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Intersections of machine learning and epidemiological methods for health services research
1Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA, 02115, USA.
International Journal of Epidemiology
|April 3, 2020
Summary
Machine learning offers new tools for health services research, though its use is limited. Future directions involve integrating machine learning with advanced epidemiological methods for better healthcare insights.
Area of Science:
- Health Services Research
- Epidemiology
- Machine Learning
Background:
- Health services research addresses complex healthcare system questions.
- Epidemiology significantly contributes to health services research.
- Traditional parametric regression dominates, with limited machine learning adoption.
Purpose of the Study:
- To review the current application of machine learning in health services research.
- To identify future research directions at the intersection of machine learning and epidemiology.
Main Methods:
- Review of machine learning applications in health care spending, outcomes, and quality.
- Discussion of underleveraged epidemiological methods in health services research.
Main Results:
- Machine learning is emerging in specific health services research areas.
- Significant advances in epidemiological methods remain underutilized.
Conclusions:
- Machine learning adoption in health services research is growing but faces challenges.
- Integrating machine learning with epidemiological methods presents key future opportunities.
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