Essential Regulatory Update for Implementation of Clinical Decision Support Software in the Psychiatric Field

Abstract

Decision-making in the field of psychiatry, including diagnosis, is highly complex and is hindered by use of a dichotomous categorical classification system that is ill-suited to mental disorders which are multi-factorial behavioral conditions. In addition, traditional use of results from randomized controlled trials to guide medical device regulation in the field of psychiatry is also problematic, as marked heterogeneity exists within psychiatric patient groups, which precludes results from these trials being projected onto the general patient population. In the past 20 years, clinical decision support software (CDSS) has been found to improve decision-making abilities, but the traditional regulatory approach based on the categorical classification system and randomized controlled trials does not allow the necessary flexibility for CDSS-based decision-making in psychiatry. In this article, we will use Major Depressive Disorder as an example and will discuss regulatory considerations for CDSS, including artificial intelligence, in psychiatry. We will also provide an adjusted life-cycle framework for CDSS in psychiatry, given that the particular complexity of psychiatric disorders demands new and innovative decision support tools. We suggest that any new software would need to perform at least as well as the standard-of-care, which in psychiatry is an unfortunate trial-and-error process. This would be demonstrated during the pre-market validation stage using clinical data from back-end testing of the CDSS. We propose that pre-market evidence of CDSS efficacy should be based on parameters that are used to measure the software success rate, with evidence of safety including demonstration of the low risk of CDSS due to human involvement in the decision-making process. In the post-market stage, CDSS would be used by doctors to generate real-world data that would allow ongoing evaluation and improvement of the algorithms. Furthermore, CDSS would collect data beyond the initial intended-use patient population, allowing the CDSS to learn about related indications. These data would inform the pre-market phase, during which the CDSS could be updated with an expanded patient population. We anticipate that such changes would support effective use of CDSS in psychiatry and improved patient care, which is particularly important given the trial-and-process that comprises the current standard-of-care in the field.