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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.

Background: Prior studies on disparities in postoperative opioid prescribing have focused primarily on in-hospital opioid administration or long-term opioid use, with fewer studies examining discharge opioid prescribing for adults, and no studies looking across different surgical subspecialties or service lines. We sought to examine racial and ethnic disparities in postoperative opioid prescribing to test the hypothesis that racial and ethnic minority groups receive opioid prescriptions at postoperative discharge with lower morphine milligram equivalents (MME) than non-Hispanic whites (NHW). Methods: We utilized 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). The sample included adult patients undergoing a procedure during the study period, discharged to home with an opioid prescription. Cross-sectional, secondary data analyses were conducted to examine differences by race and ethnicity in opioid prescribing, measured as 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. Results: Among 61,564 patients, on adjusted regression analysis, non-Hispanic Black (NHB) patients received prescriptions with higher mean MME than 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 postoperative prescribing can reduce disparities and overall excess prescribing.

Poorly controlled cardiometabolic biometric health gap measures [e.g.,uncontrolled blood pressure (BP), HbA1c, and low-density lipoprotein cholesterol (LDL-C)] are mediated by medication adherence and clinician-level therapeutic inertia (TI). The study of comparing relative contribution of these two factors to disease control is lacking. We conducted a retrospective cohort study using 7 years of longitudinal electronic health records (EHR) from primary care cardiometabolic patients who were 35 years or older. Cox-regression modeling was applied to estimate how baseline proportion of days covered (PDC) and TI were associated with cardiometabolic related health gap closure. 92,766 patients were included in the analysis, among which 89.9%, 85.8%, and 73.3% closed a BP, HbA1c, or LDL-C gap, respectively, with median days to gap closure ranging from 223 to 408 days. Patients who did not retrieve a medication were the least likely to achieve biometric control, particularly for LDL-C (HR = 0.58, 95% CI: 0.55–0.60). TI or uncertainty of TI was associated with a high risk of health gap persistence, particularly for LDL-C (HR ranges 0.46–0.48). Both poor medication adherence and TI are independently associated with persistent health gaps, and TI has a much higher impact on disease control compared to medication adherence, implying disease management strategies should prioritize reducing TI.

Background Electronic health record (EHR) systems are becoming increasingly complicated, leading to concerns about rising physician burnout, particularly for primary care physicians (PCPs). Managing the most common cardiometabolic chronic conditions by PCPs during a limited clinical time with a patient is challenging. Objective This study aimed to evaluate a Cardiometabolic Sutter Health Advanced Reengineered Encounter (CM-SHARE), a web-based application to visualize key EHR data, on the EHR use efficiency. Methods We developed algorithms to identify key clinic workflow measures (eg, total encounter time, total physician time in the examination room, and physician EHR time in the examination room) using audit data, and we validated and calibrated the measures with time-motion data. We used a pre-post parallel design to identify propensity score–matched CM-SHARE users (cases), nonusers (controls), and nested-matched patients. Cardiometabolic encounters from matched case and control patients were used for the workflow evaluation. Outcome measures were compared between the cases and controls. We applied this approach separately to both the CM-SHARE pilot and spread phases. Results Time-motion observation was conducted on 101 primary care encounters for 9 PCPs in 3 clinics. There was little difference ( Conclusions Combining EHR audit log files and clinical information, our approach offers an innovative and scalable method and new measures that can be used to evaluate clinical EHR efficiency of digital tools used in clinical settings.

