Buchan, I

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The University of Manchester ResearchDigital Curation for the Public's Health: Ethics, Security &TrustLink to publication record in Manchester Research ExplorerCitation for published version (APA):Buchan, I. (2010). Digital Curation for the Public's Health: Ethics, Security & Trust. DCC/RIN Research DataManagement Forum, Manchester.Citing this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. 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Jun. 2022Digital Curation for the Public’s Health  Ethics, Security & Trust DCC/RIN Research Data Management Forum “Dealing with Sensitive Data: managing ethics, security and trust”  10th March 2010   Prof. Iain Buchan University of Manchester  Digital Curation for the Public’s Health • Where does the public’s health need digital innovation?  • How can research curators promote this innovation? – Ethics – Security – Trust  • Is a framework required? – Social contract – Digital & operational infrastructure: e-Lab   CITIZEN-LED PREVENTION & EARLY INTERVENTION Public Health Needs for Digital Innovation: I Source: Whelton PK, et al. Primary prevention of hypertension: Clinical and public health advisory from The National High Blood Pressure Education Program. JAMA 2002;288:1882–8. Approaches to Healthcare • Clinical model: – Rescue the ill – Resource  illness – Specialise to optimise  • Public Health model: – Rescue the at-risk – Resource  disease/risk – Generalise to optimise Reactive Proactive Healthcare Digital Innovation is Mostly Clinical • Specialist-driven • Excellent in niches • Incremental …while public health technologies may be left to perverse market-forces.  A life-course view of personal health Birth Death Amount of Potentially Healthy Years Lifecourse Personal cost: healthy years of life lost  Societal cost: large proportion of healthcare  resource used Hip fracture Instability + structured exercise programme State-led: Health Promotion Citizen-led: Health & Wellbeing Know Act Interact Feel Persuasive Technology Social Networks Social Marketing Re-wire the brain to resist over-malnutrition? Burning Fat Depositing Fat (ketone/other) molecues on skin Active Polymers in wristband +/- other signals & data Frequent Choices Care costs escalate without prevention Public Health Prevention Wellbeing Clinical Care Social Care Science Life-course view (barely visible investment) Life of a person Sickness view (fire-fighting investments) Life of a parliament State > Citizen Unified Modelling Digital Coherence Citizen > State DYNAMIC LINKAGE OF RESEARCH INTO PRACTICE AND PRACTICE INTO RESEARCH Public Health Needs for Digital Innovation: II Research Evidence Patient & Community Values Healthcare Information Optimal Decision Primary Care Secondary Care Healthcare Problem: Gaps in Communication & Organisation Self Care Clinical Care Hospital A Hospital B Primary Care Secondary Care Digital Bridges Since 1990s: Integrated Care Pathways (Disease-specific) Self Care Clinical Care Hospital A Hospital B Primary Care Secondary Care Missing: Patient & Community ‘Big-picture’ Across Disease / Specialist Pathways Self Care Clinical Care Hospital A Hospital B  = Avatar Diabetology: Glucose control Ophthalmology: Diabetic eye care Nephrology: Chronic kidney disease Person: Experiencing n of 1 trials Future Health ‘Avatar’: Fuse research evidence about the average person with a longitudinal model of the person represented Large scale inference Unified Graphical Model Electronic Health Records (eHR) Data Expertise Expertise Expertise Multi-scale & Multi-system Health: • Research • Policy • Care Model refinement Data Data Health Records & Knowledge Bases Data-intensive Paradigm -shift Health Avatars & Dynamic Models  models = Avatar Open Unifying Modelling: Across mechanisms and contexts e.g. Lung cancer e.g. Chronic obstructive pulmonary disease Trans-disciplinary Analysis of Clinical Research Data Machine Learning in Epidemiology... Introducing Health Sciences Signal Paths to Physical Scientists & Engineers Problem Space Observation Space Data Space ...like resolving an image through a prism through a doyley y = b1x1 + b2x2 + b3x3 + c Hypothesis-driven Epidemiology: