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Evaluating the operational and financial performance of Community-Based visiting nursing center for elderly with the balanced scorecard model
Evaluating the operational and financial performance of Community-Based visiting nursing center for elderly with the balanced scorecard model
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Evaluating the operational and financial performance of Community-Based visiting nursing center for elderly with the balanced scorecard model
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Evaluating the operational and financial performance of Community-Based visiting nursing center for elderly with the balanced scorecard model
Evaluating the operational and financial performance of Community-Based visiting nursing center for elderly with the balanced scorecard model

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Evaluating the operational and financial performance of Community-Based visiting nursing center for elderly with the balanced scorecard model
Evaluating the operational and financial performance of Community-Based visiting nursing center for elderly with the balanced scorecard model
Journal Article

Evaluating the operational and financial performance of Community-Based visiting nursing center for elderly with the balanced scorecard model

2025
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Overview
Background Aging populations have increased demand for long-term care, raising concerns about quality, efficiency, and sustainability. While the Balanced Scorecard (BSC) is widely used for performance evaluation, it is underutilized in Community-Based Elderly Visiting Nursing Centers (CB-EVNC). This study applies the BSC model to evaluate performance across four perspectives—learning and growth, internal processes, customer, and financial—to identify key performance indicators (KPIs) and enhance operational efficiency. Methods A secondary data analysis was conducted using simulation data from the 3 S Business Intelligence System (3 S-BIS) program, reflecting operational data from a home-based long-term care facility in Seoul (January–December 2021). Pearson correlation and path analysis were performed using IBM AMOS 25.0. Model fit was evaluated using chi-square tests, Goodness of Fit Index (GFI), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA). Results Total pay correlated positively with visit frequency ( r  =.938, p  <.001) and staff count ( r  =.931, p  <.001), while visit frequency negatively correlated with beneficiary status ( r =–.330, p  <.001). Path analysis showed that visit frequency significantly increased with staff count (β = 0.917, p  <.001), but total performance rate decreased with higher staff numbers (β = − 0.466, p  <.001). Beneficiary status was positively influenced by visit frequency (β = 1.328, p  <.001) and performance rate (β = 0.273, p  <.001) but negatively affected by staff count (β = − 1.439, p  <.001). Financial performance was primarily driven by visit frequency (β = 0.977, p  <.001). The final path model demonstrated acceptable model fit, supporting the interconnected nature of operational efficiency and service quality. Conclusions The study confirms that staffing levels and process efficiency significantly influence financial sustainability and service quality in home-based long-term care facilities. While increased staffing improves service delivery, it may reduce efficiency, highlighting the need for strategic workforce management and continuous professional training. Future research should expand to multiple institutions and adopt longitudinal designs to assess long-term impacts. Standardized performance management systems using the BSC model could enhance service quality and support long-term care policy development.