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By Truven Staff

Barriers to Adoption of Clinical Data Analytics for Population Health

Larry Yuhasz imageIn the recent article, Data Analytics Continues Upward Trend, the authors discuss the growth potential of healthcare analytics and the factors that will enable or inhibit this growth. Although the clinical data analytics trend is casting upward, momentum is being held back by a few key factors. First of all, the lack of electronic medical record (EMR) interoperability tends to silo clinical data by care setting and facility. Many analytic requirements to develop predictive risk scores, prevent readmissions, and measure risk require the ability to analyze clinical data across care settings and facilities. The demands of payment reform will ultimately prevail, yet Integrating the Healthcare Enterprise (IHE) vendor protection of proprietary formats remains strong.

Second, the majority of the U.S. health system is operating in a fee-for-service business model. Not until the majority of revenue shifts to at-risk models will the requirements for population health analytics really blossom. This is happening in enlightened pockets across the country and requires leadership education and changing HIT investment strategies to take root.

Third, many hospital systems are not operationally experienced in implementing enterprise-wide decision support. Unlike health plans and carriers who have been leveraging information to manage their business for decades, hospitals have tended to manage their operations along siloed service lines, with their physician network ultimately calling the shots on resource requirements. Payment reform fundamentally changes this dynamic and sees many more physicians being employed by physicians and connected with analytical platforms that can guide not only point of care decision making, but also retrospective review of clinical and cost performance.

Finally, all healthcare data emanates from patient encounters. Claims data is triggered via coding work flow to optimize billing, whereas clinical data is captured based upon proprietary EMR data entry requirements.  In today’s fee-for-service world, the clinical coding leveraged for claims purposes may or may not jive with the clinical data fields entered for EMR collection purposes. This creates downstream data aggregation and analytical methodology challenges. Over time, as payment reform stimulates a higher percentage of value-based care, the collection of administrative and clinical data must not only become more efficient at the encounter level, but also more analytically relevant for real time and retrospective analytical purposes.

Ultimately, the pace of analytical growth will be enabled through a combination of payment reform, operational change across all healthcare stakeholder groups, and technical innovation that overcomes barriers to data flow and utility.

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Larry Yuhasz
Director for Strategy and Business Development
3709

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