phd in health technology assessment

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Health Technology Assessment (HTA)

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Health Technology Assessment

The doctoral programme in health technology assessment (HTA) conveys methods in evidence-based medicine (EBM), clinical epidemiology, health economics and decision science to the users and creators of evidence-based records and HTA reports, and prepares stakeholders and tomorrow’s healthcare leaders to perform their future duties and tasks.

The research focus and thesis topics include: assessment of preventive, diagnostic, therapeutic, rehabilitative and management/system-related procedures in terms of efficacy, safety, benefit-risk balance, cost effectiveness, and ethical, legal and social implications (ELSI). Quantitative methods are applied, e.g. data set analysis, meta analysis, indirect comparisons, analysis of relative effectiveness, benefit/risk analysis, cost effectiveness analysis and budget impact analysis as well as qualitative procedures from the field of ELSI.

The doctoral degree gives students the opportunity to participate in the ongoing national and international HTA research activities of the Department für Public Health, Health Services Research and Health Technology Assessment.

The doctoral programme can be completed in German or English.

Selected thesis supervisors:

• Univ.-Prof. Dr. Uwe Siebert, Institut für Public Health, Medical Decision Making und HTA

Academic Faculty Representative:

Univ.-Prof. Dr. Uwe Siebert Institute of Public Health, Medical Decision Making und HTA [email protected]

• Doctoral Regulations

• Netherlands
• Utrecht University via AcademicTransfer
• Posted on: 5 June 2024

PhD in Health Technology Assessment

Job Information

Offer description.

The uncertainty about the added value of orphan medicinal products (OMPs) is often substantially high because the available clinical studies contain limited information about clinical outcomes and long-term effectiveness. Despite the uncertainties, there is social pressure to make these highly prized OMPs quickly available for a small group of patients with life-threatening conditions. The question is whether a cyclic Health Technology Assessment (HTA) approach could contribute to more controlled and affordable access to these medicines. Your job You will be on the frontline of developing a cyclic HTA approach to OMPs. Your work will focus on:

• mapping current international activities on cyclic HTA for OMPs. The emphasis will be on which elements of cyclic HTA approach, such as horizon scanning, scientific advice, assessment of efficacy and cost-effectiveness, and appropriate use, should have an initial priority in establishing cyclic HTA of OMPs;
• assessing whether and how the available national and international horizon scanning programmes maybe used to facilitate cyclic HTA of OMPs;
• evaluating current scientific methods for determining the value of OMPs (relative effectiveness and cost-effectiveness) and considering whether new, more experimental methods might be used in the future;
• determining the role of international patient registries for orphan diseases in monitoring appropriate care and using managed entry arrangements for OMPs.

The methods you will apply to study these questions include comparative document analyses, quantitative descriptive and association studies, and complex systems analyses. This PhD position is part of the Academic Research Network HTA, a collaboration between the National Health Care Institute (Zorginstituut Nederland (ZIN)), Erasmus University Rotterdam, and Utrecht University. The network aims to closely bridge HTA research and HTA policy by supporting specific HTA research projects closely linked to policy questions. That means you will work closely with other PhD candidates within this network and structurally interact with the HTA policy officers at ZIN. Moreover, your work will entail close collaboration with stakeholders such as patients, clinicians and health technology developers on a national and international level.

Where to apply

Requirements.

We are looking for a candidate who meets the following criteria:

• an MSc in pharmaceutical or (bio)medical sciences, health economics and/or health technology assessment, epidemiology, or another relevant field;
• the ability and enthusiasm to learn quantitative and qualitative methods within Health Technology Assessment;
• an interest in cross-disciplinary work within Health Technology Assessment;
• the ability to work independently, be self-reflective, and take initiative and responsibility for your projects.
• good oral and written communication skills in English and Dutch are essential to ensure effective collaboration and communication with the Dutch health technology assessment stakeholders’ community.

We consider it a plus if you have experience with:

• complex health systems (i.e. international health policies);
• data analyses; qualitative (e.g. NVivo) and/or quantitative (e.g. R, SAS, or SPSS);
• working with different types of stakeholders, such as HTDs, clinicians, and healthcare decision-makers from public institutes or governments;
• working in a dynamic environment on multiple projects simultaneously;
• teaching graduate students and supervising bachelor and master research projects within the pharmaceutical policy domain.

Additional Information

• a position (1.0 FTE) for one year, with an extension to a total of four years upon successful assessment;
• a full-time gross salary between €2,770 in the first year and €3,539 in the fourth year of employment in scale P of the Collective Labour Agreement Dutch Universities (CAO NU);
• 8% holiday bonus and 8.3% end-of-year bonus;
• a pension scheme, partially paid parental leave, and flexible employment conditions based on the Collective Labour Agreement Dutch Universities.

In addition to the employment conditions from the CAO for Dutch Universities, Utrecht University has a number of its own arrangements. These include agreements on professional development , leave arrangements, sports and cultural schemes and you get discounts on software and other IT products. We also give you the opportunity to expand your terms of employment through the Employment Conditions Selection Model. This is how we encourage you to grow. For more information, please visit working at Utrecht University .

As Utrecht University, we want to be a home for everyone. We value staff with diverse backgrounds, perspectives and identities, including cultural, religious or ethnic background, gender, sexual orientation, disability or age. We strive to create a safe and inclusive environment in which everyone can flourish and contribute. If you are enthusiastic about this position, just apply via the "Apply now" button! Please enclose:

• your letter of motivation;
• your Curriculum Vitae;
• the names, telephone numbers, and email addresses of at least two references.

If this specific opportunity isn’t for you, but you know someone else who may be interested, please forward this vacancy to them. Some connections are fundamental – Be one of them #FundamentalConnection

For more information, please contact Prof. dr. Wim G. Goettsch at [email protected] . Do you have a question about the application procedure? Please send an email to [email protected] .

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Health Economics & Health Technology Assessment PhD

University of glasgow, different course options.

• Key information

Course Summary

Tuition fees, entry requirements, university information, similar courses at this uni, key information data source : idp connect, qualification type.

PhD/DPhil - Doctor of Philosophy

Subject areas

Health Economics Medical Technology

Course type

Our aim is to be the world-leading centre for health economic and health technology assessment research and education, working to improve health and wellbeing through better decision-making.

The Institute of Health & Wellbeing (IHW) spans medical and social sciences and offers students an opportunity to train in a unique and vibrant interdisciplinary environment.

Health Economics and Health Technology Assessment (HEHTA) covers a broad set of activities relating to the appraisal of health service interventions including policies, procedures, devices, medicines and diagnostics. Health Technology Assessment (HTA) is the assessment of relevant evidence on the effects and consequences of healthcare technologies. Our work contributes to priority-setting and decision-making in relation to preventative, diagnostic, treatment and rehabilitation strategies.

Postgraduate research (PGR) students in HEHTA graduate with a thorough and robust skillset that is transferable to future employment in academia and public, private and third sector organisations. Postgraduate research students’ development is supported by a programme of high quality internal training, a dedicated and cohesive team, and exposure to our extensive network of international colleagues. They also have access to the researcher training programmes in the Colleges of Medical, Veterinary & Life Sciences (MVLS) and the College of Social Sciences (CoSS). Our students’ work results in high quality publications and international conference presence, and contributes to HEHTA’s international reputation across our seven research themes.

Our research objectives are:

• through our new research theme of Global HTA, to devise innovative methodological and applied research in the international context, with a particular focus on low and middle income countries
• to strengthen our existing research portfolio by focusing on synergistic working across our research themes
• to produce high quality evidence-based research that is relevant and impactful
• to collaborate and engage with national and global policy makers to undertake relevant research and influence decision-making.

