Sayeed et al report the completion of a digital health research study with an architecture using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard to express the research workflow components, thus introducing the potential for a shared ecosystem for FHIR-based communication of research processes. Beyond the framework offered by these authors, the HL7 FHIR standard has been broadened to include resources for the reporting of research datasets and research findings (evidence), and can now support an architecture for the complete cycle of generation, analysis, dissemination, and application of health research. Making this cycle computable is especially important to reduce the delay in translating research into practice. In 2018, HL7 approved the FHIR Resources for Evidence-Based Medicine Knowledge Assets (EBMonFHIR) project to extend the methods and infrastructure of the HL7 FHIR standard to provide an interoperability standard for the electronic exchange of biomedical knowledge from and about clinical research and recommendations.1 As of 2025, the Evidence Based Medicine on FHIR Implementation Guide (EBMonFHIR IG) defines 90 profiles for FHIR resources for the representation of scientific knowledge and is intended for developers of systems using FHIR for data exchange of scientific knowledge and for authors of more specialized implementation guides in this domain.2

OBJECTIVE: We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years). MATERIALS AND METHODS: In our retrospective case study from the DiCAYA Network, SARS-CoV-2 was identified using labs and diagnoses from June 1, 2020 to December 31, 2021. Patients were followed through December 31, 2022 for new diabetes diagnoses. Sites examined incident diabetes by COVID-19 status using Cox proportional hazards models. Results were pooled in meta-analyses. A bias analysis examined potential impact of COVID-19 misclassification scenarios on results, guided by hypotheses that sensitivity would be <50% and would be higher among those who developed diabetes. RESULTS: Prevalence of documented COVID-19 was low overall and variable across sites (children: 4.4%-7.7%, young adults: 6.2%-22.7%). Individuals with documented COVID-19 were at higher risk of incident diabetes compared to those with no documented infection, but results were heterogeneous across sites. Findings were highly sensitive to COVID-19 misclassification assumptions. Observed results could be biased away from the null under several differential misclassification scenarios. DISCUSSION: Although EHR-based documentation of COVID-19 was associated with incident diabetes, COVID-19 phenotypes likely had low sensitivity, with considerable variation across sites. Misclassification assumptions strongly impacted interpretation of results. CONCLUSION: Given the potential for low phenotype sensitivity and misclassification, caution is warranted when interpreting analyses of COVID-19 and incident diabetes using clinical or administrative databases.

OBJECTIVE: Value-based care (VBC) represents a fundamental shift from volume-driven reimbursement to models focused on improving patient outcomes and reducing costs. Informatics plays an essential, but often underappreciated, role in enabling VBC. Traditional discussions of informatics emphasize data and technology; however, a broader sociotechnical view highlights how people, organizations, workflows, and policies interact with technology to influence the success of VBC initiatives. In this article, we apply the Informatics Stack as a heuristic framework to examine how informatics shapes VBC across 4 phases: research, policy setting, healthcare implementation, and local assessment within learning health systems. MATERIALS AND METHODS: We applied the Informatics Stack as a heuristic framework to analyze VBC across four phases: research, policy setting, healthcare implementation, and local assessment. To provide a grounded analysis, the study focused on the Healthcare Implementation phase, utilizing vascular claudication management as a primary illustrative case to demonstrate how high-level VBC policies are converted into granular clinical workflows and algorithms. RESULTS: We present "As-Is" characterizations of informatics in VBC at multiple levels of the Stack, ranging from world-level regulatory forces to organizational values, to business processes, workflows, information systems, modules, algorithms, data, and underlying technologies. We also outline "To-Be" opportunities, including computable clinical guidelines, interoperable data platforms, algorithm performance monitoring, and integration of multimodal data streams into decision support. To provide a grounded analysis, we narrow our focus to the Healthcare Implementation phase, using vascular claudication management as our primary illustrative case. Managing claudication in a VBC model requires preventing low-value care, such as early, aggressive peripheral vascular interventions, while optimizing patient-specific outcomes. We will used this clinical example to walk down the levels of the Stack, demonstrating how informatics converts high-level VBC policy into granular clinical workflows and algorithm. DISCUSSION: In this article, we apply the Informatics Stack as a heuristic framework to examine how informatics shapes VBC across four phases, specifically focusing on the Healthcare Implementation phase using vascular claudication management as an illustrative case. We present "As-Is" characterizations of the current state of informatics alongside "To-Be" opportunities, including computable clinical guidelines, interoperable data platforms, and algorithm performance monitoring. Concluding that VBC requires a socio-technical perspective beyond mere data and technology, we propose the Stack as a diagnostic tool for health leaders and offer a "VBC Informatics Gap Analysis Toolkit" to help organizations identify alignment gaps in their implementation strategies. CONCLUSION: In teaching informatics and in generating an assessment of where a problem or a field is at any point in time, we have found the Stack to help students, universally, and many alumni, to apply in their work. However, for the health system leader attempting to implement VBC, the Stack must be more than a theoretical model; it must be a diagnostic tool. It does not prescribe specific software purchases, but rather points out to an organization where they may have inconsistencies in their conception of their informatics infrastructure or frank gaps in that conceptualization.

