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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
Development and Implementation of a Predictive
Model of Hospitalization Risk among Pediatric
Home Care Patients
Daniel Kurowski
, David Russell
, Tasha Hamilton
, Rocco Napoli
, Robert Rosati
redictive models of hospitalization risk can be used to identify patients who may benet from more inten-
sive services and transitional care interventions. Previous work on predictive hospitalization risk models for
adult home care patients has been undertaken. The aim of this presentation is to describe the development
and implementation of a predictive model of hospitalization risk for a pediatric homecare population using
information collected at the start of home care. We examined a range of demographic and clinical characteristics including:
referral source, prior admission status, the presence of a complex chronic condition, self-rated general health, the use of
therapy equipment, and prescription medications.
Methods: We conducted a retrospective observational study of pediatric home care patients who were served by a large,
urban, not-for-prot home healthcare agency and who were admitted and discharged from 2008 to 2011 (N=14,216). In-
formation was obtained from patient intake, clinical assessment and medication history databases. We employed a logistic
regression model with backward selection to examine the demographic and clinical characteristics associated with hospitaliza-
tion within 60 days of admission to home care. Predicted probabilities were calculated from the model and used to estimate
each patient’s level of hospitalization risk. Concordance between predictive model scores and judgments of hospitalization
risk made via chart reviews by three clinical nurse specialists was evaluated using Cohen’s kappa statistic.
Results: A number of clinical factors were found to be signicantly associated with hospitalization, including: the pres-
ence of a complex chronic condition, use of medical therapy equipment (oxygen, IV/infusion therapy, and enteral/parenteral
nutrition), fair or poor self-rated health, and prescriptions within certain therapeutic classes of medications (i.e. ulcer med-
ications, psychotropic medications, anticonvulsants, anti-nauseates, penicillins, hematinics, and sedative barbiturates). We
also identied service characteristics that were signicantly associated with hospitalization risk, including: referral from the
hospital, a prior history of home care service, and enrollment in a managed care insurance plan. Patients with the highest
predicted level of risk had a hospitalization rate of 28.7% compared to a hospitalization rate of 1.2% among patients with
the lowest predicted level of risk. Results from the model validation indicated a moderate level of agreement between the
predictive model and the nurse specialist’s risk judgment (Cohen’s kappa=0.44); reasons for moderate agreement will be
reviewed in detail. Hospitalization risk scores for newly admitted patients are disseminated to supervisors once a week via
e-mail using an automated SAS program.
Discussion: Our ndings suggest that information collected at the start of home care can be used to identify pediatric
patients who have a greater risk of hospitalization. In our population of pediatric patients, 63% of hospitalized home care
patients were discharged to the hospital within 14 days of admission to home care. Special attention must be paid to the
information technology (IT) requirements associated with moving data into an analytic warehouse that is capable of process-
ing and returning a risk score. IT applications that reduce the lag time between assessment and processing may be useful to
home care organizations.
Conclusion: The use of data-driven risk measures can be used to assist administrative sta in managing clinical resources
for pediatric home care patients, however, special attention should be made to ensure timely dissemination. Further research
is needed to evaluate whether strategies implemented to mitigate risk factors are eective in reducing hospitalization rates
among this population.
Visiting Nurse Service of New York
CenterLight Healthcare
VNA Health Group
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