The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach.

Objectives: This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling.

Methods: To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis.

A national nudge study of differently framed messages to increase COVID-19 vaccine uptake in Saudi Arabia: A randomized controlled trial.

Background: During the COVID-19 pandemic, Saudi Arabia witnessed hesitancy from a proportion of the population toward taking the vaccine; thus, it was necessary to nudge them to uptake it. This study was conducted to assess the impact of using different types of messages to nudge the public to increase the proportion of vaccinated individuals.

Methods: This study is a multi-arm randomized controlled trial aiming to assess the efficacy of using differently framed messages that appear as pop-notifications in Sehatty application.

Using machine learning to reduce unnecessary rehospitalization of cardiovascular patients in Saudi Arabia.

Objectives: Patient readmission is a costly and preventable burden on healthcare systems. The main objective of this study was to develop a machine-learning classification model to identify cardiovascular patients with a high risk of readmission.

Methods: Inpatient data were collected from 48 Ministry of Health hospitals (MOH) in Saudi Arabia from 2016 to 2019.

Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models.

Objective: Several opioid risk assessment tools are available to prescribers to evaluate opioid analgesic abuse among chronic patients. The objectives of this study are to 1) identify variables available in the literature to predict opioid abuse; 2) explore and compare methods (population, database, and analysis) used to develop statistical models that predict opioid abuse; and 3) understand how outcomes were defined in each statistical model predicting opioid abuse.

Design: The OVID database was searched for this study.

Integration of Provider, Pharmacy, and Patient-Reported Data to Improve Medication Adherence for Type 2 Diabetes: A Controlled Before-After Pilot Study.

Background: Patients with diabetes often have poor adherence to using medications as prescribed. The reasons why, however, are not well understood. Furthermore, most health care delivery processes do not routinely assess medication adherence or the factors that contribute to poor adherence.