The speakers for Wednesday’s Big Data in Mental Health event have kindly made their abstracts available. Hope you’re all looking forward to it! Take a look below for details of the talks.
Michael Lynskey, King’s College London
Title: Integrating epidemiological, geographical and statewide administrative data
In Missouri, access to Sate-wide vital records (birth, marriage/ divorce, driver’s license and death records) has been used to enhance epidemiological research, primarily through identification of informative samples. Now, given increasing capacity for linking to existing data sets and for geocoding locations of events we have the capacity to examine environmental influences on health outcomes at a population level. This talk will discuss the rationale for – and challenges in – integrating population wide data with geographic and epidemiologic data.
Maxim Ozipov, University of Oxford
Title: From questionnaires to objective data – continuous monitoring of mania and depression symptoms using mobile phone sensors
The diagnosis of mental disorders depends heavily on clinical judgment. Numerous mental health scales have been suggested to bring objectivity into the diagnostic process, however assessments require periodic encounters with a patient and compliance with electronic self-reports can be low. An automated assessment of mental health may provide more timely interventions and decrease the severity of significant events. Changes in sleep and activity patterns are important symptoms of mood disorders and such changes can be indicative of deteriorations in a patient’s clinical state. Using smart phones we can continuously monitor changes in activity patterns, as well as social interactions, thus potentially providing a low-cost pervasive monitoring modality for the assessment of clinical status in mental health patients. Here we describe an implementation of such a system, as well as the first results and challenges arising from a pilot study on healthy volunteers. We also propose a framework for continuous symptom assessment in the presence of real-world challenges.
Richard Jackson, King’s College London
Title: Concept extraction from clinical free text, and integration with research datasets.
In the course of clinical care, vast quantities of unstructured clinical notes are generated. These store and communicate vital pieces of information between stakeholders in patient care. In Mental Health, clinical notes are an especially important, due less reliance on discrete diagnostic categories relative to other health disciplines. However, additional uses of clinical data for the betterment of public health are not possible while it is in unstructured format. This talk explores the mass information extraction techniques the South London and Maudsley NHS Trust employs to derive meaningful, structured data from the Trust’s 20 million clinical documents, and the discoveries that result.
Amos Folarin, King’s College London
Title: Quantitative monitoring of the patient behavior and environment using small wearable devices and mobile phone sensors – a feasibility study on automated sleep measurement
Rapid and continued advancement in ‘omics technologies have provided us with a wealth of high resolution information on the inner workings of disease and genetic variability. A collateral rise in technological advances and widespread availability of mobile phones has at the same time been observed. These mobile technologies promise the potential of gaining equally fine grained information on patient behaviour and environment, especially when paired with additional small wearable probes that report specific information on the user. The ability to capture this data and report it back into the clinical record will be of immediate benefit, such as providing clinical trials with high resolution data for patient stratification, real-time response to treatment, and directly improve patient care and efficiency of intervention.
SLaM employs an electronic health record (EHR): the Patient Journey System (PJS) and has implemented a de-identified derivative of this EHR for research use: the Case Register Interactive Search (CRIS). Research projects can apply for access to CRIS data through a well-developed governance framework. To give patients a more interactive relationship with their clinical record, SLaM is using MyHealthLocker, a system built on top of Microsoft HealthVault. Clinicians can elect to share EHR data with their patients and patients can submit data to their own health record and elect to share parts of that record with their treatment team.
The Purple Robot application, developed by the Centre for Behavioural Intervention Technology (CBITS) at Northwestern University, provides both an interface to the on-board mobile phone probes (e.g. GPS, light, temperature) and also a framework for integrating other mobile-aware probes e.g. worn accelerometers, heart rate, perspiration, temperature, oxygen saturation, blood pressure, blood glucose etc. Using this platform, we have built a framework to enable patient reported outcome (PROM) data from smartphone applications, smartphone sensors and other sensor devices to be collected and analysed. Pertinent summary data can then be exported into HealthVault and, through a MyHealthLocker widget, patients can access and visualise their data and choose to share that data with their care team or researchers.
In this talk I will discuss the use of this framework in the development of a proof-of-concept application which tracks sleep (quantity and quality).
