Dataset Information

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study.

ABSTRACT: BACKGROUND:Health systems routinely implement changes to the design of electronic health records (EHRs). Physician behavior may vary in response and methods to identify this variation could help to inform future interventions. The objective of this study was to phenotype primary care physician practice patterns and evaluate associations with response to an EHR nudge for influenza vaccination. METHODS AND FINDINGS:During the 2016-2017 influenza season, 3 primary care practices at Penn Medicine implemented an active choice intervention in the EHR that prompted medical assistants to template influenza vaccination orders for physicians to review during the visit. We used latent class analysis to identify physician phenotypes based on 9 demographic, training, and practice pattern variables, which were obtained from the EHR and publicly available sources. A quasi-experimental approach was used to evaluate response to the intervention relative to control practices over time in each of the physician phenotype groups. For each physician latent class, a generalized linear model with logit link was fit to the binary outcome of influenza vaccination at the patient visit level. The sample comprised 45,410 patients with a mean (SD) age of 58.7 (16.3) years, 67.1% were white, and 22.1% were black. The sample comprised 56 physicians with mean (SD) of 24.6 (10.2) years of experience and 53.6% were male. The model segmented physicians into groups that had higher (n = 41) and lower (n = 15) clinical workloads. Physicians in the higher clinical workload group had a mean (SD) of 818.8 (429.1) patient encounters, 11.6 (4.7) patient appointments per day, and 4.0 (1.1) days per week in clinic. Physicians in the lower clinical workload group had a mean (SD) of 343.7 (129.0) patient encounters, 8.0 (2.8) patient appointments per day, and 3.1 (1.2) days per week in clinic. Among the higher clinical workload group, the EHR nudge was associated with a significant increase in influenza vaccination (adjusted difference-in-difference in percentage points, 7.9; 95% CI, 0.4-9.0; P = .01). Among the lower clinical workload group, the EHR nudge was not associated with a significant difference in influenza vaccination rates (adjusted difference-in-difference in percentage points, -1.0; 95% CI, -5.3-5.8; P = .90). CONCLUSIONS:A model-based approach categorized physician practice patterns into higher and lower clinical workload groups. The higher clinical workload group was associated with a significant response to an EHR nudge for influenza vaccination.

SUBMITTER: Changolkar S

PROVIDER: S-EPMC7239439 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study.

Changolkar Sujatha S Rewley Jeffrey J Balachandran Mohan M Rareshide Charles A L CAL Snider Christopher K CK Day Susan C SC Patel Mitesh S MS

PloS one 20200520 5

<h4>Background</h4>Health systems routinely implement changes to the design of electronic health records (EHRs). Physician behavior may vary in response and methods to identify this variation could help to inform future interventions. The objective of this study was to phenotype primary care physician practice patterns and evaluate associations with response to an EHR nudge for influenza vaccination.<h4>Methods and findings</h4>During the 2016-2017 influenza season, 3 primary care practices at P ...[more]

PMID: 32433678

Similar Datasets

Project description:BackgroundMultiple policy initiatives encourage more cautious prescribing of opioids in light of their risks. Electronic health record (EHR) redesign can influence prescriber choices, but some redesigns add to workload.ObjectiveTo estimate the effect of an EHR prescribing redesign on both opioid prescribing choices and keystrokes.DesignQuality improvement quasi-experiment, analyzed as interrupted time series.ParticipantsAdult patients of an academic multispecialty practice and a federally qualified health center (FQHC) who received new prescriptions for short-acting opioids, and their providers.InterventionIn the redesign, new prescriptions of short-acting opioids defaulted to the CDC-recommended minimum for opioid-naïve patients, with no alerts or hard stops, such that 9 keystrokes were required for a guideline-concordant prescription and 24 for a non-concordant prescription.Main measuresProportion of guideline-concordant prescriptions, defined as new prescriptions with a 3-day supply or less, calculated per 2-week period. Number of mouse clicks and keystrokes needed to place prescriptions.Key resultsAcross the 2 sites, 22,113 patients received a new short-acting opioid prescription from 821 providers. Before the intervention, both settings showed secular trends toward smaller-quantity prescriptions. At the academic practice, the intervention was associated with an immediate increase in guideline-concordant prescriptions from an average of 12% to 31% of all prescriptions. At the FQHC, about 44% of prescriptions were concordant at the time of the intervention, which was not associated with an additional significant increase. However, total keystrokes needed to place the concordant prescriptions decreased 62.7% from 3552 in the 6 months before the intervention to 1323 in the 6 months afterwards.ConclusionsAutocompleting prescription forms with guideline-recommended values was associated with a large increase in guideline concordance in an organization where baseline concordance was low, but not in an organization where it was already high. The redesign markedly reduced the number of keystrokes needed to place orders, with important implications for EHR-related stress.Trial registrationwww.ClinicalTrials.gov protocol 1710018646.

