Validation of 2 New Measures of Continuity of Care Based on Year-to-Year Follow-up With Known Providers of Health Care

PURPOSE

In a primary care context favoring group practices, we assessed the validity of 2 new continuity measures (both versions of known provider continuity, KPC) that capture the concentration of care over time from multiple physicians (multiple provider continuity, KPC-MP) or from the physician seen most often (personal provider continuity, KPC-PP).

METHODS

Patients with diabetes or cardiovascular disease (N = 765) were approached in the waiting rooms of 28 primary care clinics in 3 regions of the province of Quebec, Canada; answered a survey questionnaire measuring relational continuity, interpersonal communication, coordination within the clinic, coordination with specialists, and overall coordination; and gave permission for their medical records to be reviewed and their medical services utilization data for the previous 2 years to be accessed to measure KPC. Using generalized linear mixed models, we assessed the association between KPC and the patients’ responses.

RESULTS

Among the 5 different patient-reported measures or their combination, KPC-MP was significantly related with overall coordination of care: for high continuity, the odds ratio (OR) = 2.02 (95% CI, 1.33–3.07), and for moderate continuity, OR = 1.61 (95% CI, 1.06–2.46). KPC-MP was also related with the combined continuity score: for high continuity, OR = 1.52 (95% CI, 1.11–2.09), and for moderate continuity, OR = 1.48 (95% CI, 1.10–2.00). KPC-PP was not significantly associated with any of the survey measures.

CONCLUSIONS

The KPC-MP measure, based on readily available administrative data, is associated with patient-perceived overall coordination of care among multiple physicians. KPC measures are potentially a valuable and low-cost way to follow the effects of changes favoring group practice on continuity of care for entire populations. They are easy to replicate over time and across jurisdictions.     

Development and Validation of a Score for Adjusting Health Care Costs in General Practice

ObjectiveTo develop and validate the Italian Health Search Morbidity (HSM) Index to adjust health care costs in general practice.MethodsThe study population comprised 1,076,311 patients registered in the Health Search CSD Longitudinal Patient Database between January 1, 2008, and December 31, 2010. We randomly selected 538,254 and 538,057 patients to form the development and validation cohorts, respectively. To ensure model convergence, 5% of the aforementioned cohorts were selected randomly to create development and validation samples. The outcome was the total direct health care costs covered by the national health system. Interaction between age and sex, chronic diseases, and acute diseases were entered in a multilevel generalized linear latent mixed model with random intercepts (province of residence and general practitioner) to identify determinants associated with increased or decreased costs. The estimated coefficients were linearly combined to create the HSM Index for individual patients. The score was applied to the validation sample, and measures of predictive accuracy, explained variance, and the observed/predicted ratio were computed to evaluate the model’s accuracy.ResultsThe mean yearly cost was €414.57 per patient, and the HSM Index had a median value of 5.08 (25th–75th range 4.44–5.98). The HSM Index explained 50.17% of the variation in costs. Concerning calibration, in 80% of the population, the margin of error in the estimation of costs was around 10%.ConclusionsThe HSM Index is a reliable case-mix system that could be implemented in general practice for costs adjustment. This tool should ensure fairer scrutiny of resource use and allocation of budgets among general practitioners.     

The Impact of Massachusetts Health Care Reform on Access,Quality, and Costs of Care for the Already-Insured

ObjectiveTo assess the impact of Massachusetts Health Reform (MHR) on access, quality, and costs of outpatient care for the already-insured.ConclusionsMHR was not associated with worsening in access or quality of outpatient care for the already-insured, and it had modest effects on costs. This has implications for other states expanding insurance coverage under the Affordable Care Act.     

Cost to Primary Care Practices of Responding to Payer Requests for Quality and Performance Data

