It is widely accepted that the term CG describes an organizational accountability framework useful to improve clinical care, safeguard standards and work towards excellence . The introduction of CG into the National Health Service can be seen as a fundamental shift in the regulatory relationship between the state and medical professionals , while at the organizational level it can be considered a process that involves a move towards "encoded knowledge" through the use of "soft bureaucracy" . In this view, according to Iedema et al (2005), one can distinguish between moralizing and disciplinary devices, the latter useful for inspecting data generation and analysis, performance monitoring and management, accreditation, guidelines and protocol production and implementation, and the close integration of clinical and financial data .
The OPTIGOV methodology, described in this article, can be seen in the perspective of the above mentioned disciplinary devices. In particular, it may appear to be similar in nature to hospital accreditation protocols [32–35] and a degree of overlap does in fact exist. Nevertheless, the distinctive perspective of OPTIGOV is focused on the existence and the level of implementation of CG tools. Unlike accreditation agencies, OPTIGOV does not issue any certifications, but is solely aimed at implementing CG and improving the quality of health care. Therefore traditional accreditation agencies and OPTIGOV reflect a different culture towards quality improvement in those who choose them and could have a different impact on the cultural change of health professionals operating at all levels within the organization.
Furthermore, although attempts to address the question of organic and flexible evaluation of the implementation of CG have already been made, the evaluation systems they have produced are either experimental ones, with a broadly qualitative approach  or else they suffer from a limited scope, as they were designed for very specific sectors or one-off studies [25, 26].
Surveys on the effectiveness of good practices in medicine have revealed that the single most important problem in their application is lack of dedicated time and resources as well as low motivation on the management side [36–39]. These observations suggest that, for good practices to be effective in a single institution, they must not only be known to health professionals, but they must represent a permanent part of work routines; and that it is reasonable to ascertain, as OPTIGOV does, that they are a well-defined component of normal behaviours and a priority shared by all members of the medical equipe - which may not be the case, even when physicians maintain they know the essentials of those practices.
An important aspect addressed since the early stages of the development of OPTIGOV was the choice of the subjects to be held responsible for each area and interviewed. Over the last decade, debate over the accountability in implementing CG has suggested that responsibility is shared by managers and physicians alike, though to different degrees [11, 41, 42]. This observation has been taken into account by the authors in establishing how to determine who should be interviewed about what.
The choice of the areas to be explored and the subareas to be considered as their components was based on the earlier definitions of CG, but subsequent discussion on the real contents of good practices as well as questions that have been raised by health care reforms were taken into account. In particular, consideration of the growing trend of transferring scientific research into more and more levels of health care and the subsequent need to evaluate research skills  supported the inclusion of the area "Research & Development" and the careful weighing of its contribution to the final score. The controversies over the perceived importance and the evolution of the practice of Clinical Audit [44, 45] were also considered when developing the correspondent set of questions and scores. Especially in the field of error and risk management, all of the aspects that were signalled as relevant in the literature were included [46, 47].
OPTIGOV is currently being put to the test in the setting of several projects, at the moment restricted to Italy, involving a number of health care institutions of different types . The next step in its development will thus be the evaluation of the results of its application. The opinions of health administrators will be collected, so as to check for any weaknesses in its completeness, effectiveness, ease of applicability and flexibility. In particular, the ability of the results of an OPTIGOV diagnostic review to establish priorities, provide basis for tailored interventions and influence subsequent clinical and management decisions has already been partially assessed after the first testing of OPTIGOV . The latter led in fact to the triggering and implementation of a series of improvement actions (e.g. activation of training programmes on evidence-based medicine and clinical audits, and definition and dissemination of risk management procedures), some of which are still in progress.
A long-term analysis of the effect of an OPTIGOV diagnostic review on the level of implementation of CG in the structures involved will also serve as an indirect evaluation of the improvement actions that have been introduced following the reviews themselves. The effectiveness of these actions will be tested through a comparison of data before and after the interventions prompted by OPTIGOV.
OPTIGOV also offers the opportunity to make intra- and inter-organizations comparisons, by showing differences in the level of adoption and spreading of adequate CG tools.
The development of the methodology could have been affected by some limitations. First, the selection of the areas to be considered and the questions they include could be questionable; however, we selected the areas on the basis of the definition of CG and a review of the scientific literature on this issue. Moreover, the first results of OPTIGOV  lead to hypothesize the reliability and reproducibility of the methodology.
Another critical point could be represented by the way data are collected in OPTIGOV and the risk of information bias. The main element which controls information bias is the double origin of data: face-to-face interviews of single professionals indicated by the organization's Board are the first source; then, the relevant official documentation of the organization is consulted, and it is compared to the information provided by the interviewees so as to correct it if contradictions are detected.