A Web-based clinical decision support system for depression care management
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, Jeffrey L Smith, 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, Jeffrey L Smith
Abstract
Objective: To inform the design of future informatics systems that support the chronic care model.
Study design: We 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.
Methods: The 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.
Results: The 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.
Conclusion: Intervention 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.
Conflict of interest statement
Author Disclosures: The authors (JCF, JMP, CAS, JSW, RGH, AKL, WNR, BAA, LMB,JLS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
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Source: PubMed