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Improving Doctor-patient Communication with AI

A computer analysis of hundreds of thousands of secure email messages between doctors and patients found that most doctors use language that is too complex for their patients to understand. The study also uncovered strategies some doctors use to overcome communication barriers.

Experts on health literacy, as well as leading health care organisations, have advised that doctors always use simple language when explaining things to their patients, to avoid confusing those with the least health literacy. But the study found that most doctors did not do this. Only about 40% of patients with low health literacy had doctors who used simple language with them.

Effective electronic communication is becoming increasingly important, as doctors and patients rely more on secure messaging, an innovation that has rapidly expanded during the COVID-19 pandemic. The study found that the doctors who performed best in surveys of how well patients understood their care tended to tailor their electronic messages to their patients’ level, wherever it was on the spectrum of health literacy.

The study employed computer algorithms and machine learning to measure the linguistic complexity of the doctors’ messages and the health literacy of their patients. Using data from over 250,000 secure messages exchanged between diabetes patients and their doctors through Kaiser Permanente’s secure email portal, the study sets a new bar for the scale of research on doctor-patient communication, which is usually done with much smaller data sets and often does not use objective metrics.

The algorithms evaluated whether patients were cared for by doctors whose language matched theirs. Then, the research team analysed the individual doctors’ overall patterns, to see if they tended to tailor their communications to their patients’ different levels of health literacy.

Our computer algorithms extracted dozens of linguistic features beyond the literal meaning of words, looking at how words were arranged, their psychological and linguistic characteristics, what part of speech they were, how frequently they were used and their emotional saliency.

– Nicholas Duran, Cognitive Scientist and Associate Professor, School of Social and Behavioral Sciences, Arizona State University

Patients’ assessments of how well they understood their doctors most likely reflected how they felt about their doctor’s verbal and written communications. But the ratings nevertheless strongly correlated with the doctor’s written communication style.

Unlike a clinic encounter, where a doctor can use visual cues or verbal feedback from each patient to verify understanding, in an email exchange, a doctor can never be sure that their patient understood the written message. The findings suggest that patients benefit when doctors tailor their email messages to match the complexity of language the patient uses.

As reported by OpenGov Asia, another study shows that machine learning techniques can offer powerful new tools for advancing personalised medicine, care that optimises outcomes for individual patients based on unique aspects of their biology and disease features.

The research with machine learning, a branch of Artificial Intelligence (AI) in which computer systems use algorithms and statistical models to look for trends in data, tackles long-unsolvable problems in biological systems at the cellular level.

Those systems tend to have high complexity—first, because of the vast number of individual cells and second, because of the highly nonlinear way in which cells can behave. Nonlinear systems present a challenge for upscaling methods, which is the primary means by which researchers can accurately model biological systems at the larger scales that are often the most relevant.

The promises of individualised medicine are rapidly becoming a reality. The combination of multiple disciplines—such as molecular biology, applied mathematics and continuum mechanics—are being combined in new ways to make this possible. One of the key components of this will certainly be the continuing advances in machine learning methods.

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