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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.

References

  1. Choy, G. et al. Current applications and future impact of machine learning in radiology. Radiology 288, 318–328 (2018).

    Article  Google Scholar 

  2. Winasti, W., Elkhuizen, S., Berrevoets, L., van Merode, G. & Berden, H. Inpatient flow management: a systematic review. Int. J. Health Care Qual. Assur. 31, 718–734 (2018).

    Article  Google Scholar 

  3. Zhao, Y. et al. Bottleneck detection for improvement of emergency department efficiency Bus. Process Manag. J. 21, 564–585 (2014).

    Article  Google Scholar 

  4. Benneyan, J. C. An introduction to using computer simulation in healthcare: patient wait case study. J. Soc. Health Diabetes 5, 1–15 (1997).

    Google Scholar 

  5. Duguay, C. & Chetouane, F. Modeling and improving emergency department systems using discrete event simulation. SAGE J. 83, 311–320 (2007).

    Google Scholar 

  6. Ghanes, K. et al. A comprehensive simulation modeling of an emergency department: a case study for simulation optimization of staffing levels. In Proc. 2014 Winter Simulation Conference (IEEE, 2014).

  7. Rossetti, M. D. Simulation Modeling and Arena (John Wiley and Sons, 2009).

  8. Subramaniyan, M., Skoogh, A., Gopalakrishnan, M. & Salomonsson, H. An algorithm for data-driven shifting bottleneck detection. Cogent Eng. 3, 1–19 (2016).

    Google Scholar 

  9. Tayne, S., Merrill, C. & Saxena, R. Maximizing operational efficiency using an in-house ambulatory surgery model at an academic medical center. Found. Am. Coll. Healthc. Exec. 63, 118–129 (2018).

    Google Scholar 

  10. Attarian, D. E., Wahl, J. E., Wellman, S. S. & Bolognesi, M. P. Developing a high-efficiency operating room for total joint arthroplasty in an academic setting. Clin. Orthop. Relat. Res. 471, 1832–1836 (2013).

    Article  Google Scholar 

  11. Schwarz, P. et al. Lean processes for optimizing OR capacity utilization: prospective analysis before and after implementation of value stream mapping (VSM). Langenbeck’s Arch. Surg. 396, 1047–1053 (2011).

    Article  Google Scholar 

  12. Wolf, F. A., Way, L. W. & Stewart, L. The efficacy of medical team training: improved team performance and decreased operating room delays. Ann. Surg. 252, 477–483 (2010).

    Google Scholar 

  13. Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P. & Bokrantz, J.A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. Comput. Ind. Eng. 125, 533–544 (2018).

    Article  Google Scholar 

  14. Priore, P., Gómez, A., Pino, R. & Rosillo, R. Dynamic scheduling of manufacturing systems using machine learning: an updated review. Artif. Intell. Eng. Des. Anal. Manuf. 28, 83–97 (2014).

    Article  Google Scholar 

  15. Thomas, T. E., Koo, J., Chaterji, S. & Bagchi, S. MINERVA: a reinforcement learning-based technique for optimal scheduling and bottleneck detection in distributed factory operations. In Proc. 10th Int. Conf. Communication Systems and Networks (IEEE, 2018).

  16. Elhenawy, M. M. Z. Applying Machine and Statistical Learning Techniques to Intelligent Transport Systems: Bottleneck Identification and Prediction, Dynamic Travel Time Prediction, Driver Stop-Run Behavior Modeling, and Autonomous Vehicle Control at Intersections. PhD thesis, Virginia Polytechnic Institute and State Univ. (2015).

  17. Fadlullah, Z. M. et al. State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surv. Tutorials 19, 2432–2455 (2017).

    Article  Google Scholar 

  18. Matsunaga, A. & Fortes, J. A. B. On the use of machine learning to predict the time and resources consumed by applications. In Proc. 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (IEEE, 2010).

  19. Joshi, M. V., Agarwal, R. C. & Kumar, V. Predicting rare classes: can boosting make any weak learner strong? In KDD ‘02 Proc. 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2002).

  20. Freund, Y. & Schapire, R. E. A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14, 771–780 (1999).

    Google Scholar 

  21. Holbrook, A. et al. Shorter perceived outpatient MRI wait times associated with higher patient satisfaction. J. Am. Coll. Radiol. 13, 505–509 (2016).

