Abstract
AbstractThe number of available hospital beds is decreasing in many countries. Reducing the length of hospital stay (LOS) and increasing bed turnover could improve patient flow. We evaluated whether robot-assisted surgery (RAS) had a beneficial impact on the LOS in a French hospital trust with a long-established robotic program (Assistance Publique–Hôpitaux de Paris, AP-HP). We extracted data from “Programme de Médicalisation des Systèmes d’Information” to determine the median LOS for adults in our trust after RAS versus laparoscopy and open surgery in 2021–2022 for eight target procedures, and compared data nationally and at similar academic centres (same database). We also calculated the number of hospitalisation days ‘saved’ using RAS. Overall, 9326 target procedures were performed at AP-HP: 3864 (41.4%) RAS, 2978 (31.9%) laparoscopies, and 2484 (26.6%) open surgeries. The median LOS for RAS was lower than laparoscopy and open surgery for all procedures, apart from hysterectomy and colectomy (equivalent to laparoscopy). Results for urological procedures at AP-HP reflected national values. The equivalent of 5390 hospitalisation days was saved in 2021–2022 using RAS instead of open surgery or laparoscopy at AP-HP; of these, 86% represented hospitalisation days saved using RAS in urological procedures. Using RAS instead of open surgery or laparoscopy (particularly in urological procedures) reduced the median LOS and may save thousands of hospitalisation days every year. This should help to increase patient turnover and facilitate patient flow.
Publisher
Springer Science and Business Media LLC
Reference21 articles.
1. OECD. Hospital beds (indicator) (2023). https://data.oecd.org/healtheqt/hospital-beds.htm (https://doi.org/10.1787/0191328e-en); Accessed 27 May 2023
2. Buchan J, Catton H. Recover to rebuild. Investing in the nursing workforce for health system effectiveness (International Council of Nurses, March 2023). (2023). https://www.icn.ch/system/files/2023-03/ICN_Recover-to-Rebuild_report_EN.pdf; Accessed 27 May 2023
3. Stone K, Zwiggelaar R, Jones P, Mac Parthaláin N (2022) A systematic review of the prediction of hospital length of stay: towards a unified framework. PLOS Digit Health 1:e0000017. https://doi.org/10.1371/journal.pdig.0000017
4. Rojas-García A, Turner S, Pizzo E, Hudson E, Thomas J, Raine R (2018) Impact and experiences of delayed discharge: a mixed-studies systematic review. Health Expect 21:41–56. https://doi.org/10.1111/hex.12619
5. Ravaghi H, Alidoost S, Mannion R, Bélorgeot VD (2020) Models and methods for determining the optimal number of beds in hospitals and regions: a systematic scoping review. BMC Health Serv Res 20:186. https://doi.org/10.1186/s12913-020-5023-z