Translating information into rules is also subjective.Two different teams are likely to express and code the same information into rules differently. As a result, two CDS systems will behave differently even if the information that was used to define each one’s rules is the same.

ML algorithms can alleviate the effort for rules development and maintenance, and reduce the number of potential errors and conflicts. By using a rules-based system and recommendations that they generate as a reference baseline, they can learn on a training data set and take patient-specific characteristics into consideration, making these recommendations more relevant, targeted, and precise.

They also can perform numerous decision support tasks that conventional rules-based systems are simply unable to perform, including extracting and presenting diagnostic information for clinicians from radiology images, such as the presence of cancer cells, or types of skin abnormalities from photographs supplied by the patient. AI-powered Natural Language Processing (NLP) simplifies interactions with EHR systems, and provides new ways to deliver decision support to clinicians.

Challenges with ML algorithms in CDS systems
The quality of recommendations from ML algorithms is entirely dependent on the data sets these algorithms are trained on. The size of the data set may be too small or may contain “bias”, for example if the distribution of patient characteristics doesn’t realistically reflect the general population of patients.

Scientists recently found evidence of racial bias in a widely used algorithm that was meant to predict which patients will benefit from extra medical care. However, the algorithm was based on using healthcare costs to establish illness severity.

“Less money is spent on black patients who have the same level of need, and the algorithm thus falsely concludes that black patients are healthier than equally sick white patients,” said the scientists. “Reformulating the algorithm so that it no longer uses cost as a proxy for needs eliminates the racial bias in predicting who needs extra care.”

Data can also be of poor quality or insufficiently structured, which can be challenging for ensuring a seamless use of ML algorithms. Additionally, ML algorithms are essentially “black boxes” – it is not very easy to understand why a given recommendation was generated, while decisions produced by the rules-based systems can usually be traversed and justified.

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