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Health information systems (HISs) provide automated solutions for the clinical, informational, and managerial life of a hospital. These systems are optimally used when the knowledge workers are adequately equipped with the needed competencies. Three Kuwaiti hospitals have been engaged in adopting and using the same HIS. Jahra Public Hospital, one of the three, has been making progress in the use of HIS. It was hypothesised that capabilities and preparedness of knowledge workers are correlated with their extent of use of the HIS. Data were gathered from 124 healthcare knowledge workers using stratified random sampling. It was found that skills and abilities of these knowledge professionals are strongly correlated with their use of information and communication technologies (ICT) and HIS. Almost all the professionals used HIS, yet there was a wide variation in the use of HIS modules and applications. We also examined whether personal attributes of knowledge workers made any difference in their ICT and HIS skills and use. Results however revealed no such association. These professionals attached a great deal of importance with formal training, but urged that these knowledge workers had enhanced and involved role.

Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. This is a tedious and time consuming process and also involves a lot of monetary resources. The faster approval of a drug helps the patients who are in need of the drug. The in silico prediction of Adverse Drug Reactions can help speed up the aforementioned process. The challenges involved are lack of negative data present and predicting ADR from just the chemical structure. Although many models are already available to predict ADR, most of the models use biological activities identifiers, chemical and physical properties in addition to chemical structures of the drugs. But for most of the new drugs to be tested, only chemical structures will be available. The performance of the existing models predicting ADR only using chemical structures is not efficient. Therefore, an efficient prediction of ADRs from just the chemical structure has been proposed in this paper. The proposed method involves a separate model for each ADR, making it a binary classification problem. This paper presents a novel CNN model called Drug Convolutional Neural Network (DCNN) to predict ADRs using chemical structures of the drugs. The performance is measured using the metrics such as Accuracy, Recall, Precision, Specificity, F1 score, AUROC and MCC. The results obtained by the proposed DCNN model outperform the competing models on the SIDER4.1 database in terms of all the metrics. A case study has been performed on a COVID-19 recommended drugs, where the proposed model predicted the ADRs that are well aligned with the observations made by medical professionals using conventional methods.

The purpose of this study is to investigate the perceptions, level of knowledge, attitudes and behaviour of female nursing students at the Technical Institute for Health Training (TIHT) in Riyadh, Saudi Arabia, towards using electronic medical records (EMRs). A questionnaire comprised of closed and open questions was distributed to 70 female nursing students of whom 56 (80%) responded. Results showed that while 58.9% of students have heard of EMR, 78.6% of them have not received any formal EMR training. The findings show that there is no significant relation between age and the level of the study, and the knowledge or attitude towards EMR, although there is a significant relationship between age and the readiness to use EMR, with students over 23 years of age being more prepared to use EMR than those who are under 23 years.

One of the major change drivers which emerged in the 21st century is the birth of the internet. Internet-led digitalisation has impacted almost all the sectors across the board positively and in some cases negatively. The healthcare sector is no exception to the transformation. We are witnessing dramatic changes in the healthcare sector across all its verticals due to increasing digital health innovations. The Government of India is aggressively pushing for digital health reforms in India. The inception of key healthcare digitalisation initiatives, like national digital health mission, legalisation of telemedicine, E-pharmacies and the Health ID project, indicates the government’s strong resolve towards taking forward the digitalisation transformation at a rapid pace.

All these digital health innovations led to new strata of medical consumers like Digital Medical Consumers. In this chapter, a conceptual framework of three types of medical consumers based on their buying behaviour has been postulated. In addition, digital health innovations are playing a major role in the evolution of the holistic digital health ecosystem in India. We anticipate the evolution of Holistic Digital Health Start-ups (HDHSs) because of the government’s push towards digitalisation in health and its subsequent willingness to create digital health infrastructure and the rising penetration of health informatics. Hence, this paper has proposed a conceptual framework for Holistic Digital Health Start-ups (HDHSs).