PURPOSE We wanted to determine how much it costs primary care practices to participate in programs that require them to gather and report data on care quality indicators.METHODS Using mixed quantitative-qualitative methods, we gathered data from 8 practices in North Carolina that were selected purposively to be diverse by size, ownership, type, location, and medical records. Formal practice visits occurred between January 2008 and May 2008. Four quality-reporting programs were studied: Medicare’s Physician Quality Reporting Initiative (PQRI), Community Care of North Carolina (CCNC), Bridges to Excellence (BTE), and Improving Performance in Practice (IPIP). We estimated direct costs to the practice and on-site costs to the quality organization for implementation and maintenance phases of program participation.RESULTS Major expenses included personnel time for planning, training, registry maintenance, visit coding, data gathering and entry, and modification of electronic systems. Costs per full-time equivalent clinician ranged from less than $1,000 to $11,100 during program implementation phases and ranged from less than $100 to $4,300 annually during maintenance phases. Main sources of variation included program characteristics, amount of on-site assistance provided, experience and expertise of practice personnel, and the extent of data system problems encountered.CONCLUSIONS The costs of a quality-reporting program vary greatly by program and are important to anticipate and understand when undertaking quality improvement work. Incentives that would likely improve practice participation include financial payment, quality improvement skills training, and technical assistance with electronic system troubleshooting.     

美国整合卫生保健主要做法及启示

随着人口老龄的快速发展、慢性病负担逐步加重,居民对综合、协调、连续、安全、可及的卫生保健服务需求日益增加。整合卫生保健有助于提高卫生保健服务的可及性、公平性、效率和患者满意度。文章梳理美国在整合卫生保健领域的做法,总结特点和成效,提出中国开展整合卫生保健的建议。     

Salary and Quality Compensation for Physician Practices Participating in Accountable Care Organizations

BACKGROUNDThe accountable care organization (ACO) is a new organizational form to manage patients across the continuum of care. There are numerous questions about how ACOs should be optimally structured, including compensation arrangements with primary care physicians.METHODSUsing data from a national survey of physician practices, we compared primary care physicians’ compensation between practices in ACOs and practices that varied in their financial risk for primary care costs using 3 groups: practices not participating in a Medicare ACO and with no substantial risk for primary care costs; practices not participating in an ACO but with substantial risk for primary care costs; and practices participating in an ACO regardless of their risk for primary care costs. We measured physicians’ compensation as the percentage of compensation based on salary, productivity, clinical quality or patient experience, and other factors. Regression models estimated physician compensation as a function of ACO participation and risk for primary care costs while controlling for other practice characteristics.RESULTSPhysicians in ACO and non-ACO practices with no substantial risk for costs on average received nearly one-half of their compensation from salary, slightly less from productivity, and about 5% from quality and other factors. Physicians not in ACOs but with substantial risk for primary care costs received two-thirds of their compensation from salary, nearly one-third from productivity, and slightly more than 1% from quality and other factors. Participation in ACOs was associated with significantly higher physician compensation for quality; however, participation was not significantly associated with compensation from salary, whereas financial risk was associated with much greater compensation from salary.CONCLUSIONAlthough practices in ACOs provide higher compensation for quality, compared with practices at large, they provide a similar mix of compensation based on productivity and salary. Incentives for ACOs may not be sufficiently strong to encourage practices to change physician compensation policies for better patient experience, improved population health, and lower per capita costs.     

Impact of Continuity of Care on Mortality and Health Care Costs: A Nationwide Cohort Study in Korea

PURPOSE

Continuity of care is considered a core element of high-quality primary care, but its impact on mortality and health care costs is unclear. We aimed to determine the impact of continuity of care on mortality, costs, and health outcomes in patients with newly diagnosed cardiovascular risk factors.

METHODS

We conducted a cohort study of a 3% nationwide random sample of Korean National Health Insurance enrollees. A total of 47,433 patients who had received new diagnoses of hypertension, diabetes, hypercholesterolemia, or their complications in 2003 or 2004 were included. We determined standard indices of continuity of care—most frequent provider continuity (MFPC), modified, modified continuity index (MMCI), and continuity of care index (COC)—and evaluated their association with study outcomes over 5 years of follow-up. Outcome measures included overall mortality, cardiovascular mortality, incident cardiovascular events, and health care costs.

RESULTS

The multivariable-adjusted hazard ratios (HRs) for all-cause mortality, cardiovascular mortality, incident myocardial infarction, and incident ischemic stroke comparing participants with COC index below the median to those above the median were HR = 1.12 (95% CI, 1.04–1.21), 1.30 (1.13–1.50), 1.57 (1.28–1.95), and 1.44 (1.27–1.63), respectively. Similar findings were obtained for other indices of continuity of care. Lower continuity of care was also associated with increased inpatient and outpatient days and costs.

CONCLUSIONS

Lower indices of continuity of care in patients with newly diagnosed hypertension, diabetes, and hypercholesterolemia were associated with higher all-cause and cardiovascular mortality, cardiovascular events, and health care costs. Health care systems should be designed to support long-term trusting relationships between patients and physicians.     

