Motivation

Motivation:

"Never have people in the West lived so long, or been so healthy, and never have medical achievements been so great. Yet, paradoxically, rarely has medicine drawn such intense doubt and disapproval as today"

Roy Porter, the distinguished medical historian, begins the introduction to The Cambridge History to Medicine with this sentiment. In a sense this describes how modern medicine is becoming the victim of its own success. Modern clinical practice has been further described by Alan Rector or Manchester University as "a humanly impossible task". As the knowledge base of medicine continues to grow inexorably, the demands on those who are required to deliver consistent, high quality, safe and accountable care are effectively becoming impossible to meet unaided. The gap between best evidence and practice has been consistently reported in health service research over the past decade [Grol et al, 2003]. In areas like cancer, it is estimated that the medical literature may be doubling every 22 months. With this unprecedented pace at which cancer research is evolving, and the reported average delay in its implementation [Balas et al, 1999], the evidence-practice gap can only get worse without some form of intervention.

There are also growing concerns about sub-optimal medical care in the developed world substantiated by the series of influential reports from the US Institute of Medicine that attempted to quantify the problem of medical errors [To Err is Human, Crossing Quality Chiasm]. In the UK, it has been found that about 850,000 medical errors occur in NHS hospitals every year, resulting in some 40,000 deaths and other consequences [Aylin et al 20041]. Though exact figures are debatable, it is generally agreed that as the overall complexity of medicine continues to grow, the ability of unaided and overworked health practitioners to achieve the highest standards for their patients will be further eroded. At the same time, the medical profession is witnessing a worldwide steady increase in litigation [Kessler et al 20062]. The media and politicians typically respond by demanding greater "efficiency" of healthcare professionals, and insisting upon improved performance and better management (often without increasing resources). But improving the performance of skilled but busy people is not generally achieved by merely exhorting them to work better and faster. As human beings, healthcare professionals probably make decisions, plan their time and remember what they need to remember as well as they can. Blaming the individuals or organizations that provide the services (and punishing them through professional sanctions or the courts when they fail) is no ultimate solution. If services are to improve significantly, they are likely to require new ways of working including improved tools.

Among the most promising new developments that may help to address these challenges and reduce the burdens on clinical and other healthcare professionals are point-of-care information and computerised decision support (CDS) systems. Although a growing body of evidence reporting on their effectiveness exists [Garg et al 2005 3 et al, Chaudhary et al4], the majority of the decision support systems tested to date in randomised controlled trials and discussed in the literature belong to either simple rule-based reminder/alerting systems or calculators such as prescribing systems. Few sophisticated decision support systems capable of supporting dynamic and multifaceted clinical decision-making and complex workflows have been evaluated. Moreover, many decision support system evaluation studies that have been carried out have been found to be methodologically flawed [Garg et al 2005 3] and have failed to answer important practical questions such as system usability and the potential of CDS to support health care in its totality rather than as niche applications designed for narrow and specific purposes e.g. dose prescribing, or laboratory test alerts.

This forms the background to our CREDO project. In CREDO, we are adopting a systematic, integrated approach to designing, implementing and evaluating a sophisticated decision-support technology to assist the entire patient journey of a woman being treated at NHS for breast cancer.. 'The journey is designed to serve as a model of complex and chronic disease management generally.

Breast cancer is the most commonly diagnosed cancer in women, accounting for about 30% of all such cancers. One in nine women will develop breast cancer at some point in their lives in the United Kingdom. In the year 2003-04, a total of 36,500 women were newly diagnosed with breast cancer in England. Though the overall care delivery to women diagnosed with breast cancer in this country continues to improve, it is far from optimal as defined by best practice. A number of reports [REFS] have identified inconsistencies in the delivery of breast cancer care covering, for example, delays in early detection, diagnostic policies and treatment options offered. A recent article published in the Lancet Oncology highlights inferior breast cancer survival rates in the UK compared with many other Western European countries [REF]. Recent policy documents and audits have emphasised the importance of using evidence-based guidelines to ensure the consistency and quality of cancer care, and have expressed concerns about the extent to which this policy is followed in a transparent and demonstrable manner.

The set of sophisticated decision support and workflow services to be deployed and evaluated in the CREDO project has been developed after many years of leading research (at Cancer Research UK) in the field of field of computerised decision support aimed to help clinicians maintain the highest possible quality and safety of care in breast cancer domain.

References

1. Aylin, P., Tanna, S., Bottle, A. & Jarman, B. How often are adverse events reported in English hospital statistics? BMJ 329, 369 (2004).
2. Kessler, D.P., Summerton, N. & Graham, J.R. Effects of the medical liability system in Australia, the UK, and the USA. Lancet 368, 240-246 (2006).
3. Garg, A.X., et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293, 1223-1238 (2005).
4. Chaudhry, B., et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 144, 742-752 (2006).
5. Schreiber, G., et al. Knowledge Engineering and Management: The CommonKADS Methodology, (The MIT Press, 2000).
6. Grol, R. & Grimshaw, J. From best evidence to best practice: effective implementation of change in patients' care. Lancet 362, 1225-1230 (2003).