Background: Decision-making about treatment when planning a pregnancy (family planning) is complex for women living with multiple sclerosis (MS). Decision tools can help this process, in 2016 MS Trust launched their online digital treatment decision tool to support people with MS.
Objectives: To evaluate user-experience of this tool by exploring women's opinions about its content, interface, and usefulness in the context of family planning; and to synthesize recommendations to improve the tool.
Methods: Thirty participants qualitatively evaluated the tool using Think Aloud methodology. Sessions were conducted online using Microsoft Teams and were video recorded. Transcription was automated and data were thematically analyzed.
Results: Women's first impression was that the tool presented a lot of information at once, which was difficult to take in, and they found it difficult to navigate. Although the tool was helpful in allowing them to compare treatment options, the filters were confusing, and the information related to pregnancy sometimes contradicted advice from their healthcare practitioners. They suggested rewording the pregnancy recommendations and filters, updating some content, and making some changes to the interface to meet users' cognitive needs.
Conclusion: The MS Trust treatment decision tool is excellent in helping women with treatment choices at initial diagnosis. However, it is not currently as useful when considering family plans. Recommendations were conveyed to MS Trust where some are now applied to the new live version and the rest are to be considered for future updating projects.
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http://dx.doi.org/10.1177/20552173241262181 | DOI Listing |
JMIR Hum Factors
January 2025
Department of Value Improvement, St. Antonius Hospital, Nieuwegein, Netherlands.
Background: Patients with cerebrovascular accident (CVA) should be involved in setting their rehabilitation goals. A personalized prediction of CVA outcomes would allow care professionals to better inform patients and informal caregivers. Several accurate prediction models have been created, but acceptance and proper implementation of the models are prerequisites for model adoption.
View Article and Find Full Text PDFBackground: Medication-related adverse events are common in pregnant women, and most are due to misunderstanding medication information. The identification of appropriate medication information sources requires adequate medical information literacy (MIL). It is important for pregnant women to comprehensively evaluate the risk of medication treatment, self-monitor their medication response, and actively participate in decision-making to reduce medication-related adverse events.
View Article and Find Full Text PDFArq Bras Cir Dig
January 2025
Antenor Orrego Private University, School of Medicine, Trujillo, La Libertad, Peru.
Background: Laparoscopic cholecystectomy is considered safe; however, it is not free from complications, such as bile duct injuries, bleeding, and infection of the surgical site.
Aims: The aim of this study was to determine the effectiveness of two prediction tools, the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) calculator and the surgical Apgar, in predicting post-cholecystectomy complications.
Methods: A cross-sectional, analytical, and comparative study was conducted on patients over 18 years old diagnosed with acute cholecystitis who underwent open or laparoscopic cholecystectomy at the Regional Teaching Hospital of Trujillo between 2015 and 2019.
PLoS One
January 2025
Department of Management Science, Strathclyde Business School, University of Strathclyde, Glasgow, Scotland.
Objective: To conceptualise the cognitive processes of early expert decision-making in urgent care.
Background: Expert clinicians in the UK frequently determine suitable urgent care patient pathways via telephone triage. This strategy is promoted by policymakers but how it is performed, and its effectiveness has not been evaluated.
Anesth Analg
January 2025
From the Department of Anesthesiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands.
Background: Rotational thromboelastometry (ROTEM) is widely used for point-of-care coagulation testing to reduce blood transfusions. Accurate interpretation of ROTEM data is crucial and requires substantial training. This study investigates the inter- and intrarater reliability of ROTEM interpretation among experts and compares their interpretations with a ROTEM-guided algorithm.
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