Building a Clinical Predictive Analytic RFP: Questions

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jvion Building a Clinical Predictive Analytic RFP: Questions Every Provider Needs to Ask

Predictive analytics is a fuzzy term. The healthcare industry in general and especially providers are challenged with how to understand, apply, and drive value from new predictive analytic tools. Compounding this confusion is the broad spectrum of solutions claiming to be predictive. Strategies, implementation timelines, and pricing vary widely. And the definition of what comprises a “predictive analytic solution” is far from solidified. There are, however, key considerations every provider needs think about when evaluating a clinical predictive analytic solution. From data to IT resources, each tool will bring with it differing methods and technology to reach a prediction. To help navigate this apples-tooranges landscape, we compiled the questions that providers should think about when evaluating and comparing predictive analytic solutions. This list is by no means exhaustive. It is intended to give you and your organization footing to obtain an informed, educated, and clearer understanding of the predictive market within healthcare.

Prediction Quality and Accuracy • At what level are predictions delivered (e.g. patient, population, geographic area)? • What approach is used to reach predictions? • Machine learning: relies on artificial intelligence technologies to reach a prediction • Statistical algorithm: relies on a set of established variables to reach a risk score • Trending: relies on historical data to forecast trends and possible risk areas • How is accuracy tested? • What visibility do you have into accuracy levels? Scalability • Which use cases are pre-seeded within the solution? • How long does it take to embed a new use case? • What types of clinical environments are supported by the solution (e.g. outpatient, inpatient, post-acute, long-term care, behavioral)? Resources and Timeline • Does the solution require the establishment of an enterprise data warehouse or equivalent? • Does the solution require in-house data science resources to test and train the predictive models? • What historical data elements are required to train the predictive solution? • What maintenance costs are required? • How long does it take to start delivering predictions? Adoption and Value • What changes to the clinical work flow are required to enable the solution? • How intuitive are the outputs? • How does the solution avoid alarm fatigue? • Is the solution enabled for mobile devises? • How long will it take before you start to see a return on investment • How is return on investment calculated? #PredictWhatMatters @jvionhealth | www.jvion.com

About Jvion At Jvion, we apply predictive analytics to solve healthcare’s most pressing challenges. With the goal of improving health outcomes, our solution combines deep machine learning technologies, clinical intelligence, and advanced Clinical Patient Pod (CPP) technology to deliver the same kind of predictive genius used by search engine and consumer goods giants. We call our predictive platform RevEgis and it allows us to develop use cases for every aspect of the care continuum. Patient health sits at the center of what we do. So our predictive capabilities don’t stop at the hospital doors; they extend to support the care activities that happen within the community while delivering the strategic insights to ensure organizational viability and strength.

For Hospitals We provide predictive solutions designed to help hospitals thrive under a changing healthcare ecosystem. By delivering the most accurate patient-level predictive capabilities, we are helping hospitals reduce target illnesses, prevent patient deterioration, reduce the cost of care, and improve community health efforts. Using RevEgis, hospitals are reducing sepsis, lowering readmissions, stopping pressure ulcers, and reducing the number of heart attacks just to name a few use cases.

For Accountable Care Organizations (ACOs)

Predict \pri-’dikt\ : to declare or indicate in advance; especially : foretell on the basis of observation, experience, or scientific reason

Analytic \,a-nƏ-’li-tik\ : of or relating to analysis or analytics; especially : separating something into component parts or constituent elements

Through RevEgis, we enable ACOs to better manage population risks, determine market opportunities, track performance against Value Based Purchasing measures, and forecast Medicare spend per beneficiary. The resulting insights help ensure ACO success in today’s healthcare landscape as well as tomorrow’s.

For Integrated Networks RevEgis is designed to help integrated delivery networks and clinically integrated networks manage utilization, network performance, and financial risk while providing the predictive insights that drive improvements in health outcomes and care. RevEgis is patient-centered and physician-focused to ensure that the right and most accurate data are getting to the right people at the right time.

jvion www.jvion.com | @jvionhealth | #PredictWhatMatters | ©Jvion 2016

Definitions from Merriam-Webster: http://www.merriam-webster.com/