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.

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).

Development of High-Risk Geriatric Polypharmacy Electronic Clinical Quality Measures and a Pilot Test of EHR Nudges Based on These Measures.

BACKGROUND: Inappropriate polypharmacy, prevalent among older patients, is associated with substantial harms. OBJECTIVE: To develop measures of high-risk polypharmacy and pilot test novel electronic health record (EHR)-based nudges grounded in behavioral science to promote deprescribing. DESIGN: We developed and validated seven measures, then conducted a three-arm pilot from February to May 2019. PARTICIPANTS: Validation used data from 78,880 patients from a single large health system. Six physicians were pre-pilot test environment users. Sixty-nine physicians participated in the pilot. MAIN MEASURES: Rate of high-risk polypharmacy among patients aged 65 years or older. High-risk polypharmacy was defined as being prescribed ≥5 medications and satisfying ≥1 of the following high-risk criteria: drugs that increase fall risk among patients with fall history; drug-drug interactions that increase fall risk; thiazolidinedione, NSAID, or non-dihydropyridine calcium channel blocker in heart failure; and glyburide, glimepiride, or NSAID in chronic kidney disease. INTERVENTIONS: Physicians received EHR alerts when renewing or prescribing certain high-risk medications when criteria were met. One practice received a "commitment nudge" that offered a chance to commit to addressing high-risk polypharmacy at the next visit. One practice received a "justification nudge" that asked for a reason when high-risk polypharmacy was present. One practice received both. KEY RESULTS: Among 55,107 patients 65 and older prescribed 5 or more medications, 6256 (7.9%) had one or more high-risk criteria. During the pilot, the mean (SD) number of nudges per physician per week was 1.7 (0.4) for commitment, 0.8 (0.5) for justification, and 1.9 (0.5) for both interventions. Physicians requested to be reminded to address high-risk polypharmacy for 236/833 (28.3%) of the commitment nudges and acknowledged 441 of 460 (95.9%) of justification nudges, providing a text response for 187 (40.7%). CONCLUSIONS: EHR-based measures and nudges addressing high-risk polypharmacy were feasible to develop and implement, and warrant further testing.

Effect of Peer Benchmarking on Specialist Electronic Consult Performance in a Los Angeles Safety-Net: a Cluster Randomized Trial.

BACKGROUND: Since the advent of COVID-19, accelerated adoption of systems that reduce face-to-face encounters has outpaced training and best practices. Electronic consultations (eConsults), structured communications between PCPs and specialists regarding a case, have been effective in reducing face-to-face specialist encounters. As the health system rapidly adapts to multiple new practices and communication tools, new mechanisms to measure and improve performance in this context are needed. OBJECTIVE: To test whether feedback comparing physicians to top performing peers using co-specialists' ratings improves performance. DESIGN: Cluster-randomized controlled trial PARTICIPANTS: Eighty facility-specialty clusters and 214 clinicians INTERVENTION: Providers in the feedback arms were sent messages that announced their membership in an elite group of "Top Performers" or provided actionable recommendations with feedback for providers that were "Not Top Performers." MAIN MEASURES: The primary outcomes were changes in peer ratings in the following performance dimensions after feedback was received: (1) elicitation of information from primary care practitioners; (2) adherence to institutional clinical guidelines; (3) agreement with peer's medical decision-making; (4) educational value; (5) relationship building. KEY RESULTS: Specialists showed significant improvements on 3 of the 5 consultation performance dimensions: medical decision-making (odds ratio 1.52, 95% confidence interval 1.08-2.14, p<.05), educational value (1.86, 1.17-2.96) and relationship building (1.63, 1.13-2.35) (both p<.01). CONCLUSIONS: The pandemic has shed light on clinicians' commitment to professionalism and service as we rapidly adapt to changing paradigms. Interventions that appeal to professional norms can help improve the efficacy of new systems of practice. We show that specialists' performance can be measured and improved with feedback using aspirational norms. TRIAL REGISTRATION: clinicaltrials.gov NCT03784950.

Clinician-Level Variation in Three Measures Representing Overuse Based on the American Geriatrics Society Choosing Wisely Statement.

