Incorporating statistical uncertainty in the use of physician cost profiles

<p>Abstract</p> <p>Background</p> <p>Physician cost profiles (also called efficiency or economic profiles) compare the costs of care provided by a physician to his or her peers. These profiles are increasingly being used as the basis for policy applications such as tiered physician networks. Tiers (low, average, high cost) are currently defined by health plans based on percentile cut-offs which do not account for statistical uncertainty. In this paper we compare the percentile cut-off method to another method, using statistical testing, for identifying high-cost or low-cost physicians.</p> <p>Methods</p> <p>We created a claims dataset of 2004-2005 data from four Massachusetts health plans. We employed commercial software to create episodes of care and assigned responsibility for each episode to the physician with the highest proportion of professional costs. A physicians' cost profile was the ratio of the sum of observed costs divided by the sum of expected costs across all assigned episodes. We discuss a new method of measuring standard errors of physician cost profiles which can be used in statistical testing. We then assigned each physician to one of three cost categories (low, average, or high cost) using two methods, percentile cut-offs and a t-test (p-value ≤ 0.05), and assessed the level of disagreement between the two methods.</p> <p>Results</p> <p>Across the 8689 physicians in our sample, 29.5% of physicians were assigned a different cost category when comparing the percentile cut-off method and the t-test. This level of disagreement varied across specialties (17.4% gastroenterology to 45.8% vascular surgery).</p> <p>Conclusions</p> <p>Health plans and other payers should incorporate statistical uncertainty when they use physician cost-profiles to categorize physicians into low or high-cost tiers.</p

Fundamental Reform of Payment for Adult Primary Care: Comprehensive Payment for Comprehensive Care

Primary care is essential to the effective and efficient functioning of health care delivery systems, yet there is an impending crisis in the field due in part to a dysfunctional payment system. We present a fundamentally new model of payment for primary care, replacing encounter-based imbursement with comprehensive payment for comprehensive care. Unlike former iterations of primary care capitation (which simply bundled inadequate fee-for-service payments), our comprehensive payment model represents new investment in adult primary care, with substantial increases in payment over current levels. The comprehensive payment is directed to practices to include support for the modern systems and teams essential to the delivery of comprehensive, coordinated care. Income to primary physicians is increased commensurate with the high level of responsibility expected. To ensure optimal allocation of resources and the rewarding of desired outcomes, the comprehensive payment is needs/risk-adjusted and performance-based. Our model establishes a new social contract with the primary care community, substantially increasing payment in return for achieving important societal health system goals, including improved accessibility, quality, safety, and efficiency. Attainment of these goals should help offset and justify the costs of the investment. Field tests of this and other new models of payment for primary care are urgently needed

CTCF cis-Regulates Trinucleotide Repeat Instability in an Epigenetic Manner: A Novel Basis for Mutational Hot Spot Determination

At least 25 inherited disorders in humans result from microsatellite repeat expansion. Dramatic variation in repeat instability occurs at different disease loci and between different tissues; however, cis-elements and trans-factors regulating the instability process remain undefined. Genomic fragments from the human spinocerebellar ataxia type 7 (SCA7) locus, containing a highly unstable CAG tract, were previously introduced into mice to localize cis-acting “instability elements,” and revealed that genomic context is required for repeat instability. The critical instability-inducing region contained binding sites for CTCF—a regulatory factor implicated in genomic imprinting, chromatin remodeling, and DNA conformation change. To evaluate the role of CTCF in repeat instability, we derived transgenic mice carrying SCA7 genomic fragments with CTCF binding-site mutations. We found that CTCF binding-site mutation promotes triplet repeat instability both in the germ line and in somatic tissues, and that CpG methylation of CTCF binding sites can further destabilize triplet repeat expansions. As CTCF binding sites are associated with a number of highly unstable repeat loci, our findings suggest a novel basis for demarcation and regulation of mutational hot spots and implicate CTCF in the modulation of genetic repeat instability

