OBJECTIVES: There is rapidly growing interest in learning health systems (LHSs) nationally and globally. While the critical role of informatics is recognized, the informatics community has been relatively slow to formalize LHS as a priority area. MATERIALS AND METHODS: We compiled results from a short survey of LHS leaders and American Medical Informatics Association (AMIA) members, discussion from an LHS reception at the AMIA annual meeting, and a follow-up survey to inform priorities at the intersection of LHS and informatics. RESULTS: We present opportunities between informatics and LHS which fell into themes of: Understanding and Context, Shared Resources, Collaboration, Education, Data, Evaluation, and Patient Centeredness. Immediate LHS informatics priorities identified include establishing informatics LHS forum(s), case reports of LHS informatics successes and failures, LHS informatics education resources, and improved understanding of LHS principles in informatics. CONCLUSION: Increased informatics and LHS alignment is critical for advancing this transformative national priority.

INTRODUCTION: The rapid advancement of artificial intelligence (AI) has led to significant transformations in health and healthcare. As AI technologies continue to evolve, there is an urgent need to establish a unified framework that guides the design, implementation, and evaluation of AI-driven interventions across individual and population health contexts. APPROACH: In response to this need, the National Academy of Medicine (NAM) has initiated the development of an AI code of conduct (AICC) through its Digital Health Action Collaborative. This code of conduct is grounded in shared principles and commitments, aiming to actualize ethical and effective AI practices within the broader health and healthcare ecosystem. Given its specialized expertise and insight, the biomedical informatics (BMI) community plays a pivotal role in shaping and applying these guidelines. RECOMMENDATIONS: We, as members of the AICC Steering Committee and the NAM Digital Health Action Collaborative, urge BMI educators, researchers, and practitioners to engage actively in refining and implementing the AICC. This involvement is critical to ensuring that the code is robust, applicable, and continuously improved to meet the evolving challenges facing health and healthcare.

OBJECTIVE: Diagnosis codes documented in electronic health records (EHR) are often relied upon to clinically phenotype patients for biomedical research. However, these diagnoses can be incomplete and inaccurate, leading to false negatives when searching for patients with phenotypes of interest. This study aims to determine whether PheMAP, a comprehensive knowledgebase integrating multiple clinical terminologies beyond diagnosis to capture phenotypes, can effectively identify patients lacking relevant EHR diagnosis codes. MATERIALS AND METHODS: We investigated a collection of 3.5 million patient records from Vanderbilt University Medical Center's EHR and focused on 4 well-studied phenotypes: (1) type 2 diabetes mellitus (T2DM), (2) dementia, (3) prostate cancer, and (4) sensorineural hearing loss. We applied PheMAP to match structured concepts in patient records and calculated a phenotype risk score (PheScore) to indicate patient-phenotype similarity. Patients meeting predefined PheScore criteria but lacking diagnosis codes were identified. Clinically knowledgeable experts adjudicated randomly selected patients per phenotype as Positive, Possibly Positive, or Negative. RESULTS: Our approach indicated that 5.3% of patients lacked a diagnosis for T2DM, 4.5% for dementia, 2.2% for prostate cancer, and 0.2% for sensorineural hearing loss. The expert review indicated 100% precision (for Possibly Positive or Positive cases) for dementia and sensorineural hearing loss, and 90.0% and 85.0% precision for T2DM and prostate cancer, respectively. Excluding Possibly Positive cases, the precision for T2DM and prostate cancer was 88.9% and 81.3%, respectively. CONCLUSIONS: Leveraging clinical terminologies incorporated by PheMAP can effectively identify patients with phenotypes who lack EHR diagnosis codes, thereby enhancing phenotyping quality and related research reliability.

OBJECTIVE: To develop ImageVU, a scalable research imaging infrastructure that integrates clinical imaging data with metadata-driven cohort discovery, enabling secure, efficient, and regulatory-compliant access to imaging for secondary and opportunistic research use. This manuscript presents a detailed description of ImageVU's key components and lessons learned to assist other institutions in developing similar research imaging services and infrastructure. METHODS: ImageVU was designed to support the secondary use of radiological imaging data through a dedicated research imaging store. The system comprises four interconnected components: a Research PACS, an Ad Hoc Backfill Host, Cloud Storage System, and a De-Identification System. Imaging metadata are extracted and stored in the Research Derivative (RD), an identified clinical data repository, and the Synthetic Derivative (SD), a de-identified research data repository, with access facilitated through the RD Discover web portal. Researchers interact with the system via structured metadata queries and multiple data delivery options, including web-based viewing, bulk downloads, and dataset preparation for high-performance computing environments. RESULTS: The integration of metadata-driven search capabilities has streamlined cohort discovery and improved imaging data accessibility. As of December 2024, ImageVU has processed 12.9 million MRI and CT series from 1.36 million studies across 453,403 patients. The system has supported 75 project requests, delivering over 50 TB of imaging data to 55 investigators, leading to 66 published research papers. CONCLUSION: ImageVU demonstrates a scalable and efficient approach for integrating clinical imaging into research workflows. By combining institutional data infrastructure with cloud-based storage and metadata-driven cohort identification, the platform enables secure and compliant access to imaging for translational research.