IMPORTANCE: The extent of clinician-level variation in the overuse of testing or treatment in older adults is not well understood. OBJECTIVE: To examine clinician-level variation for three new measures of potentially inappropriate use of medical services in older adults. DESIGN: Retrospective analysis of overall means and clinician-level variation in performance on three new measures. SUBJECTS: Adults aged 65 years and older who had office visits with outpatient primary or immediate care clinicians within a single academic medical center health system between July 1, 2016, and June 30, 2017. MEASURES: Two electronic clinical quality measures representing potentially inappropriate use of medical services in older adults: prostate-specific antigen testing against guidelines (PSA) in men aged 76 and older; urinalysis or urine culture for non-specific reasons in women aged 65 and older; and one intermediate outcome measure: hemoglobin A1c less than 7.0 in adults aged 75 and older with diabetes mellitus treated with insulin or oral hypoglycemic medication. RESULTS: Sixty-nine clinicians and 2009 patients contributed observations to the PSA measure, 144 clinicians and 5933 patients contributed to the urinalysis/urine culture measure, and 42 clinicians and 665 patients contributed to the diabetes measure. Meaningful clinician-level performance variation was greatest for the PSA measure (intraclass correlation coefficient [ICC] = 0.27), followed by the urinalysis/urine culture measure (ICC = 0.18), and the diabetes measure (ICC = 0.024). The range of possible overuse across clinician quartiles was 8-54% for the PSA measure, 3-35% for the urinalysis/urine culture measure, and 13-49% for the diabetes measure. The odds ratios of overuse in the highest quartile compared with the lowest for the PSA, urinalysis/urine culture, and diabetes measures were 99.3 (95% CI 43 to 228), 15.7 (10 to 24), and 6.0 (3.3 to 11), respectively. CONCLUSIONS: Within the same health system, rates of potential overuse in elderly patients varied greatly across clinicians, particularly for the process measures examined.

Efficient determination of equivalence for encrypted data.

Secure computation of equivalence has fundamental application in many different areas, including health-care. We study this problem in the context of matching an individual's identity to link medical records across systems under the socialist millionaires' problem: Two millionaires wish to determine if their fortunes are equal without disclosing their net worth (Boudot, et al. 2001). In Theorem 2, we show that when a "greater than" algorithm is carried out on a totally ordered set it is easy to achieve secure matching without additional rounds of communication. We present this efficient solution to assess equivalence using a set intersection algorithm designed for "greater than" computation and demonstrate its effectiveness on equivalence of arbitrary data values, as well as demonstrate how it meets regulatory criteria for risk of disclosure.

Behavioral Economics Interventions to Improve Outpatient Antibiotic Prescribing for Acute Respiratory Infections: a Cost-Effectiveness Analysis.

BACKGROUND: Behavioral economics interventions have been shown to effectively reduce the rates of inappropriate antibiotic prescriptions for acute respiratory infections (ARIs). OBJECTIVE: To determine the cost-effectiveness of three behavioral economic interventions designed to reduce inappropriate antibiotic prescriptions for ARIs. DESIGN: Thirty-year Markov model from the US societal perspective with inputs derived from the literature and CDC surveillance data. SUBJECTS: Forty-five-year-old adults with signs and symptoms of ARI presenting to a healthcare provider. INTERVENTIONS: (1) Provider education on guidelines for the appropriate treatment of ARIs; (2) Suggested Alternatives, which utilizes computerized clinical decision support to suggest non-antibiotic treatment choices in lieu of antibiotics; (3) Accountable Justification, which mandates free-text justification into the patient's electronic health record when antibiotics are prescribed; and (4) Peer Comparison, which sends a periodic email to prescribers about his/her rate of inappropriate antibiotic prescribing relative to clinician colleagues. MAIN MEASURES: Discounted costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios. KEY RESULTS: Each intervention has lower costs but higher QALYs compared to provider education. Total costs for each intervention were $178.21, $173.22, $172.82, and $172.52, and total QALYs were 14.68, 14.73, 14.74, and 14.74 for the control, Suggested Alternatives, Accountable Justification, and Peer Comparison groups, respectively. Results were most sensitive to the quality-of-life of the uninfected state, and the likelihood and costs for antibiotic-associated adverse events. CONCLUSIONS: Behavioral economics interventions can be cost-effective strategies for reducing inappropriate antibiotic prescriptions by reducing healthcare resource utilization.

