Readiness and Acceptance of Health Providers using Clinical Decision Support System at Probolinggo Primary Healthcare Centers
Abstract
Introduction: The government targets a significant reduction in antibiotic resistance by 2030 through wise antibiotic management, including implementing the Clinical Decision Support System (CDSS) for Health Providers in Non-Pneumonia acute respiratory infections (ISPA) and Non-specific Diarrhea in Primary Healthcare Centre (Puskesmas). However, the readiness and acceptance of Health Providers to CDSS need to be evaluated. This study evaluates the readiness and acceptance of doctors, nurses, midwives, pharmacists, and pharmacists' assistants in Puskesmas using CDSS.
Methods: The method used was a cross-sectional quantitative survey with snowball sampling of 185 respondents at the Probolinggo Puskesmas in July-August 2024. The questionnaire was developed based on a combination of the Technology Readiness Index and the Technology Acceptance Model (TRAM), and the data was analyzed using SEM-PLS (Structural Equation Modeling-Partial Least Squares).
Result: This study revealed that optimism contributed positively to the perception of ease of use (PEOU) and perception of benefits (PU) of CDSS. Innovation also contributed positively to PEOU, but not significantly to PU. Optimism increases PEOU and PU, while innovation only increases PEOU. Although innovation is insignificant to PU, it has a more significant impact on PEOU than optimism.
Conclusion: These findings also show that PU affects the attitude of Health Providers to use CDSS (? = 0.286, p < 0.001) but does not directly affect behavioral intentions (? = 0.081, p = 0.250). PEOU significantly affected PU (? = 0.617, p < 0.001) and attitude (? = 0.661, p < 0.001). Attitudes towards CDSS greatly influenced the behavioral intentions of healthcare providers to use it (? = 0.851, p < 0.001), making it a strong predictor of CDSS adoption. The integration of TRI and TAM in predicting the readiness of Health Providers has proven helpful in understanding the factors of CDSS adoption in Puskesmas. These findings highlight the importance of training for health providers to improve CDSS readiness and acceptance. In addition, the results of this study can be the basis for policy development in implementing CDSS Primary Healthcare Centers to support a more rational use of antibiotics.
References
Mudenda S, Chabalenge B, Daka V, Mfune RL, Salachi KI, Mohamed S, et al. Global Strategies to Combat Antimicrobial Resistance: A One Health Perspective. Pharmacol & Pharm [Internet]. 2023;14(08):271–328. Available from: https://www.scirp.org/journal/doi.aspx?doi=10.4236/pp.2023.148020
Altieri E, Grove J, Lawe Davies O, Bach Habersaat K, Okeibunor J, Samhouri D, et al. Harnessing the power of behavioural science to improve health. Bull World Health Organ [Internet]. 2021 Nov 1;99(11):754–754. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542273/pdf/BLT.21.287375.pdf
Huang Z, George MM, Tan YR, Natarajan K, Devasagayam E, Tay E, et al. Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore. J Glob Antimicrob Resist [Internet]. 2023;35:76–85. Available from: https://doi.org/10.1016/j.jgar.2023.08.016
Uddin TM, Chakraborty AJ, Khusro A, Zidan BRM, Mitra S, Emran T Bin, et al. Antibiotic resistance in microbes: History, mechanisms, therapeutic strategies and future prospects. J Infect Public Health [Internet]. 2021 Dec;14(12):1750–66. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1876034121003403
Hossain MJ, Jabin N, Ahmmed F, Sultana A, Abdur Rahman SM, Islam MR. Irrational use of antibiotics and factors associated with antibiotic resistance: Findings from a cross?sectional study in Bangladesh. Heal Sci Reports [Internet]. 2023 Aug 28;6(8):1–11. Available from: https://onlinelibrary.wiley.com/doi/10.1002/hsr2.1465
