Association of Self-reported Primary Care Physician Tolerance for Uncertainty With Variations in Resource Use and Patient Experience.

Importance: Inappropriate variations in clinical practice are a known cause of poor quality and safety, with variations often associated with nonclinical factors, such as individual differences in cognitive processing. The differential response of physicians to uncertainty may explain some of the variations in resource use and patient experience. Objective: To examine the association of physician tolerance for uncertainty with variations in resource use and patient experience. Design, Setting, and Participants: This survey study linked physician survey data (May to June 2019), patient experience survey data (January 2016 to December 2019), and billing data (January 2019 to December 2019) among primary care physicians (PCPs) at Massachusetts General Hospital with at least 10 visits in 2019. The statistical analysis was performed in 2021. Main Outcomes and Measures: The analysis examined associations of PCP tolerance for uncertainty with the tendency to order diagnostic tests, the frequency of outpatient visits, hospital admissions, emergency department visits, and patient experience data (focused on physician communication and overall rating). A 2-stage hierarchical framework was used to account for clustering of patients under PCPs. Binary outcomes were modeled using a hierarchical logistic model, and count outcomes were modeled using hierarchical Poisson or negative binomial models. The analysis was adjusted for patient demographic variables (age, sex, and race and ethnicity), socioeconomic factors (payer and neighborhood income), and clinical comorbidities. Results: Of 217 included physicians, 137 (63.1%) were women, and 174 (80.2%) were adult PCPs. A total of 62 physicians (28.6%) reported low tolerance, 59 (27.2%) reported medium tolerance, and 96 (44.2%) reported high tolerance for uncertainty. Physicians with a low tolerance for uncertainty were less likely to order complete blood cell counts (odds ratio [OR], 0.66; 95% CI, 0.50-0.88), thyroid tests (OR, 0.67; 95% CI, 0.52-0.88), a basic metabolic profile (OR, 0.78; 95% CI, 0.60-1.00), and liver function tests (OR, 0.72; 95% CI, 0.53-0.99) than physicians with a high tolerance for uncertainty. Physicians who reported higher tolerance for uncertainty were more likely to receive higher patient experience scores for listening to patients carefully (OR, 0.65; 95% CI, 0.50-0.83) and higher overall ratings (OR, 0.80; 95% CI, 0.66-0.98) than physicians with medium tolerance. Conversely, no association was found between physician tolerance for uncertainty and patient outpatient visits, hospital admissions, or emergency department visits. Conclusions and Relevance: In clinical practice, identifying and effectively managing inappropriate variations and improving patient experience have proven to be difficult, despite increased attention to these issues. This study supports the hypothesis that physicians' tolerance for uncertainty is associated with differences in resource use and patient experience. Whether enhancing physicians' tolerance for uncertainty could help reduce unwarranted practice variations, improve quality and patient safety, and improve patient's experience remains to be established.

Comparing neighborhood-based indices of socioeconomic risk factors and potentially preventable emergency department utilization.

BACKGROUND: Neighborhood stress score (NSS) and area deprivation index (ADI) are two neighborhood-based composite measures used to quantify an individual's socioeconomic risk based on home location. In this analysis, we compare the relationships between an individual's socioeconomic risk, based on each of these measures, and potentially preventable acute care utilization. METHODS: Using emergency department (ED) visit data from two academic medical centers in Boston, Massachusetts, we conducted adjusted Poisson regressions of ADI decile and NSS decile with counts of low acuity ED visits, admissions for ambulatory care sensitive conditions (ACSCs), and patients with high frequency ED utilization at the census block group (CBG) level within the greater Boston area. RESULTS: Both NSS and ADI decile were associated with elevated rates of utilization, although the associated incidence rate ratios (IRRs) for NSS were higher than those for ADI across all three measures. NSS decile was associated with IRRs of 1.11 [95% CI: 1.10-1.12], 1.16 [1.14-1.17], and 1.22 [1.19-1.25] for ACSC admissions, low acuity ED visits, and patients with high frequency ED utilization, respectively; compared with 1.04 [1.04-1.05], 1.11 [1.10-1.11], and 1.10 [1.08-1.12] for ADI decile. CONCLUSION: ADI and NSS both represent effective tools to assess the potential impact of geographically-linked socioeconomic drivers of health on potentially preventable acute care utilization. NSS decile was associated with a greater effect size for each measure of utilization suggesting that this may be a stronger predictor, however, additional research is necessary to evaluate these findings in other contexts.

Predictors of MRI Leakage Among Patients Attributed to an Academic Medical Center Commercial Risk-Shared Insurance Contract.

PURPOSE: For health care organizations engaged in risk-shared insurance contracts, leakage of advanced diagnostic imaging to imaging sites not affiliated with the risk-sharing organization may undermine performance on financial and quality metrics. The goal of this study was to identify factors that are predictive of leakage of MRI examinations among patients attributed to an academic health care organization's risk-shared commercial insurance contract. METHODS: Administrative claims data from 2015 through 2016 for patients attributed to a single risk-shared commercial insurance contract at a large academic medical center (AMC) were analyzed. Primary outcome was MRI leakage: an outpatient MRI study performed at a site not affiliated with the AMC's integrated health care system. Ordering provider alignment with the AMC's risk-shared insurance contract was categorized as strong, weak, or none. Multivariate regression analyses were conducted to evaluate the relationship between provider alignment and MRI leakage, while adjusting for selected covariates. RESULTS: Among 8,215 patients meeting inclusion criteria, there were 13,272 MRI encounters. The overall proportion of leaked MRI studies was 12.7%. MRI studies ordered by providers with weak AMC alignment (odds ratio, 3.16; 95% confidence interval, 2.49-4.02) or no AMC alignment (odds ratio, 3.68; 95% confidence interval, 3.12-4.33) were more likely to leak than MRI studies ordered by providers with strong AMC alignment. CONCLUSIONS: An ordering provider with no alignment with an AMC's commercial risk-shared insurance contract was the strongest predictor of MRI leakage. Population health management initiatives aimed at reducing leakage should consider the impact of provider networks and clinical referral patterns that drive imaging utilization.

Reduction in pediatric gastroenterology ED visits can be sustained through physician accountability and financial incentives.

OBJECTIVE: There have been various interventions to reduce ED utilization. Little is known about the sustainability of outcomes of interventions to reduce ED overcrowding. We sought to investigate whether the outcomes from one of successful interventions to reduce ED utilization, specialist physician level reporting were sustained over time and how this practice change was sustained over time. METHOD: This study is a longitudinal analysis of the pre and post intervention ED utilization data collected on ED pediatric patients who were followed by pediatric gastroenterologists in an urban, academic hospital. The primary outcome was the mean rate of ED visits per 1000 office visits from January, 2013 to June, 2017 using a u control chart with three sigma limits. RESULTS: There were continuous leadership's support, physicians' engagement and communications among different members involved in the intervention. The rate of gastrointestinal (GI)-related ED visits after an intervention decreased by 54% from 4.89 to 2.23 during all hours and by 59% from 2.19 to 0.91 during office hours. DISCUSSION: Physician-level reporting reduced ED utilization over a four year period. The outcomes could be sustained over time with sustained leadership and physicians' engagement.

Development of an electronic health record-based chronic kidney disease registry to promote population health management.

BACKGROUND: Electronic health record (EHR) based chronic kidney disease (CKD) registries are central to population health strategies to improve CKD care. In 2015, Partners Healthcare System (PHS), encompassing multiple academic and community hospitals and outpatient care facilities in Massachusetts, developed an EHR-based CKD registry to identify opportunities for quality improvement, defined as improvement on both process measures and outcomes measures associated with clinical care. METHODS: Patients are included in the registry based on the following criteria: 1) two estimated glomerular filtration rate (eGFR) results < 60 ml/min/1.73m2 separated by 90 days, including the most recent eGFR being < 60 ml/min/1.73m2; or 2) the most recent two urine protein values > 300 mg protein/g creatinine on either urine total protein/creatinine ratio or urine albumin/creatinine ratio; or 3) an EHR problem list diagnosis of end stage renal disease (ESRD). The registry categorizes patients by CKD stage and includes rates of annual testing for eGFR and proteinuria, blood pressure control, use of angiotensin converting enzyme inhibitors (ACE-Is) or angiotensin receptor blockers (ARBs), nephrotoxic medication use, hepatitis B virus (HBV) immunization, vascular access placement, transplant status, CKD progression risk; number of outpatient nephrology visits, and hospitalizations. RESULTS: The CKD registry includes 60,503 patients and has revealed several opportunities for care improvement including 1) annual proteinuria testing performed for 17% (stage 3) and 31% (stage 4) of patients; 2) ACE-I/ARB used in 41% (stage 3) and 46% (stage 4) of patients; 3) nephrotoxic medications used among 23% of stage 4 patients; and 4) 89% of stage 4 patients lack HBV immunity. For advanced CKD patients there are opportunities to improve vascular access placement, transplant referrals and outpatient nephrology contact. CONCLUSIONS: A CKD registry can identify modifiable care gaps across the spectrum of CKD care and enable population health strategy implementation. No linkage to Social Security Death Master File or US Renal Data System (USRDS) databases limits our ability to track mortality and progression to ESRD.