Characterizing treatment pathways at scale using the OHDSI network.

Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.

Patient Commitment to Health (PACT-Health) in the Heart Failure Population: A Focus Group Study of an Active Communication Framework for Patient-Centered Health Behavior Change.

BACKGROUND: Over 6 million Americans have heart failure, and 1 in 8 deaths included heart failure as a contributing cause in 2016. Lifestyle changes and adherence to diet and exercise regimens are important in limiting disease progression. Health coaching and public commitment are two interactive communication strategies that may improve self-management of heart failure. OBJECTIVE: This study aimed to conduct patient focus groups to gain insight into how best to implement health coaching and public commitment strategies within the heart failure population. METHODS: Focus groups were conducted in two locations. We studied 2 patients in Oakland, California, and 5 patients in Los Angeles, California. Patients were referred by local cardiologists and had to have a diagnosis of chronic heart failure. We used a semistructured interview tool to explore several patient-centered themes including medication adherence, exercise habits, dietary habits, goals, accountability, and rewards. We coded focus group data using the a priori coding criteria for these domains. RESULTS: Medication adherence barriers included regimen complexity, forgetfulness, and difficulty coping with side effects. Participants reported that they receive little instruction from care providers on appropriate exercise and dietary habits. They also reported personal and social obstacles to achieving these objectives. Participants were in favor of structured goal setting, use of online social networks, and financial rewards as a means of promoting health lifestyles. Peers were viewed as better motivating agents than family members. CONCLUSIONS: An active communication framework involving dissemination of diet- and exercise-related health information, structured goal setting, peer accountability, and financial rewards appears promising in the management of heart failure.

A Novel Stakeholder Engagement Approach for Patient-centered Outcomes Research.

INTRODUCTION/OBJECTIVES: The engagement of patients and other stakeholders is a critical element in the design of patient-centered outcomes research studies. However, methodology for scalable engagement in research management particularly activities such as operationalization of principles and setting of priorities is not well-developed. The objective of this study is to describe a novel approach for scalable stakeholder engagement in research aligned with the Patient-Centered Outcomes Research Institute (PCORI) engagement principles, which was evaluated in a national clinical data research network. MATERIALS AND METHODS: Patient, patient advocate, clinician, and researcher stakeholders were recruited from clinical sites, as well as social media sites related to the 3 conditions of focus, heart failure, obesity, and Kawasaki disease. The engagement strategy was designed, implemented, and mapped to the PCORI engagement principles. Evaluation included internal assessment and quantitative measures of online engagement. RESULTS: We operationalized the PCORI principles with 12 stakeholder engagement strategies and convened stakeholder advisory boards and online research prioritization panels to determine research priorities in a rigorous, deliberative process. A total of 46 advisors (20 patients) and 339 panelists (159 patients) actively participated. There were not significant differences between patients and clinicians in level of online engagement. Nonetheless, while patients reported a slightly greater challenge with following online discussion, they overall had a more favorable opinion about use of the online format. DISCUSSION/CONCLUSION: An efficient way to engage large numbers of representative stakeholders in research is a necessary first step to assure the public of trustworthy use of data networks for health research. This paper describes a comprehensive approach to engagement in patient-centered outcomes research management that informs ongoing development of rigorous methodologies in this area.

Perfectly Secure and Efficient Two-Party Electronic-Health-Record Linkage.

Patient health data are often found spread across various sources. However, precision medicine and personalized care requires access to the complete medical records. The first step towards this is to enable the linkage of health records spread across different sites. Existing record linkage solutions assume that data is centralized with no privacy/security concerns restricting sharing. However, that is often untrue. Therefore, we design and implement a portable method for privacy-preserving record linkage based on garbled circuits to accurately and securely match records. We also develop a novel approximate matching mechanism that significantly improves efficiency.