Sieving Associations Association Bias Type Explanation CM Cause-effect Real Cause-effect MIC Reverse Real Effect-cause C?MI Confounding Real Effect-effect C </> MI Random error Spurious Chance C </> MI Systematic error Spurious Bias C = caffeine, MI = myocardial infarction (heart attack)  Disciplined approach to causal inference, Bradford-Hill: Criteria (temporality, strength, dose-response, consistency, plausibility, consideration of alternatives, open to experiment, specificity, coherence) Need to Address Complexity & Scale The big public health problems e.g. Type 2 Diabetes have “complex webs of causes” Problem 1: Dwindling hits from tools to detect independent “causes” Problem 2: Knowledge can’t be managed by reading papers any more The “data-set” and structure extend beyond the study’s observations A Graphical Model of Asthma Ma nc hets t e r eAAs tdhnm a a l l r g y Su dyTo Infer Unsupervised Clustering The Atopy Model Ma nc hets t e r eAAs tdhnm a a l l r g y Su dyAtopy Classes and Asthma 1.66 0 11.1 9.59 42.2 0102030405060No atopy "Atopy 4" "Atopy 3" "Atopy 2" "Atopy 1"% with asthma (symptomatic on challenge) Ma nc hets t e r eAAs tdhnm a a l l r g y Su dyMultiple Early Sensitisations  odds ratio (OR) [95% CI]: 30.5 [12.4-75.3] c.f. Ever atopic group:  Trans-disciplinary Simulation of Public Health Impacts Digital Assets  Policy Decisions... PhysicalActivityDietSmokingDeprivationObesity(BMI)Diabetesor IGTCholesterol LDL(& HDL)Blood PressureCombined CVD RiskUnstableAnginaChronic AnginaEarly Heart FailureSevere Heart FailureMIsurvivorsAcuteMIStrokePADetcAdditional CVD Risk FactorsFromany StateOutputs: Population-based incidence, prevalence; Deaths prevented; Life-Years; Life expectancy; Costs; Cost-effectiveness ratiosPopulation Policies & BehavioursBiological Risk FactorsCombinedCVD RiskCVD PatientGroupsOUTPUTSCHD DeathNon-CHD DeathRecurrentMISUDSNON-SUDSPulling evidence together into one, realistically-complex model: e.g. MRC IMPACT IIRecombine evidence around probabilistic graphical models of disease & care-services Ask ‘what if’ invest in statins vs. smoking cessation etc. Open Unified Models for Health Policy WCurrent FutureCLAHRC SystemsSoftwareEngineersStatisticiansEpidemiologistsHealthEconomistsCliniciansPeerReviewersExpert InterfacesUnifiedOpenModelCo-thinkingSeparate ThinkingResearchersPractitionersPolicyMakersModellersHealthcareManagersPublicHealth WorkersOpen to ScrutinyLeading Methodology Local Scenario PlanningNatural Experiments & Hypothesis-generating Patterns of Healthcare       Healthcare Evidence Base Can models be built from literature? “Not fully” 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 DIGITAL & OPERATIONAL INFRASTRUCTURE: E-LAB Framework for Digital Research in Healthcare: I Finance Public Health Clinical Healthcare Remote Data Tomb Research Deposit Use Digital Dust (data deposit > use) Data supply is not the bottleneck Data ↑↑↑ Methods/Models ↑ Contextual expertise ↔ Cloud of millions of care messages in the local health economy Structured Data Organise Transform & Examine Structured Data & Metadata GPGPGPGPHosp.Deaths, Demographics etc.Link on NHS numberTrusted personposes question(s)BiomicsDataDataRepository in PCTReal-timePerson-identifiableand sensitive informationremovedAnonymisedData Repositoryin PCT24-hourlyupdatesF I R E W A L LOutputsOptometristEye screeningCommunitynursesPodiatrySense-making software & support NHS e-Lab: Salford Pilot Federation: More local use  better quality data Health Community 1 e-Lab e-Lab e-Lab Research Networks Research Community development F I R E W A L L Health Community 2 Health Community 3 10 developers Data/Workflow Analysis Scripts Annotations Slides Manuscripts References Research Object Workflows Packages of Outputs Services Reproduce Share Morph Inherit Work/Research Object Encapsulated  (DAG) discovery? e-Lab Socially-stimulating science, in-silico Research Object Find Share Reuse Data-sources Data-preparation scripts Research protocol Statistical analysis scripts Slides Working datasets Figures/Graphics Manuscripts References Analysis-logs & notes Prototype NHS e-Lab Next: Merge with visualisation research ETHICS Digital Curators Promoting Healthcare Innovation: I Ethical Principles • Respect for autonomy  • Beneficence  • Non-maleficence  • Justice Beauchamp and Childress; Principles Biomedical Ethics, OUP, 5th edition 2001 Respect for Autonomy • Patient/subject – Consent • Opt-in • Opt-out – Right to participate or not – Advocates where appropriate • Clinical; carer; guardian; data custodian  • Investigator – Access to patients/citizens?  