“If you can't measure it, you can't manage it” – essential truth, or costly myth?

In this issue of the journal, Kilbourne et al1 make a reasoned and cogent argument for a more structured approach to the delivery of mental health care, with the aim of driving up the quality of care and its outcomes. They see mental health services as innovators in models of care delivery (i.e., community-based, multidisciplinary, and person-centred) but as laggards in learning from and adopting advances in monitoring and improving the quality of continuing care derived from other chronic disease disciplines. The paper advocates enhancements oriented to the structure proposed in Donabedian's framework, namely the organization of care, the clinical care processes, and the health care outcomes achieved. The authors are right to highlight the importance of measurement, on the principle that “if you can't measure it, you can't manage it”. Health management information systems are a core “building block” for well-functioning health systems. The purpose of those information systems is to routinely generate quality health information, and they are used directly for management decisions to improve health care delivery – supporting resource management, service monitoring, supervision and quality improvement. On the other hand, W. Edwards Deming2 warned that the above principle could be “a costly myth”. He could be right, and for several reasons. First, intuitively obvious qualitative enhancements can be made without data either to diagnose the problem or confirm the benefits. Data can provide a mechanism to support incremental improvement, but the fundamental transformation towards a “learning health system” is cultural. Well-functioning health management information systems are just one essential component of an interactive suite of health system strengthening measures that may prove necessary developing: a peer-driven quality improvement culture; non-technical workforce skills (leadership, teamwork and communication); and person-centred care practices guided by values and preferences, with agreed care plans, and patients empowered for self-management. Beyond evidence-based guidelines, consideration may also be given to the introduction of care pathways3. Every patient goes through a care process, and this varies among patients with particular conditions. Care pathways are about planning and managing those processes, in advance, for defined groups of individuals. Critically, this establishes explicit standards for care processes and outcomes, against which performance can then be judged. Not all health care activities lend themselves to this approach, since not all care is provided for a “well-defined patient group” and a “well-defined period of time”. For continuing care of mental health conditions, pathways may need to be drawn up and delivered flexibly, contingent upon differing needs, clinical trajectories and treatment responses. A “stepped care” approach is often used, whereby a patient first receives the most effective, least invasive, least expensive and shortest form of assessment or intervention, escalated to the next level where necessary. Second, problems can arise with the bureaucratization of the data process. All too often health management information systems are over-burdensome. Too many indicators are collected focusing on morbidity, basic service activity and routine risk assessment, with no obvious application to improvement in the quality of care or its outcomes. The problem is compounded when data are merely collected and then reported to higher levels of the health system for aggregation, analysis and centralized decision making, with no information used for improving performance and service delivery at the periphery. Such data systems do not fulfil the basic requirements of a health management information system. With the data collectors disengaged from the process, data quality is poor. These problems may be addressed through simplification and democratization. Keeping things simple, there is much in common between care for hypertension, diabetes and chronic obstructive pulmonary disease on the one hand, and psychosis, epilepsy and depression on the other. Reduced to basics, people with these conditions need to be identified (detection and diagnosis), engaged on an agreed management plan (linkage to care), encouraged and supported to participate actively in the care process (adherence), and be retained in care (retention/drop out), having their treatment reviewed and revised to ensure optimal outcomes (treatment to target). At minimum, therefore, just five items of data need to be collected, although adherence and outcome monitoring need to be continuous across the episode of care. Democratization of the data process involves two key elements: public and patient involvement in the design and governance of the system (“nothing about us without us”), and the ability to aggregate, analyze and use the data at every level of the health system, including facilities, teams and individual health professionals. Smartphones or tablets, linked by mobile data to cloud servers, can promote the collection, aggregation, timely analysis and use of health management information systems data. After detection of a condition requiring continuing care, the app would generate a bespoke care pathway with follow-up appointments, and prompted actions and assessments (attendance, adherence, and outcome monitoring) to be carried out on each occasion. These basic health management information systems generate an electronic medical record for any health care professional providing care (promoting information and provider continuity), and a patient registry to track patients’ progress. Providers can target care toward patients with the greatest need (not adherent, not attending, not improving or meeting the clinical targets defined by the program). Treatment adjustment may involve addressing barriers for patients with poor adherence, or modifying treatment, or considering referral for patients who do not improve despite adhering to care plans, until improvement occurs. Rapid feedback of aggregated data can be used, inter alia, to compare care quality and outcomes across health professionals, facilities and districts; to target supervision and support; to identify best performing professionals and facilities to mentor others; and to inform quality improvement initiatives with real time data to track effectiveness. A “global perspective” is a bold undertaking. Kilbourne et al's paper cites examples from the US health care system, which is complex and particular in its financing models, and its highly fragmented nature. Fragmentation imposes challenges for quality improvement at the national level, where the reach of the state may be limited. At the same time, the authors correctly point out the barriers to implementing reforms in more unitary national or regional health services, such as those in the UK or Canada. Independent private or not-for-profit providers can be fleeter of foot. From a global mental health perspective, the focus on equity is welcome, but data stratification should extend beyond quality and outcomes of care to include treatment coverage. The “treatment gap” for mental health care services is an affront to the fundamental right to health worldwide, particularly in low- and middle-income countries. It, too, needs to be measured to be reduced. The important role of primary care also deserves more attention. In high-income countries, task-shifting (to lower levels of the health care system, supported to provide care through task-sharing with specialist services) can reduce costs through greater allocative efficiency, and may provide more holistic, integrated and person-centred care, particularly in the context of physical comorbidity. In resource poor and lower-income settings, task-shifting is understood to be an essential strategy for closing the treatment gap. In either type of setting, a more structured approach to care, supported by data used for quality improvement, can be an essential development. Martin Prince King's Global Health Institute, King's College London, London, UK M. Prince receives salary support for his role as Director of the National Institute of Health Research (NIHR) Global Health Research Unit on Health System Strengthening in Sub-Saharan Africa, King's College London (GHR Unit: 16/136/54). This research was commissioned by the NIHR using official development assistance (ODA) funding. The views expressed in this paper are those of the author and not necessarily those of the UK National Health Service, the NIHR or the UK Department of Health.

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