Qualitative and Economic Impact of Standardized and Digitalized Operation Room Processes in Obesity Surgery.
Costs
Digitization
Efficiency
Obesity surgery
Quality
Workflow management system
Journal
Obesity surgery
ISSN: 1708-0428
Titre abrégé: Obes Surg
Pays: United States
ID NLM: 9106714
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
24
06
2023
accepted:
25
09
2023
revised:
16
09
2023
medline:
30
11
2023
pubmed:
23
10
2023
entrez:
22
10
2023
Statut:
ppublish
Résumé
The introduction of innovative digital solutions in healthcare lags compared to other industries but promises high potential to create value in efficiency and quality. Increasing economic pressure forces hospitals to optimize operating room (OR) processes, in which such solutions might provide additional support. This retrospective case-control and monocentric study investigated if digitalized and standardized intraoperative surgical workflows of laparoscopic Roux-en-Y gastric bypass (LRYGB) have a significant impact on efficiency, quality, and economics. Logistic and linear regression models were used to apply propensity score matching (PSM) for efficiency and odds ratio for the quality analysis. The study included 49 patients per group. The results demonstrate a significant increase in efficiency and cost-effectiveness in the treatment group. Length of stay (LoS) was 1.2 days less than in the control group (5.6 vs. 4.4). The mean of total OR and skin-to-skin time increased by 3.7% (142.00 vs. 136.80) and 8.5%, respectively (93.88 vs. 85.94). The standard deviation (SD) of total OR and skin-to-skin time decreased by 7.36 min (26.86 vs. 34.22) and 8.98 min (23.20 vs. 32.18) in the treatment group. The results of the odds ratio did not provide any conclusions on quality. Overall, costs were reduced by 318 € per patient and total revenue improved by 10,073 €. The implementation of digital workflow management systems in obesity surgery improves economic efficiency. Hospital management and payors should evaluate further support in research of the digitization of the OR, followed by reimbursement to increase and facilitate the accessibility to digital support systems.
Identifiants
pubmed: 37867185
doi: 10.1007/s11695-023-06868-w
pii: 10.1007/s11695-023-06868-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3860-3870Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Bundesamt S. Gesundheitsausgaben im Jahr 2020 auf über 440 Milliarden gestiegen [Internet]. 2022 [cited 2022 Nov 9]. Available from: https://www.destatis.de/DE/Presse/Pressemitteilungen/2022/04/PD22_153_236.html
vdek. Daten zum Gesundheitswesen: Ausgaben [Internet]. Berlin: Verband der Ersatzkassen e.V.; 2023 Mar. Available from: https://www.vdek.com/presse/daten/d_versorgung_leistungsausgaben.html
Klein S, Krupka S, Behrendt S, et al. Weißbuch Adipositas - Versorgungssituation. In: GmbH II, editor. Deutschland. Berlin: MWV Medizinisch Wissenschaftliche Verlagsgesellschaft; 2016.
OECD. The heavy burden of obesity: Oecd Heal Pol Stud; 2019.
doi: 10.1787/67450d67-en
OECD. Union E. Health at a glance: Europe 2020: Heal Glance Europe. p. 2020.
Zapp W. Strategic development in hospitals: key figures – portfolio – geocoding – occupancy management: Stuttgart: Verlag W. Kohlhammer; 2014.
Barbagallo S, Corradi L, De Ville de Goyet J, et al. Optimization and planning of operating theatre activities: an original definition of pathways and process modeling. Bmc Med Inform Decis. 2015;15:38.
doi: 10.1186/s12911-015-0161-7
Behar BI, Guth C, Salfeld R. Modernes Krankenhausmanagement. 4th ed. Berlin: Springer Gabler; 2016.
doi: 10.1007/978-3-642-36132-6
Fong AJ, Smith M, Langerman A. Efficiency improvement in the operating room. J Surg Res. 2016;204:371–83.
doi: 10.1016/j.jss.2016.04.054
pubmed: 27565073
Neumuth T, Jannin P, Schlomberg J, et al. Analysis of surgical intervention populations using generic surgical process models. Int J Comput Ass Rad. 2011;6:59–71.
Feige K, Gollnick I, Schmitz P, et al. The application of surgical procedure manager (SPM): first experience with FESS. Eur Arch Oto-rhino-l. 2017;274:3407–16.
doi: 10.1007/s00405-017-4658-9
Lahmann B, Hampel D. Impact of digital supported process workflow optimization for hip joint endoprosthesis implantation on hospital - specific process and quality ratios. Acta Univ Agric Et Silvic Mendelianae Brunensis. 2020;68:755–63.
doi: 10.11118/actaun202068040755
Graichen H, Lekkreusuwan K, Scior W. How will digitalization affect patient treatment in arthroplasty? Part I: Intraoperative aspects. J Orthop. 2020;17:A1–5.
pubmed: 32021011
Athanasiadis D, Monfared S, Whiteside JA, et al. Reducing operating room inefficiencies via a novel surgical app shortens the duration of laparoscopic Roux-en-Y gastric bypass. J Am Coll Surgeons. 2021;233:S19.
doi: 10.1016/j.jamcollsurg.2021.07.013
von Schudnat C, Schoeneberg K-P, Albors-Garrigos J, et al. The economic impact of standardization and digitalization in the operating room: a systematic literature review. J Med Syst. 2022;47:55.
doi: 10.1007/s10916-023-01945-0
Birkmeyer JD, Finks JF, O’Reilly A, et al. Surgical skill and complication rates after bariatric surgery. New Engl J Medicine. 2013;369:1434–42.
doi: 10.1056/NEJMsa1300625
Mannaerts GHH, van Mil SR, Stepaniak PS, et al. Results of implementing an enhanced recovery after bariatric surgery (ERABS) protocol. Obes Surg. 2016;26:303–12.
doi: 10.1007/s11695-015-1742-3
pubmed: 26003552
Fantola G, Agus M, Runfola M, et al. How can lean thinking improve ERAS program in bariatric surgery? Surg Endosc. 2021;35:4345–55.
doi: 10.1007/s00464-020-07926-5
pubmed: 32856155
Harris H, Horst JS. A brief guide to decisions at each step of the propensity score matching process. Pract Assess Res Evaluation. 2016;21
Hahs-Vaughn DL. Applied multivariate statistical concepts: New York: Routledge; 2016.
doi: 10.4324/9781315816685
Leite W. Practical propensity score methods using R: Sage Publications; 2017.
doi: 10.4135/9781071802854
Zhao Q-Y, Luo J-C, Su Y, et al. Propensity score matching with R: conventional methods and new features. Ann Transl Medicine. 2021;9:812–2.
doi: 10.21037/atm-20-3998
Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Research. 2011;3:399–424.
doi: 10.1080/00273171.2011.568786
mbH DKV. Calculation of treatment costs manual for use in hospitals [Kalkulation von Behandlungskosten Handbuch zur Anwendung in Krankenhäusern] [Internet]. 2016 [cited 2023 May 15]. Available from: https://www.g-drg.de/kalkulation/drg-fallpauschalen-17b-khg/kalkulationshandbuch
Garrow CR, Kowalewski K-F, Li L, et al. Machine learning for surgical phase recognition: a systematic review. Ann Surg. 2020;273:684–93.
doi: 10.1097/SLA.0000000000004425
Junger D, Beyersdorffer P, Kücherer C, et al. Service-oriented device connectivity interface for a situation recognition system in the OR. Int J Comput Ass Rad. 2022;17:2161–71.
Khan DZ, Luengo I, Barbarisi S, et al. Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0). J Neurosurg. 2022;137:51–8.
doi: 10.3171/2021.6.JNS21923