Beneficence • Duty to deliver good for the data donors  • Under-use is unethical  • Audit the context of use  • Measuring good – Research quality – Clinical utility – Patient/citizen involvement   Linked Health Records Audit; Research; Intelligence Unclear Public Good Clear Public Good Linked Health Records Work Objects De-identify Local Ownership Asset Enrichment e-Lab for a defined community Non-maleficence • Part of NHS & University contracts  • Part of clinical and research information governance protocols  • Not dealt with by restricting access to data Justice • Knowing the uses of your health records • Knowing how your practice is measured • Fair access to data • Fair access to methods • Fair access to models • Intellectual property protection • Fair networking opportunities for investigators Data Curation Example: Obesity e-Lab The e-Science target: Fragmented understanding of public health problems such as obesity  ...data, methods/models and expertise split across disciplines (e.g. Social vs. Biomedical) and settings (e.g. Academia vs. Healthcare)  Fifths of IDAC 2004 Red (light) = most deprived Red (dark) Purple Blue (dark) Blue (light) = most affluent   Child poverty map (households with children: % on benefits in 2001-3) Wirral (0.3M), UK   BMI of 3 yr olds 1988 - 1989 Fifths of BMI SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest Wirral (0.3M), UK   BMI of 3 yr olds 1990 - 1991 Fifths of BMI SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest   BMI of 3 yr olds 1992 - 1993 Fifths of BMI SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest   BMI of 3 yr olds 1994 - 1995 Fifths of BMI SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest   BMI of 3 yr olds 1996 - 1997 Fifths of BMI SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest   BMI of 3 yr olds 1998 - 1999 Fifths of BMI SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest   BMI of 3 yr olds 2000 – 2001 Fifths of BMI SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest   BMI of 3 yr olds 2002 - 2003 Fifths of BMI SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest 1 2 3 4 5 Men Women 25 25.5 26 26.5 27 27.5 BMI Income fifth (low to high) Women and not men from low-income households  are fatter in England Data from Health Survey for England 1 2 3 4 5 Men Women 25 25.5 26 26.5 27 27.5 BMI Income fifth (low to high) Women from low-income households  and men from high-income households are fatter in Greater Manchester Data from Health Survey for England Beware Discipline Clouds Previous slides show social-biomedical signals about obesity from under-used datasets Biomedical Research: Data, methods & people Social Research: Data, methods & people Obesity e-Lab Aim ..to increase the sharing and reuse of  data sources & extracts  and data processing methods  in one in-silico environment (‘e-Lab’)  shared by social and health researchers  Focus • Health Surveys for England – Large-scale (participants * variables) – Annual since early 90s – Under-used by NHS who fund it – Key barrier: extracting a research-ready subset of data – Data archive  playground = e-Lab  Supporting and developing interdisciplinary understanding Sharing resources – tools, methods, data Sharing expertise – discussions and reuse around shared resources Promoting interdisciplinary working Developing interdisciplinary understanding – language, tacit assumptions, methods First step - sharing of resources   Shared resources provide the basis for discussion    Discussions lead to deeper interdisciplinary understanding   Understanding of other domains promotes more effective interdisciplinary working MethodBox users include NHS Public Health analysts and Department of Health Public Health Observatory analysts, as well as academic social scientists and epidemiologists. Download methods and scripts shared by other Methodbox