Making health data work for Patients and Populations
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The document discusses the historical and contemporary use of health data in improving patient care and population health, highlighting key events such as epidemics and the evolution of public health statistics. It emphasizes the importance of data sharing, analytics, and patient engagement in creating effective healthcare interventions and enhancing decision-making processes. The content illustrates the transition from traditional health data methodologies to modern computational approaches and the challenges faced in implementing evidence-based practices in primary care.
![• Who self-weighs?
• UK Withings smart-scale users vs. Health Survey for England (2011)
Ubiquitous (Almost) Technology
Difference in Mean BMI
Fatter men use smart-scalesSlimmer women use smart-scales
-2 1 0 1 2
Men point-estimate = 1.26 [95% CI: 0.84,1.69]Women point-estimate = -1.62 [95% CI: -2.22,-1.03]
Sperrin M et al. Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between
Smart Scale Users and the General Population in England. J Med Internet Res. 2016;18(1):e17.](https://image.slidesharecdn.com/buchankocmar17-170320105834/85/Making-health-data-work-for-Patients-and-Populations-26-320.jpg)
Making health data work for Patients and Populations
- 1. Making Health Data Work for Patients and Populations Iain Buchan Farr Institute @ HeRC & University of Manchester www.herc.ac.uk Koç University Hospital, 20th March 2017 @profbuchan #DataSavesLives
- 2. Ancient, yet Relevant, Public Health Statistics Plague Of Justinian (541–542) • 40% Constantinople dead • 25-50 m dead globally over 200y Black Death (1338/1346–1353) • China to Constantinople by 1347 • 30-60% Europe’s population dead Wagner DM et al. Yersinia pestis and the plague of Justinian 541-543 AD: a genomic analysis. Lancet Infect Dis. 2014;14(4):319-26.
- 3. London 1600s: Plague; Fire; Data; Society Parish (deaths) Number of hearths (fireplaces) as a proxy for house size and over-crowding 1 2 3 4 5 6 7 8+ St James Clerkenwell (44) St Botolph without Aldgate (41) St Dunstan in the West (49) St Michael Queenhithe (20) St Saviour Southwark (42) 31.7 39.8 31.2 26.5 15.5 10.7 35.1 24.3 14.2 30.0 20.8 19.3 25.2 33.1 11.7 7.5 24.3 12.9 31.8 33.5 24.7 16.7 22.3 24.4 12.6 7.3 16.2 20.0 28.2 20.1 9.7 7.3 8.3 5.1 20.2 14.3 5.4 7.1 12.4 7.5 2.6 3.9 4.7 3.8 8.6 11.9 5.4 15.0 6.9 3.8 3.2 2.7 4.7 4.1 5.0 10.5 2.7 10.0 4.3 2.9 1.2 1.9 1.5 1.5 4.7 8.7 2.7 2.1 0.7 0.9 6.2 8.4 2.2 1.7 21.7 29.1 8.1 8.5 1.5 1.3 From: Epidemic Disease in London, ed. J.A.I. Champion (Centre for Metropolitan History Working Papers Series, No.1, 1993): pp. 35-52 …any man's death diminishes me, because I am involved in mankind, and therefore never send to know for whom the bells tolls; it tolls for thee. Epidemiology and politics preceding the Great Plague of London (1665; 25% population die) followed by the Great Fire of 1666 then social and structural reform. John Donne, London 1624
- 4. Health Data Computation: 1841 Farr (1807-1883) HPC for life tables public health reform 1700s: Bernoulli & DeMoivre introduce probability theory to quantifying (health) risks Early 1800s: Laplace then Louis apply probability theory to showing some treatments to be ineffective – rebuked by medical profession – Quetelet’s concept of ‘the average man’ adds fuel to the fire Letting the data speak computationally… Babbage (1791-1871)
- 5. Evidence Based Care Mid-late 1800s: Lister uses statistical arguments and Pasteur’s germ theory to revolutionise surgery with carbolic spray Early 1900s: Statistical Movement, strong in Agriculture and emerging in Medicine Mid 1900s: Experimental (statistical) discipline into Medicine and NHS founded (1948) 1970-80s onward: Disciplined implementation of evidence into practice
- 6. NHS: Learning System Legacy and Duty 30 years of GPs coding in routine primary care Needs-based Constitution NHS Computability: 1970s onward • Administrative consistency: Körner to ICD to HES to QOF • Clinical utility: GP home-grown IT to patient apps • Research integration: VAMP to CPRD & registries to Farr
- 7. 1988 AAH MEDITEL advertisement courtesy of Tim Benson 30 Years of Structured Primary Care Data Schulz EB, Price C, Brown PJ. Symbolic anatomic knowledge representation in the Read Codes version 3: structure and application. J Am Med Inform Assoc. 1997 Jan-Feb;4(1):38-48. UK: 30 years of GPs coding in routine primary care
- 8. Healthcare Data Analytic Partnerships MISSED OPPORTUNITIES DETECTOR Find patients relevant to care pathway Exclude if target inappropriate e.g. A&E asthma terminal illness Exclude if target achieved Follow-up < 48h Identify how care could be improved Rx & social review Integrated Care Record BLIZZARD OF DATABASES (Salford: 53 GP offices + 1 Hospital) Salford Resident Population Care Quality Management Patients’ Decisions ACTIONABLE INFORMATION Actionable information attracts: trust & traction from patients, public and practitioners… and better data quality. Brown B et al. Missed opportunities mapping: computable healthcare quality improvement. Stud Health Technol Inform. 2013;192:387-91.
