PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model

There exist a sizable body of research addressing the evaluation of eHealth/health information technology (HIT) readiness using standard readiness model in the domain of Information Systems (IS). However, there is a general lack of reliable indicators used in measuring readiness assessment factors, resulting in limited predictability. The availability of reliable measuring tools could help improve outcomes of readiness assessments. In determining the predictive relevance of developed HIT model we collected quantitative data from clinical and non-clinical (administrators) staff at Komfo Anokye Teaching Hospital (KATH), Kumasi Ghana using the traditional in-person distribution of paper-based survey, popularly known as drop and collect survey (DCS). We then used PLS-SEM path analysis to measure the predictive relevance of a block of manifest indicators of the readiness assessment factors. Three important readiness assessment factors are thought to define and predict the structure of the KATH HIT/eHealth readiness survey data (Technology readiness (TR); Operational resource readiness (ORR); and Organizational cultural readiness (OCR). As many public healthcare organizations in Ghana have already gone paperless without any reliable HIT/eHealth guiding policy, there is a critical need for reliable HIT/eHealth regulatory policies readiness (RPR) and some improvement in HIT/eHealth strategic planning readiness (core readiness). The final model (R² = 0.558 and Q² = 0.378) suggest that TR, ORR, and OCR explained 55.8% of the total amount of variance in HIT/eHealth readiness in the case of KATH and the relevance of the overall paths of the model was predictive. Fit values (SRMR = 0.054; d_ULS = 6.717; d_G = 6.231; Chi² = 6,795.276; NFI = 0.739). Generally, the GoF for this SEM are encouraging and can substantially be improved.