Siahaan S, Herman MJ, Fitri N. Antimicrobial Resistance Situation in Indonesia: A Challenge of Multisector and Global Coordination. Wang M, editor. J Trop Med [Internet]. 2022 Feb 7;2022:1–10. Available from: https://www.hindawi.com/journals/jtm/2022/2783300/
Bakhit M, Hoffmann T, Scott AM, Beller E, Rathbone J, Del Mar C. Resistance decay in individuals after antibiotic exposure in primary care: a systematic review and meta-analysis. BMC Med [Internet]. 2018 Dec 7;16(1):126. Available from: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-018-1109-4
Jeffs L, McIsaac W, Zahradnik M, Senthinathan A, Dresser L, McIntyre M, et al. Barriers and facilitators to the uptake of an antimicrobial stewardship program in primary care: A qualitative study. PLoS One [Internet]. 2020;15(3):1–14. Available from: http://dx.doi.org/10.1371/journal.pone.0223822
Giamarellou H, Galani L, Karavasilis T, Ioannidis K, Karaiskos I. Antimicrobial Stewardship in the Hospital Setting: A Narrative Review. Antibiotics [Internet]. 2023 Oct 21;12(10):1557. Available from: https://www.mdpi.com/2079-6382/12/10/1557
Dache A, Dona A, Ejeso A. Inappropriate use of antibiotics, its reasons and contributing factors among communities of Yirgalem town, Sidama regional state, Ethiopia: A cross-sectional study. SAGE Open Med [Internet]. 2021 Jan 3;9:205031212110424. Available from: http://journals.sagepub.com/doi/10.1177/20503121211042461
Rahbarimanesh A, Mojtahedi SY, Sadeghi P, Ghodsi M, Kianfar S, Khedmat L, et al. Antimicrobial stewardship program (ASP): an effective implementing technique for the therapy efficiency of meropenem and vancomycin antibiotics in Iranian pediatric patients. Ann Clin Microbiol Antimicrob [Internet]. 2019 Dec 29;18(1):6. Available from: https://doi.org/10.1186/s12941-019-0305-1
Shah P, Maheshwari T, Patel D, Patel Z, Dikkatwar MS, Rathod MM. An overview: Implementation and core elements of antimicrobial stewardship programme. Clin Epidemiol Glob Heal [Internet]. 2024 Sep;29(February):101543. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2213398424000393
Harun MGD, Anwar MMU, Sumon SA, Hassan MZ, Mohona TM, Rahman A, et al. Rationale and guidance for strengthening infection prevention and control measures and antimicrobial stewardship programs in Bangladesh: a study protocol. BMC Health Serv Res [Internet]. 2022 Oct 7;22(1):1239. Available from: https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-022-08603-0
Limato R, Lazarus G, Dernison P, Mudia M, Alamanda M, Nelwan EJ, et al. Optimizing antibiotic use in Indonesia: a systematic review and synthesis of current evidence to inform opportunities for intervention [Internet]. medRxiv. 2022. p. 2022.02.20.22271261. Available from: https://www.medrxiv.org/content/10.1101/2022.02.20.22271261v1%0Ahttps://www.medrxiv.org/content/10.1101/2022.02.20.22271261v1.abstract
Llor C, Frimodt-Møller N, Miravitlles M, Kahlmeter G, Bjerrum L. Optimising antibiotic exposure by customising the duration of treatment for respiratory tract infections based on patient needs in primary care. eClinicalMedicine [Internet]. 2024 Aug;74:102723. Available from: https://doi.org/10.1016/j.eclinm.2024.102723
Sadeq AA, Hasan SS, AbouKhater N, Conway BR, Abdelsalam AE, Shamseddine JM, et al. Exploring Antimicrobial Stewardship Influential Interventions on Improving Antibiotic Utilization in Outpatient and Inpatient Settings: A Systematic Review and Meta-Analysis. Antibiotics [Internet]. 2022;11(10). Available from: https://www-ncbi-nlm-nih-gov.translate.goog/pmc/articles/PMC9598859/?_x_tr_sl=en&_x_tr_tl=id&_x_tr_hl=id&_x_tr_pto=sc
Meunier P-Y, Raynaud C, Guimaraes E, Gueyffier F, Letrilliart L. Barriers and Facilitators to the Use of Clinical Decision Support Systems in Primary Care: A Mixed-Methods Systematic Review. Ann Fam Med [Internet]. 2023 Jan;21(1):57–69. Available from: http://www.annfammed.org/lookup/doi/10.1370/afm.2908
Armando LG, Miglio G, de Cosmo P, Cena C. Clinical decision support systems to improve drug prescription and therapy optimisation in clinical practice: a scoping review. BMJ Heal Care Informatics [Internet]. 2023 May;30(1):e100683. Available from: https://informatics.bmj.com/lookup/doi/10.1136/bmjhci-2022-100683
Alotaibi YK, Federico F. The impact of health information technology on patient safety. Saudi Med J [Internet]. 2017 Dec;38(12):1173–80. Available from: https://smj.org.sa/lookup/doi/10.15537/smj.2017.12.20631
Amjad A, Kordel P, Fernandes G. A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence. Sustainability [Internet]. 2023 Apr 14;15(8):6655. Available from: https://www.mdpi.com/2071-1050/15/8/6655
Laka M, Milazzo A, Merlin T. Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management. Int J Environ Res Public Health [Internet]. 2021 Feb 16;18(4):1901. Available from: https://www.mdpi.com/1660-4601/18/4/1901
Horwood C, Luthuli S, Mapumulo S, Haskins L, Jensen C, Pansegrouw D, et al. Challenges of using e-health technologies to support clinical care in rural Africa: a longitudinal mixed methods study exploring primary health care nurses’ experiences of using an electronic clinical decision support system (CDSS) in South Africa. BMC Health Serv Res [Internet]. 2023 Jan 13;23(1):30. Available from: https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-022-09001-2
Mumtaz H, Riaz MH, Wajid H, Saqib M, Zeeshan MH, Khan SE, et al. Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review. Front Digit Heal [Internet]. 2023 Sep 28;5(September):1–8. Available from: https://www.frontiersin.org/articles/10.3389/fdgth.2023.1203945/full
Afrizal SH, Handayani PW, Hidayanto AN, Eryando T, Budiharsana M, Martha E. Barriers and challenges to Primary Health Care Information System (PHCIS) adoption from health management perspective: A qualitative study. Informatics Med Unlocked [Internet]. 2019;17(May):100198. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352914819300747
Safi S, Thiessen T, Schmailzl KJG. Acceptance and Resistance of New Digital Technologies in Medicine: Qualitative Study. JMIR Res Protoc [Internet]. 2018 Dec 4;7(12):e11072. Available from: http://www.researchprotocols.org/2018/12/e11072/
Renukappa S, Mudiyi P, Suresh S, Abdalla W, Subbarao C. Evaluation of challenges for adoption of smart healthcare strategies. Smart Heal [Internet]. 2022 Dec;26(September):100330. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352648322000642
Shahmoradi L, Safdari R, Ahmadi H, Zahmatkeshan M. Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects. Med J Islam Repub Iran [Internet]. 2021 Apr 30;35(1):1–16. Available from: http://mjiri.iums.ac.ir/article-1-5828-en.html
Ackerhans S, Huynh T, Kaiser C, Schultz C. Exploring the role of professional identity in the implementation of clinical decision support systems—a narrative review. Implement Sci [Internet]. 2024 Feb 12;19(1):11. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/s13012-024-01339-x
Chen Z, Liang N, Zhang H, Li H, Yang Y, Zong X, et al. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Hear [Internet]. 2023 Nov 28;10(2):e002432. Available from: https://openheart.bmj.com/lookup/doi/10.1136/openhrt-2023-002432
Parasuraman A. Technology Readiness Index (TRI): A Multipleitem Scale To Measure Readiness To Embrace New Technologies. J Serv Res. 2000;2:307(May).
Kolade O, Odumuyiwa V, Abolfathi S, Schröder P, Wakunuma K, Akanmu I, et al. Technology acceptance and readiness of stakeholders for transitioning to a circular plastic economy in Africa. Technol Forecast Soc Change [Internet]. 2022 Oct;183(July):121954. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0040162522004759
El Barachi M, Salim TA, Nyadzayo MW, Mathew S, Badewi A, Amankwah-Amoah J. The relationship between citizen readiness and the intention to continuously use smart city services: Mediating effects of satisfaction and discomfort. Technol Soc [Internet]. 2022 Nov 1 [cited 2024 Oct 9];71:102115. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0160791X22002561
Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989;13(3):319–40.
McCord M. Technology Acceptance Model. In: Handbook of Research on Electronic Surveys and Measurements [Internet]. IGI Global; 2007. p. 306–8. Available from: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-59140-792-8.ch038
Kampa RK. Combining technology readiness and acceptance model for investigating the acceptance of m-learning in higher education in India. Asian Assoc Open Univ J [Internet]. 2023 Nov 7;18(2):105–20. Available from: https://www.emerald.com/insight/content/doi/10.1108/AAOUJ-10-2022-0149/full/html
Sohn K, Kwon O. Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telemat Informatics [Internet]. 2020 Apr;47(April 2019):101324. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0736585319308160
Mahmood A, Imran M, Adil K. Modeling Individual Beliefs to Transfigure Technology Readiness into Technology Acceptance in Financial Institutions. SAGE Open [Internet]. 2023 Jan 18;13(1):215824402211497. Available from: http://journals.sagepub.com/doi/10.1177/21582440221149718
Sanaky MM. ANALISIS FAKTOR-FAKTOR KETERLAMBATAN PADA PROYEK PEMBANGUNAN GEDUNG ASRAMA MAN 1 TULEHU MALUKU TENGAH. J SIMETRIK [Internet]. 2021 Aug 6;11(1):432–9. Available from: https://ejournal-polnam.ac.id/index.php/JurnalSimetrik/article/view/615
Rosalyn Gracya. ANALISIS PENGGUNAAN AKTUAL SISTEM INFORMASI MANAJEMEN BARANG MILIK DAERAH DENGAN PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL DI PEMERINTAH DAERAH KABUPATEN KEPULAUAN YAPEN. J Soc Econ Res [Internet]. 2023 May 14;5(1):078–90. Available from: https://idm.or.id/JSER/index.php/JSER/article/view/72
Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev [Internet]. 2019 Jan 14;31(1):2–24. Available from: https://www.emerald.com/insight/content/doi/10.1108/EBR-11-2018-0203/full/html
Fornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J Mark Res [Internet]. 1981 Feb;18(1):39. Available from: https://www.jstor.org/stable/3151312?origin=crossref
Sukamdani HB, Sulistyadi Y, Sukwika T, Sukamdani NB, Eddyono F. Decision Making Factors In An Ecotourism Visit In The Situ Gunung Natural Tourism Park West Java. J Appl Manag Res [Internet]. 2024 Jun 21;4(1):45–54. Available from: https://jurnal.usahid.ac.id/index.php/jamr/article/view/2212
Cheung GW, Cooper-Thomas HD, Lau RS, Wang LC. Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific J Manag [Internet]. 2024 Jun 30;41(2):745–83. Available from: https://doi.org/10.1007/s10490-023-09871-y
Ford E, Edelman N, Somers L, Shrewsbury D, Lopez Levy M, van Marwijk H, et al. Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners. BMC Med Inform Decis Mak [Internet]. 2021 Dec 21;21(1):193. Available from: https://doi.org/10.1186/s12911-021-01557-z
Westerbeek L, Ploegmakers KJ, de Bruijn G-J, Linn AJ, van Weert JCM, Daams JG, et al. Barriers and facilitators influencing medication-related CDSS acceptance according to clinicians: A systematic review. Int J Med Inform [Internet]. 2021 Aug;152(April):104506. Available from: https://doi.org/10.1016/j.ijmedinf.2021.104506
Edo OC, Ang D, Etu E-E, Tenebe I, Edo S, Diekola OA. Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy. Int J Inf Manag Data Insights [Internet]. 2023 Nov;3(2):100186. Available from: https://doi.org/10.1016/j.jjimei.2023.100186
Ndlovu K, Stein N, Gaopelo R, Annechino M, Molwantwa MC, Monkge M, et al. Evaluating the Feasibility and Acceptance of a Mobile Clinical Decision Support System in a Resource-Limited Country: Exploratory Study. JMIR Form Res [Internet]. 2023 Oct 10;7:e48946. Available from: https://formative.jmir.org/2023/1/e48946
Zha H, Liu K, Tang T, Yin Y-H, Dou B, Jiang L, et al. Acceptance of clinical decision support system to prevent venous thromboembolism among nurses: an extension of the UTAUT model. BMC Med Inform Decis Mak [Internet]. 2022 Aug 19;22(1):221. Available from: https://doi.org/10.1186/s12911-022-01958-8
Lin C, Shih H, Sher PJ. Integrating technology readiness into technology acceptance: The TRAM model. Psychol Mark [Internet]. 2007 Jul 24;24(7):641–57. Available from: https://onlinelibrary.wiley.com/doi/10.1002/mar.20177