BACKGROUND: As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time. OBJECTIVE: Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360° continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles. DISCUSSION: We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.

INTRODUCTION: Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources. RECOMMENDATIONS: Here, we propose five independent fundamental dimensions of phenotyping algorithms-complexity, performance, efficiency, implementability, and maintenance-through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities.

We aimed to assess ChatGPT's performance on the Clinical Informatics Board Examination and to discuss the implications of large language models (LLMs) for board certification and maintenance. We tested ChatGPT using 260 multiple-choice questions from Mankowitz's Clinical Informatics Board Review book, omitting 6 image-dependent questions. ChatGPT answered 190 (74%) of 254 eligible questions correctly. While performance varied across the Clinical Informatics Core Content Areas, differences were not statistically significant. ChatGPT's performance raises concerns about the potential misuse in medical certification and the validity of knowledge assessment exams. Since ChatGPT is able to answer multiple-choice questions accurately, permitting candidates to use artificial intelligence (AI) systems for exams will compromise the credibility and validity of at-home assessments and undermine public trust. The advent of AI and LLMs threatens to upend existing processes of board certification and maintenance and necessitates new approaches to the evaluation of proficiency in medical education.

The Supreme Court recently overturned settled case law that affirmed a pregnant individual's Constitutional right to an abortion. While many states will commit to protect this right, a large number of others have enacted laws that limit or outright ban abortion within their borders. Additional efforts are underway to prevent pregnant individuals from seeking care outside their home state. These changes have significant implications for delivery of healthcare as well as for patient-provider confidentiality. In particular, these laws will influence how information is documented in and accessed via electronic health records and how personal health applications are utilized in the consumer domain. We discuss how these changes may lead to confusion and conflict regarding use of health information, both within and across state lines, why current health information security practices may need to be reconsidered, and what policy options may be possible to protect individuals' health information.

OBJECTIVE: We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors. MATERIALS AND METHODS: We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities. RESULTS: Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media. DISCUSSION: Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19. CONCLUSIONS: The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

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: To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods: We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results: We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion: Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion: CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

While great progress has been made in digitizing the US health care system, today's health information technology (IT) infrastructure remains largely a collection of systems that are not designed to support a transition to value-based care. In addition, the pursuit of value-based care, in which we deliver better care with better outcomes at lower cost, places new demands on the health care system that our IT infrastructure needs to be able to support. Provider organizations pursuing new models of health care delivery and payment are finding that their electronic systems lack the capabilities needed to succeed. The result is a chasm between the current health IT ecosystem and the health IT ecosystem that is desperately needed.In this paper, we identify a set of focal goals and associated near-term achievable actions that are critical to pursue in order to enable the health IT ecosystem to meet the acute needs of modern health care delivery. These ideas emerged from discussions that occurred during the 2015 American Medical Informatics Association Policy Invitational Meeting. To illustrate the chasm and motivate our recommendations, we created a vignette from the multistakeholder perspectives of a patient, his provider, and researchers/innovators. It describes an idealized scenario in which each stakeholder's needs are supported by an integrated health IT environment. We identify the gaps preventing such a reality today and present associated policy recommendations that serve as a blueprint for critical actions that would enable us to cross the current health IT chasm by leveraging systems and information to routinely deliver high-value care.

Large amounts of personal health data are being collected and made available through existing and emerging technological media and tools. While use of these data has significant potential to facilitate research, improve quality of care for individuals and populations, and reduce healthcare costs, many policy-related issues must be addressed before their full value can be realized. These include the need for widely agreed-on data stewardship principles and effective approaches to reduce or eliminate data silos and protect patient privacy. AMIA's 2012 Health Policy Meeting brought together healthcare academics, policy makers, and system stakeholders (including representatives of patient groups) to consider these topics and formulate recommendations. A review of a set of Proposed Principles of Health Data Use led to a set of findings and recommendations, including the assertions that the use of health data should be viewed as a public good and that achieving the broad benefits of this use will require understanding and support from patients.

