Making NHS data work for everyone (I): the value of NHS data
Earlier this month, Reform held a panel event on how to make NHS data work for everyone. The event explored: (i) the different ways of defining the value of NHS data and (ii) what constitutes a mutually beneficial public-private partnership between patients, the NHS and industry when there is access to data to create a product. To respect the Chatham House rule Reform has decided to produce as series of three blogs covering the main ideas raised during the event.
Information is the lifeblood of the NHS. It is passed on from patients to healthcare practitioners – and from one healthcare practitioner to another – who can use it to diagnose and treat. This information gets codified and standardised and becomes data. Data can be more easily analysed as it is formatted in a specific way. It can, for example, allow comparisons to be drawn between the performance of one acute trust and another.
NHS organisations such as GP practices, social care providers or hospitals are huge information repositories. It transpired from the conversation during the event that there is a moral duty to use healthcare data for the good of patients, the NHS and wider society. This does not only mean using information for direct patient care, but also to increase the quality of care by studying which health interventions work best for a given population. The healthcare system has an incredible amount of data which still remains unused. It was highlighted that is both inefficient, as it is a waste of the resources used to collect that data, and a great source of untapped potential to construct a better NHS.
During the event, an agreement seemed to emerge on the determinants of the value of NHS data. These were:
- The type of data – for example, the value of patient records can be different to the value of data generated by appointments and booking systems. The value of personal identifiable data (e.g. data including name, date of birth, contact details…) can be different from the value of anonymised data.
- The use of the data – the same data could have a different value depending on what it is used for and applied to. For example, A&E admissions data could be used as a simple statistical reporting of how many patients are admitted into A&E. That same data could be used for better demand management and triaging of patients by exploring the reasons why people were admitted to A&E and if their case could have been treated elsewhere.
- The quality of the data – the better the quality of the data, the greater value it has. This is because of the accuracy of the insights derived from the data and because of the potential reduction in the cost of the data cleaning process.
- The amount of data – the value of a single patient record is different to the value of a collection of patient records. This because the smaller the sample size the harder it is to find robust results.
- The linking of data – the value of a dataset can increase if it is linked with another. For example, by combining different datasets on different disease types, new patterns of comorbidity can be discovered.
The meaning of value was extensively discussed, and no consensus emerged as to a single definition. This is because it is dependent on the context in which the data is used. There is a value to the patient in terms of increased quality of care and better health and care outcomes. Value can be expressed in terms of efficiency gains for the healthcare system. There is value in using NHS data to advance medical research, value in using data for better policy making and wider societal benefits. There is also value to the private sector. While definitions vary, it is undeniable that NHS data has value.
During the event, an agreement seemed to emerge on the determinants of the value of NHS data. These were:
- The type of data – for example, the value of patient records can be different to the value of data generated by appointments and booking systems. The value of personal identifiable data (e.g. data including name, date of birth, contact details…) can be different from the value of anonymised data.
- The use of the data – the same data could have a different value depending on what it is used for and applied to. For example, A&E admissions data could be used as a simple statistical reporting of how many patients are admitted into A&E. That same data could be used for better demand management and triaging of patients by exploring the reasons why people were admitted to A&E and if their case could have been treated elsewhere.
- The quality of the data – the better the quality of the data, the greater value it has. This is because of the accuracy of the insights derived from the data and because of the potential reduction in the cost of the data cleaning process.
- The amount of data – the value of a single patient record is different to the value of a collection of patient records. This because the smaller the sample size the harder it is to find robust results.
- The linking of data – the value of a dataset can increase if it is linked with another. For example, by combining different datasets on different disease types, new patterns of comorbidity can be discovered.
The meaning of value was extensively discussed, and no consensus emerged as to a single definition. This is because it is dependent on the context in which the data is used. There is a value to the patient in terms of increased quality of care and better health and care outcomes. Value can be expressed in terms of efficiency gains for the healthcare system. There is value in using NHS data to advance medical research, value in using data for better policy making and wider societal benefits. There is also value to the private sector. While definitions vary, it is undeniable that NHS data has value.
The meaning of value was extensively discussed, and no consensus emerged as to a single definition. This is because it is dependent on the context in which the data is used.
The meaning of value was extensively discussed, and no consensus emerged as to a single definition. This is because it is dependent on the context in which the data is used.