Skip to main content

All Content

Search Tips
Save
Selection
Format
Download
Published Date
Original Publication Date
Original Publication Date
PSNet Publication Date
Narrow Results By
PSNet Original Content
Commonly Searched Resource Types
1 - 4 of 4
Choudhury A, Asan O. JMIR Med Inform. 2020;8:e18599.
This systematic review explored how artificial intelligence (AI) based on machine learning algorithms and natural language processing is used to address and report patient safety outcomes. The review suggests that AI-enabled decision support systems can improve error detection, patient stratification, and drug management, but that additional evidence is needed to understand how well AI can predict safety outcomes.  
Fortman E, Hettinger AZ, Howe JL, et al. J Am Med Inform Asso. 2020.
Physicians from different health systems using two computerized provider order entry (CPOE) systems participated in simulated patient scenarios using eye movement recordings to determine whether the physician looked at patient-identifying information when placing orders. The rate of patient identification overall was 62%, but the rate varied by CPOE system. An expert panel identified three potential reasons for this variation – visual clutter and information density, the number of charts open at any given time, and the importance placed on patient identification verification by institutions.  
Becker RE. J Patient Saf. 2020;16.
This commentary explores two scientific cultures in modern medicine. A ‘traditional culture’ leaves error control up to individuals and groups of healthcare practitioners; the author describes how this culture leads to an overconfidence among practitioners about personal abilities to reduce errors. In contrast, a ‘modern scientific culture’ considers errors as inevitable and pervasive throughout medicine and beyond individuals or groups to control. The author describes the competing priorities of these cultures, and suggests that error control efforts in medicine will be more successful if there is a paradigm shift towards a more ‘modern’ attitude.
Russo E, Sittig DF, Murphy DR, et al. Healthc (Amst). 2016;4:285-290.
Using a case study on missed and delayed follow-up of test results, this commentary explores challenges and opportunities that data from electronic health records present for patient safety research. Key barriers to utilizing electronic health record data to inform improvement work include restricted access to data, difficulty interpreting data, and workforce issues.