OBJECTIVE: To support long COVID research in National COVID Cohort Collaborative (N3C), the N3C Phenotype and Data Acquisition team created data designs to aid contributing sites in enhancing their data. Enhancements include long COVID specialty clinic indicator; Admission, Discharge, and Transfer transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation. MATERIALS AND METHODS: For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way. RESULTS: As of June 2024, 29 sites have added at least one data enhancement to their N3C pipeline. DISCUSSION: The use of common data models is critical to the success of N3C; however, these data models cannot account for all needs. Project-driven data enhancement is required. This should be done in a standardized way in alignment with common data model specifications. Our approach offers a useful pathway for enhancing data to improve fit for purpose. CONCLUSION: In this initiative, we rapidly produced project-specific data modeling guidance and documentation in support of long COVID research while maintaining a commitment to terminology standards and harmonized data.

OBJECTIVE: The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance. MATERIALS AND METHODS: The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach. RESULTS: A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources. DISCUSSION: The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies. CONCLUSION: Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future.

The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations.

OBJECTIVES: Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. TARGET AUDIENCE: Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. SCOPE: We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.

OBJECTIVE: The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. MATERIALS AND METHODS: The National COVID Cohort Collaborative (N3C) table of laboratory measurement data-over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. RESULTS: Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). DISCUSSION: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. CONCLUSION: The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

OBJECTIVE: This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip code-level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely tested zip codes (which contained most of the data) were similar between original and synthetically derived datasets. Analyses of sparsely tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression-an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Dear JAMIA Editors, A 2020 perspective article published in Journal of the American Medical Informatics Association (JAMIA) posed a timely question, “Is it time for computable evidence synthesis?”1 The shortest answer is, yes. The novel coronavirus disease 2019 (COVID-19) pandemic poses an immediate demand for evidence synthesis, given that nearly 30 000 articles have been published in fewer than 6 months since that first case in Wuhan, China.2 It provides the informatics community with a unique opportunity to accelerate development and interoperability of many systems to realize the aspirations of computable evidence synthesis. In this letter, we describe the origins and status of the COVID-19 Knowledge Accelerator (COKA). There are tremendous inefficiencies in our current scientific dissemination systems, in which many researchers compute the results then convert the data to various noncomputable forms for human-readable displays, and then many other knowledge processors work with the various human-readable displays to extract the data and enter it into computable form for evidence synthesis. This inefficient pattern is repeated incrementally across multiple steps in an extended series of processes while reports are re-evaluated and reused in subsequent reports. Thus, structured (computable) results directly from research and research publications would greatly accelerate evidence synthesis. Trial registries such as ClinicalTrials.gov are a good place for identifying early system developments for processing structured results data, but structured results data would be especially useful as a companion to scholarly publications, preprint publications, and derivative works in which systematic reviewers and other evidence processors are evaluating currently unstructured results data. For example, COVID-19 studies have already resulted in hundreds of systematic reviews. Achieving a state of structured results data as standard practice will not likely occur through a single universal repository, but we believe that it can be achieved with universal standards for data exchange, and multiple component standards that account for the many types of data that represent and support research results. Several groups—including the Guidelines International Network—seeking to accelerate evidence synthesis through collaborations set out to define standards for computable expressions of evidence, statistics, and evidence variables. In 2018, we started a project through Health Level Seven (HL7) International to extend the Fast Healthcare Interoperability Resources (FHIR) standard to achieve this. The group is called Evidence-Based Medicine on FHIR (EBMonFHIR).3 In less than 2 years, the EBMonFHIR project established draft standards for expression of evidence (http://build.fhir.org/evidence.html), evidence variables (http://build.fhir.org/evidencevariable.html), Statistics (http://build.fhir.org/statistic.html), and ordered distributions for statistical arrays (http://build.fhir.org/ordereddistribution.html). Recent developments to overcome the COVID-19 pandemic have stimulated many researchers, scholars, and information professionals to initiate large consortium-based efforts to share their work and advance our knowledge of the virus and the pandemic. Examples include the COVID-19 Open Research Dataset (CORD-19) (https://cset.georgetown.edu/research/covid-19-open-research-dataset-cord-19/), the COVID-19 Evidence Network to support Decision-making (COVID-END) (https://www.mcmasterforum.org/networks/covid-end), and the Australian National Clinical Evidence Taskforce (https://covid19evidence.net.au/). Several of these consortia asked to leverage EBMonFHIR efforts to provide standards for interoperable evidence syntheses. In response to these requests, our group initiated COKA (https://www.gps.health/covid19_knowledge_accelerator.html). The specific strategy of COKA is to establish universal standards for each component of knowledge exchanged and thus enable stakeholders to share and reuse their efforts by using the same format for electronic data exchange. As of May 11, 2020, COKA had 50 working meetings with more than 40 active participants from more than 25 organizations from academia, industry, government, and nonprofits in 7 countries. The group has created additional draft FHIR standards for expressions of citations (http://build.fhir.org/citation.html) and evidence reports (http://build.fhir.org/evidencereport.html) that provide compositions of all the preceding concepts. We strongly encourage developers of systems for evidence identification, evaluation, and dissemination to use these resources now as foundational elements to create a computational evidence ecosystem. This environment includes the building blocks for achieving computable evidence synthesis. Other resources not noted previously, such as resources for computational logic expressions, may ultimately be needed for the complete ecosystem. If we can develop standards for each granular component, we can then weave together the many overlapping systems and consortia to accelerate realization of this complex evidence ecosystem. Computable evidence synthesis is not the endpoint, but rather is another step in a larger knowledge ecosystem. For instance, the EBMonFHIR project is closely related to a CPGonFHIR project (http://build.fhir.org/ig/HL7/cqf-recommendations/) extending FHIR to support clinical guidelines. Past and present consortia efforts that have or are considering advancements for this ecosystem include the Agency for Healthcare Research and Quality evidence-based Care Transformation Support (ACTS) initiative (https://digital.ahrq.gov/acts), the Centers for Disease Control and Prevention's Adapting Clinical Guidelines for the Digital Age (https://www.cdc.gov/ddphss/clinical-guidelines/), Logica (https://covid-19-ig.logicahealth.org/), Mobilizing Computable Biomedical Knowledge (MCBK) (http://mobilizecbk.org/), and the Patient-Centered Clinical Decision Support Learning Network (PCCDS LN) (https://pccds-ln.org/). Although creating a computational environment for evidence could be done for any domain or subject matter,4,5 COVID-19 currently presents a unique human interest with urgency and impact, thus providing a special openness to collaboration. We invite your participation at https://www.gps.health/covid19_knowledge_accelerator.html. VS is supported in part by the National Science Foundation under grant #1838745 and the Arizona Board of Regents’ Technology Research and Innovation Fund. All listed authors contributed to this correspondence. BSA was employed by EBSCO Information Services and owns Computable Publishing LLC but the COKA and EBMonFHIR efforts are open, noncommercial activities. The other authors have no competing interests to report.