Rahimi B, Nadri H, Lotfnezhad Afshar H, Timpka T. A Systematic Review of the Technology Acceptance Model in Health Informatics. Appl Clin Inform [Internet]. 2018 Jul 15;09(03):604–34. Available from: http://www.thieme-connect.de/DOI/DOI?10.1055/s-0038-1668091
Zamani SZ. Small and Medium Enterprises (SMEs) facing an evolving technological era: a systematic literature review on the adoption of technologies in SMEs. Eur J Innov Manag [Internet]. 2022 Dec 19;25(6):735–57. Available from: https://www.emerald.com/insight/content/doi/10.1108/EJIM-07-2021-0360/full/html
Hubert M, Blut M, Brock C, Zhang RW, Koch V, Riedl R. The influence of acceptance and adoption drivers on smart home usage. Eur J Mark [Internet]. 2019 Jun 10;53(6):1073–98. Available from: https://www.emerald.com/insight/content/doi/10.1108/EJM-12-2016-0794/full/html
Abdullah F, Ward R, Ahmed E. Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Comput Human Behav [Internet]. 2016 Oct;63:75–90. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0747563216303387
Wicaksono A, Maharani A. The Effect of Perceived Usefulness and Perceived Ease of Use on the Technology Acceptance Model to Use Online Travel Agency. J Bus Manag Rev [Internet]. 2020 Nov 26;1(5):313–28. Available from: https://profesionalmudacendekia.com/index.php/jbmr/article/view/50
Chao CM. Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Front Psychol. 2019;10(JULY):1–14.
Kelly S, Kaye S-A, Oviedo-Trespalacios O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telemat Informatics [Internet]. 2023 Feb;77(November 2022):101925. Available from: https://doi.org/10.1016/j.tele.2022.101925
Alsyouf A, Lutfi A, Alsubahi N, Alhazmi FN, Al-Mugheed K, Anshasi RJ, et al. The Use of a Technology Acceptance Model (TAM) to Predict Patients’ Usage of a Personal Health Record System: The Role of Security, Privacy, and Usability. Int J Environ Res Public Health [Internet]. 2023 Jan 11;20(2):1347. Available from: https://www.mdpi.com/1660-4601/20/2/1347
Wijnhoven F. Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project. Inf Syst Front [Internet]. 2022 Jun 9;24(3):731–44. Available from: https://doi.org/10.1007/s10796-021-10206-9
Kirchherr J, Charles K. Enhancing the sample diversity of snowball samples: Recommendations from a research project on anti-dam movements in Southeast Asia. Guetterman TC, editor. PLoS One [Internet]. 2018 Aug 22;13(8):e0201710. Available from: https://dx.plos.org/10.1371/journal.pone.0201710
Kennedy-Shaffer L, Qiu X, Hanage WP. Snowball Sampling Study Design for Serosurveys Early in Disease Outbreaks. Am J Epidemiol [Internet]. 2021 Sep 1;190(9):1918–27. Available from: https://academic.oup.com/aje/article/190/9/1918/6217343
Meraya AM, Syed MH, Shabi AA, Madkhali HA, Yatimi YA, Khobrani KY, et al. Assessment of community pharmacists’ knowledge, attitudes and their willingness to provide vaccination services in Saudi Arabia. Ansari M, editor. PLoS One [Internet]. 2024 May 28;19(5):e0304287. Available from: https://dx.plos.org/10.1371/journal.pone.0304287
Forsetlund L, O’Brien MA, Forsén L, Mwai L, Reinar LM, Okwen MP, et al. Continuing education meetings and workshops: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev [Internet]. 2021 Sep 15;2021(9). Available from: http://doi.wiley.com/10.1002/14651858.CD003030.pub3
Laka M, Milazzo A, Merlin T. Can evidence-based decision support tools transform antibiotic management? A systematic review and meta-analyses. J Antimicrob Chemother. 2020;75(5):1099–111.
Tokgöz P, Hafner J, Dockweiler C. Factors influencing the implementation of decision support systems for antibiotic prescription in hospitals: a systematic review. BMC Med Inform Decis Mak [Internet]. 2023 Feb 6;23(1):27. Available from: https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02124-4
Laka M, Carter D, Milazzo A, Merlin T. Challenges and opportunities in implementing clinical decision support systems (CDSS) at scale: Interviews with Australian policymakers. Heal Policy Technol [Internet]. 2022 Sep;11(3):100652. Available from: https://doi.org/10.1016/j.hlpt.2022.100652
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