Harnessing Real-world Data: for Adaptive Healthcare Systems

1. Harnessing Real-world Data:for Adaptive Healthcare Systems PRISME SIG: Patient Centred R&D,Alderley Park, 21st May 2012 Iain Buchan

2. Patient at the Centre of What? Organisation/Pathway 1 System Organisation/Pathway 2 Organisation/Pathway k… Real-world evidence = Evidence base (average patient)  Me (and n-of-1 of my responses)  My care setting Evidence base aboutthe average patientpredicting < 30% oftreatment response

3. Troublesome Threes • 3 Ingredients • Data; Models; Expertise • 3 Myths • Big data warehouses are the solution • Science provides the models to utilise the data • Clinicians will continue to be the main source of data • 3 Pipelines • R&D; Quality Improvement; Payor & Public Health

4. Dormant Data Body Mass Index (BMI) trend in Wirral 3y-olds from 1988 to 2003 0.5 0.4 0.3 0.2 0.1 Three-monthly rolling average BMI SDS 0 Actions -0.1 Clues -0.2 -0.3 -0.4 Mar-88 Jul-89 Nov-90 Apr-92 Aug-93 Jan-95 May-96 Sep-97 Feb-99 Jun-00 Nov-01 Mar-03 Aug-04 Month of measurement by Health Visitor SDS = standard deviation score from 1990 British Growth Reference charts – adjusts for age and sex of the child

5. Easily Disregarded Data Trends in liver function sought in patients with long-term conditions. Most did not have regular repeat tests. Complex time structure to model.

6. Improved Liver Function on Starting Glitazone First glitazoneRx Mean log(ALT) before Mean log(ALT) after Fitted trends do not join evidence of change of glitazone effect on ALT Decrease (pooled) = 0.15 (se=0.009), p<1x10-59 Similar irrespective of co-prescriptions {G = 0.12; GM = 0.16; GS = 0.16; GSM = 0.15G = glitazone, M = metformin, S = sulponylurea}

7. Rosiglitazone to Pioglitazone: No Liver Signal Rosi Pio ALT on Rosi ALT on Pio Lines almost join  no evidence of a change in ALT

8. Trapped Data • Narrative • Vioxx risk of MI detectable pre-2004 • By text mining 154k RA patient notes • Not by ICD-9 codes from same notes • Repeated for 2 other risks LePendu et al. 2012, J BioMedSem 3(1):S5 • Rubrics • Code for type 2 diabetes annotated “DM r/o” • Access blocked by some ethics committees

9. Miscoded DataUK Diabetes Prevalence from Different Databases Data from Tim Doran, University of Manchester

10. Clinical (audit) question leading to scientific finding:required local metadata (assay change) not in national datasets Anaemia at lower levels of kidney impairment than commonly thought

11. Data Warehouses are not the Solution • Need contextual metadatacontinuously adding valueto big data • Need engaged communitiesadding the metadata • OMOP lessons • Current big data only for crude effects • Different answers from different datasets & methods

12. Big Data in Context or Datasets (+ models) (searched by experts) • Data  Models  Expertise • “sense-making network”

13. Fragmented Evidence & Workflows Patient is the union, not the sum of pathways Need usefully complex models of health & care Public Health Self Care Weight control Physical activity Secondary Care Primary Care Diabetology: Glucose focus Weight  Blood pressure Nephrology: Blood pressure focus Specialist A Specialist B

14. Health Records& Knowledge Silos Health Avatars& Dynamic Models Data-intensive Paradigm -shift Open Unifying Modelling: Across mechanisms and contexts  models = Avatar e.g. Coronary heart disease Expertise Expertise Expertise e.g. Chronic obstructivepulmonary disease e.g. Lung cancer • Multi-scale & • Multi-system • Health: • Research • Policy • Care Unified Graphical Model Electronic Health Records(eHR) Large scale inference Model refinement Data Data Data

15. Not About Mining Big Data Problem Space Data Space Observation Space y = b1x1 + b2x2 + b3x3 + c ...like squinting at an image through a doyley and prismNeed to harness networks of reasoning about models Not just structure in big data

16. Model-based Machine Learning • Suspected myth: false division of children into allergic tendency (atopy) or not • Life-course data: birth cohort of 1,000 children from Manchester with careful measurements • Approach: unsupervised search forpatterns of sensitisation  shape hypotheses

17. Unsupervised Clustering ofAllergic Sensitisation Development 1,053 Children Sensitization Class switch class 8 Allergens P(Sens’n) in year 1 Sensitized Age 1 Sensitized Age 3 Sensitized Age 5 Sensitized Age 8 Mite Cat Dog Pollen Egg Milk Mold Peanut P(Gain) P (Loose) Sens’n 3 intervals Skin Test Age 1 Skin Test Age 3 Skin Test Age 5 Skin Test Age 8 Blood Test Age 1 Blood Test Age 3 Blood Test Age 5 Blood Test Age 8 Sens’n state P(+ skin) Sens’ P(+ blood) Sens’ P(+ skin) Not Sens’ P(+ blood) Not Sens’

