South Florida Hospital News
Monday October 26, 2020
Quote

test 2

August 2017 - Volume 14 - Issue 2
Advertisements

NovaMPH.jpg
advertizehere.gif

Why Predictive Analytics?

You didn’t have to pass statistics to deploy predictive analytics! But you must understand its value and use the right people to unleash its power.

Predictive analytics’ power comes from foretelling the future and taking action before events occur. It offers decision risk and operating cost amelioration, and improves outcomes through individualized patient care. Traditional data analysis tells you what happened. Predictive analytics tells you:
• How to better manage patient populations in real time.
• How to save on pharmacy costs.
• Market share expected from launching a new service.
• How to set optimal inventory levels.
 
Consider the value of predicting and intervening on:
• Heart failure patients at high risk for readmission - Parkland Health and Hospital System in Dallas, TX reduced readmissions by 26%.1
• Newborns with infections. Kaiser Permanente of North California reduced excessive use of antibiotics -- “0.05% of all newborns have infection confirmed by blood culture, 11% of them receive antibiotics.”2
• Patients at high risk for central line-associated bloodstream infections. Thousands of preventable deaths occur each year and billions of dollars in waste.3
• Patients at high risk for non-adherence to medication protocols. Annually, non-adherence causes 125,000 deaths, 10% rate of unnecessary hospitalizations, and $100 to $289 billion in healthcare spending waste.4
• Urgent care center characteristics that maximizes patient volume at maximum profitability.
 
Adoption of advanced predictive modeling is slow among hospitals – about 15% deployed the technique in 2015.5 Jvion survey results show these early adopters focused on patient risk or illness; most notably, “readmissions, patient deterioration, sepsis, and general patient health.”6
 
There are a number of healthcare systems that are early adopters of predictive analytics. Not surprisingly, these early adopters appear to be the most innovative and market leaders, and likely will be the most profitable in the transition from fee-for-service to payment for quality and cost-effectiveness. Their clinical divisions can identify quality considerations and their business units can identify cost considerations, and they can coordinate on predictive analytic models to increase quality and decrease costs. Doing so will be the keys to success under value-based health care.
 
Given the lack of sufficient data analytics professionals in this country, many healthcare systems have to triage the projects on which they will work, minimizing their ability to effectively manage care. However, those healthcare systems that already have data analytics professionals and do predictive modeling, have a good head start on those that do not. Some healthcare systems are slow to adopt new innovations, hoping to observe how they are adopted by others. Doing so in this space can be a prescription for disaster. It is imperative that healthcare systems understand the value of predictive analytics, and that its value is communicated to leadership. Chief Medical Officers, Chief Medical Informatics Officers, and those responsible for quality need to be cheerleaders in this regard.
 
Pushing for adoption and building a capable team should come from finance, strategic planning, quality, and management engineering departments. Return on investment and effort is many folds. But an independent assessment will help senior leadership understand the benefits, cost, and hurdles of building advanced analytic and modeling capabilities.
 
You often hear the saying culture, leadership, innovation, etc. starts at the top. Boards of Directors and Trustees of health systems, along with their Chief Executive Officers will need to appreciate the importance of predictive analytics and its use in their organizations. They will need to ensure that management is at the forefront of such issues, looking for opportunities, managing them, and employing the necessary resources, including qualified data analytics professionals. Just as boards of companies that have used off-balance financing vehicles should understand such vehicles, when to employ them, how, and when not to, healthcare systems should understand the power of predictive analytics, how they are used, and when to employ predictive analytics to increase quality and decrease costs to be competitive in this ever-changing healthcare landscape.

Richard Klass, Vice President and Co-founder, KCI Partners, Inc., can be reached at rklass@kcipartners.com. Paul DeMuro, Of Counsel, Broad and Cassel LLP, can be reached at pdemuro@broadandcassel.com.

 
 
1 R. Parkikh, Z. Obermeyer, and D. Bates (April 2016), Making Predictive Analytics a Routine Part of Patient Care, Harvard Business Review. Found at: https://hbr.org/2016/04/making-predictive-analytics-a-routine-part-of-patient-care
2 Ibid.
3 Centers for Disease Control and Prevention. Found at: https://www.cdc.gov/hai/bsi/bsi.html
4 M. Viswanathan, C, Golin, C. Jones, M. Ashok, et al. (December 2012), Interventions to Improve Adherence to Self-administered Medications for Chronic Diseases in the United States: A Systematic Review, Annals of Internal Medicine. Found at: https://www.ncbi.nlm.nih.gov/pubmed/22964778
5 Jvion (March 2015), Jvion Releases Findings from Latest Predictive Analytics in Healthcare Survey, Chimecentral, News & Press. Found at: https://chimecentral.org/jvion-releases-findings-latest-predictive-analytics-healthcare-survey/
6 Ibid.
Share |