INTEGRATIVE INSIGHTS
ON EMERGING OPPORTUNITIES

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First Analysis Healthcare Technology Team
Matthew Nicklin
Managing Director
Andrew Walsh
Managing Director
Joseph Munda
Vice President
Tracy Marshbanks
Managing Director
Healthcare Analytics
A closer look at the healthcare analytics market
April 14, 2015
  • Analytic tools and technologies have become critical across sub-sectors of healthcare, including revenue cycle management (RCM), clinical interventions, and population health management, and represent a multi-billion market opportunity we believe will be largely captured by a group of emerging vendors.
  • The majority of our report focuses on these new emerging private companies such as Health Catalyst, Apixio, Pulse8, and VisiQuate. There are currently only a handful of publicly traded healthcare analytics companies, including Verisk Health and Inovalon, both focused on the payer side of the market and growing at healthy double-digit rates.
  • More than one-quarter of hospital leaders are planning to purchase an enterprise analytics suite or at least one analytics tool in 2015, with 30% of these purchases being first-time selections vs. replacements of existing solutions. EHR vendors have mindshare but lack the analytic expertise and data independence/neutrality to dominate the market while the horizontal vendors, such as Tableau and MicroStrategy, and stack vendors, such as Oracle and IBM, lack the industry knowledge to develop the necessary workflow and content to deliver a complete solution. (Nonetheless, on April 13, IBM announced the acquisition of Explorys to strengthen its newly formed Watson Health business unit and its position in healthcare analytics.)
  • There are four basic forms of business intelligence (BI), and while most of the healthcare industry is at Stage 1 (Descriptive) or 2 (Diagnostic), several organizations are moving to Stage 3 (Predictive) or Stage 4 (Prescriptive) and using technologies such as natural language processing (NLP) and Hadoop.

TABLE OF CONTENTS

Includes profiles of 30 public and private companies

Drivers

The horizontal players

Inpatient inertia...Epic and Cerner

The payer side

Provider side

Industry background

Cognitive computing: machine learning and NLP

Social analytics

INTRODUCTION

Analytics is a broad term and critical component underlying popular technologies in areas such as revenue cycle management (RCM), clinical interventions, and population health management (PHM). In particular, data integration and aggregation, performance measurement and monitoring, and actionable intelligence are essential to PHM. Analytics is also of great interest to many industry participants, with a steady flow of new initiatives and investments. In March 2015, for example, UPMC, a major Pittsburgh-area health system, partnered with the University of Pittsburgh and Carnegie Mellon University to expand its research in health data analytics with the hope of delivering commercial products in the future. UPMC will fund these new research centers with plans to spend $10 million to $20 million annually for a period of six years, along with hundreds of millions in existing grant funds. The R&D center at Carnegie Mellon, The Center for Machine Learning, will focus on a number of areas, including big health data analytics, personalized medicine and disease modeling, privacy and security issues with big data, and a new general framework for big data in the healthcare environment.

We use the terms business intelligence and analytics interchangeably throughout our report, but both have broad meaning in terms of the underlying technologies (databases, data integration tools, predictive analytics, and machine learning) and use cases. Our focus is the area of clinical, financial, and operational analytic solutions targeting healthcare providers (primarily hospitals and health systems) vs. those vendors serving payers or pharmaceutical companies. Table 1 shows four basic forms of business intelligence (BI), and while most of the healthcare industry is at Stage 1 (Descriptive) or 2 (Diagnostic), several organizations are moving to Stage 3 (Predictive) or Stage 4 (Prescriptive) and using technologies such as NLP and Hadoop. For example, Penn Medicine uses predictive analytics to develop an early warning for sepsis (the most expensive hospital condition with a cost of more than $20 billion annually and leading cause of death) and has reduced mortality rates from 17% to 13%.

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