JAMIA Open最新文献

Objectives: To enable interactive visualization of the vaginal microbiome across the pregnancy and facilitate discovery of novel insights and generation of new hypotheses.

Material and methods: Vaginal Microbiome Atlas during Pregnancy (VMAP) was created with R shiny to generate visualizations of structured vaginal microbiome data from multiple studies.

Results: VMAP (http://vmapapp.org) visualizes 3880 vaginal microbiome samples of 1402 pregnant individuals from 11 studies, aggregated via open-source tool MaLiAmPi. Visualized features include diversity measures, VALENCIA community state types, and composition (phylotypes, taxonomy) that can be filtered by various categories.

Discussion: This work represents one of the largest and most geographically diverse aggregations of the vaginal microbiome in pregnancy to date and serves as a user-friendly resource to further analyze vaginal microbiome data and better understand pregnancies and associated outcomes.

Conclusion: VMAP can be obtained from https://github.com/msirota/vmap.git and is currently deployed as an online app for non-R users.

Objectives: Development of search queries for systematic reviews (SRs) is time-consuming. In this work, we capitalize on recent advances in large language models (LLMs) and a relatively large dataset of natural language descriptions of reviews and corresponding Boolean searches to generate Boolean search queries from SR titles and key questions.

Materials and methods: We curated a training dataset of 10 346 SR search queries registered in PROSPERO. We used this dataset to fine-tune a set of models to generate search queries based on Mistral-Instruct-7b. We evaluated the models quantitatively using an evaluation dataset of 57 SRs and qualitatively through semi-structured interviews with 8 experienced medical librarians.

Results: The model-generated search queries had median sensitivity of 85% (interquartile range [IQR] 40%-100%) and number needed to read of 1206 citations (IQR 205-5810). The interviews suggested that the models lack both the necessary sensitivity and precision to be used without scrutiny but could be useful for topic scoping or as initial queries to be refined.

Discussion: Future research should focus on improving the dataset with more high-quality search queries, assessing whether fine-tuning the model on other fields, such as the population and intervention, improves performance, and exploring the addition of interactivity to the interface.

Conclusions: The datasets developed for this project can be used to train and evaluate LLMs that map review descriptions to Boolean search queries. The models cannot replace thoughtful search query design but may be useful in providing suggestions for key words and the framework for the query.

Objectives: To integrate a computerized adaptive test for depression into the electronic health record (EHR) and establish systems for administering assessments in-clinic and via a patient portal to improve depression care.

Materials and methods: This article reports the adoption, implementation, and maintenance of a health information technology (IT) quality improvement (QI) project, Patient Outcomes Reporting for Timely Assessment of Life with Depression (PORTAL-Depression). The project was conducted in a hospital-based primary care clinic that serves a medically underserved metropolitan community. A 30-month (July 2017-March 2021) QI project was designed to create an EHR-embedded system to administer adaptive depression assessments in-clinic and via a patient portal. A multi-disciplinary team integrated 5 major health IT innovations into the EHR: (1) use of a computerized adaptive test for depression assessment, (2) 2-way secure communication between cloud-based software and the EHR, (3) improved accessibility of depression assessment results, (4) enhanced awareness and documentation of positive depression results, and (5) sending assessments via the portal. Throughout the 30-month observational period, we collected administrative, survey, and outcome data.

Results: Attending and resident physicians who participated in the project were trained in depression assessment workflows through presentations at clinic meetings, self-guided online materials, and individual support. Developing stakeholder relationships, using an evaluative and iterative process, and ongoing training were key implementation strategies.

Conclusions: The PORTAL-Depression project was a complex and labor-intensive intervention. Despite quick adoption by the clinic, only certain aspects of the intervention were sustained in the long term due to financial and personnel constraints.

Objective: To investigate the impact of missing laboratory measurements on sepsis diagnostic delays.

Materials and methods: In adult patients admitted to 2 University of California San Diego (UCSD) hospitals from January 1, 2021 to June 30, 2024, we evaluated the relative time of organ failure (T _OF) and time of clinical suspicion of sepsis (T _suspicion) in patients with sepsis according to the Centers for Medicare & Medicaid Services (CMS) definition.

Results: Of the patients studied, 48.7% (n = 2017) in the emergency department (ED), 30.8% (n = 209) in the wards, and 14.4% (n = 167) in the intensive care unit (ICU) had T _OF after T _suspicion. Patients with T _OF after T _suspicion had significantly higher data missingness of 1 or more of the 5 laboratory components used to determine organ failure. The mean number of missing labs was 4.23 vs 2.83 in the ED, 4.04 vs 3.38 in the wards, and 3.98 vs 3.19 in the ICU.

Discussion: Our study identified many sepsis patients with missing laboratory results vital for the identification of organ failure and the diagnosis of sepsis at or before the time of clinical suspicion of sepsis. Addressing data missingness via more timely laboratory assessment could precipitate an earlier recognition of organ failure and potentially earlier diagnosis of and treatment initiation for sepsis.

Conclusions: More prompt laboratory assessment might improve the timeliness of sepsis recognition and treatment.

Objectives: This article focuses on the role of the electronic health record (EHR) to generate meaningful formative feedback for medical students in the clinical setting. Despite the scores of clinical data housed within the EHR, medical educators have only just begun to tap into this data to enhance student learning. Literature to-date has focused almost exclusively on resident education.