Project description:Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping.Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance.We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p=0.039), J48 (p=0.003) and JRIP (p=0.003).ILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts.Relational learning using ILP offers a viable approach to EHR-driven phenotyping.

Project description:BACKGROUND:Development of electronic health record (EHR) prediction models to improve palliative care delivery is on the rise, yet the clinical impact of such models has not been evaluated. OBJECTIVE:To assess the clinical impact of triggering palliative care using an EHR prediction model. DESIGN:Pilot prospective before-after study on the general medical wards at an urban academic medical center. PARTICIPANTS:Adults with a predicted probability of 6-month mortality of ??0.3. INTERVENTION:Triggered (with opt-out) palliative care consult on hospital day 2. MAIN MEASURES:Frequencies of consults, advance care planning (ACP) documentation, home palliative care and hospice referrals, code status changes, and pre-consult length of stay (LOS). KEY RESULTS:The control and intervention periods included 8 weeks each and 138 admissions and 134 admissions, respectively. Characteristics between the groups were similar, with a mean (standard deviation) risk of 6-month mortality of 0.5 (0.2). Seventy-seven (57%) triggered consults were accepted by the primary team and 8 consults were requested per usual care during the intervention period. Compared to historical controls, consultation increased by 74% (22 [16%] vs 85 [63%], P?<?.001), median (interquartile range) pre-consult LOS decreased by 1.4 days (2.6 [1.1, 6.2] vs 1.2 [0.8, 2.7], P?=?.02), ACP documentation increased by 38% (23 [17%] vs 37 [28%], P?=?.03), and home palliative care referrals increased by 61% (9 [7%] vs 23 [17%], P?=?.01). There were no differences between the control and intervention groups in hospice referrals (14 [10] vs 22 [16], P?=?.13), code status changes (42 [30] vs 39 [29]; P?=?.81), or consult requests for lower risk (<?0.3) patients (48/1004 [5] vs 33/798 [4]; P?=?.48). CONCLUSIONS:Targeting hospital-based palliative care using an EHR mortality prediction model is a clinically promising approach to improve the quality of care among seriously ill medical patients. More evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.

