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Quæfacta GCCCF May 22th, 2019

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Quæfacta’s presentation for the GCCCF roundtable held in Paris Healthcare Week, 2019

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Quæfacta GCCCF May 22th, 2019

  1. 1. GCCCF Roundtable, Paris, May 22nd Blockchain in Healthcare May 22nd, 2019 Quæfacta Lea Dias, CEO & Co-founder David Andrianavalontsalama, CPO & Co-founder
  2. 2. Blockchain 1. Decentralised, not owned by a single entity; 2. Data is cryptographically stored inside a block; 3. Immutable, tamper resistance; 4. Transparent, so data can be tracked. !3
  3. 3. Participant A Participant B Participant C Participant … Blockchain !4 …… Data is immutable & transparent Participants can join / leave A distributed consensus protocol is shared by all participants block 1834 block 1835 block 1836 block 1837
  4. 4. Use cases in healthcare   Sharing of information  – between tertiary health care facilities, community healthcare and telemedicine including; pathology and radiology results, discharge information, medicines information;  Clinical trials traceability  – for clinical research and development of medications;  Genomics research – precision, tailored-made medicine for individual genetic makeups; Medication supply chain  – tackling issues such as counterfeit medications, opioid misuse and vaccination distribution;   Interoperability  – IoT, medical devices, robotics, wearable devices, sensors, applications; Partial views  – mandatory reporting for governments, health departments and health institutions. !5
  5. 5. !6 Improve quality of healthcare: 1. Intelligent workflow management 2. Smart data Let’s start with a case study…
  6. 6. !7 Patient
  7. 7. !8 Case study Patient: • Mr J. Doe • 57yo male, morbidly obese, smoker • PMHx: hypertension, hypercholesterolaemia, NIDDM • Presents: Stroke like symptoms, dizziness, confusion,
 weakness in limbs, speech difficulty, facial drooping • Medications: perindopril 4mg om, frusemide 40mg om, pravastatin 20mg on, metformin 500mg bd, gliclazide 80mg od • Risk factors: hypertension, diabetes, smoking, obesity, lack of exercise
  8. 8. !9 1. Examinations 2. Diagnosis 3. Emergency treatment with medications 4. Procedure 5. Treatment team may include: 6. Medications post stroke Ischaemic stroke J. Doe Intravenous injection of tissue plasminogen activator (tPA) Angioplasty and carotid artery stent • Doctor trained in brain conditions (neurologist) • Rehabilitation doctor (physiatrist) • Nurse • Dietitian • Physical therapist • Occupational therapist • Recreational therapist • Speech pathologist • Social worker • Case manager • Psychologist or psychiatrist High blood pressure medication Blood thinners Anti - coagulants Diuretics Cholesterol medication Anti-fibrillation drug Diabetic medication Antidepressants Physical examination Blood tests CT scan MRI scan Carotid ultrasound Cerebral angiogram Echocardiogram
  9. 9. !10 ADMISSION ICU INPATIENT DISCHARGE • Patient brought to ED by ambulance • Patient is non responsive • Admitted to hospital recorded on PAS • Pathology, radiology investigations performed • Call for medical records • Close neurologic and hemodynamic monitoring provided in the ICU to minimize the risk of secondary injury • Monitor ventilation • Commence IV saline and mannitol 20% • Access to pathology and radiology results with ICU systems • Managed care on ward • Rehabilitation begins • Allied health and pharmacy follow up • Discharge summary prepared on inhouse software system • Communication with GP via phone, fax, mail • Follow up outpatient appointment booked manual NO INTEGRATION (NI) MANUAL PROCESSES (M) • GP medical history, medications or allergies (NI) • Paper record of ambulance information (M) • Medical information record (M) • PAS with inpatient system (NI, M) • Allergies recorded on PAS and paper chart (NI, M) • Pathology, radiology systems (NI) • Smart pumps and ICU system (NI) • Medical devices and ICU systems (NI) • ICU and theatres booking system (NI) • ICU and inpatient paper recorded (NI, M) • ICU system and paper record (NI, M) • Pathology and radiology systems (NI) • Allied health information (NI, M) • Medication reconciliation (M) • Medication reconciliation (M) • Pathology, radiology input (M) • Allied health information (NI, M)
