Modern medicine has brought remarkable advances. The application of scientific rigour to the art of healing has resulted in a better understanding of diseases, a proliferation of new treatments and has given hope to many. In large areas of medicine however, the complexity of the health condition and the heterogeneity of patient characteristics mean that experimental studies such as randomised controlled trials are too costly to conduct (Wallace 2015). When they are conducted, the exclusion criteria often make it difficult to generalise the results to real patients (Loder, Bunt et al. 2013).
Furthermore, the proliferation of new studies means that it is impossible for practitioners to keep abreast of the latest developments in all but the very narrowest of fields (Davidoff, Haynes et al. 1995). Even evidence based medicine approaches, such as the development of systematic review methodologies (Sackett, Rosenberg et al. 1996) can only partially address this problem because of the volume and complexity of studies. The net result is that much medical practice still relies on gut feeling and all of the associated biases.(Matzen 2003, Loder 2015)
In addition to the rapid expansion in the evidence base, health care faces other challenges from growing and ageing populations (The King’s Fund 2014), rising levels of chronic illness (The King’s Fund 2014), constrained budgets (Rubin 2015), health inequalities, and the proliferation of high cost interventions that bring diminishing returns in terms of the health improvements that they provide (NHS England 2014, Fund 2014). Improvements in productivity are urgently sought (Lafond, Charlesworth et al. 2015). Variations in practice, that cannot be justified, are increasingly seen as unacceptable (Appleby, Raleigh et al. 2011), but healthcare organisations and clinicians often do not have the capability, opportunity or motivation to drive change, because of other pressures.
A recent Health Foundation report identified seven key success factors (Figure 1) for promoting change at any level in the healthcare system (Allcock, Dormon et al. 2015). The report maps these factors onto eight other models of change dating back almost three decades. That so many models of change have been developed and that successful change remains an elusive goal, suggests that at least one fundamentally new development will be required to improve the situation.
Figure 1: Seven success factors for change in the NHS (Reproduced from Allcock, Dormon et al. 2015)
That these factors can be mapped into at least eight other models, dating back almost three decades and that achieving widespread change is still an elusive goal, suggests that at least one fundamentally new development will be required to improve the situation.
This report argues that there are at least three such fundamental developments are within sight and that their realisation could enable Learning Healthcare Systems that would facilitate more successful change within healthcare. These developments are:
- Routine data collection and analytics
- Outcomes measurement
- Systematic behaviour change theory and techniques
Routinely collected data has long been used to improve healthcare. Indeed, Florence Nightingale in the nineteenth century (Nightingale 1863) and Ernest Codman in the early twentieth century (Codman, Mayo et al. 1913), famously used routine data to compile outcome measures. Modern behaviour change efforts also date back to the 1930s (Skinner 1938) and before. The factor that can now help to bring each of these developments to fruition within healthcare is the development of digital infrastructure (McGinnis 2015). This can automate processes that were previously infeasible, apply enormous computational power and instantly link data recorded in distant geographies. To date, healthcare has lagged other industries in its use of data (Wallace 2015) but the roll out of Electronic Health Records (EHRs) and recent development of standards suggest that this could change.
Realising the benefits of these developments will require a new ethics framework that enables rapid learning while also reassuring patients and the public. Outcomes measurement, routine data analytics and systematic behavior change techniques, along with a new supportive ethics framework, are discussed in the section entitled, Building Blocks of a Learning Health System.
Evidence: