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LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan

Author

Listed:
  • Mei-Chin Su

    (Department of Nursing, Taipei Veterans General Hospital, Taipei 112, Taiwan
    Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei 112, Taiwan)

  • Yu-Chun Chen

    (Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
    Department of Family Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
    School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan)

  • Mei-Shu Huang

    (Department of Nursing, Taipei Veterans General Hospital, Taipei 112, Taiwan)

  • Yen-Hsi Lin

    (Department of Family Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan)

  • Li-Hwa Lin

    (Department of Nursing, Taipei Veterans General Hospital, Taipei 112, Taiwan
    Institute of Public Health, National Yang Ming Chiao Tung University, Taipei 112, Taiwan)

  • Hsiao-Ting Chang

    (Department of Family Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
    School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan)

  • Tzeng-Ji Chen

    (Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
    Department of Family Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
    School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan)

Abstract

Background: Effectively predicting and reducing readmission in long-term home care (LTHC) is challenging. We proposed, validated, and evaluated a risk management tool that stratifies LTHC patients by LACE predictive score for readmission risk, which can further help home care providers intervene with individualized preventive plans. Method: A before-and-after study was conducted by a LTHC unit in Taiwan. Patients with acute hospitalization within 30 days after discharge in the unit were enrolled as two cohorts (Pre-Implement cohort in 2017 and Post-Implement cohort in 2019). LACE score performance was evaluated by calibration and discrimination (AUC, area under receiver operator characteristic (ROC) curve). The clinical utility was evaluated by negative predictive value (NPV). Results: There were 48 patients with 87 acute hospitalizations in Pre-Implement cohort, and 132 patients with 179 hospitalizations in Post-Implement cohort. These LTHC patients were of older age, mostly intubated, and had more comorbidities. There was a significant reduction in readmission rate by 44.7% (readmission rate 25.3% vs. 14.0% in both cohorts). Although LACE score predictive model still has room for improvement (AUC = 0.598), it showed the potential as a useful screening tool (NPV, 87.9%; 95% C.I., 74.2–94.8). The reduction effect is more pronounced in infection-related readmission. Conclusion: As real-world evidence, LACE score-based risk management tool significantly reduced readmission by 44.7% in this LTHC unit. Larger scale studies involving multiple homecare units are needed to assess the generalizability of this study.

Suggested Citation

  • Mei-Chin Su & Yu-Chun Chen & Mei-Shu Huang & Yen-Hsi Lin & Li-Hwa Lin & Hsiao-Ting Chang & Tzeng-Ji Chen, 2021. "LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan," IJERPH, MDPI, vol. 18(3), pages 1-13, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1135-:d:488318
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    References listed on IDEAS

    as
    1. Mei-Chin Su & Yi-Jen Wang & Tzeng-Ji Chen & Shiao-Hui Chiu & Hsiao-Ting Chang & Mei-Shu Huang & Li-Hui Hu & Chu-Chuan Li & Su-Ju Yang & Jau-Ching Wu & Yu-Chun Chen, 2020. "Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated H," IJERPH, MDPI, vol. 17(3), pages 1-17, February.
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    Cited by:

    1. Pietro Ferrara & Luciana Albano, 2022. "Advances in Population-Based Healthcare Research: From Measures to Evidence," IJERPH, MDPI, vol. 19(20), pages 1-4, October.

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