Factors identified by experts to support decision making for post acute referral

Abstract

Background: Each year, more than 13 million post acute referral decisions are made for Medicare recipients, yet there are no national, empirically derived decision support tools to assist in making these important decisions.

Objectives: The aim of this study was to elicit expert knowledge about factors important to referral decision making and identify the characteristics of hospitalized patients who need a post acute referral.

Methods: This was a retrospective and prospective mixed-methods study of the referral decisions made by discharge planning experts for 355 hospitalized older adults. Variables included sociodemographics, living arrangement, insurance, diagnosis, comorbid conditions, adverse events, medications, home care use, hospitalization in last 30 days or 6 months, patients' perception of need for and use of assistive devices or post acute services, length of stay, cognition, self-rated health, depression, functional status, and post acute referral decision.

Results: The final model identified six factors associated with the need for a post acute referral. A cutpoint was derived with a sensitivity and specificity of 87.6% and 63.2%, respectively. Experts were more likely to refer patients who had no or intermittent help available (odds ratio [OR] = 3.0), major walking restrictions (OR = 6.5), less than excellent self-rated health (3.1 and 4.0 times more likely with good and fair-poor health, respectively), remained in the hospital longer (OR = 1.2), and had higher depression scores (OR = 1.1) or number of comorbidities (OR = 1.2).

Discussion: This study begins to identify information useful to clinicians caring for hospitalized older adults who may benefit from post acute services. By assuring the systematic, valid, and reliable collection of these items, the multidisciplinary team is alerted to patients who may benefit from post acute services. Further work is needed to increase the specificity and generalizability of the model and to test its effects on patient and clinician outcomes.