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Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Анализ на въздействието върху бюджета (BIA)× | Модел на Марковска верига в здравната икономика× | |
|---|---|---|
| Област | Икономика на здравеопазването | Икономика на здравеопазването |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2005 | 1983 |
| Създател≠ | Sullivan, Mauskopf, and colleagues (ISPOR task force) | Beck & Pauker (medical decision analysis, Massachusetts General Hospital) |
| Тип | Method | Method |
| Основополагащ източник≠ | Sullivan, S. D., Mauskopf, J. A., Augustovski, F., et al. (2014). Budget Impact Analysis—Principles of Good Practice: Report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force. Value in Health, 17(1), 5-14. DOI ↗ | Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458. DOI ↗ |
| Други названия | BIA, financial impact assessment, budget consequence analysis | Markov model, state transition model, cohort simulation |
| Свързани | 5 | 5 |
| Резюме≠ | Budget impact analysis estimates the financial consequences (net costs or savings) of implementing a new health technology in a specific healthcare system or population over a short time horizon (typically 1–5 years). Distinct from cost-effectiveness analysis (which compares health outcomes per dollar), BIA answers a budgetary question: 'If we adopt this new drug/device, how much will it cost our health system next year?' Widely used by hospital procurement committees, insurance formularies, and government health budgets to assess financial feasibility and reimbursement decision. | A Markov model is a decision-analytic tool that simulates disease progression through defined health states over time, calculating cumulative costs and quality-adjusted life years (QALYs) to enable cost-effectiveness analysis. Developed by Beck and Pauker in 1983, Markov models are now the standard framework for projecting long-term outcomes of health interventions, especially chronic diseases where patients transition between clinical states (treatment response, disease progression, remission, death). Used by health technology assessment bodies and pharmaceutical companies to predict intervention value beyond trial duration. |
| ScholarGateНабор от данни ↗ |
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