  10. 10. Global EMR adoption Current State of Healthcare (1/4) Patient information is siloed Incomplete information Vulnerability & Exposure BCMA (Barcode Medication Administration) EMR Adoption Model In US, 2016, 97% of hospitals unit dosing, 96% CPOE adoption, 94% BCMA and 40% paperless hospitals (200-400 beds) OpenEHR Standards for customisable, flexible, open source platforms facilitating interoperability !11
  11. 11. Patient information is siloed Global EMR adoption Current State of Healthcare (2/4) Incomplete information Vulnerability & Exposure There is fragmentation and gaps in the transfer of information between hospital care and community care Patient Hospital Providers Hospital HealthcareCommunity Healthcare Outpatient clinicsGP Clinic / Community Health Home Health Pharmacy Wearable devices Laboratory Rehabilitation Screening & diagnosis Ambulatory care
  12. 12. Global EMR adoption Current State of Healthcare (3/4) Patient information is siloed Incomplete information Vulnerability & Exposure Medicines information, inpatient records, admission and discharge information are often missing or poorly communicated by health professionals within hospitals and to community health providers. This may lead to: ‣ hospital readmissions; ‣ adverse drugs events; ‣ compromised patient care; ‣ serious or fatal outcomes; ‣ litigation. !13
  13. 13. Global EMR adoption Current State of Healthcare (4/4) Patient information is siloed Incomplete information Vulnerability & Exposure Patients and health providers are left feeling vulnerable and exposed. !14
  14. 14. 1. Observable gaps in the transfer of information 2. Lack of interoperability — Many devices and practitioners interact and do not share the full data 3. Procedures that should be implemented are not, or not followed, or incomplete !15
  15. 15. !16 1. Intelligent workflow management to improve quality of healthcare
  16. 16. !17 Patient Role of blockchain Securely sharing information Interoperability Traceability Accountability Fraud detection Incentives Data privacy Analytics & AI Digital Identity Matching
  17. 17. !18 Digital Identity Matching Patient “Matching the correct individual to his or her health data is critical to their medical care.” “Statistics show that up to one in five patient records are not accurately matched even within the same health care system. As many as half of the patient records are mismatched when data is transferred between healthcare systems.” — Shaun Grannis, Director of Center for Biomedical Informatics (CBMI)
  18. 18. Multi-vendor + smart contracts Vendor A Vendor B Vendor C Auditing system hash data data data hash hash The data and results are accurate certification! command + hash data Anchoring system using blockchain + smart contracts !19 Interoperability
  19. 19. Patient Hospital Providers Hospital HealthcareCommunity Healthcare Outpatient clinicsGP Clinic / Community Health Home Health Pharmacy Wearable devices Laboratory Rehabilitation Screening & diagnosis Ambulatory care !20 Securely sharing information
  20. 20. !21 Pharma Pharma Pharma / Med device Product Development Innovation Active Pharmaceutical Ingredient Manufacturing Secondary Manufacturing ERP ERP ERP Logistics Logistics Distribution Supplier ERP ERP Logistics Pharmacist Customer Wholesaler Reseller (Pharmacist dispenses Rx
 2D scan - WF1) Patient ERP ERP PIS Smart device Direct to patient Retail Pharmacy (Pharmacist dispenses Rx
 2D scan - WF1) Hospital Pharmacy (Pharmacist dispenses Rx
 2D scan - WF1) Supply chain logistics workflowTraceability
  21. 21. !22 TR TR TR TR TR TR TR TR TR TR TR TR Acquiring traces
  22. 22. !23 Traceability content: Who? What? Where? When? Why? Traceability actor: Any known user + key Acquisition tools Anchoring: Any known blockchain Metrics: How many traces per device? How often? How long? What a trace holds
  23. 23. !24 How we acquire a trace Tool suite: • API • Mobile & desktop apps • Dashboards
  24. 24. !25 TR TR TR TR-1 TR-2 TR-3
  25. 25. !26 TR TR TR TR-1 TR-2 TR-3 TR TR-4smart contract
  26. 26. !27 TR TR-XXX = 2000 lines
  27. 27. !28 TR TR-XXX = 2000 lines smart contract TR TR-XXX-1 TR TR-XXX-2 TR TR-XXX-2000 TR TR-XXX-3 …
  28. 28. !29 Chain reconstitution Whole chain Partial view #1 etc.data analysis
  29. 29. !30 Chain anchoring Whole chain Partial view #1 etc. + supervisor validation + supervisor validation smart contract smart contract TR TR-x TR TR-y
