The Changing Landscape of Healthcare Data Analytics


Legislation such as the HITECH Act, as well as the shift toward fee-for-value reimbursement models, is driving the need to extract meaning from data and accelerating the market adoption of data producing technology.  Data analytics – broadly defined as the process of cleaning, transforming, and modeling data with the goal of extracting useful information – is gaining popularity within healthcare due to the proliferation of data-generating devices and infrastructures which create and store such information.   Data analytics unlock the true power of data by using statistical analysis and rules based engines to arrive at relationships and inferences that are beyond the human capacity.

  • Payers and providers are dedicating considerable time and resource to augmenting their uses of these technologies by aligning with specialized software and services firms – below are two examples. Health Language’s software based medical terminology management (MTM) solution which allows healthcare providers, vendors and payers manage updating, mapping, distributing and accessing standard medical vocabularies and administrative codes.  In standardizing code sets, a key component of health data, Health Language provides clients confidence that their data is normalized, up-to-date, and contains sufficient granularity to satisfy reporting requirements.  The “clean data” that the technology delivers enables enterprise-wide data interoperability of disparate systems, HIEs, ACOs, ICD-10 conversion, Meaningful Use compliance, and a myriad of other payer / provider challenges.  The company created this sub vertical over a decade ago and has developed their technology into an embedded, high RIO data platform for a wide variety of clients around the world.  (Disclosure: TripleTree was the exclusive advisor to HealthLanguage in its acquisition by Wolters Kluwer, who recognized the powerful core technology and multitude of relevant market applications for their business).
  • Cerecons, provide SaaS based care coordination and population management solutions to Accountable Care Organizations (ACOs) and other risk-bearing provider organizations across the U.S.   The company provides innovative and scalable care coordination workflow, analytics and reporting, and point-of-care clinical content software to more than 70 leading provider organizations, covering more than 1.5 million patients.  The core Cerecons technology is comprised of sophisticated rules engines that effectively analyze large volumes of clinical data to identify patients who need care as well as provide vital information to the provider at the point of care.  Utilizing cloud technology, Cerecons platform seamlessly integrates and analyzes data from disparate sources to provide a comprehensive view of the patient.  (Disclosure: TripleTree was the exclusive advisor to Cerecons in its recent acquisition by MEDecision, a provider of collaborative health management solutions for healthcare payers).

These are just two examples of how, despite the use case, value can be derived from healthcare data.

The healthcare universe has a wide variety of data types and in order to extract meaningful insights from data, our team has learned firsthand that it must be standardized and auditable.  Health Language checks both boxes and their outputs can be analyzed by special purpose software (such as Cerecons) to provide insight into best treatment practices, identifying gaps in care, and an array of other applications that address:

  • Structured, unstructured and  image based data
  • The increasing granularity of health records data (ICD-9 to ICD-10, SNOMED)
  • And changing reimbursement models that challenge providers to provide efficient care

The convergence of increasing amounts of available data and refined analytics technologies means that data analytics will be a core consideration for every healthcare CIO in the months and years ahead.  As well, it’s a continued focus of for our work at TripleTree.

Let us know what you think.

Jeff Farnell
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