VisualDecisionLinc: Real-Time Decision Support for Behavioral Health (North Carolina)

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Summary:

The societal burden of psychiatric disorders is large. Improving the initial selection of treatments for these disorders has the potential to reduce the time to remission, leading to a reduction in the likelihood of medication errors and adverse events when medications are changed due to initial treatment failure. Guidelines usually lack treatment algorithms that are tailored to a patient’s symptom profile and disease history. Supplementing clinical guidelines with data on treatment response from patients sharing similar profiles would likely narrow the range of treatment options to those with the best available evidence. Incorporating evidence-based recommendations into decision support tools has enormous potential to improve psychiatric care, including initial treatment strategies.

This project designed and developed approaches to identify, aggregate, and present treatment-response information on individual patients and comparative populations as “data views.” These data views were then aggregated to build a visual analytics–based clinical decision support prototype called VisualDecisionLinc (VDL). VDL was designed to improve clinical decisionmaking through the use of integrated data and knowledge derived from electronic medical records (EMRs).

The specific aims of the project were to:

  • Develop and validate expert-driven, guideline-driven, and data-driven attribute sets for the creation of comparative populations. 
  • Develop a data visualization-based user interface to aid in the selection of treatment choices. 
  • Conduct an exploratory effectiveness evaluation of VisualDecisionLinc in preparation for a larger scale, health information technology implementation research methods. 

The project began by aggregating de-identified patient data from MindLinc, the largest available warehouse of psychiatry data, for the design and development of the VDL. The project focused on patients with a primary diagnosis of major depressive disorder (MDD).

Usability testing was conducted with three participants who reviewed a video highlighting the VDL user interface (UI) features. Participants used VDL with simulated patient data and provided verbal feedback that was captured by a screen-capture tool. Information from this initial evaluation was used to make changes to the VDL UI. A second evaluation with six participants focused on how well the VDL UI aligned with the clinician’s workflow. Pre- and post-test results were analyzed. Overall, the results were encouraging. The participants liked the different data views and the ability to customize the evidence to meet their needs.

VisualDecisionLinc: Real-Time Decision Support for Behavioral Health - 2012

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-269: Exploratory and Developmental Grant to Improve Health Care Quality Through Health Information Technology (IT) (R21)
  • Grant Number: 
    R21 HS 019023
  • Project Period: 
    August 2011 – July 2013
  • AHRQ Funding Amount: 
    $299,997
  • PDF Version: 
    (PDF, 313.87 KB)

Summary: In 2000, the societal burden of psychiatric disorders was estimated at $83 billion, $26 billion of which was attributable to direct medical expenses. An Agency for Healthcare Research and Quality- supported research review of psychiatric disorders recommended that studies build evidence on the most appropriate initial treatment strategies for maintaining a favorable response. Improving the initial selection of treatments has the potential to reduce the time to remission, as well as reduce the likelihood of medication errors and the adverse events caused by medication switching. There is consensus among clinicians and health policy experts that mental health decision support tools that aid clinical decisionmaking hold enormous potential to improve psychiatric care, including initial treatment strategies. One strategy is providing clinicians with expert and evidence-derived knowledge at the point of care. General guidelines often lack treatment algorithms that are tailored to a patient’s symptom profile and disease history. Thus, supplementing clinical guidelines with data on treatment response from patients sharing similar profiles would narrow the range of treatment options to those based on the best available evidence.

To address these needs, Dr. Mane and his research team are developing a software-based decision support prototype known as VisualDecisionLinc (VDL). The VDL is designed to provide decision support for treatments of major depressive disorder (MDD), one of the most prevalent and burdensome psychiatric disorders. The project is: 1) developing new approaches to selecting comparative patient populations based on expert, guideline, and data-driven approaches; 2) developing software user interfaces (UIs) to allow clinicians to determine quickly which treatment approaches have been effective for patients similar to the presenting patient; and 3) providing an initial evaluation of approaches in preparation of a larger- scale deployment and test of clinical effectiveness. In 2011, the research team built a database to maintain and clean patient data from the electronic medical record (EMR) so that it can be imported into the VLD. In collaboration with psychiatrists, the data were evaluated to identify a set of similarity attributes (SSAs) to define a comparative population. The SSAs include demographics such as race, gender, and age, comorbid conditions, and prescribed medications. The research has the potential to identify novel ways to leverage historical patient databases and demonstrate a health information technology (IT) approach to optimize treatment choices for behavioral health care.

Specific Aims:

  • Develop and validate expert-driven, guideline-driven, and data-driven attribute sets for the creation of comparative populations. (Ongoing)
  • Develop a data visualization-based user interface to aid in the selection of treatment choices. (Ongoing)
  • Conduct an exploratory effectiveness evaluation of VisualDecisionLinc in preparation for a larger scale, health IT implementation research. (Upcoming)

2012 Activities: In 2012, the focus was the development and evaluation of the VDL UI. The UI was integrated with the analytical engine developed earlier, which filters the database of patient information to identify a comparative population similar to the target patient. The UI was designed to give providers the capability to click to select SSAs of interest and get instant and updated views of the presented evidence. Aggregated evidence on prescribed medications was organized by medication class to facilitate the understanding of medication combinations prescribed to the comparative population. Additional data views were built to provide an at-a-glance view of comorbid conditions for the comparative population as well as an overview of the patient’s medical profile, including medications, outcomes, and comorbidities. All data views were designed to work in coordination so that any change in one view triggers automatic changes in the other views. The UI also integrated a guideline view that shows patient data in relation to the Texas Medication Algorithm Project, which developed guidelines for the treatment of the MDD patients.