Individual research projects are tailored around the expertise of principal investigators within HEHTA, the Institute of Health & Wellbeing, and the student’s interests. Our supervisors use a variety of approaches to research including decision analysis modelling, cost benefit and effectiveness analyses, risk and prediction modeling, data linkage and advanced meta-analysis. We have excellent engagement with government agencies, the NHS and local authorities, other statutory public sector and regulatory bodies, and third sector organisations.

Our research spans seven main themes:

• economic evidence alongside clinical trials
• evidence synthesis
• economic aspects of population health
• statistical analysis of linked health data
• decision analytic modeling and simulation
• incorporating qualitative evidence into health economic and health technology studies.

UK fees Course fees for UK students

For this course (per year)

International fees Course fees for EU and international students

A 2.1 Honours degree or equivalent.

The University of Glasgow is one of four ancient universities in Scotland, founded back in 1451. Alumni include seven Nobel Prize winners, Scotland’s First Minister and a Prime Minister, while Albert Einstein gave a seminal lecture on the theory of relativity there in 1933. The university consists of four colleges: College of Arts College of Medical, Veterinary and Life Sciences College of Science and Engineering College of... more

Health Economics and Health Technology Assessment MSc

Online | 36 months | SEP-24

Public Health (Health Economics) MPH/PgDip/PgCert

Full time | 12 months | 23-SEP-24

Health Economics & Health Technology Assessment MSc (Research)

Full time | 1 year | 23-SEP-24

Health Economics and Health Technology Assessment PgDip

Online | 24 months | SEP-24

Health Economics and Health Technology Assessment PgCert

Online | 12 months | SEP-24

We have 5 health technology assessment PhD Projects, Programmes & Scholarships for European Students (exc UK)

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health technology assessment PhD Projects, Programmes & Scholarships for European Students (exc UK)

Warning system for extreme weather events, awareness technology for healthcare, equitable delivery, and resilience (weather), phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (Students Worldwide)

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Perioperative brain state monitoring algorithms based on EEG big data and deep learning technology

Development of a multi-gene detection model for chinese luminal-type early breast cancer and its clinical translational study on the predictive value of breast cancer recurrence risk, wearable healthcare sensor for continuous monitoring of critical biomarkers and therapeutic drugs, competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Materials for the detection of minority species in optofluidic waveguides

Funded phd project (european/uk students only).

This project has funding attached for UK and EU students, though the amount may depend on your nationality. Non-EU students may still be able to apply for the project provided they can find separate funding. You should check the project and department details for more information.

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PhD: HSR Program Details

IHPME’s HSR PhD is designed for researchers interested in extensive training in health services research theory and methodologies. Graduates will feel prepared to take on senior leadership roles in academia, research, policy, and planning in both the public and private sectors.

Entry Term: Fall

Accepting Applications: September 23, 2024

HSR Application Deadline: November 15, 2024

Study Options: Full time, flex-time

Time Commitment: 4-6 years

Supervisor: Although it does not guarantee admission, communicating with potential supervisors is helpful in structuring the letter of intent required for your application . Review Faculty Profiles and Research and Initiatives to find potential supervisors that align with your research interests.

Fees and Funding: Accepted full-time PhD students are eligible to receive a funding package .

Studying with an Emphasis

HSR PhD researchers should select an emphasis that aligns with their professional background and interests. Students must select an emphasis in:

Health Economics

Faculty lead: Boriana Miloucheva & Alex Hoagland

Students will gain an in-depth understanding of fundamental economic principles as they relate to the healthcare sector. This emphasis builds capacity in mathematical and statistical techniques while providing students with practical knowledge on how to effectively communicate research motivations, study designs, findings, and implications to various audiences including academics and decision-makers. Students will feel equipped to critically analyze health policy issues and have a deeper understanding of resource allocation, health services supply, and how healthcare markets work.

Areas of study include: 

• Health economic theory
• Health economic evaluation
• Health econometrics and machine learning

Health Informatics Research

Faculty lead: Nelson Shen & Nur Camellia Zakaria

Students will design, evaluate, and use health informatics capabilities to better manage information and improve healthcare delivery. This highly interdisciplinary emphasis tackles major issues around the design, development, and evaluation of electronic solutions in consumer, community, and acute care settings. Students will be prepared with the necessary research tools, including the use of conceptual frameworks and research methods, to investigate specific areas of interest. 

• Development and evaluation of digital health innovations
• Implementation of digital health innovations
• Health informatics theory

Health Policy

Faculty lead: Fiona Miller

Students will investigate the political, social, and economic conditions that produce and distribute health and illness across populations and jurisdictions, and examine the systems devoted to sustaining public health and to financing, governing, and delivering healthcare and related social services.

• Comparative health policy and systems
• Public health policy
• Healthcare policy
• Health technology policy

Health Services Organization and Management Studies

Faculty lead: Lianne Jeffs

Students will explore organizational behaviour, organizational theory, strategic management, implementation science, sociology, and industrial-organizational psychology to understand the organization of health services and the impact of management and organizational practices on performance. This highly interdisciplinary field will explore diverse topic areas including how healthcare organizations are managed, leadership, healthcare practitioners, patient safety and quality of care, team functioning, organizational change, inter-organizational relationships and networks, governance, and evidence-based management.

• Health practitioner outcomes (e.g. burnout, turnover)
• Motivation and leadership in HSR organizations
• Strategic decision making
• Change implementation

Health Services Outcomes and Evaluation

Faculty lead: Kelly Smith & Patricia Trbovich

The Health Services Outcomes and Evaluation emphasis draws upon several academic disciplines including epidemiology, program evaluation, and economics to systematically examine the impacts of health services on the health status of various populations. Students should have demonstrated knowledge of quantitative, qualitative and mixed methods, primary data collection and secondary data sources, and the strengths, weaknesses and appropriate application of different research designs and data analysis strategies.

• Program evaluation
• Comparative effectiveness, safety, economic and other outcomes of health systems, services & programs 
• Methods for health services research 

Health Technology Assessment

Faculty lead: David Naimark

Health Technology Assessment (HTA) is an interdisciplinary field that advances and applies theories, concepts and methods in order to inform decision-makers on the introduction, use, and dissemination of health technology. The HTA emphasis encompasses quantitative and qualitative methods to equip students with skills within the main pillars of HTA to be able to critically analyze health policy issues related to health technology. 

• Evidence synthesis
• Economic evaluation
• Social, legal and ethical consequences of emerging technologies

Knowledge Translation

The Knowledge Translation (KT) area of study explores the broad domain of KT and implementation science in healthcare. Students will learn about theories and frameworks that help to inform KT, research approaches, methods and methodological challenges, and current and future KT and implementation science research relevant to the healthcare sector. Students interested in this area of study can add it to any of the HSR emphases (for both MSc and PhD), or the course-based MSc, by taking two of the KT courses.

See the KT courses within the HSR Course Descriptions .

Program Outcomes 

The PhD in HSR provides in-depth and comprehensive training that equips professionals with the necessary knowledge and skills necessary for senior roles in academic or within public and private sectors. HSR has cultivated collaborative research opportunities with prominent industry, government agencies, and non-government agencies. This extensive network provides students with unique research opportunities to publish in leading academic journals.

Finance Your Degree

At IHPME, we offer a variety of financial supports to help you succeed in our graduate programs.

Learn More About this Program

Hsr program co-director.

Emily Seto Email Address: emily.seto@​utoronto.ca

Katie N. Dainty Email Address: katie.dainty@​utoronto.ca

Co-leads the management of the HSR Program.

Graduate Administrator

Zoe Downie-Ross Phone Number: (416) 946-3486 Email Address: ihpme.grad.admin@​utoronto.ca

Coordinates student records, graduate funding, and student-related awards.

Graduate Admissions

Christina Lopez Email Address: ihpme.admissions@​utoronto.ca

Manages admissions and responds to all related inquiries.

