I have blogged extensively about the deployment of AI in healthcare (see, for example: CMS Offers $1M Reward for AI to Predict Unplanned Hospitalizations). I have learned that the simplest AI applications may sometimes have the largest beneficial effects (see: Medical Community Braces for Algorithm to Reduce Unnecessary Imaging Orders). That was my reaction when I read a recent Dark Daily article (see: Florida Hospital Utilizes Machine Learning Artificial Intelligence Platform to Reduce Clinical Variation in Its Healthcare...). Below is an excerpt from it:
Variation in how individual physicians order, interpret, and act upon clinical laboratory test results is regularly shown by studies in peer-reviewed medical journals to be one reason why some patients get great outcomes and other patients get less-than-desirable outcomes....At Flagler Hospital....,a new tool is being used to address variability in clinical care. It is a machine learning platform called Symphony AyasdiAI for Clinical Variation Management (AyasdiAI) from Silicon Valley-based SymphonyAI Group. Flagler is using this system to develop its own clinical order set built from clinical data contained within the hospital’s electronic health record (EHR) and financial systems....
Clinical order sets are designed to be used as part of clinical decision support systems (CDSS) installed by hospitals for physicians to standardize care and support sound clinical decision making and patient safety. However, when doctors don’t adhere to those pre-defined standards, the results can be disadvantageous, ranging from unnecessary services and tests being performed to preventable complications for patients, which may increase treatment costs....Flagler first used the AI program to examine trends among their pneumonia patients....[A] “smart software solution” will be inserted into the clinical workflow of physicians. This solution will automatically guide the physician to follow the established care protocol. In turn, this will give the medical laboratory the simple role of accepting a lab test order, performing the analysis, and reporting the results.
My initial reaction to the use of lab order sets years ago was that they were too rigid and bureaucratic. Having said this, I did realize, even early on, that there was a place for them. Many physician continue to use lab testing strategies that they learned years before as residents. Some relevant and necessary tests may thus be overlooked for admission lab test orders. However, I was struck by the following sentence in the excerpt above: Clinical order sets are designed to be used as part of clinical decision support systems (CDSS) installed by hospitals for physicians to standardize care and support sound clinical decision making and patient safety.
We now have the ability to develop test order sets that are more flexible and sophisticated and integrated with clinical and lab test data in the EHR and supported by predictive analytic tools (see: Diagnostic and Predictive Analytics and Their Possible Link to the Future of the LIS; Three Examples of Predictive Analytics in Clinical Practice). The deployment of sophisticated CDSS thus has the potential to diagnose patients faster as well as reduce length-of-stay (see: Mayo Develops CareSelect Tool to Guide Best Lab Practices for Clinicians; Dr. Alexa Will See You now -- An Extension of Paging Dr. Google).
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