New study finds more accurate diagnoses and shorter hospital stays
Integrating BMJ Best Practice into a hospital clinical decision support system is associated with more accurate diagnoses and shorter hospital stays, finds a new study from China published in JMIR Medical Informatics.
The researchers, based at Peking University Third Hospital in Beijing, believe this application “has the potential to improve the quality of health and health care.”
BMJ Best Practice is a clinical decision support tool that gives healthcare professionals fast and easy access to the latest information when making diagnosis and treatment decisions. It draws on the latest evidence-based research, guidelines and expert opinion to give guidance on diagnosis, prognosis, treatment and prevention.
BMJ Best Practice is currently freely available to university medical departments across China to help healthcare professionals tackle challenges, such as the COVID-19 outbreak, as part of BMJ’s commitment to helping to create a healthier world.
Clinical decision support systems (CDSS) have been used for many years to improve healthcare decision-making and quality of patient care.
More recently, the development of artificial intelligence (AI-based) systems have the potential to further improve diagnosis and treatment, but their use in “real-world” clinical practice remains controversial.
So the researchers set out to assess the effects on patient care of implementing an AI-based clinical decision support system integrated with BMJ Best Practice at their hospital.
Their findings are based on diagnosis data for 34,113 hospital patients in six clinical departments from December 2016 to February 2019.
After CDSS implementation, the researchers found improved consistency between the admission and the discharge diagnoses (from 70% to 73%), shorter confirmed diagnosis times, and shorter average hospital stays (from 7 days to 6 days).
These results were similar after further analyses, indicating that BMJ Best Practice has a positive impact on patient outcomes.
“The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians’ diagnoses,” write the authors. “Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies.”
These findings “highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials,” they conclude.
Find out more at: https://bestpractice.bmj.com/
Notes to Editors:
Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study JMIR Med Inform 2020;8(1):e16912) doi: 10.2196/16912
Click here to view full study: https://medinform.jmir.org/2020/1/e16912/
Siyan Zhan, PhD, Research Center of Clinical Epidemiology, Peking University Third Hospital, Haidian District Beijing China
Phone: +86 10 82 265 732
Hong Ji, Prof., Information Management and Big Data Center, Peking University Third Hospital, Haidian District Beijing China
Phone: +86 15 61 190 8189
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