QResearch Professor Julia Hippisley-Cox PHCSG, Stratford 2014 JHC roles • • • • • • • Sessional GP in Nottingham Professor epidemiology & GP Notts Uni Director QResearch Member PHSCG committee Member EMIS NUG committee Director ClinRisk Ltd Member Confidentiality Advisory Group Acknowledgements • • • • • • • Contributing practices EMIS NUG EMIS University of Nottingham QResearch Advisory Board ClinRisk (software) Co-authors/researchers Overview • • • • • QResearch QData Linkage Project Risk prediction tools QSurveillance QFeedback QResearch – what is it? • • • • • • • • Patient level pseudonymised database Not for profit partnership UoN & EMIS Started in 2002 Ethical research EMIS systems used in > 55% of UK First research database from EMIS Pilot including 40 practice for 1 year Currently >900 general practices QResearch – why set up? • David Stables co-founder of EMIS (1987) & QResearch (2002) • Designed emis systems to facilitate research to improve health care & help GPs make better diagnoses & Rx decisions • UoN strong track record using MIQUEST to extract GP data for academic research • MIQUEST not working & not scalable • Both interested in ethical research QResearch Governance • Strategic decisions taken by Manageemnt Board representing EMIS & UoN • Dr Shaun O’Hanlon (ex-GP Chief Medical officer) • JHC (current GP & academic) • Scientific board approves access to data • Advisory board sets policy & oversees operation & governance database • Annual review by REC • Assessment by ECC/CAG QResearch Advisory ToR • • • • • Oversee general working Oversee comms with & benefits to practices Agree criteria & principles of access Oversee application of criteria Review any changes to context, content or data usage • Advise on policy for access • Advise on professional issues QResearch Advisory Members • • • • • • • EMIS NUG Patient representation RCGP BMA/GPC Society for Academic Primary Care UoN EMIS QResearch - what used for? • • • • • • Solely used for research projects Generation new knowledge Testing or generating hypotheses Intended for publication in academic journal Subject to rigorous peer review All projects must be published QResearch CANNOT be used for • • • • • • Non-research projects Identifying patients or practices Clinical trials Delivering interventions Political purposes Projects which wont be published QResearch practice engagement • Practices need to opt in by activating sharing agreement in EMIS. • Can be de-activated at any time • Individual patients can opt out or opt in • Just re-consented 900 + practices with move to EMIS Web • http://emisnug.org.uk/video/enabling-sharingagreements-qsurveillance-and-qresearch QResearch benefits • Contributing to bona fide research by sharing data safely • Results from all research publically available to maximise public benefit • Publications here • Includes disease epidemiology, drug safety, health inequalities • Helps development of tools & utilities in clinical systems QResearch–who can access? • • • • • • • • Lead researchers based in UK universities Track record undertaking research Team must include a GP Freedom & intent to publish Clinical custodian signs declaration Undertakes not to try to identify patients Data stored securely on site in university Signed licence agreement Peer review projects • Clear research question or hypothesis likely to lead to generalisable finding? • Output suitable for publication? • Does team have track record of research • QResearch appropriate database? • Are methods appropriate? • Any risks to ethical position including identification of patients or practices? QResearch what GP data included? • • • • • • • • Pseudonymised NHS number; year birth Only coded data Clinical events, values, diagnoses Prescriptions Consultations, referrals No strong identifiers No free text or attachments No confidential patients or data items or data from patients who have opted out QResearch: what data be accessed? • • • • • • Samples of database not whole database Up to 100,000 GP records Studies needing > 100K done on site Studies needing linked data done on site Remote monitored access to prepared data Access to development datasets to prepare code which are run on site • Only use for specified project • No onward disclosure/re-use QResearch–how researchers specify data requested? • Application form on