Perancangan Clinical Decision Support System (CDSS) untuk Drug Drug Interaction (DDI) pada e-Prescription

https://doi.org/10.22146/jmpf.74506

Resia Perwirani(1*), Ika Puspitasari(2)

(1) Universitas Gadjah Mada
(2) Departemen Farmakologi, Fakultas Farmasi, Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Not all drugs side-effect that occur can be avoided, but those caused by drug-drug interactions (DDI) are among the most likely to be prevented and managed due to their predictability. The increasing number of drugs co-prescribed, affects the potential for drug interactions exponentially. Clinical Decision Support System (CDSS) is a promising strategy to prevent patient safety risks caused by drug interactions. This study aims to design a CDSS for DDI on e-Prescription. This research is qualitative study with action research design. The research was carried out at Digital Health Innovation Studio (DHIS) UGM, and at Budi Rahayu Hospital Magelang with the implementation time November 2021 - April 2022. Data collection for user needs analysis was carried out by interviewing management, doctors and pharmacists at the hospital, and also pharmacologists. Design and development of CDSS-DDI was executed in collaboration with DHIS UGM programmers. The evaluation was done by interviews and a System Usability Scale (SUS) questionnaire filled in by 17 system-related users. CDSS-DDI successfully developed according to user needs, it can be accessed by doctors and pharmacy units. The drug interaction warning display pop-up appears on one screen in the e-Prescription menu with a description of drug interactions in Bahasa. Drug interaction data refers to the National Drug Information Center (PIONas) which is managed by the POM. CDSS-DDI then implemented in hospital after going through socialization. Based on evaluation with SUS data processing tools, the CDSS-DDI received a score of 83 in the acceptable category and excellent rating. Based on results of evaluation interviews, CDSS for DDI is considered to have been successfully developed with the principle of user centered design and optimally efficient to help improve the quality of patient care.


Keywords


Clinical Decision Support System (CDSS); Drug Drug Interaction (DDI); User Centered Design

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References

  1. Institude of Medicine. Preventing Medication Errors. (Aspden P, Wolcott J, Bootman JL, Cronenwett LR, eds.). The National Academies Press; 2007.
  2. Id WNI, Whittlesea C, Alwafi H, Man KKC, Chapman S, Wei L. Prevalence of adverse drug reactions in the primary care setting : A systematic review and. Published online 2021:1-24.
  3. Corrigan J. To Err Is Human.; 2015.
  4. Pusat Farmakovigilans/MESO Nasional BPOM. Laporan e-meso tahun 2021.
  5. Phansalkar S, Wright A, Kuperman GJ, et al. Towards meaningful medication-related clinical decision support: recommendations for an initial implementation. Appl Clin Inform. 2011;2(1):50-62.
  6. Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Informatics Assoc. 2017;24(4):806-812.
  7. Slone Epidemiology Center. Patterns of Medication Use in the United States 2006. Bost Bost Univ. Published online 2006:1-24. http://scholar.google.com/scholar hl=en&btnG=Search&q=intitle:PATTERNS+OF+MEDICATION+USE+IN+THE+UNITED+STATES+2006:+A+REPORT+FROM+THE+SLONE+SURVEY#0
  8. Malone DC, Armstrong EP, Abarca J, et al. Identification of Serious Drug–Drug Interactions: Results of the Partnership to Prevent Drug–Drug Interactions. J Am Pharm Assoc. 2004;44(2):142-151.
  9. Garg AX, Adhikari NKJ, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. J Am Med Assoc. 2005;293(10):1223-1238.
  10. Calloway S, Akilo H, Bierman K. Impact of a clinical decision support system on pharmacy clinical interventions, documentation efforts, and costs. Hosp Pharm. 2013;48(9):744-752.
  11. Winter R, Munn-Giddings C. A Handbook for Action Research in Health and Social Care. Taylor & Francis; 2001.
  12. Kock NF. Information Systems Action Research : An Applied View of Emerging Concepts and Methods. Springer; 2007.
  13. Bradbury H. The SAGE Handbook of Action Research. Published online 2015.
  14. Utarini A. Tak Kenal Maka Tak Sayang: Penelitian Kualitatif Dalam Pelayanan Kesehatan. Gadjah Mada University Press; 2021.
  15. Perwirani R, Clinical Decision Support System (CDSS) Design for Drug Drug Interaction (DDI) on e-Prescription. Tesis, FKKMK, UGM, 2022.
  16. Karen L. McGraw KH. User-Centered Requirements: The Scenario-Based Engineering Process. CRC Press; 2020.
  17. Dopp AR, Parisi KE, Munson SA, Lyon AR. A glossary of user-centered design strategies for implementation experts. Transl Behav Med. 2019;9(6):1057-1064.
  18. Horn J, Ueng S. The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study. Ann Pharmacother. 2019;53(11):1087-1092.
  19. Pirnejad H, Amiri P, Niazkhani Z, et al. Preventing potential drug-drug interactions through alerting decision support. Int J Med Inform. 2019;(April).
  20. Standing C, Jackson P, Maruster L, Faber NR, Peters K. Sustainable information systems: A knowledge perspective. J Syst Inf Technol. 2008;10(3):218-231.
  21. Nyström T, Mustaquim MM. Finding sustainability indicators for information system assessment. Acad 2015 - Proc 19th Int Acad Mindtrek Conf. 2015;(September):106-113.
  22. M. Eltajoury W, M. Maatuk A, Denna I, K. Elberkawi E. Physicians’ Attitudes towards Electronic Prescribing Software: Perceived Benefits and Barriers. In: International Conference on Data Science, E-Learning and Information Systems 2021. DATA’21. Association for Computing Machinery; 2021:47–53.
  23. Oktarlina RZ. E-prescribing: Benefit, barrier, and adopting challenge in electronic prescribing. J Med. 2020;21(2):98-101.
  24. Van De Sijpe G, Quintens C, Walgraeve K, et al. Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey. BMC Med Inform Decis Mak. 2022;22(1):1-11.
  25. Alamdar R, Mathews A, Kaur S. A Perception on Integrated Medicine Management System by Healthcare Professionals. In: 2021 7th International Conference on Research and Innovation in Information Systems (ICRIIS). ; 2021:1-6.
  26. Humphrey KE, Mirica M, Phansalkar S, Ozonoff A, Harper MB. Clinician Perceptions of Timing and Presentation of Drug-Drug Interaction Alerts. Appl Clin Inform. 2020;11(3):487-496.
  27. McMullin ST, Lonergan TP, Rynearson CS, Doerr TD, Veregge PA, Scanlan ES. Impact of an evidence-based computerized decision support system on primary care prescription costs. Ann Fam Med. 2004;2(5):494-498.



DOI: https://doi.org/10.22146/jmpf.74506

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