The other major focus was to develop a data-driven approach to build a model to predict the next clinical global impression (CGI) score for clinical improvement and clinical severity. The CGI scale considers symptom severity, treatment response, and efficacy of treatments for patients with mental disorders. Analysis of the EMR data identified five predictors of the next CGI score: 1) CGI measured at the previous visit; 2) type of medicine prescribed; 3) psychiatric comorbidities; 4) type of treatment; and 5) demographic characteristics. The model had a predictive power of approximately 76 percent for CGI improvement and approximately 89 percent for CGI severity. The team is exploring the factors that may be driving the differences, including demographic characteristics, adverse drug events, and MDD.

As last self-reported in the AHRQ Research Reporting Systems, project progress is mostly on track, and project spending is on target.

Preliminary Impact and Findings: To evaluate the UI, the research team prepared a demonstration video with an explanation on the use of the VDL UI. Based on agile usability principles, the team conducted evaluations with two or three participants per cycle for quick feedback, optimized the UI design, and then retested it in followup evaluation cycles. Overall, the feedback was encouraging. Participants reported that they liked the view of patient-level demographics and the information about the patient’s predicted and actual response to treatment. However, participants reported that it was difficult to distinguish between the predicted and the actual outcome. The team is working on this issue and plans to write a manuscript about the usability studies.

Target Population: Chronic Care*, Mental Health/Depression

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

*This target population is one of AHRQ’s priority populations.

VisualDecisionLinc: Real-Time Decision Support for Behavioral Health - 2011

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-269: Exploratory and Developmental Grant to Improve Health Care Quality through Health Information Technology (IT) (R21)
  • Grant Number: 
    R21 HS 019023
  • Project Period: 
    August 2011 - July 2013
  • AHRQ Funding Amount: 
    $299,997
  • PDF Version: 
    (PDF, 477.31 KB)

Summary: In 2000, the societal burden of psychiatric disorders was estimated at $83 billion, with $26 billion attributable to direct medical expenses. A research review of psychiatric disorders, supported by the Agency for Healthcare Research and Quality, recommended that studies build the evidence-base on the most appropriate initial treatment strategies for maintaining a favorable response. Improving the initial selection of treatments has the potential to reduce the time to remission, as well as reduce the likelihood of medication errors and the adverse events caused by medication switching. There is consensus among clinicians and health policy experts that mental health decision support tools that aid clinical decision-making hold enormous potential to improve psychiatric care, including initial treatment strategies. One strategy is providing clinicians with expert and evidence-derived knowledge at the point of care. General guidelines often lack specific treatment algorithms that are tailored to a patient's unique symptom profile and disease history. Thus, supplementing clinical guidelines with data on treatment response from patients sharing similar profiles would narrow the range of treatment options to those based on the best available evidence.

To address these needs, Dr. Mane and his research team are developing a software-based decision support prototype known as VisualDecisionLinc (VDL). The VDL is designed to provide decision support for treatments of major depressive disorder (MMD), one of the most prevalent and burdensome psychiatric disorders. The project will: 1) develop new approaches to selecting comparative patient populations based on expert-, guidelines-, and data-driven approaches; 2) develop software user interfaces to quickly allow clinicians to determine which treatment approaches have been effective for patients similar to the presenting patient; and 3) provide an initial evaluation of approaches in preparation of a larger scale deployment and test of clinical effectiveness. The research has the potential to help understand novel ways to leverage historical patient databases and to demonstrate a health information technology (IT) approach to optimize treatment choices for behavioral health care.

Specific Aims:

  • Develop and validate expert-driven, guideline-driven, and data-driven attribute sets for the creation of comparative populations. (Ongoing)
  • Develop a data visualization based user-interface to aid in the selection of treatment choices. (Ongoing)
  • Conduct an exploratory effectiveness evaluation of VisualDecisionLinc in preparation for a larger scale, health IT implementation research. (Upcoming)

2011 Activities: The research team laid the ground work for this project by building a database to maintain and clean patient data from the electronic medical record (EMR) so that it may be imported into VLD. In collaboration with psychiatrists, the data were evaluated to identify a set of similarity attributes (SSAs) to define a comparative population. The SSAs include demographics such as race, gender, and age; comorbid conditions; and prescribed medications. The SSAs will form the basis of the analytical engine, which is a query that filters the database of patient information to identify a comparative population similar to the target patient. Subsequently, a VDL user-interface (UI) was developed and integrated with the analytical engine and the EMR such that providers may click to select SSAs of interest.

At the UI level, the prescribed medications were organized by medication class to facilitate the understanding of medication combinations prescribed to the comparative population. Additional data views were built to provide an at-a-glance view of comorbid conditions for the comparative population as well as an overview of the patient's medical profile, including medications, outcomes, and comorbidities. The UI also integrates a guideline view that shows patient data in relation to the Texas Medication Algorithm Project, which developed guidelines for the treatment of the MDD patients.

As last self-reported in the AHRQ Research Reporting Systems, project progress is mostly on track, and project spending is on target.

Preliminary Impact and Findings: This project has no findings to date.

Target Population: Mental Health/Depression

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decision-making through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

VisualDecisionLinc: Real-Time Decision Support for Behavioral Health - Final Report

Citation:
Mane K. VisualDecisionLinc: Real-Time Decision Support for Behavioral Health - Final Report. (Prepared by the University of North Carolina, Chapel Hill under Grant No. R21 HS019023). Rockville, MD: Agency for Healthcare Research and Quality, 2014. (PDF, 772.46 KB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services. 
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