PubMed · 10153751
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
C B Grimm. 1996. Healthcare computing: achieving mobility.. https://pubmed.ncbi.nlm.nih.gov/10153751/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
GOALS OF WORK: Economic and regulatory healthcare policy is limited by the lack of population-based information on the cost of supportive care of cancer patients. The goal of this study was to estimate these costs and to explore the impact of these costs on policy decisions. PATIENTS AND METHODS: We identified estimates of the cost of outpatient and inpatient supportive care from published reports in the literature. The range of these costs (from lowest to highest) is described for each supportive care condition. These estimates were supplemented by computing the cost of all hospitalizations for these conditions in Texas between June 2000 and December 2001. Medicare payments were used to estimate average reimbursement. Costs (not charges) were standardized to 2002 US dollars using the consumer price index for medical care and Medicare's cost-to-charge ratio for Texas. MAIN RESULTS: Inpatient care for most conditions exceeded 7,000 US dollars per episode. Our population-based estimates of the cost of care were similar to literature-based estimates, except in the case where conditions could be managed in the outpatient setting. Average Medicare payments were often far lower than the estimated cost of care. For example, the estimated cost of hospitalization for management of diarrhea was 6,616 US dollars while the average Medicare payment was only 2,809 US dollars. CONCLUSIONS: Many supportive care interventions are quite expensive. In an environment focused on cost containment, a risk-based approach to expensive supportive care treatments is essential. Further study of the cost effectiveness of supportive care management strategies is indicated.
Explore the source record for details and available documents.
BACKGROUND: An outpatient setting typically includes experienced and novice resident physicians who are supervised by senior staff physicians. The performance of this kind of outpatient setting, for a given mix of experienced and novice resident physicians, is determined by the number of senior staff physicians available for supervision. The optimum mix of human resources may be determined using discrete-event simulation. METHODS: An outpatient setting represents a system where concurrency and resource sharing are important. These concepts can be modelled by means of timed Coloured Petri Nets (CPN), which is a discrete-event simulation formalism. We determined the optimum mix of resources (i.e. the number of senior staff physicians needed for a given number of experienced and novice resident physicians) to guarantee efficient overall system performance. RESULTS: In an outpatient setting with 10 resident physicians, two staff physicians are required to guarantee a minimum level of system performance (42-52 patients are seen per 5-hour period). However, with 3 senior staff physicians system performance can be improved substantially (49-56 patients per 5-hour period). An additional fourth staff physician does not substantially enhance system performance (50-57 patients per 5-hour period). CONCLUSION: Coloured Petri Nets provide a flexible environment in which to simulate an outpatient setting and assess the impact of any staffing changes on overall system performance, to promote informed resource allocation decisions.