Measuring individual physician clinical productivity in an era of consolidated group practices.

BACKGROUND: As physician groups consolidate and value-based payment replaces traditional fee-for-service systems, physician practices have greater need to accurately measure individual physician clinical productivity within team-based systems. We compared methodologies to measure individual physician outpatient clinical productivity after adjustment for shared practice resources. METHODS: For cardiologists at our hospital between January 2015 and June 2016, we assessed productivity by examining completed patient visits per clinical session per week. Using mixed-effects models, we sequentially accounted for shared practice resources and underlying baseline characteristics. We compared mixed-effects and Generalized Estimating Equations (GEE) models using K-fold cross validation, and compared mixed-effect, GEE, and Data Envelopment Analysis (DEA) models based on ranking of physicians by productivity. RESULTS: A mixed-effects model adjusting for shared practice resources reduced variation in productivity among providers by 63% compared to an unadjusted model. Mixed-effects productivity rankings correlated strongly with GEE rankings (Spearman 0.99), but outperformed GEE on K-fold cross validation (root mean squared error 2.66 vs 3.02; mean absolute error 1.89 vs 2.20, respectively). Mixed-effects model rankings had moderate correlation with DEA model rankings (Spearman 0.692), though this improved upon exclusion of outliers (Spearman 0.755). CONCLUSIONS: Mixed-effects modeling accounts for significant variation in productivity secondary to shared practice resources, outperforms GEE in predictive power, and is less vulnerable to outliers than DEA. IMPLICATIONS: With mixed-effects regression analysis using otherwise easily accessible administrative data, practices can evaluate physician clinical productivity more fairly and make more informed management decisions on physician compensation and resource allocation.

Behavioral Health Factors as Predictors of Emergency Department Use in the High-Risk, High-Cost Medicare Population.

OBJECTIVE: This study measured the presence, extent, and type of behavioral health factors in a high-cost Medicare population and their association with the probability and intensity of emergency department (ED) use. METHODS: Retrospective claims analysis and a comprehensive electronic medical record-based review were conducted for patients enrolled in a 65-month prospective care management program at an academic tertiary medical center (N=3,620). A two-part model used multivariable logistic regression to evaluate the effect of behavioral health factors on the probability of ED use, complemented by a Poisson model to measure the number of ED visits. Control variables included demographic characteristics, poststudy survival, and hierarchical condition category risk score. RESULTS: After analyses controlled for comorbidities and other relevant variables, patients with two or more behavioral health diagnosis categories or two or more behavioral health medications were about twice as likely as those without such categories or medications to use the ED. Patients with a diagnosis category of psychosis, neuropsychiatric disorders, sleep disorders, or adjustment disorders were significantly more likely than those without these disorders to use the ED. Most primary ED diagnoses were not of behavioral health conditions. CONCLUSIONS: Behavioral health factors had a substantial and significant effect on the likelihood and number of ED visits in a population of high-cost Medicare patients. Attention to behavioral health factors as independent predictors of ED use may be useful in influencing ED use in high-cost populations.

Variation in the Echocardiographic Surveillance of Primary Mitral Regurgitation.