Project description:IntroductionElectronic health records (EHR) are linked together to examine disease history and to undertake research into the causes and outcomes of disease. However, the process of constructing algorithms for phenotyping (e.g., identifying disease characteristics) or health characteristics (e.g., smoker) is very time consuming and resource costly. In addition, results can vary greatly between researchers. Reusing or building on algorithms that others have created is a compelling solution to these problems. However, sharing algorithms is not a common practice and many published studies do not detail the clinical code lists used by the researchers in the disease/characteristic definition. To address these challenges, a number of centres across the world have developed health data portals which contain concept libraries (e.g., algorithms for defining concepts such as disease and characteristics) in order to facilitate disease phenotyping and health studies.ObjectivesThis study aims to review the literature of existing concept libraries, examine their utilities, identify the current gaps, and suggest future developments.MethodsThe five-stage framework of Arksey and O'Malley was used for the literature search. This approach included defining the research questions, identifying relevant studies through literature review, selecting eligible studies, charting and extracting data, and summarising and reporting the findings.ResultsThis review identified seven publicly accessible Electronic Health data concept libraries which were developed in different countries including UK, USA, and Canada. The concept libraries (n = 7) investigated were either general libraries that hold phenotypes of multiple specialties (n = 4) or specialized libraries that manage only certain specialities such as rare diseases (n = 3). There were some clear differences between the general libraries such as archiving data from different electronic sources, and using a range of different types of coding systems. However, they share some clear similarities such as enabling users to upload their own code lists, and allowing users to use/download the publicly accessible code. In addition, there were some differences between the specialized libraries such as difference in ability to search, and if it was possible to use different searching queries such as simple or complex searches. Conversely, there were some similarities between the specialized libraries such as enabling users to upload their own concepts into the libraries and to show where they were published, which facilitates assessing the validity of the concepts. All the specialized libraries aimed to encourage the reuse of research methods such as lists of clinical code and/or metadata.ConclusionThe seven libraries identified have been developed independently and appear to replicate similar concepts but in different ways. Collaboration between similar libraries would greatly facilitate the use of these libraries for the user. The process of building code lists takes time and effort. Access to existing code lists increases consistency and accuracy of definitions across studies. Concept library developers should collaborate with each other to raise awareness of their existence and of their various functions, which could increase users' contributions to those libraries and promote their wide-ranging adoption.

Project description:ImportanceThe use of electronic health records (EHRs) is directly associated with physician burnout. An underlying factor associated with burnout may be EHR-related fatigue owing to insufficient user-centered interface design and suboptimal usability.ObjectiveTo examine the association between EHR use and fatigue, as measured by pupillometry, and efficiency, as measured by mouse clicks, time, and number of EHR screens, among intensive care unit (ICU) physicians completing a simulation activity in a prominent EHR.Design, setting, and participantsA cross-sectional, simulation-based EHR usability assessment of a leading EHR system was conducted from March 20 to April 5, 2018, among 25 ICU physicians and physician trainees at a southeastern US academic medical center. Participants completed 4 simulation patient cases in the EHR that involved information retrieval and task execution while wearing eye-tracking glasses. Fatigue was quantified through continuous eye pupil data; EHR efficiency was characterized through task completion time, mouse clicks, and EHR screen visits. Data were analyzed from June 1, 2018, to August 31, 2019.Main outcomes and measuresPrimary outcomes were physician fatigue, measured by pupillometry (with lower scores indicating greater fatigue), and EHR efficiency, measured by task completion times, number of mouse clicks, and number of screens visited during EHR simulation.ResultsThe 25 ICU physicians (13 women; mean [SD] age, 32.1 [6.1] years) who completed a simulation exercise involving 4 patient cases (mean [SD] completion time, 34:43 [11:41] minutes) recorded a total of 14 hours and 27 minutes of EHR activity. All physician participants experienced physiological fatigue at least once during the exercise, and 20 of 25 participants (80%) experienced physiological fatigue within the first 22 minutes of EHR use. Physicians who experienced EHR-related fatigue in 1 patient case were less efficient in the subsequent patient case, as demonstrated by longer task completion times (r = -0.521; P = .007), higher numbers of mouse clicks (r = -0.562; P = .003), and more EHR screen visits (r = -0.486; P = .01).Conclusions and relevanceThis study reports high rates of fatigue among ICU physicians during short periods of EHR simulation, which were negatively associated with EHR efficiency and included a carryover association across patient cases. More research is needed to investigate the underlying causes of EHR-associated fatigue, to support user-centered EHR design, and to inform safe EHR use policies and guidelines.

Project description:Introduction:Electronic health record (EHR)-driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time-consuming, error-prone, and platform-specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high-throughput, cross-platform phenotyping. Methods:We selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results. Results:CQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross-platform execution resulted in identical patient cohorts generated by both data platforms. Conclusions:CQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross-platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR-driven phenotyping and scale in learning health systems.

Dataset Information

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study.

Publications

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets