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Objective: To understand the relative role of prices versus utilization in the variation in total spending per patient across medical groups. Data Sources: We conducted a cross‐sectional analysis of medical claims for commercially insured adults from a large national insurer in 2018. Study Design: After assigning patients to a medical group based on primary care visits in 2018, we calculated total medical spending for each patient in that year. Total spending included care provided by clinicians within the medical group and care provided by other providers, including hospitals. It did not include drug spending. We estimated the case mix adjusted spending per patient for each medical group. Within each market, we categorized medical groups into quartiles based on the group's spending per patient. To decompose spending variation into price versus utilization, we compared spending differences between highest and lowest quartile medical groups under two scenarios: (1) using actual prices (2) using a standardized price (same price used for a given service across the nation). Principal Findings: In total, 3,921,736 patients were assigned to 7284 medical groups. Per‐patient spending in the highest quartile of spending medical groups was $1813 higher than per‐patient spending in the lowest spending quartile of medical groups (50% higher relative spending). This overall difference was primarily driven by differences in inpatient care, imaging, and specialty care. In the scenario where we used standardized prices, the difference in spending between medical groups in the top and bottom quartiles decreased to $1425, implying that 79% of the $1813 difference in spending between the top and bottom quartile groups is explained by utilization and the remaining 21% by prices. The likely explanation for the modest impact of prices is that patients cared for by a given medical group receive care across a wide range of providers. Conclusions: Prices explained a modest fraction of the differences in spending between medical groups. [ABSTRACT FROM AUTHOR]

Objective To validate classification of race/ethnicity based on the Bayesian Improved Surname Geocoding method ( BISG) and assess variations in validity by gender and age. Data Sources/Study Setting Secondary data on members of Kaiser Permanente Georgia, an integrated managed care organization, through 2010. Study Design For 191,494 members with self-reported race/ethnicity, probabilities for belonging to each of six race/ethnicity categories predicted from the BISG algorithm were used to assign individuals to a race/ethnicity category over a range of cutoffs greater than a probability of 0.50. Overall as well as gender- and age-stratified sensitivity, specificity, positive predictive value ( PPV), and negative predictive value ( NPV) were calculated. Receiver operating characteristic ( ROC) curves were generated and used to identify optimal cutoffs for race/ethnicity assignment. Principal Findings The overall cutoffs for assignment that optimized sensitivity and specificity ranged from 0.50 to 0.57 for the four main racial/ethnic categories (White, Black, Asian/Pacific Islander, Hispanic). Corresponding sensitivity, specificity, PPV, and NPV ranged from 64.4 to 81.4 percent, 80.8 to 99.7 percent, 75.0 to 91.6 percent, and 79.4 to 98.0 percent, respectively. Accuracy of assignment was better among males and individuals of 65 years or older. Conclusions BISG may be useful for classifying race/ethnicity of health plan members when needed for health care studies. [ABSTRACT FROM AUTHOR]

Objective. To simplify the decision-making process, we propose and implement an approach to assess the stability of health plan performance over time when multiple indicators of performance exist. Data Source. National Committee for Quality Assurance Health Care Effectiveness Data and Information Set data for childhood immunization for both publicly and nonpublicly reporting health plans between 1998 and 2002. Data/Study Design. We use longitudinal data to examine whether plan quality ratings are stable from year to year. We estimate a parametric Multiple Indicator Multiple Cause Model, a model which allows us to aggregate the multiple measures of performance. The model controls for observed characteristics of the plan and market, allowing for unmeasured heterogeneity. Principal Findings. We find moderate persistence in plan performance over time. A plan in the upper tier of performance in the year 1999 has only a 0.47 probability of remaining in the upper tier in the year 2001. Multiple years of good performance increase the probability of good performance in the future. For example, from the subset of plans in the upper tier of performance in 1999, 63 percent continued to perform in the upper tier in 2000. However, from the subset of plans in the upper tier in both 1998 and 1999, about three-fourths of the plans continued to perform in the upper tier in the year 2000. Finally, better performance in the more recent past is more indicative of better performance in the future than better performance in the more distant past. Conclusions. Although there is some persistence in health plan ratings over time, it is not uncommon for ratings of plans to change between when the data are generated and when actions based on that data, such as employers' contracting decisions or consumers' enrollment decisions, may take effect. Decision makers should be cognizant of this issue and methods should be developed to mitigate its consequences. [ABSTRACT FROM AUTHOR]

Objective: To efficiently estimate race/ethnicity using administrative records to facilitate health care organizations' efforts to address disparities when self-reported race/ethnicity data are unavailable.Data Source: Surname, geocoded residential address, and self-reported race/ethnicity from 1,973,362 enrollees of a national health plan.Study Design: We compare the accuracy of a Bayesian approach to combining surname and geocoded information to estimate race/ethnicity to two other indirect methods: a non-Bayesian method that combines surname and geocoded information and geocoded information alone. We assess accuracy with respect to estimating (1) individual race/ethnicity and (2) overall racial/ethnic prevalence in a population.Principal Findings: The Bayesian approach was 74 percent more efficient than geocoding alone in estimating individual race/ethnicity and 56 percent more efficient in estimating the prevalence of racial/ethnic groups, outperforming the non-Bayesian hybrid on both measures. The non-Bayesian hybrid was more efficient than geocoding alone in estimating individual race/ethnicity but less efficient with respect to prevalence (p<.05 for all differences).Conclusions: The Bayesian Surname and Geocoding (BSG) method presented here efficiently integrates administrative data, substantially improving upon what is possible with a single source or from other hybrid methods; it offers a powerful tool that can help health care organizations address disparities until self-reported race/ethnicity data are available. [ABSTRACT FROM AUTHOR]

Objectives. The purpose of this study was to examine the extent to which measures of health plan clinical performance and measures of patient perceptions of care are associated with health plan organizational characteristics, including the percentage of care provided based on a group or staff model delivery system, for-profit (tax) status, and affiliation with a national managed care firm. Data Sources. Data describing health plans on region, age of health plan, for-profit status, affiliation with a national managed care firm, percentage of Medicare business, total enrollment, ratio of primary care physicians to specialists, HMO penetration, and form of health care delivery system (e.g., IPA, network, mixed, staff, group) were obtained from InterStudy. Clinical performance measures for women's health screening rates, child and adolescent immunization rates, heart disease screening rates, diabetes screening rates, and smoking cessation were developed from HEDIS® data. Measures of patient perceptions of care are obtained from CAHPS® survey data submitted as Healthplan Employer Data and Information Set, Consumer Assessment of Health Plans 2.0 H. Study Design. Multivariate regression cross-sectional analysis of 272 health plans was used to evaluate the relationship of health plan characteristics with measures of clinical performance and patient perceptions of care. Principal Findings. The form of delivery system, measured by percent of care delivered by staff and group model systems, is significantly related ( p≤.05) with four of the five clinical performance indices but none of the three satisfaction performance indices. Other variables significantly associated with performance were being geographically located in the Northeast, having nonprofit status, and for patient satisfaction, not being part of a larger insurance company. Conclusions. These comparative results provide evidence suggesting that the type of delivery system used by health plans is related to many clinical performance measures but is not related to patient perceptions of care. These findings underscore the importance of the form of the delivery system and the need for further inquiry that examines the relationship between organizational form and performance. [ABSTRACT FROM AUTHOR]

To develop a framework of factors to characterize health plans, to identify how plan characteristics were measured in a national survey, and to apply our findings to an analysis of the predictors of screening mammography. The primary data were from the 1996 Medical Expenditure Panel Survey. Women ages 40+, with private insurance, and no history of breast cancer were included in the study ( N=2,909). We used multivariate logistic regression to estimate mammography utilization in the past two years relative to health plan and demographic factors. Health plan measures included whether there is a defined provider network, whether coverage is restricted to a network, use of gatekeepers, level of cost containment, copayment and deductible amounts, coinsurance rate, and breadth of benefit coverage. We found no significant difference in reported mammography utilization using a dichotomous comparison of individuals enrolled in managed care versus indemnity plans. However, women in health plans with a defined provider network were more likely to report having received a mammogram in the past two years than those without networks (adjusted OR=1.21, 95 percent CI=1.07–1.36), and women in gatekeeper plans were more likely to report receiving mammography than those without gatekeepers (adjusted OR=1.18, 95 percent CI=1.03–1.36). Restricted out-of-network coverage, use of cost containment, enrollee cost sharing, and breadth of benefit coverage did not appear to affect mammography use. It is important to examine the effect of individual health plan components on the utilization of health care, rather than use the traditional broader categorizations of managed versus nonmanaged care or simple health plan typologies. [ABSTRACT FROM AUTHOR]

Objective. To describe how hospitals’ negotiating leverage with managed care plans changed from 1996 to 2001 and to identify factors that explain any changes. Data Sources. Primary semistructured interviews, and secondary qualitative (e.g., newspaper articles) and quantitative (i.e., InterStudy, American Hospital Association) data. Study Design. The Community Tracking Study site visits to a nationally representative sample of 12 communities with more than 200,000 people. These 12 markets have been studied since 1996 using a variety of primary and secondary data sources. Data Collection Methods. Semistructured interviews were conducted with a purposive sample of individuals from hospitals, health plans, and knowledgeable market observers. Secondary quantitative data on the 12 markets was also obtained. Principal Findings. Our findings suggest that many hospitals’ negotiating leverage significantly increased after years of decline. Today, many hospitals are viewed as having the greatest leverage in local markets. Changes in three areas—the policy and purchasing context, managed care plan market, and hospitalmarket—appear to explain why hospitals’ leverage increased, particularly over the last two years (2000–2001). Conclusions.Hospitals’ increased negotiating leverage contributed to higher payment rates, which in turn are likely to increase managed care plan premiums. This trend raises challenging issues for policymakers, purchasers, plans, and consumers. [ABSTRACT FROM AUTHOR]