What Makes Healthcare Data So Different

Below is an excerpt from the white paper Implementing Data Governance in Complex Healthcare Organizations: Challenges and Strategies by senior consultant, Carol Newcomb. The more complex the healthcare organization, the greater the need for enterprise data governance. The paper suggests building a data governance program within the context of a unified framework and explains how. You can download the full paper here.

Use of data in healthcare organizations over the past two centuries has changed dramatically, and the sheer amount of data available has grown exponentially. The emerging number of specialized applications under one roof that inform clinical and administrative decisions keeps increasing. Advancements in scientific understanding and technological developments in diagnostic techniques, surgical options and pharmaceutical sophistication, as well as the overriding need to share information between provider organizations, regulators and insurer networks have put data management issues high on the radar of healthcare executives. Given the tight payer environment for cost control and increasingly competitive pressures to demonstrate quality while also maintaining privacy, the need for data governance in healthcare organizations has become acute.

What Makes Healthcare Data So Different

The very nature of healthcare business requirements makes the collection, storage and organization of the data highly complex. Requirements that guide data availability, for example, include:

• Data should be available at the point of care, upon demand, to support clinical decision making regardless of the care setting (hospital ER, ICU, office setting, long term care, lab or radiology department, etc.);

• Data should include patient-specific, longitudinal diagnostic and procedural information coded using standard classifications, terminology and modifiers;

• Reference data (billing codes, diagnostic codes, lab and medication catalogues, etc.) need to refer to patient encounter dates, as these classifications change over time;

• Data should include patient-specific family and social histories collected from the patient or through practitioner observation;

• Data should include orders for tests and procedures as well as corresponding results, which may include specific values, images or written notes;

• Data should be collected across different practice settings, practitioners and geographic locations;

• All data should tie back to a unique patient regardless of the practitioner, care setting or location;

• Data should be secure, auditable and available to practitioners, billing organizations and oversight bodies based on HIPAA confidentiality requirements;

• De-identification of data should not remove the ability to link specific diagnoses, procedures, results and demographic characteristics to an individual patient;

• Data should be aggregated at multiple levels, including encounter date, provider, provider type, facility, department, disease, procedure, result, outcome, geography, demographics, insurance group, and many others;

• Clinical data should be linked to demographic data about the patient and family;

• Financial payment and billing data should be supported through clear clinical documentation;

• Staffing and facility operations data (inventory, equipment, rooms) should be available for analysis relative to patient encounter dates and provider data.

Given this subset of data requirements, consider all the transactional sources of data within a healthcare organization. Such a complex interaction system usually results in a “spaghetti diagram”—like the one in Figure 3 (below) that fully illustrates the pain of sorting through different clinical and administrative applications, and designing standards to link data from one system to other relevant systems for analytical purposes.

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