OBJECTIVE: To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. MATERIALS AND METHODS: We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. RESULTS: Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. DISCUSSION: We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. CONCLUSIONS: There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

Time motion studies were first described in the early 20th century in industrial engineering, referring to a quantitative data collection method where an external observer captured detailed data on the duration and movements required to accomplish a specific task, coupled with an analysis focused on improving efficiency. Since then, they have been broadly adopted by biomedical researchers and have become a focus of attention due to the current interest in clinical workflow related factors. However, attempts to aggregate results from these studies have been difficult, resulting from a significant variability in the implementation and reporting of methods. While efforts have been made to standardize the reporting of such data and findings, a lack of common understanding on what "time motion studies" are remains, which not only hinders reviews, but could also partially explain the methodological variability in the domain literature (duration of the observations, number of tasks, multitasking, training rigor and reliability assessments) caused by an attempt to cluster dissimilar sub-techniques. A crucial milestone towards the standardization and validation of time motion studies corresponds to a common understanding, accompanied by a proper recognition of the distinct techniques it encompasses. Towards this goal, we conducted a review of the literature aiming at identifying what is being referred to as "time motion studies". We provide a detailed description of the distinct methods used in articles referenced or classified as "time motion studies", and conclude that currently it is used not only to define the original technique, but also to describe a broad spectrum of studies whose only common factor is the capture and/or analysis of the duration of one or more events. To maintain alignment with the existing broad scope of the term, we propose a disambiguation approach by preserving the expanded conception, while recommending the use of a specific qualifier "continuous observation time motion studies" to refer to variations of the original method (the use of an external observer recording data continuously). In addition, we present a more granular naming for sub-techniques within continuous observation time motion studies, expecting to reduce the methodological variability within each sub-technique and facilitate future results aggregation.

OBJECTIVE: Clinical documentation is central to the medical record and so to a range of healthcare and business processes. As electronic health record adoption expands, computerized provider documentation (CPD) is increasingly the primary means of capturing clinical documentation. Previous CPD studies have focused on particular stakeholder groups and sites, often limiting their scope and conclusions. To address this, we studied multiple stakeholder groups from multiple sites across the USA. METHODS: We conducted 14 focus groups at five Department of Veterans Affairs facilities with 129 participants (54 physicians or practitioners, 34 nurses, and 37 administrators). Investigators qualitatively analyzed resultant transcripts, developed categories linked to the data, and identified emergent themes. RESULTS: Five major themes related to CPD emerged: communication and coordination; control and limitations in expressivity; information availability and reasoning support; workflow alteration and disruption; and trust and confidence concerns. The results highlight that documentation intertwines tightly with clinical and administrative workflow. Perceptions differed between the three stakeholder groups but remained consistent within groups across facilities. CONCLUSIONS: CPD has dramatically changed documentation processes, impacting clinical understanding, decision-making, and communication across multiple groups. The need for easy and rapid, yet structured and constrained, documentation often conflicts with the need for highly reliable and retrievable information to support clinical reasoning and workflows. Current CPD systems, while better than paper overall, often do not meet the needs of users, partly because they are based on an outdated 'paper-chart' paradigm. These findings should inform those implementing CPD systems now and future plans for more effective CPD systems.

AMIA, as other professional societies, has a long-standing interest in promoting a strong ethical framework for its membership. This white paper presents the latest AMIA Code of Professional and Ethical Conduct. It was approved in November of 2011 by the AMIA Board of Directors. This document constitutes a revision of, and update to, the first code, approved and published in J Am Med Inform Assoc1 in 2007. In an effort to keep pace with the field's vitality, the code presented here is intended to be a dynamic document, and will continue to evolve as AMIA and the field itself evolve. AMIA will publish on its web site this version of the code as part of a process that seeks ongoing response from, and involvement by, AMIA members. The code is meant to be practical and easily understood, so it is compact and uses general language. Unlike the ethics codes of some professional societies, the AMIA code is not intended to be prescriptive or legislative; it is aspirational, and as such, provides the broad strokes of a set of important ethical principles especially 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, students, and others—and with whom they interact. The AMIA Board and the AMIA Ethics Committee encourage members to offer suggestions for improvements and other changes. In this way, the code will continue to progress and best serve AMIA and the larger informatics community.

OBJECTIVE: Inadequate participant recruitment is a major problem facing clinical research. Recent studies have demonstrated that electronic health record (EHR)-based, point-of-care, clinical trial alerts (CTA) can improve participant recruitment to certain clinical research studies. Despite their promise, much remains to be learned about the use of CTAs. Our objective was to study whether repeated exposure to such alerts leads to declining user responsiveness and to characterize its extent if present to better inform future CTA deployments. METHODS: During a 36-week study period, we systematically documented the response patterns of 178 physician users randomized to receive CTAs for an ongoing clinical trial. Data were collected on: (1) response rates to the CTA; and (2) referral rates per physician, per time unit. Variables of interest were offset by the log of the total number of alerts received by that physician during that time period, in a Poisson regression. RESULTS: Response rates demonstrated a significant downward trend across time, with response rates decreasing by 2.7% for each advancing time period, significantly different from zero (flat) (p<0.0001). Even after 36 weeks, response rates remained in the 30%-40% range. Subgroup analyses revealed differences between community-based versus university-based physicians (p=0.0489). DISCUSSION: CTA responsiveness declined gradually over prolonged exposure, although it remained reasonably high even after 36 weeks of exposure. There were also notable differences between community-based versus university-based users. CONCLUSIONS: These findings add to the limited literature on this form of EHR-based alert fatigue and should help inform future tailoring, deployment, and further study of CTAs.

