Defining AMIA's artificial intelligence principles.

Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including "safe failure"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.

Effect of a Default Order vs an Alert in the Electronic Health Record on Hepatitis C Virus Screening Among Hospitalized Patients: A Stepped-Wedge Randomized Clinical Trial.

Importance: Hepatitis C virus (HCV) screening has been recommended for patients born between 1945 and 1965, but rates remain low. Objective: To evaluate whether a default order within the admission order set increases HCV screening compared with a preexisting alert within the electronic health record. Design, Setting, and Participants: This stepped-wedge randomized clinical trial was conducted from June 23, 2020, to April 10, 2021, at 2 hospitals within an academic medical center. Hospitalized patients born between 1945 and 1965 with no history of screening were included in the analysis. Interventions: During wedge 1 (a preintervention period), both hospital sites had an electronic alert prompting clinicians to consider HCV screening. During wedge 2, the first intervention wedge, the hospital site randomized to intervention (hospital B) had a default order for HCV screening implemented within the admission order set. During wedge 3, the second intervention wedge, the hospital site randomized to control (hospital A) had the default order set implemented. Main Outcomes and Measures: Percentage of eligible patients who received HCV screening during the hospital stay. Results: The study included 7634 patients (4405 in the control group and 3229 in the intervention group). The mean (SD) age was 65.4 (5.8) years; 4246 patients (55.6%) were men; 2142 (28.1%) were Black and 4625 (60.6%) were White; and 2885 (37.8%) had commercial insurance and 3950 (51.7%) had Medicare. The baseline rate of HCV screening in wedge 1 was 585 of 1560 patients (37.5% [95% CI, 35.1%-40.0%]) in hospital A and 309 of 1003 patients (30.8% [95% CI, 27.9%-33.7%]) in hospital B. The main adjusted model showed an increase of 31.8 (95% CI, 29.7-33.8) percentage points in test completion in the intervention group compared with the control group (P <. 001). Conclusions and Relevance: This stepped-wedge randomized clinical trial found that embedding HCV screening as a default order in the electronic health record substantially increased ordering and completion of testing in the hospital compared with a conventional interruptive alert. Trial Registration: Clinicaltrials.gov: NCT04525690.

Design, Implementation, and Validation of an Automated, Algorithmic COVID-19 Triage Tool.

OBJECTIVE: We describe the design, implementation, and validation of an online, publicly available tool to algorithmically triage patients experiencing severe acute respiratory syndrome coronavirus (SARS-CoV-2)-like symptoms. METHODS: We conducted a chart review of patients who completed the triage tool and subsequently contacted our institution's phone triage hotline to assess tool- and clinician-assigned triage codes, patient demographics, SARS-CoV-2 (COVID-19) test data, and health care utilization in the 30 days post-encounter. We calculated the percentage of concordance between tool- and clinician-assigned triage categories, down-triage (clinician assigning a less severe category than the triage tool), and up-triage (clinician assigning a more severe category than the triage tool) instances. RESULTS: From May 4, 2020 through January 31, 2021, the triage tool was completed 30,321 times by 20,930 unique patients. Of those 30,321 triage tool completions, 51.7% were assessed by the triage tool to be asymptomatic, 15.6% low severity, 21.7% moderate severity, and 11.0% high severity. The concordance rate, where the triage tool and clinician assigned the same clinical severity, was 29.2%. The down-triage rate was 70.1%. Only six patients were up-triaged by the clinician. 72.1% received a COVID-19 test administered by our health care system within 14 days of their encounter, with a positivity rate of 14.7%. CONCLUSION: The design, pilot, and validation analysis in this study show that this COVID-19 triage tool can safely triage patients when compared with clinician triage personnel. This work may signal opportunities for automated triage of patients for conditions beyond COVID-19 to improve patient experience by enabling self-service, on-demand, 24/7 triage access.

Transitions from One Electronic Health Record to Another: Challenges, Pitfalls, and Recommendations.

