Identifying Optimal Acute Care Comparators to Inform the Evaluation of an Advanced Care at Home Pilot Program.
Martin ElliottGina Rinetti-VargasPatricia KipnisAriel R HermKent WongAgnieszka WitkowskiJesica DeputyVivian ReyesFernando BarredaLaura C MyersVincent X LiuPublished in: The Permanente journal (2023)
Background Hospital at Home (H@H) programs-which seek to deliver acute care within a patient's home-have become more prevalent over time. However, existing literature exhibits heterogeneity in program structure, evaluation design, and target population size, making it difficult to draw generalizable conclusions to inform future H@H program design. Objective The objective of this work was to develop a quality improvement evaluation strategy for a H@H program-the Kaiser Permanente Advanced Care at Home (KPACAH) program in Northern California-leveraging electronic health record data, chart review, and patient surveys to compare KPACAH patients with inpatients in traditional hospital settings. Methods The authors developed a 3-step recruitment workflow that used electronic health record filtering tools to generate a daily list of potential comparators, a manual chart review of potentially eligible comparator patients to assess individual clinical and social criteria, and a phone interview with patients to affirm eligibility and interest from potential comparator patients. Results This workflow successfully identified and enrolled a population of 446 comparator patients in a 5-month period who exhibited similar demographics, reasons for hospitalization, comorbidity burden, and utilization measures to patients enrolled in the KPACAH program. Conclusion These initial findings provide promise for a workflow that can facilitate the identification of similar inpatients hospitalized at traditional brick and mortar facilities to enhance outcomes evaluations for the H@H programs, as well as to identify the potential volume of enrollees as the program expands.
Keyphrases
- end stage renal disease
- electronic health record
- ejection fraction
- quality improvement
- newly diagnosed
- acute care
- chronic kidney disease
- healthcare
- prognostic factors
- systematic review
- physical activity
- palliative care
- emergency department
- type diabetes
- machine learning
- skeletal muscle
- metabolic syndrome
- case report
- deep learning
- cross sectional
- adipose tissue
- weight loss
- artificial intelligence
- drug induced