- 9. Theoretical Framework Perception Acceptance Desire Action Clinical performance Intention Data analysis Message Data collection Interaction Organisational Individual patient Verification Unintended outcomes Task Action Audit Message Patient Co- interventions Recipients Organisation USABILITY/DESIGN TEAM DELIVERY ALGORITHM ACCURACY DATA CREDIBILITY ACTION PLANNING NHS: a decade of dashboards Business intelligence tools Provider management led No theoretical framework
- 10. Connecting Population Analytics with Care • Audit & Feedback Theory • Eye Tracking Experiments • Field Trials in Salford, UK • Patients Asking for Safety Alerts • Now Targeting Antimicrobial Resistance
- 11. Instrumenting Alternative Views Fraccaro, P et al. "Patients’ online access and interpretation of laboratory test results: a human computer interaction study”. Digital Health and Care Congress 2016: www.kingsfund.org.uk/events/digital-health-and-care-congress-2016 In search of a better conversation of healthcare over shared records, brokered by informatics that is context-aware.
- 12. Reusable Health Analytics: Trials & Audits National Proteomics Centre: Stoller Biomarker Discovery Clinical Audits and Service Planning for the local population
- 13. Evidence Translation Singularity Evidence Practice Evidence Practice Implementation Evidence (Co-)Practice System Asynchronous Healthcare Information Synchronous Translation More Specialisation More Underpinning Biology Precision Medicine? Ironing out Variation in Care Real-world Evidence Managed Self-care Translation
- 14. Problem: Big Data & Blunt Evidence Mental health team… Primary care team… Zak… 47y; asthma since early childhood; schizophrenia since teenage; overweight; smoker Weight Respiratory team… Evidence needed is the union not sum of models ~ Mood Primary Care Respiratory Medicine Psychiatry Smoking cessation; social support; weight control; work Inhaled steroid adherence Antipsychotic medication adherence
- 15. Over-implementing Blunt Evidence Current evidence-base predicts < 30% healthcare outcomes: so why try to “iron out variation”? Primary Care Respiratory Medicine Better healthcare needs information on how care works across diseases, providers and daily-life contexts Psychiatry Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann. Fam. 2009;7:357–363.
- 16. Precision Medicine Horizons: Asthma(s) • Life-course complexity indicates multiple (sub-)diseases • Usually starts young • May progress, remit or relapse over life • Inconsistent gene-environment interactions indicates multiple (sub-)diseases • Variable effects of genetic polymorphisms, e.g. CD14 • Variable treatment-setting interactions C allele associated T allele associated No association CD14 Endotoxin Receptor Simpson A et al. Endotoxin exposure, CD14, and allergic disease: an interaction between genes and the environment. Am J Respir Crit Care Med. 2006;174(4):386-92. 50-60% heritability in twin studies but < 2% phenotype explained by current genomics
- 17. Seeking Sub-disease Patterns in Data Mite Cat Dog Pollen Egg Milk Mold Peanut Sensitized Age 1 Sensitized Age 3 Sensitized Age 5 Sensitized Age 8 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 Sensitization Groupswitch group P(Sens’n) in year 1 P(Gain) P (Loose) Sens’n 3 intervals P(+ skin) Sens’ P(+ skin) Not Sens’ P(+ blood) Sens’ P(+ blood) Not Sens’ Sens’n state 1,053 Children 8 Allergens Machine-learning software & partial statistical models ATOPY (allergic tendency) Crude clinical label not explained by genomic studies
- 18. New Risk Factor for Asthma Discovered Allergic sensitisation patterns ‘learned’ from data Simpson A, Tan VY, Winn J, Svensén M, Bishop CM, Heckerman DE, Buchan I, Custovic A. Beyond atopy: multiple patterns of sensitization in relation to asthma in a birth cohort study. Am J Respir Crit Care Med. 2010;181(11):1200-6.