Materials and methods: Development of EHR auto-logging and triggered notifications are discussed as specific use cases in providing enhanced feedback for medical students.

Results: By incorporating predictive and prescriptive analytics into the EHR, there is an opportunity to create powerful educational tools which may also support general clinical activity.

Discussion: This article explores the possibilities of EHR as an educational resource. This serves as a call to action for educators and technology developers to work together on creating health record user-centric tools, acknowledging the ongoing work done to improve student-level attribution to patients.

Conclusion: EHR analytics and tools present a novel approach to enhancing clinical clerkship education for medical students.

Objectives: Serum creatinine (SCr) is the primary biomarker for assessing kidney function; however, it may lag behind true kidney function, especially in instances of acute kidney injury (AKI). The objective of the work is to develop Nephrocast, a deep-learning model to predict next-day SCr in adult patients treated in the intensive care unit (ICU).

Materials and methods: Nephrocast was trained and validated, temporally and prospectively, using electronic health record data of adult patients admitted to the ICU in the University of California San Diego Health (UCSDH) between January 1, 2016 and June 22, 2024. The model features consisted of demographics, comorbidities, vital signs and laboratory measurements, and medications. Model performance was evaluated by mean absolute error (MAE) and root-mean-square error (RMSE) and compared against the prediction day's SCr as a reference.

Results: A total of 28 191 encounters met the eligibility criteria, corresponding to 105 718 patient-days. The median (interquartile range [IQR]) MAE and RMSE in the internal test set were 0.09 (0.085-0.09) mg/dL and 0.15 (0.146-0.152) mg/dL, respectively. In the prospective validation, the MAE and RMSE were 0.09 mg/dL and 0.14 mg/dL, respectively. The model's performance was superior to the reference SCr.

Discussion and conclusion: Our model demonstrated good performance in predicting next-day SCr by leveraging clinical data routinely collected in the ICU. The model could aid clinicians in in identifying high-risk patients for AKI, predicting AKI trajectory, and informing the dosing of renally eliminated drugs.

Objective: The Multiple Chronic Conditions (MCCs) Electronic Care (e-Care) Plan project aims to establish care planning data standards for individuals living with MCCs. This article reports on the portion of the project focused on long COVID and presents the process of identifying and modeling data elements using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard.

Materials and methods: Critical data elements for managing long COVID were defined through a consensus-driven approach involving a Technical Expert Panel (TEP). This involved 2 stages: identifying data concepts and establishing electronic exchange standards.

Results: The TEP-identified and -approved long COVID data elements were mapped to the HL7 US Core FHIR profiles for syntactic representation, and value sets from standard code systems were developed for semantic representation of the long COVID concepts.

Discussion: Establishing common long COVID data standards through this process, and representing them with the HL7 FHIR standard, facilitates interoperable data collection, benefiting care delivery and patient-centered outcomes research (PCOR) for long COVID. These standards may support initiatives including clinical and pragmatic trials, quality improvement, epidemiologic research, and clinical and social interventions.By building standards-based data collection, this effort accelerates the development of evidence to better understand and deliver effective long COVID interventions and patient and caregiver priorities within the context of MCCs and to advance the delivery of coordinated, person-centered care.

Conclusion: The open, collaborative, and consensus-based approach used in this project will enable the sharing of long COVID-related health concerns, interventions, and outcomes for patient-centered care coordination across diverse clinical settings and will facilitate the use of real-world data for long COVID research.

Objectives: To report lessons from integrating the methods and perspectives of clinical informatics (CI) and implementation science (IS) in the context of Improving the Management of symPtoms during and following Cancer Treatment (IMPACT) Consortium pragmatic trials.

Materials and methods: IMPACT informaticists, trialists, and implementation scientists met to identify challenges and solutions by examining robust case examples from 3 Research Centers that are deploying systematic symptom assessment and management interventions via electronic health records (EHRs). Investigators discussed data collection and CI challenges, implementation strategies, and lessons learned.

Results: CI implementation strategies and EHRs systems were utilized to collect and act upon symptoms and impairments in functioning via electronic patient-reported outcomes (ePRO) captured in ambulatory oncology settings. Limited EHR functionality and data collection capabilities constrained the ability to address IS questions. Collecting ePRO data required significant planning and organizational champions adept at navigating ambiguity.

Discussion: Bringing together CI and IS perspectives offers critical opportunities for monitoring and managing cancer symptoms via ePROs. Discussions between CI and IS researchers identified and addressed gaps between applied informatics implementation and theory-based IS trial and evaluation methods. The use of common terminology may foster shared mental models between CI and IS communities to enhance EHR design to more effectively facilitate ePRO implementation and clinical responses.

Conclusion: Implementation of ePROs in ambulatory oncology clinics benefits from common understanding of the concepts, lexicon, and incentives between CI implementers and IS researchers to facilitate and measure the results of implementation efforts.

Objectives: Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face limitations in portability and privacy due to their need for circulating user data in remote servers for operation. We overcome this by porting iCARE to the web platform.

Materials and methods: We refactored R-iCARE into a Python package (Py-iCARE) and then compiled it to WebAssembly (Wasm-iCARE)-a portable web module, which operates within the privacy of the user's device.

Results: We showcase the portability and privacy of Wasm-iCARE through 2 applications: for researchers to statistically validate risk models and to deliver them to end-users. Both applications run entirely on the client side, requiring no downloads or installations, and keep user data on-device during risk calculation.

Conclusions: Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.