We continue to exist in a world overrun with information. Too often this information is bloated with answers to questions nobody has asked or wants to know, is incomplete or fails to answer the question posed. Finding the salient information in amongst the deluge has always been a challenge and never more so than now. Adopting a more personalised solution in the form of mHealth data collection offers a major strategy in healthcare that can help address this.
The basis of any data collection scheme through an mHealth product is that users can directly feed responses back to the healthcare system, be this the doctor, hospital or clinical trial that they are participating in. Each question asked is being posed directly to the person it is intended for, and there is less of a “one size fits all” approach to data collection. The data generated are far removed from the healthcare usage statistics and process measures, which although have their place, are relatively blunt instruments when it comes to answering more nuanced clinical and policy questions.
mHelath data collection really started with researchers in the early 2000s using SMS texts to capture respondent information. It remains a very disruptive technology, due to its simplicity and reliability, although repeated SMS responses can provoke user disengagement and lead to poor or incomplete data collection. SMS data can still have a lot to offer however, and on-going work in health systems in the developing world often in more rural locations have made continuing use of this strategy. Smartphones can capture a lot more, and increasing handset uptake across populations in middle and higher income countries has allowed mHealth data collection to realise new potential.
Two main strategies have emerged in providing platforms for mHealth user generated data collection since the explosion of interest in this field a few years ago. The first is to embed a data collection form within a dedicated app that runs on individual handsets. Once downloaded to the device the user can respond to the questions and the app then sends the responses to the server directly or by email. The second option is to house the data collection tool within a mobile website, with the user interface usually designed to give the look and feel of a native app. This option has the advantage of working across devices as it will run in any browser, but does require a cellular data network signal or wifi to function.
mHealth data collection has broad applications across the sector. Benchmarking the performance of a clinical unit within a hospital using a validated patient reported outcome measure (PROM) is a key area of interest, and the Imperial PROMs app is a good example of how this can be delivered. Using smartphones to capture clinical trial data using bespoke apps developed for major pharmaceutical companies is another major growth area. These are just two examples, but the approach allows healthcare providers are free to ask the questions that are useful to establishing follow-up and outcome data, as well as the experiencial data that is getting so much attention in recently.
The one real sticking point that mhealth data collection has the opportunity to circumvent, when compared to current and previous forms of outcome measurement in the health sector surrounds feedback. Data collection previously took time to achieve, longer to input and even longer to report on. mHealth data collection is instant and feedback reports can be based on familiar analytics, so that clinicians and healthcare decision makers know what the results are, and can act on them.
Lack of useful health data is among the greatest obstacles facing health decision makers. mHealth data collection has great potential to deliver the right data from the right patient group to the right people in the right timeframe.