PSYCHOMETRIC PROPERTIES OF THE TURKISH VERSION OF THE HEALTH SYSTEMS USABILITY SCALE FOR CLINICAL DECISION SUPPORT SYSTEMS: RELIABILITY AND VALIDITY STUDY

Yıl 2024, Cilt: 27 Sayı: 4, 577 - 592, 23.12.2024

Onur Gözübüyük , Arzu Bulut

https://doi.org/10.61859/hacettepesid.1451287

Öz

The aim of this study was to examine the psychometric properties of the Turkish form of the Health Systems Usability Scale (HSUSS), which can be used to assess the usability of clinical decision support systems (CDSS) by nurses, and to test its validity and reliability. The study was conducted in a university hospital with 335 nurses who voluntarily agreed to participate in the study. Data were obtained using an online survey method. R and JASP (Version 0.18.1) programs were used for statistical analysis. The construct validity of the HSUS was examined by CFA. As a result of CFA, 22 items and 4-factor structure of the scale were confirmed (χ2/df= 2.58, CFI=0.939, TLI=0.929, RMSEA=0.069, 90% CI [0.062, 0.076] and SRMR=0.03). The factor loadings of the items were between 0.50 and 0.94. The correlation coefficients between the factors of the HSUS ranged between 0.679 and 0.865. Cronbach's α coefficient for the internal consistency of the scale is 0.956, and the sub-factors are between 0.822 and 0.914. The results of this study supported that the Turkish version of the CDSS is a tool with good psychometric properties, convergent-divergent validity and reliability that can be used to assess the usability of clinical decision support systems by nurses. The CDSS can help to better understand the usability issues of clinical decision support systems in context, address potential medical adverse events before they occur, and early identification of usability issues that may cause medical adverse events and contribute to nurses' awareness of the CDSS.

Anahtar Kelimeler

Validity, Reliability, Decision support system, Health systems usability

KLİNİK KARAR DESTEK SİSTEMLERİ İÇİN SAĞLIK SİSTEMLERİ KULLANILABİLİRLİK ÖLÇEĞİ TÜRKÇE FORMUNUN PSİKOMETRİK ÖZELLİKLERİ: GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI

Öz

Bu çalışmanın amacı, Klinik Karar Destek Sistemleri’nin (KKDS) hemşireler tarafından kullanılabilirliğinin değerlendirilmesinde kullanılabilecek Sağlık Sistemleri Kullanılabilirlik Ölçeği (SSKÖ) Türkçe formunun psikometrik özelliklerini incelemek, geçerlik ve güvenirliğini test etmektir. Araştırma bir üniversite hastanesinde çalışmaya gönüllü katılmayı kabul eden 335 hemşire ile gerçekleştirilmiştir. Veriler çevirimiçi anket yöntemi kullanılarak elde edilmiştir. İstatistiksel analizler için R ve JASP (Version 0.18.1) programları kullanılmıştır. SSKÖ’nin yapı geçerliği DFA ile incelenmiştir. DFA sonucu ölçeğin 22 madde ve 4 faktörlü yapısı doğrulanmıştır (χ2/df= 2,58, CFI=0,939, TLI=0,929, RMSEA=0,069, %90 GA [0,062, 0,076] ve SRMR=0,03). Maddelerin faktör yükleri 0,679 ile 0,865 arasındadır. SSKÖ faktörleri arasındaki korelasyon katsayıları 0,579 ile 0,786 arasında değişmektedir. Ölçeğin iç tutarlılığı için Cronbach’s α katsayısı 0,956, alt faktörlerin ise 0,822 ile 0,914 arasındadır. Bu çalışmanın bulguları SSKÖ Türkçe versiyonunun klinik karar destek sistemlerinin hemşireler tarafından kullanılabilirliğini değerlendirmek amacı ile kullanılabilecek iyi psikometrik özellikler, yakınsak-ayrışım geçerliliği ve güvenirliğine sahip bir araç olduğunu desteklemiştir. SSKÖ, klinik karar destek sistemlerinin kullanılabilirlik sorunlarının bağlam içinde daha iyi anlaşılmasına, potansiyel tıbbi olumsuz olaylar meydana gelmeden önce ele almasına ve tıbbi advers olaylara neden olabilecek kullanılabilirlik sorunlarının erken tanımlanmasına yardımcı olabilir ve hemşirelerin KKDS’ye yönelik farkındalığına katkıda bulunabilir.

