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. A terminology infrastructure should: (A) Include discrete care plan concepts; (B) Include sets of profession-specific concerns, decisions, and interventions; (C) Communicate rationales, anticipatory guidance, and guidelines that inform decisions among the care team; (D) Define semantic linkages across clinical events and professions; (E) Define sets of shared patient goals and sub-goals, including patient stated goals; (F) Capture evaluation toward achievement of goals. These requirements were mapped to AHRQ Care Coordination Measures Framework. Limitations: This study used a constrained set of clinician-validated clinical scenarios. Terminology models for goals and decisions are unavailable in SNOMED CT, limiting the ability to evaluate these aspects of the proposed infrastructure. Conclusions: Defining and linking subsets of care planning concepts appears to be feasible, but also essential to model interprofessional care planning for common co-occurring conditions and chronic diseases. We recommend the creation of goal dynamics and decision concepts in SNOMED CT to further enable the necessary models. Systems with flexible terminology management infrastructure may enable intelligent decision support to identify conflicting and aligned concerns, goals, decisions, and interventions in shared care plans, ultimately decreasing documentation effort and cognitive burden for clinicians and patients.