18. From 2 to 5 Useful Classes of Atopy

19. Predicted Real-world Outcomes First Admitted > 3 Years Old (remove early virus wheeze) Admitted at Any Age

20. Mirage of Evidence Health Sciences Evidence Base Algorithm Care Audit Research Commissioning Reality: feedback is too little too late Algorithms may be out of date by the time they are “validated” Note EU Directive 2007/47

21. Predicting Mortality from Cardiac Surgery 0.08 Observed in UK (SCTS audit) Expected via EuroSCORE I model 0.07 0.06 Drifting EuroSCORE Calibration 0.05 In-hospital mortality rate 0.04 0.03 0.02 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year Source: G. Hickey 2012

22. Re-inventing Reuse • 3 Pipelines • Research and Development • Provider Quality Improvement • Payor & Public Health Evidence • 1 Bottle-neck • Human resource to make sense of data

23. Potential to Borrow Strength Clinical Trial: 1 vs. 2 per day dosing  adherence (* deprivation) Clinical Audit: Depression vs. readmission Public Health: Recalibrated deprivation score “Users who selected the variables in your basket also selected these variables and these models…”

24. Health Intelligence ‘e-Lab’from/for a Population Local Value Adding Anonymised linked records Firewall 1 Metadata Analysis UnifiedHealth Intelligence Platform 2nd Level Privacy Control Global Value Adding Firewall 2 External collaborator/client

25. 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and j.EntryDate>@p5DateLimit1 ) ) and PatientID in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j inner join (select PatientID, MAX(EntryDate) as max_entrydate from Journal where ReadCode in 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and EntryDate<@p6DateLimit1 group by PatientID) x on j.PatientID = x.PatientID and j.EntryDate=x.max_entrydate where j.ReadCode in 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and j.EntryDate<@p6DateLimit1 ) ) and PatientID in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j inner join (select PatientID, MAX(EntryDate) as max_entrydate from Journal where ReadCode in ('.22K.','22K..','X76CN','X76CO','X76CP','XUHPV','XUSxA') group by PatientID) x on j.PatientID = x.PatientID and j.EntryDate=x.max_entrydate where j.ReadCode in ('.22K.','22K..','X76CN','X76CO','X76CP','XUHPV','XUSxA') and j.CodeValue<=@p7CodeValue1 ) ) and PatientID not in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j where j.ReadCode in ('Xa5sa','Xa5sb','Xa5sc','Xa5sd','Xa5se','Xa5sf','Xa5sg','Xa5sh','Xa5sH','Xa5si','Xa5sI','Xa5sj','Xa5sJ','Xa5sk','Xa5sK','Xa5sl','Xa5sL','Xa5sm','Xa5sM','Xa5sn','Xa5sN','Xa5so','Xa5sO','Xa5sp','Xa5sP','Xa5sq','Xa5sQ','Xa5sR','Xa5sS','Xa5sT','Xa5sU','Xa5sV','Xa5sW','Xa5sX','Xa5sY','Xa5sZ') ) )

26. …to this

27. Clinical Study Feasibility Assessment

28. Clinical Study Recruitment

29. Preserving Consent for Consent Local Health Community Integrated Health Record Essential Local Metadata SQL Study Recruitment Tool Bulk ETL with Anonymisation e-Lab email HTTPS e-Lab Anonymised Repository Researcher /Manager Clinician SQL HTTPS Study Protocol Designer Query Objects HTTPS Clinical Care Boundary Patient-Clinician Relationships HTTPS

30. 1 Website Many uses Many users Common data Population focus

31. e-Lab Currency: Work Object Work Object (Research Object) Research protocol Statistical analysis scripts Data-sources Analysis-logs & notes VisibleValue-Adding Data-preparation scripts Figures/Graphics Working datasets Manuscripts References Slides Socially Stimulating Science & Service Intelligence, In-silico

32. Data Sharing: Contextualised Local Community Integrated Health Record Collaboration with othertrusted e-Labs Depersonalised records SharedWork object Local uses Each locality can police the passage of objects at its boundary. Changes to objects are fully auditable.

33. SOA for Borrowing Strength Federation of e-Lab communities shares work objects without remote data warehousing Strength is borrowedand costs reducedby pooling expertise

34. Harness Heterogeneity Diversity of settings (disease-risk & treatment)across regionscould be harnessedto tease out evidencewith smaller samplesfaster than at present Ref: Thompson et al. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. International Journal of Epidemiology 2010;39:1345–1359

35. Toward m-Health: n-of-1 Gateway • Dark arease.g. adherence vs. antibiotic resistance • Current signal“how did you get on with your medicine?...” • m-Health • Smartphone app. with drug • Quality of life scores • Position and acceleration • Watch/plaster • Temperature • Pulse Patient/Citizen Co-produced Health Record Algorithm/ System Clinician/ Health Ally

36. Med-App Ecosystem:Reducing Schizophrenia Relapse Note: EU Directive 2007/47 (algorithm = medical device)

37. Conclusion • Current healthcare evidencepredicts less than 30% of true outcomes • Smart networking of data, models and expertise across environments is key tohealth system adaptation • Payer, provider, industry andacademia need to co-adaptin a social machine of R&D Rousseau: “Social Contract” Pre-competitive alliance framework