OBJECTIVES: The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes. MATERIALS AND METHODS: A process mining-based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared. RESULTS: Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9-246.0] minutes vs 295.2 [95% confidence, 289.2-297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists. DISCUSSION: The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay. CONCLUSIONS: Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

AMIA has a longstanding interest in and a professional obligation to promote a strong ethical framework for the field of biomedical and health informatics. This white paper presents the latest AMIA Code of Professional and Ethical Conduct. The original Code was approved in 20071 by the AMIA Board of Directors. Recognizing the need to update the Code to ensure that it remains current and relevant, this document constitutes a revision of and update to the second code, approved in 2012 and published in the Journal of the American Medical Informatics Association in 2013.2 The code presented here remains an evolving document, with modifications expected as the information technology, informatics, and healthcare environments change over time. AMIA will publish on its web site the most recent version of the Code of Ethics as part of a process that seeks ongoing response from and involvement by AMIA members. Because the Code is meant to be practical, applicable in real life, and easily understood, it is compact and uses general language. The AMIA Code of Ethics is not intended to be prescriptive or legislative; it is aspirational and extends beyond regulatory and legal obligations to provide the broad strokes of a set of important ethical principles pertinent to the field of biomedical and health informatics. The Code is organized around the common roles of AMIA members and the constituents they serve, including patients, caregivers, clinicians, researchers, students, agencies, hospitals and practices, medical organizations, vendors, insurance companies, and others with whom they interact. The AMIA Board and the AMIA Ethics Committee encourage members to offer suggestions for improvements and changes. In this way, the Code will continue to evolve to best serve AMIA and the larger informatics community. The Code’s authors are aware that all professionals will, from time to time, find themselves in situations shaped by what has been called “dual agency” or “multiple agency.” In these circumstances, a professional encounters conflicting commitments, duties, or loyalties. An informatics professional may have conflicting duties to patients, to colleagues, to society, and to an employer. Few, if any, codes of ethics are nimble enough to provide guidance in such situations. Further, AMIA’s Ethics Committee is a resource to members who find themselves in ethically unclear or challenging situations. AMIA members may contact the AMIA Ethics Committee, which can provide guidance in some circumstances. AMIA members are professionally diverse,3,4 and include those who are, or are in training to be, nurses, physicians, pharmacists, dentists, informaticians, computer scientists, and others. In many cases, these professions have their own codes of ethics.5–12 The International Medical Informatics Association, an international federation for which AMIA serves as the U.S. membership organization, also has a revised “Code of Ethics for Health Information Professionals.”13 This document incorporates issues covered by other documents and laws bearing on ethics and professional conduct: AMIA’s “Conflict of Interest Policy,” which governs the organization’s employees and leaders in regard to some of their financial and other interactions with outside entities.14 AMIA’s “Meeting Anti-Harassment Policy,” which describes AMIA’s commitment to providing an atmosphere that is welcoming to all members and supports learning and professional growth.15 The International Committee of Medical Journal Editors’ “Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.”16 This document is widely accepted as identifying standards for publication and authorship, and is paralleled by the editorial policies for the past17 and current18 publishers of the Journal of the American Medical Informatics Association, as well as the publisher of Applied Clinical Informatics.19 Privacy laws. Several sections herein address patient privacy or the rights of patients to view and control access to their health information. These sections are intended to parallel and make explicit duties under the law. In the United States, for instance, the Privacy Rule under the Health Insurance Portability and Accountability Act20 lays out many duties for those who are entrusted with health information. Many other countries have similar laws to protect patient data. Informatics professionals are expected to be familiar with and follow the laws governing their practice. Members of the Ethics Committee are unanimous in their view that those who work in informatics, much as in other health professions, are duty-bound to embrace a patient-centered approach to their work, even if that work does not involve direct patient care or human subjects research. As elsewhere in the health professions, vulnerable populations or those with special needs may be entitled to additional considerations. The importance of professionalism and ethics has been recognized for millennia by health professionals and organizations,21 now including information technology professionals. This code of ethics emphasizes AMIA’s commitment to adhere to and promote the highest standard of ethical and professional behavior. AMIA members acknowledge as their professional duty to uphold the following principles of and guidelines for ethical conduct. AMIA members are expected to know to seek the advice of institutional ethics committees, AMIA’s Ethics Committee, or appropriate institutional review boards, as necessary. The following code details address patient care, interactions with colleagues, responsibilities to employers, and roles regarding society and research. I. Key ethical guidelines regarding patients, guardians, and their authorized representatives (called here collectively “patients”) AMIA members involved in patient care should: A. Recognize that patients and their loved ones and caregivers have the right to know about the existence and use of electronic records containing their personal healthcare information, and have the right to create and maintain their own personal health records and manage personal health information using a variety of platforms including mobile devices. In this context AMIA members should: Not mislead patients about the collection, use, or communication of their healthcare information. Enable and — as appropriate, within reason and the scope of their position and in accord with independent ethical and legal standards — facilitate patients’ rights and ability to access, review, and correct their electronic health information. Recognize that patient-provided/generated health data, such as those collected on mobile devices, deserve the same diligence and protection as biomedical and health data gathered in the process of providing health care. B. Advocate and work as appropriate to ensure that