Introduction: Health information systems play a crucial role in the delivery of efficient and effective healthcare. Poor usability is one of the reasons for their lack of acceptance and low usage by users. The aim of this study was to identify the usability problems of a national comprehensive health information system using the concurrent think-aloud method in the recording of childcare data.
Methods: A descriptive cross-sectional study was conducted in the health centers of Kashan University of Medical Sciences, Iran, in 2020. Ten healthcare providers as system's users were purposively selected to evaluate the system. To identify problems, a concurrent think-aloud evaluation was conducted. Two administrators of the system designed scenarios for ten childcare data recording tasks. By analysing the recorded files, usability problems were identified. The severity of the problems was then determined with the help of the users and problems were assigned to usability attributes based on their impact on the user.
Results: A total of 68 unique problems were identified in the system, of which 47.1% were rated as catastrophic problems. The participants assigned 47 problems (69%) to the user satisfaction attribute and 45 problems (66%) to the efficiency attribute; they also did not assign any problems to the effectiveness attribute.
Conclusion: The problems identified in the national comprehensive health information system using the think-aloud method were rated as major and catastrophic, which indicates poor usability of this system. Therefore, resolving the system problems will help increase user satisfaction and system efficiency, allowing more time to be spent on patient care and parent's education as well as improving overall quality of care.
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http://dx.doi.org/10.1186/s12911-024-02746-2 | DOI Listing |
Infant Behav Dev
January 2025
Department of Psychology, Arizona State University, USA.
Background: Early intervention is effective for reducing ADHD symptoms and related impairments, yet methods of identifying young children in need of services are lacking. Most early predictors of ADHD previously identified are of limited clinical utility. This study examines several theoretically relevant predictors of ADHD in infancy and toddlerhood and whether assessment at multiple time points improves prediction.
View Article and Find Full Text PDFJ Exp Child Psychol
January 2025
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, LMU University Hospital, LMU Munich, 80336 München, Germany.
Early spelling depends on the ability to understand the alphabetic principle and to translate speech sounds into visual symbols (letters). Thus, the ability to associate sound-symbol pairs might be an important predictor of spelling development. Here, we examined the relation between sound-symbol learning (SSL) and early spelling skills.
View Article and Find Full Text PDFCir Cir
January 2025
Department of Anesthesiology and Critical Care, Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey.
Objective: The agitation that can occur in patients undergoing vitreoretinal surgery on awakening from general anesthesia is a serious post-operative problem. In our study, we aimed to compare the effects of different anesthesia methods on emergence agitation in patients undergoing vitreoretinal surgery.
Method: Patients undergoing vitreoretinal surgery were divided into two groups: Total intravenous anesthesia (Group T) and inhalation anesthesia (Group D) according to the maintenance of anesthesia applied by consulting the records.
J Med Internet Res
January 2025
Department of Pediatrics, Medical School, University of Michigan, Ann Arbor, MI, United States.
Background: The mental health crisis among college students intensified amid the COVID-19 pandemic, suggesting an urgent need for innovative solutions to support them. Previous efforts to address mental health concerns have been constrained, often due to the underuse or shortage of services. Mobile health (mHealth) technology holds significant potential for providing resilience-building support and enhancing access to mental health care.
View Article and Find Full Text PDFNeural Comput
January 2025
Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, U.K.
The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned patterns from their noisy variants. Solutions to this problem may be required to memorize vast numbers of patterns based on limited training data and subsequently recall the patterns in the presence of noise.
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