Designing Health Information Technologies for Uptake: Development and Implementation of Measurement Feedback Systems in Mental Health Service Delivery. Introduction to the Special Section.

Driven by recent policy developments that emphasize accountability and data sharing (e.g., Patient Protection and Affordable Care Act 2010; HITECH Act 2009), health information technologies (HIT) are rapidly becoming ubiquitous within the contemporary healthcare landscape. Specific subtypes of HIT support service quality monitoring and can be classified within the ‘‘quality management’’ set of implementation strategies articulated by Powell et al. (2012, 2015). Despite their potential to function as an implementation strategy, these technologies often require their own strategic implementation supports to be successfully installed and effectively used in new service systems (Cohen 2015; Ruud 2015). Although there is widespread recognition of the potential for HIT to usher in new cost savings in healthcare, issues related to technology design and implementation processes have interfered with the extent to which those savings have been realized (Carroll 2015; Leviss 2011; Ribitzky et al. 2010). Measurement feedback systems (MFS; Bickman 2008) are one type of HIT that support service quality monitoring and directly inform care decisions through (a) the ongoing collection of intervention process and outcome data and (b) data-based feedback to providers (i.e., routine outcome monitoring; ROM). Because it is a core component of numerous evidence-based psychotherapies (e.g., Cognitive Behavioral Therapy, Interpersonal Psychotherapy) and in light of the burgeoning evidence for its positive impact on service recipient outcomes, ROM has been identified a potential minimal intervention needed for change (MINC; Glasgow et al. 2014; Scott and Lewis 2015). MFS represent a leading strategy to enable ROM in mental health service delivery offering two basic functions: (1) They include, or provide the ability to input into the system, quantitative measures that are administered regularly throughout treatment to collect ongoing information about the process and progress of the intervention; and (2) They provide automated presentation of that information to support timely and clinically-useful feedback to mental health providers about their cases (Bickman 2008). Importantly, these functions do not necessitate a freestanding system. Although MFS technologies often exist as stand-alone products, they may also be integrated into other HIT, such as electronic health records (Douglas et al. 2014; Steinfeld et al. 2015). MFS have seen rapid proliferation, with close to 50 such systems identified (Lyon et al., this issue). Despite the growth of MFS and their widespread endorsement in the mental health services literature (e.g., Garland et al. 2013; Halford et al. 2012; SAMHSA 2012), little empirical work has examined the strategies through which these systems are developed (e.g., contextual inquiry, software design, system usability testing) and implemented (e.g., training, consultation, use of incentives) in real world mental health service delivery settings. The integration and effective use of MFS is beset with the typical implementation challenges common across behavior change efforts (Cohen 2015). However, technology development and roll out are perhaps uniquely complicated and often unexpectedly protracted tasks in which mental health professionals (e.g., administrators, researchers, practitioners) and resource-poor settings are rarely & Aaron R. Lyon lyona@uw.edu