How EMRs improve medical attention by health personnel

There is growing enthusiasm in the United States about the use of electronic medical records (EMR) in patients. Important sums of money have been allocated to subsidize the adoption of this technology by physicians. The magazine Family Medicine highlights in an article both the promises and the disadvantages of this technology.

A study by Schriefer et al showed that the addition of instructions for patients with a high body mass index (BMI) resulted in a higher rate of diagnosis of obesity and indications for treatment with diet and exercise. Establishing when a patient is obese is not a difficulty or a novelty for the doctor, but the fact that the computer clearly marks it is surely an additional stimulus for the professional to act on it. Other studies also showed that EMR systems encouraged the necessary action against streptococcal pharyngitis, diabetes and hypertension.

There are several types of electronic record implementation that affect the results. In situations where the patient is seen by several different doctors, the electronic record improves the results, since it avoids the difficulty of reading the famous bad calligraphy of colleagues. But in addition, when the protection of bad calligraphy disappears, the users of the system feel more obliged to write correctly and completely the required data. And since the system often requires the entry of certain data, the excuse of forgetfulness disappears in the records.

This also applies to auxiliary personnel, because when they have access to the data entered by the doctor in a clear and indicative manner, they can and should perform the additional tasks that may correspond to them, such as taking measurements or taking samples, asking the patient to fill out forms or that is committed to other stages of the care process.

Automated systems with “artificial intelligence” that help the doctor to perform their task, can be a help or a nuisance, but to the extent that they are more complete and are better implemented, they help significantly. For example, a doctor may receive a warning from the system that he is not asking for enough studies for a certain quality of patients. For example, it does not ask for serology for venereal young adults. And this has a statistical basis, which arises from comparing the activity of a user of the system with the general average of users, so there is a chance that the computer, despite the medical user, is right and knows in one more case medicine that the doctor himself.