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.

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.

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.

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.

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.

Use of imaging in the emergency department: physicians have limited effect on variation.

PURPOSE: To quantify interphysician variation in imaging use during emergency department (ED) visits and examine the contribution of factors to this variation at the patient, visit, and physician level. MATERIALS AND METHODS: This study was HIPAA compliant and approved by the institutional review board of Partners Healthcare System (Boston, Mass), with waiver of informed consent. In this retrospective study of 88 851 consecutive ED visits during 2011 at a large urban teaching hospital, a hierarchical logistic regression model was used to identify multiple predictors for the probability that low- or high-cost imaging would be ordered during a given visit. Physician-specific random effects were estimated to articulate (by odds ratio) and quantify (by intraclass correlation coefficient [ICC]) interphysician variation. RESULTS: Patient- and visit-level factors found to be statistically significant predictors of imaging use included measures of ED busyness, prior ED visit, referral source to the ED, and ED arrival mode. Physician-level factors (eg, sex, years since graduation, annual workload, and residency training) did not correlate with imaging use. The remaining amount of interphysician variation was very low (ICC, 0.97% for low-cost imaging; ICC, 1.07% for high-cost imaging). These physician-specific odds ratios of imaging estimates were moderately reliable at 0.78 (95% confidence interval [CI]: 0.77, 0.79) for low-cost imaging and 0.76 (95% CI: 0.74, 0.78) for high-cost imaging. CONCLUSION: After careful and comprehensive case-mix adjustment by using hierarchical logistic regression, only about 1% of the variability in ED imaging utilization was attributable to physicians.

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.

Increasing the appropriateness of outpatient imaging: effects of a barrier to ordering low-yield examinations.

PURPOSE: To determine the effect of a computerized radiology order entry system rule that prevented nonclinician support staff from completing orders for outpatient computed tomographic, magnetic resonance imaging, and nuclear medicine examinations that received initial low-yield decision support scores in the order entry system. MATERIALS AND METHODS: This retrospective HIPAA-compliant study was approved by the institutional review board; the requirement for informed consent was waived. The control group consisted of 42737 consecutive orders for examinations in which decision support was provided that were placed from April to December 2006. The study group consisted of 76238 consecutive orders that were placed from April to December 2007. During the latter time period, a new rule in the order entry system was implemented: Examinations that had low-yield decision support scores could not be scheduled when the orders were placed by nonclinician support staff. To schedule the blocked examinations, the responsible clinician was required to personally log in to complete the process. System event logs and records of outpatient imaging procedures were extracted, counted, and analyzed to determine which ordering sessions resulted in examinations being scheduled and performed and which sessions resulted in modified or cancelled examinations. Results were correlated with user status and decision support scores. The Cochran-Mantel-Haenszel technique was used to control for the status of the order initiator and to allow testing for significance of the effect of the intervention on the "fate" of ordering events. RESULTS: After the intervention, the proportion of total examination requests initiated by clinicians directly logging in almost doubled: from 11,243 (26.31%) of 4,737 to 41,450 (54.37%) of 76238 examinations (P < .001). The fraction of low-yield (decision support score, 1-3) examinations requested through the order entry system that were later scheduled and performed decreased from 2106 (5.43%) of 38,801 to 1261 (1.92%) of 65,765 (P < .001). This is in contrast to requests for examinations with higher initial decision support scores that were not affected by the policy change and were scheduled at the same rate (relative risk, 0.988) before and after the change. CONCLUSION: A simple change in the business logic of the order entry system resulted in a substantially decreased rate of low-yield imaging examinations and a markedly increased percentage of tests personally ordered by clinicians.

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.

Effect of computerized order entry with integrated decision support on the growth of outpatient procedure volumes: seven-year time series analysis.

