Gap Analysis and Refinement Recommendations of Skin Alteration and Pressure Ulcer Enterprise Reference Models against Nursing Flowsheet Data Elements.

Reference models are an essential instrument to provide structure and guidance in the creation and use of data elements within an organizations' electronic health record (EHR). Standardization of data elements is imperative to ensure clinical data is consistently and reliably captured for use in clinical documentation, care communication, and a variety of downstream data uses. Ongoing assessment and refinement of reference models and data elements are necessary to ascertain clinical data capture is applicable and inclusive across a variety of caregivers and domains.

Next generation terminology infrastructure to support interprofessional care planning.

Objective: Develop a prototype of an interprofessional terminology and information model infrastructure that can enable care planning applications to facilitate patient-centered care, learn care plan linkages and associations, provide decision support, and enable automated, prospective analytics.

Design: The study steps included a 3 step approach: (1) Process model and clinical scenario development, and (2) Requirements analysis, and (3) Development and validation of information and terminology models.

Results: Components of the terminology model include: Health Concerns, Goals, Decisions, Interventions, Assessments, and Evaluations.

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.