• John C Fortney, Jeffrey M Pyne, Christopher A Steven, J Silas Williams, Richard G Hedrick, Amanda K Lunsford, William N Raney, Betty A Ackerman, Loretta O Ducker, Laura M Bonner, and Jeffrey L Smith.
• HSR&D Center for Mental Health and Outcomes Research, Central Arkansas Veterans Healthcare System, North Little Rock, AR, USA. fortneyjohnc@uams.edu
• Am J Manag Care. 2010 Nov 1; 16 (11): 849854849-54.

ObjectiveTo inform the design of future informatics systems that support the chronic care model.Study DesignWe describe the development and functionality of a decision support system for the chronic care model of depression treatment, known as collaborative care. Dissemination of evidence-based collaborative care models has been slow, and fidelity to the evidence base has been poor during implementation initiatives. Implementation could be facilitated by a decision support system for depression care managers, the cornerstone of the collaborative care model. The Net Decision Support System (https://www.netdss.net/) is a free Web-based system that was developed to support depression care manager activities and to facilitate the dissemination of collaborative care models that maintain high fidelity to the evidence base.MethodsThe NetDSS was based on intervention materials used for a randomized trial of depression care management that improved clinical outcomes compared with usual care. The NetDSS was developed jointly by a cross-functional design team of psychiatrists, depression care managers, information technology specialists, technical writers, and researchers.ResultsThe NetDSS has the following functional capabilities: patient registry, patient encounter scheduler, trial management, clinical decision support, progress note generator, and workload and outcomes report generator. The NetDSS guides the care manager through a self-documenting patient encounter using evidence-based scripts and self-scoring instruments. The NetDSS has been used to provide evidence-based depression care management to more than 1700 primary care patients.ConclusionIntervention protocols can be successfully converted to Web-based decision support systems that facilitate the implementation of evidence-based chronic care models into routine care with high fidelity.

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