"There are too many, but never enough": qualitative case study investigating routine coding of clinical information in depression.

We sought to understand how clinical information relating to the management of depression is routinely coded in different clinical settings and the perspectives of and implications for different stakeholders with a view to understanding how these may be aligned

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

The body speaks its mind : the BodyMind Approach® for patients with medically unexplained symptoms in primary care in England

Date of Acceptance: 22/12/2014This article documents an experience of translating research into the real-world of the National Health Service (NHS) in the England. Transferring new knowledge from research is problematic particularly when negotiating within the context of the changing NHS England. An overview of the pitfalls/challenges and some of the tried and tested methods which were designed to overcome these is provided. The evidence-based intervention, offered by a University of Hertfordshire spin-out company Pathways2Wellbeing, is a service called Symptoms Groups to patients, and termed The Medically Unexplained Symptoms (MUS) Clinic to health professionals. The groups use The BodyMind Approach (TBMA)®2, based on a bio-psychosocial model derived from dance movement psychotherapy, which has been specifically researched with patients with MUS. These patients have no specific pathway for supporting their wellbeing and are high health utilizers at the interface of primary and community care. They suffer with chronic, physical symptoms or conditions which do not appear to have an organic, medical diagnosis, previously known as psychosomatic conditions.Peer reviewe

Tourette syndrome research highlights 2015 [version 1; referees: 3 approved]

We present selected highlights from research that appeared during 2015 on Tourette syndrome and other tic disorders. Topics include phenomenology, comorbidities, developmental course, genetics, animal models, neuroimaging, electrophysiology, pharmacology, and treatment. We briefly summarize articles whose results we believe may lead to new treatments, additional research or modifications in current models of TS

Health Care for Veterans: Suicide Prevention

[Excerpt] Congress has attempted to address the problem of suicide among veterans through legislation and oversight hearings, both on prevention of veteran suicide specifically and on veteran mental health more broadly. A task as challenging as preventing suicide requires collaboration among federal agencies, state and local governments, other organizations, communities, and individuals. This report, however, focuses on activities of the Veterans Health Administration (VHA) within the Department of Veterans Affairs (VA). The VHA’s approach to suicide prevention is based in part on the National Strategy for Suicide Prevention, which involves multiple federal departments, including the VA, Defense (DOD), and Education (ED), as well as several agencies within Health and Human Services (HHS). While this CRS report focuses on suicide prevention efforts of the VHA, activities of other entities are discussed as they relate to VHA activities. This CRS report begins with a brief overview of the public health framework for suicide prevention, which forms the basis for both the National Strategy for Suicide Prevention and the VHA’s approach to suicide prevention. The three subsequent parts of the report correspond to the three major components of the public health framework: (1) suicide surveillance, (2) suicide risk factors and protective factors, and (3) suicide prevention interventions. The final section addresses potential issues for Congress, and the Appendix summarizes provisions of public laws addressing suicide prevention among veterans