Graduate Assistant

Nadia Ismail Phone Number: (416) 946-4100 Email Address: ihpme.grad.assist@​utoronto.ca

Coordinates various graduate initiatives including defences, student events, and graduation.

HSR Program Assistant

Anita Morehouse Phone Number: 416-946-3922 Email Address: ihpme.hsr.courses@​utoronto.ca

Manages the HSR courses including enrolment, grades, and access to Quercus.

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Health Economics and Health Technology Assessment

Our aim in the Health Economics and Health Technology Assessment programme from University of Glasgow is to be the world-leading centre for health economic and health technology assessment research and education, working to improve health and wellbeing through better decision-making.

University of Glasgow Multiple locations Glasgow , Scotland , United Kingdom Top 0.5% worldwide Studyportals University Meta Ranking 4.3 Read 178 reviews Featured by University of Glasgow

Health Economics and Health Technology Assessment (HEHTA) at University of Glasgow covers a broad set of activities relating to the appraisal of health service interventions including policies, procedures, devices, medicines and diagnostics. 

Features 

• Health Technology Assessment (HTA) is the assessment of relevant evidence on the effects and consequences of healthcare technologies. 
• Our work contributes to priority-setting and decision-making in relation to preventative, diagnostic, treatment and rehabilitation strategies.

Programme Structure

Our research spans seven main themes:

• economic evidence alongside clinical trials
• evidence synthesis
• economic aspects of population health
• statistical analysis of linked health data
• decision analytic modeling and simulation
• incorporating qualitative evidence into health economic and health technology studies

Key information

• 36 months

Start dates & application deadlines

Disciplines, academic requirements, english requirements, student insurance.

Make sure to cover your health, travel, and stay while studying abroad. Even global coverages can miss important items, so make sure your student insurance ticks all the following:

• Additional medical costs (i.e. dental)
• Repatriation, if something happens to you or your family
• Home contents and baggage

We partnered with Aon to provide you with the best affordable student insurance, for a carefree experience away from home.

Starting from €0.53/day, free cancellation any time.

Remember, countries and universities may have specific insurance requirements. To learn more about how student insurance work at University of Glasgow and/or in United Kingdom, please visit Student Insurance Portal .

Other requirements

General requirements.

• A 2.1 Honours degree or equivalent.

Tuition Fee

International, living costs for glasgow.

The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.

In order for us to give you accurate scholarship information, we ask that you please confirm a few details and create an account with us.

Scholarships Information

Below you will find PhD's scholarship opportunities for Health Economics and Health Technology Assessment.

Available Scholarships

You are eligible to apply for these scholarships but a selection process will still be applied by the provider.

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Stanford Online

Health innovation.

Stanford School of Medicine

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Discover how health innovation can unlock the future of healthcare, driving transformative change in industry practices, patient care, and beyond.

In today's rapidly evolving healthcare landscape, embracing innovation is not just a trend - it's essential. With programs and courses covering the latest developments in health and medicine, you can drive meaningful impact. 

In this program you will:

• Explore the digital revolution shaping healthcare - from idea to impact 
• Understand the development of products, addressing stakeholders, regulations, and accessibility
• Master clinical trial design, including formulating questions, recruitment strategies, and data communication
• Address healthcare challenges and maximize the impact of technology on healthcare  
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Digital Health Product Development

Clinical Trials: Design, Strategy, and Analysis

What you'll earn.

You’ll earn a Stanford Certificate of Completion in Health Innovation when you successfully complete each of the 3 required courses in this program.

This Stanford Certificate of Completion represents a minimum of 20-36 hours of Stanford coursework and other relevant criteria established by the Stanford School of Engineering.

Because your credential will be delivered as a digital certificate verified on the blockchain, you’ll be able to share your accomplishments with your network on your LinkedIn profile or other social platforms, verify your credentials to employers, and communicate the scope of your acquired expertise.

Teaching Team

Oliver O. Aalami, MD

Clinical Professor, Surgery

• School of Medicine

Afsana Akhter

Feel Therapeutics

Associate, Patents and Innovations

Wilson Sonsini Goodrich & Rosati

Dr. Narges Baniasadi

Adjunct Professor

Bioengineering

Clinical Professor

Stanford Medicine

Golnaz Moeini

Founder and Principal Consultant

MedEdge Regulatory & Quality Consulting

Regina Nuzzo

Gallaudet University

Kristin Sainani

Epidemiology and Population Health

Kristin Sainani (née Cobb) is an associate professor at Stanford University and also a health and science writer. After receiving an MS in statistics and a PhD in epidemiology from Stanford University, she studied science writing at the University of California, Santa Cruz. She has taught statistics and writing at Stanford for more than a decade and has received several Excellence in Teaching Awards from the graduate program in epidemiology. Dr. Sainani writes about science and health for a range of audiences. She authored the health column Body News for Allure magazine for a decade. She is also the statistical editor for the journal Physical Medicine & Rehabilitation; and she authors a statistics column, Statistically Speaking, for this journal.

Minang (Mintu) Turakhia

Professor, Cardiovascular Medicine

Ross Venook

Senior Lecturer of Bioengineering

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Conceptualising, measuring and valuing the impact of Health Technology Assessment

Grieve, Eleanor (2020) Conceptualising, measuring and valuing the impact of Health Technology Assessment. PhD thesis, University of Glasgow.

How do we assess the impact of Health Technology Assessment (HTA)? Whilst high-income countries (HIC) may have led the way, lower-income countries are increasingly beginning to develop HTA processes to assist in their healthcare decision-making. Understanding how we might quantify the costs and benefits of investing in HTA is important to policy makers and donors. Very few studies have, however, estimated the benefits of the process of HTA in terms of its value to the health system. The global expansion of HTA, its variable implementation, the lack of quantified evidence on health outcomes, along with an increasing investment in these processes at the systems level in low- and middle-income countries (LMIC) has generated greater interest from policy makers about the value and return on investment (ROI) of HTA. A lack of longer-term impact assessment (IA) may undermine its importance and value.

To fill this research gap, we have developed a methodological framework to estimate the ROI in HTA using net health benefits (NHB) as our measure of value. This is the difference between QALYs gained by an intervention and QALYs that could have been gained if the money required to deliver it had been spent on other interventions. We use a mixed-methods approach to quantify the value of HTA and to produce explanatory programme theory on the mechanisms by which HTA impact can be optimised. It is also important to consider opportunity costs when establishing HTA processes but which are often overlooked. The aim is to convey the concepts of potential and realised population NHB, and what we can attribute to the HTA process. Central to understanding this is the ‘value of implementation’ (VOImp). Theory-driven approaches will be used to generate and test contextual explanations for gaps between expected and actual gains in population health.

We envisage the use of this research will encourage accountability of spending decisions and help to optimise the impact of HTA in an era of investment and expansion, in particular, for LMICs, through better understanding of HTA’s role in delivering health outcomes and value for money at the system level. This research will offer a forward-looking model that LMICs can point to as a reference for their own implementation.

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Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity

Hassane alami.

1 Public Health Research Center, Université de Montréal, Montreal, QC, Canada

2 Department of Health Management, Evaluation and Policy, École de santé publique de l’Université de Montréal, Montreal, QC, Canada

3 Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada

Pascale Lehoux

Yannick auclair, michèle de guise, marie-pierre gagnon.

4 Research Center on Healthcare and Services in Primary Care, Université Laval, Quebec, QC, Canada

5 Faculty of Nursing Science, Université Laval, Quebec, QC, Canada

6 Joint Centre for Bioethics, University of Toronto, Toronto, ON, Canada

7 Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada

Richard Fleet

8 Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Quebec, QC, Canada

9 Research Chair in Emergency Medicine, Université Laval - CHAU Hôtel-Dieu de Lévis, Lévis, QC, Canada

Mohamed Ali Ag Ahmed

10 Research Chair on Chronic Diseases in Primary Care, Université de Sherbrooke, Chicoutimi, QC, Canada

Jean-Paul Fortin

11 Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec, QC, Canada

Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI’s value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.

