Applying concept mapping to solving in-patient mental health recidivism.

Concept mapping is a powerful research tool using visual representation to expose the complementary impact and synergy of factors affecting a specific process. This article outlines an example, in the domain of mental health, of concept mapping applied to the specific issue of readmissions or recidivism of mental health patients. Concept mapping is used to unify the diverse perspectives found across the existing literature and in mental health care delivery so that patient input and engagement in the care process can be maximally applied to improvement in the delivery of local inpatient mental health care and, penultimately, to transformation of an inefficacious care delivery model.

Toward determining the structure of psychiatric visit nonadherence.

Visit nonadherence, that is, "no shows," in psychiatry costs the US healthcare $100 billion every year. Nonadherent visits undermine healthcare quality improvement efforts and erode patients' health. Previous research has focused on patient demographics or on redundant scheduling, rather than on the actual structure of visit nonadherence. Drawing on a comprehensive literature review and a series of 3 studies, we identify 22 determinants that contribute to visit nonadherence. Significantly, 8 of these determinants seem to account for most of the variance in modeling visit nonadherence. This work lays the foundation to develop prognostic tools for reducing nonadherent visits in ambulatory care.

Using electronic data sources to understand the determinants of psychiatric visit non-adherence.

Technology cannot be effectively used to drive improvements in health care quality and health care cost reduction until significantly more healthcare visits are attended. Visit attendance is often perceived as an intractable problem. This formative study identified and analyzed a set of visit adherence determinants to delineate a structure of adherence. The study is distinguished from previous work because it employs three types of determinants (socio-economic, diagnosis, and logistical), captured from an ADT system and an EMR, to predict visit non-adherence.