BACKGROUND: Clinical outcomes after surgical treatment of mitral regurgitation are worse if intervention occurs after deterioration of left ventricular size and function. Transthoracic echocardiographic (TTE) surveillance of patients with mitral regurgitation is indicated to avoid adverse ventricular remodeling. Overly frequent TTEs can impair patient access and reduce value in care delivery. This balance between timely surveillance and overutilization of TTE in valvular disease provides a model to study variation in the delivery of healthcare services. We investigated patient and provider factors contributing to variation in TTE utilization and hypothesized that variation was attributable to provider practice even after adjustment for patient characteristics. METHODS AND RESULTS: We obtained records of all TTEs from 2001 to 2016 completed at a large echocardiography laboratory. The outcome variable was time interval between TTEs. We constructed a mixed-effects linear regression model with the individual physician as the random effect in the model and used intraclass correlation coefficient to assess the proportion of outcome variation because of provider practice. Our study cohort was 55 773 TTEs corresponding to 37 843 intervals ordered by 635 providers. The mean interval between TTEs was 12.4 months, 17.0 months, 18.3 months, and 17.4 months for severe, moderate, mild, and trace mitral regurgitation, respectively, with 20% of providers deemed overutilizers of TTEs and 25% underutilizers. CONCLUSIONS: We conclude that there is substantial variation in follow-up intervals for TTE assessment of mitral regurgitation, despite risk-adjustment for patient variables, likely because of provider factors.

Optimizing the Use of Electronic Health Records to Identify High-Risk Psychosocial Determinants of Health.

BACKGROUND: Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data. However, not all clinically relevant data reside in claims and formatted data. Recently, there has been increasing interest in including patients with complex psychosocial determinants of health in care coordination programs. Psychosocial risk factors, including social determinants of health, mental health disorders, and substance abuse disorders, are less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data, and due to US Health Insurance Portability and Accountability Act (HIPAA) regulations are often not available as claims data. OBJECTIVE: The objective of our study was to develop a systematic approach using word recognition software to identifying psychosocial risk factors within any part of a patient's electronic health record (EHR). METHODS: We used QPID (Queriable Patient Inference Dossier), an ontology-driven word recognition software, to scan adult patients' EHRs to identify terms predicting a high-risk patient suitable to be followed in a care coordination program in Massachusetts, USA. Search terms identified high-risk conditions in patients known to be enrolled in a care coordination program, and were then tested against control patients. We calculated precision, recall, and balanced F-measure for the search terms. RESULTS: We identified 22 EHR-available search terms to define psychosocial high-risk status; the presence of 9 or more of these terms predicted that a patient would meet inclusion criteria for a care coordination program. Precision was .80, recall .98, and balanced F-measure .88 for the identified terms. For adult patients insured by Medicaid and enrolled in the program, a mean of 14 terms (interquartile range [IQR] 11-18) were present as identified by the search tool, ranging from 2 to 22 terms. For patients enrolled in the program but not insured by Medicaid, a mean of 6 terms (IQR 3-8) were present as identified by the search tool, ranging from 1 to 21. CONCLUSIONS: Selected informatics tools such as word recognition software can be leveraged to improve health care delivery, such as an EHR-based protocol that identifies psychosocially complex patients eligible for enrollment in a care coordination program.

Utilization Management of High-Cost Imaging in an Outpatient Setting in a Large Stable Patient and Provider Cohort over 7 Years.

Purpose To quantify the effect of a comprehensive, long-term, provider-led utilization management (UM) program on high-cost imaging (computed tomography, magnetic resonance imaging, nuclear imaging, and positron emission tomography) performed on an outpatient basis. Materials and Methods This retrospective, 7-year cohort study included all patients regularly seen by primary care physicians (PCPs) at an urban academic medical center. The main outcome was the number of outpatient high-cost imaging examinations per patient per year ordered by the patient's PCP or by any specialist. The authors determined the probability of a patient undergoing any high-cost imaging procedure during a study year and the number of examinations per patient per year (intensity) in patients who underwent high-cost imaging. Risk-adjusted hierarchical models were used to directly quantify the physician component of variation in probability and intensity of high-cost imaging use, and clinicians were provided with regular comparative feedback on the basis of the results. Observed trends in high-cost imaging use and provider variation were compared with the same measures for outpatient laboratory studies because laboratory use was not subject to UM during this period. Finally, per-member per-year high-cost imaging use data were compared with statewide high-cost imaging use data from a major private payer on the basis of the same claim set. Results The patient cohort steadily increased in size from 88 959 in 2007 to 109 823 in 2013. Overall high-cost imaging utilization went from 0.43 examinations per year in 2007 to 0.34 examinations per year in 2013, a decrease of 21.33% (P < .0001). At the same time, similarly adjusted routine laboratory study utilization decreased by less than half that rate (9.4%, P < .0001). On the basis of unadjusted data, outpatient high-cost imaging utilization in this cohort decreased 28%, compared with a 20% decrease in statewide utilization (P = .0023). Conclusion Analysis of high-cost imaging utilization in a stable cohort of patients cared for by PCPs during a 7-year period showed that comprehensive UM can produce a significant and sustained reduction in risk-adjusted per-patient year outpatient high-cost imaging volume. © RSNA, 2017.