Anahtar Kelimeler

Geçerlilik, Güvenilirlik, Karar destek sistemi, Sağlık sistemleri kullanılabilirlik

Kaynakça

• Abedin, M. Z., Guotai, C., Moula, F. E., Azad, A. S., & Khan, M. S. U. (2019). Topological applications of multilayer perceptrons and support vector machines in financial decision support systems. International Journal of Finance & Economics, 24(1), 474-507.
• Anderson, J.C. & Gerbing, D.W. (1984). The effect of sampling error on convergence, ımproper solutions, and goodness of fit ındices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
• Ash, J. S., Sittig, D. F., Campbell, E. M., Guappone, K. P., & Dykstra, R. H. (2007). Some unintended consequences of clinical decision support systems. In Amia annual Symposium proceedings. American Medical Informatics Association, 26.
• Bates, D. W., Kuperman, G. J., Wang, S., Gandhi, T., Kittler, A., Volk, L., ... & Middleton, B. (2003). Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association, 10(6), 523-530. Bentler, P. M., & Chou, C. P. (1987). Practical ıssues in structural modeling. Sociological Methods & Research, 16(1), 78-117. https://doi.org/10.1177/0049124187016001004
• Berner, E. S., & La Lande, T. J. (2016). Overview of clinical decision support systems. Clinical decision support systems: Theory and Practice, 1-17.
• Bollen, K. A. (1989). Structural equations with latent variables. New York: Jon Wiley & Sons. https://doi.org/10.1002/9781118619179
• Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., ... & Lobach, D. (2012). Effect of clinical decision-support systems: a systematic review. Annals of Internal Medicine, 157(1), 29-43.
• Brislin, R., Lonner, W. & Thorndike, R. (1973). Cross-cultural research methods. New York: John Wiley.
• Bryant, F. B., & Yarnold, P. R. (1995). Comparing five alternative factor-models of the student jenkins activity survey: Separating the wheat from the chaff. Journal of Personality Assessment, 64(1), 145-158. https://doi.org/10.1207/s15327752jpa6401_10
• Cabı, E. (2016). Dijital teknolojiye yönelik tutum ölçeği. Kastamonu Eğitim Dergisi, 24(3), 1229-1244.
• Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309-319.
• Czimber, K., & Gálos, B. (2016). A new decision support system to analyse the impacts of climate change on the Hungarian forestry and agricultural sectors. Scandinavian Journal of Forest Research, 31(7), 664-673.
• Çelik, M., Güneş, D., Akbaş, G., & Özkan, A. (2019). Hemşirelikte klinik karar destek sistemleri kullanımı: Dr. Siyami Ersek Hastanesi örneği. Cardiovasc Perf Nurs, 1(1):10-19
• Delaney, B. C., Fitzmaurice, D. A., Riaz, A., & Hobbs, F. R. (1999). Can computerised decision support systems deliver improved quality in primary care?. Bmj, 319(7220), 1281.
• DeVellis, R. F. (2014). Ölçek geliştirme: Kuram ve uygulamalar (Çev. Ed. T. Totan). Ankara: Nobel.
• Donaldson, M. S., Corrigan, J. M., & Kohn, L. T. (2000). To err is human: building a safer health system. Natıonal Academy Press, Washington, D.C.
• Evans, J.D. (1996). Straightforward statistics for the behavioral sciences. USA: Pacific Grove, Brooks/Cole Publishing.
• Fieschi, M., Dufour, J. C., Staccini, P., Gouvernet, J., & Bouhaddou, O. (2003). Medical decision support systems: old dilemmas and new paradigms?. Methods of Information in Medicine, 42(03), 190-198.
• Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.
• Fuerst, W. L., & Cheney, P. H. (1982). Concepts, theory, and techniques: Factors affecting the perceived utilization of computer‐based decision support systems in the oil industry. Decision Sciences, 13(4), 554-569. Ghorayeb, A., Darbyshire, J. L., Wronikowska, M. W., & Watkinson, P. J. (2023). Design and validation of a new Healthcare Systems Usability Scale (HSUS) for clinical decision support systems: a mixed-methods approach. BMJ Open, 13(1), e065323.
• Gliem, J. A., & Gliem, R. R. (2003). Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for likert-type scales. Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education, The Ohio State University, Columbus, OH.
• Gjersing, L., Caplehorn, J. R., & Clausen, T. (2010). Cross-cultural adaptation of research instruments: Language, setting, time and statistical considerations. BMC Medical Research Methodology, 10, 13.
• Hägglund, M., & Scandurra, I. (2021). User evaluation of the swedish patient accessible electronic health record: system usability scale. JMIR Human Factors, 8(3), e24927.
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2009). Multivariate data analysis. 5 vols. UpperSaddle River, NJ: Prentice Hall.
• Holden, R. J. (2020,). A simplified system usability scale (SUS) for cognitively impaired and older adults. In proceedings of the ınternational symposium on human factors and ergonomics in health care (Vol. 9, No. 1, pp. 180-182). Sage CA: Los Angeles, CA: SAGE Publications.
• Hsu, L., & Wu, P. (2013). Electronic-tablet-based menu in a full service restaurant and customer satisfaction-a structural equation model. International Journal of Business, Humanities and Technology, 3(2), 61-71.
