The healthcare industry had been facing a loss due to the open or no show slots. When considered individually, a patient’s no show may not appear as a topic of concern but when the data was evaluated for an annual basis it was large enough, proving to be a matter which needed serious attention.
Artificial intelligence model was developed to solve the problem.
A team of researchers collected records of 32,957 outpatient MRI appointments which were placed between January 2016 and December 2018 from their institution’s radiology information system where they found that the overall no-show rate was 17.4%.
The whole data was then used to train the artificial intelligence model which could predict and analyse the pattern of no show in the outpatient MRI and can further solve the issue by trimming down the no show.
The model developed could show the list of top 25% of the highest risk of an appointment no-show patients. The reminders for such patients were made in the form of phone calls for a period of six months.
The results obtained after 6 months showed a decrease in no show to 15.9% compared with 19.3% seen in the pre-intervention period of preceding 12 months.
Also the patients in the group at high risk of appointment no-shows when contacted with reminders showed 17.5% no-show rates as compared to the 40.3% no-show rate among those who were not contacted.
Also a poll was conducted to find out the most effective way for reminding patients, which showed texting is the most efficient way of cutting down appointment no show whereas some medical setups also used phone calls and emails.
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