ArticleDigital health
Should employees be empowered? Resource allocation in telemedicine.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study investigates optimal resource allocation strategies in a multichannel healthcare system that integrates telemedicine and offline services. It compares hospital-led and physician-led decision-making models under varying market coverage conditions. Methods: A game-theoretical model is developed to analyze how hospitals and autonomous physicians make decisions regarding telemedicine and offline healthcare service provision. The model incorporates factors such as revisit risk, patient copayment ratios, and market coverage. Analytical and numerical methods are used to evaluate patient choice behavior, hospital profit, and social welfare under different decision modes. Results: In fully covered markets, physician autonomy improves resource flexibility and maximizes social welfare. Conversely, in partially covered markets, centralized hospital decision-making yields better outcomes, particularly under moderate revisit risk and copayment conditions. In high-risk scenarios, a hybrid strategy balancing both models is most effective. Numerical simulations further validate these findings, highlighting key differences in outcomes under varied patient cost-sharing and risk settings. Conclusion: Decision-making autonomy should be context-dependent. In fully covered healthcare markets, hospitals may benefit from empowering physicians to improve adaptability and efficiency. In contrast, centralized management remains preferable in partially covered markets. A flexible combination of both models offers the best performance under high uncertainty and revisit risk.
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