Evaluation of a Clinical Decision Support Tool to Guide Adoption of the American Heart Association Telemetry Monitoring Practice Standards.

BACKGROUND: The objectives of this study were to (1) evaluate telemetry use pre- and postimplementation of clinical decision support tools to support American Heart Association practice standards for telemetry monitoring and (2) understand the factors that may contribute to variation of telemetry monitoring in practice. METHODS AND RESULTS: First, we captured overall variability in telemetry use pre- and postimplementation of the clinical decision support intervention. We then conducted semistructured interviews with telemetry-ordering providers to identify key barriers and facilitators to adoption. During the study period, 399 physicians met criteria for inclusion and were divided into excessive and nonexcessive orderers. Distribution of telemetry use was bimodal. Among nonexcessive users, 24.4% of patient days were with telemetry compared with 51.6% among excessive users. On average, both excessive (6.1% reduction) and nonexcessive users (2.8% reduction) decreased telemetry use postimplementation, and these reductions were sustained over a 16-month period. Sixteen interviews were conducted. Physicians believed that the tool was successful because it caused them to more closely consider if telemetry was indicated for each patient. Physicians also voiced frustration with interruptions to their workflow, and some noted that they commonly use telemetry outside of practice standards to monitor patients who were acutely but not critically ill. CONCLUSIONS: Embedding telemetry practice standards into the electronic health record in the form of clinical decision support is effective at reducing excess telemetry use. Although the intervention was well received, there are persistent barriers, such as preexisting views on telemetry and existing workflow habits, that may inhibit higher adoption of standards.

Implementing Telestroke in the Inpatient Setting: Identifying Factors for Success.

BACKGROUND: Inpatient telestroke programs have emerged as a solution to provide timely stroke care in underserved areas, but their successful implementation and factors influencing their effectiveness remain underexplored. This study aimed to qualitatively evaluate the perspectives of inpatient clinicians located at spoke hospitals participating in a newly established inpatient telestroke program to identify implementation barriers and facilitators. METHODS: This was a formative evaluation relying on semistructured qualitative interviews with 16 inpatient providers (physicians and nurse practitioners) at 5 spoke sites of a hub-and-spoke inpatient telestroke program. The Integrated-Promoting Action on Research Implementation in Health Services framework guided data analysis, focusing on the innovation, recipients, context, and facilitation aspects of implementation. Interviews were transcribed and coded using thematic analysis. RESULTS: Fifteen themes were identified in the data and mapped to the Integrated-Promoting Action on Research Implementation in Health Services framework. Themes related to the innovation (the telestroke program) included easy access to stroke specialists, the benefits of limiting patient transfers, concerns about duplicating tests, and challenges of timing inpatient telestroke visits and notes to align with discharge workflow. Themes pertaining to recipients (care team members and patients) were communication gaps between teams, concern about the supervision of inpatient telestroke advanced practice providers and challenges with nurse empowerment. With regard to the context (hospital and system factors), providers highlighted familiarity with telehealth technologies as a facilitator to implementing inpatient telestroke, yet highlighted resource limitations in smaller facilities. Facilitation (program implementation) was recognized as crucial for education, standardization, and buy-in. CONCLUSIONS: Understanding barriers and facilitators to implementation is crucial to determining where programmatic changes may need to be made to ensure the success and sustainment of inpatient telestroke services.

Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial.

BACKGROUND: Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted. OBJECTIVE: Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT). METHODS: A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery. RESULTS: This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024. CONCLUSIONS: Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record. TRIAL REGISTRATION: ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52882.

A population-based analysis on the incidence of metachronous colon cancer after endoscopic resection of advanced adenomas with high-grade dysplasia: does location matter?

