Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3145
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Introduction: Technological advancements like digital monitoring tools, disease modifying therapies and artificial intelligence have been shown to improve the clinical management of neurocognitive diseases like Alzheimer's disease (AD). To enhance implementation in daily practice, users' input is essential in the technology development process. This study aimed to determine clinician's perspective of clinical decision support systems (CDSS) in the management of dementia and AD.
Method: A survey was conducted targeting clinicians practicing in the field of dementia across Europe. A sixty-five-item digital questionnaire was administered, and opinions were enquired across the domains of diagnosis, disease modifying therapy and prognosis, including factors that affect tool implementation and utilization.
Results: Eighty-four clinicians (including specialist physicians, psychologists and nurses) responded to this survey, and more than 50% had no knowledge or experience with CDSS. Most of the respondents reported the ability to predict the likelihood of AD as the most important diagnostic function. It was surprising to find the middling responses for the ability to predict amyloid positivity. The majority indicated assessment of treatment eligibility for disease-modifying therapy as vital, and the ability to predict cognitive and functional decline as the most important prognostic functions. Data accuracy and ease of use were noted as most necessary to facilitate CDSS adoption and implementation.
Conclusion: Findings from this study contribute to the future development of CDSS in this field, especially regarding the approval and imminent use of disease modifying therapies, a comprehensive tool that is precise and user friendly would improve clinical decisions and efficiency.
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http://dx.doi.org/10.1159/000544801 | DOI Listing |
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