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Patient's Health Status (patient + health_status)
Selected AbstractsA systematic review of evaluation in formal continuing medical educationTHE JOURNAL OF CONTINUING EDUCATION IN THE HEALTH PROFESSIONS, Issue 1 2007Jing Tian MD Abstract Introduction: Physicians spend a considerable amount of time in Continuing Medical Education (CME) to maintain their medical licenses. CME evaluation studies vary greatly in evaluation methods, levels of evaluation, and length of follow-up. Standards for CME evaluation are needed to enable comparison among different studies and to detect factors influencing CME evaluation. Methods: A review of the CME evaluation literature was conducted on primary research studies published from January 2000 to January 2006. Studies assessing only satisfaction with CME were excluded, as were studies where fewer than 50% of the participants were practicing physicians. Thirty-two studies were included in the analyses. Determinations were made about evaluation methods, outcome measures, and follow-up assessment. Results: Only 2 of 32 reviewed studies addressed all evaluation levels: physician changes in knowledge and attitudes (level 2), practices (level 3), and improved patient health status (level 4). None of the studies using self-developed instruments (n = 10) provided reliability and validity information. Only 6 studies used validated scales. Twenty studies had a follow-up period of 6 months or less, and 11 had a follow-up period between 1 and 2 years. Discussion: A gold standard for evaluating the effectiveness of CME would include assessment of all 4 levels of evaluation. A valid, reliable, and adaptable CME evaluation questionnaire addressing variables in the second level is needed to allow comparison of effectiveness across CME interventions. A minimum 1-year postintervention follow-up period may also be indicated to investigate the sustainability of intervention outcomes. [source] Getting Real Performance Out of Pay-for-PerformanceTHE MILBANK QUARTERLY, Issue 3 2008SEAN NICHOLSON Context: Most private and public health insurers are implementing pay-for-performance (P4P) programs in an effort to improve the quality of medical care. This article offers a paradigm for evaluating how P4P programs should be structured and how effective they are likely to be. Methods: This article assesses the current comprehensiveness of evidence-based medicine by estimating the percentage of outpatient medical spending for eighteen medical processes recommended by the Institute of Medicine. Findings: Three conditions must be in place for outcomes-based P4P programs to improve the quality of care: (1) health insurers must not fully understand what medical processes improve health (i.e., the health production function); (2) providers must know more about the health production function than insurers do; and (3) health insurers must be able to measure a patient's risk-adjusted health. Only two of these conditions currently exist. Payers appear to have incomplete knowledge of the health production function, and providers appear to know more about the health production function than payers do, but accurate methods of adjusting the risk of a patient's health status are still being developed. Conclusions: This article concludes that in three general situations, P4P will have a different impact on quality and costs and so should be structured differently. When information about patients' health and the health production function is incomplete, as is currently the case, P4P payments should be kept small, should be based on outcomes rather than processes, and should target physicians' practices and health systems. As information improves, P4P incentive payments could be increased, and P4P may become more powerful. Ironically, once information becomes complete, P4P can be replaced entirely by "optimal fee-for-service." [source] Quality-Adjusted Survival Estimation with Periodic ObservationsBIOMETRICS, Issue 3 2001Pai-Lien Chen Summary. Quality-adjusted survival is a measure that integrates both longevity and quality-of-life information. The analysis of quality-adjusted survival in a clinical study with data collected at periodic intervals encounters difficulties due to incomplete information. Based on observed time points, the time axis is partitioned into a set of disjoint time intervals, and under a Markovian assumption on patient's health status, the expected quality-adjusted survival is estimated as the summed product of the quality of life and its mean sojourn time of each health state within partitioned intervals. It is shown that the estimator is asymptotically normal with a simple variance calculation. A simulation study is conducted to investigate the behavior of the estimator, and a stroke study illustrates the use of the estimator. [source] The Impact of Private Insurance Coverage on Veterans' Use of VA Care: Insurance and Selection EffectsHEALTH SERVICES RESEARCH, Issue 1p1 2008Yujing Shen Objective. To examine private insurance coverage and its impact on use of Veterans Health Administration (VA) care among VA enrollees without Medicare coverage. Data Sources. The 1999 National Health Survey of Veteran Enrollees merged with VA administrative data, with other information drawn from American Hospital Association data and the Area Resource File. Study Design. We modeled VA enrollees' decision of having private insurance coverage and its impact on use of VA care controlling for sociodemographic information, patients' health status, VA priority status and access to VA and non-VA alternatives. We estimated the true impact of insurance on the use of VA care by teasing out potential selection bias. Bias came from two sources: a security selection effect (sicker enrollees purchase private insurance for extra security and use more VA and non-VA care) and a preference selection effect (VA enrollees who prefer non-VA care may purchase private insurance and use less VA care). Principal Findings. VA enrollees with private insurance coverage were less likely to use VA care. Security selection dominated preference selection and naïve models that did not control for selection effects consistently underestimated the insurance effect. Conclusions. Our results indicate that prior research, which has not controlled for insurance selection effects, may have underestimated the potential impact of any private insurance policy change, which may in turn affect VA enrollees' private insurance coverage and consequently their use of VA care. From the decline in private insurance coverage from 1999 to 2002, we projected an increase of 29,400 patients and 158 million dollars for VA health care services. [source] Measuring performance status in pediatric patients with brain tumors,experience of the HIT-GBM-C protocol,,PEDIATRIC BLOOD & CANCER, Issue 3 2010Johannes E. A. Wolff MD Abstract Background Measuring the quality of life or performance status in pediatric neurooncology has proven a challenge. Here, we report in a treatment protocol for pediatric patients with high-grade glioma and diffuse intrinsic pontine glioma. Procedure The Fertigkeitenskala Münster,Heidelberg (FMH) is a 56-item quantitative measure of health status. The number of yes answers is transformed to age-dependent percentiles. Physicians were also asked the patients' health status by their own judgment on a 1,5 scale: normal, mild handicap, age-normal activity severely reduced but patient not in bed, in bed, and in ICU. Results Assessments were available from 50 of 97 eligible patients. For 22 patients both questionnaire and the physicians score obtained. At the beginning of the treatment, only 5 patients scored over 40 FMH%, and 4 of these survived. Of 16 patients who initially scored less than 40 FMH%, 15 died. During later assessments, most FMH measures became gradually worse. FMH scores improved in three patients. The physician's judgment was documented at diagnosis and during treatment (n,=,50). Per physician, 22% of the patients were normal before chemotherapy, decreasing to 16% in the middle of the protocol. At diagnosis only 16% of patients had severely reduced activity, which increased to 30.6% in the middle of the protocol. The FMH% correlated well with the physicians' judgments (P,<,0.005). Conclusion The FMH scale is easily obtained and provides a valid assessment of health status. Patients with poor performance at diagnosis had a poorer prognosis. Pediatr Blood Cancer. 2010;55:520,524. © 2010 Wiley-Liss, Inc. [source] | ||||||||||