Introduction

Artificial intelligence (AI) raises many expectations in all sectors of society. There is no universally agreed upon definition of what AI encompasses. Generically, it refers to a branch of informatics that develops systems that—through their ability to learn —imitate the characteristics associated with human intelligence: reasoning, learning, adaptation, self-correction, sensory comprehension, and interaction [ 1 , 2 ].

AI is seen as a strategic lever to improve access, quality, and efficiency of health care and services [ 3 ]. For example, by exploiting exhaustive data sets from complex systems, it could contribute to improving clinical decision making (eg, diagnosis, screening, and treatment), service organization (eg, flow optimization, triage, and resource allocation), and patient management and follow-up (eg, drug administration and compliance) [ 4 ].

However, research on the application of AI in health focuses primarily on technological performance in experimental contexts or on ethical issues. Although relevant, these studies do not fully address the broader systemic policy questions surrounding their use in a real-world context of care and services. In a recent meta-analysis, Lieu et al [ 5 ] concluded that despite a diagnostic performance equivalent to that of health care professionals, the diagnostic applications of AI have not been externally validated in a real-world context of care and services. Poor reporting is also prevalent in studies on AI, which limits the reliable interpretation of results. Thus, before being integrated into clinical routine, AI applications should overcome what is called the AI chasm , that is, the gap between reported performance in laboratory conditions and its performance and impacts in a real-world context of care and services [ 6 ]. AI raises issues of different types, but they are, in practice, closely interconnected: economic, professional, organizational, clinical, human, cognitive, legal, ethical, and technological. To date, few scholars have examined these issues in a systemic and holistic manner [ 7 ].

In this viewpoint paper, relying on the health technology assessment (HTA) core model [ 8 ], which is a methodological framework used to facilitate production and sharing of HTA information [ 9 ], we examine, based on our own experience as HTA academics and practitioners and in light of the emerging literature on the subject, issues raised by the use of AI. More specifically, we contrast the expectations specific to the health sector and the risks that should be mitigated for AI to be deployed responsibly. We limit our analysis to AI-based applications for clinical use (eg, diagnostic), some of which would be classified by the US Food and Drug Administration (FDA) as software as a medical device : “software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device” [ 10 ]. They are subject to formal regulatory approval [ 6 , 11 ].

In this paper, we offer critical observations and reflections that are informed by our various roles in HTA as health technology governance experts, researchers-evaluators, and/or decision makers. The analysis primarily adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, funding, and reimbursing technologies [ 12 ].

On the basis of the HTA core model, we summarize key challenges posed by AI in a real-world context of care and services, which include (1) technological, (2) clinical, (3) human and cognitive (patient and clinician), (4) professional and organizational, (5) economic, and (6) legal and ethical dimensions ( Textbox 1 ). We provide examples for each of these dimensions and underline how decision makers could approach them in a more systemic and holistic manner.

Synthesis of some key challenges posed by artificial intelligence.

Technological

• Laboratory performance versus a real-world context of care and services
• Data quality and representativeness of the general population or other contexts
• Black box: how and why the decision is made?
• Is artificial intelligence (AI) reliable and free of biases or technical failures?
• How AI would react in situations where input data deviate from initial data?
• Cybersecurity: data and model (algorithm)
• Interoperability: fragmented systems and unstructured data
• Reproduction of tropism of practice models
• Actual clinical added value in a real-world context of care and services: difficult to distinguish the effect of the AI's decision from the rest of the preventive and/or therapeutic strategy
• The level of accuracy of AI in diagnosis and recommendations (reference standard) in a real-world context of care and services

Human and cognitive (patients)

• Evolution of the nature and quality of the clinician-patient relationship
• Loss of human contact: isolation of some people
• Unrealistic expectations in some patients regarding clinical outcomes
• Black box: could be perceived as a restriction on the patient’s right to make a free and informed decision
• AI could be beneficial for one part of the population and not be for others: what is the good target population?

Human and cognitive (clinicians)

• How to integrate AI into the electronic health record (EHR) and clinical routine with minimal effort and disruption for clinicians?
• Nonintuitive technologies: weigh-down workflows and burden for clinicians and cognitive overload
• Disruption of interpersonal communication styles (eg, clinician-clinician and clinician-patient)
• AI as clinical mind: challenge of clinician’s decision-making autonomy
• Absolute confidence in AI: technical dependence

Professional and organizational

• How will it fit into the patient care and services trajectory?
• How will it be integrated into the clinical-administrative processes and workflows of organizations and health system?
• What changes will result in terms of service organization (eg, waiting time, primary care and specialized services relationships)?
• How will it impact on professional jurisdictions (eg, reserved activities, responsibility, training, new skills, and expertise)?
• Investments required: continuous performance tests, software and data quality tests, infrastructure and equipment upgrades, human expertise, and training
• Clinical tropism and reimbursement/billing biases: costs for patients, clinicians, organizations, and health system
• Need of new financing mechanisms, appropriate remuneration and/or reimbursement models, and insurance models

Legal and ethical

• When is AI considered as a decision-making support tool? When is it considered as a decision-making tool?
• What are the limits of technology and their potential legal implications?
• If the AI makes a mistake (eg, black box), who will be held responsible? If the patient is harmed, who will pay for the repairs?
• What would be the consequence if the clinician does not comply with the recommendations of an AI and this leads to an error?
• AI needs access to data from different sources: consent is becoming more complex, as patients will be asked to authorize the use of diversified amounts of data
• Protection and confidentiality: origin of the data, how consent was obtained, and authorization to use and/or reuse the data
• Who owns the data? Who is responsible for it? Who can use (or reuse) it and under what conditions?

Technological Dimension

Generalizability and reproducibility.

Studies that focus on technological issues indicate that AI should provide the same level of performance in a real-world context of care and services as that obtained in laboratory conditions. However, this requirement is difficult to achieve [ 13 - 16 ]. The majority of AI applications reported in the literature are not exploitable in clinical practice [ 17 ]. AI is often trained with so-called clean (exclusion of poor-quality images) and complete data sets (elimination of imperfect data) [ 18 ]. It may not be operational in other contexts where data are incomplete or of poor quality (electronic health record [EHR] with missing data and/or erroneously entered data) [ 19 - 21 ]. This applies to some categories of the patient population (eg, low economic status and psychosocial problems) who receive care and services in a fragmented way in several organizations (institutional wandering) [ 21 - 24 ]. In addition, AI is usually trained on data specific to certain sites (hospital) and patients who are not necessarily representative of the general population. This includes decontextualized data (lack of psychosocial and organizational indicators) and data about disproportionately sick individuals (data enriched by metastases cases), men, and those from a European origin (ethnodiversity) [ 23 , 25 - 28 ].

Health organizations and systems produce and manage data in different ways. Variations may exist in clinical protocols (eg, diagnosis, procedures, and vital parameters) and devices (eg, different types of scanners, EHRs, and laboratory devices) on which AI applications are trained and those on which they are expected to operate [ 29 , 30 ]. These variations could affect the AI performance in a real-world context of care and services [ 31 ]. For example, an AI application trained on data from 2 hospitals in the United States performed poorly in a third hospital [ 13 , 32 ]. In its decision, the AI application had as predictors the image characteristics (magnetic resonance imaging machines specifications), imperceptible to humans, specific to the technological systems of the hospitals where it was trained. The AI solution had adapted to noise rather than to the signal of clinical interest [ 33 ]. When used in the third hospital, it was deprived of these expected predictors ( noise ), which affected its anticipated performance [ 32 ]. In the same vein, the use of data from the Framingham Heart Study to predict the risk of cardiovascular events produced biased results, which both overestimated and underestimated risk when AI was used in non-white populations [ 34 , 35 ]. The ability of AI to operate without bias or confounding factors on different devices and protocols remains a major challenge [ 36 , 37 ]. Thus, the fact that an algorithm was trained on large data sets does not mean that its results are generalizable.

Interpretability and Transparency

The interpretability and transparency of AI are important issues. The black box logic makes some AI applications vulnerable and at risk to false discoveries via spurious associations: how is the decision made and on what basis (justification and process description) [ 24 , 38 , 39 ]. This issue is central because these technologies will be diffused on a large scale. The error of a defective AI could have a greater impact (several patients) than a clinician's error on a single patient [ 20 , 31 , 40 ].

Interpretability and transparency are also necessary to identify the origin of errors, biases, or failures that should be prevented and/or avoided in the future [ 3 , 21 , 41 ]. For example, an AI application could lead to many undesirable impacts related to: (1) poor-quality training data, which could lead to erroneous or biased knowledge ( garbage in, garbage out ), whereas technology may further amplify how poor data produce poor results (noisy data and missing values); (2) the presence of a technical flaw in the algorithm (code), which could lead to erroneous inferences, even if good-quality data are used; (3) decision-making criteria that may not be universally acceptable; and (4) the emergence of new situations for which AI could not adapt, even with good-quality data and code [ 21 , 30 , 42 - 45 ]. For example, the emergence of new treatments or practices may require changes in clinical protocols; however, at present, AI applications are not developed to manage temporal data naturally in a real-world context of care and services. However, diseases and treatments evolve in a nonlinear manner [ 18 , 45 ]. The question thus remains regarding how AI would react, with observable indicators, in situations where input data deviate from initial data (EHRs and real-time monitoring devices), in the medium and long term [ 45 , 46 ].

The risk of cyberattacks is also a major concern. The data could be modified and/or fed by other false or biased data in a way that is difficult to detect [ 1 ]. For example, a slight intentional modification of laboratory results in a patient's EHR resulted in significant changes in the estimates of a well-trained AI of the same patient's risk of mortality [ 24 ]. For AI, the issue is two-fold because it is necessary to ensure the security of the data and that of the model (the algorithm). Interoperability is also a significant issue. The integration of AI in fragmented and noninteroperable information technology systems and organizations could create more problems than it will solve; to deliver its full potential, AI needs integrated and interoperable systems with fluent and optimal data circulation and exchange [ 17 ].

Finally, addressing interpretably and transparency in AI could be compromised by intellectual property issues, competitive strategy, and financial advantages that make companies reluctant to disclose their source codes [ 3 ].

Clinical Dimension

AI can entrench and disseminate practice models specific to particular contexts (organizations or health systems) and not necessarily accepted or used in others (tropism) [ 38 ]. For example, clinicians in some countries stopped using IBM Watson for Oncology because it reflected US specificity in cancer treatment [ 1 , 47 ].

To use AI in their decision making, clinicians should understand how it makes decisions in the first place [ 38 , 45 , 48 ]. They need the evidence to support a given conclusion to be able to carry out the necessary verifications or even corrections [ 14 ]: Why this decision (what information or image—or part of the image—tipped the final decision of the AI)? Why not another option (or choice)? When may I consider that the decision is correct? When should I accept this decision? How can I correct the error when it occurs?

AI should provide clinically added value for the patient. In a real-world context of care and services, much information, decisions, and diagnoses could intersect (eg, symptom assessment, laboratory tests, and radiology). At present, it is difficult to distinguish the effect of an AI-based decision from the overall preventive and/or therapeutic strategy of patient care [ 49 , 50 ].

Another clinical issue is determining the level of accuracy of AI for diagnosis and recommendations. In practice, decisions physicians make could diverge or even contradict each other in many situations. The gold standard is not always easy to define in a process that involves complex judgments [ 38 , 51 , 52 ]. In this case, should the standard reflect that of the lead clinician (or clinicians) in the organization? Or the one accepted by the majority of clinicians? Or the one reported in similar contexts? Some authors believe that for technologies that aim to provide pragmatic solutions under suboptimal conditions, AI performance should correspond to clinically acceptable practice in a given context and not necessarily to recommended practices [ 32 ]. This last point is likely to be problematic, particularly in a context where health systems are trying to overcome the challenge of practice variations to be able to provide equitable and quality services for all citizens.

Human and Cognitive Dimensions

AI could affect the nature and quality of the clinician-patient relationship and their expectations for care and follow-up [ 53 , 54 ]. The loss of human contact could lead to increased isolation of some people (replacement of health care providers) [ 1 ]. Some patients may feel able to control and manage their disease, with passive surveillance and/or less contact with the clinician, whereas others may feel overwhelmed by additional responsibilities [ 55 ]. AI may also create unrealistic expectations in some patients regarding clinical outcomes, which could have a negative impact on their care and service experience [ 56 ]. In addition, some AI-based decisions could be perceived as a restriction on the patient's right to make a free and informed decision [ 1 , 53 ]. Cultural and social aspects could play an important role in how patients will respond to AI and therefore how effective it can prove in practice [ 57 ]. Hence, it is important to know on which basis one may define the target population that can benefit from it [ 58 ]. In this regard, the question of social acceptability (acceptable risk and public confidence) also needs to be considered, which goes beyond the simple question of the effectiveness and usability of AI [ 59 ].

For clinicians, the challenge is to integrate AI into the EHR and clinical routine with minimal effort while respecting their decision-making autonomy [ 24 ]. Nonintuitive technologies could encumber workflows and become a burden for clinicians without improving service delivery [ 30 , 60 ]. Otherwise, the ability of AI to combine data from the scientific literature with learning from practice data could generate a repository of clinical practices ( clinical mind ), which could give AI an unwanted power or authority [ 35 ]. In some situations, AI may reduce the clinician's ability to take into account patient values and preferences. In contrast, some clinicians may develop absolute confidence and become dependent on AI, thus relinquishing their responsibility to verify or double-check its decisions [ 1 ].

In short, if clinicians feel overloaded and workflows become more complex, AI may be rejected because of self-perceived inefficacy and performance, alert fatigue, cognitive overload, and disruption of interpersonal communication routines [ 54 , 61 - 63 ].

Professional and Organizational Dimensions

Global appreciation of the added value of AI should take into account the nature and magnitude of the professional and organizational changes required for its use [ 6 ]. For example, the FDA has approved an AI application used for diabetic retinopathy screening, which may be used in primary care clinics [ 11 ]. As in some countries, the screening procedure is performed by an ophthalmologist (specialist), some questions arise: How will this technology fit into patient care and services trajectory? How will it be integrated into the clinical-administrative processes of organizations and the health system? If used at the primary care level, will general practitioners, nurses, or optometrists be allowed to supervise the AI? If so, under what conditions? What will be the impact on professional jurisdictions (regulated activities, remuneration, and training)? What changes will result in terms of service organization and clinical-administrative workflows (waiting time at primary care level, primary care, and specialized services relationships)?

Thus, AI could lead to a redistribution of work between different professional scopes of practice and highlight the need for other clinical, administrative, and technical skills and expertise. This will require clarifying new rules and processes (clinical and administrative), negotiating and reframing professional jurisdictions, responsibilities, and privileges associated with them and reassessing the number of positions needed and the new skills required to work (with) and/or perform other tasks that accompany its use. This will have to take into consideration how new roles in terms of skills in informatics and data science and the ability to liaise may be introduced within clinical teams [ 64 ].

