Reflections on the Use of Qualitative Research with Users – Benefits, Barriers and New Opportunities Jenny Ure
Care is now provided in an extended, digitally mediated environment where the problems and the opportunities are often unseen adn unreported • qualitative research with users is increasingly needed to inform large scale clinical trials and service developments
I will present a few vignettes of how I have used qualitative and collaborative action research in different eHealth and Telehealth projects, highlighting theory and practice • I will highlight some of the things that can go wrong in a qualitative research project, (which I which I had known at the start of my career!) and some of the strategies I have seen successful researchers adopt to avoid them
Research in theory Research in the wild
Patient Data integration • across sites(horizontal) • across scales (vertical …think Google Earth)
Seven HealthGrid projects • PsyGrid • NeuroGrid • BIRN • NeuroBase • CARMEN • DGEMap • EMAGE • P3G Observatory https://wikis.ac.uk/mod/Main_Page
Recurring problem: solution scenarios at different stages the human process 1.sampling 2.collecting 3.coding 4.cleaning 5.linkage 6.analysis 7.use the technical process
Sampling, collecting scenarios • Different populations • Different collection protocols • Different contexts and criteria for collection
Standardisation Strategies Do Not Ensure the Quality of Data Collection • A 46:36 waist/hip ratio reading – is it an input error or just a typical sample from West Lothian
Adapted from Keator et al (2006) Presentation to the UK-BIRN workshop Different Disease Effects or Different Scanner Calibration?
Strategies include... • Wireless notepads for data collection • Provenance metadata • Links to original data • Local QA/ethics/linkage committees • Error trawls and spot checks combined with error-trapping software
Harmonising different tools and platforms? • Microarray • In situ hybridisation • Scanners
Shared protocols? • Trace a line around the region of interest in all subjects • Compare differences in area across control and experimental groups • Unambiguous?
…there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns -- the ones we don't know we don't know.
The vision of seamless data sharing! Reliability of technology? Reliability of transmission? Usability of interfaces/kit? Patient factors? Local Factors? Unknown unknowns.....
30% Collection Errors ? • Missing data i.e. data that was known but was not filled in • Incomplete data - the patient ID specified but not the issuer of the patient ID • Incorrect data - the patient's name being entered as "brain" • Incorrectly formatted data • Data in the wrong field - the series being described as “knee” • Inconsistent data within a single file - patient's age inconsistent with scan date minus birth date.
Effect or Artefact? • Different equipment • Different populations • Different raters • Different contexts • Different protocols • Different coding • Different metadata
Ethical/Legal Issues in Data Collection/Linkage. New technical infrastructures can outstrip the development of social governance structures the human process 1.sampling 2.collecting 3.coding 4.cleaning 5.linkage 6.analysis 7.use the technical process
Data linkage enhances knowledge discovery • in relation to disease • in relation to patients and their families • DeCODE Different solutions cost, risk, benefit in different ways
Options - Role Based Access • The de facto standard • Persistent linked datasets more likely • Getting access is easier • Monitoring misuse is hard
An additional layer? • Checking for risks arising from linkage between particular datasets
An additional human layer • Linkage assessment panel • Also combines ethical and quality roles • Existing roles and responsibilities support effective intervention to enhance security and quality
Designing for e-Health: Recurring Scenarios in Developing Grid-based Medical Imaging Systems • Conclusions • In organic communities, the processes of structuring collaboration, coordination and control structures happens as a matter of course. NeuroGrid is employing an early prototype to generate engagement and dialogue, to enable early discussion of requirements for more complex services, compute capability and workflows, as well as data quality and configurational issues. • In addition to ameliorating the recurring issue of requirements ‘creep’, late in the design process, it allows disparate groups to engage with the real issues, and possible solutions in a shared context. • Introduction • NeuroGRID www.neurogrid.ac.uk is a three-year, £2.1M project funded through the UK Medical Research Council to:- • develop a Grid-based research environment to facilitate the sharing of MR and CT scans of the brain and clinical patient data in the diagnosis of psychoses, dementia and stroke • bring together clinicians, researchers and e-scientists at Oxford, Edinburgh, Nottingham and London • create a toolset for image registration, analysis, normalisation, anonymisation, real-time acquisition and error trapping • ensure rapid, reliable and secure access, authentication and data sharing Data Quality Issues: The Social Life of Information Challenge: The large scale aggregation of diverse datasets offers both potential benefits and risks, particularly if the outputs are to be used with patients in a clinical context. Thus aggregating data is a key issue for e-Health, yet data is not independent of the context in which it is generated. Within small communities of practice a degree of shared and updated knowledge and experience allows judicious use of resources whose provenance is known and whose weaknesses are often already transparent. The same is not true of aggregated data from multiple sources. Approach: Early use of prototypes to provide a ‘sandpit’ for promoting both technical and inter-community dialogue and engagement, and start the process of identifying, sharing and updating knowledge of emerging issues. Early trials with known datasets aim to generate an awareness of the types of variance that can arise and ways in which it might be minimized, harmonized, or made transparent to users Socio-technical Issues Aligning Technical and Human Systems Challenge:Integrating