Behavioral interventions to reduce inappropriate antibiotic prescribing: a randomized pilot trial.

BACKGROUND: Clinicians frequently prescribe antibiotics inappropriately for acute respiratory infections (ARIs). Our objective was to test information technology-enabled behavioral interventions to reduce inappropriate antibiotic prescribing for ARIs in a randomized controlled pilot test trial. METHODS: Primary care clinicians were randomized in a 2 × 2 × 2 factorial experiment with 3 interventions: 1) Accountable Justifications; 2) Suggested Alternatives; and 3) Peer Comparison. Beforehand, participants completed an educational module. Measures included: rates of antibiotic prescribing for: non-antibiotic-appropriate ARI diagnoses, acute sinusitis/pharyngitis, all other diagnoses/symptoms of respiratory infection, and all three ARI categories combined. RESULTS: We examined 3,276 visits in the pre-intervention year and 3,099 in the intervention year. The antibiotic prescribing rate fell for non-antibiotic-appropriate ARIs (24.7 % in the pre-intervention year to 5.2 % in the intervention year); sinusitis/pharyngitis (50.3 to 44.7 %); all other diagnoses/symptoms of respiratory infection (40.2 to 25.3 %); and all categories combined (38.7 to 24.2 %; all p < 0.001). There were no significant relationships between any intervention and antibiotic prescribing for non-antibiotic-appropriate ARI diagnoses or sinusitis/pharyngitis. Suggested Alternatives was associated with reduced antibiotic prescribing for other diagnoses or symptoms of respiratory infection (odds ratio [OR], 0.62; 95 % confidence interval [CI], 0.44-0.89) and for all ARI categories combined (OR, 0.72; 95 % CI, 0.54-0.96). Peer Comparison was associated with reduced prescribing for all ARI categories combined (OR, 0.73; 95 % CI, 0.53-0.995). CONCLUSIONS: We observed large reductions in antibiotic prescribing regardless of whether or not study participants received an intervention, suggesting an overriding Hawthorne effect or possibly clinician-to-clinician contamination. Low baseline inappropriate prescribing may have led to floor effects. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01454960 .

Cognitive reflection and antibiotic prescribing for acute respiratory infections.

BACKGROUND: Variation in clinical decision-making could be explained by clinicians' tendency to make 'snap-decisions' versus making more reflective decisions. One common clinical decision with unexplained variation is the prescription of antibiotics for acute respiratory infections (ARIs). OBJECTIVE: We hypothesized that clinicians who tended toward greater cognitive reflection would be less likely to prescribe antibiotics for ARIs. METHODS: The Cognitive Reflection Test (CRT) is a psychological test with three questions with intuitive but incorrect answers that respondents reach if they do not consider the question carefully. The CRT is scored from 0 to 3, representing the number of correct answers. A higher score indicates greater cognitive reflection. We administered the CRT to 187 clinicians in 50 primary care practices. From billing and electronic health record data, we calculated clinician-level antibiotic prescribing rates for ARIs in 3 categories: all ARIs, antibiotic-appropriate ARIs and non-antibiotic-appropriate ARIs. RESULTS: A total of 57 clinicians (31%) scored 0 points on the CRT; 38 (20%) scored 1; 51 (27%) scored 2; and 41 (22%) scored 3. We found a roughly U-shaped association between cognitive reflection and antibiotic prescribing. The antibiotic prescribing rate for CRT scores of 0, 1, 2 and 3 for all ARIs (n = 37080 visits) was 32%, 26%, 25% and 30% (P = 0.10); for antibiotic-appropriate ARIs (n = 11220 visits) was 60%, 55%, 54% and 58% (P = 0.63); and for non-antibiotic-appropriate ARIs (n = 25860 visits) was 21%, 17%, 13% and 18%, respectively (P = 0.03). CONCLUSIONS: In contrast to our hypothesis, there appears to be a 'sweet-spot' of cognitive reflection for antibiotic prescribing for non-antibiotic-appropriate ARIs. Differences in clinicians' cognitive reflection may be associated with other variations in care.