protected health information (PHI),20 personally identifiable information (PII), and other biomedical data are transmitted, acquired, recorded, stored, maintained, analyzed, and communicated in an appropriately safe, reliable, secure, and confidential manner, and that such data management is consistent with applicable laws, local privacy and security policies, and accepted informatics standards. C. Never knowingly disclose PHI, PII, or biomedical or health data in violation of legal requirements or accepted local confidentiality practices, or in ways that are inconsistent with the explanation of data disclosure and use to the patient. AMIA members should understand that inappropriate disclosure of biomedical information can cause harm, and so should work to prevent such disclosures. AMIA members should avoid acquiring data through means that run the risk of, or fail to prevent, inappropriate disclosure. Likewise, even if an action does not involve disclosure, one should not use — or through negligence permit the use of — patient information and data in ways inconsistent with the stated purposes, goals, or intentions of the patient or organization responsible for these data, except as appropriate for public health, previously approved and communicated research uses, or reporting as required under the law. II. Key ethical guidelines regarding colleagues AMIA members should: A. Endeavor, as appropriate, to support and foster colleagues’ and/or team members’ work, in a timely, respectful, and conscientious way to support their roles in healthcare and/or research and education. B. Support and foster the efforts of patients to be actively involved in the collection, management, and curation of their health data. C. Advise colleagues and others, as appropriate, about actual or potential information or systems issues (including system flaws, bugs, usability issues, etc.) that negatively affect patient safety, privacy, data security, or outcomes or could hinder colleagues’ ability to delegate responsibilities to patients, other colleagues, involved institutions, or other stakeholders. D. If a leader, an AMIA member should: Be familiar with these guidelines and their applicability to their practice, unit, or organization. Communicate as appropriate about these ethical guidelines to those they lead. Strive to promote familiarity with, and use of, these ethical guidelines. III. Key ethical guidelines regarding institutions, employers, business partners, and clients (called here collectively “employers”) AMIA members should: A. Understand their duties and obligations to current and former employers and fulfill them to the best of their abilities within the bounds of ethical and legal norms. B. Understand and appreciate that employers have legal and ethical rights and obligations, including those related to intellectual property. Understand and respect the obligations of their employers, and comply with local policies and procedures to the extent that they do not violate ethical and legal norms. Consider the tradeoffs that occur with the configuration and use of technologies (eg, decision support systems) before implementation, and monitor and manage results when the optimal approach is unclear. C. Inform the employer and act in accordance with ethico-legal mandates and patient rights when employer actions, policies, or procedures would violate ethical or legal obligations, contracts, or other agreements made with patients. Maintain a safe and high-quality environment even while implementing innovation, recognizing that all changes in a complex adaptive environment generate unanticipated consequences and potential harm. IV. Key ethical guidelines regarding society and regarding research AMIA members involved in research should: A. Be aware of the Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subjects), which should guide all human subject research, including research that involves users of informatics tools and interventions as human subjects (eg, workflow analysis studies, clinical decision support systems analysis, patient care innovations, analysis, etc.).22,23 Recognize that duty and care to colleagues exist regardless of whether such responsibilities are acknowledged by institutional review boards, vendors, and others involved in informatics activities. B. Be mindful and respectful of the social or public health implications of their work, ensuring that the greatest good for society is balanced by ethical obligations to individual patients. C. Avoid any plagiarism or self-plagiarism or other misrepresentations of the truth in the publication of research and other work. D. Disseminate new knowledge — both positive and negative — expeditiously, to allow the field to advance and to permit others to take advantage of novel discoveries to improve patient care. E. Strive as appropriate in the context of one’s position to foster the generation of knowledge and biomedical advances through appropriate support for ethical and institutionally approved research efforts facilitated through informed consent and disclosure processes and procedures, particularly when third-party entities not meeting the definition of business associates are involved. F. Know and abide by the applicable governmental regulations and local policies that define ethical research in their professional environment. V. General professional and ethical guidelines AMIA members should: A. Maintain competence as informatics professionals: Obtain applicable continuing education and be dedicated to a culture of lifelong learning and improvement; Recognize technical and ethical limitations and seek consultation when needed, particularly in ethically conflicting situations; Contribute to the education and mentoring of students, junior members, and others, as appropriate; Promote a culture of inclusivity in their work and professional conduct. B. Strive to encourage the adoption of informatics approaches supported by adequate evidence to improve health and healthcare; and to encourage and support efforts to improve the amount and quality of such evidence. C. Be mindful that their work and actions reflect on the profession and on AMIA. As a matter of personal and professional integrity, adherence to the principles laid out here is expected of all who have the privilege of serving in the field of biomedical and health informatics. Those whose skills allow them to contribute in one way or another to the health of individuals and populations carry important responsibilities, and this Code of Ethics delineates how informaticians may best do so. None. Not commissioned; not peer reviewed. Conflict of interest statement. None. The authors and the AMIA Ethics Committee would like to thank the AMIA Board of Directors for its continuing interest in refining and publishing these guidelines. Phyllis Burchman, AMIA’s Director of Office Operations and Human Resources, provided invaluable support to the Ethics Committee in its work. Members of the AMIA Ethics Committee who contributed to the second version of the code in 2012 and are not otherwise listed here include Samantha Adams, Robert Hsiung, John Hurdle, and Dixie A. Jones. This version of the code also owes much to the members of AMIA’s Ethical, Legal, and Social Issues (ELSI) Working Group.

