Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study.

BACKGROUND: Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare. OBJECTIVE: This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system. METHODS: All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months. RESULTS: Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6% (13,268/14,644), a sensitivity of 70.2% (153/218), and a specificity of 90.91% (13,115/14,426). CONCLUSIONS: Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.

Real world performance of the 21st Century Cures Act population-level application programming interface.

OBJECTIVE: To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. MATERIALS AND METHODS: We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. RESULTS: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. DISCUSSION: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. CONCLUSION: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.

Cumulus: A federated EHR-based learning system powered by FHIR and AI.

Objective: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app 'listener' that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and AI for processing unstructured text. Results: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across five healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs (2), increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence.

OBJECTIVE: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). METHODS: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. RESULTS: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. DISCUSSION AND CONCLUSION: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

Increased Incidence of Vestibular Disorders in Patients With SARS-CoV-2.

Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. Study Design: Retrospective. Setting: Clinical data in the National COVID Cohort Collaborative database (N3C). Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29-2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48-3.78; P < 0.001), delta (OR, 3.03; CI, 2.94-3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90-3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35-9.27; P < 0.001). Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings.

Discovering medical conditions associated with periodontitis using linked electronic health records.

AIM: To use linked electronic medical and dental records to discover associations between periodontitis and medical conditions independent of a priori hypotheses. MATERIALS AND METHODS: This case-control study included 2475 patients who underwent dental treatment at the College of Dental Medicine at Columbia University and medical treatment at NewYork-Presbyterian Hospital. Our cases are patients who received periodontal treatment and our controls are patients who received dental maintenance but no periodontal treatment. Chi-square analysis was performed for medical treatment codes and logistic regression was used to adjust for confounders. RESULTS: Our method replicated several important periodontitis associations in a largely Hispanic population, including diabetes mellitus type I (OR = 1.6, 95% CI 1.30-1.99, p < 0.001) and type II (OR = 1.4, 95% CI 1.22-1.67, p < 0.001), hypertension (OR = 1.2, 95% CI 1.10-1.37, p < 0.001), hypercholesterolaemia (OR = 1.2, 95% CI 1.07-1.38, p = 0.004), hyperlipidaemia (OR = 1.2, 95% CI 1.06-1.43, p = 0.008) and conditions pertaining to pregnancy and childbirth (OR = 2.9, 95% CI: 1.32-7.21, p = 0.014). We also found a previously unreported association with benign prostatic hyperplasia (OR = 1.5, 95% CI 1.05-2.10, p = 0.026) after adjusting for age, gender, ethnicity, hypertension, diabetes, obesity, lipid and circulatory system conditions, alcohol and tobacco abuse. CONCLUSIONS: This study contributes a high-throughput method for associating periodontitis with systemic diseases using linked electronic records.

Real World Performance of the 21st Century Cures Act Population Level Application Programming Interface.

Objective: To evaluate the real-world performance in delivering patient data on populations, of the SMART/HL7 Bulk FHIR Access API, required in Electronic Health Records (EHRs) under the 21st Century Cures Act Rule. Materials and Methods: We used an open-source Bulk FHIR Testing Suite at five healthcare sites from April to September 2023, including four hospitals using EHRs certified for interoperability, and one Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across six types of FHIR resources. Results: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8,000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1,555-2,500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12,000 resources/min. Discussion: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. Conclusion: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.

Research data collection methods: from paper to tablet computers.

BACKGROUND: Primary data collection is a critical activity in clinical research. Even with significant advances in technical capabilities, clear benefits of use, and even user preferences for using electronic systems for collecting primary data, paper-based data collection is still common in clinical research settings. However, with recent developments in both clinical research and tablet computer technology, the comparative advantages and disadvantages of data collection methods should be determined. OBJECTIVE: To describe case studies using multiple methods of data collection, including next-generation tablets, and consider their various advantages and disadvantages. MATERIALS AND METHODS: We reviewed 5 modern case studies using primary data collection, using methods ranging from paper to next-generation tablet computers. We performed semistructured telephone interviews with each project, which considered factors relevant to data collection. We address specific issues with workflow, implementation and security for these different methods, and identify differences in implementation that led to different technology considerations for each case study. RESULTS AND DISCUSSION: There remain multiple methods for primary data collection, each with its own strengths and weaknesses. Two recent methods are electronic health record templates and next-generation tablet computers. Electronic health record templates can link data directly to medical records, but are notably difficult to use. Current tablet computers are substantially different from previous technologies with regard to user familiarity and software cost. The use of cloud-based storage for tablet computers, however, creates a specific challenge for clinical research that must be considered but can be overcome.

A survey of informatics platforms that enable distributed comparative effectiveness research using multi-institutional heterogenous clinical data.

Comparative effectiveness research (CER) has the potential to transform the current health care delivery system by identifying the most effective medical and surgical treatments, diagnostic tests, disease prevention methods, and ways to deliver care for specific clinical conditions. To be successful, such research requires the identification, capture, aggregation, integration, and analysis of disparate data sources held by different institutions with diverse representations of the relevant clinical events. In an effort to address these diverse demands, there have been multiple new designs and implementations of informatics platforms that provide access to electronic clinical data and the governance infrastructure required for interinstitutional CER. The goal of this manuscript is to help investigators understand why these informatics platforms are required and to compare and contrast 6 large-scale, recently funded, CER-focused informatics platform development efforts. We utilized an 8-dimension, sociotechnical model of health information technology to help guide our work. We identified 6 generic steps that are necessary in any distributed, multi-institutional CER project: data identification, extraction, modeling, aggregation, analysis, and dissemination. We expect that over the next several years these projects will provide answers to many important, and heretofore unanswerable, clinical research questions.

