Drivers of Variation in Opioid Prescribing after Common Surgical Procedures in a Large Multihospital Healthcare System.

BACKGROUND: Misuse of prescription opioids is a well-established contributor to the US opioid epidemic. The primary objective of this study was to identify which level of care delivery (ie patient, prescriber, or hospital) produced the most unwarranted variation in opioid prescribing after common surgical procedures. STUDY DESIGN: Electronic health record data from a large multihospital healthcare system were used in conjunction with random-effect models to examine variation in opioid prescribing practices after similar inpatient and outpatient surgical procedures between October 2019 and September 2021. Unwarranted variation was conceptualized as variation resulting from prescriber behavior unsupported by evidence. Covariates identified as drivers of warranted variation included characteristics known to influence pain levels or patient safety. All other model variables, including prescriber specialty and patient race, ethnicity, and insurance status were characterized as potential drivers of unwarranted variation. RESULTS: Among 25,188 procedures with an opioid prescription at hospital discharge, 53.5% exceeded guideline recommendations, corresponding to 13,228 patients receiving the equivalent of >140,000 excess 5 mg oxycodone tablets after surgical procedures. Prescribing variation was primarily driven by prescriber-level factors, with approximately half of the total variation in morphine milligram equivalents prescribed observed at the prescriber level and not explained by any measured variables. Unwarranted covariates associated with higher prescribed opioid quantity included non-Hispanic Black race, Medicare insurance, smoking history, later hospital discharge times, and prescription by a surgeon rather than a hospitalist or primary care provider. CONCLUSIONS: Given the large proportion of unexplained variation observed at the provider level, targeting prescribers through education and training may be an effective strategy for reducing postoperative opioid prescribing.

Peer Comparison or Guideline-Based Feedback and Postsurgery Opioid Prescriptions: A Randomized Clinical Trial.

Importance: Excess opioid prescribing after surgery can result in prolonged use and diversion. Email feedback based on social norms may reduce the number of pills prescribed. Objective: To assess the effectiveness of 2 social norm-based interventions on reducing guideline-discordant opioid prescribing after surgery. Design, Setting, and Participants: This cluster randomized clinical trial conducted at a large health care delivery system in northern California between October 2021 and October 2022 included general, obstetric/gynecologic, and orthopedic surgeons with patients aged 18 years or older discharged to home with an oral opioid prescription. Interventions: In 19 hospitals, 3 surgical specialties (general, orthopedic, and obstetric/gynecologic) were randomly assigned to a control group or 1 of 2 interventions. The guidelines intervention provided email feedback to surgeons on opioid prescribing relative to institutionally endorsed guidelines; the peer comparison intervention provided email feedback on opioid prescribing relative to that of peer surgeons. Emails were sent to surgeons with at least 2 guideline-discordant prescriptions in the previous month. The control group had no intervention. Main Outcome and Measures: The probability that a discharged patient was prescribed a quantity of opioids above the guideline for the respective procedure during the 12 intervention months. Results: There were 38 235 patients discharged from 640 surgeons during the 12-month intervention period. Control-group surgeons prescribed above guidelines 36.8% of the time during the intervention period compared with 27.5% and 25.4% among surgeons in the peer comparison and guidelines arms, respectively. In adjusted models, the peer comparison intervention reduced guideline-discordant prescribing by 5.8 percentage points (95% CI, -10.5 to -1.1; P = .03) and the guidelines intervention reduced it by 4.7 percentage points (95% CI, -9.4 to -0.1; P = .05). Effects were driven by surgeons who performed more surgeries and had more guideline-discordant prescribing at baseline. There was no significant difference between interventions. Conclusions and Relevance: In this cluster randomized clinical trial, email feedback based on either guidelines or peer comparison reduced opioid prescribing after surgery. Guideline-based feedback was as effective as peer comparison-based feedback. These interventions are simple, low-cost, and scalable, and may reduce downstream opioid misuse. Trial Registration: ClinicalTrials.gov NCT05070338.

Antibiotic prescribing for acute respiratory infections during the coronavirus disease 2019 (COVID-19) pandemic: Patterns in a nationwide telehealth service provider.

