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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
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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
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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
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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
Takeaway Points

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.
  • Our tool can be used within 24 hours of a patient’s admission to a general internal medicine unit.
  • The tool is a scoring system that has been derived from a statistical model.
  • It will help care managers in effectively classifying, at the point of admission, patients requiring disposition to facilities other than home.
Although the Department of Veterans Affairs (VA) health system is not necessarily reimbursement-based, it strives to achieve high quality care and efficient resource utilization via evidence-based practice and continuous measurement and improvement. Effectively managing patient flow through VA medical centers requires the proactive identification of appropriate level of care and services. Early planning and coordination by the healthcare team, including physicians, social workers, rehabilitation specialists, and postacute care services, improves inpatient care quality and access while reducing costs.1,2 The ability to predict discharge disposition—whether a patient can return home or requires placement in a care facility—could expedite rehabilitation, improve coordination of care among consultants, prepare caregivers, and help community agencies plan for needed resources. It also helps reduce length of hospital stay, which, in turn, may mitigate the risks of hospitalization and improve patient recovery.3-7 Consequently, early, accurate, and effective discharge planning has emerged as a high priority for both patients and hospital systems.8

The primary objective of this study was to develop an approach that could serve as an early warning decision aid to care providers for predicting, within 24 hours of admission, the discharge disposition of hospitalized veterans based on the available clinical and health utilization factors at index and previous hospitalizations.

METHODS

Setting

The study setting was the inpatient medical service at the West Roxbury campus of the VA Boston Healthcare System (VA-BHS). At the time of the study, discharge planning occurred within an interdisciplinary group consisting of a nurse case manager, a social worker, and a clinician representing the medical team. Consultations with other services, such as physical and/or occupational therapy, were made based on the individual assessments of these groups, mostly prompted by clinical judgment.

Data Collection

We obtained 4817 records (3187 were unique patients) from the corporate data warehouse of the VA-BHS for January 1, 2013, through December 31, 2013. After excluding 57 records (1.18%) with missing discharge disposition, the final data set consisted of 4760 records. We considered factors for inclusion in this study that would be readily available to the care team within 24 hours of admission.

Index admission. For the index admission, we ascertained demographic information (ie, age, sex, race, presence of non-VA health insurance, and marital status), clinical factors (primary diagnosis, number of diagnoses), source of admission, and specialty of the admitting ward. The primary diagnosis at the time of admission is not an available field in VA administrative data; however, the primary hospitalization diagnosis, recorded clinically at the time of discharge, is routinely transcribed from the admission history and physical notes and thus represents the likely primary diagnosis at the time of admission. These diagnoses were grouped into 19 categories based on International Classification of Diseases, Ninth Revision standard code groups. We counted all recorded discharge diagnoses associated with that admission and included this sum as the number of diagnoses. Admission sources included VA nursing home, VA domiciliary, transfer from other VA hospital, outpatient treatment, and other direct admissions (eg, walk-ins, directly admitted from home, transfers from non-VA facilities). Admitting ward specialties included general (acute medicine), cardiac intensive care unit (ICU), medical ICU, medical step down, telemetry, and hospice for acute care. We included ICU patients because the variables considered in predicting their disposition are comparable with those of patients admitted to non–critical care units. We further derived the 31 Elixhauser comorbidities using the updated International Classification of Diseases, Ninth Revision, Clinical Modification coding schema of Quan et al.9 This study was approved by the VA-BHS Institutional Review Board.

Historical factors. We also derived several historical clinical and health services factors using a 12-month “look back” into 2012 records of each of the unique patients. The clinical factors included the primary diagnosis of the immediately preceding hospital admission, the number of diagnoses on the immediately preceding admission, and the discharge disposition for the immediately preceding admission. The health services/utilization factors included the number of admissions in the 12 months prior to the index admission and an indicator of whether the index admission was an all-cause readmission within 30 days.

Main outcome measure. The main outcome was the patient’s discharge disposition, home versus facility. Patients discharged to the community, including those who were homeless and referred to a shelter, were considered discharged to “home.” Facility locations included VA nursing home care units (NHCUs), also known as Community Living Centers, and non-VA nursing homes.


 
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