Linkage study of fibrinogen levels: the Strong Heart Family Study

<p>Abstract</p> <p>Background</p> <p>The pathogenesis of atherosclerosis involves both hemostatic and inflammatory mechanisms. Fibrinogen is associated with both risk of thrombosis and inflammation. A recent meta-analysis showed that risk of coronary heart disease may increase 1.8 fold for 1 g/L of increased fibrinogen, independent of traditional risk factors. It is known that fibrinogen levels may be influenced by demographic, environmental and genetic factors. Epidemiologic and candidate gene studies are available; but few genome-wide linkage studies have been conducted, particularly in minority populations. The Strong Heart Study has demonstrated an increased incidence of cardiovascular disease in the American Indian population, and therefore represents an important source for genetic-epidemiological investigations.</p> <p>Methods</p> <p>The Strong Heart Family Study enrolled over 3,600 American Indian participants in large, multi-generational families, ascertained from an ongoing population-based study in the same communities. Fibrinogen was determined using standard technique in a central laboratory and extensive additional phenotypic measures were obtained. Participants were genotyped for 382 short tandem repeat markers distributed throughout the genome; and results were analyzed using a variance decomposition method, as implemented in the SOLAR 2.0 program.</p> <p>Results</p> <p>Data from 3535 participants were included and after step-wise, linear regression analysis, two models were selected for investigation. Basic demographic adjustments constituted model 1, while model 2 considered waist circumference, diabetes mellitus and postmenopausal status as additional covariates. Five LOD scores between 1.82 and 3.02 were identified, with the maximally adjusted model showing the highest score on chromosome 7 at 28 cM. Genes for two key components of the inflammatory response, i.e. interleukin-6 and "signal transducer and activator of transcription 3" (<it>STAT3</it>), were identified within 2 and 8 Mb of this 1 LOD drop interval respectively. A LOD score of 1.82 on chromosome 17 between 68 and 93 cM is supported by reports from two other populations with LOD scores of 1.4 and 1.95.</p> <p>Conclusion</p> <p>In a minority population with a high prevalence of cardiovascular disease, strong evidence for a novel genetic determinant of fibrinogen levels is found on chromosome 7 at 28 cM. Four other loci, some of which have been suggested by previous studies, were also identified.</p

Physicians in Retainer (“Concierge”) Practice: A National Survey of Physician, Patient, and Practice Characteristics

BACKGROUND: Retainer practices represent a new model of care whereby physicians charge an up-front fee for services that may not be covered by health insurance. The characteristics of these practices are largely unknown. DESIGN, SETTING, AND PARTICIPANTS: We conducted a cross-sectional mail survey of 144 retainer physicians (58% response rate) and a national random sample of 463 nonretainer physicians (50% response rate) to compare retainer and nonretainer practices. Outcomes of interest included physician demographics, size and case-mix of patient panel, services offered and, for retainer practices, characteristics of practice development. RESULTS: Retainer physicians have much smaller patient panels (mean 898 vs 2303 patients, P<.0001) than their nonretainer counterparts, and care for fewer African-American (mean 7% vs 16%, P<.002), Hispanic (4% vs 14%, P<.001), or Medicaid (5% vs 15%, P<.001) patients. Physicians in retainer practices are more likely to offer accompanied specialist visits (30% vs 1%), house calls (63% vs 26%), 24-hour direct physician access (91% vs 40%), and several other services (all P values <.05). Most retainer physicians (85%) converted from nonretainer practices but kept few of their former patients (mean 12%). Most retainer physicians (84%) provide charity care and many continue to see some patients (mean 17%) who do not pay retainer fees. CONCLUSIONS: Despite differences between retainer and nonretainer practices, there is also substantial overlap in services provided. These findings, in conjunction with the scope of patient discontinuity when physicians transition to retainer practice, suggest that ethical and legal debates about the standing of these practices will endure