BACKGROUND: Advanced adenomas (AAs) with high-grade dysplasia (HGD) represent a risk factor for metachronous neoplasia, with guidelines recommending short-interval surveillance. Although the worse prognosis of proximal (vs distal) colon cancers (CCs) is established, there is paucity of evidence on the impact of laterality on the risk of subsequent neoplasia for these AAs. METHODS: Adults with HGD adenomas undergoing polypectomy were identified in the Surveillance, Epidemiology, and End Results database (2000-2019). Cumulative incidence of malignancy was estimated using the Kaplan-Meier method. Fine-Gray models assessed the effect of patient and disease characteristics on CC incidence. RESULTS: Of 3199 patients, 26% had proximal AAs. A total of 65 cases of metachronous adenocarcinoma were identified after polypectomy of 35 proximal and 30 distal adenomas with HGD. The 10-year cumulative incidence of CC was 2.3%; when stratified by location, it was 4.8% for proximal vs 1.4% for distal adenomas. Proximal location was significantly associated with increased incidence of metachronous cancer (adjusted hazard ratio, 3.32; 95% CI, 2.05-5.38). CONCLUSION: Proximal location of AAs with HGD was associated with >3-fold increased incidence of metachronous CC and shorter time to diagnosis. These data suggest laterality should be considered in the treatment and follow-up of these patients.

Evolving Interoperability Across a State Public Health Immunization Registry and Electronic Health Records.

The critical need for system interoperability and robust information infrastructure in public health was highlighted during the COVID-19 pandemic. An assessment of the evolving interoperability between immunization information system (IIS) in a state-based public health agency and electronic health records (EHRs) including pandemic-driven evolution/use was conducted. The Minnesota Immunization Information Connection (MIIC), the IIS for Minnesota (US) supports interoperability with EHRs using HL7v2.5.1 standards-based queries. Structured interviews were conducted with 28 experts across 12 healthcare systems and public health clinics (n=286 sites) between April - July 2022. Though all reported use of MIIC, most (83%) had MIIC integration within their EHRs, and high EHR queries to MIIC (∼6 million/month), numerous organizational/technical barriers were identified including standard vaccine-naming need in EHRs, app access issues, limited resources and informatics-staff shortage in public health. Results underscore vital role of IIS, on-going interoperability evaluation to address issues and promote standards-based bi-directional EHR-IIS data exchanges.

Promoting Learning Health System Cycles by Optimizing EHR Data Clinical Concept Encoding Processes.

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.

Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge.

Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge. Our results showed that the additional use of data-driven features statistically improved the model performances, with best AUC=0.766 by using logistic regression.

Post-Implementation Outcomes of a Remote Patient Monitoring Program After Emergency Department Discharge.

To better communicate and improve post-visit outcomes, a remote patient monitoring (RPM) program was implemented for patients discharged from emergency departments (ED) across 10 hospitals. The solution was offered to patients at the time of ED discharge and staffed by a group of care coordinators to respond to questions/urgent needs. Of 107,477 consecutive patients offered RPM, 28,425 patients (26.4%) engaged with the program. Activated patients with RPM were less likely to return to the ED within 90 days of their index visit [19.8% compared to 23.6%, p<.001]. While activation rates were modest, we observed fewer return visits to the ED in patients using RPM, with a 16.2% lower hazard of returning in the next year. Future research is needed to understand methods to improve RPM activation, any causal effects of RPM activation on return ED visits, and external validation of these findings.

Advanced Care Planning Content Encoding with Natural Language Processing.

While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its documentation variable. Natural language processing (NLP) approaches hold promise for supporting ACP, including its use for decision support to improve ACP gaps at the point of care. ACP themes were annotated on palliative care notes across four annotators (Fleiss kappa = 0.753) and supervised models trained (Huggingface models bert-base-uncased and Bio_ClinicalBERT) using 5-fold cross validation (F1=0.8, precision=0.75, recall=0.86, any theme). When applied across the full note corpus of 12,711 notes, we observed variability in documentation of ACP information. Our findings demonstrate the promise of NLP approaches for informatics-based approaches for ACP and patient-centered care.

A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients.

Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases. A manual case review to obtain this estimate indicated our sample incidence of PASC (13%) was representative of the estimated population proportion (95% CI: 19±6.22%). However, the high number of cases classified as indeterminate demonstrates the challenges in classifying PASC even among experienced clinicians. Lastly, this study developed a dashboard to display views of aggregated PASC symptoms and measured its utility using the System Usability Scale. Overall comments related to the dashboard's potential were positive. This pipeline is crucial for monitoring post-COVID-19 patients with potential for use in clinical settings.

Large Language Model-Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures.

