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GCCCF Roundtable, Paris, May 22nd
Blockchain in Healthcare
May 22nd, 2019
Quæfacta
Lea Dias, CEO & Co-founder

David Andrianavalontsalama, CPO & Co-founder
Quæfacta GCCCF May 22th, 2019
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
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
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
!6
Improve quality of healthcare:

1. Intelligent workflow management

2. Smart data

Let’s start with a case study…
!7
Patient
!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
!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
!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)
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
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
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
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
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
!16
1. Intelligent workflow management to improve quality of healthcare
!17
Patient
Role of blockchain
Securely sharing
information
Interoperability
Traceability
Accountability
Fraud detection
Incentives
Data privacy
Analytics & AI
Digital Identity
Matching
!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)
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
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
!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
!22
TR TR TR
TR
TR
TR
TR
TR
TR
TR
TR
TR
Acquiring traces
!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
!24
How we acquire a trace
Tool suite:

• API

• Mobile & desktop apps

• Dashboards
!25
TR TR TR
TR-1 TR-2 TR-3
!26
TR TR TR
TR-1 TR-2 TR-3
TR
TR-4smart contract
!27
TR
TR-XXX = 2000 lines
!28
TR
TR-XXX = 2000 lines
smart contract
TR TR-XXX-1
TR TR-XXX-2
TR TR-XXX-2000
TR TR-XXX-3
…
!29
Chain reconstitution
Whole chain
Partial view #1
etc.data

analysis
!30
Chain anchoring
Whole chain
Partial view #1
etc.
+ supervisor
validation
+ supervisor
validation
smart

contract
smart

contract
TR
TR-x
TR
TR-y
!31
Trace composition

Supply chain model

Diversity of actors

Incremental level of trust

What a chain holds
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
+ 1 level

of trust
Data

Lake
!33
TR
BLOCKCHAINS Analysis
+ supervisor
validation
BUSINESS RULES
TR
TR-SPEC TR-SPEC
!34
Analytics & AI
Weighting the trust

Feedback loops

Pattern matching from theoretical chains

Statistical inference from actual chains
!35
2. Smart data to improve quality of healthcare
!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
!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
!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.
!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 
!40
Viz.AI Contact 2018
!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
!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.
!43
4,567 steps
2 coins
cash in
!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.
!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
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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Quæfacta GCCCF May 22th, 2019

  • 1. GCCCF Roundtable, Paris, May 22nd Blockchain in Healthcare May 22nd, 2019 Quæfacta Lea Dias, CEO & Co-founder David Andrianavalontsalama, CPO & Co-founder
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. !6 Improve quality of healthcare: 1. Intelligent workflow management 2. Smart data Let’s start with a case study…
  • 8. !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
  • 9. !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
  • 10. !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)
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. 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
  • 15. 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
  • 16. !16 1. Intelligent workflow management to improve quality of healthcare
  • 17. !17 Patient Role of blockchain Securely sharing information Interoperability Traceability Accountability Fraud detection Incentives Data privacy Analytics & AI Digital Identity Matching
  • 18. !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)
  • 19. 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
  • 20. 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
  • 21. !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
  • 23. !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
  • 24. !24 How we acquire a trace Tool suite: • API • Mobile & desktop apps • Dashboards
  • 25. !25 TR TR TR TR-1 TR-2 TR-3
  • 26. !26 TR TR TR TR-1 TR-2 TR-3 TR TR-4smart contract
  • 28. !28 TR TR-XXX = 2000 lines smart contract TR TR-XXX-1 TR TR-XXX-2 TR TR-XXX-2000 TR TR-XXX-3 …
  • 29. !29 Chain reconstitution Whole chain Partial view #1 etc.data analysis
  • 30. !30 Chain anchoring Whole chain Partial view #1 etc. + supervisor validation + supervisor validation smart contract smart contract TR TR-x TR TR-y
  • 31. !31 Trace composition Supply chain model Diversity of actors Incremental level of trust What a chain holds
  • 32. 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
  • 33. + 1 level
 of trust Data
 Lake !33 TR BLOCKCHAINS Analysis + supervisor validation BUSINESS RULES TR TR-SPEC TR-SPEC
  • 34. !34 Analytics & AI Weighting the trust Feedback loops Pattern matching from theoretical chains Statistical inference from actual chains
  • 35. !35 2. Smart data to improve quality of healthcare
  • 36. !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
  • 37. !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
  • 38. !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.
  • 39. !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 
  • 41. !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
  • 42. !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.
  • 44. !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.
  • 45. !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
  • 46. 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