Health Information Technology Commons^™

Clinician Satisfaction With In-House And Cloud-Based Ehr Vendors, Massara Alazazi, Jun Sun, Nan Xiao

Information Systems Faculty Publications and Presentations

This paper examines the factors that affect clinician satisfaction with electronic health records (EHR) vendors that provide in-house and cloud-based solutions. The study is concerned with the organizational level of different healthcare organizations. The model combines the IS Success model and the Task Technology Fit model, to best fit the health information systems. Five research hypotheses derived from the model and are going to be empirically tested using a field survey of EHR systems characteristics and its influence on the organization’s tasks achievement and mission alignment.

Coping Strategies Among Caregivers Of Children With Autism Spectrum Disorders: A Cluster Analysis, Noor Ismael, Lisa Mische Lawson, Murad Moqbel, Lauren Little

Information Systems Faculty Publications and Presentations

Introduction/Background: The variability in coping strategies among caregivers of children with ASD may contribute to broader health outcomes; however, it is unclear if specific coping strategies are related to overall strain in this population. Therefore, this study identified groups of caregivers based on coping mechanisms and investigated differences in strain among groups.

Materials & Methods: This study utilized a secondary data analysis, and included survey responses of 273 caregivers of children with ASD. Measures consisted of the COPE Inventory, and the Caregiver Strain Questionnaire. Data analyses consisted of cluster analysis to group caregiver coping strategies, and Analysis of Variance to …

Mobile Personal Health Monitoring For Automated Classification Of Electrocardiogram Signals In Elderly, Luis J. Mena, Vanessa G. Felix, Alberto Ochoa, Rodolfo Ostos, Eduardo Gonzalez, Javier Aspuru, Pablo Velarde, Gladys E. Maestre

School of Medicine Publications and Presentations

Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). …