web • Qweb query tools to define data requirements • Includes code libraries for • • • • Read codes/snomed Drug codes ICD9 & 10 OPCS codes • Defining study cohorts inclusion/exclusion QResearch safeguards • Clinician lead • Strong IG framework which limits purposes (research) and users (researchers) • Strong oversight by advisory board • No strong identifiers • Pseudonymisation-at-source • Minimisation of variables & sample size • Use of onsite analysis for large samples/linked data QResearch Data Linkage Project QResearch Data Linkage Project • QResearch database already linked to • deprivation data • cause of death data • Very useful for research • better definition & capture of outcomes • Health inequality analysis • Improved performance of QRISK and similar scores QResearch data linkage project • • • • • • Inpatient data Outpatient data Maternity Critical care Cancer registration Mortality registration New approach pseudonymisation • Need approach which doesn’t extract identifiable data but still allows linkage • • • • • • Legal, ethical and NIGB approvals Secure, Scalable Reliable, Affordable Generates ID which are Unique to Project Applied within the heart of the clinical system Minimise disclosure Pseudonymisation: method • Scrambles NHS number BEFORE extraction from clinical system • Takes NHS number + project specific encrypted ‘salt code’ • One way hashing algorithm (SHA2-256) • Cant be reversed engineered • Applied twice in to separate locations before data leaves EMIS • Apply identical software to external dataset • Allows two pseudonymised datasets to be linked Openpseudonymiser.org • • • • • • • • Website has .NET and JAVA implementation Desk top batch processor Libraries for integration Test harness Documentation Key server Screencasts Used by EMIS, TPP, HSCIC, ONS + various other CCGs/CSU/companies Individual assessment Who is most at risk of current or preventable disease? Who is likely to benefit from interventions? What is the balance of risks and benefits for my patient? Enable informed consent and shared decisions Population risk stratification Identification of rank ordered list of patients for recall or reassurance GP systems integration Allow updates tool over time, audit of impact on services and outcomes Major cause morbidity & mortality Represents real clinical need Related intervention which can be targeted Related to national priorities (ideally) Necessary data in clinical record All then available as open & closed source software & for integration into clinical systems Embargoed until publication QScores embedded • EMIS • INPS • SystmOne • Microtest • Pharmacies -Boots • Telehealth • Occupational Health • Jaguar • Morrison’s • National Grid! ALREADY IN EMIS WEB QRISk2 QDiabetes QStroke QFracture QAdmissions IN PLANNING PHASES QCancer (release Nov 14) QKidney QThrombosis QBleed QIntervention EMIS NUG screen casts courtesy of Dr Geoff Schrecker & EMIS NUG http://emisnug.org.uk/video/addingcalculation-template-emis-web http://emisnug.org.uk/video/runningcalculation-eg-qrisk-group-patients-batchadd http://www.emisnug.org.uk/ QSurveillance QSurveillance • • • • • • • Real time surveillance system Daily data from 4000 EMIS practices 30 million patients Infectious diseases Vaccine uptake Only aggregated count data by age/sex Alerts to and helps manage pandemic QSurveillance real-time surveillance •Largest real time surveillance system worldwide •Used to monitor seasonal outbreaks of disease e.g. influenza, norovirus •Real time response to public health incidents •Compliments NHS Direct, RCGP, National Pandemic Service Influenza virus particles Flooding, Oxfordshire, 2007 © HPA, Jane Bradley QSurveillance •Core part of the Emergency Response. Reports to • • • • • • Public health England Department of Health CMO’s Office Cobra WHO Academic Modellers •Used to make national and local policy decisions Examples: Ricin and London Bombings Examples:Heatwaves Examples: Buncefield 2005 Examples: Avon Floods 2007 Examples: Pandemic 2009 September 2009 QFeedback QFeedback: update • Interactive tool based on QSurveillance • Allows practices to view own data compared • PCT, SHA, UK • Similar practices • Graphs, Maps, Export data to excel • Deployed to 4000 EMIS practice since 2011 • Final of E Heath innovation awards QFeedback dashboard Example maps General application