  30. 30. !31 Trace composition Supply chain model Diversity of actors Incremental level of trust What a chain holds
  31. 31. Data
 Lake !32 TR TR TR BLOCKCHAINS Analysis TR-SPEC TR-SPEC Trace reports Traceability chains Traces validated data Data retrieval Analytics dashboard BUSINESS RULES event anchoring
  32. 32. + 1 level
 of trust Data
 Lake !33 TR BLOCKCHAINS Analysis + supervisor validation BUSINESS RULES TR TR-SPEC TR-SPEC
  33. 33. !34 Analytics & AI Weighting the trust Feedback loops Pattern matching from theoretical chains Statistical inference from actual chains
  34. 34. !35 2. Smart data to improve quality of healthcare
  35. 35. !36 The 4 P’s of Personalised healthcare Identification of individual risks of developing certain diseases based on the person’s genetic profile and other personal information Predictive Methods and treatments to avoid, reduce and monitor the risk of developing certain diseases Preventive Clinical interventions based on the unique genetic, medical and environmental characteristics of each patient-citizen, and genomic profile of his/her diseases Personalised Citizens are fully engaged in personal health management Participatory
  36. 36. !37 Case study Patient: • Mr J. Doe • 57yo male, morbidly obese, smoker • PMHx: hypertension, hypercholesterolaemia, NIDDM • Presents: Stroke like symptoms, dizziness, confusion,
 weakness in limbs, speech difficulty, facial drooping • Medications: perindopril 4mg om, frusemide 40mg om, pravastatin 20mg on, metformin 500mg bd, gliclazide 80mg od • Risk factors: hypertension, diabetes, smoking, obesity, lack of exercise
  37. 37. !38 Analytics & AI Google AI team: • Analyse retinal images, extract personal health risks, and make predictions based on the knowledge received. • Identifying risk factors critical for CV and stroke, • body mass index (BMI) • hemoglobin A1c (HbA1c) • systolic and diastolic blood pressure • smoking status.  Smart data to diagnose ischaemic stroke? Researchers reported their algorithms succeeded in predicting the chances of particular patients developing stroke or heart attack in a five-year period with a 70 percent accuracy.
  38. 38. !39 Analytics & AI FDA Approved, Viz.AI Contact 2018 AI Algorithm Clinical decision support for triage Analyse CT scans and detect stroke signs in medical images Detects slightest deviations on CT and MRI scans ML algorithms can distinguish ischaemic from haemorrhagic stroke System suspects stroke, alerts neurovascular specialist via smartphone Specialist’s attention refocused to the acute cases Radiologist proceeds with review of less urgent scans AI-enabled process optimization ensures timely care for patients 
  39. 39. !40 Viz.AI Contact 2018
  40. 40. !41 Analytics & AI • Support health specialists and provide actionable insights to accelerate diagnosis. • Ensure accurate medication and intervention decisions in the shortest possible time. • Reduce the risk of developing conditions, elicit subtle warning patterns and alert clinicians to upcoming crisis. Artificial Intelligence
  41. 41. !42 Incentives • Insurance companies may incentivise patient’s (data) for good behaviour via a reward mechanism.
 e.g. tokens for following a care plan or staying healthy. • Pharma companies/medical institutions may incentivise patients who provide data for research and clinical trials.
  42. 42. !43 4,567 steps 2 coins cash in
  43. 43. !44 Fraud detection Pharma companies • Detection of counterfeit medications. Governments/healthcare • Detection of opioid/medication misuse, abuse and theft; • Detection of inappropriate use of medications (including high cost medication). Insurance companies • false claims/information by patients and providers to receive payable benefits.
  44. 44. !45 “Blockchain is not meant for storage of large data sets. Blockchain is not an analytics platform. Blockchain has very slow transactional performance. However, as a tamperproof public ledger, blockchain is ideal for proof of work. Blockchain is highly resilient”. — John Halamka, CIO of Beth Israel Deaconess Medical Center in Boston
  45. 45. Quæfacta !46 We do: • Blockchain traceability solutions in healthcare • AI, data acquisition and analytics https://quaefacta.com contact@quaefacta.com Thank you! May 22nd, 2019

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