Evaluating Unwarranted Variation in Treatment Patterns Using Unblinded Data

Background: Unwarranted practice variation is an issue for most health care systems and is sometimes caused by external factors. We examined the peer group Variation Reduction (VR), a program in which clinicians are shown data about their treatment patterns alongside other clinicians and are usually surprised by the variation. The information drives a desire to understand the variation in how they care for their patients and can lead to a change in clinician behavior. We sought to evaluate VR projects aimed at reducing brand name prescribing and increasing appropriate documentation of end-of-life (EOL) wishes in a large integrated health care system. Methods: In this physician-controlled process, providers reviewed and discussed data in a safe environment ruled by principles that confirm confidentiality, safety and group control. Verbal consent of all group members is required for each VR step, including topic selection and discussion of relevant data. For analyzing projects, we chose a pre-post parallel design. Providers having at least 12 months of data prior to the VR program and 3 months of data after the VR program were included in the analysis. The control group was comprised of clinicians not involved in the VR program who had enough data for the selected projects. Orders in the 3 months after the VR program were compared to the 12 months before the VR program for the intervention and control physicians. Repeated measures within clinicians were modeled using generalized estimating equations. Standard errors were estimated using nonparametric bootstrap with 1,000 iterations, and 95% confidence intervals (CI) are reported. Results: Postintervention, accounting for the 12-month pre-period, physicians in the intervention group prescribed generics for corticosteroids and nasal steroids significantly more than physicians in the control group, and EOL wishes were documented more frequently in the intervention group: 9.4% (CI: 5.4–14.3) corticosteroids; 3.2% (CI: 1.8–4.6) nasal steroids; 0.5% (CI: 0.2–0.8) documenting EOL wishes. Conclusion: For all three projects presented, the VR intervention was associated with a reduction in variation in the practices. These results indicate that clinician behavior can be changed with a peer-group process, without the need for additional incentives

Imputation of missing values for electronic health record laboratory data

Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients’ comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method

Disparities in postoperative opioid prescribing by race and ethnicity: an electronic health records-based observational study from Northern California, 2015–2020

Abstract Objectives To examine racial and ethnic disparities in postoperative opioid prescribing. Data sources Electronic health records (EHR) data across 24 hospitals from a healthcare delivery system in Northern California from January 1, 2015 to February 2, 2020 (study period). Study design Cross-sectional, secondary data analyses were conducted to examine differences by race and ethnicity in opioid prescribing, measured as morphine milligram equivalents (MME), among patients who underwent select, but commonly performed, surgical procedures. Linear regression models included adjustment for factors that would likely influence prescribing decisions and race and ethnicity-specific propensity weights. Opioid prescribing, overall and by race and ethnicity, was also compared to postoperative opioid guidelines. Data extraction Data were extracted from the EHR on adult patients undergoing a procedure during the study period, discharged to home with an opioid prescription. Principal findings Among 61,564 patients, on adjusted regression analysis, non-Hispanic Black (NHB) patients received prescriptions with higher mean MME than non-Hispanic white (NHW) patients (+ 6.4% [95% confidence interval: 4.4%, 8.3%]), whereas Hispanic and non-Hispanic Asian patients received lower mean MME (-4.2% [-5.1%, -3.2%] and − 3.6% [-4.8%, -2.3%], respectively). Nevertheless, 72.8% of all patients received prescriptions above guidelines, ranging from 71.0 to 80.3% by race and ethnicity. Disparities in prescribing were eliminated among Hispanic and NHB patients versus NHW patients when prescriptions were written within guideline recommendations. Conclusions Racial and ethnic disparities in opioid prescribing exist in the postoperative setting, yet all groups received prescriptions above guideline recommendations. Policies encouraging guideline-based prescribing may reduce disparities and overall excess prescribing

Persistent Cardiometabolic Health Gaps: Can Therapeutic Care Gaps Be Precisely Identified from Electronic Health Records

The objective of this study was to determine the strengths and limitations of using structured electronic health records (EHR) to identify and manage cardiometabolic (CM) health gaps. We used medication adherence measures derived from dispense data to attribute related therapeutic care gaps (i.e., no action to close health gaps) to patient- (i.e., failure to retrieve medication or low adherence) or clinician-related (i.e., failure to initiate/titrate medication) behavior. We illustrated how such data can be used to manage health and care gaps for blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), and HbA1c for 240,582 Sutter Health primary care patients. Prevalence of health gaps was 44% for patients with hypertension, 33% with hyperlipidemia, and 57% with diabetes. Failure to retrieve medication was common; this patient-related care gap was highly associated with health gaps (odds ratios (OR): 1.23–1.76). Clinician-related therapeutic care gaps were common (16% for hypertension, and 40% and 27% for hyperlipidemia and diabetes, respectively), and strongly related to health gaps for hyperlipidemia (OR = 5.8; 95% CI: 5.6–6.0) and diabetes (OR = 5.7; 95% CI: 5.4–6.0). Additionally, a substantial minority of care gaps (9% to 21%) were uncertain, meaning we lacked evidence to attribute the gap to either patients or clinicians, hindering efforts to close the gaps