Implementation of a centralized discharge planning office during the COVID-19 pandemic: translating the experience from the emergency to routine clinical practice
Keywords:
Patient discharge; hospital discharge planning; centralized discharge planning office; COVID-19.Abstract
Background and aim During the COVID-19 emergency, the lombardy region (northern Italy) implemented a regional Centralized Discharge Planning Office (CDPO) to promptly manage the discharge requests, rapidly match the needs of discharge hospitals with the availability of admission facilities and ensure the management of the entire discharge process. To improve the discharge process in routine clinical practice, maintaining the role of the CDPO could be of great interest. This paper describes the experience of the CDPO during the COVID-19 pandemic and discusses the possibility to translate this operational model to routine clinical practice.
Methods: The PRIAMO web portal was developed to manage discharge requests with centralized and standardized procedures. The activity on PRIAMO consisted of three stages: discharge request, sorting process, and discharge follow-up phase. To evaluate the activity of the CDPO, these indicators were considered: average time (hours) between patient discharge and transfer acceptance; average time (hours) between patient discharge and effective admission to the new facility; percentage of transfers whose destination was found directly by the CDPO; percentage of reallocations beyond 24 hours; mean distance between discharge and admission facilities.
Results: Process indicator evaluation showed a great reduction in the time between the discharge and the admission to post-acute care facilities. Transfers whose destination was found directly by the CDPO progressively increased. Reallocations beyond 24 hours by the CDPO decreased, suggesting an improvement in the quality of the operations.
Conclusions: Centralized discharge planning has enabled timely and efficient management of discharge requests even in the moment of a surge, saving time and costs for acute care hospitals.
References
Gustavson AM, Toonstra A, Johnson JK, Ensrud KE. Reframing hospital to home discharge from ‘should we?’ to ‘how can we?’: COVID-19 and beyond. J Am Geriatr Soc 2021; 69: 608–609.
Weerahandi H, Mak W, Burack OR, Canter BE, Reinhardt JP, Boockvar KS. Discharge processes in a skilled nursing facility affected by COVID-19. J Am Geriatr Soc 2021; 69: 2437–2439.
Kim Y, Sung HK, Bang JH, Koh IS, Joh JS, Ju YS, Min HS, Chin BS, Chung KH. Letter to editor: strategy for hospitalization and discharge of COVID-19 patients: based on the nationwide clinical course analysis. J Korean Med Sci 2020; 35: e353.
Donker T, Bürkin FM, Wolkewitz M, Haverkamp C, Christoffel D, Kappert O, Hammer T, Busch HJ, Biever P, Kalbhenn J, Bürkle H, Kern WV, Wenz F, Grundmann H. Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity. Infect Control Hosp Epidemiol 2021; 42: 653-658.
WHO. Critical preparedness, readiness and response actions for COVID‐19. World Health Organization; 2020. https://apps.who.int/iris/bitstream/handle/10665/331494/WHO-2019-nCoV-Community_Actions-2020.2-eng.pdf Accessed May 30, 2022.
Borgen I, Romney MC, Redwood N, Delgado B, Alea P, George BH, Puzziferro J, Shihabuddin L. From hospital to home: an intensive transitional care management intervention for patients with COVID-19. Popul Health Manag 2021; 24: 27-34.
Civil Protection Department, Presidency of the Council of Ministers, Italian Government, Coronavirus dashboards, http://arcg.is/C1unv Accessed on May 30, 2022.
Delibera della Giunta Regionale XI/2906 dell’8 Marzo 2020, Disposizioni in ordine alle Strutture Extra Ospedaliere.
McGinley A, Pearse RM. A national early warning score for acutely ill patients. BMJ 2012; 345: e5310.
Vaes AW, Machado FVC, Meys R, Delbressine JM, Goertz YMJ, Van Herck M, Houben-Wilke S, Franssen FME, Vijlbrief H, Spies Y, Van 't Hul AJ, Burtin C, Janssen DJA, Spruit MA. Care Dependency in Non-Hospitalized Patients with COVID-19. J Clin Med 2020; 9: 2946.
Dorogush AV, Gulin A, Gusev G, Kazeev N, Prokhorenkova L, Vorobev A. Fighting biases with dynamic boosting. arXiv:1706.09516, 2017.
Dorogush AV, Gulin A, Gusev G. CatBoost: gradient boosting with categorical features support. Workshop on ML Systems at NIPS 2017. https://catboost.ai/docs/concepts/python-reference_parameters-list.html
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