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Improving healthcare operations management with machine learning

Nature Machine Intelligence volume 2, pages 266–273 (2020)Cite this article

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Abstract

Healthcare institutions need modern and powerful technology to provide high-quality, cost-effective care to patients. However, despite the considerable progress in the computerization and digitization of medicine, efficient and robust management tools have yet to materialize. One important reason for this is the extreme complexity and variability of healthcare operations, the needs of which have outgrown conventional management. Machine learning algorithms, scalable and adaptive to complex patterns, may be particularly well suited to solving these problems. Two major advantages of machine learning—the power of building strong models from a large number of weakly predictive features, and the ability to identify key factors in complex feature sets—have a particularly direct connection to the principal operational challenges. The main goal of this work was to study this relationship using two major types of operational problems: predicting operational events, and identifying key workflow drivers. Using practical examples, we demonstrate how machine learning can improve human ability to understand and manage healthcare operations, leading to more efficient healthcare.

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Fig. 1: From standards to analyses for operational data in healthcare.
Fig. 2: Predicting wait time.
Fig. 3: Implementing the wait time model in a hospital.
Fig. 4: Predicting workflow overloads in ED imaging (logistic random forest model, 75 predictors).
Fig. 5: The relationship between the number of predictor variables N and the percentage of model error for three ML models corresponding to three different workflows in a facility.
Fig. 6: Using ML to discover main operational features.
Fig. 7: ML versus pre-ML estimates.

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Data availability

The clinical facility wait/delay time dataset described in this work is available from our web site: https://medicalanalytics.group/operational-data-challenge.

Code availability

The Matlab code used to analyse the clinical facility wait/delay time dataset is available from our website: https://medicalanalytics.group/operational-data-challenge.

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Acknowledgements

We acknowledge C. Crowley and other former and present members of the Medical Analytics Group who contributed to these projects.

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Authors and Affiliations

  1. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Oleg S. Pianykh, Steven Guitron, Darren Parke, Pari Pandharipande, James Brink & Daniel Rosenthal

  2. Massachusetts Institute of Technology, Cambridge, MA, USA

    Chengzhao Zhang

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  1. Oleg S. Pianykh
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Contributions

O.P. and D.R. conceived the study. O.P., S.G., D.P. and C.Z. analysed the data. All authors contributed to interpreting the results. All authors contributed to writing the manuscript.

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Correspondence to Oleg S. Pianykh.

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Pianykh, O.S., Guitron, S., Parke, D. et al. Improving healthcare operations management with machine learning. Nat Mach Intell 2, 266–273 (2020). https://doi.org/10.1038/s42256-020-0176-3

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