Evidence map›Paper›PMID 40826076›Full record

ArticleBMC health services research2025

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

Ji Young Lim, Seong Kwang Kim

Abstract read
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Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Ji Young LimSchool of Nursing, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, 22212, Republic of Korea.ORCID http://orcid.org/0000-0001-9561-4678
Seong Kwang KimDepartment of Nursing, Gangneung-Wonju National University, 150 Namwonro Heungup-Myun, Wonju, 26403, Gangwondo, Republic of Korea. ksk1677@naver.com.ORCID http://orcid.org/0000-0002-8029-2722

Funding

National Research Foundation of Korea (NRF) No. RS-2023-00277435
6 · The paper itself

Abstract

backgroundAging 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.

methodsA 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).

resultsTotal 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.

conclusionsThe 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.

Indexed as

Community Health NursingEfficiency, OrganizationalAgedFemaleHumansMaleQuality Indicators, Health CareRepublic of KoreaSeoulAgedBalanced scorecard modelCommunity health servicesFinancial performance

Identifiers

PMID40826076
PMCPMC12363046

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.