and there is an increasing drive towards adopting national terminologies that are drawn on across electronic systems facilitating data exchange between settings [12]. This poses significant new challenges for coding of clinical information when compared to paper-based record systems. Mental health offers important insights into the challenges associated with electronic clinical coding and with classification systems: complex information is predominantly recorded as free- text narrative and diagnoses tend to be formulated over lengthy periods of time, which makes clinical coding of diagnoses extremely challenging. However, existing mental disorders classi- fication systems may lack clinical utility [6,7]. They are often perceived to fail to accommodate existing clinical practices as well as communication needs between care settings, resulting in a lack of immediate patient care benefits [6,7]. This shortcoming is also recognized by the World Health Organization (WHO), which advocates that ‘‘a mental health information system is a system for action: it should exist not simply for the purpose of gathering data, but also for enabling well-informed decision-making’’ [4]. So why do coding and classification systems for mental disorders frequently fall short of clinical utility? Although there is a dearth of empirical work in this area, many have argued that existing systems do not appropriately reflect the reality and specific characteristics of mental healthcare [6,13,14]. They are often based on more physical diagnoses and are not well suited to accounting for multi-disciplinary, multi-agency care pathways [8,13]. Concerns have also been expressed that diagnostic systems may mask the complexity of disorders and may lead clinicians to focus on symptoms present in a classification system, whilst potentially neglecting other significant factors such as contextual dynamics (e.g. social circumstances) [14–16]. Whilst clinical utility is clearly essential, it is also very important that managerial needs are met. We sought to explore how a balance in this respect may be achieved and if and how mental health information systems contribute to clinical decision-making. In doing so, we explored how clinical information relating to depression is structured and/or coded in primary and secondary care settings and the viewpoints of, and implications for, different stakeholders. Materials and Methods As mental health is a large area, we focused on the area of depression as one of the most common mental disorders with numbers steadily increasing worldwide [16,17]. Internationally, depression is usually diagnosed and treated in primary care [18]. Clinical codes across care settings are most commonly mapped onto the ICD-10 (developed by the WHO) and the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition published by the American Psychiatric Association [19–21]. Design We conceptualized the disorder depression as a case in order to focus our data collection activities. This case study design allowed us to explore the phenomenon of interest (i.e. routine coding of clinical information in depression) within context, drawing on multiple sources of evidence.[22–26] We collected qualitative data from a range of stakeholders and geographical locations, spanning primary and secondary care settings in the UK over a period of four months. Exploring a range of NHS information structures within the UK (i.e. primary and secondary care settings) and obtaining data from a variety of qualitative data sources, allowed us to gain an insight into context-specific and cross-cutting issues relating to clinical coding in depression within one political setting. ‘‘There Are Too Many, but Never Enough’’ Our data collection and analysis strategy was theoretically informed drawing on sociotechnical principles [25,26]. In essence, this approach emphasizes the interrelated nature of social and technical dimensions and we therefore sampled individual participants on the basis of their relationship with coding of clinical computerized information relating to depres- sion, exploring how individual behaviors shaped technical outputs and vice versa. Ethical and Site-specific Approvals We obtained ethical approval from the Brighton West Ethics Committee (Reference: 10/H1111/25). Site-specific approvals, advanced disclosures and honorary contracts were obtained for the lead researcher (KC). Written informed consent was obtained from all participants and data obtained were anonymised. Recruitment of Participants We initially purposefully sampled a range of academic General Practitioners (GPs) with an interest in clinical coding and/or depression through personal contacts [27]. Interviews with academic GPs helped to provide an insight into secondary uses of data for research purposes and a preliminary overview of clinical coding practice as well as tensions in the primary care setting. Participants were asked to recommend other potential informants, which helped us to snowball sample additional primary care stakeholders including those with no particular interest in clinical coding as well as clinical coders. We also recruited secondary care staff at a mental health Trust (our main secondary care research site) identified through the UK Clinical Research Network [28], purposefully sampling diverse stakeholders with an insight into depression and/or clinical coding practices, including healthcare professionals (i.e. nurses, doctors, allied health professionals), managers and clinical coders [27]. This allowed exploring the dynamics of everyday clinical depression coding from a variety of perspectives. Participant characteristics and their involvement in data collection activities are summarized in Table 1. Data Generation and Handling Our work was informed by a review of the existing literature and involved a combination of 28 semi-structured face-to-face and telephone interviews (our main data source), a focus group with clinical coders, a two-hour period of participant observation of selected clinical coding activities in primary care, and collection of documents to aid understanding of context (e.g. information on clinical coding standards). We gathered data from a variety of data sources to facilitate the credibility of our findings (triangulation). Our overall dataset is summarized in Table 2. Key issues explored included attitudes towards clinical coding in relation to depression; perceived benefits, barriers and facilitators; and recommendations for improvement (Table 3). Topic guides were tailored to the roles of individual participants and refined throughout the research, with emerging issues being recorded as field notes and used to inform subsequent rounds of data collection. Interviews were informal and centered on issues that interviewees perceived to be important. A focus group with clinical coders working in secondary care and participant observation of clinical coding in primary care were undertaken to complement interviews and provide greater understanding of the context in which coding decisions are made [29]. The observation involved the researcher practicing clinical coding on dummy patients (i.e. coding diagnoses and symptoms on fictional patients that were used for training system users locally) whilst discussing activities with the relevant healthcare profession- PLOS ONE | www.plosone.org 2 August 2012 | Volume 7 | Issue 8 | e43831