Journal of the American Medical Informatics Association最新文献

Objective: Patients of different race have different outcomes following renal transplantation. Patients of different race also undergo renal transplantation at different hospitals. We used a novel decentralized multisite approach to quantitatively assess the effect of site of care on racial disparities between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in post-transplantation survival times.

Materials and methods: In this study, we develop a communication-efficient federated learning algorithm to assess site-of-care associated racial disparities based on decentralized time-to-event data, called Communication-Efficient Distributed Analysis for Racial Disparity in Time-to-event Data (CEDAR-t2e). The algorithm includes 2 modules. Module I is to estimate the site-specific proportional hazards model for time-to-event outcomes in a distributed manner, in which the Poissonization is used to simplify the estimation procedure. Based on the estimated results from Module I, Module II calculates how long the kidney failure time of NHB patients would be extended had they been admitted to transplant centers in the same distribution as NHW patients were admitted.

Results: With application to United States Renal Data System data covering 39 043 patients across 73 transplant centers, we found no evidence suggesting the presence of site-of-care associated racial disparities in post-transplantation survival times. In particular, restricting to one year after transplantation, the counterfactual graft failure time would have been extended by only 0.61 days on average if NHB had the same admission distribution to transplant centers as NHW patients.

Discussion: The proposed approach offers a quantitative measure to evaluate site-of-care associated racial disparities.

Conclusion: Our approach has the potential to be extended to investigate site-of-care related disparities in other time-to-event outcomes, thus promoting health equity and improving patient health in various fields.

Objective: To assess whether HL7 Fast Healthcare Interoperability Resources (FHIR) can underpin a fully standards-based, end-to-end digital research architecture, demonstrate it in a live study, and quantify its benefits for interoperability and development efficiency.

Materials and methods: We designed a generalizable standards-based architecture to accelerate digital health research relying on FHIR as the sole transactional model throughout a participant research lifecycle starting from API-based study discovery to results. It was instantiated for People Heart Study, a real-world digital health cardiovascular-risk assessment study with its protocol transformed into FHIR resources (eligibility, consent, tasks, and results). Evaluation examined workflow coverage, validator conformance across independent servers, and points requiring custom extensions or app logic.

Results: The architecture was implemented using cloud managed FHIR stores including an illustrative public research discovery API for first-/third-party apps. A participant-facing iOS app was published on the App Store. Our evaluation reveals that 6 of 10 research app workflows could be executed entirely from FHIR artifacts; 2 were partially standards-driven and 2 remained limited requiring custom development. All FHIR resources passed structural, semantic validation with minimal custom extension usage and terminology integrity issues.

Discussion: Our approach addresses persistent challenges in digital health research by enhancing data interoperability, minimizing redundant development, and supporting the full research lifecycle. The architecture aligns with national priorities and complements healthcare standardization efforts.

Conclusion: By leveraging FHIR, our architecture enables generalizability, interoperability, and reuse across diverse digital health research contexts, transforming study design into data modeling rather than software development, and fostering a more inclusive and agile digital health ecosystem.

Objectives: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients. Effective management of AF in this setting remains a subject of debate, with current guidelines often derived from outpatient studies. This study aims to evaluate the effectiveness of different AF management strategies-both, rhythm, rate, or no control-in reducing mortality in ICU patients using a deep learning-based causal inference model.

Materials and methods: Data from the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV were utilized, encompassing ICU admissions with documented AF. Exposures included both rhythm and rate, only rhythm, and only rate, or no control. A deep learning-based causal inference model analyzed treatment effects. Additionally, the characteristics of patients who benefited more from rhythm control compared to rate control were identified using treatment effect sizes and multivariable logistic regression.

Results: The study population comprised 13 583 patients. Both rhythm and rate control, rhythm control-only, and rate control-only strategies significantly reduced in-hospital mortality compared to no control, with average treatment effects of -1.23% (-1.43% to -1.03%), -2.32% (-2.48% to -2.15%), and -9.11% (-9.29% to -8.93%), respectively. Rhythm control proved more effective than rate control in specific subgroups: older age, higher maximum heart rate, presence of new-onset AF, absence of hypertension, absence of diabetes, chronic liver disease, not having undergone heart surgery, and the use of vasopressor agents.

Conclusion: Using a deep learning-based causal inference model, we quantified mortality reduction for each treatment strategy and identified the patient characteristics associated with the most favorable outcomes for each strategy.

