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Background: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. Methods: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. Results: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. Conclusions: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment. [ABSTRACT FROM AUTHOR]

Background: In fiscal year (FY) 2008, 133,658 patients were provided services within substance use disorders treatment programs (SUDTPs) in the U.S. Department of Veterans Affairs (VA) health care system. To improve the effectiveness and cost-effectiveness of SUDTPs, we analyze the impacts of staffing mix on the benefits and costs of specialty SUD services. This study demonstrates how cost-effective staffing mixes for each type of VA SUDTPs can be defined empirically.Methods: We used a stepwise method to derive prediction functions for benefits and costs based on patients' treatment outcomes at VA SUDTPs nationally from 2001 to 2003, and used them to formulate optimization problems to determine recommended staffing mixes that maximize net benefits per patient for four types of SUDTPs by using the solver function with the Generalized Reduced Gradient algorithm in Microsoft Excel 2010 while conforming to limits of current practice. We conducted sensitivity analyses by varying the baseline severity of addiction problems between lower (2.5 %) and higher (97.5 %) values derived from bootstrapping.Results and Conclusions: Compared to the actual staffing mixes in FY01-FY03, the recommended staffing mixes would lower treatment costs while improving patients' outcomes, and improved net benefits are estimated from $1472 to $17,743 per patient. [ABSTRACT FROM AUTHOR]

Background: In order to monitor and ultimately improve the quality of addiction treatment, professional societies, health care systems, and addiction treatment programs must establish clinical practice standards and then operationalize these standards into reliable, valid, and feasible quality measures. Before being implemented, quality measures should undergo tests of validity, including predictive validity. Predictive validity refers to the association between process-of-care quality measures and subsequent patient outcomes. This study evaluated the predictive validity of two process quality measures of residential substance use disorder (SUD) treatment. Methods: Washington Circle (WC) Continuity of Care quality measure is the proportion of patients having an outpatient SUD treatment encounter within 14 days after discharge from residential SUD treatment. The Early Discharge measure is the proportion of patients admitted to residential SUD treatment who discharged within 1 week of admission. The predictive validity of these process measures was evaluated in US Veterans Health Administration patients for whom utilization-based outcome and 2-year mortality data were available. Propensity score-weighted, mixed effects regression adjusted for pre-index imbalances between patients who did and did not meet the measures' criteria and clustering of patients within facilities. Results: For the WC Continuity of Care measure, 76 % of 10,064 patients had a follow-up visit within 14 days of discharge. In propensity score-weighted models, patients who had a follow-up visit had a lower 2-year mortality rate [odds ratio (OR) = 0.77, p = 0.008], but no difference in subsequent detoxification episodes relative to patients without a follow-up visit. For the Early Discharge measure, 9.6 % of 10,176 discharged early and had significantly higher 2-year mortality (OR = 1.49, p < 0.001) and more subsequent detoxification episodes. Conclusions: These two measures of residential SUD treatment quality have strong associations with 2-year mortality and the Early Discharge measure is also associated with more subsequent detoxification episodes. These results provide initial support for the predictive validity of residential SUD treatment quality measures and represent the first time that any SUD quality measure has been shown to predict subsequent mortality. [ABSTRACT FROM AUTHOR]

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Background: Substance use disorders (SUDs) are a substantial problem in the United States (U.S.), affecting far more people than receive treatment. This is true broadly and within the U.S. military veteran population, which is our focus. To increase funding for treatment, the Veterans Health Administration (VA) has implemented several initiatives over the past decade to direct funds toward SUD treatment, supplementing the unrestricted funds VA medical centers receive. We study the 'flypaper effect' or the extent to which these directed funds have actually increased SUD treatment spending. Methods: The study sample included all VA facilities and used observational data spanning years 2002 to 2010. Data were analyzed with a fixed effects, ordinary least squares specification with monetized workload as the dependent variable and funding dedicated to SUD specialty clinics the key dependent variable, controlling for unrestricted funding. Results: We observed different effects of dedicated SUD specialty clinic funding over the period 2002 to 2008 versus 2009 to 2010. In the earlier period, there is no evidence of a significant portion of the dedicated funding sticking to its target. In the later period, a substantial proportion-38% in 2009 and 61% in 2010-of funding dedicated to SUD specialty clinics did translate into increased medical center spending for SUD treatment. In comparison, only five cents of every dollar of unrestricted funding is spent on SUD treatment. Conclusions: Relative to unrestricted funding, dedicated funding for SUD treatment was much more effective in increasing workload, but only in years 2009 and 2010. The differences in those years relative to prior ones may be due to the observed management focus on SUD and SUD-related treatment in the later years. If true, this suggests that in a centrally directed healthcare organization such as the VA, funding dedicated to a service is a necessary, but not sufficient condition for increasing resources expended for that service. [ABSTRACT FROM AUTHOR]

Background: Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients.Methods: Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools.Results: The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools.Conclusions: Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations. [ABSTRACT FROM AUTHOR]

Background: Genetic testing is increasingly used as a tool throughout the health care system. In 2011 the number of clinically available genetic tests is approaching 2,000, and wide variation exists between these tests in their sensitivity, specificity, and clinical implications, as well as the potential for discrimination based on the results.Discussion: As health care systems increasingly implement electronic medical record systems (EMRs) they must carefully consider how to use information from this wide spectrum of genetic tests, with whom to share information, and how to provide decision support for clinicians to properly interpret the information. Although some characteristics of genetic tests overlap with other medical test results, there are reasons to make genetic test results widely available to health care providers and counterbalancing reasons to restrict access to these test results to honor patient preferences, and avoid distracting or confusing clinicians with irrelevant but complex information. Electronic medical records can facilitate and provide reasonable restrictions on access to genetic test results and deliver education and decision support tools to guide appropriate interpretation and use.Summary: This paper will serve to review some of the key characteristics of genetic tests as they relate to design of access control and decision support of genetic test information in the EMR, emphasizing the clear need for health information technology (HIT) to be part of optimal implementation of genetic medicine, and the importance of understanding key characteristics of genetic tests when designing HIT applications. [ABSTRACT FROM AUTHOR]