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OBJECTIVE: To examine the effect of enrollee switching from a broad-network accountable care organization (ACO) health maintenance organization (HMO) to a high performance ACO-HMO with a selective narrow network and comprehensive patient navigation system on access, utilization, expenditures, and enrollee experiences. DATA SOURCES: Secondary administrative data were obtained for 2016-2020, and primary interview and survey data in 2021. STUDY DESIGN: Fixed-effects instrumental variable analyses of administrative data and regression analyses of survey data. Outcomes included access, utilization, expenditures, and enrollee experience. Background information was gathered via interviews. DATA COLLECTION/EXTRACTION METHODS: We obtained medical expenditure/enrollment and access data on continuously enrolled members in a broad-network ACO-HMO (n = 24,555), a subset of those who switched to a high-performance ACO-HMO in 2018 (n = 7664); interviews of organizational leaders (n = 13); and an enrollee survey (n = 512). PRINCIPAL FINDINGS: Health care effectiveness data and information Set (HEDIS) access measures were not different across plans. However, annual utilization dropped by 15.5 percentage points (95% CI: 18.1, 12.9) more in the high-performance ACO-HMO, with relative annual expenditures declining by $1251 (95% CI: $1461, $1042) per person per year. High-performance ACO-HMO enrollees were 10.1 percentage points (95% CI 0.001, 0.201) more likely to access primary care usually or always as soon as needed and 11.2 percentage points (95% CI 0.007, 0.217) more likely to access specialty care usually or always as soon as needed. Plan satisfaction was 7.1 percentage points (95% CI: -0.001, 0.138) higher in the high-performance ACO-HMO. Interviewees noted the comprehensive patient navigation system was designed to ensure patients remained in the narrow network to receive care. CONCLUSIONS: ACO and HMO contracts with selective narrow networks supported by comprehensive patient navigation can reduce expenditures and improve specialty access and patient satisfaction compared to broad-network plans that lack these features. Payers should consider implementing narrow networks with comprehensive support systems.

Photographic identification (photo ID) is a well-established, non-invasive, and relatively cost-effective technique to collect longitudinal data from species that can be individually recognised based on natural markings. This method has been improved by computer-assisted pattern recognition software which speed up the processing of large numbers of images. Freely available algorithms exist for a wide range of species, but the choice of software can have significant effects on the accuracy of individual capture histories and derived demographic parameter estimates. We tested the performance of three open source, semi-automated pattern recognition software algorithms for harbour seal (Phoca vitulina vitulina) photo ID: ExtractCompare, I3S Pattern and Wild-ID. Performance was measured as the ability of the software to successfully score matching images higher than non-matching images using the cumulative density function (CDF). The CDF for the top ranked potential match was highest for Wild-ID (CDF1 = 0.34–0.58), followed by ExtractCompare (CDF1 = 0.24–0.36) and I3S pattern (CDF1 = 0.02–0.3). This trend emerged regardless of how many potential matches were inspected. The highest performing aspects in ExtractCompare were left heads, whereas in I3S Pattern and Wild-ID these were front heads. Within each aspect, images collected using a camera and lens performed higher than images taken by a camera and scope. Data processing within ExtractCompare took  > 4 × longer than Wild-ID, and  > 3 × longer than I3S Pattern. We found that overall, Wild-ID outperformed both ExtractCompare and I3S Pattern under tested scenarios, and we therefore recommend its assistance in harbour seal photo ID. [ABSTRACT FROM AUTHOR]