Albert Duntugan said that UCLA Health is doing a lot of early binding of data through their Epic data warehouse, which requires substantial upfront resources to do mapping and documentation of lineage. He also said that the changing specifications from payers and monitoring of data quality requires constant vigilance from a small team of business analysts, data-quality specialists and ETL programmers. Regarding payer-provided data, Duntugan said that payers have varying levels of quality and completeness depending on the size and sophistication of the payer organization.
Partners’ Dr. Lara Terry said, “In our Enterprise Data Warehouse we combine claims and clinical data in an integration layer. It’s very effective and facilitates our analysts’ work.”
The group raised the issue of incorporating unstructured data into analytics. Ascension is experimenting with using Natural Language Processing (NLP) for physician transcriptions and nursing notes to derive meaningful clinical information. The challenge is defining standard dictionary sets. Billings Clinic uses unstructured data more for clinical decision support than for true analytics. Their transcription vendor runs their notes and reports through an NLP engine.
The conversation then turned to data quality and the issues of data accuracy, timeliness and completeness. The language of healthcare data has many ‘dialects’ that continue to interfere with the sharing and mapping of patient information. While standards exist, too many entities along the prospective interoperability pipeline don’t use them. It was noted, “If standards were used in telecom the way they are used in healthcare, then switchboard operators would still be connecting calls.” Standards exist on the billing side, with ICD-10 and CPT codes, but formal, structured, clinical data across disparate systems doesn’t exist.
Data Security Issues
The demand for role-level security is increasing with increasing data integration. Defining who gets access to what subsets of data is the responsibility of several leadership groups at Ascension. Rick Howard said, “We have some location-specific security roles that we’re trying to identify. For example, with our employed physicians, we have some information that only the dyad leadership can review because of the sensitivity of the data.”
Having enterprise-level and role-based access needs uniform roles defined across the entire enterprise. This calls for some handshake activity with HR to rebuild infrastructure for mapping access to titles. Eric Yablonka said, “We are trying to do the right thing, but we can’t continue to do it in an agile way without process improvements,” thus implying the need for this infrastructure remodel.
Christine Watts voiced a concern with the volume and variety of software vendors and their impact on security. “We integrate data across many different solutions across our organization. Some of these are custom-built solutions, but many are packaged software solutions that are hosted both internally and externally to our environment. We must move data across our entire network. As we consider outsourcing, offshoring and sharing information on exchanges, we have to consider how we secure that data in and outside of the organization.”
Deborah O’Dell said that, with regard to vendor contracting, there are challenges with people signing contracts when they don’t have any experience in backend data platforms. This can result in one vendor being out of compliance with basic data-security protocols because they outsource to—and share data with—a second vendor.
The security conversation continued with the majority of attendee organizations identifying at a SOC II level security protocol.
The Need for Social Determinants of Health
Along with patient-reported outcomes and activity-based costing data, population health requires incorporating social determinants of health: education, employment, income, community support, and family and social support data. This introduces new elements of data consent, data storage and data curation, as well as the time to track it all. Tina Esposito said this adds more to how we govern data. “We’re late to the party. We pull all this data together and it’s clear that we don’t have one person as an asset in the organization who knows all the data. We have to bring leaders of knowledge together. We need to be clear on how we use all these datasets.”
…and Longitudinal Data Management
Population health requires lifetime engagement of entire patient populations and managing longitudinal data. To manage the profile of a patient over time can involve 60 or 70 source transactional systems flowing into a data warehouse. Longitudinally connecting an individual patient’s data from all these sources requires a unique strategy and some thought about who owns the data. Dr. Randy Thompson said, “We get all this different data that lands in our data mart, but there’s no way to see the sequence of events as a patient progresses through our system.”
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