OBJECTIVE: The Johns Hopkins Center for Population Health IT hosted a 1-day symposium sponsored by the National Library of Medicine to help develop a national research and development (R&D) agenda for the emerging field of population health informatics (PopHI). MATERIAL AND METHODS: The symposium provided a venue for national experts to brainstorm, identify, discuss, and prioritize the top challenges and opportunities in the PopHI field, as well as R&D areas to address these. RESULTS: This manuscript summarizes the findings of the PopHI symposium. The symposium participants' recommendations have been categorized into 13 overarching themes, including policy alignment, data governance, sustainability and incentives, and standards/interoperability. DISCUSSION: The proposed consensus-based national agenda for PopHI consisted of 18 priority recommendations grouped into 4 broad goals: (1) Developing a standardized collaborative framework and infrastructure, (2) Advancing technical tools and methods, (3) Developing a scientific evidence and knowledge base, and (4) Developing an appropriate framework for policy, privacy, and sustainability. There was a substantial amount of agreement between all the participants on the challenges and opportunities for PopHI as well as on the actions that needed to be taken to address these. CONCLUSION: PopHI is a rapidly growing field that has emerged to address the population dimension of the Triple Aim. The proposed PopHI R&D agenda is comprehensive and timely, but should be considered only a starting-point, given that ongoing developments in health policy, population health management, and informatics are very dynamic, suggesting that the agenda will require constant monitoring and updating.

