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Working Paper: Case Study

Trinity Health: Using Digitization, Unification, and Data Analytics to Tame the Quality, Cost, and Accessibility Problems of Healthcare

Trinity Health sought to tame quality, cost and accessibility problems with enterprise-wide digitization, unification and analytical decision making.
CASE STUDY

An in-depth description of a firm’s approach to an IT management issue (intended for MBA and executive education)

Abstract

Trinity Health of Novi, Michigan has sought to tame healthcare’s quality, cost, and accessibility problems by using enterprise-wide digitization, unification, and analytical decision making. It started by making strategic commitments to an enterprise-wide digital platform and a unified operating model. These commitments created a foundation for analytical decision making and organizational improvements. Operating units across the enterprise sought to adopt standardized information systems applications, standardized clinical and operational processes, and standardized data types and definitions. The standardization of applications, processes, and data definitions increased the quality of performance metrics and fostered performance benchmarking across similar operating units. Transparency of performance results strengthened accountability and fostered collaboration and best practice sharing across operating units. Lower performing units started to learn and adopt the best practices of higher performing units. This organizational learning enabled Trinity Health to increase the quality and efficiency of its care. The resources freed up by the efficiencies were used to increase the accessibility of Trinity Health’s services to poor and uninsured patient populations. Going forward, Trinity Health faces challenges in maintaining and upgrading its digitization, unification, and analytics foundation as it acquires new entities and co-evolves with the rapidly changing healthcare industry; expanding the foundation to all entities in its continuum of care; and building more sophisticated analytical skills and expertise to enable a more predictive enterprise.

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About the Authors

MIT CISR Researcher

Huseyin Tanriverdi, University of Texas at Austin and Visiting Scholar at MIT Sloan Center for Information Systems Research (CISR)

MIT CISR Researcher

Kui Du, University of Texas at Austin

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Jeanne W. Ross, MIT Sloan Center for Information Systems Research (CISR)

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