Written by Dr. Janak Gunatilleke
Since starting my role at Mindwave Ventures in June, I have been thinking about how patient-centered solutions such as Personal Health Records can help maximize the value from healthcare data, building upon the existing open data in
The current state of open data in the NHS
The NHS publishes a number of datasets — for example, NHS Digital and Public Health England publish various operational and performance datasets including waiting times, GP prescribing and information on health-related behaviors such
as smoking and alcohol intake.
Within Mental health, the Clinical Record Interactive Search (CRIS) system provides access to anonymized information from the South London and Maudsley NHS Foundation Trust patient record system.
Social media platforms present another interesting dimension. On one hand, information shared specifically — for example, a woman who shared a video sharing her experience of living with a particular mental health condition — provides
opportunities for peer support through sharing of similar experiences. On the other hand, it has been demonstrated data from publicly available Twitter feeds can be collected and analyzed to provide useful insights into conditions
such as bipolar, PTSD and depression.
Underlying technology trends and opportunities
I believe that there are 3 key elements.
First, with the increasing use of smartphones — 72% accessed the internet on their mobiles in 2018 compared to 17% in 2008 — is the opportunity to collect and use additional data in real-time, and deliver interventions through
smartphones. For example, as described by Huckvale (2019), the relationship between smartphone sensor and location data, and established outcome measure such as the PHQ-9 have been studied. This leads to potential predictive
value in linking activities such as sleep to severe depressive episodes and relapses.
Second, is the need for personalization. As well as genomics-based precision medicine, there are also opportunities to better understand and provide information relevant to what service users are seeking. For example, a study by
Crangle (2015) explored the different types questions services users asked and demonstrated that even well-respected sources such as the US National Institute of Mental Health didn’t result in effective answers to the specific
questions asked. This highlights opportunities to better understand nuances in the meaning behind questions and respond with individualized content.
Third, are wider opportunities that can be delivered through developments in technologies including Artificial Intelligence (AI). Woebot is a chatbot and a randomised control trial with university students in 2017 demonstrated
a 20% improvement in PHQ-9 scores within 2 weeks. High levels of engagement — students were using the app almost every day — were thought to have contributed to the results, highlighting the need to consider user adoption
and engagement when designing and implementing solutions.
Challenges to optimizing the use of open data
The wider healthcare ecosystem has 5 main stakeholder groups with varying priorities:
There are 3 key challenges that arise from the above complexities:
Potential solutions to addressing the challenges
Personal Health Records can help accelerate improvement
Personal Health Records (PHRs) provide individuals access to their clinical records (which are held within hospital or GP systems), opportunities to enrich the record by adding and collecting further information (i.e. manually
or through fitness trackers) and to share the record with relatives and other healthcare professionals.
PHRs could support many elements of the potential solutions described above.
Dynamic consent is described as enabling users to give or change consent over time as projects and their own situation changes. This approach gives users more time to think, provides visibility of what’s happening with their
data and confidence that their data is being managed appropriately. It gives data processors flexibility and opportunities to get additional users engaged. With a PHR that covers many clinical pathways or more than one organization,
it provides a good ‘digital front door’ to present and gain dynamic consent.
‘Federated learning’ is analyzing the data within the storage environment of each organization rather than transferring and processing it at a central and ‘third party’ location. This could provide both individual organizations
and the citizens more confidence that their data is secure. For example, Owkin — https://owkin.com/ — is a startup that is applying machine learning to medical research using Federated
learning. The benefits of this approach fit it well where a single PHR platform is used across many clinical pathways and disparate organizations.
It has been demonstrated that providing tools to enable analysis and data visualization can motivate users to donate data. PHRs are a great backdrop to provide visualisations of user data and insights based on trends over time.
For example, this could include charting various metrics such as sleep and mood over time, and also super-imposing other collated data points including changes to medications.
Additionally, PHRs provides options, with appropriate consent and safeguards, to connect users with other similar users to share experiences and foster belonging.
PHRs also present an opportunity to improve the quality of data being shared. Data archetypes — a specification for the various data elements that need to be captured — can provide consistency and improve the ability to share
data across systems.
PHRs could be an interesting solution to resolving conflicts of interests mentioned earlier. For example, if developed by an independent party to the established hospital and other provider systems, it could be an objective aggregator
across regions and disparate systems enabling the secure and efficient sharing of data. The opportunity to create a thriving ecosystem could be further strengthened by adopting open technology standards that enable maximum
interoperability between systems.
Support and infrastructure
MedMij is a PHR technical framework and set of standards that is established in the Netherlands which aims to create an ecosystem covering apps across primary care, hospitals, pharmacies, etc.
In the NHS, there are opportunities to build on the groundwork including allocated funding through the Global Digital Exemplars program, publishing of standards and best practice including the PHR adoption toolkit,
and national IT contracts based on interoperability such as the GP IT futures program .
Bigger picture / expanding the concept
Open source integration engines such as MIRTH Connect (NextGen Connect) — https://www.nextgen.com/products-and-services/integration-engine —
provides a framework to enable to connect disparate healthcare information systems and to establish interoperability. A number of API ecosystems — from HumanAPI (https://www.humanapi.co/)
and Validic (https://validic.com/) that focusses more on connections to healthcare institutions and consumers provide infrastructure to integrate more data sources into a PHR solution.
Over time and with adequate functionality, as more users consider a PHR as a single ‘digital front door’, it presents a great opportunity to be scaled further across an increased scope and more varied data sources.
If you want to chat or get involved in the work Mindwave Ventures are doing to develop data archetypes for mental health, please drop me an email via firstname.lastname@example.org
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