Importance: Informed consent is a critical component of patient care before invasive procedures, yet it is frequently inadequate. Electronic consent forms have the potential to facilitate patient comprehension if they provide information that is readable, accurate, and complete; it is not known if large language model (LLM)-based chatbots may improve informed consent documentation by generating accurate and complete information that is easily understood by patients. Objective: To compare the readability, accuracy, and completeness of LLM-based chatbot- vs surgeon-generated information on the risks, benefits, and alternatives (RBAs) of common surgical procedures. Design, Setting, and Participants: This cross-sectional study compared randomly selected surgeon-generated RBAs used in signed electronic consent forms at an academic referral center in San Francisco with LLM-based chatbot-generated (ChatGPT-3.5, OpenAI) RBAs for 6 surgical procedures (colectomy, coronary artery bypass graft, laparoscopic cholecystectomy, inguinal hernia repair, knee arthroplasty, and spinal fusion). Main Outcomes and Measures: Readability was measured using previously validated scales (Flesh-Kincaid grade level, Gunning Fog index, the Simple Measure of Gobbledygook, and the Coleman-Liau index). Scores range from 0 to greater than 20 to indicate the years of education required to understand a text. Accuracy and completeness were assessed using a rubric developed with recommendations from LeapFrog, the Joint Commission, and the American College of Surgeons. Both composite and RBA subgroup scores were compared. Results: The total sample consisted of 36 RBAs, with 1 RBA generated by the LLM-based chatbot and 5 RBAs generated by a surgeon for each of the 6 surgical procedures. The mean (SD) readability score for the LLM-based chatbot RBAs was 12.9 (2.0) vs 15.7 (4.0) for surgeon-generated RBAs (P = .10). The mean (SD) composite completeness and accuracy score was lower for surgeons' RBAs at 1.6 (0.5) than for LLM-based chatbot RBAs at 2.2 (0.4) (P < .001). The LLM-based chatbot scores were higher than the surgeon-generated scores for descriptions of the benefits of surgery (2.3 [0.7] vs 1.4 [0.7]; P < .001) and alternatives to surgery (2.7 [0.5] vs 1.4 [0.7]; P < .001). There was no significant difference in chatbot vs surgeon RBA scores for risks of surgery (1.7 [0.5] vs 1.7 [0.4]; P = .38). Conclusions and Relevance: The findings of this cross-sectional study suggest that despite not being perfect, LLM-based chatbots have the potential to enhance informed consent documentation. If an LLM were embedded in electronic health records in a manner compliant with the Health Insurance Portability and Accountability Act, it could be used to provide personalized risk information while easing documentation burden for physicians.

Development and implementation of an interoperability tool across state public health agency's disease surveillance and immunization information systems.

Public health information systems have historically been siloed with limited interoperability. The State of Minnesota's disease surveillance system (Minnesota Electronic Disease Surveillance System: MEDSS, ∼12 million total reportable events) and immunization information system (Minnesota Immunization Information Connection: MIIC, ∼130 million total immunizations) lacked interoperability between them and data exchange was fully manual. An interoperability tool based on national standards (HL7 and SOAP/web services) for query and response was developed for electronic vaccination data exchange from MIIC into MEDSS by soliciting stakeholder requirements (n = 39) and mapping MIIC vaccine codes (n = 294) to corresponding MEDSS product codes (n = 48). The tool was implemented in March 2022 and incorporates MIIC data into a new vaccination form in MEDSS with mapping of 30 data elements including MIIC demographics, vaccination history, and vaccine forecast. The tool was evaluated using mixed methods (quantitative analysis of user time, clicks, queries; qualitative review with users). Comparison of key tasks demonstrated efficiencies including vaccination data access (before: 50 clicks, >2 min; after: 4 clicks, 8 s) which translated directly to staff effort (before: 5 h/week; after: ∼17 min/week). This case study demonstrates the contribution of improving public health systems interoperability, ultimately with the goal of enhanced data-driven decision-making and public health surveillance.

Crowdsourcing Electronic Health Record Improvements at Scale across an Integrated Health Care Delivery System.

