15 August 2019
The expanding availability of digital technologies is generating large amounts of detailed health-related data outside formal healthcare settings. Interest in this citizen generated data (CGD) has to date focused on how the information it provides about an individual can be used to improve the health of that same individual. This policy briefing explores the potential CGD has to improve the health of populations.
Read our other CGD resources:
What is citizen generated data?
Citizen generated data: the ethics of remote patient monitoring
In order to prevent disease, promote health and prolong life, public health requires datasets to understand what the key health issues are and how they are distributed across society.
Interest in citizen generated data has up to now focused on how data about an individidual can be used to improve the health of that person. In principle, the aggregation of CGD may also have benefits for populations by providing opportunities to: Enrich existing health datasets
CGD may also be useful in informing, evaluating, and even delivering innovative public health interventions.
Current gaps in data used to monitor population health include data on conditions known to be considerably underreported (e.g. mental health conditions) and conditions commonly managed without seeing a doctor (e.g. hayfever). Furthermore, most health data is currently gathered within a healthcare setting, but a diverse range of factors that influence health arise in the home and other environments. These are rarely captured, but potentially very valuable.
Citizen generated data could help to address these data gaps. For example, smartphone associated sensor technology could help to quantify determinants such as pollution (see example box) or internet search data could be used to monitor population-level health symptoms, such as the spread of flu. For some conditions there is evidence that health data generated from citizen sources correlates well with health data gathered via more established methods (such as clinical consultations). However, lack of information about, for example, the person searching online and why they are searching, greatly limits the scope for accurately interpreting and taking action on CGD unless it is in conjunction with data from established sources.
The need for accurate, consistent data over time is also limiting the current utility of CGD for health surveillance, although there is potential for providing targeted qualitative information at a local level to better profile the health of a community. Such insights may prove useful in informing public health initiatives.
Promoting healthy lifestyle choices and behavioural changes is a significant public health challenge. The tools that generate CGD could be used to prompt and target behaviour change, for example through a combination of mobile phone applications and wearable technology to monitor and encourage a person’s physical activity.
However current evidence indicates mixed results as to whether there has been any impact. Where change has been reported, it was generally short term with a high rate of drop off over time. When used to prompt behaviour change, there is better evidence of impact when targeting to individuals who could benefit most from them, but the challenge to sustain use remains.
Patients are using the internet to describe their experiences of healthcare, providing a readily available source of information to inform healthcare improvement and ultimately the quality of healthcare services. The use of internet captured views is currently being assessed by groups such as HDRUK and the Health Foundation to determine whether CGD can be a source of viable information.
The utility of CGD to public health is just beginning to be explored and understanding how best to use this data and approaches for doing so still need to be developed. Actual use of CGD in public health has tended to be geographically localised, and limited to small scale studies. Scope for widescale adoption is low, constrained by general infrastructure challenges as well as the quality of available studies.
Whilst CGD may support surveillance and inform current public health initiatives, it will not replace them for the forseeable future. Developing the use of CGD to improve public health will depend on finding answers to important questions:
Acceptance, adoption and sustained use by the public, patients and professionals will depend on demonstrating how data is of clinical benefit. To date studies have not generally focused on the impact on clinical outcomes of this nacent development.
Public trust is crucial if the health system is to have access to data generated by citizens where the primary intent was not to infer conclusions about their personal health. The regulation around the devices that generate and collect CGD and how data can be analysed and used is uncertain.
The population group producing and using certain types of CGD (e.g. data from health wearables) are generally healthier – differential usage and acceptance will need to be factored into design and development for its use to be targeted for public health purposes. Furthermore, there may be differences in the demographics of those who are willing to share data.
While there are many challenges, the changing ways in which health data is generated and the scope of CGD means its potential benefits for public health should not be dismissed.
Public health professionals should be aware of its potential and what is necessary to progress work in this field. To realise the potential that CGD may offer public health as a support to established data sources, end-users must be involved in the design of data gathering processes and devices. Collaboration between technology companies and public health providers will also be essential.