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BlockMed Whitepaper

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  1. 1. BlockMed: Open Protocol for Medical IoT Wayne Chung, Andrew Lee Dec. 8th, 2018 Abstrac​t We present an open protocol for medical IoT devices that connects payer, provider and patients on a public Ethereum blockchain that allows for trustless, secure smart contract execution. Our protocol allows for BlockMed token, BMD to serve as a utility token which allows 3 core innovations: 1) Distributed encryption/decryption of HIPAA protected medical IoT patient data for prevention of hacking and ransomware type attacks in remote monitoring settings. 2) Augmented deep learning and blockchain platform to allow faster, more efficient development of crowdsourced AI algorithms. 3) Machine to machine payment of smart contract parameters from medical IoT data based on individual and community standards. 1
  2. 2. Contents I. Introduction…………………………………………………………… 3 II. Current State ………………………………………………………….. 4 III. Enhanced Cybersecurity ………………………………………………. 5 IV. Crowdsourced AI Algorithms ………………………………………….14 V. Machine to Machine Payments ……………………………………….. 20 VI. Token Economics & Governance……………………………………... 23 VII. Timeline ………………………………………………………………. 24 VIII. Summary ……………………………………………………………… 26 IX. Acknowledgements …………………………………………………… 26 X. References …………………………………………………………….. 27 2
  3. 3. I. ​Introduction The rapid proliferation of medical IoT devices recording patient data remotely outside the hospital and clinic by connecting to the mobile-cloud is creating increasing cybersecurity vulnerabilities for the health IT infrastructure. Provider based EHRs are understandably concerned and reluctant to open their APIs to the myriad of remote monitoring devices connecting to the mobile-cloud for fear of hacking and ransomware type attacks. Enhanced cybersecurity of existing health IT and EHRs is top of mind for CIO’s of all major health organizations. BlockMed proposes an open protocol which allows remote medical IoT data to be HIPAA compliant on and off the blockchain so there is record of sensor data verified with the patient, provider and payer. Username-password compromises, as seen in centralized databases allowing current ransomware attacks, will be more difficult on the distributed platform proposed here. The exponential explosion of medical IoT data is becoming a double-edge sword regarding utility and value created from such data. Providers are increasingly overwhelmed from alarm fatigue and false positives which generate unnecessary calls, visits, and further diagnostics, potentially creating more legal liability. The fundamental value of this data resides in the actionable algorithms created to predict and inform therapeutic action. Creating such actionable algorithms depends heavily on the quantity and quality of the data, as compared to the entire body of data from the individual and community. Remote medical IoT data analysis are currently limited to the number of patients which can generate that data and the team members within an individual company to create actionable algorithms from that data. BlockMed proposes a novel open platform whereby such remotely generated medical IoT data can be crowdsourced out to the BMD community to then develop superior algorithms, that overcomes standard overfitting bias based on static historical data as opposed to new continuously generated live data. This deep learning blockchain platform will 3
  4. 4. allow faster, more efficient development of crowdsourced AI algorithms, that can then result in machine to machine payment of smart contract value from medical IoT data based on real-time live data as opposed to traditional population health models currently in existence. II. ​Current State Blockchain technology applied to medical IoT is still in its infancy. Decentralized distributed ledger technology is ideal for immutable records of crowdsourced algorithm performance and machine to machine payments where secure identification and verification of algorithm results is crucial for widespread adoption. Many other proposed projects identify the EHR and existing legacy IT systems as starting point for a blockchain based replacement. This rapidly growing eco-system can accommodate many approaches, but in our opinion the medical IoT space is relatively new like blockchain--therefore a prime opportunity to rapidly establish a widely adopted and scalable use case as there is no current EHR or IT legacy system to overcome and replace. Other proposed projects attempt to combine predictive algorithms with blockchain for better disease prediction on existing data sets from current hospital or clinic-based systems. Such projects have issues of ownership of data, as the existing legacy hospital and clinic systems are reluctant to enter into profitable agreements to share this data for the proposed types of blockchain projects. Without this legacy data in actionable formats from current hospital or clinic-based IT systems, blockchain development of predictive algorithms in these types of projects may be much slower in getting massive data to scale and yielding superior actionable results. Supply chain management of hospital and medical device supplies are other proposed blockchain projects. Given the complicated logistics involved with large legacy systems, this area is better suited for large existing IT vendors such as IBM, Microsoft, AWS to offer blockchain services 4