We examined 3,046,538 acute respiratory infection (ARI) encounters with 6,103 national telehealth physicians from January 2019 to October 2021. The antibiotic prescribing rates were 44% for all ARIs; 46% were antibiotic appropriate; 65% were potentially appropriate; 19% resulted from inappropriate diagnoses; and 10% were related to coronavirus disease 2019 (COVID-19) diagnosis.

Use of Telehealth Information for Early Detection: Insights From the COVID-19 Pandemic.

Objectives. To examine whether the addition of telehealth data to existing surveillance infrastructure can improve forecasts of cases and mortality. Methods. In this observational study, we compared accuracy of 14-day forecasts using real-time data available to the National Syndromic Surveillance Program (standard forecasts) to forecasts that also included telehealth information (telehealth forecasts). The study was performed in a national telehealth service provider in 2020 serving 50 US states and the District of Columbia. Results. Among 10.5 million telemedicine encounters, 169 672 probable COVID-19 cases were diagnosed by 5050 clinicians, with a rate between 0.79 and 47.8 probable cases per 100 000 encounters per day (mean = 8.37; SD = 10.75). Publicly reported case counts ranged from 0.5 to 237 916 (mean: 53 913; SD = 47 466) and 0 to 2328 deaths (mean = 1035; SD = 550) per day. Telehealth-based forecasts improved 14-day case forecasting accuracy by 1.8 percentage points to 30.9% (P = .06) and mortality forecasting by 6.4 percentage points to 26.9% (P < .048). Conclusions. Modest improvements in forecasting can be gained from adding telehealth data to syndromic surveillance infrastructure. (Am J Public Health. 2024;114(2):218-225. https://doi.org/10.2105/AJPH.2023.307499).

Reducing Care Overuse in Older Patients Using Professional Norms and Accountability : A Cluster Randomized Controlled Trial.

BACKGROUND: Effective strategies are needed to curtail overuse that may lead to harm. OBJECTIVE: To evaluate the effects of clinician decision support redirecting attention to harms and engaging social and reputational concerns on overuse in older primary care patients. DESIGN: 18-month, single-blind, pragmatic, cluster randomized trial, constrained randomization. (ClinicalTrials.gov: NCT04289753). SETTING: 60 primary care internal medicine, family medicine and geriatrics practices within a health system from 1 September 2020 to 28 February 2022. PARTICIPANTS: 371 primary care clinicians and their older adult patients from participating practices. INTERVENTION: Behavioral science-informed, point-of-care, clinical decision support tools plus brief case-based education addressing the 3 primary clinical outcomes (187 clinicians from 30 clinics) were compared with brief case-based education alone (187 clinicians from 30 clinics). Decision support was designed to increase salience of potential harms, convey social norms, and promote accountability. MEASUREMENTS: Prostate-specific antigen (PSA) testing in men aged 76 years and older without previous prostate cancer, urine testing for nonspecific reasons in women aged 65 years and older, and overtreatment of diabetes with hypoglycemic agents in patients aged 75 years and older and hemoglobin A1c (HbA1c) less than 7%. RESULTS: At randomization, mean clinic annual PSA testing, unspecified urine testing, and diabetes overtreatment rates were 24.9, 23.9, and 16.8 per 100 patients, respectively. After 18 months of intervention, the intervention group had lower adjusted difference-in-differences in annual rates of PSA testing (-8.7 [95% CI, -10.2 to -7.1]), unspecified urine testing (-5.5 [CI, -7.0 to -3.6]), and diabetes overtreatment (-1.4 [CI, -2.9 to -0.03]) compared with education only. Safety measures did not show increased emergency care related to urinary tract infections or hyperglycemia. An HbA1c greater than 9.0% was more common with the intervention among previously overtreated diabetes patients (adjusted difference-in-differences, 0.47 per 100 patients [95% CI, 0.04 to 1.20]). LIMITATION: A single health system limits generalizability; electronic health data limit ability to differentiate between overtesting and underdocumentation. CONCLUSION: Decision support designed to increase clinicians' attention to possible harms, social norms, and reputational concerns reduced unspecified testing compared with offering traditional case-based education alone. Small decreases in diabetes overtreatment may also result in higher rates of uncontrolled diabetes. PRIMARY FUNDING SOURCE: National Institute on Aging.

Integrating REDCap patient-reported outcomes with the HealtheIntent population health platform: proof of concept.

