How far down the managed care road? A comparison of primary care outpatient services in a Veterans Affairs medical center and a capitated multispecialty group practice
ABSTRACT Under increasing pressure to provide more efficient, higher-quality care, the Department of Veterans Affairs (VA) is expanding primary care and implementing other managed care techniques. To assess the magnitude of performance improvement possible in the VA and to investigate potential barriers to implementation of new techniques, we compared a VA facility with similar managed care organizations on specific managed care performance benchmarks. METHODS AND DATA COLLECTION: Detailed case studies of a large VA medical center and a large capitated multispecialty group practice in the same region were carried out. Various qualitative and quantitative data were collected between October 1, 1994, and September 30, 1997. Unstructured and semistructured interviews, participant and direct observations, document review, electronic data abstractions, and patient surveys were used to collect the data.
Patients in the VA medical center were poorer (average income, $13300 per year), older (36.5% aged 65 years and older), and more likely to be homeless (10.5%). The VA patients saw more specialists and made more emergency department visits than managed care patients. Although the VA had better electronic information flows, its providers saw fewer patients, had more unscheduled visits, and received fewer consultant reports, and its patients waited longer. Inpatient utilization was also higher (length of stay averaged 8 days) among VA primary care patients.
On many dimensions the VA did not compare favorably with the efficiency or lower utilization of the capitated managed care practice. Part of the reason must be attributed to the VA's multiple missions, which include teaching and research; another reason is the VA's role to be a service provider to all eligible veterans regardless of sociodemographic or health characteristics. Whether these differences are also caused by different case mix, or differences in socioeconomic status of patients, surprisingly is not well understood. This hampers future efforts to use managed care techniques to improve the operation of the VA.
SourceAvailable from: Peter A GlassmanJAMA The Journal of the American Medical Association 04/2000; 283(13). DOI:10.1001/jama.283.13.1715 · 30.39 Impact Factor
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ABSTRACT: Given the size of the patient population of the Veterans Health Administration (VHA), it is likely the largest single provider of health care for sexual and gender minority (SGM) individuals in the United States, including lesbian, gay, bisexual, and transgender persons. However, current VHA demographic data-collection strategies limit the understanding of how many SGM veterans there are, thereby making a population-based understanding of the health needs of SGM veterans receiving care in VHA difficult. In this article, we summarize the emergent research findings about SGM veterans and the first initiatives that have been implemented by VHA to promote quality care. Though the research on SGM veterans is in its infancy, it suggests that SGM veterans share some of the health risks noted in veterans generally and also risks associated with SGM status. Some promising resiliency fac-tors have also been identified. These findings have implications for both VHA and non-VHA systems in the treat-ment of SGM veterans. However, more research on the unique needs of SGM veterans is needed to fully understand their health risks and resiliencies in addition to health-care utilization patterns.
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ABSTRACT: In paper, we propose a robust security instrument that can detect the location and identity of talker in variable noisy environment. This instrument calculates the value of the signal-to-noise (SNR) using a discrete-valued SNR estimator. This SNR value is used to adapt the performance of speech/nonspeech classifier to the surrounding noisy environment. If the detected signal is speech then a novel multi-engine talker identification (TT) will determine the identity of the talker, else an audio classification system will determine the audio type of the detected signal (e.g. widows breaking, and wind sounds). Multi-engine TI utilizes the SNR value to select the TI engine, from a set of six engines (five different SNR environments and a "clean" environment), with the SNR training condition that best matches the surrounding environment; greater TI accuracy should be achieved when training and test environments are similar. The performance of this instrument is evaluated using twelve test environments, with the SNR ranging from -10 dB to clean environment (SNR > 50 dB). The proposed instrument achieves an average classification accuracy of 92% over an SNR range of 10 dB to clean environments; an enhancement of 38% over the instrument trained in a clean environment.Conference Record - IEEE Instrumentation and Measurement Technology Conference 01/2008; DOI:10.1109/IMTC.2008.4547192