In 2009-2010, Blue Cross Blue Shield of Massachusetts entered into global payment contracts (the Alternative Quality contract, AQC) with 11 provider organizations. We evaluated the impact of the AQC on spending and utilization of several categories of medical technologies, including one considered high value (colonoscopies) and three that include services that may be overused in some situations (cardiovascular, imaging, and orthopedic services).
Approximately 420,000 unique enrollees in 2009 and 180,000 in 2010 were linked to primary care physicians whose organizations joined the AQC. Using three years of pre-intervention data and a large control group, we analyzed changes in utilization and spending associated with the AQC with a propensity-weighted difference-in-differences approach adjusting for enrollee demographics, health status, secular trends, and cost-sharing.
In the 2009 AQC cohort, total volume of colonoscopies increased 5.2 percent (p=0.04) in the first two years of the contract relative to control. The contract was associated with varied changes in volume for cardiovascular and imaging services, but total spending on cardiovascular services in the first two years decreased by 7.4% (p=0.02) while total spending on imaging services decreased by 6.1% (p<0.001) relative to control. In addition to lower utilization of higher-priced services, these decreases were also attributable to shifting care to lower-priced providers. No effect was found in orthopedics.
As one example of a large-scale global payment initiative, the AQC was associated with higher use of colonoscopies. Among several categories of services whose value may be controversial, the contract generally shifted volume to lower-priced facilities or services.
Under new bundled payment models, hospitals are financially responsible for post-acute care delivered by providers such as skilled nursing facilities (SNFs) and home health agencies (HHAs). The hope is that hospitals will use post-acute care more prudently and better coordinate care with post-acute providers. However, little is known about existing patterns in hospitals׳ referrals to post-acute providers.
Post-acute provider referrals were identified using SNF and HHA claims within 14 days following hospital discharge. Hospital post-acute care network size and concentration were estimated across hospital types and regions. The 2008 Medicare Provider Analysis and Review claims for acute hospitals and SNFs, and the 100% HHA Standard Analytic Files were used.
The mean post-acute care network size for U.S. hospitals included 57.9 providers with 37.5 SNFs and 23.4 HHAs. The majority of these providers (65.7% of SNFs, 60.9% of HHAs) accounted for 1 percent or less of a hospital׳s referrals and classified as “low-volume”. Other post-acute providers we classified as routine. The mean network size for routine providers was greater for larger hospitals, teaching hospitals and in regions with higher per capita post-acute care spending.
The average hospital works with over 50 different post-acute providers. Moreover, the size of post-acute care networks varies considerably geographically and by hospital characteristics. These results provide context on the complex task hospitals will face in coordinating care with post-acute providers and cutting costs under new bundled payment models.
Implementation of a patient centered medical home challenges primary care providers to change their scheduling practices to enhance patient access to care as well as to learn how to use performance metrics as part of a self-reflective practice redesign culture. As medical homes become more commonplace, health care administrators and primary care providers alike are eager to identify barriers to implementation. The objective of this study was to identify non-technological barriers to medical home implementation from the perspective of primary care providers. We conducted qualitative interviews with providers implementing the medical home model in Department of Veterans Affairs clinics-the most comprehensive rollout to date. Primary care providers reported favorable attitudes towards the model but discussed the importance of data infrastructure for practice redesign and panel management. Respondents emphasized the need for administrative leadership to support practice redesign by facilitating time for panel management and recognizing providers who utilize non-face-to-face ways of delivering clinical care. Health care systems considering adoption of the medical home model should ensure that they support both technological capacities and vertically aligned expectations for provider performance.
Published by Elsevier Inc.
The Triple Aim of better health, better care, and lower costs has become a fundamental framework for understanding the need for broad health care reform and describing health care value. While the framework is not specific to any clinical setting, this article focuses on the alignment between the framework and Emergency Department (ED) care. The paper explores where emergency care is naturally aligned with each Aim, as well as current barriers which must be addressed to meet the full vision of the Triple Aim. We propose a vision of EDs serving as a nexus for care coordination optimally consistent with the Triple Aim and the requirements for such a role. These requirements include: (1) substantial integration in coordinated care models; (2) development of reliable and actionable data on ED quality, population health, and cost outcomes; (3) specific initiatives to control and optimize ED utilization; and (4) payment models which preserve surge and disaster response capacity.
Published by Elsevier Inc.
Existing national health-related surveys take several months or years to become available. The Affordable Care Act will bring rapid changes to the health care system in 2014. We analyzed the Gallup-Healthways׳ Well-Being Index (WBI) in order to assess its ability to provide real-time estimates of the impact of the ACA on key health-related outcomes.
We compared the Gallup-Healthways WBI to established surveys on demographics, health insurance, access to care, and health. Data sources were the Gallup-Healthways WBI, the Current Population Survey, the American Community Survey, the Medical Expenditure Panel Survey, the National Health Interview Survey, and the Behavioral Risk Factor Surveillance System. Demographic measures included age, race/ethnicity, education, and income. Insurance outcomes were coverage rates by type, state, and year. Access measures included having a usual source of care and experiencing cost-related delays in care. Health measures were self-reported health and history of specific diagnoses.
Most differences across surveys were statistically significant (p<0.05) due to large sample sizes, so our analysis focused on the absolute magnitude of differences. The Gallup-Healthways WBI post-weighted sample was similar in age, race/ethnicity, and education to other surveys, though the Gallup-Healthways WBI sample is slightly older, has fewer minorities, and is more highly educated than in other national surveys. In addition, income was more frequently missing. The Gallup-Healthways WBI produced similar national, state, and time-trend estimates on uninsured rates, but far lower rates of public coverage. Access to care and health status were similar in the Gallup-Healthways WBI and other surveys.
The Gallup-Healthways WBI is a valuable complement to existing data sources for health services research. The Gallup-Healthways WBI estimates for uninsured rates and access to care were similar to established national surveys and may allow for rapid estimates of the ACA׳s impact on the uninsured in 2014. Estimates of insurance type are less comparable, particularly for public coverage, which likely limits the utility of the Gallup-Healthways WBI for analyzing changes in particular types of coverage.
Prior research has shown that provider positive attitudes about EHRs are associated with their successful adoption. There is no evidence on whether comfort with technology and more positive attitudes about EHRs affect use of EHR functions once they are adopted.
We used data from a survey of providers in the Primary Care Information Project, a bureau of the New York City Department of Health and Mental Hygiene and measures of use from their EHRs. The main predictor variables were scores on three indices: comfort with computers, positive attitudes about EHRs, and negative attitudes about EHRs. The main outcome measures were four measures of use of EHR functions. We used linear regression models to test the association between the three indices and measures of EHR use.
The mean comfort with computers score was 2.37 (SD 0.53) on a scale of 1–3 with 3 being the most comfortable. The mean positive attitude score was 2.74 (SD 0.40) on a scale of 1–3 with 3 being more positive. The mean negative attitude score was 1.81 (SD 0.54) on a scale of 1–3 with 3 being more negative. Within the first twelve months of having the EHR, 59.5% of visits had allergy information entered into a structured field, 64.8% had medications reviewed, and 74.3% had blood pressured entered. Among visits with a prescription generated, 24.5% had prescriptions electronically prescribed. In multivariate regression analysis, we found no significant correlations between comfort with computers, positive attitudes about EHRs, or negative attitudes about EHRs and any of the measures of use.
Comfort with computers and attitudes about EHRs did not predict future use of the EHR functions. Our findings suggest that meaningful use of the EHR may not be affected by providers׳ prior attitudes about EHRs.