OBJECTIVE: We address the challenges of transitioning from one electronic health record (EHR) to another-a near ubiquitous phenomenon in health care. We offer mitigating strategies to reduce unintended consequences, maximize patient safety, and enhance health care delivery. METHODS: We searched PubMed and other sources to identify articles describing EHR-to-EHR transitions. We combined these references with the authors' extensive experience to construct a conceptual schema and to offer recommendations to facilitate transitions. RESULTS: Our PubMed query retrieved 1,351 citations: 43 were relevant for full paper review and 18 met the inclusion criterion of focus on EHR-to-EHR transitions. An additional PubMed search yielded 1,014 citations, for which we reviewed 74 full papers and included 5. We supplemented with additional citations for a total of 70 cited. We distinguished 10 domains in the literature that overlap yet present unique and salient opportunities for successful transitions and for problem mitigation. DISCUSSION: There is scant literature concerning EHR-to-EHR transitions. Identified challenges include financial burdens, personnel resources, patient safety threats from limited access to legacy records, data integrity during migration, cybersecurity, and semantic interoperability. Transition teams must overcome inadequate human infrastructure, technical challenges, security gaps, unrealistic providers' expectations, workflow changes, and insufficient training and support-all factors affecting potential clinician burnout. CONCLUSION: EHR transitions are remarkably expensive, laborious, personnel devouring, and time consuming. The paucity of references in comparison to the topic's salience reinforces the necessity for this type of review and analysis. Prudent planning may streamline EHR transitions and reduce expenses. Mitigating strategies, such as preservation of legacy data, managing expectations, and hiring short-term specialty consultants can overcome some of the greatest hurdles. A new medical subject headings (MeSH) term for EHR transitions would facilitate further research on this topic.

Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems.

BACKGROUND: Electronic health record (EHR) alert fatigue, while widely recognized as a concern nationally, lacks a corresponding comprehensive mitigation plan. OBJECTIVES: The goal of this manuscript is to provide practical guidance to clinical informaticists and other health care leaders who are considering creating a program to manage EHR alerts. METHODS: This manuscript synthesizes several approaches and recommendations for better alert management derived from four U.S. health care institutions that presented their experiences and recommendations at the American Medical Informatics Association 2019 Clinical Informatics Conference in Atlanta, Georgia, United States. The assembled health care institution leaders represent academic, pediatric, community, and specialized care domains. We describe governance and management, structural concepts and components, and human-computer interactions with alerts, and make recommendations regarding these domains based on our experience supplemented with literature review. This paper focuses on alerts that impact bedside clinicians. RESULTS: The manuscript addresses the range of considerations relevant to alert management including a summary of the background literature about alerts, alert governance, alert metrics, starting an alert management program, approaches to evaluating alerts prior to deployment, and optimization of existing alerts. The manuscript includes examples of alert optimization successes at two of the represented institutions. In addition, we review limitations on the ability to evaluate alerts in the current state and identify opportunities for further scholarship. CONCLUSION: Ultimately, alert management programs must strive to meet common goals of improving patient care, while at the same time decreasing the alert burden on clinicians. In so doing, organizations have an opportunity to promote the wellness of patients, clinicians, and EHRs themselves.

Computerized prescriber order entry-related patient safety reports: analysis of 2522 medication errors.

Objective: To examine medication errors potentially related to computerized prescriber order entry (CPOE) and refine a previously published taxonomy to classify them. Materials and Methods: We reviewed all patient safety medication reports that occurred in the medication ordering phase from 6 sites participating in a United States Food and Drug Administration-sponsored project examining CPOE safety. Two pharmacists independently reviewed each report to confirm whether the error occurred in the ordering/prescribing phase and was related to CPOE. For those related to CPOE, we assessed whether CPOE facilitated (actively contributed to) the error or failed to prevent the error (did not directly cause it, but optimal systems could have potentially prevented it). A previously developed taxonomy was iteratively refined to classify the reports. Results: Of 2522 medication error reports, 1308 (51.9%) were related to CPOE. Of these, CPOE facilitated the error in 171 (13.1%) and potentially could have prevented the error in 1137 (86.9%). The most frequent categories of "what happened to the patient" were delays in medication reaching the patient, potentially receiving duplicate drugs, or receiving a higher dose than indicated. The most frequent categories for "what happened in CPOE" included orders not routed to or received at the intended location, wrong dose ordered, and duplicate orders. Variations were seen in the format, categorization, and quality of reports, resulting in error causation being assignable in only 403 instances (31%). Discussion and Conclusion: Errors related to CPOE commonly involved transmission errors, erroneous dosing, and duplicate orders. More standardized safety reporting using a common taxonomy could help health care systems and vendors learn and implement prevention strategies.

Order sets in electronic health records: principles of good practice.

Electronic health record (EHR) order sets are common. Order sets represent one clinical decision support (CDS) tool within computerized provider order entry systems that may promote safe, efficient, and evidence-based patient care. A small number of order sets account for the vast majority of use, suggesting that some order sets have a higher value to clinicians than others. While EHR order sets can save time and improve processes of care, it remains less clear that EHR order sets have shown definite patient outcome benefits. There are general guidelines on CDS and usability that can be applied to the development of EHR order sets. Order set success requires leadership, planning, and resources as well as ongoing maintenance and evaluation. This article describes one academic medical center's experience in developing an EHR order set embedded with evidence-based principles and lessons learned for future order set development.