users The owner of the script can create links back to other methods and scripts used in the development of this script. SECURITY Digital Curators Promoting Healthcare Innovation: II Major Issues with Clinical Studies • Bias & generalisability  • >50% over-run  • >30% don’t hit recruitment targets  • Unrealistic feasibility assessment  • Consent-management confusion openCDMS An Open Source System for: • multi-centre remote electronic data collection; • highly configurable security system employing Role Based Access Control; • fully customisable data set definition including data elements, validation rules and scheduling; • fully configurable online randomisation with email and SMS text message notification; • project management reporting including recruitment, completion and UKCRN accrual; • on-line and off-line data collection; • flexible query system for identifying eligible trial participants and nested case-control studies; • designed for compliance with 21 CFR part 11; EMEA GCP; ISO 27001  openCDMS in use • PsyGrid study – cohort of 700+ schizophrenics followed from first episode for 18 months  • Running numerous mental health trials  • ADDRESS – Type I+II diabetes 10y cohort study  • DARE – Diabetes cohort (phenotype and genotype) Investigator-shaped data capture Clean Data ± Link External Data Publish Generate Hypothesis Test Hypothesis Recruit Participants Seek Ethical Approval ± Collect Observations Fuse Data Test Feasibility Design Study Explain Results Governance  (Helsinki & local) Managing clinical studies Making sense of local healthcare + community Electronic Health Record Anonymised Care Record Clinical Care Boundary Analyst Clinician Query Query Object Anonymisation Query Rewriter Consent-for-consent ...is the consent to   search an individual’s health record   to determine whether or not  they should be invited  to take part in a clinical study. FARSITE  Feasibility Assessment  and Recruitment System  for Improving  Trial Efficiency Study Protocol (sponsor) Recruitment Estimate (researcher) [de-identified records] +/- Tweak Refined Search Email to attending clinicians “Click to identify your eligible patients”  [health e-record] Study Management  (identifiable) Study Management  (e-Lab: de-identified) F I R E W A L L Rapid, Consistent Feasibility & Audit Realistic Recruitment Estimates Salford Integrated Record (SIR) Salford e-Lab e-Lab Anonymised Repository Trial Protocol Designer Clinical Care Boundary Trial Recruitment Tool Salford Health Community Clinician Researcher /Manager Bulk ETL with Anonymisation Query Objects SQL SQL HTTPS HTTPS HTTPS HTTPS email TRUST Digital Curators Promoting Healthcare Innovation: III Clinical Research Integrated Health Anonymised e-Lab Clinical Clinical Information Governance Database-centred e-Lab: Community-centred Ethical Oversight Research Governance x Health Communities x Health Agencies x Research Agencies Now Future? Trust & Benefit in Research across Health Records Public Involvement • Patient / Citizen Scientist  • Social network  investigator  • Early & mobile signals beyond clinical reach  • Relevant outcomes SOCIAL CONTRACT Framework for Digital Research in Healthcare: II Digital Curation for the Public’s Health: What is the Social Contract? • UK has much already in place in the laws and governance across NHS, higher education and allied public services  • Law, standards, regulations and some infrastructure work nationally  • Trust and capacity-building to provide the best data and analyses works locally  more attention Open Unifying e-Lab (open models)    Easy computation    Abstract reasoning and motivation of domain experts = More complete insights  More reusable evidence  Better management across diseases  Earlier intervention  Greater citizen involvement 

Digital Curation for the Public's Health: Ethics, Security &amp; Trust

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Digital Curation for the Public's Health: Ethics, Security & Trust

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Digital Curation for the Public's Health: Ethics, Security &amp; Trust

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Digital Curation for the Public's Health: Ethics, Security & Trust