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The n-by-T Target Discharge Strategy for Inpatient Units

Author

Listed:
  • Pratik J. Parikh
  • Nicholas Ballester
  • Kylie Ramsey
  • Nan Kong
  • Nancy Pook

Abstract

Background. Ineffective inpatient discharge planning often causes discharge delays and upstream boarding. While an optimal discharge strategy that works across all units at a hospital is likely difficult to identify and implement, a strategy that provides a reasonable target to the discharge team appears feasible. Methods. We used observational and retrospective data from an inpatient trauma unit at a Level 2 trauma center in the Midwest US. Our proposed novel n-by-T strategy—discharge n patients by the Tth hour—was evaluated using a validated simulation model. Outcome measures included 2 measures: time-based (mean discharge completion and upstream boarding times) and capacity-based (increase in annual inpatient and upstream bed hours). Data from the pilot implementation of a 2-by-12 strategy at the unit was obtained and analyzed. Results. The model suggested that the 1-by-T and 2-by-T strategies could advance the mean completion times by over 1.38 and 2.72 h, respectively (for 10 AM ≤ T ≤ noon, occupancy rate = 85%); the corresponding mean boarding time reductions were nearly 11% and 15%. These strategies could increase the availability of annual inpatient and upstream bed hours by at least 2,469 and 500, respectively. At 100% occupancy rate, the hospital-favored 2-by-12 strategy reduced the mean boarding time by 26.1%. A pilot implementation of the 2-by-12 strategy at the unit corroborated with the model findings: a 1.98-h advancement in completion times (P

Suggested Citation

  • Pratik J. Parikh & Nicholas Ballester & Kylie Ramsey & Nan Kong & Nancy Pook, 2017. "The n-by-T Target Discharge Strategy for Inpatient Units," Medical Decision Making, , vol. 37(5), pages 534-543, July.
  • Handle: RePEc:sae:medema:v:37:y:2017:i:5:p:534-543
    DOI: 10.1177/0272989X17691735
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    Cited by:

    1. Mahsa Pahlevani & Majid Taghavi & Peter Vanberkel, 2024. "A systematic literature review of predicting patient discharges using statistical methods and machine learning," Health Care Management Science, Springer, vol. 27(3), pages 458-478, September.

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