Advances in clinical and translational science, along with related national-scale policy and funding mechanisms, have provided significant opportunities for the advancement of applied clinical research informatics (CRI) and translational bioinformatics (TBI). Such efforts are primarily oriented to application and infrastructure development and are critical to the conduct of clinical and translational research. However, they often come at the expense of the foundational CRI and TBI research needed to grow these important biomedical informatics subdisciplines and ensure future innovations. In light of this challenge, it is critical that a number of steps be taken, including the conduct of targeted advocacy campaigns, the development of community-accepted research agendas, and the continued creation of forums for collaboration and knowledge exchange. Such efforts are needed to ensure that the biomedical informatics community is able to advance CRI and TBI science in the context of the modern clinical and translational science era.

Test result management is an integral aspect of quality clinical care and a crucial part of the ambulatory medicine workflow. Correct and timely communication of results to a provider is the necessary first step in ambulatory result management and has been identified as a weakness in many paper-based systems. While electronic health records (EHRs) hold promise for improving the reliability of result management, the complexities involved make this a challenging task. Experience with test result management is reported, four new categories of result management errors identified are outlined, and solutions developed during a 2-year deployment of a commercial EHR are described. Recommendations for improving test result management with EHRs are then given.

OBJECTIVES: Clinical Research Informatics, an emerging sub-domain of Biomedical Informatics, is currently not well defined. A formal description of CRI including major challenges and opportunities is needed to direct progress in the field. DESIGN: Given the early stage of CRI knowledge and activity, we engaged in a series of qualitative studies with key stakeholders and opinion leaders to determine the range of challenges and opportunities facing CRI. These phases employed complimentary methods to triangulate upon our findings. MEASUREMENTS: Study phases included: 1) a group interview with key stakeholders, 2) an email follow-up survey with a larger group of self-identified CRI professionals, and 3) validation of our results via electronic peer-debriefing and member-checking with a group of CRI-related opinion leaders. Data were collected, transcribed, and organized for formal, independent content analyses by experienced qualitative investigators, followed by an iterative process to identify emergent categorizations and thematic descriptions of the data. RESULTS: We identified a range of challenges and opportunities facing the CRI domain. These included 13 distinct themes spanning academic, practical, and organizational aspects of CRI. These findings also informed the development of a formal definition of CRI and supported further representations that illustrate areas of emphasis critical to advancing the domain. CONCLUSIONS: CRI has emerged as a distinct discipline that faces multiple challenges and opportunities. The findings presented summarize those challenges and opportunities and provide a framework that should help inform next steps to advance this important new discipline.

The AMIA Board of Directors has decided to periodically publish AMIA's Code of Professional Ethical Conduct for its members in the Journal of the American Medical Informatics Association. The Code also will be available on the AMIA Web site at www.amia.org as it continues to evolve in response to feedback from the AMIA membership. The AMIA Board acknowledges the continuing work and dedication of the AMIA Ethics Committee. AMIA is the copyright holder of this work.

OBJECTIVE: Computerized physician documentation (CPD) has been implemented throughout the nation's Veterans Affairs Medical Centers (VAMCs) and is likely to increasingly replace handwritten documentation in other institutions. The use of this technology may affect educational and clinical activities, yet little has been reported in this regard. The authors conducted a qualitative study to determine the perceived impacts of CPD among faculty and housestaff in a VAMC. DESIGN: A cross-sectional study was conducted using semistructured interviews with faculty (n = 10) and a group interview with residents (n = 10) at a VAMC teaching hospital. MEASUREMENTS: Content analysis of field notes and taped transcripts were done by two independent reviewers using a grounded theory approach. Findings were validated using member checking and peer debriefing. RESULTS: Four major themes were identified: (1) improved availability of documentation; (2) changes in work processes and communication; (3) alterations in document structure and content; and (4) mistakes, concerns, and decreased confidence in the data. With a few exceptions, subjects felt documentation was more available, with benefits for education and patient care. Other impacts of CPD were largely seen as detrimental to aspects of clinical practice and education, including documentation quality, workflow, professional communication, and patient care. CONCLUSION: CPD is perceived to have substantial positive and negative impacts on clinical and educational activities and environments. Care should be taken when designing, implementing, and using such systems to avoid or minimize any harmful impacts. More research is needed to assess the extent of the impacts identified and to determine the best strategies to effectively deal with them.