Blockchain tech in veterinary medicine

A case study on digital data

Steve Joslyn (pictured) graduated from Murdoch University and completed a residency in veterinary diagnostic imaging at the University of Glasgow. While on faculty at the University of Illinois he developed the clinical 3D printing service to assist with surgical planning in orthopaedics, ophthalmology and oncologic surgery. He was also involved with image processing techniques in pulmonary imaging with automated disease detection algorithms. He consults with referral and general practice hospitals on practice design, focusing on the imaging services and work-flow efficiencies. Steve started a new company, VetDB, which combines his passion for veterinary medicine and informatics, with new technologies such as blockchain and machine learning.

There seems to be increasing publicity of new and emerging technologies that have implications or benefits for veterinary medicine. We see them often listed together, and always with some success or proof-of-concept that was tested in human healthcare. Without attempting to group these together, they are often used in the same headline.

You could probably guess what I’m about to list: Virtual Reality (VR), Big Data, Machine Learning, Internet of Things (IoT), Wearables and 3D printing to name the usual suspects. However, there is a lesser understood cousin of these technologies that is sometimes listed, mentioned, referenced to human healthcare, and then perhaps forgotten.


It comes with many names, which may be part of the problem. Blockchain, Distributed-ledger-technology (DLT), and, of course, Bitcoin to name a few terms. Personally, I have been directly involved in veterinary technologies such as computer aided diagnoses, image processing, diagnostic coding, Big Data, 3D printing, machine learning, etc., but nothing excites me more than what is coming with Blockchain technology.

Blockchain has solved a big problem across many industries. However, the problem is so deep rooted in our everyday systems that we have simply come to accept it as normal. And then we ignore it.

The problem is trust.

Trust, or the lack thereof, is the mechanism behind middle men, central authorities, notaries, payment processors etc. We lack trust because there is so little transparency on everyday transactions and interactions. To mitigate this our financial services collect trillions of dollars’ worth in fees to provide a level of trust. In healthcare we close doors, lock our files, and make it very hard to collaborate. In a digital world, where everything becomes zeros and ones, amongst malicious actors behind firewalls and dark networks, dealing with valuable digital data is prone to copying, falsifying and corrupting; the trust issue has gotten a lot worse.

Bitcoin, the first implementation of Blockchain technology, solved the problem by removing trust, completely. “Trust-less” is the new term. Trust is no longer a factor. Bitcoin did this, through the use of cryptography and some other complex computational maths, and in turn developed a system of global truth. Distributed truth.

In the basic form, Bitcoin, and other blockchains, offer a transparent record for every user. Bitcoin, in the most fundamental action, is a ledger of who paid whom, how much and when. Starting from time zero, it knows the balances of each and every participant. “It” being the collective network. Each participant of the network has a copy of the entire history of bitcoin transactions. And every 10 minutes, their copy gets updated collectively. The important bit is that everyone can verify for themselves that they have the same up-to-date copy. Overall, this distributed truth becomes distributed consensus.

The same distributed truth is referenced at all times. It is therefore impossible, without tricking the entire network, to falsely claim you have an extra 100 bitcoin. Everyone can check their copy and see that is incorrect. It would be easier, albeit impossible, to go back in time, than it is to trick this distributed truth model.

Despite the complex maths behind blockchain technology, what I think is magical is that everyone, with absolute mathematical certainty, can verify that they have the same copy as everyone else. And if you pay Bob 1 bitcoin, for example, everyone can see that your address sent that address 1 bitcoin.

So, what the hell does this have to do with human or veterinary healthcare?

Well. We also have trust issues. Especially with respect to patient data. The integrity of data. The authorised access to data, and whether or not medical data has been changed. This applies to collaborative research, clinical medicine, patient/results pairing, patient confidentiality, and insurance claims. Data is valuable, it’s sacrosanct and it’s currently corruptible, untrustworthy, and inaccessible.

Currently we can’t trust that patient records are verifiably linked to the patient. Animals move between veterinary institutions and we have limited access to the complete medical history. And if we do, it may be PDF print-outs of clinical histories.

Blockchain technology provides us with a virtually free tool to solve the trust problem. Even without revealing the entire medical record for all to see.

