Dataset Information

Effect of Passive Choice and Active Choice Interventions in the Electronic Health Record to Cardiologists on Statin Prescribing: A Cluster Randomized Clinical Trial.

ABSTRACT:

Importance

Statin therapy is underused for many patients who could benefit.

Objective

To evaluate the effect of passive choice and active choice interventions in the electronic health record (EHR) to promote guideline-directed statin therapy.

Design, setting, and participants

Three-arm randomized clinical trial with a 6-month preintervention period and 6-month intervention. Randomization conducted at the cardiologist level at 16 cardiology practices in Pennsylvania and New Jersey. The study included 82 cardiologists and 11 693 patients. Data were analyzed between May 8, 2019, and January 9, 2020.

Interventions

In passive choice, cardiologists had to manually access an alert embedded in the EHR to select options to initiate or increase statin therapy. In active choice, an interruptive EHR alert prompted the cardiologist to accept or decline guideline-directed statin therapy. Cardiologists in the control group were informed of the trial but received no other interventions.

Main outcomes and measures

Primary outcome was statin therapy at optimal dose based on clinical guidelines. Secondary outcome was statin therapy at any dose.

Results

The sample comprised 11 693 patients with a mean (SD) age of 63.8 (9.1) years; 58% were male (n = 6749 of 11 693), 66% were White (n = 7683 of 11 693), and 24% were Black (n = 2824 of 11 693). The mean (SD) 10-year atherosclerotic cardiovascular disease (ASCVD) risk score was 15.4 (10.0); 68% had an ASVCD clinical diagnosis. Baseline statin prescribing rates at the optimal dose were 40.3% in the control arm, 39.1% in the passive choice arm, and 41.2% in the active choice arm. In adjusted analyses, the change in statin prescribing rates at optimal dose over time was not significantly different from control for passive choice (adjusted difference in percentage points, 0.2; 95% CI, -2.9 to 2.8; P = .86) or active choice (adjusted difference in percentage points, 2.4; 95% CI, -0.6 to 5.0; P = .08). In adjusted analyses of the subset of patients with clinical ASCVD, the active choice intervention resulted in a significant increase in statin prescribing at optimal dose relative to control (adjusted difference in percentage points, 3.8; 95% CI, 1.0-6.4; P = .008). No other subset analyses were significant. There were no significant changes in statin prescribing at any dose for either intervention.

Conclusions and relevance

The passive choice and active choice interventions did not change statin prescribing. In the subgroup of patients with clinical ASCVD, the active choice intervention led to a small increase in statin prescribing at the optimal dose, which could inform the design or targeting of future interventions.

Trial registration

ClinicalTrials.gov Identifier: NCT03271931.

SUBMITTER: Adusumalli S

PROVIDER: S-EPMC7542520 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ImportanceStatins are not prescribed to approximately 50% of patients who could benefit from them.ObjectiveTo evaluate the effectiveness of an automated patient dashboard using active choice framing with and without peer comparison feedback on performance to nudge primary care physicians (PCPs) to increase guideline-concordant statin prescribing.Design, setting, and participantsThis 3-arm cluster randomized clinical trial was conducted from February 21, 2017, to April 21, 2017, at 32 practice sites in Pennsylvania and New Jersey. Participants included 96 PCPs and 4774 patients not previously receiving statin therapy. Data were analyzed from April 25, 2017, to June 16, 2017.InterventionsPrimary care physicians in the 2 intervention arms were emailed a link to an automated online dashboard listing their patients who met national guidelines for statin therapy but had not been prescribed this medication. The dashboard included relevant patient information, and for each patient, PCPs were asked to make an active choice to prescribe atorvastatin, 20 mg, once daily, atorvastatin at another dose, or another statin or not prescribe a statin and select a reason. The dashboard was available for 2 months. In 1 intervention arm, the email to PCPs also included feedback on their statin prescribing rate compared with their peers. Primary care physicians in the usual care group received no interventions.Main outcomes and measuresStatin prescription rates.ResultsPatients had a mean (SD) age of 62.4 (8.3) years and a mean (SD) 10-year atherosclerotic cardiovascular disease risk score of 13.6 (8.2); 2625 (55.0%) were male, 3040 (63.7%) were white, and 1318 (27.6%) were black. In the active choice arm, 16 of 32 PCPs (50.0%) accessed the patient dashboard, but only 2 of 32 (6.3%) signed statin prescription orders. In the active choice with peer comparison arm, 12 of 32 PCPs (37.5%) accessed the patient dashboard and 8 of 32 (25.0%) signed statin prescription orders. Statins were prescribed in 40 of 1566 patients (2.6%) in the usual care arm, 116 of 1743 (6.7%) in the active choice arm, and 117 of 1465 (8.0%) in the active choice with peer comparison arm. In the main adjusted model, compared with usual care, there was a significant increase in statin prescribing in the active choice with peer comparison arm (adjusted difference in percentage points, 5.8; 95% CI, 0.9-13.5; P = .008), but not in the active choice arm (adjusted difference in percentage points, 4.1; 95% CI, -0.8 to 13.1; P = .11).Conclusions and relevanceAn automated patient dashboard using both active choice framing and peer comparison feedback led to a modest but significant increase in guideline-concordant statin prescribing rates.Trial registrationClinicalTrials.gov Identifier: NCT03021759.

