The importance of incorporating patient and provider decision-making processes in interventions research has come to the forefront of the National Institute of Mental Health (NIMH, 1999
) agenda for improving mental health interventions and services and is currently identified as a key research direction for the NIMH Primary Care Research program. Patients/healthcare users1
are increasingly recognized as key decision makers in shared treatment decision making with healthcare providers, but relatively little research has been done in mental health regarding patient/provider treatment preferences and decision-making processes. Research in this area is high priority because of individual quality of life and autonomy reasons, but also in the context of the health services research agenda to improve population health outcomes. Most patients ultimately decide for themselves what they will or will not do in regard to treatment, and this autonomy right is also increasingly recognized in community mental health and other types of health care.2
Patients' decisions impact behaviors, such as treatment initiation and continuance, which in turn can influence individual and aggregate level clinical health status and health system outcomes.
How patients make decisions, the testing of interventions to support effective decision making, and the development of measures of patient decision making have only recently begun to be studied for mental health contexts. Interest in patient decision making for mental health contexts is timely given a general increase in interest in patient-centered interventions that focus on the patient perspective in health care (van Dulmen, 2003
; Lauver et al., 2002
Recommendations 23, 24, and 26 of the NIMH Bridging Science and Service Report
consider research needs and funding of research on patient/provider decision making; for example, additional study of decision-making theory, concepts, preference assessment, and strategies for incorporating and evaluating impacts on usual clinical care. These initiatives are the mental health expression of reform challenges articulated in the Institute of Medicine report Crossing the Quality Chasm
), which addresses needs for health services quality improvements that enhance patient centeredness.
To improve mental health care quality, interventions research must better incorporate the patient/healthcare user perspective—not only in terms of more traditionally studied patient-centric concepts, such as symptoms, expectations, beliefs/attitudes, and preferences—but also in terms of decision making itself, as related to the process and outcomes of care. For example, a contemporary proposal for healthcare services redesign is embodied in the Chronic Care Model (E. Wagner, 1998) that has been applied to systemwide interventions to improve depression care (Von Korff, Unutzer, Katon, & Wells, 2001
). The Chronic Care Model addresses strategies to improve self-care management support via “productive interactions” among “informed, activated” patients and “prepared, proactive practice” teams. Central to the self-management support dimension of the Wagner model is basic research on what creates the “productive interactions” at the heart of the model for improving outcomes. In this article, we propose that improving patient self-management requires better understanding of productive interactions, as can occur via the study of patient/provider decision making.
Aims of This Article
The overall aim of this article is to describe how patient decision-making concepts are being incorporated in recent mental health research. Following a brief historical overview of decision theory, we describe a decision-making model (Rothert et al., 1997
) with its supporting empirical findings that link patient-level/“micro” variables to services-level/“macro” variables via the decision-making process that is a target for interventions. Next, we discuss the rapid growth of patient-centered decision aids (DAs), including presentation of an example research program involving descriptive and interventions research methods focused on testing the usefulness of DA technology in mental health. Finally, we discuss further challenges and future directions for incorporating patient preferences into interventions research designs.