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Classification trees for decision making in long-term care

M Quartararo1, P Glasziou, C B Kerr

  • 1Department of Public Health, University of Sydney, Australia.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences
|November 1, 1995
PubMed
Summary
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This study developed a classification tool to predict nursing home care needs, preventing inappropriate admissions. Combining Barthel Index and Mini-Mental State Examination scores proved most effective for accurate classification.

Area of Science:

  • Gerontology
  • Health Services Research
  • Biostatistics

Background:

  • Nursing home admissions require careful assessment to prevent inappropriate placement.
  • Classification tools are essential for objective decision-making in long-term care services.

Purpose of the Study:

  • To develop and evaluate a classification tool for predicting nursing home care requirements.
  • To aid in preventing unnecessary admissions to nursing homes.

Main Methods:

  • A cohort of 295 nursing home applicants in Sydney, Australia, was studied.
  • Predictor variables included demographics, social work assessments, dementia, incontinence, Barthel Index, and Mini-Mental State Examination (MMSE).
  • Classification analysis was performed using the C4.5 Program.

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Main Results:

  • Classification trees demonstrated sensitivities over 70% for predicting nursing home care needs.
  • The optimal classification tree integrated scores from the Barthel Index and the MMSE.
  • This combined approach yielded the most accurate predictions.

Conclusions:

  • Classification trees offer a simple yet effective method for patient classification.
  • The developed classification analysis holds potential for improving decision-making in long-term care.
  • This tool can support objective assessments for nursing home placement.