Currently Viewing:
The American Journal of Managed Care October 2018
Putting the Pieces Together: EHR Communication and Diabetes Patient Outcomes
Marlon P. Mundt, PhD, and Larissa I. Zakletskaia, MA
Primary Care Physician Resource Use Changes Associated With Feedback Reports
Eva Chang, PhD, MPH; Diana S.M. Buist, PhD, MPH; Matt Handley, MD; Eric Johnson, MS; Sharon Fuller, BA; Roy Pardee, JD, MA; Gabrielle Gundersen, MPH; and Robert J. Reid, MD, PhD
From the Editorial Board: Bruce W. Sherman, MD
Bruce W. Sherman, MD
Recent Study on Site of Care Has Severe Limitations
Lucio N. Gordan, MD, and Debra Patt, MD
The Authors Respond and Stand Behind Their Findings
Yamini Kalidindi, MHA; Jeah Jung, PhD; and Roger Feldman, PhD
The Characteristics of Physician Practices Joining the Early ACOs: Looking Back to Look Forward
Stephen M. Shortell, PhD, MPH, MBA; Patricia P. Ramsay, MPH; Laurence C. Baker, PhD; Michael F. Pesko, PhD; and Lawrence P. Casalino, MD, PhD
Nudging Physicians and Patients With Autopend Clinical Decision Support to Improve Diabetes Management
Laura Panattoni, PhD; Albert Chan, MD, MS; Yan Yang, PhD; Cliff Olson, MBA; and Ming Tai-Seale, PhD, MPH
Medicare Underpayment for Diabetes Prevention Program: Implications for DPP Suppliers
Amanda S. Parsons, MD; Varna Raman, MBA; Bronwyn Starr, MPH; Mark Zezza, PhD; and Colin D. Rehm, PhD
Clinical Outcomes and Healthcare Use Associated With Optimal ESRD Starts
Peter W. Crooks, MD; Christopher O. Thomas, MD; Amy Compton-Phillips, MD; Wendy Leith, MS, MPH; Alvina Sundang, MBA; Yi Yvonne Zhou, PhD; and Linda Radler, MBA
Medicare Savings From Conservative Management of Low Back Pain
Alan M. Garber, MD, PhD; Tej D. Azad, BA; Anjali Dixit, MD; Monica Farid, BS; Edward Sung, BS, BSE; Daniel Vail, BA; and Jay Bhattacharya, MD, PhD
CMS HCC Risk Scores and Home Health Patient Experience Measures
Hsueh-Fen Chen, PhD; J. Mick Tilford, PhD; Fei Wan, PhD; and Robert Schuldt, MA
Currently Reading
An Early Warning Tool for Predicting at Admission the Discharge Disposition of a Hospitalized Patient
Nicholas Ballester, PhD; Pratik J. Parikh, PhD; Michael Donlin, MSN, ACNP-BC, FHM; Elizabeth K. May, MS; and Steven R. Simon, MD, MPH

An Early Warning Tool for Predicting at Admission the Discharge Disposition of a Hospitalized Patient

Nicholas Ballester, PhD; Pratik J. Parikh, PhD; Michael Donlin, MSN, ACNP-BC, FHM; Elizabeth K. May, MS; and Steven R. Simon, MD, MPH
We developed an early warning discharge disposition prediction tool to facilitate discharge planning and coordination, potentially reducing length of hospital stay and improving patient experience.
ABSTRACT

Objectives: To develop an early warning discharge disposition prediction tool based on clinical and health services factors for hospitalized patients. Recent study results suggest that early prediction of discharge disposition (ie, whether patients can return home or require placement in a facility) can improve care coordination, expedite care planning, and reduce length of stay.

Study Design: Retrospective analysis of inpatient data; development of multiple logistic regression model and an easy-to-use score.

Methods: We used retrospective data from all patients who were admitted in 2013 to the general medical service at the Veterans Affairs Boston Healthcare System and discharged alive. A derivation-validation approach was used to build a multiple logistic regression model, which was transformed into a score for potential implementation.

Results: Of the 4760 patients discharged in 2013, 485 (10.2%) were discharged to a facility other than home. Correlates of discharge to a facility included a primary admission diagnosis of neoplasm (odds ratio [OR], 2.71; 95% CI, 1.73-4.25), diseases of the nervous system (OR, 2.53; 95% CI, 1.26-5.08), and musculoskeletal diseases (OR, 2.55; 95% CI, 1.52-4.27), as well as discharge to a facility during previous hospitalization. Patients with a prior primary diagnosis of circulatory disorder and those with comorbidity of hypertension, either complicated or uncomplicated, were less likely to be discharged to a facility. A value of 5 or greater on the 20-point scale indicated discharge to a facility with 83% sensitivity and 48% specificity.

Conclusions: A validated, easy-to-use score can assist providers in identifying upon admission those patients who may not be able to go directly home after hospitalization, thus facilitating early discharge planning and coordination, potentially reducing length of hospital stay and improving patient experience.

Am J Manag Care. 2018;24(10):e325-e331

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