PURPOSE: To determine the effect of a computerized radiology order entry (ROE) and decision support (DS) system on growth rate of outpatient computed tomography (CT), magnetic resonance (MR) imaging, and ultrasonography (US) procedure volumes over time at a large metropolitan academic medical center. MATERIALS AND METHODS: Institutional review board approval was obtained for this study of deidentified aggregate administrative data. The research was compliant with HIPAA; informed consent was waived. This was a retrospective study of outpatient advanced imaging utilization before, during, and after implementation of a Web-based ROE and DS system. Dependent variables were the quarterly volumes of outpatient CT, MR imaging, and US examinations from quarter 4 of 2000 through quarter 4 of 2007. Outpatient visits during each quarter were included as control variables. These data were analyzed as three separate time series with piecewise linear regression for simultaneous estimation of quarterly examination volume trends before and after ROE and DS system implementation. This procedure was repeated with log-transformed quarterly volumes to estimate percentage growth rates. RESULTS: There was a significant decrease in CT volume growth (274 per quarter) and growth rate (2.75% per quarter) after ROE and DS system implementation (P < .001). For MR imaging, growth rate decreased significantly (1.2%, P = .016) after ROE and DS system implementation; however, there was no significant change in quarterly volume growth. With US, quarterly volume growth (n = 98, P = .014) and growth rate (1.3%, P = .001) decreased significantly after ROE implementation. These changes occurred during a steady growth in clinic visit volumes in the associated referral practices. CONCLUSION: Substantial decreases in the growth of outpatient CT and US procedure volume coincident with ROE implementation (supplemented by DS for CT) were observed. The utilization of outpatient MR imaging decreased less impressively, with only the rate of growth being significantly lower after interventions were in effect.

Recommendations for additional imaging in radiology reports: multifactorial analysis of 5.9 million examinations.

PURPOSE: To quantify the rates of recommendation for additional imaging (RAI) in a large number of radiology reports of different modalities and to estimate the effects of 11 clinically relevant factors. MATERIALS AND METHODS: This HIPAA compliant research was approved by the institutional review board under an expedited protocol for analyzing anonymous aggregated radiology data. All diagnostic imaging examinations (n = 5 948 342) interpreted by radiologists between 1995 and 2008 were studied. A natural language processing technique specifically designed to extract information about any recommendations from radiology report texts was used. The analytic data set included three quantitative variables: the interpreting radiologist's experience, the year of study, and patient age. Categoric variables described patient location (inpatient, outpatient, emergency department), whether a resident dictated the case, patient sex, modality, body area studied, ordering service, radiologist's specialty division, and whether the examination result was positive. A multivariable logistic regression model was used to determine the effect of each of these factors on likelihood of RAI while holding all others equal. RESULTS: Recommendations increased during the 13 years of study, with the unadjusted rate rising from roughly 6% to 12%. After accounting for all other factors, the odds of any one examination resulting in an RAI increased by 2.16 times (95% confidence interval: 2.12, 2.21) from 1995 to 2008. As radiologist experience increased, the odds of an RAI decreased by about 15% per decade. Studies that had positive findings were more likely (odds ratio = 5.03; 95% confidence interval: 4.98, 5.07) to have an RAI. The remaining factors also had significant effects on the tendency for an RAI. CONCLUSION: The likelihood of RAI increased by 15% for each decade of radiologist experience and roughly doubled over 13 years of study.

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.

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.

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.

Treatment of cardiac risk factors among patients with schizophrenia and diabetes.

OBJECTIVE: The appropriateness and effectiveness of the outpatient medical management of cardiac risk factors for patients with diabetes who had a diagnosis of schizophrenia or a related psychotic syndrome were examined. METHODS: In a cross-sectional analysis of 4,236 patients with diabetes, ICD-9 billing codes were used to identify 214 patients with schizophrenia or a schizophrenia-related syndrome. Measures of treatment appropriateness and effectiveness for the management of cardiac risk factors (control of blood sugar, blood pressure, and lipids) were assessed for this group and compared with measures from patients with diabetes but no severe mental illness (N = 3,594). Odds ratios were adjusted for between-group differences in gender, race, age, and clinic setting. RESULTS: There were no statistically significant between-group differences on any of the five measures of treatment appropriateness, indicating that patients with schizophrenia received a similar regimen of medical treatment for cardiac factors. However, two of the seven measures of treatment effectiveness indicated significant deficiencies, with fewer patients with schizophrenia meeting the clinical quality benchmarks for cholesterol and low-density lipoprotein control. This disparity may have been caused by factors other than the measured treatment appropriateness variables, including the prescription of older lipid-lowering agents and a higher rate of missed appointments in the group with schizophrenia. CONCLUSIONS: Effective lipid control may be more difficult to attain for at least some patients with schizophrenia. Given that population's high rates of cardiovascular mortality, additional research to clarify the barriers to effective lipid management is essential.