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: a Multidisciplinary Journal, 6(1), 1-55.
• Hyppönen, H., Kaipio, J., Heponiemi, T., Lääveri, T., Aalto, A. M., Vänskä, J., & Elovainio, M. (2019). Developing the national usability-focused health information system scale for physicians: validation study. Journal of Medical Internet Research, 21(5), e12875.
• Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, 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.
• Kırkbir, İ. B., & Kurt, T. (2020). Hemşirelik bilişimi ve karar verme sürecinde klinik karar destek sistemlerinin önemi. Hemşirelik Bilimi Dergisi, 3(3), 28-31.
• Kline, R.B. (2011). Principles and practice of structural equation modeling (3rd Edition). New York: The Guilford Press.
• Kwan, J. L., Lo, L., Ferguson, J., Goldberg, H., Diaz-Martinez, J. P., Tomlinson, G., ... & Shojania, K. G. (2020). Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials. Bmj, 370.
• Lishinski, A. (2018). LavaanPlot: Path diagrams for Lavaan models via DiagrammeR. R package.: https://cran.r-project.org/web/packages/lavaanPlot/index.html
• Mack, E. H., Wheeler, D. S., & Embi, P. J. (2009). Clinical decision support systems in the pediatric intensive care unit. Pediatric Critical Care Medicine, 10(1), 23-28.
• Martínez-Pérez, B., de la Torre-Díez, I., López-Coronado, M., Sainz-de-Abajo, B., Robles, M., & García-Gómez, J. M. (2014). Mobile clinical decision support systems and applications: a literature and commercial review. Journal of Medical Systems, 38, 1-10.
• Mayerl, J. (2016). Environmental concern in cross-national comparison: Methodological threats and measurement equivalence. In Green European. Routledge, 182-204.
• Musen, M. A., Middleton, B., & Greenes, R. A. (2021). Clinical decision-support systems. In Biomedical informatics: computer applications in health care and biomedicine. Cham: Springer International Publishing, 795-840.
• Nair, K., Malaeekeh, R., Schabort, I., Taenzer, P., Radhakrishnan, A., & Guenter, D. (2015). A clinical decision support system for chronic pain management in primary care: usability testing and its relevance. BMJ Health & Care Informatics, 22(3).
• Nunnally, J. C. (1978). Psychometric theory. New York: McGraw Hill.
• Omididan, Z., & Hadianfar, A. M. (2011). The role of clinical decision support systems in healthcare (1980-2010): A systematic review study. Jentashapir Sceintific-Research Quarterly, 2(3), 125-34.
• Osheroff, J. A., Teich, J. M., Middleton, B., Steen, E. B., Wright, A., & Detmer, D. E. (2007). A roadmap for national action on clinical decision support. Journal of the American Medical Informatics Association, 14(2), 141-145.
• Reichenheim, M. E., & Moraes, C. L. (2007). Operationalizing the cross-cultural adaptation of epidemological measurement instruments. Revista De Saúde Pública, 41, 665-673.
• Roshanov, P. S., Misra, S., Gerstein, H. C., Garg, A. X., Sebaldt, R. J., Mackay, J. A., ... & Haynes, R.B. (2011). Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review. Implementation Science, 6, 1-16.
• Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36. Schaaf, J., Prokosch, H. U., Boeker, M., Schaefer, J., Vasseur, J., Storf, H., & Sedlmayr, M. (2020). Interviews with experts in rare diseases for the development of clinical decision support system software-a qualitative study. BMC Medical Informatics and Decision Making, 20, 1-11.
• Shibl, R., Lawley, M., & Debuse, J. (2013). Factors influencing decision support system acceptance. Decision Support Systems, 54(2), 953-961.
• Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics Association, 8(6), 527-534.
• Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 17.
• Şencan, H. (2005). Sosyal ve davranışsal ölçümlerde güvenilirlik ve geçerlilik (1. Baskı). Seçkin Yayıncılık Sanayi ve Ticaret AŞ., Ankara, 499-559.
• Temoçin, F., Köse, H., & Sürel, A. A. (2019). Enfeksiyon kontrol önlemlerine ilişkin klinik karar destek sistemlerinin hazırlanması ve etkililiğin değerlendirilmesi. Journal of Health Sciences and Medicine, 2(2), 54-57.
• Uysal, H., & Ozcan, Ş. (2011). A Turkish version of myocardial infarction dimensional assessment scale (TR-MIDAS): reliability–validity assesment. European Journal of Cardiovascular Nursing, 10(2), 115-123.
• Walsh, S., de Jong, E. E., van Timmeren, J. E., Ibrahim, A., Compter, I., Peerlings, J., ... & Lambin, P. (2019). Decision support systems in oncology. JCO Clinical Cancer Informatics, 3, 1-9.
• White, N. M., Carter, H. E., Kularatna, S., Borg, D. N., Brain, D. C., Tariq, A., ... & McPhail, S. M. (2023). Evaluating the costs and consequences of computerized clinical decision support systems in hospitals: a scoping review and recommendations for future practice. Journal of the American Medical Informatics Association, 30(6), 1205-1218.
• Yaşlıoğlu, M. M. (2017). Sosyal bilimlerde faktör analizi ve geçerlilik: Keşfedici ve doğrulayıcı faktör analizlerinin kullanılması. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 46, 74-85.