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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

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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

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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:ObjectiveElectronic 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.MethodsTwo 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.ResultsWe 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).DiscussionILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts.ConclusionRelational learning using ILP offers a viable approach to EHR-driven phenotyping.

Project description:ImportanceInfluenza vaccination rates in the United States are suboptimal near 40%, but little is known about variations in care based on clinic appointment time.ObjectivesTo compare differences in influenza vaccination rates by clinic appointment time and to evaluate the association of an active choice intervention in the electronic health record with changes in vaccination rates.Design, setting, and participantsRetrospective, quality improvement study of 11 primary care practices at the University of Pennsylvania Health System from September 1, 2014, to March 31, 2017. Participants included adults eligible for influenza vaccination. Data analysis was conducted from October 20, 2017, to March 9, 2018.InterventionsDuring the 2016 to 2017 influenza season, 3 primary care practices at the University of Pennsylvania Health System implemented an active choice intervention in the electronic health record that prompted medical assistants to ask patients about influenza vaccination during check-in and template vaccination orders for clinicians to review during the visit.Main outcomes and measuresInfluenza vaccination rates.ResultsThe sample comprised 96 291 patients with a mean (SD) age of 56.2 (17.0) years; 41 865 (43.5%) were men, 61 813 (64.2%) were white, and 23 802 (24.7%) were black. Among all practices across all 3 years, vaccination rates were approximately 44% from 8 am to 10 am, declined to 41.2% by 11 am and 38.3% at noon, increased to 40.2% at 1 pm, and then declined to 34.3% at 3 pm and 32.0% at 4 pm (P < .001 for adjusted linear trend). For the 3 years, vaccination rates were 46.9%, 47.2%, and 45.6% at control practices and 49.7%, 52.2%, and 59.3% at intervention practices, respectively. In adjusted analyses, compared with control practices over time, the active choice intervention was associated with a significant 9.5-percentage point increase in vaccination rates (95% CI, 4.1-14.3; P < .001). Vaccination rates increased similarly across times of the day.Conclusions and relevanceInfluenza vaccination rates significantly declined as the clinic day progressed. The active choice intervention was associated with a significant increase in influenza vaccination rates that were similar in magnitude throughout the day.

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:BACKGROUND:Clinical decision support (CDS), including computerized reminders for providers and patients, can improve health outcomes. CDS promoting influenza vaccination, delivered directly to patients via an electronic health record (EHR) patient portal and interactive voice recognition (IVR) calls, offers an innovative approach to improving patient care. OBJECTIVE:To test the effectiveness of an EHR patient portal and IVR outreach to improve rates of influenza vaccination in a large multispecialty group practice in central Massachusetts. METHODS:We describe a nonblinded, randomized controlled trial of EHR patient portal messages and IVR calls designed to promote influenza vaccination. In our preparatory phase, we conducted qualitative interviews with patients, providers, and staff to inform development of EHR portal messages with embedded questionnaires and IVR call scripts. We also provided practice-wide education on influenza vaccines to all physicians and staff members, including information on existing vaccine-specific EHR CDS. Outreach will target adult patients who remain unvaccinated for more than 2 months after the start of the influenza season. Using computer-generated randomization and a factorial design, we will assign 20,000 patients who are active users of electronic patient portals to one of the 4 study arms: (1) receipt of a portal message promoting influenza vaccines and offering online appointment scheduling; (2) receipt of an IVR call with similar content but without appointment facilitation; (3) both (1) and (2); or (4) neither (1) nor (2) (usual care). We will randomize patients without electronic portals (10,000 patients) to (1) receipt of IVR call or (2) usual care. Both portal messages and IVR calls promote influenza vaccine completion. Our primary outcome is percentage of eligible patients with influenza vaccines administered at our group practice during the 2014-15 influenza season. Both outreach methods also solicit patient self-report on influenza vaccinations completed outside the clinic or on barriers to influenza vaccination. Self-reported data from both outreach modes will be uploaded into the EHR to increase accuracy of existing provider-directed EHR CDS (vaccine alerts). RESULTS:With our proposed sample size and using a factorial design, power calculations using baseline vaccination rate estimates indicated that 4286 participants per arm would give 80% power to detect a 3% improvement in influenza vaccination rates between groups (?=.05; 2-sided). Intention-to-treat unadjusted chi-square analyses will be performed to assess the impact of portal messages, either alone or in combination with the IVR call, on influenza vaccination rates. The project was funded in January 2014. Patient enrollment for the project described here completed in December 2014. Data analysis is currently under way and first results are expected to be submitted for publication in 2016. CONCLUSIONS:If successful, this study's intervention may be adapted by other large health care organizations to increase vaccination rates among their eligible patients. CLINICALTRIAL:ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 (Archived by WebCite at http://www.webcitation.org/6fbLviHLH).

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

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

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