Many studies have assessed the factors associated with overall video visit use during the COVID-19 pandemic, but little is known about who is most likely to continue to use video visits and why. The authors combined a survey with electronic health record data to identify factors affecting the continued use of video visit. In August 2020, a stratified random sample of 20,000 active patients from a large health care system were invited to complete an email survey on health care seeking preferences during the COVID. Weighted logistic regression models were applied, adjusting for sampling frame and response bias, to identify factors associated with video visit experience, and separately for preference of continued use of video visits. Actual video visit utilization was also estimated within 12 months after the survey. Three thousand three hundred fifty-one (17.2%) patients completed the survey. Of these, 1208 (36%) reported having at least 1 video visit in the past, lowest for African American (33%) and highest for Hispanic (41%). Of these, 38% would prefer a video visit in the future. The strongest predictors of future video visit use were comfort using video interactions (odds ratio [OR] = 5.30, 95% confidence interval [95% CI]: 3.57-7.85) and satisfaction with the overall quality (OR = 3.94, 95% CI: 2.66-5.86). Interestingly, despite a significantly higher satisfaction for Hispanic (40%-55%) and African American (40%-50%) compared with Asian (29%-39%), Hispanic (OR = 0.46, 95% CI: 0.12-0.88) and African American (OR = 0.54, 95% CI: 0.16-0.90) were less likely to prefer a future video visit. Disparity exists in the use of video visit. The association between patient satisfaction and continued video visit varies by race/ethnicity, which may change the future long-term video visit use among race/ethnicity groups.

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.

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.

Objective Medications frequently require prior authorization from payers before filling is authorized. Obtaining prior authorization can create delays in filling prescriptions and ultimately reduce patient adherence to medication. Electronic prior authorization (ePA), embedded in the electronic health record (EHR), could remove some barriers but has not been rigorously evaluated. We sought to evaluate the impact of implementing an ePA system on prescription filling. Materials and Methods ePA was implemented in 2 phases in September and November 2018 in a large US healthcare system. This staggered implementation enabled the later-implementing sites to be controls. Using EHR data from all prescriptions written and linked information on whether prescriptions were filled at pharmacies, we 1:1 matched ePA prescriptions with non-ePA prescriptions for the same insurance plan, medication, and site, before and after ePA implementation, to evaluate primary adherence, or the proportion of prescriptions filled within 30 days, using generalized estimating equations. We also conducted concurrent analyses across sites during the peri-implementation period (Sept–Oct 2018). Results Of 74 546 eligible ePA prescriptions, 38 851 were matched with preimplementation controls. In total, 24 930 (64.2%) ePA prescriptions were filled compared with 26 731 (68.8%) control prescriptions (Adjusted Relative Risk [aRR]: 0.92, 95%CI: 0.91–0.93). Concurrent analyses revealed similar findings (64.7% for ePA vs 62.3% control prescriptions, aRR: 1.03, 95%CI: 0.98–1.09). Discussion Challenges with implementation, such as misfiring and insurance fragmentation, could have undermined its effectiveness, providing implications for other health informatics interventions deployed in outpatient care. Conclusion Despite increasing interest in implementing ePA to improve prescription filling, adoption did not change medication adherence.