Symptom trends in the last year of life from 1998 to 2010: a cohort study.

BACKGROUND: Calls for improvement in end-of-life care have focused attention on the management of pain and other troubling symptoms at the end of life. OBJECTIVE: To describe changes in pain intensity and symptom prevalence during the last year of life from 1998 to 2010. DESIGN: Observational study. SETTING: The HRS (Health and Retirement Study), a nationally representative longitudinal survey of community-dwelling U.S. residents aged 51 years or older. PARTICIPANTS: 7204 HRS participants who died while enrolled in the study and their family respondents. MEASUREMENTS: Proxy-reported pain during the last year of life and other symptoms for at least 1 month during the last year of life. Trends in pain intensity and symptom prevalence were analyzed for all decedents and within the categories of sudden death, cancer, congestive heart failure or chronic lung disease, and frailty. RESULTS: Between 1998 and 2010, proxy reports of the prevalence of any pain increased for all decedents from 54.3% (95% CI, 51.6% to 57.1%) to 60.8% (CI, 58.2% to 63.4%), an increase of 11.9% (CI, 3.1% to 21.4%). Reported prevalences of depression and periodic confusion also increased for all decedents by 26.6% (CI, 14.5% to 40.1%) and 31.3% (CI, 18.6% to 45.1%), respectively. Individual symptoms increased in prevalence among specific decedent categories, except in cancer, which showed no significant changes. The prevalence of moderate or severe pain did not change among all decedents or in any specific decedent category. LIMITATION: Use of proxy reports and limited information about some patient and surrogate variables. CONCLUSION: Despite national efforts to improve end-of-life care, proxy reports of pain and other alarming symptoms in the last year of life increased from 1998 to 2010. PRIMARY FUNDING SOURCE: National Institute of Nursing Research.

Effect of Behavioral Interventions on Inappropriate Antibiotic Prescribing Among Primary Care Practices: A Randomized Clinical Trial.

IMPORTANCE: Interventions based on behavioral science might reduce inappropriate antibiotic prescribing. OBJECTIVE: To assess effects of behavioral interventions and rates of inappropriate (not guideline-concordant) antibiotic prescribing during ambulatory visits for acute respiratory tract infections. DESIGN, SETTING, AND PARTICIPANTS: Cluster randomized clinical trial conducted among 47 primary care practices in Boston and Los Angeles. Participants were 248 enrolled clinicians randomized to receive 0, 1, 2, or 3 interventions for 18 months. All clinicians received education on antibiotic prescribing guidelines on enrollment. Interventions began between November 1, 2011, and October 1, 2012. Follow-up for the latest-starting sites ended on April 1, 2014. Adult patients with comorbidities and concomitant infections were excluded. INTERVENTIONS: Three behavioral interventions, implemented alone or in combination: suggested alternatives presented electronic order sets suggesting nonantibiotic treatments; accountable justification prompted clinicians to enter free-text justifications for prescribing antibiotics into patients' electronic health records; peer comparison sent emails to clinicians that compared their antibiotic prescribing rates with those of "top performers" (those with the lowest inappropriate prescribing rates). MAIN OUTCOMES AND MEASURES: Antibiotic prescribing rates for visits with antibiotic-inappropriate diagnoses (nonspecific upper respiratory tract infections, acute bronchitis, and influenza) from 18 months preintervention to 18 months afterward, adjusting each intervention's effects for co-occurring interventions and preintervention trends, with random effects for practices and clinicians. RESULTS: There were 14,753 visits (mean patient age, 47 years; 69% women) for antibiotic-inappropriate acute respiratory tract infections during the baseline period and 16,959 visits (mean patient age, 48 years; 67% women) during the intervention period. Mean antibiotic prescribing rates decreased from 24.1% at intervention start to 13.1% at intervention month 18 (absolute difference, -11.0%) for control practices; from 22.1% to 6.1% (absolute difference, -16.0%) for suggested alternatives (difference in differences, -5.0% [95% CI, -7.8% to 0.1%]; P = .66 for differences in trajectories); from 23.2% to 5.2% (absolute difference, -18.1%) for accountable justification (difference in differences, -7.0% [95% CI, -9.1% to -2.9%]; P < .001); and from 19.9% to 3.7% (absolute difference, -16.3%) for peer comparison (difference in differences, -5.2% [95% CI, -6.9% to -1.6%]; P < .001). There were no statistically significant interactions (neither synergy nor interference) between interventions. CONCLUSIONS AND RELEVANCE: Among primary care practices, the use of accountable justification and peer comparison as behavioral interventions resulted in lower rates of inappropriate antibiotic prescribing for acute respiratory tract infections. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01454947.

