LibGuides: HTM 552: Module 4

Module 4 Required Resources

Business intelligence in the healthcare industry: the utilization of a data-driven approach to support clinical decision making
Provides example of data-driven clinical decision support, prompting the question as to when it makes more sense for computers to decide on treatments than doctors.

Basile, L. J., Carbonara, N., Pellegrino, R., & Panniello, U. (2023). Business intelligence in the healthcare industry: the utilization of a data-driven approach to support clinical decision making. Technovation, 120, 102482.
Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis.
Highlights the inconsistency of patient care and cost-efficiency clinical decision support outcomes, prompting questions around how best to standardize its implementation.

Chen, W., Howard, K., Gorham, G., O’Bryan, C. M., Coffey, P., Balasubramanya, B., & Cass, A. (2022). Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis. Journal of the American Medical Informatics Association, 29(10), 1757-1772.

An open science approach to artificial intelligence in healthcare.
This proposal for an “open science” approach to developing helpful AI tools for health care settings, in contrast to proprietary approaches, prompts the question how we might best incentivize collective AI/ML capabilities in health care.

Paton, C., & Kobayashi, S. (2019). An open science approach to artificial intelligence in healthcare. Yearbook of Medical Informatics, 28(1), 47–51. https://doi.org/10.1055/s-0039-1677898.