Objective: We propose Heterogeneity-aware Collaborative One-shot Lossless Algorithm for Generalized Linear Model (COLA-GLM-H), a novel one-shot lossless distributed algorithm that enables the integration of heterogeneous multi-institutional data while preserving patient privacy by avoiding patient-level data sharing.

Materials and methods: Generalized Linear Models (GLMs) are widely used in medical research for analyzing diverse outcome types. In multi-institution settings, we demonstrated that the global likelihood can be reconstructed using only institution-level summary statistics, enabling lossless estimation without accessing individual records. We validated COLA-GLM-H in two real-world studies: (1) a U.S. pediatric centralized network (719,383 patients) evaluating long-term cardiovascular risks following COVID-19, and (2) an internationally decentralized network of 120,429 hospitalized patients from seven databases across three countries assessing risk factors for COVID-19 mortality.

Results: In the centralized network, COLA-GLM-H produced estimates identical to those from pooled analyses. In the decentralized setting, the algorithm effectively integrated heterogeneous data across multiple clinical institutions using a single communication round.

Conclusions: COLA-GLM-H is a privacy-preserving, lossless, and communication- and computation-efficient solution for multi-institutional research. It accounts for between-institution heterogeneity and supports all outcome types within the exponential family, enabling secure, scalable, and accurate analysis in collaborative clinical research.

Objective: To identify a brief scale to accurately assess digital skills among older adults for use in identifying need for support to use digital health tools.

Materials and methods: Patients age ≥50 speaking English, Spanish, or Cantonese completed surveys (n = 186) assessing digital health access, use, and skills. A subsample (n = 101) completed observational task assessments gauging competency on 4 tasks essential to digital health skills: (1) launch a video visit from an email/text message hyperlink, (2) visit a specific health website, (3) sign up for a patient portal, and (4) log in to a patient portal. We used exploratory factor analysis, receiver operator characteristic, logistic regression, and dominance analysis methods to identify and evaluate a scale measuring digital skills essential to using digital health tools.

Results: We found that a 9-item scale demonstrated unidimensionality and reliability (Cronbach's alpha 0.93) in measuring digital skills. Mean score was 19.3 out of 36. For each task, handout/video support was inadequate in facilitating completion for one-quarter of participants. We found high accuracy of the scale in predicting digital health competency (area under the curve 0.77-0.88).

Discussion: The Skills Measurement and Readiness Training for Digital Health (SMART Digital Health) scale is a measure of digital skills with evidence of reliability and validity to be used as a diagnostic tool to identify patients requiring support to use digital health tools.

Conclusion: This early work supports the identification of patients with digital literacy needs who may require interventions to effectively engage in digital health communication and management.

Objectives: Infections following healthcare-associated interventions drive patient morbidity and mortality, making early detection essential. Traditional predictive models utilize preoperative surgical characteristics. This study evaluated whether integrating postoperative laboratory values and their kinetics could improve outcome prediction.

Materials and methods: 91 794 surgical cases were extracted from electronic health records (EHR) and analyzed to predict bacterial infection as the endpoint. The endpoint was documented in the EHR as ICD-10 by a professional coding team. Variables were grouped as preoperative, intraoperative, or postoperative. Strategic imputation was used for postoperative missing laboratory values. Procedure-agnostic prediction models were built incorporating both static and kinetic properties of laboratory values.

Results: The integration of kinetics of laboratory values into a machine learning predictor achieved a recall, precision and ROC AUC at postoperative day 2 of 0.71, 0.69, and 0.83, respectively. Moreover, infection detection outperformed clinician-based decision-making, as reflected by the postoperative timing of antibiotic administration. The analysis identified previously unknown, informative combinations of routine markers from hepatic, renal, and bone marrow functions that predict outcome.

Discussion: Dynamic modelling of postoperative laboratory values enhanced the timeliness and accuracy of infection detection compared with static or preoperative-only models. The integration of explainable machine learning supports clinical interpretation and highlights the contribution of multiple organ systems to postoperative infection risk.

Conclusion: A surgery-independent workflow integrating time-series values from laboratory parameters to enhance baseline predictors of infection. This interpretable approach is generalizable across procedures and has the potential to optimize patient outcomes and resource use in surgical care.

