1. NHS e-Lab “Engineering Digital Health Economies” Digital Futures, 12 October 2010 Prof. Iain Buchan

2. Primary Care Secondary Care Healthcare Problem: Gaps in Communication & Organisation Self CareClinical Care Specialist ASpecialist B

3. Primary Care Secondary Care Digital Bridges Since 1990s: Integrated Care Pathways (Disease-specific) Self CareClinical Care Specialist ASpecialist B

4. Primary Care Secondary Care Missing: Patient & Community ‘Big-picture’ Across Diseases/Services/Pathways Self CareClinical Care Specialist ASpecialist B Diabetology: Glucose control Diabetology: Glucose control Ophthalmology: Diabetic eye care Ophthalmology: Diabetic eye care Nephrology: Chronic kidney disease Nephrology: Chronic kidney disease Future: Realistically complex and dynamic models of individual or community care: i.e. Mr Smith’s care pathway, not diabetes + eye + kidney care pathways

5. Finance Public Health Clinical Healthcare Data Tomb Research Deposit Use Fuel for models? Warehouse = Digital Dust

6. Digital Bottleneck = Expertise Data ↑↑↑ Methods/Models/Applications ↑ Relevant expertise ↔

7. NHS e-Lab GP Local Community Integrated Health Record Hosp. Optometrist Community nurse Podiatrist Biobanks Local surveys Individual research ONS vital statistics Local authority socio-economic Public health NHS no. Postcode & NHS to other ID Usual suppliers Commissioning Audit Public Health Research “unified sense-making” Depersonalised records

8. Change data extraction from this… select distinct PatientID, GPPracticeCode from Patients where DeathDate is null and PatientID in (select distinct PatientID from Patients where Dob =1931 ) ) and PatientID in (select distinct PatientID from Patients where PatientID in (select distinct j.PatientID from Journal j where j.ReadCode in ('.C21.','.C2A.','.C2D.','.C2G2','C1021','C109.','C1094','C1095','C1097','C1099','C109D','C109F','C109G','C109H','C109J','C10F.','C10F4','C10F5','C10F7','C10F9','C10FD','C10FF','C10FG','C10FH','C10FJ','L1806','X40J5','X40J6','X40Jk','XaELQ','XaFn7','XaFn8','XaFn9','XaFWI','XSETp','XU70f','XU71F','XUK O0','XULXc','XUPHn','XUSbx') 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 ('','f1...','f13..','f221.','f25W.','fw2..','x006f','x05d1','x05d2','X797a','X797n','XU6gD','XU6gE','XU6gF','XU6gH','XU6gI','XU6gJ','XU6gK','XU6gL','XU6gM','XU6gN','XU6gQ','XUbmy','XUbxv','XUby2','XUby3','XUby4','XUc1k','XUc2i','XUcdE','XUcdF','XUcdG','XUd1d','XUd1e','XUd1f','XUd1g','XUd1h','XUd1i','XUd1j','XUd1k','XUd1l','XUd1m','XUd1n','XUd1o','XUd1p','XUd1q','XUd1r','XUd1s','XUd1t','XUdPn','XUdPo','XUdSP','XUe2f','XUe6n','XUe6o','XUfQE','XUfrN','XUgJm','XUhyH','XUhyI','XUUng','XUUnh','XUUni','XUUnj','XUUnm','XUUns','XUUnv','XUUo4','XUUo5','XUUo6','XUUo7','XUUo8','XUUo9','XU UoA','XUUoB','XUUoC','XUUoD','XUUoE','XUUoF','XUUoG','XUUoH','XUUoI','XUUoj','XUUoJ','XUUoK','XUUoL','XUUom','XUUoM','XUUoN','XUUoO','X UUoP','XUUoQ','XUUoR','XUUoS','XUUoT','XUUoU','XUUox','XUUpF','XUUpG','XUUpH','XUUpI','XUUpJ','XUUpO','XUVCz','XUVKo','XUWCm','XUWCn',' 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('','f1...','f13..','f221.','f25W.','fw2..','x006f','x05d1','x05d2','X797a','X797n','XU6gD','XU6gE','XU6gF','XU6gH','XU6gI','XU6gJ','XU6gK','XU6gL','XU6gM','XU6gN','XU6gQ','XUbmy','XUbxv','XUby2','XUby3','XUby4','XUc1k','XUc2i','XUcdE','XUcdF','XUcdG','XUd1d','XUd1e','XUd1f','XUd1g','XUd1h','XUd1i','XUd1j','XUd1k','XUd1l','XUd1m','XUd1n','XUd1o','XUd1p','XUd1q','XUd1r','XUd1s','XUd1t','XUdPn','XUdPo','XUdSP','XUe2f','XUe6n','XUe6o','XUfQE','XUfrN','XUgJm','XUhyH','XUhyI','XUUng','XUUnh','XUUni','XUUnj','XUUnm','XUUns','XUUnv','XUUo4','XUUo5','XUUo6','XUUo7','XUUo8','XUUo9','XU UoA','XUUoB','XUUoC','XUUoD','XUUoE','XUUoF','XUUoG','XUUoH','XUUoI','XUUoj','XUUoJ','XUUoK','XUUoL','XUUom','XUUoM','XUUoN','XUUoO','X UUoP','XUUoQ','XUUoR','XUUoS','XUUoT','XUUoU','XUUox','XUUpF','XUUpG','XUUpH','XUUpI','XUUpJ','XUUpO','XUVCz','XUVKo','XUWCm','XUWCn',' 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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','Xa5 sn','Xa5sN','Xa5so','Xa5sO','Xa5sp','Xa5sP','Xa5sq','Xa5sQ','Xa5sR','Xa5sS','Xa5sT','Xa5sU','Xa5sV','Xa5sW','Xa5sX','Xa5sY','Xa5sZ') ) )

9. ..to this

10. Cloud of millions of care messages in the local health economy Cloud of millions of care messages in the local health economy Structured Data Organise Transform & Examine Structured Data & Metadata

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

14. Disseminating New Models: Policy Simulator Models from research  into NHS practice via e-Lab Export “Method Object” from research Ask “what if” scenario planning questions e.g. “what is the likely public health impact of 500k spend on statins vs. smoking cessation”

15. e-Lab Currency = Work Object Stimulating healthcare intelligence 1) Find one your colleague made earlier; 2) take it apart and learn how it was done; 3) refill it with local data; 4) republish it (+/- enhanced); 5) get points for sharing and reusing Work / Method Object Find Share Reuse Data-sources Data-preparation scripts Work protocolStatistical analysis scripts Slides Working datasets Figures/Graphics Reports References Analysis-logs & notes

16. Data-sharing is Context-specific Local Community Integrated Health Record Depersonalised records Local uses Collaboration with other trusted NWeH e-Lab: say over large-scale research Shared Work object Localities only share the data items relevant to the work, packaged into a work object that is checked by a local officer before being shared – all subsequent work on the object is audited and visible to the originator

17. “Borrowing Strength” along Service Buses Federation of e-Lab communities shares work or method objects without remote data warehousing. Strength is borrowed and costs reduced by pooling expertise.

18. Summary: e-Lab Principle … assemble a digital ecosystem of people, data and methods People with relevant expertise and authorisation State-of-the-art algorithms Quality assured integrated data

19. Conclusion Healthcare futures will be more data and model intensive Healthcare information pipelines don’t scale NHS e-Lab harnesses radical sharing of sense-making resources around integrated health records to support digital health economies See YouTube channel “NIBHI1”