Determinants predicting the electronic medical record adoption in healthcare: A SEM-Artificial Neural Network approach

Abstract

An Electronic Medical Record (EMR) has the capability of promoting knowledge and awareness regarding healthcare in both healthcare providers and patients to enhance interconnectivity within various government bodies, and quality healthcare services. This study aims at investigating aspects that predict and explain an EMR system adoption in the healthcare system in the UAE through an integrated approach of the Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Model (TAM) using various external factors. The collection of data was through a cross-section design and survey questionnaires as the tool for data collection among 259 participants from 15 healthcare facilities in Dubai. The study further utilised the Artificial Neural Networks (ANN) algorithm and the Partial Least Squares Structural Equation Modeling (PLS-SEM) in the analysis of the data collected. The study’s data proved that the intention of using an EMR system was the most influential and predictor of the actual use of the system. It was also found that TAM construct was directly influenced by anxiety, innovativeness, self-efficacy, and trust. The behavioural intention of an individual regarding EMR was also proved to positively influence the use of an EMR system. This study proves to be useful practically by providing healthcare decision-makers with a guide on factors to consider and what to avoid when implementing strategies and policies.

Implementasi of technology acceptance model (TAM) in electronic documentation of nursing care: phenomenology explorative

“…Computer Self Efficacy indirectly strongly influences behavioral intention to use technology (Chow et al, 2013). Individual behavioral intentions related to EMR have also been shown to positively affect the use of EMR systems (Almarzouqi et al, 2022;Supriyati & Cholil, 2017). Self-efficacy can predict how much effort can be done and how long individuals can survive difficult and obstacles.…”

Post-Traumatic Growth Among Patients Suffering From Burn: A Cross-Sectional Study

“…The survey was conducted over a period of approximately 15 minutes, during which data was primarily collected from the outpatient clinic or counselling room of the hospital. The researchers employed the G*Power 3.1 software to determine the most suitable sample size for their investigation [23][24][25][26] The minimum required sample size was calculated to be 98, taking into account a worth level (p-value) of 0.05 and an influence (1-β) of 0.79. The study had a group of 97 individuals in the acute period and 94 individuals in the rehabilitation stage.…”

Implementasi Rekam Medis Elektronik Dengan Pendekatan Metode Technology Acceptance Model

“…Adapun beberapa artikel membahas mengenai penerimaan electronic health record, meskipun ada perbedaan definisi antara electronic health record dengan RME, namun artikel tersebut tetap penulis gunakan sebagai data pendukung dari penerimaan teknologi. (Almarzouqi et al, 2022) Pengumpulan data melalui desain cross-sectional dan kuesioner survei sebagai alat untuk pengumpulan data di antara 259 peserta dari 15 fasilitas kesehatan di Dubai. Data penelitian membuktikan bahwa niat menggunakan sistem EMR adalah yang paling berpengaruh dan prediktor dari penggunaan sistem yang sebenarnya.…”

“…Banyak tantangan yang dihadapi, seperti permasalahan biaya awal yang tinggi dan manfaat keuangan atau keuntungan yang tidak pasti, teknologi yang sulit digunakan, serta dukungan yang tidak memadai masih menjadi penghambat dalam implementasi rekam medis elektronik (Honavar, 2020;Singh et al, 2020). Tantangan lainnya adalah resistensi perubahan terhadap hal yang baru (Aqleh et al, 2019), faktor kecemasan terhadap inovasi, self-efficacy, dan kepercayaan juga turut mempengaruhi penerimaan seseorang terhadap penggunaan teknologi, dalam hal ini rekam medis elektronik (Almarzouqi et al, 2022).…”

“…Penelitian yang dilakukan oleh Almarzouqi et al (2022) menjelaskan bahwa niat dan motivasi pengguna merupakan salah satu faktor yang paling berpengaruh terhadap keberhasilan dari implementasi RME. Penegasan terhadap hal tersebut di atas dapat dilihat dari hasil penelitian yang telah dilakukan terlebih dahulu oleh Aqleh et al (2019), mengenai penerimaan RME.…”