Research methods & information tools
We undertake a range of projects that exploit the large amounts of existing data on care. The scale and sophistication of these datasets continues to grow and with the right analytical techniques they can make a significant contribution to research, policy analysis and patient care.
The range and volume of information collected about health services continues to grow every year. We are developing a series of linked 'pseudonymous' datasets to capture events for large populations in order to promote more comprehensive analysis and greater understanding of care services provided at the person level.
We are interested in ways that we can exploit these datasets to inform health policy. We are particularly interested in how the linkage between datasets can reveal a fuller picture of what is happening to people as they use different care services.
By linking data effectively it is possible to see an individual’s pathway through care services
Many preventive interventions are available at the NHS’ disposal, ranging from case management for high-risk patients, the use of technology, such as telehealth and telecare, to monitor patients remotely, and changes to reimbursement systems for care provided by hospitals.
One particularly robust way of evaluating interventions is by conducting a large randomised controlled trial – where one group receives the new intervention and a control group receives usual care – such as our project that is evaluating the impact of telecare and telehealth.
However, trials like this are not always a feasible option and so we are also developing alternative approaches. For example, person-based risk-adjusted evaluation allows us to put together a control group for comparative purposes by linking up information stored on patients’ care to the health and social care interventions that we are commissioned to evaluate.
Dr Martin Bardsley of the Nuffield Trust on managing financial risk in health and the possible strategies to help commissioners manage risk in future
This means that our researchers can now match an individual receiving a new service to a control group that were similar in terms of age, sex, recorded health diagnoses and prior hospital use, and who lived in an area with a similar level of deprivation. In the past this would have been very challenging.
This kind of evaluation is economical and can be applied retrospectively, so that pilots can be evaluated even if at first they were not developed with evaluation in mind. The method can be applied in real time too, so it can be used to refine a service in response to emerging evaluation findings.
We are also experienced in using predictive risk adjustment tools to determine the expected future health care resource use of each individual in a population. Such tools can be used to estimate future events for people at different levels of risk, providing commissioners with more accurate estimates of likely future costs.
Photo credit: US Navy, Flickr