One Health: Insights from Organizational & Social, Technology Assessment and Human Factors Perspectives.

OBJECTIVES: To offer diverse but complementary perspectives on how biomedical and health informatics can be informed by and help to achieve the vision of One Health. METHODS: Overview of key considerations and critical discussion of common themes, barriers and opportunities, based on collaborative review by International Medical Informatics Association (IMIA) working group members active in related fields. RESULTS: Health and care systems are complex sociotechnical systems that need explicit design and implementation strategies to align with the goals of One Health. The evidence-based health informatics paradigm and associated frameworks for evaluation of digital health technologies need to broaden their scope to take full account of the One Health approach. Informatics has specific contributions to make to One Health, for example by improved user experience reducing energy consumption and effective app design enhancing medication adherence. CONCLUSIONS: One Health is inherently intertwined with ergonomic, sociotechnical and evaluation perspectives in biomedical and health informatics. Health is a planetary issue that requires interdisciplinary collaborative action. The theories and principles of biomedical and health informatics offer many opportunities to transform digital health technology to better serve the One Health agenda.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision.

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

Automating Electronic Health Record Data Quality Assessment.

Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies and tools to help ensure that available data are fit for use. However, developing reliable data quality assessment (DQA) tools necessary for guiding and evaluating improvement efforts has remained a fundamental challenge. This review examines the state of research on operationalising EHR DQA, mainly automated tooling, and highlights necessary considerations for future implementations. We reviewed 1841 articles from PubMed, Web of Science, and Scopus published between 2011 and 2021. 23 DQA programs deployed in real-world settings to assess EHR data quality (n = 14), and a few experimental prototypes (n = 9), were identified. Many of these programs investigate completeness (n = 15) and value conformance (n = 12) quality dimensions and are backed by knowledge items gathered from domain experts (n = 9), literature reviews and existing DQ measurements (n = 3). A few DQA programs also explore the feasibility of using data-driven techniques to assess EHR data quality automatically. Overall, the automation of EHR DQA is gaining traction, but current efforts are fragmented and not backed by relevant theory. Existing programs also vary in scope, type of data supported, and how measurements are sourced. There is a need to standardise programs for assessing EHR data quality, as current evidence suggests their quality may be unknown.

The Role of Formative Evaluation in Promoting Digitally-based Health Equity and Reducing Bias for Resilient Health Systems: The Case of Patient Portals.

OBJECTIVES: Patient portals are increasingly implemented to improve patient involvement and engagement. We here seek to provide an overview of ways to mitigate existing concerns that these technologies increase inequity and bias and do not reach those who could benefit most from them. METHODS: Based on the current literature, we review the limitations of existing evaluations of patient portals in relation to addressing health equity, literacy and bias; outline challenges evaluators face when conducting such evaluations; and suggest methodological approaches that may address existing shortcomings. RESULTS: Various stakeholder needs should be addressed before deploying patient portals, involving vulnerable groups in user-centred design, and studying unanticipated consequences and impacts of information systems in use over time. CONCLUSIONS: Formative approaches to evaluation can help to address existing shortcomings and facilitate the development and implementation of patient portals in an equitable way thereby promoting the creation of resilient health systems.

Managing Pandemic Responses with Health Informatics - Challenges for Assessing Digital Health Technologies.

OBJECTIVES: To highlight the role of technology assessment in the management of the COVID-19 pandemic. METHOD: An overview of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS: Evaluation of digital health technologies for COVID-19 should be based on their technical maturity as well as the scale of implementation. For mature technologies like telehealth whose efficacy has been previously demonstrated, pragmatic, rapid evaluation using the complex systems paradigm which accounts for multiple sociotechnical factors, might be more suitable to examine their effectiveness and emerging safety concerns in new settings. New technologies, particularly those intended for use on a large scale such as digital contract tracing, will require assessment of their usability as well as performance prior to deployment, after which evaluation should shift to using a complex systems paradigm to examine the value of information provided. The success of a digital health technology is dependent on the value of information it provides relative to the sociotechnical context of the setting where it is implemented. CONCLUSION: Commitment to evaluation using the evidence-based medicine and complex systems paradigms will be critical to ensuring safe and effective use of digital health technologies for COVID-19 and future pandemics. There is an inherent tension between evaluation and the imperative to urgently deploy solutions that needs to be negotiated.

Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications.

OBJECTIVES: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. METHOD: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. CONCLUSION: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.

Computerised medical record systems that guide and protect - reflections on the Bawa-Garba case.

Lawrence Weed proposed we develop computerised, problem orientated medical records that guide and teach.  The Bawa-Garba case outcomes might have been different if care had been supported by computerised medical record (CMR) systems. CMR systems can reduce prescribing errors and could be develop to flag gaps in supervision.   However, CMR systems are not a panacea and need to be fit for purpose.  Our informatics perspective on this case is to call for widespread use of CMR systems - designed to guide and protect.