    Article  Google Scholar 

  22. Anderson, R. T., Camacho, F. T. & Balkrishnan, R. Willing to wait?: The influence of patient wait time on satisfaction with primary care. BMC Health Serv. Res. 7, 31 (2007).

    Article  Google Scholar 

  23. Brandenburg, L., Gabow, P., Steele, G., Toussaint, J. & Tyson, B. Innovation and best practices in health care scheduling. NAM Perspect. 5, 1–24 (2015).

    Article  Google Scholar 

  24. Dibble, E. H., Baird, G. L., Swenson, D. W. & Healey, T. T. Psychometric analysis and qualitative review of an outpatient radiology-specific patient satisfaction survey: a call for collaboration in validating a survey instrument. J. Am. Coll. Radiol. 14, 1291–1297 (2017).

    Article  Google Scholar 

  25. Singh, S. C., Sheth, R. D., Burrows, J. F. & Rosen, P. Factors influencing patient experience in pediatric neurology. Pediatr. Neurol. 60, 37–41 (2016).

    Article  Google Scholar 

  26. Kuhn, M. Applied Predictive Modeling (Springer, 2013).

  27. Jaworsky, C., Pianykh, O. & Oglevee, C. Patient feedback on waiting time displays. Am. J. Med. Qual. 32, 108–108 (2016).

    Article  Google Scholar 

  28. Bertsimas, D., King, A. & Mazumder, R. Best subset selection via a modern optimization lens. Ann. Stat. 44, 813–852 (2016).

    Article  MathSciNet  Google Scholar 

  29. Khalid, S., Khalil, T. & Nasreen, S. A survey of feature selection and feature extraction techniques in machine learning. In Proc. Science and Information Conference (Science and Information Conference, 2014).

  30. Bottou, L., Curtis, F. E. & Nocedal, J. Optimization methods for large-scale machine learning. Soc. Ind. Appl. Math. Rev. 60, 223–311 (2018).

    MathSciNet  MATH  Google Scholar 

  31. Dietterich, T. Overfitting and undercomputing in machine learning. ACM Comput. Surv. 27, 326–327 (1995).

    Article  Google Scholar 

  32. Benjamin, A. S. et al. Modern machine learning far outperforms GLMs at predicting spikes. Preprint at https://doi.org/10.1101/111450 (2017).

  33. Austin, P. C. A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality. Stat. Med. 26, 2937–2957 (2007).

    Article  MathSciNet  Google Scholar 

  34. Mohri, M., Rostamizadeh, A. & Talwalkar, A. Foundations of Machine Learning (MIT Press, 2012).

  35. Hastie, T. & Tibshirani, R. Generalized Additive Models (Chapman and Hall, 1990).

  36. Dominici, F., McDermott, A., Zeger, S. L. & Samet, J. M. On the use of generalized additive models in time-series studies of air pollution and health. Am. J. Epidemiol. 156, 193–203 (2002).

    Article  Google Scholar 

  37. Prasad, A. M., Iverson, L. R. & Liaw, A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9, 181–199 (2006).

    Article  Google Scholar 

  38. Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. Variable selection using random forests. Pattern Recognit. Lett. 31, 2225–2236 (2010).

    Article  Google Scholar 

  39. Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).

    Article  Google Scholar 

  40. De’ath, G. Boosted trees for ecological modeling and prediction. Ecology 88, 243–251 (2007).

    Article  Google Scholar 

  41. Haykin, S. Neural Networks: A Comprehensive Foundation (Prentice Hall, 1994).

  42. Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015).

    Article  Google Scholar 

  43. Olden, J. D. & Jackson, D. A. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model. 154, 135–150 (2002).

    Article  Google Scholar 

  44. Hemaya, S. & Locker, T. How accurate are predicted waiting times, determined upon a patient’s arrival in the emergency department? Emergency Med. J. 29, 316–318 (2012).

    Article  Google Scholar 

  45. Halford, G. S., Baker, R., McCredden, J. E. & Bain, J. D. How many variables can humans process? Psychol. Sci. 16, 70–76 (2005).

    Article  Google Scholar 

  46. Iyengar, S. S. & Lepper, M. R. When choice is demotivating: can one desire too much of a good thing? J. Pers. Soc. Psychol. 79, 995–1006 (2000).

    Article  Google Scholar 

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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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