Nudging physician prescription decisions by partitioning the order set: results of a vignette-based study.

BACKGROUND: Healthcare professionals are rapidly adopting electronic health records (EHRs). Within EHRs, seemingly innocuous menu design configurations can influence provider decisions for better or worse. OBJECTIVE: The purpose of this study was to examine whether the grouping of menu items systematically affects prescribing practices among primary care providers. PARTICIPANTS: We surveyed 166 primary care providers in a research network of practices in the greater Chicago area, of whom 84 responded (51% response rate). Respondents and non-respondents were similar on all observable dimensions except that respondents were more likely to work in an academic setting. DESIGN: The questionnaire consisted of seven clinical vignettes. Each vignette described typical signs and symptoms for acute respiratory infections, and providers chose treatments from a menu of options. For each vignette, providers were randomly assigned to one of two menu partitions. For antibiotic-inappropriate vignettes, the treatment menu either listed over-the-counter (OTC) medications individually while grouping prescriptions together, or displayed the reverse partition. For antibiotic-appropriate vignettes, the treatment menu either listed narrow-spectrum antibiotics individually while grouping broad-spectrum antibiotics, or displayed the reverse partition. MAIN MEASURES: The main outcome was provider treatment choice. For antibiotic-inappropriate vignettes, we categorized responses as prescription drugs or OTC-only options. For antibiotic-appropriate vignettes, we categorized responses as broad- or narrow-spectrum antibiotics. KEY RESULTS: Across vignettes, there was an 11.5 percentage point reduction in choosing aggressive treatment options (e.g., broad-spectrum antibiotics) when aggressive options were grouped compared to when those same options were listed individually (95% CI: 2.9 to 20.1%; p = .008). CONCLUSIONS: Provider treatment choice appears to be influenced by the grouping of menu options, suggesting that the layout of EHR order sets is not an arbitrary exercise. The careful crafting of EHR order sets can serve as an important opportunity to improve patient care without constraining physicians' ability to prescribe what they believe is best for their patients.

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.

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.

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.

Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems.

INTRODUCTION: The need for a common format for electronic exchange of clinical data prompted federal endorsement of applicable standards. However, despite obvious similarities, a consensus standard has not yet been selected in the comparative effectiveness research (CER) community. METHODS: Using qualitative metrics for data retrieval and information loss across a variety of CER topic areas, we compare several existing models from a representative sample of organizations associated with clinical research: the Observational Medical Outcomes Partnership (OMOP), Biomedical Research Integrated Domain Group, the Clinical Data Interchange Standards Consortium, and the US Food and Drug Administration. RESULTS: While the models examined captured a majority of the data elements that are useful for CER studies, data elements related to insurance benefit design and plans were most detailed in OMOP's CDM version 4.0. Standardized vocabularies that facilitate semantic interoperability were included in the OMOP and US Food and Drug Administration Mini-Sentinel data models, but are left to the discretion of the end-user in Biomedical Research Integrated Domain Group and Analysis Data Model, limiting reuse opportunities. Among the challenges we encountered was the need to model data specific to a local setting. This was handled by extending the standard data models. DISCUSSION: We found that the Common Data Model from the OMOP met the broadest complement of CER objectives. Minimal information loss occurred in mapping data from institution-specific data warehouses onto the data models from the standards we assessed. However, to support certain scenarios, we found a need to enhance existing data dictionaries with local, institution-specific information.