A Clinical Decision Support Tool for Electronic Health Records
Small Business Information
320 NEEDHAM STREET, SUITE 100, NEWTON, MA, -
Name: EMIL CHIAUZZI
Phone: (617) 332-6028
Phone: (617) 332-6028
Name: DEBORAH TROTTIER
Phone: (603) 672-6628
Phone: (603) 672-6628
AbstractDESCRIPTION (provided by applicant): Substance abuse treatment is often complicated by problems such as depression, family and interpersonal conflicts, and legal issues. When these issues are addressed with effective, evidence-based treatments (EBTs), outcomes improve. To improve the quality of addiction treatment, experts have called for the adoption of a continuing care management approach, involving a more comprehensive integration of EBTs in the treatment process. However, it is often difficult to integrate EBTs into the clinical workflow, so clinicians rely on old patterns of care. EBTs are most likely to be implemented if information about them is available to the clinician at the point of service. The expansion of electronic health record (EHR) use offers an important opportunity to overcome this problem. Although CDS systems are generally not available within behavioral health settings, SAMHSA has encouraged the integration of behavioral health into EHRs and is wants to increase the number of behavioral health providers who use EHRs. The next step is to build CDS capabilities that will be available as EHRs inevitably expand across behavioral health settings. This proposal will address this gap through a partnership between two companies that have developed a national standing in substance abuse treatment. Inflexxion has developed a computerized version of the Addiction Severity Index called the ASI-MV, now used in over 600 treatment centers nationwide. FEI's flagship product is the Web Infrastructure for Treatment Services (WITS), a web application that captures extensive client-level treatment data, and is used in 22 states, by more than 1,000,000 clients. Inflexxion and FEI will develop a CDS component that will plug into the WITS user interface. Applying advanced statistical and data mining techniques to the extensive WITS data set (that includes ASI-MV data), we will assess the relationships among client profiles (key predictive variables), services, and outcomes, to provide recommendations about treatment services that will yielded the best outcomes (e.g., successful recovery and decreased depression, increased duration of employment, fewer legal/judicial problems). Rather than rely on static algorithms, they system will gain intelligence as the number of cases increases. The advantages of this approach are: (1) it utilizes statistical analysis and data mining to match effective interventions with client profiles; (2) assists clinicians in identifying the best service combinations, EBTs, resources fortheir clients at the point of service; and (3) assists organizations in identifying EBT implementation and outcomes. The public health potential of this is very significant, and will shift the paradigm away from an asynchronous, training-based EBT implementation model to a data-driven, continuing care model. The current proposal represents a significant innovation that addresses the traditional barriers to EBT implementation. Because FEI and Inflexxion have both attained a significant collective presence in substance abuse treatment field, there will be an opportunity to implement the system in a large number of treatment settings. We envision a commercial model that initially sells subscription access to the proposed system within the WITS application and,eventually to users of other EHR systems, to facilitate broader healthcare integration. PUBLIC HEALTH RELEVANCE: When co-morbid issues are addressed in substance abuse treatment, via evidence-based approaches, patient outcomes improve. Evidence-based treatments (EBTs) are most likely to be used if information about them is available to the clinician at the point of care. However, the use of EBTs in substance abuse treatment continues to lag. We propose developing a system to: (1) match effective interventions with client profiles; (2) identify the most effective combinations of service and (3) make the data available to the provider at the point of care by integrating the system in an electronic health record. This would represent a significant innovation that addresses major barriers to EBT implementation, and enhances the use of electronic health records in substance abuse treatment.
* information listed above is at the time of submission.