BACKGROUND AND OBJECTIVE: Despite widespread adoption of electronic health records (EHRs), these systems have significant room for improved efficiency and efficacy. While the idea of crowdsourcing EHR improvement ideas has been reported, little is known about how this might work across an integrated health care delivery system in practice. METHODS: Our program solicited EHR improvement submissions during two timeframes across 10 hospitals and 60 clinics in an upper-Midwest integrated health care delivery system. Submissions were primarily collected via an EHR help feature. RESULTS: A total of 262 and 294 submissions were received in 2019 and 2022, with a majority initiated from physicians (73.5 and 46.9%, 2019 and 2022) specializing in family medicine (52.0 and 59.3%). In 2022, the program reached a larger variety of personnel than 2019, with 53.0% of submissions from advanced practice providers, nurses, administrative staff, and other roles (p < 0.0001). Many ideas (36.4 and 50.0% in 2019 and 2022) reflected a lack of user understanding of EHR features and were addressed through training/education. Significant (27.1 and 25.9%) or simple (24.0 and 14.7%) EHR optimizations were required to address most remaining suggestions, with a number part of planned EHR improvement projects already (16.3 and 17.6%). CONCLUSION: Our experience using a crowdsourcing approach for EHR improvement ideas provided clinicians and staff the opportunity to address frustrations with the EHR and offered concrete feedback and solutions. While previous studies have suggested EHR technology improvements as paramount, we observed large numbers of users having a misunderstanding of EHR features, highlighting the need for improved EHR user competency and training.

A novel, evidence-based, comprehensive clinical decision support system improves outcomes for patients with traumatic rib fractures.

BACKGROUND: Traumatic rib fractures are associated with high morbidity and mortality. Clinical decision support systems (CDSS) have been shown to improve adherence to evidence-based (EB) practice and improve clinical outcomes. The objective of this study was to investigate if a rib fracture CDSS reduced hospital length of stay (LOS), 90-day and 1-year mortality, unplanned ICU transfer, and the need for mechanical ventilation. The independent association of two process measures, an admission EB order set and a pain-inspiratory-cough score early warning system, with LOS were investigated. METHODS: The CDSS was scaled across nine US trauma centers. Following multiple imputation, multivariable regression models were fit to evaluate the association of the CDSS on primary and secondary outcomes. As a sensitivity analysis, propensity score matching was also performed to confirm regression findings. RESULTS: Overall, 3,279 patients met inclusion criteria. Rates of EB practices increased following implementation. On risk-adjusted analysis, in-hospital LOS preintervention versus postintervention was unchanged (incidence rate ratio [IRR], 1.06; 95% confidence interval [CI], 0.97-1.15, p = 0.2) but unplanned transfer to the ICU was reduced (odds ratio, 0.28; 95% CI, 0.09-0.84, p = 0.024), as was 1-year mortality (hazard ratio, 0.6; 95% CI, 0.4-0.89, p = 0.01). Provider utilization of the admission order bundle was 45.3%. Utilization was associated with significantly reduced LOS (IRR, 0.87; 95% CI, 0.77-0.98; p = 0.019). The early warning system triggered on 34.4% of patients; however, was not associated with a significant reduction in hospital LOS (IRR, 0.76; 95% CI, 0.55-1.06; p = 0.1). CONCLUSION: A novel, user-centered, comprehensive CDSS improves adherence to EB practice and is associated with a significant reduction in unplanned ICU admissions and possibly mortality, but not hospital LOS. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.

Endoscopic polypectomy for malignant polyps: Should tumor location (right versus left side) guide clinical decisions?

BACKGROUND: Current guidelines consider endoscopic resection appropriate treatment for malignant colon polyps with negative margins, low-grade histology, and no lymphovascular invasion. While increasing literature demonstrates a worse prognosis for advanced stage right- versus left-sided colon cancers after curative treatment, there is paucity of data regarding prognostic effect of location in patients undergoing endoscopic resection of T1 polyps. We hypothesized the more aggressive biologic behavior observed in advanced right-sided cancers would be similarly represented in malignant polyps, and this location would be associated with lower overall survival. METHODS: The National Cancer Database was queried for adults with T1NxMx tumors who underwent endoscopic polypectomy (2004-2017). Patients with positive margins or without follow-up information were excluded. RESULTS: A total of 2,337 patients met inclusion criteria; 22% had right-sided polyps. Endoscopically excised proximal tumors were more common in elderly, and those with public insurance and more comorbidities (all P < .01). Among patients with complete pathologic data, there were no statistical differences between right- and left-sided polyps with 1 cm median size, >92% without lymphovascular invasion, and 100% without tumor deposits. Univariate analysis showed 73% vs 86% 5-year overall survival for right versus left polyps (P < .01). After adjustment for available confounders, right-sided location remained significantly associated with worse overall survival (hazard ratio 1.49, 95% confidence interval 1.21-1.83). CONCLUSION: In this national cohort of patients with endoscopically excised malignant polyps, we identified right colon location as an independent prognostic factor associated with increased risk of mortality. Our data suggest polyp location should be taken into consideration when making clinical decisions regarding treatment and/or surveillance.