The health informatics field continues to evolve and grow. The adoption of electronic health records has increased dramatically. Integrated medical devices, telemedicine, consumer-directed apps, and precision medicine are mainstream. There are continued developments in genomics data mining and pattern recognition. The reimbursement models are rapidly changing and require detailed accountability via measurement and high-quality, integrated data. The data that are collected in the continuum of care can prove truly helpful in evidence-based decision-making and in the sciences, to verify or disprove existing models or theories. The rapid changes in health IT, along with an increasing growth in the reliance on IT in health care, has resulted in an increasing demand for trained workers. A survey by Hoffman and Ash1 and a later study by Hersh2 indicated that the most important skills for health informaticians include knowledge of the following: the use of information in clinical care, change management, relational databases, interoperability standards, and project management and best practices for IT use in the health care setting. The ability to analyze large amounts of structured and unstructured data has also become a requirement for future informaticians. It is indeed challenging to educate the future wave of informaticians and the health professionals who interact with these technologies.

The PaTH (University of Pittsburgh/UPMC, Penn State College of Medicine, Temple University Hospital, and Johns Hopkins University) clinical data research network initiative is a collaborative effort among four academic health centers in the Mid-Atlantic region. PaTH will provide robust infrastructure to conduct research, explore clinical outcomes, link with biospecimens, and improve methods for sharing and analyzing data across our diverse populations. Our disease foci are idiopathic pulmonary fibrosis, atrial fibrillation, and obesity. The four network sites have extensive experience in using data from electronic health records and have devised robust methods for patient outreach and recruitment. The network will adopt best practices by using the open-source data-sharing tool, Informatics for Integrating Biology and the Bedside (i2b2), at each site to enhance data sharing using centrally defined common data elements, and will use the Shared Health Research Information Network (SHRINE) for distributed queries across the network.

At the 2011 American College of Medical Informatics (ACMI) Winter Symposium we studied the overlap between health IT and economics and what leading healthcare delivery organizations are achieving today using IT that might offer paths for the nation to follow for using health IT in healthcare reform. We recognized that health IT by itself can improve health value, but its main contribution to health value may be that it can make possible new care delivery models to achieve much larger value. Health IT is a critically important enabler to fundamental healthcare system changes that may be a way out of our current, severe problem of rising costs and national deficit. We review the current state of healthcare costs, federal health IT stimulus programs, and experiences of several leading organizations, and offer a model for how health IT fits into our health economic future.

OBJECTIVE: To evaluate non-response rates to follow-up online surveys using a prospective cohort of parents raising at least one child with an autism spectrum disorder. A secondary objective was to investigate predictors of non-response over time. MATERIALS AND METHODS: Data were collected from a US-based online research database, the Interactive Autism Network (IAN). A total of 19,497 youths, aged 1.9-19 years (mean 9 years, SD 3.94), were included in the present study. Response to three follow-up surveys, solicited from parents after baseline enrollment, served as the outcome measures. Multivariate binary logistic regression models were then used to examine predictors of non-response. RESULTS: 31,216 survey instances were examined, of which 8772 or 28.1% were partly or completely responded to. Results from the multivariate model found non-response of baseline surveys (OR 28.0), years since enrollment in the online protocol (OR 2.06), and numerous sociodemographic characteristics were associated with non-response to follow-up surveys (all p<0.05). DISCUSSION: Consistent with the current literature, response rates to online surveys were somewhat low. While many demographic characteristics were associated with non-response, time since registration and participation at baseline played the greatest role in predicting follow-up survey non-response. CONCLUSION: An important hazard to the generalizability of findings from research is non-response bias; however, little is known about this problem in longitudinal internet-mediated research (IMR). This study sheds new light on important predictors of longitudinal response rates that should be considered before launching a prospective IMR study.

