Algorithm flags health practitioners who pose threats to patient safety

Health profession regulators have their eye on an algorithm that can identify practitioners at high risk of complaints.

Called PRONE-HP (Predicted Risk of New Event for Health Practitioners) and developed by a team from The University of Melbourne and Stanford University, the tool sought to fix a challenge regulators face – identifying health practitioners who pose substantial threats to patient safety.

Among the characteristics and markers that the model uses to estimate risk are demographics like age, sex and practice locations; profession and speciality; and information of the number and nature of prior complaints.

Associate Professor Matthew Spittal, ARC future fellow at The University of Melbourne, said if a practitioner had a high score then it would be “a 'red-flag’ to look more deeply to see, for instance, if there was a pattern of troubling behaviour or an imminent risk to patients”.

In their paper, the study’s authors said: “Because the score can be re-calculated each time a complaint is lodged, it could be useful for flagging cases that warrant deeper review – for example, reviewing previous complaints against a practitioner or other information held by the regulator to ascertain if there are troubling patterns of care or imminent risks to patients.”

They said this may be the most appropriate way to use the model.

Still, Spittal noted that the score is not perfectly predictive of risk.

“There may be practitioners that have a high score, for example, because of the area of medicine that they work in, but do not pose a risk to patient safety,” he explained. “For this reason, a score alone will never be sufficient to classify a practitioner as risky or not.

“A score isn't a substitute for a thorough investigation, but it may assist in prioritising notifications for investigation.”

So will the tool be used for the nursing profession in the near future?

Spittal said the model is not likely to be useful for all professions.

While the research team found that the tool showed promise for identifying high-risk doctors and dentists and showed potential for identifying high-risk chiropractors, psychologists, pharmacists and podiatrists, it was less successful when it came to other professions that the Australian Health Practitioner Regulation Agency (AHPRA) regulates, like nurses and midwives, due to the number of false positives.

There were far too many instances where practitioners who were identified as high risk but did not have another notification, Spittal explained.

“Partly, this is because the number of notifications relative to the overall number of nurses and midwives is low – much lower than for doctors and dentists.”

The team said profession-specific algorithms might perform better.

AHPRA was interested in seeing how the tool could be used in practice, Spittal said, but he added that will require more work.

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