The National COVID Cohort Collaborative: Analyses of Original and Computationally Derived Electronic Health Record Data.

BACKGROUND: Computationally derived ("synthetic") data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic. OBJECTIVE: We aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes. METHODS: We used the National COVID Cohort Collaborative's instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-19-related measures and outcomes, and constructing their epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data. RESULTS: For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. Although the synthetic and original data yielded overall nearly the same results, there were exceptions that included an odds ratio on either side of the null in multivariable analyses (0.97 vs 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts. CONCLUSIONS: This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights.

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.

Association of Integrated Team-Based Care With Health Care Quality, Utilization, and Cost.

IMPORTANCE: The value of integrated team delivery models is not firmly established. OBJECTIVE: To evaluate the association of receiving primary care in integrated team-based care (TBC) practices vs traditional practice management (TPM) practices (usual care) with patient outcomes, health care utilization, and costs. DESIGN: A retrospective, longitudinal, cohort study to assess the association of integrating physical and mental health over time in TBC practices with patient outcomes and costs. SETTING AND PARTICIPANTS: Adult patients (aged ≥18 years) who received primary care at 113 unique Intermountain Healthcare Medical Group primary care practices from 2003 through 2005 and had yearly encounters with Intermountain Healthcare through 2013, including some patients who received care in both TBC and TPM practices. EXPOSURES: Receipt of primary care in TBC practices compared with TPM practices for patients treated in internal medicine, family practice, and geriatrics practices. MAIN OUTCOMES AND MEASURES: Outcomes included 7 quality measures, 6 health care utilization measures, payments to the delivery system, and program investment costs. RESULTS: During the study period (January 2010-December 2013), 113,452 unique patients (mean age, 56.1 years; women, 58.9%) accounted for 163,226 person-years of exposure in 27 TBC practices and 171,915 person-years in 75 TPM practices. Patients treated in TBC practices compared with those treated in TPM practices had higher rates of active depression screening (46.1% for TBC vs 24.1% for TPM; odds ratio [OR], 1.91 [95% CI, 1.75 to 2.08), adherence to a diabetes care bundle (24.6% for TBC vs 19.5% for TPM; OR, 1.26 [95% CI, 1.11 to 1.42]), and documentation of self-care plans (48.4% for TBC vs 8.7% for TPM; OR, 5.59 [95% CI, 4.27 to 7.33]), lower proportion of patients with controlled hypertension (<140/90 mm Hg) (85.0% for TBC vs 97.7% for TPM; OR, 0.87 [95% CI, 0.80 to 0.95]), and no significant differences in documentation of advanced directives (9.6% for TBC vs 9.9% for TPM; OR, 0.97 [95% CI, 0.91 to 1.03]). Per 100 person-years, rates of health care utilization were lower for TBC patients compared with TPM patients for emergency department visits (18.1 for TBC vs 23.5 for TPM; incidence rate ratio [IRR], 0.77 [95% CI, 0.74 to 0.80]), hospital admissions (9.5 for TBC vs 10.6 for TPM; IRR, 0.89 [95% CI, 0.85 to 0.94]), ambulatory care sensitive visits and admissions (3.3 for TBC vs 4.3 for TPM; IRR, 0.77 [95% CI, 0.70 to 0.85]), and primary care physician encounters (232.8 for TBC vs 250.4 for TPM; IRR, 0.93 [95% CI, 0.92 to 0.94]), with no significant difference in visits to urgent care facilities (55.7 for TBC vs 56.2 for TPM; IRR, 0.99 [95% CI, 0.97 to 1.02]) and visits to specialty care physicians (213.5 for TBC vs 217.9 for TPM; IRR, 0.98 [95% CI, 0.97 to 0.99], P > .008). Payments to the delivery system were lower in the TBC group vs the TPM group ($3400.62 for TBC vs $3515.71 for TPM; ß, -$115.09 [95% CI, -$199.64 to -$30.54]) and were less than investment costs of the TBC program. CONCLUSIONS AND RELEVANCE: Among adults enrolled in an integrated health care system, receipt of primary care at TBC practices compared with TPM practices was associated with higher rates of some measures of quality of care, lower rates for some measures of acute care utilization, and lower actual payments received by the delivery system.