  5. 5. for large companies to deploy--as IBM has already done with the cargo shipping industry. Simply replicating entire aspects of legacy health IT infrastructures with blockchain is unlikely to gain rapid adoption and scale if the complexity of legacy systems and lack of data access continues--as will likely be the case while the legacy incumbents slowly adopt blockchain on their own. Areas which fundamentally lends itself to open protocol development have the best chance for rapid network adoption and true scaling potential for existing healthcare players. BlockMed is proposing an open protocol which allows medical IoT data largely untethered to current legacy IT systems, to be rapidly adopted by payers, providers, and patients in a BMD token eco-system with core innovations in enhanced cybersecurity, crowdsourced AI algorithms and machine to machine payment. III. ​Enhanced Cybersecurity BlockMed will utilize a patented wearable multi-functional sensor​[1]​ to initially provide data for its proposed open protocol for enhanced cybersecurity of medical IoT. This proprietary sensor has been developed for over 5 years by Wireless Medical for remote monitoring of cardiac disease. It produces 9 real-time data streams from a credit card size sensor worn on chest that sends data via Bluetooth to an iOS app on iPhone, which streams the data to AWS cloud server, where it is stored and sent back to iOS app for display of data to physician. For HIPAA compliance, the data streamed from sensor via Bluetooth will be encrypted with AES-128 protocols. The data streamed from iOS app to web app will be protected by SSL encryption, while the data stored in AWS cloud server will have dedicated hardware provided for encrypted data at rest. For sensor’s 9 real-time data streams, no such encryption is necessary as long as there is no pairing with the HIPAA compliant patient identifiers. The raw sensor data will be stored on public distributed file storage protocols such as IPFS​[2]​ via BlockMed open 5
  6. 6. source API​[3]​ (See Figure 1). This not only enables other technology and framework such as IoT Fog/Edge computing framework proposed by Cisco​[4]​ to take advantage of IPFS that has better performance than others such as Amazon S3​[5]​ , but also shows better performance gain with IPFS​[6]​ . Figure 1 The proposed open protocol will utilize a public Ethereum​[7]​ blockchain for the smart contract execution, while the HIPAA compliant patient identifier data will be stored off-chain such as an AWS encrypted databases as necessary for proper onboarding of medical sensor usage. The private keys of public Ethereum blockchain can be kept on mobile app of patient. In case of loss of mobile app/smart phone, this unique key can have appropriate recovery or replacement mechanisms provided by BlockMed​[8]​ . Furthermore, the API kit can be extended to support data encryption on top of IPFS as an extra layer of security to keep data encrypted and give users full control of their data. In addition to user data, the open protocol also provides a channel to compute Private Contracts, in other word, confidential code that can be service through off-chain service provider that provides TEE (Intel SGX) backed compute-nodes​[9][10]​ . In Oasis Lab’s Ekiden​[11]​ protocol, clients send inputs to confidentiality-preserving smart contracts, which are executed within a TEE at any compute node. The blockchain stores encrypted contract state. 6
  7. 7. This provides opportunity for regulated industries to utilize the open protocol and compute public data from IPFS along with private data off-chain with proprietary code. The owner of the data who holds the Private Key can authorize different service providers with the proper key to decrypt the data and still track of each service provider on how the authorization are distributed and applied. Other services such as Enigma​[12]​ can also take advantages to corporate data privacy off-chain​[13]​ by exchanging the authorized secret key access from user via Ethereum smart contract while the data are still kept on the public IPFS for further usage. Enigma also opens the door to multi-party computation, MPC​[14][15]​ as well. This also leaves a trace of who accessed the data on the public chain as an evidence and audit. (See Figure 2) Figure 2 The distributed ledger technology of public Ethereum blockchain provides enhanced security with immutable records of transactions on nodes. As such, it is very difficult to hack in traditional ways seen in centralized databases such as AWS, often through username-password compromises seen frequently in ransomware attacks on hospital systems. BlockMed’s proposed open protocol cooperating with private off-chain, AWS encrypted database for patient, physician ID, and public IPFS file storage for raw sensor data serves as redundancy for both EHRs’ and Payers’ centralized databases. (See Figure 3) 7