Key Points Question Was the implementation of mandatory consultation with a prescription drug monitoring program (PDMP) that integrates electronic health record (EHR) alerts associated with changes in the postoperative quantity of opioid medications prescribed at discharge after general, obstetric and gynecologic, and orthopedic surgery? Findings In this cross-sectional study of 93 760 adult patients within a large health care system who received a postoperative prescription for an opioid medication, implementation of mandatory PDMP consultation with EHR-based alerts was associated with significant decreases in postoperative opioid prescription quantities during the quarter in which the mandate was implemented after accounting for preimplementation prescribing patterns. Meaning This study found that implementation of legislation mandating PDMP consultation and the use of EHR alerts may be associated with changes in postoperative opioid prescribing behavior and decreases in the excess number of opioid tablets in circulation., Importance Legislation mandating consultation with a prescription drug monitoring program (PDMP) was implemented in California on October 2, 2018. This mandate requires PDMP consultation before prescribing a controlled substance and integrates electronic health record (EHR)–based alerts; prescribers are exempt from the mandate if they prescribe no more than a 5-day postoperative opioid supply. Although previous studies have examined the consequences of mandated PDMP consultation, few have specifically analyzed changes in postoperative opioid prescribing after mandate implementation. Objective To examine whether the implementation of mandatory PDMP consultation with concurrent EHR-based alerts was associated with changes in postoperative opioid quantities prescribed at discharge. Design, Setting, and Participants This cross-sectional study performed an interrupted time series analysis of opioid prescribing patterns within a large health care system (Sutter Health) in northern California between January 1, 2015, and February 1, 2020. A total of 93 760 adult patients who received an opioid prescription at discharge after undergoing general, obstetric and gynecologic (obstetric/gynecologic), or orthopedic surgery were included. Exposures Mandatory PDMP consultation before opioid prescribing, with concurrent integration of an EHR alert. Prescribers are exempt from this mandate if prescribing no more than a 5-day opioid supply postoperatively. Main Outcomes and Measures The primary outcome was the total quantity of opioid medications (morphine milligram equivalents [MMEs] and number of opioid tablets) prescribed at discharge before and after implementation of the PDMP mandate, with separate analyses by surgical specialty (general, obstetric/gynecologic, and orthopedic) and most common surgical procedure within each specialty (laparoscopic cholecystectomy, cesarean delivery, and knee arthroscopy). The secondary outcome was the proportion of prescriptions with a duration of longer than 5 days. Results Of 93 760 patients (mean [SD] age, 46.7 [17.6] years; 67.9% female) who received an opioid prescription at discharge, 65 911 received prescriptions before PDMP mandate implementation, and 27 849 received prescriptions after implementation. Most patients received general or obstetric/gynecologic surgery (48.6% and 30.1%, respectively), did not have diabetes (90.3%), and had never smoked (66.0%). Before the PDMP mandate was implemented, a decreasing pattern in opioid prescribing quantities was already occurring. During the quarter of implementation, total MMEs prescribed at discharge further decreased for all 3 surgical specialties (eg, medians for general surgery: β = −10.00 [95% CI, −19.52 to −0.48]; obstetric/gynecologic surgery: β = −18.65 [95% CI, −22.00 to −15.30]; and orthopedic surgery: β = −30.59 [95% CI, −40.19 to −21.00]) after adjusting for the preimplementation prescribing pattern. The total number of tablets prescribed also decreased across specialties (eg, medians for general surgery: β = −3.02 [95% CI, −3.47 to −2.57]; obstetric/gynecologic surgery: β = −4.86 [95% CI, −5.38 to −4.34]; and orthopedic surgery: β = −4.06 [95% CI, −5.07 to −3.04]) compared with the quarters before implementation. These reductions were not consistent across the most common surgical procedures. For cesarean delivery, the median number of tablets prescribed decreased during the quarter of implementation (β = −10.00; 95% CI, −10.10 to −9.90), but median MMEs did not (β = 0; 95% CI, −9.97 to 9.97), whereas decreases were observed in both median MMEs and number of tablets prescribed (MMEs: β = −33.33 [95% CI, −38.48 to −28.19]; tablets: β = −10.00 [95% CI, −11.17 to −8.82]) for laparoscopic cholecystectomy. For knee arthroscopy, no decreases were found in either median MMEs or number of tablets prescribed (MMEs: β = 10.00 [95% CI, −22.33 to 42.33; tablets: β = 0.83; 95% CI, −3.39 to 5.05). The proportion of prescriptions written for longer than 5 days also decreased significantly during the quarter of implementation across all 3 surgical specialties. Conclusions and Relevance In this cross-sectional study, the implementation of mandatory PDMP consultation with a concurrent EHR-based alert was associated with an immediate decrease in opioid prescribing across the 3 surgical specialties. These findings might be explained by prescribers’ attempts to meet the mandate exemption and bypass PDMP consultation rather than the PDMP consultation itself. Although policies coupled with EHR alerts may be associated with changes in postoperative opioid prescribing behavior, they need to be well designed to optimize evidence-based opioid prescribing., This cross-sectional study examines whether the implementation of mandated consultation with a prescription drug monitoring program was associated with changes in the quantity of postoperative opioid medications prescribed at discharge among adult patients in a health care system in California.

Graphical abstract, Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response.