The effectiveness of pharmaceutical risk minimization programs in protecting public health has been the subject of intense regulatory scrutiny over the past decade. These programs, which represent a population-based type of drug safety measure, are mandated by regulators for products with serious risks, either as a condition of marketing authorization or to investigate safety information newly identified in the post-market period [1, 2]. Under the terms of the regulatory commitment, these programs must be evaluated to determine whether they have been implemented as intended and are effective and sustainable in the context of real-world clinical care.

Risk minimization programs can be defined as ‘complex interventions’ [3, 4]. Characteristic features of complex interventions are that they have several interacting components, require numerous behaviors by those delivering or receiving the intervention (e.g., healthcare professionals [HCPs], patients), target different groups or organizational levels (e.g., individual, healthcare setting, healthcare system), involve a range of outcomes, and may require some flexibility or tailoring of the intervention to optimally fit within different healthcare settings [4].

When designing and evaluating complex interventions, it is essential to specify the theoretical (or empirical) basis for how the program is expected to achieve the desired effects. Similarly, it is important to conduct both a formative evaluation (to determine intervention feasibility and acceptability) and a comprehensive process evaluation (to determine the quality and fidelity of program implementation, and whether any adaptations occurred). A process evaluation is especially valuable when program evaluation results show lack of effect as it can clarify whether this was due to problems in program implementation or to the ineffectiveness of the program itself [4]. Lastly, a range of measures are needed to fully understand program effects, including measures of reach, adoption, impact, and ongoing maintenance, as well as whether the program was successful in reducing the incidence of the targeted risk(s), imposed undue burden on the healthcare system, impeded patient access to the drug, or had other unintended consequences [2, 4].

In recent years, three high-profile assessments of risk minimization programs—one in Europe for valproate products, and two in the United States (US) (one generally, and a second for opioid analgesics specifically)—revealed that program implementation had been incomplete and evaluation data insufficient [5,6,7]. Similarly, published reviews have shown that both the quality and comprehensiveness of risk minimization evaluation studies are highly variable, assessment of program implementation and context often inadequate, and findings regarding program effects limited or uncertain [8,9,10,11].

In an effort to build the evidence base in this area, a group of researchers under the auspices of the International Society for Pharmacoepidemiology (ISPE) developed a quality reporting checklist called the Reporting recommendations Intended for pharmaceutical risk Minimization Evaluation Studies (RIMES) [12]. The RIMES Checklist consists of 43 items and was designed to guide standardized, comprehensive, and transparent reporting of risk minimization evaluation study results. The RIMES was intended to be reviewed and updated periodically to remain abreast of the evolving science and regulatory guidance in this area.

Since its publication in 2018, the RIMES Checklist has had a significant impact on the field of drug safety. First, it engendered two comprehensive reviews of risk minimization evaluation studies, one focusing on studies published in the peer-reviewed literature, and a second focusing on risk minimization evaluation study reports submitted to the European Medicines Agency’s (EMA’s) Pharmacovigilance Risk Assessment Committee (PRAC) [13, 14]. These reviews highlighted numerous shortcomings in risk minimization reporting, including that evaluations rarely referenced the use of theories, models, or frameworks to guide program design and evaluation, and descriptions of program implementation, adaptations, and context were inadequate or missing.

Second, prominent pharmacovigilance organizations in Europe, including the EMA and the European Network Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) [1, 15], cited the RIMES Checklist in relevant guidance documents and recommended that it be used when reporting the results of risk minimization evaluation studies. A translated version of the RIMES Checklist along with guidance on how it should be applied were also published in a Chinese pharmacovigilance journal [16].

Globally, healthcare delivery organizations, including pharmacovigilance bodies, have been facing mounting pressures to transform into learning healthcare systems [17, 18]. Implementation science, defined as “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice,” is instrumental to such a transformation [19]. Consistent with this, and contemporaneously with the development of the RIMES, a new checklist, the Standards for Reporting of Implementation studies (StaRI), was published. The StaRI Statement and Checklist were intended to guide standardized, transparent, and complete reporting of IS research [20].

The application of implementation science for drug safety and risk minimization has been gaining increasing recognition [3, 19, 21,22,23,24,25,26]. Recently, pharmacovigilance regulatory guidance documents have begun incorporating concepts, constructs, and terminology from implementation science that are consistent with recommendations for evaluating complex interventions [1, 2, 4].

The purpose of the current work was to further refine and harmonize the RIMES with updated frameworks from implementation science, namely, by reviewing and including relevant items from StaRI to create a RIMES StaRI extension (RIMES-SE).

The RIMES-SE fills a niche in the growing array of quality checklists developed for the reporting of healthcare research [27]. The field of therapeutic risk minimization seeks to implement, scale up, and maintain effective interventions (e.g., behavioral, educational, healthcare process improvements) in the context of real-world clinical care to minimize the harmful effects of exposure to product-related risks. The programs are mandated to be implemented in full according to the terms of the marketing authorization commitment at the time of product launch. As a result, randomized experimental designs are not feasible and evaluators must employ non-experimental study designs (e.g., observational, time series, and/or mixed methods approaches) for program evaluation purposes. Given the variety of drug-related risks that may be targeted for minimization and the diversity of HCPs, healthcare settings, and patients involved, a heterogeneous array of data sources and data collection methods are appropriate and relevant to use.

Existing checklists for observational research in healthcare (i.e., STROBE [28]), or for research using routinely collected health data (RECORD [29]; RECORD-PE [30]) focus on a limited set of study designs, methods, and data sources. Other checklists developed for reporting quality improvements in healthcare (SQUIRE [31]) or for reporting behavioral and public health evaluations using non-randomized designs (TREND [32]) are not fully applicable due to their lack of emphasis on program implementation and maintenance.