Kennon R. Copeland

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.

Managing Chronic Illness: Physician Practices Increased The Use Of Care Management And Medical Home Processes

The effective management of patients with chronic illnesses is critical to bending the curve of health care spending in the United States and is a crucial test for health care reform. In this article we used data from three national surveys of physician practices between 2006 and 2013 to determine the extent to which practices of all sizes have increased their use of evidence-based care management processes associated with patient-centered medical homes for patients with asthma, congestive heart failure, depression, and diabetes. We found relatively large increases over time in the overall use of these processes for small and medium-size practices as well as for large practices. However, the large practices used fewer than half of the recommended processes, on average. We also identified the individual processes whose use increased the most and show that greater use of care management processes is positively associated with public reporting of patient experience and clinical quality and with pay-for-performance.

Independent Practice Associations And Physician-Hospital Organizations Can Improve Care Management For Smaller Practices

Pay-for-performance, public reporting, and accountable care organization programs place pressures on physicians to use health information technology and organized care management processes to improve the care they provide. But physician practices that are not large may lack the resources and size to implement such processes. We used data from a unique national survey of 1,164 practices with fewer than twenty physicians to provide the first information available on the extent to which independent practice associations (IPAs) and physician-hospital organizations (PHOs) might make it possible for these smaller practices to share resources to improve care. Nearly a quarter of the practices participated in an IPA or a PHO that accounted for a significant proportion of their patients. On average, practices participating in these organizations provided nearly three times as many care management processes for patients with chronic conditions as nonparticipating practices did (10.4 versus 3.8). Half of these processes were provided only by IPAs or PHOs. These organizations may provide a way for small and medium-size practices to systematically improve care and participate in accountable care organizations.

Quantifying bias in a health survey: modeling total survey error in the national immunization survey.

Random-digit-dial telephone surveys are experiencing both declining response rates and increasing under-coverage due to the prevalence of households that substitute a wireless telephone for their residential landline telephone. These changes increase the potential for bias in survey estimates and heighten the need for survey researchers to evaluate the sources and magnitudes of potential bias. We apply a Monte Carlo simulation-based approach to assess bias in the NIS, a land-line telephone survey of 19-35 month-old children used to obtain national vaccination coverage estimates. We develop a model describing the survey stages at which component nonsampling error may be introduced due to nonresponse and under-coverage. We use that model and components of error estimated in special studies to quantify the extent to which noncoverage and nonresponse may bias the vaccination coverage estimates obtained from the NIS and present a distribution of the total survey error. Results indicated that the total error followed a normal distribution with mean of 1.72 per cent(95 per cent CI: 1.71, 1.74 per cent) and final adjusted survey weights corrected for this error. Although small, the largest contributor to error in terms of magnitude was nonresponse of immunization providers. The total error was most sensitive to declines in coverage due to cell phone only households. These results indicate that, while response rates and coverage may be declining, total survey error is quite small. Since response rates have historically been used to proxy for total survey error, the finding that these rates do not accurately reflect bias is important for evaluation of survey data. Published in 2011 by John Wiley & Sons, Ltd.

Spending per Medicare Beneficiary Is Higher in Hospital-Owned Small- and Medium-Sized Physician Practices

OBJECTIVE To examine the relationship of physician versus hospital ownership of small- and medium-sized practices with spending and utilization of care. DATA SOURCE/STUDY SETTING/DATA COLLECTION Survey data for 1,045 primary care-based practices of 1-19 physicians linked to Medicare claims data for 2008 for 282,372 beneficiaries attributed to the 3,010 physicians in these practices. STUDY DESIGN We used generalized linear models to estimate the associations between practice characteristics and outcomes (emergency department visits, index admissions, readmissions, and spending). PRINCIPAL FINDINGS Beneficiaries linked to hospital-owned practices had 7.3 percent more emergency department visits and 6.4 percent higher total spending compared to beneficiaries linked to physician-owned practices. CONCLUSIONS Physician practices are increasingly being purchased by hospitals. This may result in higher total spending on care.

Calculating Adjusted Survival Functions for complex sample survey data and application to vaccination coverage studies with National Immunization Survey (NIS).

Background: In vaccination studies with complex sample survey data, non-parametric survival functions may be useful. Recent publications have proposed several methods for evaluating the adjusted survival functions in non-population-based studies. However, alternative methods for calculating adjusted survival functions for complex sample survey data have not been described. Objectives: 1) Propose and describe two methods for calculating adjusted survival functions in the complex sample survey setting; 2) implement these two methods with SUDAAN software package; and 3) apply these two methods to 2011 National Immunization Survey (NIS) data. Methods: (1) The inverse probabilities of being in a certain group are defined as the new weights and applied to obtain the inverse probability weighting (IPW) adjusted Kaplan-Meier survival function. (2) Survival functions are evaluated for each of the unique combination of all levels of covariates in a complex sample survey obtained from a single Cox proportional hazards (PH) model, and the weighted average of these individual functions is calculated, with weights equal to the weighted sample size of the individual function, to obtain the Cox corrected group (CCG) adjusted survival function. Illustrative example: For illustration of the basic techniques rather than a thorough epidemiologic investigation of a specific research question, the two proposed methods were applied to 2011 National Immunization Survey (NIS) data. We estimated the adjusted survival function by age in days of children receiving the first dose of varicella vaccination by children’s family mobility status and by IPW, CCG, and crude Kaplan-Meier (KM) methods controlling for parents’ attitude toward vaccination, mother’s age group, and children first born status. Conclusions: If the Cox PH assumption is not met, then the IPW adjusted KM method is the only good choice among the two proposed methods, if adjusted survival estimates are desired. If the Cox PH assumption is valid, either the IPW or CCG methods can be used.