Background: Electronic health records (EHRs) are widely viewed as useful tools for supporting the provision of high quality healthcare. However, evidence regarding their effectiveness for this purpose is mixed, and existing studies have generally considered EHR usage a binary factor and have not considered the availability and use of specific EHR features.
Objective: To assess the relationship between the use of an EHR and the use of specific EHR features with quality of care.
Research Design: A statewide mail survey of physicians in Massachusetts conducted in 2005. The results of the survey were linked with Healthcare Effectiveness Data and Information Set (HEDIS) quality measures, and generalized linear regression models were estimated to examine the associations between the use of EHRs and specific EHR features with quality measures, adjusting for physician practice characteristics.
Subjects: A stratified random sample of 1884 licensed physicians in Massachusetts, 1345 of whom responded. Of these, 507 had HEDIS measures available and were included in the analysis (measures are only available for primary care providers).
Measure: Performance on HEDIS quality measures.
Results: The survey had a response rate of 71%. There was no statistically significant association between use of an EHR as a binary factor and performance on any of the HEDIS measure groups. However, there were statistically significant associations between the use of many, but not all, specific EHR features and HEDIS measure group scores. The associations were strongest for the problem list, visit note and radiology test result EHR features and for quality measures relating to women's health, colon cancer screening, and cancer prevention. For example, users of problem list functionality performed better on women's health, depression, colon cancer screening, and cancer prevention measures, with problem list users outperforming nonusers by 3.3% to 9.6% points on HEDIS measure group scores (all significant at the P < 0.05 level). However, these associations were not universal.
Conclusions: Consistent with past studies, there was no significant relationship between use of EHR as a binary factor and performance on quality measures. However, availability and use of specific EHR features by primary care physicians was associated with higher performance on certain quality measures. These results suggest that, to maximize health care quality, developers, implementers and certifiers of EHRs should focus on increasing the adoption of robust EHR systems and increasing the use of specific features rather than simply aiming to deploy an EHR regardless of functionality.
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http://dx.doi.org/10.1097/MLR.0b013e3181c16203 | DOI Listing |
Infect Control Hosp Epidemiol
January 2025
Department of Biostatistics and Data Science, Wake Forest University, School of Medicine, Medical Center Blvd, Winston-Salem, NC27157, USA.
Objective: Environmental features of a patient's room depend on the patient's level of acuity and their clinical manifestations upon admission and during their hospital stay. In this study, we wish to apply statistical methodology to explore the association between room features and hospital onset infections caused by (HO-CDI) while accounting for room assignment.
Method: We conducted a nested case-control study using retrospective electronic health record (EHR) data of patients hospitalized at the Ohio State University Wexner Medical Center (OSUWMC) between January 2019 and April 2021.
Kidney360
January 2025
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, US.
Background: Patients on hemodialysis (HD) have a high burden of emotional and physical symptoms. These symptoms are often under-recognized. NLP can be used to identify patient symptoms from the EHR.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Great Ormond Street Institute of Child Health, University College London, London, UK.
Introduction: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
Design: We applied document embedding algorithms to real-world paediatric intensive care (PICU) EHR data to extract patient-specific features from 1853 patients' PICU journeys using 647 unique lab tests and medication events. We evaluated the clinical utility of the patient features via a K-means clustering analysis.
J Gen Intern Med
January 2025
Icahn School of Medicine at Mount Sinai, Institute for Health Equity Research, New York, USA.
Background: Over 60 million patients in the USA have limited English proficiency (LEP) and experience barriers in care. Still, there exists no standardized method of monitoring the utilization of language interpreting services (LIS).
Objective: To introduce a methodological approach to systematically monitor utilization of LIS for LEP patients.
BMC Med Res Methodol
January 2025
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030-R, Boston, MA, 02120, USA.
Background: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an "out of the box" implementation of open-source large language models (LLMs) without any fine-tuning can accurately extract social determinants of health (SDoH) data from free-text clinical notes.
Methods: We conducted a cross-sectional study using EHR data from the Mass General Brigham (MGB) system, analyzing free-text notes for SDoH information.
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