The Association Between Online Health Information-Seeking Behaviors and Health Behaviors Among Hispanics in New York City: A Community-Based Cross-Sectional Study.

BACKGROUND: Hispanics are the fastest-growing minority group in the United States and they suffer from a disproportionate burden of chronic diseases. Studies have shown that online health information has the potential to affect health behaviors and influence management of chronic disease for a significant proportion of the population, but little research has focused on Hispanics. OBJECTIVE: The specific aim of this descriptive, cross-sectional study was to examine the association between online health information-seeking behaviors and health behaviors (physical activity, fruit and vegetable consumption, alcohol use, and hypertension medication adherence) among Hispanics. METHODS: Data were collected from a convenience sample (N=2680) of Hispanics living in northern Manhattan by bilingual community health workers in a face-to-face interview and analyzed using linear and ordinal logistic regression. Variable selection and statistical analyses were guided by the Integrative Model of eHealth Use. RESULTS: Only 7.38% (198/2680) of the sample reported online health information-seeking behaviors. Levels of moderate physical activity and fruit, vegetable, and alcohol consumption were low. Among individuals taking hypertension medication (n=825), adherence was reported as high by approximately one-third (30.9%, 255/825) of the sample. Controlling for demographic, situational, and literacy variables, online health information-seeking behaviors were significantly associated with fruit (ß=0.35, 95% CI 0.08-0.62, P=.01) and vegetable (ß=0.36, 95% CI 0.06-0.65, P=.02) consumption and physical activity (ß=3.73, 95% CI 1.99-5.46, P<.001), but not alcohol consumption or hypertension medication adherence. In the regression models, literacy factors, which were used as control variables, were associated with 3 health behaviors: social networking site membership (used to measure one dimension of computer literacy) was associated with fruit consumption (ß=0.23, 95% CI 0.05-0.42, P=.02), health literacy was associated with alcohol consumption (ß=0.44, 95% CI 0.24-0.63, P<.001), and hypertension medication adherence (ß=-0.32, 95% CI -0.62 to -0.03, P=.03). Models explained only a small amount of the variance in health behaviors. CONCLUSIONS: Given the promising, although modest, associations between online health information-seeking behaviors and some health behaviors, efforts are needed to improve Hispanics' ability to access and understand health information and to enhance the availability of online health information that is suitable in terms of language, readability level, and cultural relevance.

Online health information seeking behaviors of Hispanics in New York City: a community-based cross-sectional study.

BACKGROUND: The emergence of the Internet has increased access to health information and can facilitate active individual engagement in health care decision making. Hispanics are the fastest-growing minority group in the United States and are also the most underserved in terms of access to online health information. A growing body of literature has examined correlates of online health information seeking behaviors (HISBs), but few studies have included Hispanics. OBJECTIVE: The specific aim of this descriptive, correlational study was to examine factors associated with HISBs of Hispanics. METHODS: The study sample (N=4070) was recruited from five postal zip codes in northern Manhattan for the Washington Heights Inwood Informatics Infrastructure for Comparative Effectiveness Research project. Survey data were collected via interview by bilingual community health workers in a community center, households, and other community settings. Data were analyzed using bivariate analyses and logistic regression. RESULTS: Among individual respondents, online HISBs were significantly associated with higher education (OR 3.03, 95% CI 2.15-4.29, P<.001), worse health status (OR 0.42, 95% CI 0.31-0.57, P<.001), and having no hypertension (OR 0.60, 95% CI 0.43-0.84, P=.003). Online HISBs of other household members were significantly associated with respondent factors: female gender (OR 1.60, 95% CI 1.22-2.10, P=.001), being younger (OR 0.75, 95% CI 0.62-0.90, P=.002), being married (OR 1.36, 95% CI 1.09-1.71, P=.007), having higher education (OR 1.80, 95% CI 1.404-2.316, P<.001), being in worse health (OR 0.59, 95% CI 0.46-0.77, P<.001), and having serious health problems increased the odds of their household members' online HISBs (OR 1.83, 95% CI 1.29-2.60, P=.001). CONCLUSIONS: This large-scale community survey identified factors associated with online HISBs among Hispanics that merit closer examination. To enhance online HISBs among Hispanics, health care providers and policy makers need to understand the cultural context of the Hispanic population. Results of this study can provide a foundation for the development of informatics-based interventions to improve the health of Hispanics in the United States.

