Our objective was to identify and address patient-perceived barriers to integrating home telehealth visits. DESIGN: We used an exploratory sequential mixedmethods design to conduct patient needs assessments, a home telehealth pilot, and formative evaluation of the pilot. SETTING: Veterans Affairs geriatrics-renal clinic. PARTICIPANTS: Patients with scheduled clinic visits from October 2019 to April 2020. MEASUREMENTS: We conducted an in-person needs assessment and telephone postvisit interviews. RESULTS: Through 50 needs assessments, we identified patient-perceived barriers in interest, access to care, access to technology, and confidence. A total of 34 (68%) patients were interested in completing a home telehealth visit, but fewer (32 (64%)) had access to the necessary technology or were confident (21 (42%)) that they could participate. We categorized patients into four phenotypes based on their interest and capability to complete a home telehealth visit: interested and capable, interested and incapable, uninterested and capable, and uninterested and incapable. These phenotypes allowed us to create trainings to overcome patient-perceived barriers. We completed 32 home telehealth visits and 12 postvisit interviews. Our formative evaluation showed that our pilot was successful in addressing many patient-perceived barriers. All interviewees reported that the home telehealth visits improved their wellbeing. Home telehealth visits saved participants an average of 166 minutes of commute time. Five participants borrowed a device from a family member, and five visits were finished via telephone. All participants successfully completed a home telehealth visit. CONCLUSIONS: We identified patient-perceived barriers to home telehealth visits and classified patients into four phenotypes based on these barriers. Using principles of implementation science, our home telehealth pilot addressed these barriers, and all patients successfully completed a visit. Future study is needed to understand methods to deploy larger-scale efforts to integrate home telehealth visits into the care of older adults.

Background The Veterans Affairs Frailty Index (VA-FI) is an electronic frailty index developed to measure frailty using administrative claims and electronic health records data in Veterans. An update to ICD-10 coding is needed to enable contemporary measurement of frailty. Methods ICD-9 codes from the original VA-FI were mapped to ICD-10 first using the Centers for Medicaid and Medicare Services (CMS) General Equivalence Mappings. The resulting ICD-10 codes were reviewed by two geriatricians. Using nationals cohort of Veterans ≥65 years old, the prevalence of deficits contributing to the VA-FI and associations between the VA-FI and mortality over years 2012-2018 were examined. Results The updated VA-FI-10 includes 6422 codes representing 31 health deficits. Annual cohorts defined on October 1 of each year included 2 266 191 to 2 428 115 Veterans, for which the mean age was 76 years, 97-98% were male, 78-79% were white, and the mean VA-FI was 0.20-0.22. The VA-FI-10 deficits showed stability before and after the transition to ICD-10 in 2015, and maintained strong associations with mortality. Patients classified as frail (VA-FI ≥0.2) consistently had a hazard of death more than two-times higher than non-frail patients (VA-FI <0.1). Distributions of frailty and associations with mortality varied with and without linkage to CMS data and with different assessment periods for capturing deficits. Conclusions The updated VA-FI-10 maintains content validity, stability, and predictive validity for mortality in a contemporary cohort of Veterans ≥65 years old, and may be applied to ICD-9 and ICD-10 claims data to measure frailty.

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