Document Type
Abstract
Publication Date
1-2020
Academic Year
2019-2020
Abstract
Background: The vast majority of healthcare costs are spent on the last decade of life. Older patients often have complex medical histories complicated further by physical, mental and social limitations. High levels of hospital readmittance and nonadherence further complicate care for senior adult patients. Continuing Care Retirement Communities (CCRCs) are long term care facilities that attempt to support this diverse array of problematic patients. Residents can live in several different groupings called “Levels of Care” (LOC). To maximize safety, quality of care, and patient satisfaction, it is important to place residents in the right context. This project seeks to drive consistency by creating a comprehensive integrated workflow for resident specific site selection.
Methods: To develop the model, surveys were conducted in a suburban CCRC called The Hill at Whitemarsh. Employee workflow was assessed in order to specifically identify problematic bottlenecks. Stakeholders were then canvassed to establish user-centered solutions. Several LOC tools were created and piloted until the current iteration was produced.
Results: Questionnaires were developed to systematically guide patient placement while respecting each patient’s needs. Qualitative data showed the LOC questionnaire is preferred over a rote decision tree because administrators were able to use their personal expertise in conjunction with the questionnaire to place residents in the best environment.
Conclusions: Restructuring the placement process will allow CCRCs to more efficiently care for this complex population. The streamlined consistency is predicted increase patient satisfaction and employee gratification. In order to validate this tool, quantitative studies are needed and scalable methods must be developed.
Recommended Citation
Klein, Austin; Safian, Nicholas; Schultheis, Grant; Ezeonu, Sopuru; and Snyderman, MD, Danielle, "Helping Continuing Care Retirement Communities Determine the Best Level of Care for Each Patient" (2020). Phase 1. Paper 16.
https://jdc.jefferson.edu/si_des_2022_phase1/16
Language
English
