Home⇛International Journal of Multidisciplinary: Applied Business and Education Research⇛vol. 5 no. 11 (2024)

Assessing the Impact of Laboratory Information System on Clinical Workflow and Patient Outcomes

Stephanie M. Antonio | May B. Dancel | Celestine N. Lim | Juan Rodrigo A. Roberto | Bernandino P. Malang | Jocelyn DS. Malang

Discipline: social sciences (non-specific)

 

Abstract:

This study looks at how Valenzuela Medical Center's (VMC) clinical workflow efficiency and patient outcomes are influenced by the installation of a Laboratory Information System (LIS). Due to time constraints, convenience sampling was used to collect data from sixty-three (63) med-ical professionals in the clinical and diagnostic laboratory departments us-ing a descriptive study design and a mixed-methods technique. To measure participant responses, descriptive statistics such as means, standard devi-ations, and frequency distributions were used. Using paired sample t-tests, inferential analysis was carried out to compare metrics before and after LIS adoption, with an emphasis on factors like error rates, turnaround times, and specimen handling accuracy. Key findings showed that LIS adoption improved data accessibility across departments, lowered transcription errors by about 28%, and cut specimen processing turnaround times by an average of 35%, all of which contributed to improved interdepartmental communication. Furthermore, 90% of respondents expressed more confidence in the accuracy of labora-tory results following LIS integration, and 85% of respondents reported higher satisfaction with data processing procedures. Significant improve-ments were also shown in patient outcomes, with quicker diagnostic processing leading to earlier treatment commencement and, in some situations, shorter hospital stays overall. In addition to demonstrating the wider advantages of incorporating cutting-edge information systems in healthcare settings, this study emphasizes the critical role that LIS plays in improving laboratory operations, cutting down on diagnostic delays, and improving the quality of patient care at VMC.



References:

  1. Ahmed, F., Jansen, M., & Roberts, L. (2023). Im-pact of rapid laboratory reporting on hos-pital  stays. Journal  of  Clinical  Pathology, 76(4), 245-252.
  2. Bates, D. W., & Gawande, A. A. (2003). "Improv-ing  Safety  with  Information  Technology." New England   Journal   of   Medicine, 348(25), 2526-2534.
  3. Hoyt, R. E., & Yoshihashi, A. K. (2014). Health In-formatics:  Practical  Guide  for  Healthcare and Information Technology Profession-als.Lulu.com.
  4. Kahn, S., Lee, T., & Patel, R. (2019). Reducing la-boratory  errors  through  information  sys-tems. Healthcare Informatics Re-search, 25(1), 12-20.
  5. Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lo-bach,  D.  F.  (2005).  "Improving  Clinical Practice  Using  Clinical  Decision  Support Systems: A Systematic Review of Trials to Identify Features Critical to Success." BMJ, 330(7494), 765-773.
  6. Marra, A., Zhang, Y., & O’Connell, M. (2020). The role of Laboratory Information Systems in enhancing  laboratory  efficiency. Clinical Biochemistry, 80, 44-50.
  7. Nair,  S.,  Gupta,  A.,  &  Thomas,  J.  (2021).  The  ef-fects  of  Laboratory  Information  Systems on clinical workflow: A systematic review. International Journal of Medical Informat-ics, 150, 104449.
  8. Smith,  R.,  Kwan,  T.,  &  Kim,  H.  (2022).  Interde-partmental communication and the role of Laboratory  Information  Systems. Journal of  Healthcare  Management,  67(2),  114-122.
  9. Singh, A., Kumar, R., & Patel, S. (2021). The im-pact  of  Laboratory  Information  Systems on clinical workflow: A systematic review. Journal of Healthcare Management, 66(3), 145-158. https://doi.org/10.1016/j.jhm.2021.01.002
  10. Singh  H,  Meyer  AN,  Thomas  EJ.  The  frequency of diagnostic errors in outpatient care: es-timations  from  three  large  observational studies  involving  US  adult  populations. BMJ Qual Saf. 2014;23(9):727–31.
  11. Georgiou,  A.,  Braithwaite,  J.,  Westbrook,  J.  I.,  & Iedema,  R.  A.  M.  (2019).  A  systematic  re-view  of  the  effects  of  computerized  pro-vider order entry systems on medical im-aging  services.  251(3),  Radiology,  629–636.
  12. The National Academy of Medicine. (2015). en-hancing  diagnosis  in  medical  treatment. Antonioet al., 2024/Assessing the Impact of Laboratory Information System on Clinical Workflow and Patient OutcomesIJMABER 4641Volume 5| Number 11| November| 2024The  National  Academies  Press,  Washing-ton, DC. https://doi.org/10.17226/21794.
  13. Balis,  U.  J.,  Tuthill,  J.  M.,  and  Pantanowitz,  L. (2013).  Theory  and  Practice  of  Pathology Informatics. Springer Publishing, Chicago.
  14. Authority    for    Philippine    Statistics    (PSA). (2021).  National  Health  Accounts  for  the Philippines. Accessible at: https://psa.gov.ph  Quezon City: PSA
  15. Varshney, D., and Sharma, R. (2020).  Obstacles to  India's  use  of  laboratory  information systems. Health Informatics Journal, 6(1), 23–29.
  16. Meyer,  A.  N.  D.,  Singh,  H.,  and  Thomas,  E.  J. (2014). Three sizable  observational stud-ies  including  adult  US  populations  were used  to  estimate  the  prevalence  of  diag-nostic  mistakes  in  outpatient  care.  727–731   in   BMJ   Quality   &   Safety,   23(9). https://doi.org/10.1136/bmjqs-2013-002627
  17. Park, H., and Williams, J. (2017). Opportunities and challenges in assessing laboratory in-formation  systems  in environments  with limited   resources.   1355626   in   Global Health Action, 10(1). https://doi.org/10.1080/16549716.2017.1355626

Tools


Copyright © 2025  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic