The use of clinical decision support (CDS) functionality is increasingly playing a prominent role in pragmatic clinical trials (PCTs)—either by enabling conduct of the trial, supporting the delivery of new interventions or being the intervention under evaluation. The key to its success has been the automation and knowledge transformation of certain tasks. Well-built CDS can save time and relieve cognitive burden on the part of the end-user. CDS can additionally assist research teams in identifying potential participants, initiating recruitment, and optimizing outcome collection. There are several examples of CDS used to support the NIH Collaboratory Trials.

In the Primary Palliative Care for Emergency Medicine (PRIM-ER) trial, CDS is used to identify seriously ill patients in the emergency department who might benefit from palliative care (Grudzen et al 2019). Here, the tool automates the process of cohort identification by querying the EHR for patient-level factors and automatically calculating risk scores. Without such a tool, manual calculations and individual searches would need to be done. Instead, this process is facilitated by the use of CDS and delivers alerts to clinicians to prioritize care and assessment. In this case, this study is supporting the testing of a new intervention with CDS as its mode of delivery.

In contrast, in the Emergency Department-Initiated Buprenorphine for Opioid Use Disorder (EMBED) trial, investigators tested CDS to identify and facilitate management of patients with opioid use disorder in the emergency department (Ahmed et al 2019; Melnick et al 2019a; Melnick et al 2019b; Ray et al 2019). Buprenorphine is a well-established therapy that is commonly used in settings other than the emergency department. Here, CDS was tested as the intervention itself and was bundled with implementation and engagement efforts (Melnick et al. 2022 ). CDS brings new potential to established interventions, expanding their effectiveness and ability to be dessiminated and implemented.

This chapter aims to define CDS, describe best practices for designing and evaluating decision support, and examine special considerations for using CDS in PCTs.