The Safe and Effective Use of Shared Data Underpinned by Stakeholder Engagement and Evaluation Practice.

OBJECTIVES: The paper draws attention to: i) key considerations involving the confidentiality, privacy, and security of shared data; and ii) the requirements needed to build collaborative arrangements encompassing all stakeholders with the goal of ensuring safe, secure, and quality use of shared data. METHOD: A narrative review of existing research and policy approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Care and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS: The technological ability to merge, link, re-use, and exchange data has outpaced the establishment of policies, procedures, and processes to monitor the ethics and legality of shared use of data. Questions remain about how to guarantee the security of shared data, and how to establish and maintain public trust across large-scale shared data enterprises. This paper identifies the importance of data governance frameworks (incorporating engagement with all stakeholders) to underpin the management of the ethics and legality of shared data use. The paper also provides some key considerations for the establishment of national approaches and measures to monitor compliance with best practice. CONCLUSION: Data sharing endeavours can help to underpin new collaborative models of health care which provide shared information, engagement, and accountability amongst all stakeholders. We believe that commitment to rigorous evaluation and stakeholder engagement will be critical to delivering health data benefits and the establishment of collaborative models of health care into the future.

Steps in Moving Evidence-Based Health Informatics from Theory to Practice.

OBJECTIVES: To demonstrate and promote the importance of applying a scientific process to health IT design and implementation, and of basing this on research principles and techniques. METHODS: A review by international experts linked to the IMIA Working Group on Technology Assessment and Quality Development. RESULTS: Four approaches are presented, linking to the creation of national professional expectations, adherence to research-based standards, quality assurance approaches to ensure safety, and scientific measurement of impact. CONCLUSIONS: Solely marketing- and aspiration-based approaches to health informatics applications are no longer ethical or acceptable when scientifically grounded evidence-based approaches are available and in use.

Theoretical Foundations for Evidence-Based Health Informatics: Why? How?

A scientific approach to health informatics requires sound theoretical foundations. Health informatics implementation would be more effective if evidence-based and guided by theories about what is likely to work in what circumstances. We report on a Medinfo 2015 workshop on this topic jointly organized by the EFMI Working Group on Assessment of Health Information Systems and the IMIA Working Group on Technology Assessment and Quality Development. We discuss the findings of the workshop and propose an approach to consolidate empirical knowledge into testable middle-range theories.

An assessment of endocrine activity in Australian rivers using chemical and in vitro analyses.

Studies on endocrine disruption in Australia have mainly focused on wastewater effluents. Limited knowledge exists regarding the relative contribution of different potential sources of endocrine active compounds (EACs) to the aquatic environment (e.g., pesticide run-off, animal farming operations, urban stormwater, industrial inputs). In this study, 73 river sites across mainland Australia were sampled quarterly for 1 year. Concentrations of 14 known EACs including natural and synthetic hormones and industrial compounds were quantified by chemical analysis. EACs were detected in 88 % of samples (250 of 285) with limits of quantification (LOQ) ranging from 0.05 to 20 ng/l. Bisphenol A (BPA; LOQ = 20 ng/l) was the most frequently detected EAC (66 %) and its predicted no-effect concentration (PNEC) was exceeded 24 times. The most common hormone was estrone, detected in 28 % of samples (LOQ = 1 ng/l), and the PNEC was also exceeded 24 times. 17α-Ethinylestradiol (LOQ = 0.05 ng/l) was detected in 10 % of samples at concentrations ranging from 0.05 to 0.17 ng/l. It was detected in many samples with no wastewater influence, and the PNEC was exceeded 13 times. In parallel to the chemical analysis, endocrine activity was assessed using a battery of CALUX bioassays. Estrogenic activity was detected in 19 % (53 of 285) of samples (LOQ = 0.1 ng/l 17ß-estradiol equivalent; EEQ). Seven samples exhibited estrogenic activity (1-6.5 ng/l EEQ) greater than the PNEC for 17ß-estradiol. Anti-progestagenic activity was detected in 16 % of samples (LOQ = 8 ng/l mifepristone equivalents; MifEQ), but the causative compounds are unknown. With several compounds and endocrine activity exceeding PNEC values, there is potential risk to the Australian freshwater ecosystems.

STAT-HI: A socio-technical assessment tool for health informatics implementations.

This paper proposes a socio-technical assessment tool (STAT-HI) for health informatics implementations. We explore why even projects allegedly using sound methodologies repeatedly fail to give adequate attention to socio-technical issues, and we present an initial draft of a structured assessment tool for health informatics implementation that encapsulates socio-technical good practice. Further work is proposed to enrich and validate the proposed instrument. This proposal was presented for discussion at a meeting of the UK Faculty of Health Informatics in December 2009.