Chris Hollis, MindTech, Nottingham
Title: NIHR MindTech HTC: Harnessing technological innovation to transform mental healthcare
MindTech (www.mindtech.org.uk) was established by NIHR in 2013 as one of eight NIHR Healthcare Technology Co-operatives (HTCs) in England. MindTech’s role is to drive forward technological innovation in mental healthcare by identifying clinical unmet needs and bringing together patients, clinicians, researchers and industry to develop and test a range of technological solutions to improve healthcare delivery and patient outcomes. User involvement in the design and testing of technology is central to our aims and we have established a national MindTech Patient Reference Group to support this work.
The growth and power of digital technology ranging from smart phone apps, sensors, automated facial, voice analysis and diagnostics is remarkable – and can provide enhanced self-management, remote on-line therapy, objective diagnostic and real-time monitoring for mental health conditions. Current research projects supported by MindTech include a randomized trial of computerised assessment of attention and activity in ADHD, automated facial and voice analysis to monitor mood, medication and self-monitoring apps linked to clinician decision support systems, investigating efficacy of remote on-line CBT and developing appropriate technology to live better with dementia.
The presentation will highlight both the opportunities as well as the challenges facing technological innovation and building the evidence-base for applications in mental healthcare.
Will Spooner, CTO Eagle Genomics
Title: The journey from 100,000 genomes to personalized medicine. Opportunities and challenges from an informatics perspective
In 2012 Eric Lander reflected: “we should remain unabashed about the ultimate impact of genomic medicine, which will be to transform the health of our children and our children’s children”. Although the molecular basis behind many diseases including several mental disorders has been uncovered, application of genome sequencing to their treatment is still very much in its infancy. Is that about to change? Genomics England Ltd (100% owned by the Department of Health) has been funded to sequence 100,000 Genomes. This project will leverage the unique position of the UK healthcare system and aims to provide an unparalleled resource for investigating clinical applications of genomics. Solving the informatics puzzle at the intended scale is challenging, and calls for specialised approaches; Eagle, a leading Cambridge-based bioinformatics services company, has been working on prototype software to tackle this problem that draws heavily on existing open source resources. The result is a robust, secure and scalable data processing and annotation platform with a distinct emphasis on metadata management that supports both primary (clinical) and secondary (research) uses of the resulting data sets.
Zina Ibrahim, King’s College London
Title: Multi-agent systems for randomized control trials in routine practice.
Results from randomized controlled trials (RCT) show that Methylphenidate is one of the most effective drugs for the treatment of Attention Deficit Hyperactivity Disorder (ADHD) with effect size ranging between 0.8 and 1.2. However, the results from RCTs do not fully represent long-term treatment or the heterogeneous population of patients with ADHD found in the clinical records.
The problem is that in order to assess the long-term effectiveness of Methylphenidate, repeated measures of treatment outcome taken at the right time should be available for patients taking the drug. However, in real practice this is seldom the case.
In order to improve the effectiveness of outcome measurements, and consequently the service to patients, we have devised a system for automatically request treatment outcomes from patients at regular intervals without clinician participation.
The system is formed of a community of software agents which cooperate to collect treatment outcomes from patients at predefined intervals. The software operates by monitoring the Electronic Patient Journey System (EPJS) containing patient records for new entries of patients prescribed Methylphenidate. The system creates a personalised scheduled for each patient, prompting them to submit new assessments on regular intervals. The patients are able to submit the outcome through MyhealthLocker and are given in return a detailed Development and Well-Being Assessment (DAWBA) report based on their submitted outcome results.
Title: Big data, better information. A clinician’s wish list
Title: How can Digital Health technologies improve outcomes, lower cost and improve care in Mental Health.
Digital Health tools & technologies promise to transform health & social care. Entrepreneurs around the globe are being encouraged to come up with new ideas to solve our biggest issues. The convergence of Big Data, Wearable Technology & the Internet of Things is creating not just new possibilities, but new challenges for healthcare systems and policy makers.
In this talk, Maneesh will go beyond the headlines, and share insights into how Digital Health is being used in Mental Health around the globe. His talk will also cover the implications of these new ideas, not just for providers and payers, but for patients and their families. He will also give a glimpse into the technologies heading our way over the next decade.
At the heart of Digital Health is data, but who will own and control this new stream of patient data? How will it integrate into existing systems? What are the privacy & security risks associated with adopting these new tools? What data is of specific value in Mental Health? Why are Apple, Google and Samsung competing to develop global platforms for collecting, storing and sharing of health data?