The current focus and funding of health information technology forces the informatics research community to ask many questions. A key one is: Are we providing policy makers the evidence they need to make the best decisions? Corollary to this question are two more: Are we using the best methods? Are we looking for evidence in all the right places? Liu and Wyatt1 present their perspective on the critical role of randomized clinical trials (RCTs) in the assessment of clinical information systems. The authors briefly review the sources of skepticism that claim that RCT-based evaluation is not useful in evaluating health information systems due to ethical and technical grounds. Liu and Wyatt present counter-arguments to make a compelling case that clinical information systems can be very influential in determining clinical outcomes, so they should be subject to the same rigorous evaluation standards as other types of clinical interventions, such as medications and procedures. Regardless of design, justification of the match between purpose and design is paramount. JAMIA agrees that RCTs are the preferred standard for evaluation in general, but it is not appropriate in all circumstances. As Liu and Wyatt point out, RCTs, which are often expensive, are appropriate for the evaluation of systems that expose added risk to subjects or that are associated with high costs. However, the growing field of comparative effectiveness research helps our thinking beyond RCTs and is directly relevant, not only because informatics systems should be evaluated using those principles, but because informatics systems often provide the data for those studies. Thus, leading comparative effectiveness research researchers provide guidance on when non-RCT designs are appropriate.2 For example, “To evaluate real-world applicability… to study multiple treatment paradigms simultaneously…to understand current practices…where trials have not been or cannot be performed…when treatment adherence differs…when providers have different training…” Each of these items applies when evaluating informatics systems: such systems can embody multiple interventions (eg, think of the many decision support options employed); baseline studies prior to implementation help us to understand current practice; trials have rarely been performed, especially of commercial systems; adherence, in terms of adoption, varies greatly within an institution; and training for HIT systems is notoriously variegated. Thus, while many reports that are based on RCTs have appeared in the journal recently, other reports evaluate system features that may not expose patients, clinicians, or healthcare workers to added risk and are relatively inexpensive to implement. These studies are important for documenting the impact of a clinical information system in clinical outcomes and/or processes and for guiding subsequent system development. These reports may ultimately influence decision makers to implement certain system features at their sites. More examples of studies not based on RCT designs appear in this issue, such as quantitative evaluations of clinical decision support interventions for appropriate test ordering in primary care and for timely discontinuation of antibiotics after surgery. Arguably, these interventions did not impose added risk to patients and were relatively inexpensive when compared to the cost of implementing a whole clinical information system. In these circumstances, other study designs may be appropriate. Similarly, JAMIA publishes qualitative evaluations and reports of studies that employ methodologies designed to model behavioral and social systems and does not restrict its publications to quantitative evaluations or studies that utilize methodologies designed to model physical systems. Therefore, JAMIA ignores the perceived dichotomy between “soft” versus “hard” sciences. The field of health and biomedical informatics is diverse and each study is unique; thus, it is important to understand what is most appropriate for a particular investigation and to avoid a priori rule-in or rule-out of particular methodologies. Beyond correct methodologies, we must study at the right level of organization. JAMIA is encouraging the submission of articles in all areas addressed in AMIA's strategic realignment,3 especially those that have been relatively under-represented in the journal. At the opposite extreme of hospital-based informatics is public health informatics. Although computer systems have been used since 1938, when Illinois used IBM tabulation equipment for vital statistics,4 the self consciousness of public health informatics (PHI) as a field is relatively new as evidenced by the 2001 Spring AMIA meeting,5 the 2003 publication of the PHI textbook,4 and the funding for syndromic surveillance in the mid-2000s by the CDC Centers for Excellence in Public Health Informatics and the Department of Homeland Security. In addition to being relatively new, the scope of PHI is also broad. In the spirit of evidence-based inquiry, we reviewed JAMIA's publications in this area, mapping article titles to PHI topics and to the 10 Essential Public Health Services.6 Of 273 articles (of all sorts) published in 2008–2010, 51 (19%) were directly related to public health and addressed only four of the functions identified by the Essential Public Health Services (see next page). From CDC. Ten Essential Public Health Services. http://www.cdc.gov/nphpsp/essentialServices.html. From CDC. Ten Essential Public Health Services. http://www.cdc.gov/nphpsp/essentialServices.html. Consider some of the functions not on the list. “Mobilize community partnerships and action to identify and solve health problems” does seem to be an epiphenomenon of health information exchange activities, but we have not published research that addresses community mobilization head on. “Enforce laws and regulations that protect health and ensure safety” is perhaps represented by articles on privacy, but those articles mostly address the technology of deidentification. Meanwhile, education of public health professionals makes no appearance. Even the functions that do appear on the list are not well-represented. While “consumer health” is on the list, most of the articles deal with personal health records, and not the public health function of “inform, educate, and empower people about health issues.” Certainly more research exists about informatics and developing countries or rural domestic areas beyond a couple of articles. The same goes for each of the other areas. The final two functions—evaluating the “effectiveness, accessibility, and quality of personal and population-based health services” and “research for new insights and innovative solutions to health problems”—are part of every JAMIA submission, but return us to the question of research methods. The broad scope of PHI means that there should be a wide range of topics that JAMIA will publish, for example, health messaging; uses of mobile technologies in developing countries; decision support for public health practice; emergent behavior of social networks and their effect on public health. We plan to issue a call for papers soon, which will address these under-represented areas in the journal. With both appropriate methodology and proper level of analysis, the best evidence will be reproducible and generalizable to different settings. Therefore, JAMIA requests that authors thoroughly justify study design choices, submit data and code as appropriate for peer-review and for potential inclusion in online appendices, and discuss study limitations explicitly. We have broadened the scope of the journal to encompass all areas of biomedical and health informatics and look forward to receiving submissions which represent research, applications, reviews, and perspectives in all areas. Our collective contributions can have a large impact in healthcare in the USA and abroad. Commissioned; internally peer reviewed.

