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Three Approaches to Predictive Analytics in Healthcare

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Predictive analytics in healthcare must be timely, role-specific, and actionable to be successful. There are also three common types of healthcare predictive analytics: Risk scores (risk stratification using CMS-HCC or other models), What-if scenarios (simulations of specific outcomes given a certain combination of events, and Geo-spatial analytics (mapping a geographical location’s patient disease burden). The common thread in all of these is the element of action, or specifically, the intervention that really matters in healthcare predictive analytics.

Published in: Health & Medicine, Technology
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Three Approaches to Predictive Analytics in Healthcare

  1. 1. © 2014 Health Catalyst www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Three Approaches to Predictive Analytics in Healthcare By David Crockett
  2. 2. © 2014 Health Catalyst www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation. Three Approaches to Predictive Analytics in Healthcare For predictive analytics to be successful in healthcare, it must have three simple characteristics: • It must be timely • It must be role-specific • It must actionable 2 At Health Catalyst, we are using three types of predictive analytics that directly support clinical decision-making and inform administrative priorities and action. Risk scores (stratification) What-if scenarios (simulation) Geo-spatial analysis (mapping)
  3. 3. © 2014 Health Catalyst www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation. Risk stratification scoring can assist in prioritizing clinical workflow, reducing system waste, and creating financially efficient population management. Well- established risk stratification scores of low-risk, high-risk, and rising-risk can play a key role in several healthcare scenarios. 3 Risk Stratification A good vendor will work with your server administration team to understand the size and footprint of your environment.
  4. 4. © 2014 Health Catalyst www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation. 4 Simulation Another type of predictive approach we use with our client partners is simulation/what-if scenarios. These tools can be invaluable when decision-makers want to ask simple “what if” questions about a given clinical area or administrative function. For example, our Key Process Analysis (KPA) application calculates the amount of opportunity dollars available to capture as variation is reduced in a specific clinical care process. Predictive analytics used in a simulation environment allows clinicians and administrators a safe glimpse into “what if” and the likely outcomes of a given combination of events.
  5. 5. © 2014 Health Catalyst www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation. 5 Mapping Geographic information systems (GIS) and geo-spatial analysis recently passed its 50th anniversary mark, yet healthcare and geomedicine is just now beginning to embrace these data analysis tools. This is a very visual and effective approach to analytics and decision-making. Mapping layers and predictive analytics are routinely used to forecast weather, optimize supply chains, and support military deployment.
  6. 6. © 2014 Health Catalyst www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation. 6 What Really Matters for Healthcare Predictive Analytics Our message is simple: prediction is the easy part, it’s the intervention that matters. Return on investment does not reside in data itself, but in timely interpretation of that data followed by appropriate intervention. Learn how data mining is used in healthcare predictive analytics or see why sometimes big data is a big mess in healthcare analytics.
  7. 7. © 2013 Health Catalyst www.healthcatalyst.com Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Why Predictive Modeling in Healthcare Requires a Data Warehouse Also by David K Crockett David K. Crockett, Ph.D. is the Senior Director of Research and Predictive Analytics. He brings nearly 20 years of translational research experience in pathology, laboratory and clinical diagnostics. His recent work includes patents in computer prediction models for phenotype effect of uncertain gene variants. Dr. Crockett has published more than 50 peer-reviewed journal articles in areas such as bioinformatics, biomarker discovery, immunology, molecular oncology, genomics and proteomics. He holds a BA in molecular biology from Brigham Young University, and a Ph.D. in biomedical informatics from the University of Utah, recognized as one of the top training programs for informatics in the world. Dr. Crockett builds on Health Catalyst’s ability to predict patient health outcomes and enable the next level of prescriptive analytics – the science of determining the most effective interventions to maintain health.

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