Online Program

Finding a predictive model for post-hospitalization adverse events

Tuesday, November 3, 2015

Henry Carretta, PhD MPH, Department of Behavioral Sciences and Social Medicine, Florida State University College of Medicine, Tallhassee, FL
Katrina McAfee, MS, College of Medicine, Florida State University, Tallahassee, FL
Dennis Tsilimingras, MD, MPH, Department of Family Medicine and Public Health Sciences, Wayne State University School of Medicine, Detroit, MI
There is urgency to determine the underlying risk factors for hospital readmissions in the current environment because of payers’ demands on hospital administrators to reduce re- hospitalizations and to take responsibility for their patients’ transition back to the community.  Hospitals with excessive readmissions, defined as an admission to a hospital within 30 days of a discharge from the same or another hospital, risk non-payment or reduced payments for treatment.  Adverse events are sometimes associated with readmission to the hospital.  This study explored possible patient demographic and health care system risk factors for adverse events post-hospitalization of 684 randomly sampled patients admitted to a large community hospital.  Eligible patients were recruited at bedside into a 10-month prospective cohort study.  Adverse events were assessed based on certainty by two physicians that the event was not due to the patient’s underlying medical conditions.  Types of events include but are not limited to the following:  fever, pain or discomfort, nausea or vomiting, cough, rash, or death. 

            Using a logistic regression model, the following factors were predictive of adverse events during a 6 week post-hospitalization period:  whether or not the primary care provider knew of the patient’s initial hospitalization, patient alcohol use, patient prescription drug use, distance to the hospital from the patient’s residence, driving time to the hospital from the patient’s residence, patient education level, and indicators for patient urbanicity classification.   The model has a 70.9% accuracy rate in the test dataset for predicting patient’s with post-hospitalization adverse events and may serve as a starting point in the discussion on how to reduce hospital readmission rates.

Learning Areas:

Administration, management, leadership
Provision of health care to the public

Learning Objectives:
Name risk factors for post-discharge adverse events

Keyword(s): Hospitals

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: 20 years of progressive public health research experience. Supervised this student's (Katrina McAffee) work in preparing the submission to APHA.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.