Finally, today, most AI applications are developed to perform a single task or a set of very specific tasks (eg, diagnosing only diabetic retinopathy and macular edema) [ 65 ]. They are unusable for other diagnoses for which they are not trained (eg, nondiabetic retinopathy lesions and eye melanoma) and are unable, at least for the moment, to replace a complete clinical examination [ 66 ]. Payers will thus be tasked to determine whether AI provides sufficient added value in relation to the nature and magnitude of the clinical, cognitive, professional, and organizational changes it could generate.

Economic Dimension

To adapt an AI to a local environment, considerable investments and expenditures may be necessary. The evolution of AI in a real-world context of care and services, by integrating large amounts of data of various types and sources, requires additional resources to ensure its proper functioning and stability: continuous performance tests, software and data quality tests, infrastructure and equipment upgrades, human expertise, and training [ 3 , 67 ]. However, many health organizations do not have a secure and scalable technological and data infrastructure as well as adequate human resources to ensure proper collection of the data necessary for the training and adaptation of AI to their local population and clinical environment [ 17 ]. The literature on AI’s promises as well as innovation policies that support its development downplays the capital-intensive requirements that are required to properly deploy AI, compared with the day-to-day work of managers in organizations.

In health systems where activity-based financing is the basis for funding health organizations, some clinicians tend to enter the highest paying codes for each clinical activity (ie, the most complex case of an intervention) to increase performance and maximize revenue. An AI application trained on data from these organizations (EHR with invoicing or reimbursement data) could amplify biases inherent in such practices that do not necessarily reflect the actual clinical condition [ 23 , 44 , 68 ]. The replication and entrenchment at a large scale of these biases could result in significant costs for patients, clinicians, organizations, and the health systems [ 35 ].

Similarly, some AI applications may be too cautious , resulting in an increase in requests for unnecessary testing and treatment, leading to overdiagnosis or overprescription [ 69 ]. Their recommendations, which are not necessarily associated with improved patient outcomes, could lead to increased costs and expenses for patients and the health system.

Legal and Ethical Dimensions

Many AI technologies are still considered today as decision-making support tools for clinicians. It could then be argued that the legal responsibility for the decision still rests with the clinician. However, with the growing performance of AI, clinicians may be increasingly influenced and may more easily accept AI decisions, even when there is clinical ambiguity. Determining the clinician's degree of responsibility becomes more complex [ 30 ]. The challenge here is to distinguish between several situations: When is it considered a decision-making support tool? When is it considered a decision-making tool? This distinction is key in defining who is legally responsible in the event of an error or a malfunction (professional misconduct) [ 30 , 51 , 70 ].

For example, if the clinical decision is based on an erroneous clinical recommendation from the AI (delayed or erroneous treatment), who will be held responsible? Is it the technology developer, technology provider, clinician, organization, or do they all share responsibility (and how)? In some jurisdictions, to confirm professional misconduct, it is necessary to prove that the standard of care was not followed. This standard is blurred when AI comes into play [ 2 ]. In addition, the likely consequence if the clinician does not comply with the recommendations of an AI and if this leads to an error must be anticipated [ 2 ]. It could be argued that the responsibility should rest with the human controller of AI, but such a responsibility becomes difficult to clarify when autonomous technologies are used [ 57 ]. In this regard, standards may shift over time: “What happens if medical practice reaches a point where AI becomes part of the standard of care?” Medical insurers and regulators will have to be able to distinguish errors inherent in the tool from those resulting from misuse by the clinician, the organization, or even the patient, an issue exacerbated by the black box of AI [ 51 , 71 ].

To generate a complete picture of the patient, AI will need access to data from different organizations (hospitals and insurers) [ 45 ]. The risk of disclosing sensitive information about patients or certain populations is real [ 45 ]. For example, some AI applications can reidentify an individual from only three different data sources [ 25 , 38 , 72 ]. In the same vein, the issue of consent is becoming more complex, as patients will be asked to authorize the use of increasingly large and diversified amounts of data about them: medical records, audio, videos, and socioeconomic data [ 58 ]. Problems could arise if the patient only consents to sharing parts of his or her data. Usually, confidentiality means that the clinician can withhold certain information—at the patient's request (or not)—and avoid entering it into the EHR. Incomplete data make AI less efficient and does not allow patients to benefit from the best possible services. AI may not be fully operational in a real-world context of care and services if specific restrictions on data access and use are applied [ 38 ].

Protection and confidentiality requirements imply the obligation to know several things: the origin of the data, how consent was obtained, and authorization to use and/or reuse the data for training and in a real-world context of care and services. As the data may come from different sources and contexts, different conditions and precautions will need to be considered [ 73 ]. Regulators will need to determine who owns the data and, in the context of public-private partnerships, who is responsible for its collection, use, transmission to third parties, and under what conditions [ 17 ]. As the answers will vary according to the nature of the data, the jurisdictions, and the purpose of use, the task at hand is sizable [ 73 ]. Finally, payers will have to recognize that the ethical implications of AI affect, directly or indirectly, all the other dimensions discussed earlier.

Conclusions

The purpose of this viewpoint paper is to provide a structured roadmap of the issues surrounding the integration of AI into health care organizations and systems. To the best of our knowledge, this is one of the few papers that offers a multidimensional and holistic analysis on the subject [ 7 ]. It contributes to current knowledge by providing a necessary basis for reflections, exchanges, and knowledge sharing among the various stakeholders concerned with AI in health care.

In light of the issues we identified, it becomes clear that regulatory and decision-making organizations as well as HTA agencies are facing unprecedented complexity: evaluating and approving so-called disruptive technologies, especially AI, requires taking several issues into consideration altogether. Many studies have reported significant technical performance of AI technologies, but very few have adopted a holistic standpoint that can situate their impacts and associated changes and transformations in health systems. Technical studies are rarely adapted to the complexity surrounding AI applications, as they overlook the context-dependent changes or adjustments the implementation and use of technology requires (variations, clinical and organizational interactions, and interdependencies) [ 74 ]. According to the frame problem [ 62 , 75 ], which highlights the difficulty for AI, beyond the specific tasks it masters, to update its set of axioms to capture the context in which it is implemented and used (eg, patient preferences, environment and social support, clinical history, personality/cultural characteristics and values that influence clinical outcomes, and empathy in medicine), the complexity inherent in the use of AI applications in the real-world context of care and services may seem difficult to overcome [ 62 ].

For informed decision making, there is a real need for evaluations that address AI as a lever of health system transformation. Given the magnitude of the implications it could have at all levels, the evaluation of AI’s value proposition should go beyond its technical performance and cost logic to incorporate its global value based on a holistic analysis in a real-world context of care and services. In this vein, technology brings value when its use in a real-world context of care and services contributes to the aims of the health system and aligns with the values of society. Global value appreciation could be based on the quintuple aim : (1) better quality and experience of care and services for patients; (2) a better state of health and well-being for the entire population; (3) reducing costs for responsible and sustainable resource management; (4) a better quality of work and satisfaction of health care providers; and (5) equity and inclusion to avoid exacerbating health disparities in the population [ 76 ]. From this perspective, further research on the evaluation of AI should no longer be limited to a technological approach, which only demonstrates quality from an engineering point of view and costs—motivated mainly by a logic of short-term savings—but should broaden its horizons to include the dimensions this paper underscored [ 77 , 78 ].

Real-world evaluations could be a major asset in informing AI decision making. In the context of uncertainty, iterative and reflective evaluation approaches should be developed to encourage dialog and collaboration among all relevant stakeholders (eg, payers, health care providers, technology providers, regulators, citizens/patients, academic researchers, and evaluation agencies) [ 63 , 78 , 79 ]. In addition, an early dialog between these stakeholders is needed to identify the evidence required to inform decision making [ 63 , 78 ]. This approach would also help AI providers to better understand the expectations of the health system [ 78 ]. This change implies that HTA should play an active role as a mediator and facilitator of transparent dialog between different stakeholders who are implicated throughout the technology’s life cycle [ 78 , 80 ].

