A primary care, multi-disciplinary disease management program for opioid-treated patients with chronic non-cancer pain and a high burden of psychiatric comorbidity

BACKGROUND: Chronic non-cancer pain is a common problem that is often accompanied by psychiatric comorbidity and disability. The effectiveness of a multi-disciplinary pain management program was tested in a 3 month before and after trial. METHODS: Providers in an academic general medicine clinic referred patients with chronic non-cancer pain for participation in a program that combined the skills of internists, clinical pharmacists, and a psychiatrist. Patients were either receiving opioids or being considered for opioid therapy. The intervention consisted of structured clinical assessments, monthly follow-up, pain contracts, medication titration, and psychiatric consultation. Pain, mood, and function were assessed at baseline and 3 months using the Brief Pain Inventory (BPI), the Center for Epidemiological Studies-Depression Scale scale (CESD) and the Pain Disability Index (PDI). Patients were monitored for substance misuse. RESULTS: Eighty-five patients were enrolled. Mean age was 51 years, 60% were male, 78% were Caucasian, and 93% were receiving opioids. Baseline average pain was 6.5 on an 11 point scale. The average CESD score was 24.0, and the mean PDI score was 47.0. Sixty-three patients (73%) completed 3 month follow-up. Fifteen withdrew from the program after identification of substance misuse. Among those completing 3 month follow-up, the average pain score improved to 5.5 (p = 0.003). The mean PDI score improved to 39.3 (p < 0.001). Mean CESD score was reduced to 18.0 (p < 0.001), and the proportion of depressed patients fell from 79% to 54% (p = 0.003). Substance misuse was identified in 27 patients (32%). CONCLUSIONS: A primary care disease management program improved pain, depression, and disability scores over three months in a cohort of opioid-treated patients with chronic non-cancer pain. Substance misuse and depression were common, and many patients who had substance misuse identified left the program when they were no longer prescribed opioids. Effective care of patients with chronic pain should include rigorous assessment and treatment of these comorbid disorders and intensive efforts to insure follow up

Selection of the logical model of the intellectual algorithm for dynamic processing of medical data (obtained through portable medical devices)

Portable devices are one of the important emerging areas of modern medicine. This article presents the rationale for the selection of the logical model of the intellectual algorithm for dynamic processing of medical data obtained through portable medical devices. The description of the main criteria for the selection and application of the method of Saaty is provided. And the conclusion about the feasibility of using fuzzy logic as a logical model for the investigated subject area is made

Use of Acupuncture in the Complex Treatment for Pain Dysfunction Syndrome of a Temporomandibular Joint

Complex treatment for pain dysfunction syndrome of a temporomandibular joint has been developed and proved pathogenetically. High efficiency in the use of acupuncture in the treatment for pain dysfunction syndrome of a temporomandibular joint as compared to pharmacotherapy and physiotherapy has been demonstrated

DataGauge: A Model-Driven Framework for Systematically Assessing the Quality of Clinical Data for Secondary Use

There is growing interest in the reuse of clinical data for research and clinical healthcare quality improvement. However, direct analysis of clinical data sets can yield misleading results. Data Cleaning is often employed as a means to detect and fix data issues during analysis but this approach lacks of systematicity. Data Quality (DQ) assessments are a more thorough way of spotting threats to the validity of analytical results stemming from data repurposing. This is because DQ assessments aim to evaluate ‘fitness for purpose’. However, there is currently no systematic method to assess DQ for the secondary analysis of clinical data. In this dissertation I present DataGauge, a framework to address this gap in the state of the art. I begin by introducing the problem and its general significance to the field of biomedical and clinical informatics (Chapter 1). I then present a literature review that surveys current methods for the DQ assessment of repurposed clinical data and derive the features required to advance the state of the art (Chapter 2). In chapter 3 I present DataGauge, a model-driven framework for systematically assessing the quality of repurposed clinical data, which addresses current limitations in the state of the art. Chapter 4 describes the development of a guidance framework to ensure the systematicity of DQ assessment design. I then evaluate DataGauge’s ability to flag potential DQ issues in comparison to a systematic state of the art method. DataGauge was able to increase ten fold the number of potential DQ issues found over the systematic state of the art method. It identified more specific issues that were a direct threat to fitness for purpose, but also provided broader coverage of the clinical data types and knowledge domains involved in secondary analyses. DataGauge sets the groundwork for systematic and purpose-specific DQ assessments that fully integrate with secondary analysis workflows. It also promotes a team-based approach and the explicit definition of DQ requirements to support communication and transparent reporting of DQ results. Overall, this work provides tools that pave the way to a deeper understanding of repurposed clinical dataset limitations before analysis. It is also a first step towards the automation of purpose-specific DQ assessments for the secondary use of clinical data. Future work will consist of further development of these methods and validating them with research teams making secondary use of clinical data