4 Essential Lessons for Adopting Predictive Analytics in Healthcare
wide range of industries and expertise. The software is built to deploy reports across cloud, in-house hardware and mobile devices. Current versions include predictive capabilities such as tools for regression, clustering and custom R scripting.
Greeewave Systems has recently introduced a new release of its collaborative predictive analytics platform, AXON Platform™. The software is also available in the cloud, on-premise or as a managed appliance. Features include data-preparation tools, model building, performance evaluation and model management tools. While the developer interface is the familiar Microsoft Excel environment, completed models can be deployed in various and flexible ways. The software can also leverage the PMML-based predictive models from SAS, SPSS, R and other platforms.
Machine learning is a well-studied discipline with a long history of success in many industries. Healthcare can learn valuable lessons from this previous success to jumpstart the utility of predictive analytics for improving patient care, chronic disease management, hospital administration and supply chain efficiencies. The opportunity that currently exists for health care systems is to define what “predictive analytics” means to them and how can it be used most effectively to make improvements within their system.
In order to be successful, clinical event prediction and subsequent intervention should be both content driven and clinician driven. Importantly, the underlying data warehouse platform is key to gathering rich data sets necessary for training and implementing predictors. Notably, prediction should be used in the context of when and where needed – with clinical leaders who have the willingness to act on appropriate intervention measures. The more specific term is prescriptive analytics, which includes evidence, recommendations and actions for each predicted category or outcome. Specifically, prediction should link carefully to clinical priorities and measurable events such as cost effectiveness, clinical protocols or patient outcomes. Finally, these predictor-intervention sets can be evaluated most effectively when they’re housed within that same data warehouse environment.
Would you like to use or share these concepts? Download this predictive analyitcs presentation highlighting the key main points.
1. Ben-Chetrit, E., et al., A simplified scoring tool for prediction of readmission in elderly patients hospitalized in internal medicine departments. Isr Med Assoc J, 2012. 14(12): p. 752-6.
2. Krumholz, H.M., et al., Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA, 2013. 309(6): p. 587-93.
3. Hosein, F.S., et al., A systematic review of tools for predicting severe adverse events following patient discharge from intensive care units. Crit Care, 2013. 17(3): p. R102.
4. Psotka, M.A. and J.R. Teerlink, Strategies to prevent postdischarge adverse events among hospitalized patients with heart failure. Heart Fail Clin, 2013. 9(3): p. 303-20, vi.
5. Bradley, E.H., et al., Hospital strategies associated with 30-day readmission rates for patients with heart failure. Circ Cardiovasc Qual Outcomes, 2013. 6(4): p. 444-50.
6. Berry, J.G., et al., Pediatric readmission prevalence and variability across hospitals. JAMA, 2013. 309(4): p. 372-80.
7. Bardach, N.S., et al., Measuring hospital quality using pediatric readmission and revisit rates. Pediatrics, 2013. 132(3): p. 429-36.
8. Eadington, W.R., The economics of casino gambling. The Journal of Economic Perspectives, 1999. 13(3): p. 173-192.
9. Croson, R. and J. Sundali, The gambler’s fallacy and the hot hand: Empirical data from casinos. Journal of Risk and Uncertainty, 2005. 30(3): p. 195-209.
10. Frees, E.W. and E.A. Valdez, Understanding relationships using copulas. North American actuarial journal, 1998. 2(1): p. 1-25.
11. Cox, S.H. and Y. Lin, Natural hedging of life and annuity mortality risks. North American Actuarial Journal, 2007. 11(3): p. 1-15.
12. Shaywitz, D. Turning Information Into Impact: Digital Health's Long Road Ahead. 2012 9/17/2013]; Available from: https://www.forbes.com/sites/davidshaywitz/2012/12/30/turning-information-into-impact-digital-healths-long-road-ahead/.
13. Crockett, D.K., et al., Utility of gene-specific algorithms for predicting pathogenicity of uncertain gene variants. J Am Med Inform Assoc, 2012. 19(2): p. 207-11.
14. Rothman, M.J., S.I. Rothman, and J.t. Beals, Development and validation of a continuous measure of patient condition using the Electronic Medical Record. J Biomed Inform, 2013. 46(5): p. 837-48.