Decision making for innovative technologies is inherently complex, in particular because of visions, perceptions, and objectives that may differ between the stakeholders involved: risk sharing is essential to strive to find a balance between uncertainty and added value [ 81 ]. In this regard, “major radical innovations never bring new technologies into the world in a fully developed form” but “appear in a crude and embryonic state with only a few specific uses” [ 81 ]. It is their use in a real-world context of care and services, through a process of learning by doing (improving users’ skills) and learning by using (improving users’ knowledge), which makes it possible to appreciate their global value [ 81 ]. With the complexity associated with AI, value appreciation becomes even more complex, challenging the traditional methodological foundations that are the basis for decision making about innovative technologies [ 82 ]. This also presents a unique opportunity for HTA to evolve and adapt (evaluative framework and contextualized data), particularly in view of the importance of contexts in the appreciation of the value of innovative technologies [ 83 , 84 ]. It is necessary for HTA scholars and practitioners to explore and exploit other avenues, complementary to traditional methods, to collect data and information that can better inform AI-related decisions [ 85 ].

Finally, this new context implies mechanisms for continuous collective learning and sharing of lessons. To do so, there is a need for learning and flexible health organizations and systems that are able to adjust and operate under uncertainty. In this regard, creating the political, regulatory, organizational, clinical, and technological conditions necessary for proper innovation is the first step. This requires building trust to ensure stakeholder engagement to guide AI developments, rapidly generate knowledge in a real-world context of care and services, and draw lessons to translate them into action.

Acknowledgments

HA was supported by the Canadian Institutes of Health Research (CIHR)’s Health System Impact Fellowship. This program is led by CIHR’s Institute of Health Services and Policy Research, in partnership with the Fonds de recherche du Québec–Santé and the Institut national d’excellence en santé et services sociaux. The authors would like to thank the reviewers and the editorial team for their insightful comments and suggestions, as these comments and suggestions led to an improvement of the manuscript.

Abbreviations

Authors' Contributions: HA and PL produced the first draft of this manuscript and received input from YA, MG, MG, JS, DR, RF, MA, and JF. All authors have read and approved the final manuscript.

Conflicts of Interest: None declared.

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Health Economics and Health Technology Assessment MSc/PgDip/PgCert: Online distance learning

Health Economics and Health Technology Assessment contributes to priorities and decisions in relation to prevention, diagnosis, treatment and rehabilitation of patients. Our programme will enable you to appraise evidence and understand the consequences of technology in order to prioritise its use and better prevent, diagnose and treat disease. Online learning at the University of Glasgow allows you to experience the outstanding education we are known for without the need for relocation.

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The scholarships above are specific to this programme. For more funding opportunities search the scholarships database

Entry requirements

To be accepted to this programme, you must have:

• At least a 2:1 Honours degree or equivalent in a relevant quantitative subject (i.e. economics, mathematics, statistics, medicine, pharmacy, epidemiology, health services research) from a recognised institution.
• Exceptionally, consideration may be given to those with a relevant professional qualification who have experience in the field of health technology assessment.

A background in health or medicine is desirable but not essential, we welcome applications from other academic disciplines.

When submitting your application, please include:

• Your Curriculum Vitae
• A supporting statement

English language requirements

For applicants whose first language is not English, the University sets a minimum English Language proficiency level.

International English Language Testing System (IELTS) Academic module (not General Training)

• 6.5 with no subtests under 6.0
• Tests must have been taken within 2 years 5 months of start date. Applicants must meet the overall and subtest requirements using a single test
• IELTS One Skill Retake accepted.

Common equivalent English language qualifications

Toefl (ibt, mybest or athome).

• 79; with Reading 13; Listening 12; Speaking 18; Writing 21
• Tests must have been taken within 2 years 5 months of start date. Applicants must meet the overall and subtest requirements , this includes TOEFL mybest.

Pearsons PTE Academic

• 59 with minimum 59 in all subtests
• Tests must have been taken within 2 years 5 months of start date. Applicants must meet the overall and subtest requirements using a single test.

Cambridge Proficiency in English (CPE) and Cambridge Advanced English (CAE) 

• 176 overall, no subtest less than 169

Oxford English Test

• Oxford ELLT 7
• R&L: OIDI level no less than 6 with Reading: 21-24 Listening: 15-17
• W&S: OIDI level no less than 6.

Trinity College Tests

• Integrated Skills in English II & III & IV: ISEII Distinction with Distinction in all sub-tests.

University of Glasgow Pre-sessional courses

• Tests are accepted for 2 years following date of successful completion.

Alternatives to English Language qualification

• students must have studied for a minimum of 2 years at Undergraduate level, or 9 months at Master's level, and must have complete their degree in that majority-English speaking country  and within the last 6 years
• students must have completed their final two years study in that majority-English speaking country  and within the last 6 years

For international students, the Home Office has confirmed that the University can choose to use these tests to make its own assessment of English language ability for visa applications to degree level programmes. The University is also able to accept UKVI approved Secure English Language Tests (SELT) but we do not require a specific UKVI SELT for degree level programmes. We therefore still accept any of the English tests listed for admission to this programme.

Pre-sessional courses

The University of Glasgow accepts evidence of the required language level from the English for Academic Study Unit Pre-sessional courses. We also consider other BALEAP accredited pre-sessional courses:

• School of Modern Languages and Cultures: English for Academic Study
• BALEAP guide to accredited courses

For further information about English language requirements, please contact the Recruitment and International Office using our  enquiry form

Computer requirements for studying online

Broadband internet connection

• 3 mbps or higher

Internet Browsers

Our online learning platform Moodle is compatible with any standards compliant web browser. This includes:

• Internet Explorer
• MobileSafari
• Google Chrome

For the best experience and optimum security, we recommend that you keep your browser up to date. 

Javascript needs to be enabled within your browser

Please note: legacy browsers with known compatibility issues with Moodle 3.3 are:

• Internet Explorer 10 and below
• Safari 7 and below

Computer specifications

• Processor: 2GHz
• Microsoft Windows Vista service pack 1
• Mac OS X v10.4.11+
• Memory: 3GB of RAM or more
• Hard disk: 300GB
• Sound card and microphone
• Speakers or headphones
• Monitor and video card with 1024x768 display or higher

Mobile device specifications

• iOS: latest (Apple Safari & Google Chrome)
• Android: 4.4+ with latest Google Chrome

Other software

• Adobe Acrobat Reader
• Media player e.g. Windows Media Player or VLC
• Word processing software (that outputs to the following file types for marking online: .doc, .docx, .html, .txt, .rft, .pdf, .ppt, .pptx, .pps, .hwp)
• Anti-virus software

Advised hardware / software

• Microsoft Office 2010
• Headset (ideally with a USB connector)

How to apply

To apply for a postgraduate taught degree you must apply online. We cannot accept applications any other way.

Please check you meet the Entry requirements for this programme before you begin your application.

As part of your online application, you also need to submit the following supporting documents:

• A copy (or copies) of your official degree certificate(s) (if you have already completed your degree)
• A copy (or copies) of your official academic transcript(s), showing full details of subjects studied and grades/marks obtained
• Official English translations of the certificate(s) and transcript(s)
• One reference letter on headed paper
• An English language certificate/evidence of your English language ability may be required
• Any additional documents required for this programme (see Entry requirements for this programme)
• A copy of the photo page of your passport (non-EU students only)

You have 42 days to submit your application once you begin the process.

You may save and return to your application as many times as you wish to update information, complete sections or upload supporting documents such as your final transcript or your language test.

For more information about submitting documents or other topics related to applying to a postgraduate taught programme, check  Frequently Asked Questions

Guidance notes for using the online application

These notes are intended to help you complete the online application form accurately; they are also available within the help section of the online application form. 