  8. 8. Figure 3 In cases of ransomware attacks on institutions, utilizing BlockMed’s proposed open protocol, the distributed and decentralized private keys stored on patients’ mobile apps provides an additional level of security for the medical IoT data. As a result, there may be little incentive to pay ransomware demands as the distributed IPFS file storage data can be easily recovered and duplicated onto legacy systems. (See Figure 4) Attack Mitigation The following illustrate how a potential attack scenario by Ransomware or compromising the server managed by central authority for business usage. 8
  9. 9. Figure 4 - Attack Scenario with Ransomware According to Figure 4, immutable records and data are already anonymized and de-identified on IPFS with the list of pointers hosted by one or more Open Source Web Server​[16]​ (via Caching and Indexing). This means by gaining access to the server or the Open Source Web Server will limit the chance to correlate and expose patient’s identities based on the randomized UUID. This Open Source Web Server can be replicated and distributed by anyone anywhere for high availability, reliability. and performance​[17]​ . The authority can decide how many of the caching servers they would like to host internally to reach a quorum for reliability in their own environment, or simply become part of the public Internet that interacts with other alike servers (however, this is likely to happen due to each authority will like to keep track of a minimal subset of the global records on IPFS, especially from their own patients). As a result, the victim authority will be able to perform the following to recover from such attack. (See Figure 5) 1. Setup a new secured server with latest security patch, etc. 2. Migrate the IP address from the old server to the new one 9
  10. 10. a. Existing sensors will retry to register on this ‘new’ server, and afterward, continue to send data to it. This provides the server a list of existing devices (aka previous registered patients). b. In addition to the new incoming data after the service availability is restored, without the existing sensors sending data, the ‘new’ server can coordinate with the Open Source Web Server to pre-load all the historical index to fetch the data from IPFS. This will allow the ‘new’ server to bootstrap the lost historical data, meanwhile, still receive new (incremental data) from the existing sensors. 3. After a certain period of time, the ‘new’ server will gain its original view prior to the attack, and hence, can mitigate the attack (e.g. ransomware). Figure 5 - Recovery Scenario to Restore Data 10
  11. 11. BlockMed’s proposed open protocol allows for developers worldwide to contribute to the sensors’ raw data storage through the BlockMed token, BMD. Wireless Medical’s proprietary sensor’s 9 real-time data streams accumulating 24/7 daily quickly adds up to large amounts of data. Blockchain technology as applied to healthcare IT security is still in its infancy. Two main issues BlockMed will address along with entire public Ethereum blockchain community include: 1) tradeoffs between maintaining security while increasing transaction speeds to handle increasingly large amounts of raw data on the public Ethereum blockchain and IPFS file storage network, (See Figure 6) Figure 6 - Tradeoff Between Ethereum and IPFS Storage 2) having useful off-chain API’s to interact with public Ethereum blockchain and IPFS file storage networks in order to handle all the various medical IoT devices, EHR’s, Insurance Payers, App/Browser eco-systems etc. that will be necessary for future scaling during mainstream adoption phase. BlockMed’s internal team and open community developers will use BMD token incentives to contribute data from Wireless Medical’s multi-functional sensor as well as their own off the shelf 11
  12. 12. sensor data to the distributed IPFS file storage platform. Consumer devices with medical applications such as Apple Health Kit API, Apple Watch, Fitbit and other app systems such as Android, and other web browser-based systems can also be tested and developed on BMD open platform by developers. This growth will be powered by worldwide community of developers incentivized through BMD token to improve functionality and security of open platform for medical IoT devices. (Figure 7) Figure 7 - Multiple Wearable Devices Integrated with Ethereum and IPFS This platform will initially interface with the existing Swift iOS app, Ruby-Python server and MongoDB database residing on AWS. (See Figure 8) 12