Objective: Patient-reported outcome measures (PROMs) are critical to drive patient-centered care and to understanding patients' perspectives on their health status, quality of life, and the overall effectiveness of the care they receive. PROMs are increasingly being used in clinical and research settings, but the mechanisms to aggregate data from different systems can be cumbersome. Materials and methods: As part of an FDA Real-World Evidence demonstration project, we enriched routine care clinical data from our Cerner electronic health record (EHR) with PROMs collected using REDCap. We used SSIS, sFTP, and the REDCap Application Programming Interface to aggregate both data sources into the Cerner HealtheIntent Population Health Platform. Results: We successfully built dashboards, reports, and datasets containing both REDCap and EHR data collected prospectively. Discussion: This technically straightforward approach using commonly available clinical and research tools can be readily adopted and adapted by others to better integrate PROMs with clinical data sources.

Clinician Job Satisfaction After Peer Comparison Feedback: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Interventions that improve clinician performance through feedback should not contribute to job dissatisfaction or staff turnover. Measurement of job satisfaction may help identify interventions that lead to this undesirable consequence. Objective: To evaluate whether mean job satisfaction was less than the margin of clinical significance among clinicians who received social norm feedback (peer comparison) compared with clinicians who did not. Design, Setting, and Participants: This secondary, preregistered, noninferiority analysis of a cluster randomized trial compared 3 interventions to reduce inappropriate antibiotic prescribing in a 2 × 2 × 2 factorial design from November 1, 2011, to April 1, 2014. A total of 248 clinicians were enrolled from 47 clinics. The sample size for this analysis was determined by the number of nonmissing job satisfaction scores from the original enrolled sample, which was 201 clinicians from 43 clinics. Data analysis was performed from October 12 to April 13, 2022. Interventions: Feedback comparing individual clinician performance to top-performing peers, delivered in monthly emails (peer comparison). Main Outcomes and Measures: The primary outcome was a response to the following statement: "Overall, I am satisfied with my current job." Responses ranged from 1 (strongly disagree) to 5 (strongly agree). Results: A total of 201 clinicians (response rate, 81%) from 43 of the 47 clinics (91%) provided a survey response about job satisfaction. Clinicians were primarily female (n = 129 [64%]) and board certified in internal medicine (n = 126 [63%]), with a mean (SD) age of 48 (10) years. The clinic-clustered difference in mean job satisfaction was greater than -0.32 (ß = 0.11; 95% CI, -0.19 to 0.42; P = .46). Therefore, the preregistered null hypothesis that peer comparison is inferior by resulting in at least a 1-point decrease in job satisfaction by 1 in 3 clinicians was rejected. The secondary null hypothesis that job satisfaction was similar among clinicians randomized to social norm feedback was not able to be rejected. The effect size did not change when controlling for other trial interventions (t = 0.08; P = .94), and no interaction effects were observed. Conclusions and Relevance: In this secondary analysis of a randomized clinical trial, peer comparison did not lead to lower job satisfaction. Features that may have protected against dissatisfaction include clinicians' agency over the performance measure, privacy of individual performance, and allowing all clinicians to achieve top performance. Trial Registration: ClinicalTrials.gov Identifiers: NCT05575115 and NCT01454947.

Ontologizing health systems data at scale: making translational discovery a reality.

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

SEnDAE: A resource for expanding research into social and environmental determinants of health.

BACKGROUND AND OBJECTIVE: Social and Environmental Determinants of Health (SEDoH) are of increasing interest to researchers in personal and public health. Collecting SEDoH and associating them with patient medical record can be challenging, especially for environmental variables. We announce here the release of SEnDAE, the Social and Environmental Determinants Address Enhancement toolkit, and open-source resource for ingesting a range of environmental variables and measurements from a variety of sources and associated them with arbitrary addresses. METHODS: SEnDAE includes optional components for geocoding addresses, in case an organization does not have independent capabilities in that area, and recipes for extending the OMOP CDM and the ontology of an i2b2 instance to display and compute over the SEnDAE variables within i2b2. RESULTS: On a set of 5000 synthetic addresses, SEnDAE was able to geocode 83%. SEnDAE geocodes addresses to the same Census tract as ESRI 98.1% of the time. CONCLUSION: Development of SEnDAE is ongoing, but we hope that teams will find it useful to increase their usage of environmental variables and increase the field's general understanding of these important determinants of health.