Re-Aiming Equity Evaluation in Clinical Decision Support: A Scoping Review of Equity Assessments in Surgical Decision Support Systems.

OBJECTIVE: We critically evaluated the surgical literature to explore the prevalence and describe how equity assessments occur when using clinical decision support systems. BACKGROUND: Clinical decision support (CDS) systems are increasingly used to facilitate surgical care delivery. Despite formal recommendations to do so, equity evaluations are not routinely performed on CDS systems and underrepresented populations are at risk of harm and further health disparities. We explored surgical literature to determine frequency and rigor of CDS equity assessments and offer recommendations to improve CDS equity by appending existing frameworks. METHODS: We performed a scoping review up to Augus 25, 2021 using PubMed and Google Scholar for the following search terms: clinical decision support, implementation, RE-AIM, Proctor, Proctor's framework, equity, trauma, surgery, surgical. We identified 1415 citations and 229 abstracts met criteria for review. A total of 84 underwent full review after 145 were excluded if they did not assess outcomes of an electronic CDS tool or have a surgical use case. RESULTS: Only 6% (5/84) of surgical CDS systems reported equity analyses, suggesting that current methods for optimizing equity in surgical CDS are inadequate. We propose revising the RE-AIM framework to include an Equity element (RE 2 -AIM) specifying that CDS foundational analyses and algorithms are performed or trained on balanced datasets with sociodemographic characteristics that accurately represent the CDS target population and are assessed by sensitivity analyses focused on vulnerable subpopulations. CONCLUSION: Current surgical CDS literature reports little with respect to equity. Revising the RE-AIM framework to include an Equity element (RE 2 -AIM) promotes the development and implementation of CDS systems that, at minimum, do not worsen healthcare disparities and possibly improve their generalizability.

How Are Leading Research Institutions Engaging with Data Sharing Tools and Programs?

With widespread electronic health record (EHR) adoption and improvements in health information interoperability in the United States, troves of data are available for knowledge discovery. Several data sharing programs and tools have been developed to support research activities, including efforts funded by the National Institutes of Health (NIH), EHR vendors, and other public- and private-sector entities. We surveyed 65 leading research institutions (77% response rate) about their use of and value derived from ten programs/tools, including NIH's Accrual to Clinical Trials, Epic Corporation's Cosmos, and the Observational Health Data Sciences and Informatics consortium. Most institutions participated in multiple programs/tools but reported relatively low usage (even when they participated, they frequently indicated that fewer than one individual/month benefitted from the platform to support research activities). Our findings suggest that investments in research data sharing have not yet achieved desired results.

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.

OBJECTIVE: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations. MATERIALS AND METHODS: We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the Federated Averaging (FedAvg) algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, and FedAMP). RESULTS: We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, P = .5) and improved model generalizability with the FedAvg model (P < .05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation. CONCLUSION: FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.

Do electronic health record systems "dumb down" clinicians?

A panel sponsored by the American College of Medical Informatics (ACMI) at the 2021 AMIA Symposium addressed the provocative question: "Are Electronic Health Records dumbing down clinicians?" After reviewing electronic health record (EHR) development and evolution, the panel discussed how EHR use can impair care delivery. Both suboptimal functionality during EHR use and longer-term effects outside of EHR use can reduce clinicians' efficiencies, reasoning abilities, and knowledge. Panel members explored potential solutions to problems discussed. Progress will require significant engagement from clinician-users, educators, health systems, commercial vendors, regulators, and policy makers. Future EHR systems must become more user-focused and scalable and enable providers to work smarter to deliver improved care.