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• W4385721328 abstract "The adoption of Electronic Health Records (EHR) has increased significantly in hospitals and physician clinics due to the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009,1, 2 but long-term care providers have lagged behind.2, 3 While previous studies focused on EHR adoption in skilled nursing facilities or home care facilities,4, 5 there is a lack of understanding of EHR adoption among Adult Day Services (ADS). ADS plays a critical role in providing post-acute care for patients discharged from hospitals as well as for those with chronic illness management concerns. Accurate documentation of patients' health information and improved communication with other healthcare providers is crucial for ADS providers to ensure patient safety and better coordination of care.7 The study aims to assess EHR adoption among ADS using nationally representative data and identify associated organizational characteristics. Data came from the 2018 National Post-acute and Long-term Care Study (NPALS), conducted by the National Center for Health Statistics (NCHS). The questionnaire was completed by Adult Day Services Centers (ADSCs) directors or knowledgeable staff. Among 6361 ADSCs in the sampling framework, 1367 were deemed eligible, and 672 of them completed the provider questionnaire, for a weighted response rate of 50%. After adjusting for eligibility and non-response, the data had a final estimated national representative sample of 4200 ADSCs.6 EHR adoption was assessed using a yes/no variable to determine if ADSCs used EHRs beyond accounting/billing purposes. Organizational characteristics, including ownership status, Medicaid licensure, Medicaid reimbursement percentage, ADSC location, capacity, occupancy rate, establishment years, and computer capabilities for functions such as recording patients' information were evaluated as factors associated with EHR adoption. Logistic regressions assessed the likelihood of ADSCs adopting EHR, with two sequential models evaluating their association with adoption. Nagelkerke R-square measured model fit. SPSS version 28, employing complex survey weights, was used for analysis. Listwise deletion handled missing data (<3% across all variables), with no identified multicollinearity issues. Statistical significance was set at p < 0.05 (two-tailed). In the fully weighted sample, 4164 ADSCs were included in the analysis, out of which, 1218 (30.2%) had adopted EHR. Logistic regression analyses (Table 1) revealed that ADSCs' capacity, location near other healthcare providers, establishment years, occupancy rate, Medicaid authorization setup, and percentage of Medicaid patients were significant predictors of EHR adoption in Model 1. The addition of computer capabilities to the model enhanced predictive power. Notably, computer capability to record clinical notes, medications, and allergies was associated with over five times the likelihood of EHR adoption. ADSCs with computer capability for recording demographics as well as Medicaid authorization setup were more than twice as likely to adopt EHR. The inclusion of computer capabilities improved the model fit, with Nagelkerke R-squared increasing from 0.119 to 0.526 in Model 1 and Model 2, respectively. The findings from this study revealed that one-third of ADSCs had adopted EHRs for purposes beyond accounting or billing. ADSCs with EHR adoption tended to have higher capacity, shorter establishment periods, higher occupancy rates, and Medicaid authorization. They also had a higher percentage of Medicaid patients and greater computer capabilities. This rate is much lower compared to skilled nursing facilities (66%) or home health agencies (78%).4 The study highlighted Medicaid reimbursement and higher percentages of Medicaid patients as significant predictors of EHR adoption in ADSCs. This suggests that Medicaid requirements could drive the adoption and use of EHRs in these settings, given Medicaid's role as a major payer for ADS services.7 Furthermore, the study found that ADSCs located near other healthcare providers were more likely to adopt EHRs, suggesting peer influence and opportunities for collaboration and care integration. Creating supportive environments that incentivize adoption and facilitate knowledge sharing, such as peer-based learning networks and financial incentives, could promote EHR adoption in ADSCs. Even after accounting for basic organizational characteristics, computer capabilities remained significantly associated with EHR adoption. This highlights the importance of investing in computer infrastructure to support EHR adoption among ADSCs. Policymakers could consider providing financial incentives, technical support, and training to encourage ADSCs to invest in necessary hardware and software. While the study provides valuable insights, there are some limitations to consider. The cross-sectional nature of the data prevents establishing causal relationships between organizational characteristics and EHR adoption. The study did not examine the cost of EHR adoption which may be a significant concern for smaller-scale ADSCs. Despite these limitations, this study contributes to a comprehensive understanding of EHR adoption in ADSCs, an essential community-based long-term care service sector. All authors planned the study. Yawen Li, Jay Chok, and Geoffrey Cui analyzed the data and verified statistical analyses. Yawen Li and Jay Chok wrote the manuscript, and Kenneth Shultz and Don Roosan provided feedback for substantial revisions. We thank William Zagorski, Chair of the National Association of Adult Day Services for his valuable insights and feedback on the initial draft that greatly contributed to the improvement of this manuscript. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors have no conflicts. No sponsor." @default.
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• W4385721328 date "2023-08-10" @default.
• W4385721328 modified "2023-10-17" @default.
• W4385721328 title "Electronic health record adoption among adult day services: Findings from the national study of long‐term care providers" @default.
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• W4385721328 doi "https://doi.org/10.1111/jgs.18549" @default.
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