One of the most widely cited papers I have written in the last decade has been one on competencies in clinical informatics for medical education . For the most part, these 13 competencies have stood the test of time, from knowing how to use the electronic health record and information retrieval systems as well as applying clinical decision support, patient privacy, personal health records, telemedicine, and more. All of these aspects of clinical informatics are essential skills for the 21st century clinicians.
But another area of required competence has come to the fore in recent years, which is the explosion of machine learning and artificial/augmented intelligence in medicine. While the impact of these in real-world clinical practice is still small, the long-term effect is likely to be substantial. Certainly clinicians should be familiar with the myriad of issues related to algorithms and models, including ethical concerns.
In the process of updating our chapter for the forthcoming 2nd edition of the textbook, Health Systems Science , my co-author Dr. Jesse Ehrenfeld and I took the opportunity to make this revision to the competencies by adding a 14th one:
14. Apply machine learning applications in clinical care
a. Discuss the applications of artificial/augmented intelligence in clinical settings
b. Describe the limitations and potential biases of data and algorithms
As with the original competencies, we encourage others to improve upon them. But it is also important to add this critical new one to the full set, which are listed below.
For cited references on this article, see original source. Dr. Hersh is a frequent contributing expert to HealthIT Answers.