Large Language Models for Efficient Medical Information Extraction.

Extracting valuable insights from unstructured clinical narrative reports is a challenging yet crucial task in the healthcare domain as it allows healthcare workers to treat patients more efficiently and improves the overall standard of care. We employ ChatGPT, a Large language model (LLM), and compare its performance to manual reviewers. The review focuses on four key conditions: family history of heart disease, depression, heavy smoking, and cancer. The evaluation of a diverse sample of History and Physical (H&P) Notes, demonstrates ChatGPT's remarkable capabilities. Notably, it exhibits exemplary results in sensitivity for depression and heavy smokers and specificity for cancer. We identify areas for improvement as well, particularly in capturing nuanced semantic information related to family history of heart disease and cancer. With further investigation, ChatGPT holds substantial potential for advancements in medical information extraction.

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.

Assessing the Predictive and Analytics Capability of Electronic Clinical Data for High-Cost Patients.

Hotspotting may prevent high healthcare costs surrounding a minority of patients when void of issues such as availability, completeness, and accessibility of information in electronic health records (EHRs). We performed a descriptive study using Barnes-Jewish Hospital patients to assess the availability and accessibility of information that can predict negative outcomes. Manual electronic chart review produced descriptive statistics for a sample of 100 High Resource and 100 Control patient records. The majority of cases were not predictive. Predictive information and their sources were inconsistent. Certain types of patients were more predictive than others, albeit a small percentage of the total. Among the largest and most predictive groups was the most difficult to classify, "Other." These findings were expected and consistent with previous studies but contrast with approaches for attempting prediction such as hotspotting. Further studies may provide solutions to the problems and limitations identified in this study.

Development of a social and environmental determinants of health informatics maturity model.

Introduction: Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps. Methods: We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration's Informatics Enterprise Committee, and a publicly available online self-assessment tool. Results: We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels. Conclusion: The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities.

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.

Simplified data science approach to extract social and behavioural determinants: a retrospective chart review.

OBJECTIVES: We aim to extract a subset of social factors from clinical notes using common text classification methods. DESIGN: Retrospective chart review. SETTING: We collaborated with a local level I trauma hospital located in an underserved area that has a housing unstable patient population of about 6.5% and extracted text notes related to various social determinants for acute care patients. PARTICIPANTS: Notes were retrospectively extracted from 43 798 acute care patients. METHODS: We solely use open source Python packages to test simple text classification methods that can potentially be easily generalisable and implemented. We extracted social history text from various sources, such as admission and emergency department notes, over a 5-year timeframe and performed manual chart reviews to ensure data quality. We manually labelled the sentiment of the notes, treating each text entry independently. Four different models with two different feature selection methods (bag of words and bigrams) were used to classify and predict housing stability, tobacco use and alcohol use status for the extracted clinical text. RESULTS: From our analysis, we found overall positive results and metrics in applying open-source classification techniques; the accuracy scores were 91.2%, 84.7%, 82.8% for housing stability, tobacco use and alcohol use, respectively. There were many limitations in our analysis including social factors not present due to patient condition, multiple copy-forward entries and shorthand. Additionally, it was difficult to translate usage degrees for tobacco and alcohol use. However, when compared with structured data sources, our classification approach on unstructured notes yielded more results for housing and alcohol use; tobacco use proved less fruitful for unstructured notes.

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."