Prioritization and Refinement of Clinical Data Elements within EHR Systems.

Standardization of clinical data element (CDE) definitions is foundational to track, interpret, and analyze patient states, populations, and costs across providers, settings and time - critical activities to achieve the Triple Aim: improving the experience of care, improving the health of populations, and reducing per capita healthcare costs. We defined and implemented two analytical methods to prioritize and refine CDE definitions within electronic health records (EHRs), taking into account resource restrictions to carry out the analysis and configuration changes: 1) analysis of downstream data needs to identify high priority clinical topics, and 2) gap analysis of EHR CDEs when compared to reference models for the same clinical topics. We present use cases for six clinical topics. Pain Assessment and Skin Alteration Assessment were topics with the highest regulatory and non-regulatory downstream data needs and with significant gaps across documention artifacts in our system, confirming that these topics should be refined first.

A Practical Approach to Governance and Optimization of Structured Data Elements.

Definition and configuration of clinical content in an enterprise-wide electronic health record (EHR) implementation is highly complex. Sharing of data definitions across applications within an EHR implementation project may be constrained by practical limitations, including time, tools, and expertise. However, maintaining rigor in an approach to data governance is important for sustainability and consistency. With this understanding, we have defined a practical approach for governance of structured data elements to optimize data definitions given limited resources. This approach includes a 10 step process: 1) identification of clinical topics, 2) creation of draft reference models for clinical topics, 3) scoring of downstream data needs for clinical topics, 4) prioritization of clinical topics, 5) validation of reference models for clinical topics, and 6) calculation of gap analyses of EHR compared against reference model, 7) communication of validated reference models across project members, 8) requested revisions to EHR based on gap analysis, 9) evaluation of usage of reference models across project, and 10) Monitoring for new evidence requiring revisions to reference model.