‘‘There Are Too Many, but Never Enough’’ Table 5. Summary of recommendations emerging from this case study. Strategic – Definitions relating to both clinical terms and related content to be based on an agreed set of criteria to ensure consistency in clinical coding practices. – Agreed clinical coding standards between care settings to facilitate information exchange. – Aligning different existing clinical coding and classification systems. – Updating structures and/or clinical codes in pace with professional practices. Development of Systems – Devise systems that are meaningful in relation to their purposes (clinical activity, secondary uses). – Arranging categories in more meaningful clusters (ideally based on hierarchies). – Reducing the number of categories – particularly in primary care. – Balancing structured and/or coded entry and free text based on severity of symptoms and settings. – Devising systems that facilitate meaningful clinical coding of interpreted syndrome components. – Devising clinical coding systems that capture relationships as well as clinical codes to derive most value out of systems (e.g. changes in diagnoses over time). Organizational – Clinical coding champions to ensure consistent approaches to clinical coding amongst teams. – Promote the relationship between clinicians and clinical coders by placing them as close to clinical work as possible. – Pay attention to professional differences in clinical coding practices. – Training of healthcare professionals in clinical coding practices and increased involvement in development of clinical codes. – Agreement on clinical coding practices within individual settings and/or health communities to ensure consistency. – Explore and encourage innovative approaches to clinical coding e.g. voice recognition, portable hardware. doi:10.1371/journal.pone.0043831.t005 associated nature of work may be more amenable to clinical coding than others. Conclusions Our work has highlighted important aspects relating to coding clinical information in the mental health setting. In line with the existing literature, we found that this area differs in important ways from the more biomedical conditions with issues surrounding definitions, cut-off points and the evolving nature of mental disorders. We have also raised some questions relating principally to the implicitly assumed benefits of coding and classification systems. We found these to be lacking in the immediate clinical context and in relation to the drive to encourage clinical coding by healthcare professionals during the clinical encounter. Based on our findings, we have made a number of suggestions that may be considered to address existing issues and explored these from a range of strategic, technical, and organizational angles. Overall, we feel that it is important to now take a step back and re-consider our own implicit assumptions about effectiveness and benefits, recognizing that these may be associated with significant trade-offs. This should involve deliberating why and for what purpose information is gathered on different levels including large- scale (e.g. epidemiological), organizational (e.g. management), and micro-environmental (e.g. the clinical encounter). The first step to achieving this, will involve formulating a challenge and then References 1. Mort M, Smith A (2009) Beyond information: Intimate relations in socio- technical practice. Sociology 43: 215–231. 2. Walker J, Pan E, Johnston D, Adler-Milstein J, Bates DW, et al. (2005) The value of health care information exchange and interoperability. Health Aff W5: 10–18. 3. O’Malley KJ, Cook KF, Price MD, Raiford Wildes K, Hurdle JF, et al. (2005) Measuring diagnoses: ICD code accuracy. Health Serv Res 40: 1620–1639. 4. World Health Organization (2005) Mental Health Policy and Service Guidance Package: Mental Health Information Systems. Switzerland: WHO. develop technical solutions to address this. Otherwise there is a danger that clinicians are asked to spend valuable clinical time on coding clinical information which may never be used. Acknowledgments We are very grateful to all interviewees who kindly gave their time and to the wider project team including Bernard Fernando, Ann Robertson, Akiko Hemmi and Rosemary Porteous. Throughout the process of undertaking this work we have had helpful support from colleagues at the NHS Connecting for Health Evaluation Programme led by Professor Richard Lilford and supported by Lee Priest, Nathalie Maillard and Jo Foster. Lee Priest kindly also represented the funders on our Independent Project Steering Committee, which was chaired by Professor Simon De Lusignan. We acknowledge the support of the National Institute for Health Research, through the Comprehensive Clinical Research Network and the Primary Care Research Network; and would like to thank the two expert reviewers who have given valuable comments on an earlier draft of this manuscript. Author Contributions Conceived and designed the experiments: AS DK. Performed the experiments: KC. Analyzed the data: AS KC ZM DK. Contributed reagents/materials/analysis tools: AS KC ZM DK. Wrote the paper: AS KC. Conceived this work: AS DK and BF. Led recruitment and undertook data generation and analysis: KC ZM. Led the drafting of the manuscript: KC AS. Commented on drafts: KC ZM DK AS. Guarantors: KC AS.. 5. The Information Centre for Health and Social Care (2010) National Renal Dataset: Human Behavioural, Organisational and Technical Guidance. National Datasets Service. 6. Reed GM (2010) Toward ICD-11: Improving the clinical utility of WHO’s international classification of mental disorders. Professional Psychology: Research and Practice 41: 457–464. 7. Andrews G, Anderson TM, Slade T, Sunderland M (2008) Classification of anxiety and depressive disorders: problems and solutions. Depress Anxiety 25: 274–281. PLOS ONE | www.plosone.org 9 August 2012 | Volume 7 | Issue 8 | e43831