Images of imaging: how to process and display imaging utilization for large populations.

OBJECTIVE: We propose a method of processing and displaying imaging utilization data for large populations. CONCLUSION: The comprehensive and finely grained picture of imaging utilization yielded by our methods is a first step toward population-based imaging utilization management. We believe that our methods for the categorization and display of imaging utilization will prove to be widely useful.

Provider Feedback about Imaging Appropriateness by Using Scores from Order Entry Decision Support: Raw Rates Misclassify Outliers.

PURPOSE: To determine the relevant physician- and practice-related factors that jointly affect the rate of low-utility imaging examinations (score of 1-3 out of 9) ordered by means of an order entry system that provides normative appropriateness feedback. MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional review board under an expedited protocol for analyzing anonymous aggregated administrative data. This is a retrospective study of approximately 250 000 consecutive scheduled outpatient advanced imaging examinations (computed tomography, magnetic resonance imaging, nuclear medicine) ordered by 164 primary care and 379 medical specialty physicians from 2008 to 2012. A hierarchical logistic regression model was used to identify multiple predictors of the probability that an examination received a low utility score. Physician- and practice-specific random effects were estimated to articulate (odds ratio) and quantify (intraclass correlation) interphysician variation. RESULTS: Fixed effects found to be statistically significant predictors of low-utility imaging included examination type, whether the examination was cancelled, status of the person entering the order, and the total number of examinations ordered by the clinician. Neither patient age nor sex had any effect, and there were no secular trends (year of study). The remaining amount of interphysician variation was moderate (intraclass correlation, 22%), whereas the variation between medical specialties and primary care practices was low (intraclass correlation, 5%). The estimated physician-specific effects had reliability of 70%, which makes them just suitable for identifying outliers. CONCLUSION: The authors found that 22% of the variation in the rate of low-utility examinations is attributable to ordering providers and 5% to their specialty or clinic.

An electronic health records study of long-term weight gain following antidepressant use.

IMPORTANCE: Short-term studies suggest antidepressants are associated with modest weight gain but little is known about longer-term effects and differences between individual medications in general clinical populations. OBJECTIVE: To estimate weight gain associated with specific antidepressants over the 12 months following initial prescription in a large and diverse clinical population. DESIGN, SETTING, AND PARTICIPANTS: We identified 22,610 adult patients who began receiving a medication of interest with available weight data in a large New England health care system, including 2 academic medical centers and affiliated outpatient primary and specialty care clinics. We used electronic health records to extract prescribing data and recorded weights for any patient with an index antidepressant prescription including amitriptyline hydrochloride, bupropion hydrochloride, citalopram hydrobromide, duloxetine hydrochloride, escitalopram oxalate, fluoxetine hydrochloride, mirtazapine, nortriptyline hydrochloride, paroxetine hydrochloride, venlafaxine hydrochloride, and sertraline hydrochloride. As measures of assay sensitivity, additional index prescriptions examined included the antiasthma medication albuterol sulfate and the antiobesity medications orlistat, phentermine hydrochloride, and sibutramine hydrochloride. Mixed-effects models were used to estimate rate of weight change over 12 months in comparison with the reference antidepressant, citalopram. MAIN OUTCOME AND MEASURE: Clinician-recorded weight at 3-month intervals up to 12 months. RESULTS: Compared with citalopram, in models adjusted for sociodemographic and clinical features, significantly decreased rate of weight gain was observed among individuals treated with bupropion (ß [SE]: -0.063 [0.027]; P = .02), amitriptyline (ß [SE]: -0.081 [0.025]; P = .001), and nortriptyline (ß [SE]: -0.147 [0.034]; P < .001). As anticipated, differences were less pronounced among individuals discontinuing treatment prior to 12 months. CONCLUSIONS AND RELEVANCE: Antidepressants differ modestly in their propensity to contribute to weight gain. Short-term investigations may be insufficient to characterize and differentiate this risk.