Objectives: Computerized clinical decision support software (CDSS) are digital health technologies that have been traditionally categorized as medical devices. However, the evaluation frameworks for traditional medical devices are not well adapted to assess the value and safety of CDSS. In this study, we identified a range of challenges associated with CDSS evaluation as a medical device and investigated whether and how CDSS are evaluated in Australia.
Methods: Using a qualitative approach, we interviewed 11 professionals involved in the implementation and evaluation of digital health technologies at national and regional levels. Data were thematically analyzed using both data-driven (inductive) and theory-based (deductive) approaches.
Results: Our results suggest that current CDSS evaluations have an overly narrow perspective on the risks and benefits of CDSS due to an inability to capture the impact of the technology on the sociotechnical environment. By adopting a static view of the CDSS, these evaluation frameworks are unable to discern how rapidly evolving technologies and a dynamic clinical environment can impact CDSS performance. After software upgrades, CDSS can transition from providing information to specifying diagnoses and treatments. Therefore, it is not clear how CDSS can be monitored continuously when changes in the software can directly affect patient safety.
Conclusion: Our findings emphasize the importance of taking a living health technology assessment approach to the evaluation of digital health technologies that evolve rapidly. There is a role for observational (real-world) evidence to understand the impact of changes to the technology and the sociotechnical environment on CDSS performance.
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http://dx.doi.org/10.1017/S0266462324000059 | DOI Listing |
Comput Methods Programs Biomed
January 2025
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, 90127, Italy. Electronic address:
J Gastrointest Surg
January 2025
Department of Gastrointestinal Surgery, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-cho, Nakamura-ku, Nagoya 453-8511, Japan; Department of Laboratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-cho, Nakamura-ku, Nagoya 453-8511, Japan. Electronic address:
Background: Few studies have examined the prognosis of long-term gastric cancer (GC) survivors after gastrectomy. This study aimed to identify the prognostic factors for 5-year recurrence-free survivors after gastrectomy for GC.
Methods: A total of 721 patients with pathological stage Ⅰ-Ⅲ GC who underwent gastrectomy between 2005 and 2018 and survived for 5 years without recurrence were enrolled.
BMC Genomics
January 2025
State Key Laboratory of Biocontrol, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China-ASEAN Belt and Road Joint Laboratory on Mariculture Technology, Guangdong Provincial Key Laboratory of Aquatic Economic Animals, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Infectious spleen and kidney necrosis virus (ISKNV) is a highly virulent and rapidly transmissible fish virus that poses threats to the aquaculture of a wide variety of freshwater and marine fish. N6-methyladenosine (mA), recognized as a common epigenetic modification of RNA, plays an important regulatory role during viral infection. However, the impact of mA RNA methylation on the pathogenicity of ISKNV remains unexplored.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Economics and Management School, Wuhan University, Wuhan, China.
Background: Artificial intelligence-driven clinical decision support systems (AI-CDSSs) are pivotal tools for doctors to improve diagnostic and treatment processes, as well as improve the efficiency and quality of health care services. However, not all doctors trust artificial intelligence (AI) technology, and many remain skeptical and unwilling to adopt these systems.
Objective: This study aimed to explore in depth the factors influencing doctors' willingness to adopt AI-CDSSs and assess the causal relationships among these factors to gain a better understanding for promoting the clinical application and widespread implementation of these systems.
Diagnostics (Basel)
December 2024
Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium.
: Orofacial pain (OFP) encompasses a complex array of conditions affecting the face, mouth, and jaws, often leading to significant diagnostic challenges and high rates of misdiagnosis. Artificial intelligence, particularly large language models like GPT4 (OpenAI, San Francisco, CA, USA), offers potential as a diagnostic aid in healthcare settings. : To evaluate the diagnostic accuracy of GPT4 in OFP cases as a clinical decision support system (CDSS) and compare its performance against treating clinicians, expert evaluators, medical students, and general practitioners.
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