the technical work of system building, with the socio-political work of generating the governance of the new risks and opportunities they generate Approach: The creation of real and virtual ‘shared spaces’ (e.g. via Access Grid) and the use of an early prototype for engagement in areas of shared professional concern, to help this new hybrid community develop its own rules of engagement, and start making collective sense of local requirements in relation to common project goals. • Semantic Issues Il nome della rosa • Challenge: • Multi-site studies raise issues such as different naming conventions for files, different coding and classification systems, different protocols, and different conceptualisations of domains. • Approach: • The project agreed on core and node specific metadata and will use an OWL-based ontology (logic-based domain map) to allow human and machine-readable searching and basic reasoning across the datasets. In this there is a trade-off between the benefits of share-ability and automated reasoning, on the one hand, and the formalisation of concepts and relationships that are evolving. • Challenge: • Aligning and representing datasets at different levels of granularity. While NeuroGrid uses MR and CT scans, other relevant datasets such as diffusion tensor imaging, genetic, proteomic datasets also contribute to an understanding of neurological processes. • Approach: • The project is adopting a two –pronged approach • developing task specific ontologies • developing a reference ontology based on the Foundational Model of Anatomy adopted by the BIRN • Human Brain Project. • This allows a degree of alignment between datasets and ontologies in future collaborations Acknowledgements The authors would like to acknowledge the support of the UK Medical Research Council (Grant Ref no: GO600623 ID number 77729), the UK e-Science programme and the NeuroGrid Consortium. Imaging Issues: Artefact or Actuality? Researchers use innovative imaging techniques to detect features that can refine a diagnosis, classify cases, track normal or often subtle physiological changes over time and improve understanding of the structural correlates of clinical features. Variance is attributable to a complex variety of procedures involved in image acquisition, transfer and storage, and it is crucial, but difficult, for true disease-related effects to be separated from those which are artifacts of the process For further information For information on this and related projects contact Jenny.Ure@ed.ac.uk or go to www.neurogrid.ac.uk Designing for e-Health: Recurring Scenarios in Developing Grid-based Medical Imaging Systems John Geddesa, Clare Mackaya, Sharon Lloydb, Andrew Simpsonb , David Powerb, Douglas Russellb, Marina Jirotkab, Mila Katzarovab, Martin Rossorc, Nick Foxc, Jonathon Fletcherc, Derek Hilld, Kate McLeishd, Yu Chend , Joseph V Hajnale, Stephen Lawrief, Dominic Jobf, Andrew McIntoshf, Joanna Wardlawg, Peter Sandercockg, Jeb Palmerg, Dave Perryg, Rob Procterh, Jenny Ureh,, Mark Hartswoodh, Roger Slackh, Alex Vossh, Kate Hoh, Philip Bathi, Wim Clarkei, Graham Watsoni aDepartment of Psychiatry, University of Oxford, bComputing Laboratory, University of Oxford, cInstitute of Neurology, University College London, dCentre for Medical Image Computing (MedIC), University College London, eImaging Sciences Department, Imperial College London, fDepartment of Psychiatry, University of Edinburgh, gDepartment of Clinical NeuroSciences, University of Edinburgh, hSchool of Informatics, University of Edinburgh, iInstitute of Neuroscience, University of Nottingham  Corresponding Author: Jenny Ure, School of Informatics, University of Edinburgh, Jenny.Ure@ed.ac.uk The concept of the collaboratory is central to the e-Science vision, yet there has been limited concern with the generation of the community and coordination infrastructures which will coordinate and sustain it. • Real or artefactual differences? • Different scanners • Different populations • Different raters • Different centres • Different protocols
2. Data Interpretation and CodingExample 2. Telehealth/Mobile Health
An accessible ‘living lab.’ for research on socio-technical systems • The shaping of data passing through many hands • The shaping and re-shaping of diagnosis as new data challenges current classifications, practices and policies • The impact of local shaping (personal, contextual and technological) on the global vision for eHealth • the impact of technology mediated access to data on the roles, risks and resources available to different actors • The implications for representation of different actors in the process • The implications of both access to data and access to representation in the design process can shape the care constituency.
Context telecare-monitoring centre
Grounded Theory • Collaborative Action research • Realistic Evaluation • Ethnographic Observation
Grounded theory • ‘The discovery of theory from data – systematically obtained and analysed in social research’ (Glaser & Strauss, 1967: 1) • ‘The methodological thrust of grounded theory is toward the development of theory, without any particular commitment to specific kinds of data, lines of research, or theoretical interests . . . A style of doing qualitative analysis that includes a number of distinct features . . . and the use of a coding paradigm to ensure conceptual development and density’ (Strauss, 1987) Grounded theory
Two Variants • Two main variants: • Strauss and Corbin’s (1990) book provides one coding paradigm (context, conditions, interactions, conditions and consequences) • Glaser’s (1978) book provides 18 coding ‘families’ giving many more options • Glaser accused Strauss and Corbin of being too restrictive and of forcing data and concepts into a preconceived mould • However the Strauss and Corbin (1990) variant is the most widely used Grounded theory
Overview of the Lothian COPD Pilot Jenny Ure Edinburgh University www.telescot.org
The home-monitoring vision • The early vision of early detection to cut hospital admissions/costs • The evolving scenarios as new opportunities and challenges arise • The process of rethinking and reconfiguring roles, risks, resources and criteria for value
Hospital admissions Should I bother the GP?