If you experience any difficulties accessing the online application, you should visit the  Application Troubleshooting/FAQs  page.

Please ensure all documents are correctly named and are uploaded before you submit your application to prevent any delays with your file.

All documentation must be attached to your online application, but please remember your student recruitment coordinator is here to support you by reviewing it before you upload it, so feel free to send them over to your student recruitment coordinator before you submit your application. 

• Name and Date of birth:  must appear exactly as they do on your passport. Please take time to check the spelling and lay-out.
• Contact Details : Correspondence address. All contact relevant to your application will be sent to this address including the offer letter(s). If your address changes, please contact us as soon as possible. 
• Choice of course : Please select carefully the course you want to study. As your application will be sent to the admissions committee for each course you select it is important to consider at this stage why you are interested in the course and that it is reflected in your application.
• Proposed date of entry:  Please state your preferred start date including the month and the year. Online taught masters degrees begin in January, April and September. 
• Education and Qualifications : Please complete this section as fully as possible indicating any relevant Higher Education qualifications starting with the most recent. Complete the name of the Institution (s) as it appears on the degree certificate or transcript. It is important to upload official copies of your transcripts and certificates for your full academic history, from Undergraduate onwards. For example, if you have a bachelors and a masters degree, you must send documents from both degrees. 
• English Language Proficiency : Please state the date of any English language test taken (or to be taken) and the award date (or expected award date if known).
• Employment and Experience : Please complete this section as fully as possible with all employments relevant to your course. Additional details may be attached in your personal statement/proposal where appropriate.

Reference : Please provide one reference. This should typically be an academic reference but in cases where this is not possible then a reference from a current employer may be accepted instead. Certain programmes, such as the MBA programme, may also accept an employer reference. If you already have a copy of a reference on letter headed paper then please upload this to your application. If you do not already have a reference to upload then please enter your referee’s name and contact details on the online application and we will contact your referee directly.

Application deadlines

• Applications for this online programme will be accepted until it starts

More information about this programme

• Meet the Academics
• Studying online
• Try our Health Technology Assessment taster course
• About online study

Watch our webinars:

• Programme overview with Professor Jim Lewsey
• Watch our webinar on the new definition of HTA led by Professor Jim Lewsey
• HTA Q&A Webinar with Programme Directors Claudia Geue & Eleanor Grieve
• Virtual Learning Environment Demonstration

Health Economics and Health Technology Assessment (HEHTA) 

• Research: Health Economics and Health Technology Assessment

Related programmes

Online postgraduate.

• See the range of online postgraduate taught programmes available

Health & Wellbeing

• Applied Neuropsychology [MSc(MedSci)/PgDip]
• Clinical Neuropsychology [MSc(MedSci)/PgDip]
• Clinical Neuropsychology Knowledge & Practice [MSc(MedSci)]
• Clinical Neuropsychology Practice [PgCert]
• Clinical Psychology [DClinPsy]

more related Health & Wellbeing programmes

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• How to apply for a postgraduate taught degree
• Postgraduate research opportunities A-Z
• How to apply for a postgraduate research degree
• Fees and funding

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Home News As AI Paves the Way for Healthcare Innovation, Can We Avoid the Potholes? back to News

As AI Paves the Way for Healthcare Innovation, Can We Avoid the Potholes?

Cu anschutz researcher explores ai in medicine at cctsi conference.

minute read

Jayashree Kalpathy-Cramer , PhD, used ChatGPT to create a professional bio. The artificial intelligence platform got many things right, correctly listing her recent publications, areas of research, past employers and current lab.

Then she asked it to add her undergraduate degree.

“It made that up completely.”

Kalpathy-Cramer is chief of the new Division of Artificial Medical Intelligence in the Department of Ophthalmology at the University of Colorado School of Medicine . She is also director of CCTSI’s Health Informatics at the Colorado Clinical and Translational Sciences Institute at the CU Anschutz Medical Campus. She said the more she tried to convince ChatGTP of its error, the more it “hallucinated,” making up sources that did not exist.

“Maybe it heard my voice and decided I'm from South India, and therefore that's where I went to school,” she said.

Kalpathy-Cramer shared the story with attendees of the 2024 CCTSI CU-CSU Summit . The annual conference took place Aug. 13 at CU Anschutz to explore innovations in health AI with CCTSI researchers from its affiliated campuses. Her presentation focused on the significant potential, and a few of the limitations, of AI in healthcare.

How AI models are used in research and beyond

Kalpathy-Cramer began with a survey of attendees to gauge faculty perceptions of AI. Some attendees had never used AI tools, while a larger percentage used them regularly, or even daily. Attendees said they use AI for tasks such as coding, generating research questions, and drafting letters of recommendation and that they were also aware of its limitations. Like Kalpathy-Cramer, many conference-goers said they had experienced frustration with the AI's overconfidence in plausible-sounding, yet incorrect, answers.

Kalpathy-Cramer provided a high-level snapshot of AI-focused projects within her department, using a slide deck made in collaboration with ChatGPT.

Her division, which includes about a dozen members, primarily data scientists, first created a research warehouse of data, including images, electronic health records and other data needed to train AI. These data are used to train a variety of AI algorithms. The researchers work on developing novel AI methods as well as the application novel AI algorithms to many clinical questions. The team is especially focused on issues such as bias and fairness and ensuring that the algorithms are suitable for patient care.

Building an AI model for retinopathy of prematurity

The department has developed AI models for imaging related to retinopathy of prematurity (ROP), a disease that primarily affects low-birthweight or premature babies. ROP is a leading cause of preventable blindness worldwide, particularly in low- and middle-income countries such as India.

Oxygen-management issues affecting premature infants increase their risk for ROP. While treatment is available if diagnosed in time, many regions lack sufficient pediatric ophthalmologists for proper diagnosis and treatment. ROP is diagnosed through imaging of retinal blood vessels, classified on a three-level severity scale. The challenge is to make AI effective in this context to improve access to care.

Kalpathy-Cramer also explored studies such as the HPV-automated visual evaluation (PAVE) for advancing cervical cancer prevention and the National Cancer Institute Cancer Moonshot research initiative to demonstrate how AI is already being used to improve access, quality, safety and efficiency of care.

Bringing together a community to address AI concerns

Kalpathy-Cramer emphasized the need for researchers to collaborate as a community to address the many questions surrounding the effective and safe use of AI in healthcare.

One challenge is overcoming bias. She used the example of how AI models can predict self-reported race with great accuracy by looking at chest X-ray s .

“Humans can’t do that,” she said. “AI is already encoding this information. What is it looking at? We have to be aware of the many ways bias can creep in from data generation and model building.”

She also talked about generalization.  

“The models tend to be brittle. They work on the device they were trained on but if there’s a software update or you put it on a different device, the model falls apart. And the hard part is you don't know that it's not working.”

It's also impossible to know if a model has stopped working if a human isn’t able to validate the AI, which raises ethical concerns.

“ AI can predict the likelihood of getting breast cancer five years in the future with very high accuracy, yet a human eye can’t see what it sees. Do we deploy that? Because maybe it helps people. But on the other hand, if no human can validate the risk, how do we know it's safe?”  

Kalpathy-Cramer also raised practical concerns related to the development and deployment of AI in healthcare. Issues like payment, reimbursement and liability must be considered when deciding to use AI.  

She concluded the talk with an AI-generated image symbolizing the intersection of data, AI and people in healthcare. She laughed at a spelling error in the image but emphasized that, from the perspective of those working in AI, there's much to be excited about.

“We really do think it has the ability to improve care, improve access to care, improve quality and safety, and make things more efficient, less expensive and safer,” she said. “But there are also lots of potholes.”

Topics: Research , Patient Care , Artificial Intelligence (AI)

Jayashree Kalpathy-Cramer, PhD

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