Influence of Blood Sampling Service Process Reengineering on Medical Services Supply: Quasi-Experimental Study.

Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE

Publication: <2011- > : Toronto : JMIR Publications
Original Publication: [Pittsburgh, PA? : s.n., 1999-

Blood Specimen Collection*/methods ; Blood Specimen Collection*/statistics & numerical data; Humans ; China ; Efficiency, Organizational/statistics & numerical data ; Tertiary Care Centers ; Delivery of Health Care

Background: Tertiary hospitals in China are confronted with significant challenges due to limited spatial capacity and workforce constraints, leading to saturated allocation of medical resources and restricted growth in medical service provision. The incorporation of digital health into medical service process reengineering (MSPR) marks a pivotal transformation and restructuring of conventional health service delivery models. Specifically, the application of MSPR to blood sampling services processes reengineering (BSSPR) holds promise for substantially enhancing the efficiency and quality of medical services through streamlining and optimizing these procedures. However, the comprehensive impact of BSSPR has been infrequently quantified in existing research.
Objective: This study aims to investigate the influence of BSSPR on the efficiency and quality of medical services and to elucidate the key informative technological support points underpinning BSSPR.
Methods: Data were collected from both the new and old laboratory information systems from August 1, 2019, to December 31, 2021. A combination of statistical description, chi-square test, and t test was used to compare check-in time and waiting time of outpatients before and after the implementation of BSSPR. An interrupted time-series design was used to analyze the impact of BSSPR on medical service efficiency and quality, enabling the control of confounding variables, including changes in medical human resources and both long- and short-term temporal trends.
Results: BSSPR had an impact on the efficiency and quality of medical services. Notably, there was a significant increase in the number of patients receiving blood sampling services, with a daily service volume increase of ~150 individuals (P=.04). The average waiting time for patients decreased substantially from 29 (SD 36) to 11 (SD 11) minutes, indicating a marked improvement in patient experience. During the peak period, the number of patients receiving blood sampling services per working hour statistically increased from 9.56 to 16.77 (P<.001). The interrupted time-series model results demonstrated a reduction in patients' waiting time by an average of 26.1 (SD 3.8; 95% CI -33.64 to -18.57) minutes. Although there was an initial decline in the number of outpatients admitted following BSSPR implementation, an upward trend was observed over time (β=1.13, 95% CI 0.91-1.36).
Conclusions: BSSPR implementation for outpatients not only reduced waiting time and improved patients' experience but also augmented the hospital's capacity to provide medical services. This study's findings offer valuable insights into the potential advantages of BSSPR and underscore the significance of harnessing digital technologies to optimize medical service processes. This research serves as a foundational basis and provides scientific support for the promotion and application of BSSPR in other health care contexts. By continuing to explore and refine the integration of digital technologies in health care, we can further enhance patient outcomes and elevate the overall quality of medical services.
(©Wenmin Liao, Rong He, Zhonglian He, Nan Shi, Dan Li, Aihua Zhuang, Feng Gan, Ying Sun, Chaofeng Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.11.2024.)

J Med Internet Res. 2021 Jul 26;23(7):e23779. (PMID: 34309581)
Stat Med. 2019 May 10;38(10):1734-1752. (PMID: 30616298)
Disaster Med Public Health Prep. 2020 Dec;14(6):808-811. (PMID: 32284090)
Acad Pediatr. 2013 Nov-Dec;13(6 Suppl):S38-44. (PMID: 24268083)
J Infect Public Health. 2016 Nov - Dec;9(6):734-743. (PMID: 27663518)
Front Med (Lausanne). 2022 Mar 07;9:802487. (PMID: 35402446)
Health Care Manag Sci. 2015 Jun;18(2):137-55. (PMID: 25012400)
Int J Equity Health. 2019 Jan 21;18(1):14. (PMID: 30665422)
BMC Health Serv Res. 2022 Jan 25;22(1):107. (PMID: 35078474)
Cancer Commun (Lond). 2021 Oct;41(10):1037-1048. (PMID: 34288593)
BMC Med Res Methodol. 2019 Jul 4;19(1):137. (PMID: 31272382)
Patient Educ Couns. 2020 Apr;103(4):819-825. (PMID: 31767241)
J Healthc Eng. 2017;2017:9034737. (PMID: 29104748)
Eur Heart J. 2016 May 07;37(18):1428-38. (PMID: 26873093)
Disaster Med Public Health Prep. 2021 Oct;15(5):e7-e16. (PMID: 32466822)
Int J Qual Health Care. 2021 Feb 3;33(1):. (PMID: 33404594)
N Engl J Med. 2020 May 14;382(20):e55. (PMID: 32212515)
Healthc Manage Forum. 1994 Winter;7(4):44-50. (PMID: 10140167)
Inquiry. 2017 Jan-Dec;54:46958017739527. (PMID: 29161947)
Int J Epidemiol. 2017 Feb 1;46(1):348-355. (PMID: 27283160)
BMJ. 2015 Jun 09;350:h2750. (PMID: 26058820)
J Clin Epidemiol. 2020 Jun;122:1-11. (PMID: 32109503)
Health Care Manag Sci. 2007 Jun;10(2):173-94. (PMID: 17608058)
Aust Health Rev. 2018 Jun;42(3):286-293. (PMID: 28355525)
Med J Islam Repub Iran. 2017 Dec 14;31:79. (PMID: 29445707)
J Med Internet Res. 2020 Nov 24;22(11):e24505. (PMID: 33141679)

Keywords: blood sampling; digital health; hospital administration; medical informatics; patient experience; process reengineering

Date Created: 20241112 Date Completed: 20241112 Latest Revision: 20250109

20250110

PMC11599894

10.2196/51412

39531265