Poor quality in health care is easily recognized, with regular scandals drawing attention to unexpected deaths and treatment mishaps. When the scandals do arise the immediate political reaction is generally to look round for someone to blame—a 'bad apple'. Bad apples do exist and dealing with them is important. It is far more important, however, to focus on the bigger picture of harm from poor quality—the harm generated from the everyday actions of staff doing their best within flawed systems.
The first step in improving quality is to define and measure it. As Shekelle et al. point out in Chapter 6.1, quality is only definable against set standards. At least in the clinical dimensions of quality those standards must be evidence based. Systematic methods of standard setting have been developed, with several packages of quality criteria now available for local adaptation.
After measurement, action to improve quality must follow, and Tomson and Massoud (Chapter 6.2) introduce us to the systems approaches. The 'central law' of improvement is that 'every system is perfectly designed to achieve the results it achieves'. To change systems a process of building 'will', the right ideas, and the support of seniors is needed. A good quality improvement team can often share ideas and build on successful projects elsewhere.
Professional roles in health care developed in the age of acute illness, and are ill‐suited to managing many chronic conditions. Davis (Chapter 6.3) identifies seven components of renewed systems, including collaborative practice models incorporating physician and support‐service providers, patient self‐management education, and routine reporting/feedback loops.
To the newcomer to health care, the idea that large variations exist in the rates of surgical operations from area to area and clinician to clinician is often rather shocking. A structured approach to analyzing these variations is described by Steel and Melzer in Chapter 6.4 (based on two key ideas from Wennberg). The first is a clear definition of unwarranted variation: care that is not consistent with a patient's preference or related to a patient's underlying illness. The second is the categorization of unwarranted variations into effective, preference‐sensitive, and supply‐sensitive care. There is at least one more critical idea in this chapter: that higher rates of activity are not generally associated with better quality.
One area of promise in improving care is the use of information technology, with programmed reminders and electronic prescribing as prominent examples. In an overview of the IT agenda, Detmer (Chapter 6.5) points to the potential of the new generation of systems to support more ambitious uses, including surveillance and decision support. He warns, however, that public policy on privacy, often fashioned with a disproportionate emphasis on personal autonomy, may undermine use of data for quality improvement and research.
Recent decades have seen an extraordinary explosion of scientific output in medicine and a wonderful inventiveness on the part of pharmaceutical, imaging, and test companies. Unfortunately some of that inventiveness has gone into hype. Health technology assessment (Stevens and Milne, Chapter 6.6) provides a structured approach to sifting through tests and treatments, be they new or old. Four deceptively simple questions must be answered: does the technology work? For whom? At what cost? How does this technology compare with alternatives?
Busy jobs and the vast scale of the literature mean that few can keep up with research. Troubling delays in implementing some key findings have resulted. Ward et al. (Chapter 6.7) point out that that interventions that address specific barriers or hurdles are more likely to succeed: for example, audit and feedback may be useful when professionals are unaware of suboptimal practice, but reminders can work when barriers relate to information processing within consultations. Effectiveness guidelines can also be useful tools describing appropriate treatment and Feder and Griffiths (Chapter 6.8) similarly identify that great care is needed in planning implementation, with a need for a good project plan, including piloting to identify barriers.
Many public health practitioners work for bodies with funding or auditing responsibilities, and have to evaluate whole services. Hicks (Chapter 6.9) explores the challenges and advises that clarity is key, including clarity of purpose and dimensions of outcomes to be considered. Good outcome data are rare, however, but health services do generate large amounts of process data. Jessop (Chapter 6.10) cuts through the noise and suggests that process data are only useful if they monitor effective care.
It is clear from the previous chapters that quality improvement has grown into a very complex set of activities, at various levels. These activities need to be pulled together into an effective system of their own. Hall and Scally (Chapter 6.11) explore the British model, under the banner of clinical governance. They point out that public health practitioners have key roles to play, technically in understanding health data but also in engaging stakeholders and promoting a population view. Perhaps most importantly, public health practitioners can help build an environment in which skilled quality improvement can flourish and apportioning blame can be minimized.