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Home Healthcare, Hospice, and Information Technology Innovations Conference

Innovations in Home Healthcare, Hospice, and Information Technology

A forum towards achieving evidence-based diffusion and implementation of innovations

Fri, Nov 3, 2017
Friday, Nov 3, 2017, Washington - D.C.
H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Developing a Tool to Support Decisions on Patient
Prioritization at Admission to Home Health Care
Maxim Topaz
1, 2
, Kathryn H. Bowles
illions of Americans are discharged from hospitals to home health every year and about third of them return
to hospitals. A signicant number of rehospitalizations (up to 60%) happen within the rst two weeks of
services. Early targeted allocation of services for patients who need them the most, have the potential to
decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to
identify high-risk patients in home health. This study aimed to (1) identify factors associated with priority for the rst home
health nursing visit and (2) to construct and validate a decision support tool for patient prioritization.
Methods: We recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care
coordination to identify factors supporting home health care prioritization. This was a predictive study of home health visit
priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included socio-demographics,
diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability,
self-rated health, depression, functional status, living arrangement, caregiver availability and ability and rst home health
visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the
nal model.
Results: The model identied ve factors associated with rst home health visit priority. A cut point for decisions on
low/medium versus high priority was derived with a sensitivity of 80% and specicity of 57.9%, area under receiver operator
curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid con-
dition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase
in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04).
Discussion: This study developed one of the rst clinical decision support tools for home health, the PREVENT- Priority
for Home Health Visit Tool. Further work is needed to improve the specicity and generalizability of the tool, implement an
electronic version and test its eects on patient outcomes.
University of Pennsylvania, Scho ol of Nursing
Brigham and WomenÕs health hospital
Visiting Nurse Service of New York
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