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Background: The digital transformation of medical data enables health systems to leverage real-world data from electronic health records to gain actionable insights for improving hypertension care., Methods and Results: We performed a serial cross-sectional analysis of outpatients of a large regional health system from 2010 to 2021. Hypertension was defined by systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or recorded treatment with antihypertension medications. We evaluated 4 methods of using blood pressure measurements in the electronic health record to define hypertension. The primary outcomes were age-adjusted prevalence rates and age-adjusted control rates. Hypertension prevalence varied depending on the definition used, ranging from 36.5% to 50.9% initially and increasing over time by ≈5%, regardless of the definition used. Control rates ranged from 61.2% to 71.3% initially, increased during 2018 to 2019, and decreased during 2020 to 2021. The proportion of patients with a hypertension diagnosis ranged from 45.5% to 60.2% initially and improved during the study period. Non-Hispanic Black patients represented 25% of our regional population and consistently had higher prevalence rates, higher mean systolic and diastolic blood pressure, and lower control rates compared with other racial and ethnic groups., Conclusions: In a large regional health system, we leveraged the electronic health record to provide real-world insights. The findings largely reflected national trends but showed distinctive regional demographics and findings, with prevalence increasing, one-quarter of the patients not controlled, and marked disparities. This approach could be emulated by regional health systems seeking to improve hypertension care.

Objective: The study sought to conduct an informatics analysis on the National Evaluation System for Health Technology Coordinating Center test case of cardiac ablation catheters and to demonstrate the role of informatics approaches in the feasibility assessment of capturing real-world data using unique device identifiers (UDIs) that are fit for purpose for label extensions for 2 cardiac ablation catheters from the electronic health records and other health information technology systems in a multicenter evaluation., Materials and Methods: We focused on data capture and transformation and data quality maturity model specified in the National Evaluation System for Health Technology Coordinating Center data quality framework. The informatics analysis included 4 elements: the use of UDIs for identifying device exposure data, the use of standardized codes for defining computable phenotypes, the use of natural language processing for capturing unstructured data elements from clinical data systems, and the use of common data models for standardizing data collection and analyses., Results: We found that, with the UDI implementation at 3 health systems, the target device exposure data could be effectively identified, particularly for brand-specific devices. Computable phenotypes for study outcomes could be defined using codes; however, ablation registries, natural language processing tools, and chart reviews were required for validating data quality of the phenotypes. The common data model implementation status varied across sites. The maturity level of the key informatics technologies was highly aligned with the data quality maturity model., Conclusions: We demonstrated that the informatics approaches can be feasibly used to capture safety and effectiveness outcomes in real-world data for use in medical device studies supporting label extensions., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background The digital transformation of medical data provides opportunities to perform digital population health surveillance and identify people inadequately managed in usual care. We leveraged the electronic health records of a large health system to identify patients with markedly elevated blood pressure and characterize their follow-up care pattern. Methods and Results We included 373 861 patients aged 18 to 85 years, who had at least 1 outpatient encounter in the Yale New Haven Health System between January 2013 and December 2017. We described the prevalence and follow-up pattern of patients with at least 1 systolic blood pressure (SBP) ≥160 mm Hg or diastolic blood pressure (DBP) ≥100 mm Hg and patients with at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Of 373 861 patients included, 56 909 (15.2%) had at least 1 SBP ≥160 mm Hg or DBP ≥100 mm Hg, and 10 476 (2.8%) had at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Among patients with SBP ≥160 mm Hg or DBP ≥100 mm Hg, only 28.3% had a follow visit within 1 month (time window of follow-up recommended by the guideline) and 19.9% subsequently achieved control targets (SBP <130 mm Hg and DBP <80 mm Hg) within 6 months. Follow-up rate at 1 month and control rate at 6 months for patients with SBP ≥180 mm Hg or DBP ≥120 mm Hg was 31.9% and 17.2%. Conclusions Digital population health surveillance with an electronic health record identified a large number of patients with markedly elevated blood pressure and inadequate follow-up. Many of these patients subsequently failed to achieve control targets.

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs. [ABSTRACT FROM AUTHOR]

Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020–March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19. [ABSTRACT FROM AUTHOR]

Background: Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth.Objective: The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform.Methods: We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics.Results: Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases.Conclusions: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research. [ABSTRACT FROM AUTHOR]