform capable of supporting regulatory insights as well as health system leader inquiries and decisions about digital health tools.

Research Impacts

This project enhances foundational requirements for regulatory science research by providing the FDA and other stakeholders with a new way to categorize and identify digital health tools based on outcomes they influence. This is particularly valuable to enable more appropriate apples-to-apples comparison of digital health tools that influence similar outcomes which could be valuable for "substantial equivalence" assessment as well as both superiority and non-inferiority considerations. The ontology is also particularly valuable for ongoing post-market surveillance.

Publications

No peer-reviewed publications to date; Investigators plan to analyze and publish follow-up study results.

Dr. Auerbach has published invited editorials in JAMA IM on digital health regulation based in part on his experiences with ADviCE.

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May 24, 2024

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Research finds connection between risk factors for periodontitis and general health

by Kari Øverby, University of Oslo

Periodontitis is a disease that affects the supporting tissues of the teeth and is associated with other types of diseases. A study conducted at the faculty of dentistry shows that patients with severe periodontitis also have a higher prevalence of cardiovascular disease, diabetes, and COPD.

The study is published in the journal BMC Oral Health with related previous research published in Journal of Clinical Periodontology .

Ida Haukåen Stødle is a specialist in periodontology and has just completed her doctorate at the faculty.

She shares the following:

The overall purpose of my thesis was to investigate the prevalence of periodontitis and to examine whether we could identify conditions that may be associated with periodontitis. Since periodontitis affects many people, these associations may also be relevant for many, and therefore we have a responsibility to address these potential associations with our patients, says Ida.

Oral health is important for overall health. Maybe we should not consider oral health as something separate but consider health in general. To highlight oral health on its own entails an artificial focus. Interaction between different diseases indicates that we must consider general health as part of periodontal treatment. We should inform patients and encourage them to speak to their general practitioner if they have severe periodontitis and other health challenges, Ida continues.

Periodontitis is an inflammatory condition that can lead to tooth loss and is caused by bacteria that naturally occur in the oral flora. The disease can develop if there is an overgrowth of these bacteria, but not for everyone. The susceptibility varies among individuals.

A new classification of periodontitis

Part of the task was to look at the prevalence of periodontitis in the surveyed population. In 2018, a new globally accepted classification of periodontal diseases was published, which forms the basis for the initial investigations, says Stødle.

We reported the overall prevalence and further prevalence of different severity levels of periodontitis. Once we had identified the participants with periodontitis, we further examined them for any other concurrent diseases, such as cardiovascular disease and diabetes.

We know that several conditions are associated with periodontitis. We selected some to see how they were expressed using the new classification. The findings largely corresponded to previous observations. We also found that those with severe periodontitis, those with significant periodontal bone loss, also have a higher prevalence of other diseases compared to those with little or no periodontitis.

We also included rheumatic conditions in this research but found no association with periodontitis for individuals with rheumatism. This was somewhat surprising because we already know about this association, and since rheumatoid arthritis is also an inflammatory condition, one would expect these diseases to occur together, Ida explains.

There may be several reasons why we did not observe any association. How we included rheumatic conditions in the material, whether we have thoroughly differentiated between healthy and sick individuals, and whether the dataset is large enough, for example. We did not attempt to distinguish between the various forms of rheumatoid arthritis. These factors contribute to the findings in our studies.

Uncertain findings for celiac disease and associations with periodontitis

We also looked at celiac disease. It was a separate study; it was exciting because there is little research on these two diseases. We had no expectations that there would be any difference in periodontal bone loss in individuals with and without celiac disease.

Nevertheless, we found that those with celiac disease have less frequent bone loss as if celiac disease protected against periodontitis. We do not know what lies behind this observation or how the relationship can be explained, and due to this uncertainty, it is hard to draw any conclusions. It would be exciting if further research could be done on this, says Ida.

Extensive and comprehensive material

The studies are based on a vast material, in which almost 5,000 adults from Nord-Trøndelag have participated. The transferability is good for small towns and rural areas. The population from larger cities, where the demography may differ, is not included. Therefore, the findings cannot necessarily represent all population groups in Norway. Population studies (the HUNT studies) have been conducted in this population in Nord-Trøndelag several times, precisely because it is particularly suitable due to stability.

The public dental service (DOT) in Trøndelag, in collaboration with the Oral Health Competence Center, TkMidt carried out the clinical oral and dental health examinations used in the doctoral work. The dentists and dental hygienists who collected this large amount of data did extensive work.

There were periodontal examinations, but also caries examinations and examinations of mucous membranes, in addition to X-rays of all the nearly 5000 participants. The X-rays consisted of a panoramic X-ray image and four smaller images from each participant.

X-rays and further work were analyzed in collaboration with my supervisors, Odd Carsten Koldsland and Anders Verket. The results of the X-ray analysis were compared with data from the clinical examinations. Statistical calculations also made up a significant part of the analyses. Several employees from TkMidt contributed to this. The work has resulted in three articles, Ida explains.