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: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:ImportanceReliance on prescription opioids for postprocedural analgesia has contributed to the opioid epidemic. With the implementation of electronic medical record (EMR) systems, there has been increasing use of computerized order entry systems for medication prescriptions, which is now more common than handwritten prescriptions. The EMR can autopopulate a default number of pills prescribed, and 1 potential method to alter prescriber behavior is to change the default number presented via the EMR system.ObjectiveTo investigate the association of lowering the default number of pills presented when prescribing opioids in an EMR system with the amount of opioid prescribed after procedures.Design, setting, and participantsA prepost intervention study was conducted to compare postprocedural prescribing patterns during the 3 months before the default change (February 18 to May 17, 2017) with the 3 months after the default change (May 18 to August 18, 2017). The setting was a multihospital health care system that uses Epic EMR (Hyperspace 2015 IU2; Epic Systems Corporation). Participants were all patients in the study period undergoing 1 of the 10 most common operations and discharged by postoperative day 1.InterventionThe default number of opioid pills autopopulated in the EMR when prescribing discharge analgesia was lowered from 30 to 12.Main outcomes and measuresLinear regression estimating the change in the median number of opioid pills and the total dose of opioid prescribed was performed. Opioid doses were converted into morphine milligram equivalents (MME) for comparison. The frequency of patients requiring analgesic prescription refills was also evaluated.ResultsThere were 1447 procedures (mean [SD] age, 54.4 [17.3] years; 66.9% female) before the default change and 1463 procedures (mean [SD] age, 54.5 [16.4] years; 67.0% female) after the default change. After the default change, the median number of opioid pills prescribed decreased from 30 (interquartile range, 15-30) to 20 (interquartile range, 12-30) per prescription (P < .001). The percentage of prescriptions written for 30 pills decreased from 39.7% (554 of 1397) before the default change to 12.9% (183 of 1420) after the default change (P < .001), and the percentage of prescriptions written for 12 pills increased from 2.1% (29 of 1397) before the default change to 24.6% (349 of 1420) after the default change (P < .001). Regression analysis demonstrated a decrease of 5.22 (95% CI, -6.12 to -4.32) opioid pills per prescription after the default change, for a total decrease of 34.41 (95% CI, -41.36 to -27.47) MME per prescription. There was no statistical difference in opioid refill rates (3.0% [4 of 135] before the default change vs 1.5% [2 of 135] after the default change, P = .41).Conclusions and relevanceLowering the default number of opioid pills prescribed in an EMR system is a simple, effective, cheap, and potentially scalable intervention to change prescriber behavior and decrease the amount of opioid medication prescribed after procedures.

Project description:BACKGROUND:Many patients with or at risk for chronic kidney disease (CKD) in the primary care setting are not receiving recommended care. OBJECTIVE:The objective of this study is to determine whether a multifaceted, low-cost intervention compared with usual care improves the care of patients with or at risk for CKD in the primary care setting. DESIGN:A pragmatic cluster-randomized trial, with an embedded qualitative process evaluation, will be conducted. SETTING:The study population comes from the Electronic Medical Record Administrative data Linked Database®, which includes clinical data for more than 140 000 rostered adults cared for by 194 family physicians in 34 clinics across Ontario, Canada. The 34 primary care clinics will be randomized to the intervention or control group. INTERVENTION:The intervention group will receive resources from the "CKD toolkit" to help improve care including practice audit and feedback, printed educational materials for physicians and patients, electronic decision support and reminders, and implementation support. MEASUREMENTS:Patients with or at risk for CKD within participating clinics will be identified using laboratory data in the electronic medical records. Outcomes will be assessed after dissemination of the CKD tools and after 2 rounds of feedback on performance on quality indicators have been sent to the physicians using information from the electronic medical records. The primary outcome is the proportion of patients aged 50 to 80 years with nondialysis-dependent CKD who are on a statin. Secondary outcomes include process of care measures such as screening tests, CKD recognition, monitoring tests, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker prescriptions, blood pressure targets met, and nephrologist referral. Hierarchical analytic modeling will be performed to account for clustering. Semistructured interviews will be conducted with a random purposeful sample of physicians in the intervention group to understand why the intervention achieved the observed effects. CONCLUSIONS:If our intervention improves care, then the CKD toolkit can be adapted and scaled for use in other primary care clinics which use electronic medical records. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02274298.

Project description:BackgroundPrescribing error represent a significant source of preventable harm to patients. Prescribing errors at discharge, including omission of pre-admission medications (PAM), are particularly harmful as they frequently propagate following discharge. This study assesses the impact of an educational intervention and introduction of an electronic patient record (EPR) in the same centre on omission of PAM at discharge using a pragmatic design. A survey of newly qualified doctors is used to contextualise findings.MethodsDischarge prescriptions and discharge summaries were reviewed at discharge, and compared to admission medicine lists, using a paper-based chart system. Discrepancies were noted, using Health Information and Quality Authority guidelines for discharge prescribing. An educational intervention was conducted. Further review of discharge prescriptions and discharge summaries took place. Following introduction of an EPR, review of discharge summaries and discharge prescriptions was repeated. A survey was administered to recently qualified doctors (interns), and analysed using descriptive statistics and thematic analysis.ResultsOmission of PAM as prescribed or discontinued items at discharge occurs frequently. An educational intervention did not significantly change prescribing error rates (U = 1255.5, p = 0.206). EPR introduction did significantly reduce omission of PAM on discharge prescribing (U = 694, p < 0.001), however there was also a reduction in the rate of deliberate discontinuation of PAM at discharge (U = 1237.5, p = 0.007). Survey results demonstrated that multiple sources are required to develop a discharge prescription. Time pressure, access to documentation and lack of admission medicine reconciliation are frequently cited causes of discharge prescribing error.ConclusionThis study verified passive educational interventions alone do not improve discharge prescribing. Introduction of EPR improved discharge prescribing, but negatively impacted deliberate discontinuation of PAM at discharge. This is attributable to reduced access to key sources of information used in formulating discharge prescriptions, and separation of the discontinuation function from the prescribing function on the EPR discharge application.