- 19. Multiplying Analytic Capacity via eLab MAAS SEATON ASHFORD ALSPAC IOW Modelling Data & Harmonized Metadata from Cohorts Data Extracts Networking: Ideas, Activities, Results, Meanings MRC STELAR and NIH CREW Consortia: www.asthmaelab.org New US New Au. Custovic A et al. The Study Team for Early Life Asthma Research (STELAR) consortium 'Asthma e-lab': team science bringing data, methods and investigators together. Thorax. 2015;70(8):799-801.
- 20. • Progression of allergy Eczema → Asthma → Rhinitis • Inferred from population summary → • Assumed causal link between eczema – asthma & rhinitis • Clinical response: target children with eczema to reduce progression to asthma Received Wisdom: Atopic March Spergel & Paller, 2003 World Allergy Organization, 2014
- 21. Model-based Machine Learning Probability Eczema Age 8 Children (n=9801) Probability Eczema Age 5 Probability Eczema Age 3 Probability Eczema Age 1 Probability Eczema Age 11 Probability Wheeze Age 8 Probability Wheeze Age 5 Probability Wheeze Age 3 Probability Wheeze Age 1 Probability Wheeze Age 11 Probability Rhinitis Age 8 Probability Rhinitis Age 5 Probability Rhinitis Age 3 Probability Rhinitis Age 1 Probability Rhinitis Age 11 Eczema Class Wheeze Class Rhinitis Class Latent Class Disease Profile Start with a well-reasoned (partial) model, not a ‘bucket of data’
- 22. Ecologic Fallacy Revealed Belgrave et al. Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies. PloS Medicine 2014;21;11(10):e1001748. MRC STELAR consortium working at scale across MAAS and ALSPACS cohorts Better: population analytics; targets for ‘omic research
- 23. Biology-Behaviour-Environment Interaction Life course Developmental genetics Disease risk environment Treatment environment ‘Persistent’ genetics Data Measurement error Mechanism knowledge Missingness ASTHMA genes * environments endotypes Transient early wheeze Late-onset wheeze Persistent troublesome wheeze Persistent controlled wheeze
- 24. My Health Data Ecosystem My Health, My Data: Where are the most predictive data?
- 25. Rhythms of Life, Health, Disease and Care Low-cost ubiquitous technologies capturing digital by-products of the life High-cost medical devices (regulated clinical algorithms) Clinicvisit8 Clinicvisit9 Patterns of disease invisible to infrequent clinical observation Precision medicine may need data on (sub)disease rhythms to realise its potential Future? My ‘health avatar’ says no to your care pathway n-of-1 trials Average patient guidelines
- 26. • Who self-weighs? • UK Withings smart-scale users vs. Health Survey for England (2011) Ubiquitous (Almost) Technology Difference in Mean BMI Fatter men use smart-scalesSlimmer women use smart-scales -2 1 0 1 2 Men point-estimate = 1.26 [95% CI: 0.84,1.69]Women point-estimate = -1.62 [95% CI: -2.22,-1.03] Sperrin M et al. Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between Smart Scale Users and the General Population in England. J Med Internet Res. 2016;18(1):e17.
- 27. • What came first, weighing or weight-loss? Complex Frequent Observation/Intervention Engagement Weight Loss • An additional monthly weighing is associated with an extra 1kg weight lost over the course of a year • Recent weight loss encourages subsequent measurement: a person who has recently lost 1kg is twice as likely to reweigh on a given day compared with someone who has remained the same weight Sperrin M et al. Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between Smart Scale Users and the General Population in England. J Med Internet Res. 2016;18(1):e17.
- 28. Rethink Experimental Designs Dwyer T et al. Objectively Measured Daily Steps and Subsequent Long Term All-Cause Mortality: The Tasped Prospective Cohort Study. PLoS One. 2015;10(11):e0141274.