Self-reported disease

The method is partly based on self-reported illness and not exclusively on medical examinations. Self-reported data is weaker than if we had confirmed medical diagnoses and it must be considered when interpreting findings. Blood samples have been obtained from all participants, including blood sugar measurements.

This strengthens the material, and studies have shown that when the disease is well-defined, such as diabetes, there is a good correlation between self-reported illness and an actual diagnosis. Self-reporting is a limitation in our data, but not to such an extensive extent that we do not believe the results.

Medical personnel also obtained many other variables, such as weight, height, and blood pressure. We had a vast amount of data on inflammatory mediators or cells. We did not use it because some choices had to be made. But the data from HUNT are extensive and good and are well-suited for a hypothesis-generating study like this, says Ida.

Preventing periodontitis and diseases associated with periodontitis

We know that the treatment of periodontitis reduces inflammation and can improve blood sugar control in some cases for diabetics. These are connections we need to keep a high focus on. We need to communicate information so patients and health care professionals can benefit from this knowledge—for treatment and to prevent disease.

Risk factors are often the same for several diseases and conditions, including periodontitis, such as smoking. By reducing smoking, the risk for multiple conditions is also reduced. We can discuss this with patients and refer them further if needed.

We should specifically pay attention to the part of the population that goes to the dentist but may not easily seek other health care services. Collaboration across health care professions and having good information flow should be central in preventing periodontitis and diseases associated with periodontitis, concludes Ida.

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Health and economic benefits of breastfeeding quantified

Among half a million scottish infants, those exclusively breastfed were less likely to use healthcare services and incurred lower costs to the healthcare system.

Breastmilk can promote equitable child health and save healthcare costs by reducing childhood illnesses and healthcare utilization in the early years, according to a new study published this week in the open-access journal PLOS ONE by Tomi Ajetunmobi of the Glasgow Centre for Population Health, Scotland, and colleagues.

Breastfeeding has previously been found to promote development and prevent disease among infants. In Scotland -- as well as other developed countries -- low rates of breastfeeding in more economically deprived areas are thought to contribute to inequalities in early childhood health. However, government policies to promote child health have made little progress and more evidence on the effectiveness of interventions may be needed.

In the new study, researchers used administrative datasets on 502,948 babies born in Scotland between 1997 and 2009. Data were available on whether or not infants were breastfed during the first 6-8 weeks, the occurrence of ten common childhood conditions from birth to 27 months, and the details of hospital admissions, primary care consultations and prescriptions.

Among all infants included in the study, 27% were exclusively breastfed, 9% mixed fed and 64% formula fed during the first 6-8 weeks of life. The rates of exclusively breastfed infants ranged from 45% in the least deprived areas to 13% in the most deprived areas.

The researchers found that, within each quintile of deprivation, exclusively breastfed infants used fewer healthcare services and incurred lower costs compared to infants fed any formula milk. On average, breastfed infants had lower average costs of hospital care per admission (£42) compared to formula-fed infants (£79) in the first six months of life and fewer GP consultations (1.72, 95% CI: 1.66 -- 1.79) than formula-fed infants (1.92 95% CI: 1.88 -- 1.94). At least £10 million of healthcare costs could have been avoided if all formula-fed infants had instead been exclusively breastfed for the first 6-8 weeks of life, the researchers calculated.

The authors conclude that breastfeeding has a significant health and economic benefit and that increasing breastfeeding rates in the most deprived areas could contribute to the narrowing of inequalities in the early years.

• Breastfeeding
• Infant's Health
• Today's Healthcare
• Health Policy
• Public Health
• Disaster Plan
• Poverty and Learning
• Education and Employment
• Early childhood education
• Upper respiratory tract infection
• Social inclusion
• Health science
• Evidence-based medicine

Story Source:

Materials provided by PLOS . Note: Content may be edited for style and length.

Journal Reference :

• Omotomilola Ajetunmobi, Emma McIntosh, Diane Stockton, David Tappin, Bruce Whyte. Levelling up health in the early years: A cost-analysis of infant feeding and healthcare . PLOS ONE , 2024; 19 (5): e0300267 DOI: 10.1371/journal.pone.0300267

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As science and health communicators, our main goal is to share our institutions’ wealth of science and health knowledge. We strive to make the information accessible to a broad range of people — from scientists and health professionals to health educators to patients and the general public. By pooling the experience and advice from experts in our community, we’ve started a list of strategies for communicating science and health research to the public.

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• Check the study’s sample size ,  methodology , and potential limitations . Given the study design, was the sample size large enough to draw firm conclusions? Were essential design elements of the study clearly communicated? Are the results consistent with previous studies? Were the results preliminary, necessitating a larger follow-up study? Ideally, the peer reviewers will ensure these are stated in the paper.
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This page last reviewed on April 3, 2024

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