System Models for Synchronous Strategies in Operational Healthcare Forecasting
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- Sophie Witter & Natasha Palmer & Dina Balabanova & Sandra Mounier‐Jack & Tim Martineau & Anna Klicpera & Charity Jensen & Miguel Pugliese‐Garcia & Lucy Gilson, 2019. "Health system strengthening—Reflections on its meaning, assessment, and our state of knowledge," International Journal of Health Planning and Management, Wiley Blackwell, vol. 34(4), pages 1980-1989, October.
- Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
- Lakshmy Subramanian, 2021. "Effective Demand Forecasting in Health Supply Chains: Emerging Trend, Enablers, and Blockers," Logistics, MDPI, vol. 5(1), pages 1-21, February.
- Bloom, David E. & Canning, David & Kotschy, Rainer & Prettner, Klaus & Schünemann, Johannes, 2024.
"Health and economic growth: Reconciling the micro and macro evidence,"
World Development, Elsevier, vol. 178(C).
- Bloom, David E. & Canning, David & Kotschy, Rainer & Prettner, Klaus & Schünemann, Johannes, 2018. "Health and Economic Growth: Reconciling the Micro and Macro Evidence," IZA Discussion Papers 11940, Institute of Labor Economics (IZA).
- David E. Bloom & David Canning & Rainer Kotschy & Klaus Prettner & Johannes J. Schünemann, 2019. "Health and Economic Growth: Reconciling the Micro and Macro Evidence," NBER Working Papers 26003, National Bureau of Economic Research, Inc.
- Schünemann, Johannes & Bloom, David E. & Canning, David & Kotschy, Rainer & Prettner, Klaus, 2018. "Health and Economic Growth: Reconciling the Micro and Macro Evidence," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181554, Verein für Socialpolitik / German Economic Association.
- Bloom, David & Canning, David & Kotschy, Rainer & Prettner, Klaus & Schünemann, Johannes, 2022. "Health and Economic Growth: Reconciling the Micro and Macro Evidence," CEPR Discussion Papers 17393, C.E.P.R. Discussion Papers.
- David E. Bloom & David Canning & Rainer Kotschy & Klaus Prettner & Johannes Schünemann & Rainer Franz Kotschy, 2022. "Health and Economic Growth: Reconciling the Micro and Macro Evidence," CESifo Working Paper Series 9806, CESifo.
- Sterman, J.D., 2006. "Learning from evidence in a complex world," American Journal of Public Health, American Public Health Association, vol. 96(3), pages 505-514.
- Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
- Said Abasse Kassim & Jean-Baptiste Gartner & Laurence Labbé & Paolo Landa & Catherine Paquet & Frédéric Bergeron & Célia Lemaire & André Côté, 2022. "Benefits and limitations of business process model notation in modelling patient healthcare trajectory: a scoping review protocol," Post-Print hal-04366441, HAL.
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Keywords
healthcare delivery; healthcare forecasting; LMICs; optimization; digital tools; artificial intelligence (AI);All these keywords.
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