OBJECTIVE: The aim of this study was to preliminarily determine the feasibility of probabilistically generating problem-specific computerized provider order entry (CPOE) pick-lists from a database of explicitly linked orders and problems from actual clinical cases. DESIGN: In a pilot retrospective validation, physicians reviewed internal medicine cases consisting of the admission history and physical examination and orders placed using CPOE during the first 24 hours after admission. They created coded problem lists and linked orders from individual cases to the problem for which they were most indicated. Problem-specific order pick-lists were generated by including a given order in a pick-list if the probability of linkage of order and problem (PLOP) equaled or exceeded a specified threshold. PLOP for a given linked order-problem pair was computed as its prevalence among the other cases in the experiment with the given problem. The orders that the reviewer linked to a given problem instance served as the reference standard to evaluate its system-generated pick-list. MEASUREMENTS: Recall, precision, and length of the pick-lists. RESULTS: Average recall reached a maximum of .67 with a precision of .17 and pick-list length of 31.22 at a PLOP threshold of 0. Average precision reached a maximum of .73 with a recall of .09 and pick-list length of .42 at a PLOP threshold of .9. Recall varied inversely with precision in classic information retrieval behavior. CONCLUSION: We preliminarily conclude that it is feasible to generate problem-specific CPOE pick-lists probabilistically from a database of explicitly linked orders and problems. Further research is necessary to determine the usefulness of this approach in real-world settings.

BACKGROUND: The use of clinical decision support systems to facilitate the practice of evidence-based medicine promises to substantially improve health care quality. OBJECTIVE: To describe, on the basis of the proceedings of the Evidence and Decision Support track at the 2000 AMIA Spring Symposium, the research and policy challenges for capturing research and practice-based evidence in machine-interpretable repositories, and to present recommendations for accelerating the development and adoption of clinical decision support systems for evidence-based medicine. RESULTS: The recommendations fall into five broad areas--capture literature-based and practice-based evidence in machine--interpretable knowledge bases; develop maintainable technical and methodological foundations for computer-based decision support; evaluate the clinical effects and costs of clinical decision support systems and the ways clinical decision support systems affect and are affected by professional and organizational practices; identify and disseminate best practices for work flow-sensitive implementations of clinical decision support systems; and establish public policies that provide incentives for implementing clinical decision support systems to improve health care quality. CONCLUSIONS: Although the promise of clinical decision support system-facilitated evidence-based medicine is strong, substantial work remains to be done to realize the potential benefits.

OBJECTIVE: To develop a model for Bayesian communication to enable readers to make reported data more relevant by including their prior knowledge and values. BACKGROUND: To change their practice, clinicians need good evidence, yet they also need to make new technology applicable to their local knowledge and circumstances. Availability of the Web has the potential for greatly affecting the scientific communication process between research and clinician. Going beyond format changes and hyperlinking, Bayesian communication enables readers to make reported data more relevant by including their prior knowledge and values. This paper addresses the needs and implications for Bayesian communication. FORMULATION: Literature review and development of specifications from readers', authors', publishers', and computers' perspectives consistent with formal requirements for Bayesian reasoning. RESULTS: Seventeen specifications were developed, which included eight for readers (express prior knowledge, view effect size and variability, express threshold, make inferences, view explanation, evaluate study and statistical quality, synthesize multiple studies, and view prior beliefs of the community), three for authors (protect the author's investment, publish enough information, make authoring easy), three for publishers (limit liability, scale up, and establish a business model), and two for computers (incorporate into reading process, use familiar interface metaphors). A sample client-only prototype is available at http://omie.med.jhmi.edu/bayes. CONCLUSION: Bayesian communication has formal justification consistent with the needs of readers and can best be implemented in an online environment. Much research must be done to establish whether the formalism and the reality of readers' needs can meet.

OBJECTIVE: To evaluate the use and effect of a computer-based histology atlas during required laboratory sessions in a medical school histology course. DESIGN: Ethnographic observation of students' interactions in a factorial, controlled setting. MEASUREMENTS: Ethnographer's observations; student and instructor self-report survey after each laboratory session with items rated from 1 (least) to 7 (best); microscope practicum scores at the end of the course. RESULTS: Between groups assigned the atlas and those not, the ethnographer found qualitative differences in the semantic categories used by students in communicating with each other and with the faculty. Differences were also found in the quality of the interactions and in the learning styles used with and without the computer present in the laboratory. The most interactive learning style was achieved when a pair of students shared a computer and a microscope. Practicum grades did not change with respect to historical controls. Students assigned the atlas, compared with those not assigned, reported higher overall satisfaction (a difference in score of 0.1, P = 0.003) and perceived their fellow students to be more helpful (a difference of 0.11, P = 0.035). They rated the usefulness of the microscope lower (a difference of 0.23, P<0.001). CONCLUSION: A computer-based histology atlas induces qualitative changes in the histology laboratory environment. Most students and faculty reacted positively. The authors did not measure the impact on learning, but they found that there are aspects of using the atlas that instructors must manipulate to make learning optimal. Ethnographic techniques can be helpful in delineating the context and defining what the interventions might be.