Physician Practice Participation in Accountable Care Organizations: The Emergence of the Unicorn

Objective

To provide the first nationally based information on physician practice involvement in ACOs.

Data Sources/Study Setting

Primary data from the third National Survey of Physician Organizations (January 2012–May 2013).

Study Design

We conducted a 40-minute phone survey in a sample of physician practices. A nationally representative sample of practices was surveyed in order to provide estimates of organizational characteristics, care management processes, ACO participation, and related variables for four major chronic illnesses.

Data Collection/Extraction Methods

We evaluated the associations between ACO participation, organizational characteristics, and a 25-point index of patient-centered medical home processes.

Principal Findings

We found that 23.7 percent of physician practices (n = 280) reported joining an ACO; 15.7 percent (n = 186) were planning to become involved within the next 12 months and 60.6 percent (n = 717) reported no involvement and no plans to become involved. Larger practices, those receiving patients from an IPA and/or PHO, those that were physician-owned versus hospital/health system-owned, those located in New England, and those with greater patient-centered medical home (PCMH) care management processes were more likely to have joined an ACO.

Conclusions

Physician practices that are currently participating in ACOs appear to be relatively large, or to be members of an IPA or PHO, are less likely to be hospital-owned and are more likely to use more care management processes than nonparticipating practices.     

无锡市万元出院病例特征分析

本文通过对万元病例和全病例比较分析,发现在费用性质、预后比例、年龄、病种方面有差异,提示增强医患双方费用意识、对大病实行统筹是必要的。     

Relationship Quality and Patient-Assessed Quality of Care in VA Primary Care Clinics: Development and Validation of the Work Relationships Scale

PURPOSE

Efforts to better understand the impact of clinic member relationships on care quality in primary care clinics have been limited by the absence of a validated instrument to assess these relationships. The purpose of this study was to develop and validate a scale assessing relationships within primary care clinics.

METHODS

The Work Relationships Scale (WRS) was developed and administered as part of a survey of learning and relationships among 17 Department of Veterans Affairs (VA) primary care clinics. A Rasch partial-credit model and principal components analysis were used to evaluate item performance, select the final items for inclusion, and establish unidimensionality for the WRS. The WRS was then validated against semistructured clinic member interviews and VA Survey of Healthcare Experiences of Patients (SHEP) data.

RESULTS

Four hundred fifty-seven clinicians and staff completed the clinic survey, and 247 participated in semistructured interviews. WRS scores were significantly associated with clinic-level reporting for 2 SHEP variables: overall rating of personal doctor/nurse (r² =0.43, P <.01) and overall rating of health care (r²= 0.25, P <.05). Interview data describing relationship characteristics were consistent with variability in WRS scores across low-scoring and high-scoring clinics.

CONCLUSIONS

The WRS shows promising validity as a measure assessing the quality of relationships in primary care settings; moreover, primary care clinics with lower WRS scores received poorer patient quality ratings for both individual clinicians and overall health care. Relationships play an important role in shaping care delivery and should be assessed as part of efforts to improve patient care within primary care settings.     

A Taxonomy of Accountable Care Organizations for Policy and Practice

ObjectiveTo develop an exploratory taxonomy of Accountable Care Organizations (ACOs) to describe and understand early ACO development and to provide a basis for technical assistance and future evaluation of performance.ConclusionsACOs can be characterized into three distinct clusters. The taxonomy provides a framework for assessing performance, for targeting technical assistance, and for diagnosing potential antitrust violations.     

Primary Care Physician Panel Size and Quality of Care: A Population-Based Study in Ontario,Canada

PURPOSE

The purpose of this study was to determine the relationship between the number of patients under a primary care physician’s care (panel size) and primary care quality indicators.

METHODS

We conducted a cross-sectional, population-based study of fee-for-service and capitated interprofessional and non-interprofessional primary health care practices in Ontario, Canada between April 2008 and March 2010, encompassing 4,195 physicians with panel sizes ≥1,200 serving 8.3 million patients. Data was extracted from multiple linked, health-related administrative databases and covered 16 quality indicators spanning 5 dimensions of care: access, continuity, comprehensiveness, and evidence-based indicators of cancer screening and chronic disease management.

