The Age-Friendly Learning Healthcare System: Replicating electronic health record based documentation metrics for 4Ms care.

BACKGROUND

University of Utah Health (UUH) is an academic medical center that achieved "committed to care excellence" in age-friendly care in 2021 and has a long-standing culture of quality improvement central to a learning health system. University of California San Francisco (UCSF) developed electronic health record (EHR) documentation metrics for inpatient assessment of the 4Ms (What Matters, Medication, Mentation, and Mobility) based on the Institute for Healthcare Improvement's recommended care practice for an Age-Friendly Healthcare System. In partnership with UCSF, we replicated the assessment and action EHR metrics with local adaptations for each of the 4Ms at UUH.

METHODS

The UCSF team shared 4Ms documentation metrics and Structured Query Language code used to assess 4Ms care at UCSF. At UUH, this code was adapted for a different relational database management system and local clinical context. We assessed 4Ms care, individual M, and composite measures of all 4Ms, for all patients aged 65 and older admitted to UU Hospital between January 1, 2019 and December 31, 2021. We conducted a clinical validation of individual patient cases to confirm accuracy of 4Ms queries.

RESULTS

In the 3-year study period, 16,489 qualifying patients, mean age 74.2, were admitted to UU Hospital in a total of 25,070 admissions with mean length of stay of 6.08 days. We were able to replicate 14 of the 16 EHR metrics of individual 4Ms developed at UCSF and five composite measures. For the composite measure addressing completeness of 4Ms care, 50% of patient encounters had all 4Ms administered during their encounter.

CONCLUSION

Indicators of the completeness of 4Ms care can be measured using EHR data to validate implementation of the 4Ms at multiple academic medical centers. Key lessons to support future scaled-up assessments include the importance of adapting EHR measures to local activities and involving expert data analysts.