Through complex mathematical functions or algorithms (eg SHA256, Merkel root signatures, public and private keys), we can anchor any piece of digital data to the blockchain – using just its signature. By doing so we time-stamp the existence of this data forever in the distributed ledger. We can reference back to it at any time, proving the integrity of whatever information we have, matching what was committed and proving it hasn’t changed. We can provide independent parties to check data authenticity themselves, without having to trust any one party. The signature itself does not allow you to reconstruct the original data or medical record and only those authorised to view records have access to it. But an impartial third party could easily attest that both untrusted stakeholders are using the same data, without seeing it themselves!

If we can prove the authenticity of clinical data at the start, we can prove the same state at the other end. We can control who has access to it, ensure it hasn’t been changed, ensure it hasn’t been falsified, and begin massive collaborative initiatives using verifiable data of which we can prove the purity with each stakeholder.

Blockchain technology in veterinary and human medicine solves confidentiality issues, but it also allows doctors, researchers, and health officials to trust that the medical data represents the correct patient, and that no one has altered it. Researchers can collect verifiable data in realtime without knowing the exact dog or owner – accessing just the relevant data. Clinicians can have immediate access to clinical notes across the animal’s entire journey from hospital to hospital.

Some blockchain examples which VetDB has developed include the ability for researchers to have instant real-time data on vaccinated populations (exact constituent strains, animal signalments, sites of injections, adverse reactions, etc.). They could see this live and verify the information themselves. However, none of the owner’s or vet’s information would be viewable, so nothing confidential is available.

For infectious disease outbreaks, local government could assess de-identified geospatial densities of the vaccinated population without revealing confidential information. This would help guide any eradication program for an efficient and targeted vaccination response.

Veterinary vaccine manufactures or the Veterinary Medicines Directorate  could issue notifications directly to the affected owners and clinics which have been implicated in a faulty batch recall.

Garbage in. Garbage out.

Blockchain doesn’t clean up data, or suddenly prove its authenticity. It helps various stakeholders ensure they are using the same data, but that doesn’t mean that data is correct, or even complete.

Large data collection initiatives like VetCompass spend a massive number of man-hours and resources cleaning up clinical data.1–5 New technologies like natural language processing (NLP) continue to have problems deciphering what one vet writes versus another, through various syntax differences, human grammatical errors or colloquialisms.3,5,6 Furthermore, a recent study found that over 35% of clinical problems discussed in GP consultations were omitted from the medical records.7 Surprisingly, this study also found that more than 40% of observed actions taken during consultation were omitted completely.

To use Blockchain technology effectively we need to be improving, automating and supporting robust clinical recording technologies. Remove the points of human data entry errors and make it easy for vets to work up their cases. At VetDB we are starting on this path by assisting veterinarians to collect reliable and verifiable data, and then securing it with blockchain technology. We have created a global digital vaccination certificate that links permanently to the animal’s microchip. Wherever that animal goes, a simple scan of the microchip will allow the attending vet instant access to the vaccination history. There are no physical cards or paper passports to lose, and it is impossible to falsify. Our tool scans the vaccine vial and automatically pulls the pertinent information meaning no writing or manual recording. We therefore save time for other aspects of the preventative health consultation.

As we build applications that produce verifiable clinical data, secured with Blockchain technology, we hope to start a new trend of high quality prospective clinical data collection. Open to immediate collaboration, powering big data initiatives, internet of things and other veterinary technologies. All of this to support the original primary care veterinary surgeon and maintaining privacy for them and the owner.


  1. Dórea FC, Sanchez J, Revie CW. Veterinary syndromic surveillance: Current initiatives and potential for development. Preventive Veterinary Medicine 2011;101:1–17.
  2. O’Neill DG, Church DB, McGreevy PD et al. Approaches to canine health surveillance. Canine genetics and epidemiology 2014;1:2.
  3. VanderWaal K, Morrison RB, Neuhauser C et al. Translating Big Data into Smart Data for Veterinary Epidemiology. Frontiers in Veterinary Science 2017;4.
  4. McGreevy P, Thomson P, Dhand N et al. VetCompass Australia: A National Big Data Collection System for Veterinary Science. Animals 2017;7:74.
  5. McCue ME, McCoy AM. The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges. Frontiers in Veterinary Science 2017;4.
  6. Baldwin T. Vet- Compass: Clinical Natural Language Processing for Animal Health. 2016.
  7. Jones-Diette J, Robinson NJ, Cobb M et al. Accuracy of the electronic patient record in a first opinion veterinary practice. Preventive Veterinary Medicine 2017;148:121–126.
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