RESULTS

The likelihood of being up-to-date on cervical, colorectal, and breast cancer screening showed relative decreases of 7.9% (P <.001), 5.9% (P = .01), and 4.6% (P <.001), respectively, with increasing panel size (from 1,200 to 3,900). Eight chronic care indicators (4 medication-based and 4 screening-based) showed no significant association with panel size. The likelihood of individuals with a new diagnosis of congestive heart failure having an echocardiogram, however, increased by a relative 8.1% (P <.001) with higher panel size. Increasing panel size was also associated with a 10.8% relative increase in hospitalization rates for ambulatory-care–sensitive conditions (P = .04) and a 10.8% decrease in non-urgent emergency department visits (P = .004). Continuity was highest with medium panel sizes (P <.001), and comprehensiveness had a small decrease (P = .03) with increasing panel size.

CONCLUSIONS

Increasing panel size was associated with small decreases in cancer screening, continuity, and comprehensiveness, but showed no consistent relationships with chronic disease management or access indicators. We found no panel size threshold above which quality of care suffered.     

Effects of Primary Care Team Social Networks on Quality of Care and Costs for Patients With Cardiovascular Disease

PURPOSE

Cardiovascular disease is the leading cause of mortality and morbidity in the United States. Primary care teams can be best suited to improve quality of care and lower costs for patients with cardiovascular disease. This study evaluates the associations between primary care team communication, interaction, and coordination (ie, social networks); quality of care; and costs for patients with cardiovascular disease.

METHODS

Using a sociometric survey, 155 health professionals from 31 teams at 6 primary care clinics identified with whom they interact daily about patient care. Social network analysis calculated variables of density and centralization representing team interaction structures. Three-level hierarchical modeling evaluated the link between team network density, centralization, and number of patients with a diagnosis of cardiovascular disease for controlled blood pressure and cholesterol, counts of urgent care visits, emergency department visits, hospital days, and medical care costs in the previous 12 months.

RESULTS

Teams with dense interactions among all team members were associated with fewer hospital days (rate ratio [RR] = 0.62; 95% CI, 0.50–0.77) and lower medical care costs (−$556; 95% CI, −$781 to −$331) for patients with cardiovascular disease. Conversely, teams with interactions revolving around a few central individuals were associated with increased hospital days (RR = 1.45; 95% CI, 1.09–1.94) and greater costs ($506; 95% CI, $202–$810). Team-shared vision about goals and expectations mediated the relationship between social network structures and patient quality of care outcomes.

CONCLUSIONS

Primary care teams that are more interconnected and less centralized and that have a shared team vision are better positioned to deliver high-quality cardiovascular disease care at a lower cost.     

The Effects of Quality of Care on Costs: A Conceptual Framework

Context

The quality of health care and the financial costs affected by receiving care represent two fundamental dimensions for judging health care performance. No existing conceptual framework appears to have described how quality influences costs.

Methods

We developed the Quality-Cost Framework, drawing from the work of Donabedian, the RAND/UCLA Appropriateness Method, reports by the Institute of Medicine, and other sources.

Findings

The Quality-Cost Framework describes how health-related quality of care (aspects of quality that influence health status) affects health care and other costs. Structure influences process, which, in turn, affects proximate and ultimate outcomes. Within structure, subdomains include general structural characteristics, circumstance-specific (e.g., disease-specific) structural characteristics, and quality-improvement systems. Process subdomains include appropriateness of care and medical errors. Proximate outcomes consist of disease progression, disease complications, and care complications. Each of the preceding subdomains influences health care costs. For example, quality improvement systems often create costs associated with monitoring and feedback. Providing appropriate care frequently requires additional physician visits and medications. Care complications may result in costly hospitalizations or procedures. Ultimate outcomes include functional status as well as length and quality of life; the economic value of these outcomes can be measured in terms of health utility or health-status-related costs. We illustrate our framework using examples related to glycemic control for type 2 diabetes mellitus or the appropriateness of care for low back pain.

Conclusions

The Quality-Cost Framework describes the mechanisms by which health-related quality of care affects health care and health status–related costs. Additional work will need to validate the framework by applying it to multiple clinical conditions. Applicability could be assessed by using the framework to classify the measures of quality and cost reported in published studies. Usefulness could be demonstrated by employing the framework to identify design flaws in published cost analyses, such as omitting the costs attributable to a relevant subdomain of quality.