Better together: Integrating biomedical informatics and healthcare IT operations to create a learning health system during the COVID-19 pandemic.

The growing availability of multi-scale biomedical data sources that can be used to enable research and improve healthcare delivery has brought about what can be described as a healthcare "data age." This new era is defined by the explosive growth in bio-molecular, clinical, and population-level data that can be readily accessed by researchers, clinicians, and decision-makers, and utilized for systems-level approaches to hypothesis generation and testing as well as operational decision-making. However, taking full advantage of these unprecedented opportunities presents an opportunity to revisit the alignment between traditionally academic biomedical informatics (BMI) and operational healthcare information technology (HIT) personnel and activities in academic health systems. While the history of the academic field of BMI includes active engagement in the delivery of operational HIT platforms, in many contemporary settings these efforts have grown distinct. Recent experiences during the COVID-19 pandemic have demonstrated greater coordination of BMI and HIT activities that have allowed organizations to respond to pandemic-related changes more effectively, with demonstrable and positive impact as a result. In this position paper, we discuss the challenges and opportunities associated with driving alignment between BMI and HIT, as viewed from the perspective of a learning healthcare system. In doing so, we hope to illustrate the benefits of coordination between BMI and HIT in terms of the quality, safety, and outcomes of care provided to patients and populations, demonstrating that these two groups can be "better together."

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).

OBJECTIVE: This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip code-level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely tested zip codes (which contained most of the data) were similar between original and synthetically derived datasets. Analyses of sparsely tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression-an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.

Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS.

Purpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: Results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).

OBJECTIVE: To evaluate whether synthetic data derived from a national COVID-19 data set could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original data set (n=1,854,968 SARS-CoV-2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip-code level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean=2.9±2.4; max=16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n=171) and for all unsuppressed zip codes (n=5,819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely-tested zip codes (which contained most of the data) were similar between original and synthetically-derived data sets. Analyses of sparsely-tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression -an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers.

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.

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

A Review of Predictive Analytics Solutions for Sepsis Patients.

BACKGROUND: Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis. OBJECTIVE: To understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality. METHODS AND RESULTS: We performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression (n = 2), logistic regression (n = 5), support vector machines (n = 4), and Markov models (n = 4), as well as population (range: 24-198,833) and feature size (range: 2-285). Many of the studies used local data sets of varying sizes and locations while others used the publicly available Medical Information Mart for Intensive Care data. Additionally, vital signs or laboratory test results were commonly used as features for training and testing purposes; however, a few used more unique features including gene expression data from blood plasma and unstructured text and data from clinician notes. CONCLUSION: Overall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.

Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research.

OBJECTIVE: Academic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of clinical data. Enterprise data warehouses have emerged as central resources for these data, but often require an informatician to extract meaningful information, limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying clinical data from heterogeneous data models and sources. MATERIALS AND METHODS: Leaf utilizes a flexible biomedical concept system to define hierarchical concepts and ontologies. Each Leaf concept contains both textual representations and SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries. RESULTS: Leaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an enterprise data warehouse with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org), and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf. DISCUSSION: Leaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication. CONCLUSIONS: Leaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly leverage existing user authentication systems and clinical databases in situ. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks, and clinical studies involving large patient cohorts.

Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States.

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems' response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave.

Potentially inappropriate medication prescribing in outpatient practices: prevalence and patient characteristics based on electronic health records.

BACKGROUND: Some older adults receive potentially inappropriate medications (PIMs), increasing their risk for adverse events. A literature search did not find any US multicenter studies that measured the prevalence of PIMs in outpatient practices based on data from electronic health records (EHRs), using both the Beers and Zhan criteria. OBJECTIVES: The aims of the present study were to compare the prevalence of PIMs using standard drug terminologies at 2 disparate institutions using EHRs and to identify characteristics of elderly patients who have a PIM on their active-medication lists. METHODS: This cross-sectional study of outpatients' active-medication lists from April 1, 2006, was conducted using data from 2 outpatient primary care settings: Intermountain Healthcare, Salt Lake City, Utah (center 1), and the Cleveland Clinic, Cleveland, Ohio (center 2). Data were included from patients who were aged > or =65 years at the time of the last office visit and had > or =2 documented clinic visits within the previous 2 years. The primary end point was prevalence of PIMs, measured according to the 2002 Beers criteria or the 2001 Zhan criteria. RESULTS: Data from 61,251 patients were included (36,663 women, 24,588 men; center 1: 37,247 patients; center 2: 24,004). A total of 8693 (23.3%) and 5528 (23.0%) patients at centers 1 and 2, respectively, were documented as receiving a PIM as per the Beers criteria; this difference was not statistically significant. Per the Zhan criteria (P < 0.001), these values were 6036 (16.2%) and 4160 (17.3%). Eight of the most common PIMs were the same at both institutions, with propoxyphene and fluoxetine (once daily) being the most prescribed. Female sex, polypharmacy (> or =6 medications), and multiple primary care visits were significantly associated with PIM prescribing. CONCLUSIONS: In this analysis of data from elderly patients at 2 outpatient centers, a small set of 8 medications accounted for the majority of PIMs at both centers, irrespective of geographic and demographic variations. Female sex, polypharmacy, and number of primary care visits were significantly associated with PIM prescribing. In this analysis of data from elderly patients at 2 outpatient centers, a small set of 8 medications accounted for the majority of PIMs at both centers, irrespective of geographic and demographic variations.