 Hi, welcome everyone. My name is Elif Tina and I would like to in this presentation I would like to share with you our work in progress In the research in the framework of the research project that aims To view the blockchain based decision support and longitudinal data storage with illustration for diabetes type 2 management So this work is done in collaboration with Rick Hu, Luca Matzola, Christian Reynolds and Peter Novotny So evidence-based medicine requires massive data acquisition and its access for diagnosis, treatment and research purposes. In order to actually put in place such approach evidence-based medicine, we need to address following things. We need to enhance decision support system. We need to be able to collect historical patient data and to share it in a secure and privacy-aware manner and share it both for the diagnosis, for the treatment and also for the search purposes. So in order to enhance those three different points, we propose to use business process and rule management and blockchain to assist healthcare decision and allow implementation and automatization on the following the latest clinical protocols. Our use case scenario is chronic dismanagement and we have chosen diabetes type 2. And the patient that have diabetes, they have to go to doctor about every three, six months. And basically the following tasks happen every time the patient comes to the doctor. So first the doctor is using some guidelines, the existing guidelines and his experience to identify the best treatment plan for the patient. Then when the plan is selected, then it has to be checked against other medications that patients is maybe taking and also the safety of the drug and possible adverse effects. Then there has to be a check for especially for the expensive and new medication. There is a need to check whether this medication will be reimbursed by the insurance company. Then the patient has to accept either possible adverse effects or maybe the need to pay for the medication in case if it's not reimbursed by his insurance company. And then also the data has to be collected, the data about patient reactions on the treatment and the progress. And in the in the best case scenario some data could also be collected for the medical research. So here we try to modeling those flow. We also try to specify different databases that are public or private and also the data or whether those databases contain only the data or also the rules such as for example guidelines for the from the medical literature. So the the difficulty of implementing such system that would basically allow a smooth execution of those tasks and optimization and their optimization lies in the following. So what we have here is the multiple dynamic data ware processes such as execution of guidelines, simulation of reimbursement decision making and collecting of the outcome data. And we have a overlapping set of participants that may have only partial access to different types of data including sensitive patient data and also all those participants can in different manner influence the full decision making regarding the treatment. And here we're talking about patients, clinicians, researchers, insurance and pharmaceutical companies and regulators. So we propose the following architecture that captures this flow with the tasks that I just presented and also allows to achieve the following functional requirements. So our system architecture consists of three layers that could be represented each of each of them can be represented as the separate blockchain or separate channel. And first layer concerns intelligent logic. So this is the this blockchain that is that can be seen by different participants, but only designated actors could write data, could basically add guidelines and then those guidelines can be verified by other participants like different medical doctors. The same as their reimbursement and their prescribing information can also be verified by the regulators. Then the second layer is the data processing layer that basically is a workflow orchestration orchestration of those tasks that is initiated by the clinician and that is more concerned to concern to the specific case specific specific patient case and involves patient data and specific medical guidelines that are required for this patient given his condition. Then we have also data processing layer that is responsible for consent management. And here is where our patient can specify what data can be used by the doctor, for example, to execute those tasks and for which doctor which doctor can access which type of data generated by also different practitioners or maybe stored in different hospitals. We also assume that there is an off-chain data storage that can have either sensitive patient information or also data for medical research. So the system works as follows. When a patient comes to the doctor, the doctor can provide some input information about the condition of the patient. So in our case, it could be diabetes type 2. And then from this first ledger, from the diligent logic ledger, the guidelines are sent to the data processing ledger. And then we know basically which patient data is required in order to run to execute those guidelines. Then when we know which date is required, then the information is sent to the data privacy ledger and to check whether there is a consent from this patient so that this doctor can assess this type of data. And then the rules are executed and here we see that either the data can be sent with the consent of the patient to the data processing ledger or we can use the private data collection in order to keep the data in the hospital and not share it on the ledger. And like this, it goes for different tasks. Next one, for example, for the prescribing information where this flow would actually be repeated. So now let's focus on how we implement this medical, how we implement this first layer with intelligent logic, what it consists of. So we started, for this specific example, we started with official recommendations for the treatment or the guidelines for treatment of type 2 diabetes where we had the text. And from this text, we constructed case management models which basically shows different outcomes based on the different rules and different input information. So here we first related whether using business process management and business process modeling annotation could be also an option. And we figured out that using case management is actually better because it fits better the structure of those text guidelines. Also we cannot have it as structured as business process as is in BPMM, business process management, because sometimes we don't know which data arrives first. And it's really the structure of the guidelines that really fits for the CMMM. Then from case management models, we created JSONs that basically describe the rules for, that basically implements the logic of the guidelines and what we have presented in CMMM. And then we used open source JSON rule engine that also lives on this intelligent ledger. So here is a specific example where we have a part of our case management model. For example, this is a representation of the logic of how to adjust the treatment for patients with chronic kidney disease where we have to have, and here is the representation in JSON manner that describes for each of the outcomes depending on the input data such as BMI and EEGFR. What are the rules that will be applied to the data, to the input data that will be specific for each patient, and that will basically automatically suggest to the medical doctor that this is the suggested medication according to the So now in this small demo, I would like to demonstrate how we implemented this specific piece of logic. And on our intelligent ledger that is that we call guideline channel because we used hyperledger fabric and we plan to have three different channels. So far we have two channels, one is the guideline channel that stores all this information about the guidelines and the second channel is the treatment channel that is concerned with the specific patient cases and implementation of those guidelines. So basically here you will see a short video where doctor is able to initiate to basically get the required model from the guideline channel and then it is sent to the treatment channel and then it is executed using patient data. So here is just a demonstration that we have those two guideline channels created. We have hospitals, the insurance company and the regulators where doctor has rights to write on the treatment channel and the insurance company and regulators are able to view the information. So here is just a screenshot from our patient data smart contract that is deployed on the treatment channel as you can see here. And here is the demo video. So here we have our case models. So this is the content of the of the first layer with the intelligent logic with our guideline channel. So here the doctor can see that okay he needs to check the information for the patient with the kidney disease is the logic that will be used. So he can see that and then he could choose already from the from the patients from the available from the information from the patients that are in his database he could initiate this patient case where the logic from that is coming from the intelligent ledger will be executed. So now he chooses to execute this logic for the patient one and see the execution and that is that basically one of those four different outcomes has been chosen based on the logic that was coming from the intelligent logic ledger and then executed on the treatment channel. So now I think I don't have time to demonstrate the demo. I just mentioned that we also have a already implemented case where the doctor may not have all the information as you can see here all the all the tasks and for example not all the information may be available and doctor has possibility to also manually enter manual entry and add different data that then would help to identify whether to identify one of those three outcomes. So the next steps we have basically first is to extend our framework to connect it with the consent management ledger implement the guidelines and also integrate other rules like prescription management and also a drug drug interaction and we have also different research challenges like how to achieve systematic sharing in use of data for research including achieving privacy and security of data and also ensure that we can translate from those textual guidelines that we can correctly translate them into the intelligent logic and this is really a very very big interesting part on how to also ensure the verification done by different parties and also make sure that this system can be adopted in the in the clinical workflow. So we have to also work with medical doctors and this is also one of our next steps to help to build better interfaces and make sure that this can be easily integrated in the clinical data flow. So thank you very much for your attention. Are there any questions?