- 29. Co-produced Observations & Outcomes Aim: To Reduce Relapse in Schizophrenia via Smartphone Drug + behaviour (information * psychological endotype) = outcome From J. Ainsworth & S. Lewis Informatics enabled observation Informatics intervention www.clintouch.com Generic: • Self-measurement • Symptom awareness • Clinical workflow integration • Self-efficacy / autonomy • Alert-fatigue avoidance
- 30. Civic AND Clinical Analytics Smartphone GPS data infer social functioning in patients with schizophrenia From Difrancesco et al. Out-of-home activity recognition from GPS data in schizophrenic patients. IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS 2016).Sport Swim ming pool Volley ball 1. Raw GPS data 2. Detection of geolocation visited 3. Geolocations visited 4. Identification of places visited 5. Places visited 6. Type of places and activities recognition 7. Out-of-home activities
- 31. Health & Care Context: Place Typical health & care data landscape: Greater Manchester • £6 billion annual care budget • >7000 care provider databases • Local bye-law “duty to share” equal to “duty to protect” • 2.7M people with low life expectancy and high inequalities
- 32. Data Sharing: Diameter of Trust Actionable information for health system optimisation National/Large Population Audits/Registers/Monitoring NOT SCALABLE Excellence provider benchmarking e.g. strokeaudit.org but no learning across disease areas and not integrated with clinical workflows SCALABLE Payer evidence, quality management, public health intelligence and research share data, infrastructure and expertise Large enough for economy of scale Small enough for a conversation with the citizenry about data sharing www.herc.ac.uk/get-involved/citizens-jury/
- 33. Trust in Predictive Analytics Academia rewards publishing papers on the same topic every 10y or so Law sees algorithms as medical devices (EU Directive 2007/47) Industry has no trusted 3rd party lab for validating algorithms/models EuroScore prediction Calibration drift: Typical of many published models Observed death rate Hickey GL et al. Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models. Eur J Cardiothorac Surg. 2013 Jun;43(6):1146-52.
- 34. Civic Health Data Analytics Data Public sector encounters Services Targeted by need Targeting Tools Ark Involved Citizens Problem Owners Data Managers Public Health Analysts Care Service Analysts Statisticians Informaticians Social Scientists Health Economists Health Service Researchers Communications Experts Service Planning and PolicyInsights SME Global Corp. Which services and how? Spin-in/out Laboratory Farr Institute & NIHR Centres Connected Health Cities Pilots 2016-9 North England twitter.com/hashtag/datasaveslives Ainsworth J, Buchan I. Combining Health Data Uses to Ignite Health System Learning. Methods Inf Med. 2015 Nov 27;54(6):479-87. www.connectedhealthcities.org Time Data Production Analysis Data ProductionData-Action Latency Insight Action Data Preparation
- 35. Stroke Pathway Learning 1 3 2 Better decision support tools for paramedics: Recognise ‘stroke mimics’ Faster, more accurate triage and improve access to neurosurgery when needed Enhanced workflows e.g. medication vs. BP reviews to prevent another stroke
- 36. • Smartwatch detects atrial fibrillation over a week, otherwise missed by a GP in a 10 minute consultation: then supports anticoagulant medication. • Virtual rehab assistant (voice/AI appliance) and smart electricity meter data alert rehab team to a change in daily living patterns. • Subsidised public transport after cardiovascular risk screening makes it cheaper to walk/tram than take the car to work: increased physical activity sustained where exercise prescriptions fail. Stroke Prevention: Civic Extensions
- 37. Borrowing Insights De-identified Records Identified Records Study Protocol /Assessment Study Recruit Clinician Researcher Commons of Metadata and Information Governance (Clinical & Research) Clinical Care Patient Research Safe Haven Encrypted (SHA1 & AES256); Certified (ISO 27001) System 1 System 3 System 2 System 4 Linkable Data Providers Analytic Objects RAPID REPLICATION • Study/audit protocol • Codes for the data • Statistical scripts • Results in progress • Report • Slides etc. Bechofer S, Buchan I et al. Why linked data is not enough for scientists. Future Generation Computer Systems 2013;29(2):599–611. Ainsworth J, Buchan I. e-Labs and Work Objects: Towards Digital Health Economies. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer, Berlin Heidelberg 2009;16:206-216. Routine Randomisation
- 38. “Learning Healthcare Systems” are an illusion if restricted to provider organisations. Health(care) can’t be optimised outside the civic context of health. Civic Imperative @profbuchan #DataSavesLives
- 39. • Grasp patient-led healthcare records/apps for self-care and clinical workflow improvement: don’t focus on legacy clinical IT. • Allow cities/regions to self-organise around healthcare workflow optimisation, and join the global Connected Health Cities: prepare for streams of place-based data that affect health and care. • Exploit the diversity of biology-behaviour-environment interactions as a global science and technology innovation asset as Turkish digital health data start to join up. Thoughts for Turkey’s Digital Health