Objective: Clinician implicit bias can impede patient-centered communication, leading to health care inequities. While the field of implicit bias education is evolving with advances in technology, clinicians' perspectives remain underexplored. This study investigated clinicians' perceptions of educational strategies to complement communication feedback technology in the implementation of an implicit bias education intervention.

Materials and methods: We recruited primary care practitioners in remote interviews to brainstorm future technologies for improving clinician awareness of implicit bias in patient-provider communication. Participants completed an online survey in which they rated the priority of educational strategies that could complement the technology. We performed inductive-deductive thematic analysis of the interview data with Implicit Bias Recognition and Management (IBRM) domains as a priori codes and used descriptive statistics to summarize the survey data.

Results: Participants (n = 16) proposed how future technology could improve clinician awareness, such as recording visits to help clinicians be more self-aware of their communication; however, some providers expressed concerns regarding feedback fatigue and the potential impact of technology on reducing time spent with patients. Participants recommended incorporating feedback regularly into training, identifying organizational incentives, and debriefing with trusted colleagues and communication experts.

Discussion: Participants brainstormed technologies and identified educational strategies, such as discussion with a facilitator, that could promote clinician receptivity to feedback and inform IBRM approaches for clinical ambient intelligence. Yet, challenges remain to incentivizing participation for practicing clinicians, and Continuing Medical Education may be one effective approach.

Conclusion: The proposed technologies and prioritized educational strategies have the potential to promote health equity by helping clinicians develop skills to manage implicit bias. In the future, these findings could inform IBRM interventions that leverage clinical ambient intelligence.

Objectives: By October 1, 2024, over 450,000 COVID-19 manuscripts were published, with 10% posted as unreviewed preprints. While they accelerate knowledge sharing, their inconsistent quality complicates systematic studies.

Materials and methods: We propose a 2-stage method to include preprints in meta-analyses. In Stage A, preprints are integrated through restriction or imputation and weighted by a confidence score reflecting their publication likelihood. In Stage B, we assess and adjust for potential publication or reporting biases.

Results: This preliminary study employed a 2-stage procedure validated with 2 COVID-19 treatment case studies. For hydroxychloroquine, the relative risk (RR) was 1.06 [95% CI: 0.62, 1.80], suggesting no mortality benefit over placebo. For corticosteroids, the RR was 0.88 [95% CI: 0.62, 1.27], which, while not statistically significant, aligns with evidence supporting a mortality benefit.

Discussion: Our research aims to bridge a significant methodological gap by providing a solution for timely evidence synthesis, particularly in the face of the overwhelming number of publications surrounding COVID-19.

Conclusion: This preliminary study presents a method to efficiently synthesize COVID-19 research, including non-peer-reviewed preprints, to support clinical and policy decisions amidst the information surge.

Objectives: To understand whether patients prefer chatbots for certain tasks in healthcare, and their motivations for doing so, recognizing that chatbots are already assisting patients with various healthcare tasks.

Materials and methods: We conducted a mixed-methods study with patient-users of a healthcare system multi-task chatbot integrated in an electronic health record. We purposively oversampled by race or ethnicity to survey 617/3089 (response rate, 20.0%) chatbot users using de novo and validated survey items. We conducted semi-structured interviews with 46 patient-users and 2 chatbot developers between November 2022 and May 2024. We used modified grounded theory to analyze interviews, descriptive statistics and Chi-square tests to compare survey results, and mixed-methods techniques to integrate findings.

Results: Patient-users preferred chatbots for administrative tasks to save providers' time, because of the chatbot availability, and to avoid unpleasant interactions. Some preferred to discuss sensitive tasks (such as mental health or gender-affirming care) with chatbots due to more privacy or anonymity and less embarrassment or judgment. Developer interviews corroborated this finding. Avoiding bias and using a preferred means of communication applied to all tasks. In surveys, patient-users were less likely to worry about being judged based on chatbot interactions (153/608, 25.2%) compared to interactions with a doctor (219/606, 36.1%) (P < .001). Patient-users preferred human clinicians for diagnostic tasks.

Discussion: Patient-users appear to simultaneously prefer chatbots for simple tasks or sensitive ones, with diverse motivations. Whether chatbots best meet patient needs while balancing ethical tensions regarding access, privacy, judgment, and bias is unclear.

Conclusion: Future chatbot design must accommodate different and diverse patient preferences.