  13. 13. Figure 8 - Initial Implementation with IPFS and BMD Smart Contract As improvements are made to the distributed IPFS file storage platform handling the open communities’ sensor data, consensus mechanisms will be utilized through BMD smart contracts to incentivize changes that achieve stated goals of increasing speed and efficiency in handling raw sensor data on distributed IPFS as well as usefulness in interacting with appropriate API’s for off chain EHR’s such as Epic which is used in Stanford, UCSF hospitals, where initial pilots will be conducted, and API’s for off chain Insurance Payers such as United Health, Humana, or others which may enter into agreements during pilot studies. In Stanford, UCSF pilot studies, the patient and physician ultimately should not notice any difference in testing the Wireless Medical multi-functional sensor and mobile app with regards to the data streams collected, between non-blockchain and BMD blockchain versions for enhanced cybersecurity. (See Figure 9) 13
  14. 14. Figure 9 - Interaction with Ethereum and IPFS are Transparent After successful pilot studies, emphasis will be on API integration and bringing on commercial partners onto BlockMed open platform with other hospital systems, EHRs, Payers, and other medical IoT devices. IV. ​Crowdsourced AI Algorithms BlockMed’s 2​nd​ core innovation is that Wireless Medical’s multi-functional proprietary sensor algorithms can be crowdsourced real-time to open community through use of BMD tokens to produce Dutch​[18]​ style multi-unit auctions that result in superior machine learning algorithms on each of the 9 sensor data streams, as well as a combined composite score. This technique can overcome issues​[19]​ of overfitting algorithms​[20]​ to static historical test data that are poor performing on newer real-time data streams. This is critical in the medical IoT space, not only for Wireless Medical but other medical IoT devices, which all constantly produce new 24/7 real-time sensor data under wide variety of variable environmental conditions. The 9 data streams produced by Wireless Medical’s sensor include: 1) acoustic heart sounds 2) skin impedance 3) EKG 4) heart rate 5) heart rate variability 6) respiratory rate 7) temperature 8) sleep angle 9) steps walked. Each of these data streams represent physiological states in 14
  15. 15. patient wearing sensor, but exist in noisy remote, non-clinical environment where movement and other factors can introduce artifacts into data streams. (See Figure 10) Figure 10 - De-identified Input Stream from IPFS to AI Frameworks In proposed open protocol, the sensor’s raw data streams, de-identified on distributed IPFS file storage network, can be made available to community of BMD token holders and developers, who can utilize various machine learning and deep learning algorithms frameworks such as TensorFlow, PyTorch, CNTK, and MXNet ​to more efficiently refine accuracy not only on static, old historical data, but on constantly generated new live data. (See Figure 11) Figure 11 - General AI Framework Tapping into BMD Protocol 15
  16. 16. A multi-unit Dutch auction can be utilized so algorithms performing better on new data get incentivized through BMD tokens issued to these developers in regularly scheduled contests​[21]​ . Developers can also use their own off the shelf consumer devices for commodity data streams such as HR, HRV, RR, Temp, Sleep Angle and Steps to submit algorithms which can be tested against other consumer devices as well as Wireless Medical’s proprietary multi-functional sensor. Complex, unique data streams such as acoustic heart sounds, skin impedance and EKG’s can only be gathered from Wireless Medical’s proprietary sensor. Initially this data will be gathered and tested on healthy developers from BlockMed’s internal team with access to proprietary sensor, but later on sick patients in the UCSF, Stanford clinical studies. These complex, unique data streams can also be crowdsourced and incentivized through a multi-unit Dutch auction to BMD developer community just like the commodity data streams. The Dutch auction’s goal will be to produce algorithm models which accurately predict behavior of both individual data streams and combined sensor score as related to true physiological state of patient. For example, raw HR data gathered from Wireless Medical’s multi-functional sensor can be used to create predictive algorithm models, then tested on Apple Watch’s raw HR data and vice versa. These models will need to account for occasional falsely high HR’s due to wearable environment as compared to clinically labelled HR as gold standards. Best models submitted by BMD token community of developers can be forward tested on wide variety of other devices new raw HR data for a continuously evolving winning model. Mechanics of reward amounts, schedule and winners can be governed by existing best practices and modified by BMD community through consensus. This Dutch auction can be applied to all 9 individual data streams and combined composite score produced by Wireless Medical’s multi-functional sensor. (See Figure 12) 16
  17. 17. Figure 12 - Derived and Published Algorithm from Crowdsourced AI gets Rewarded The BMD crowdsourced machine learning algorithms can also be applied to the mobile data generated through future voice activated app platform along with camera, video data upon app interaction by patient. True artificial intelligence through applying deep learning natural language processing and computer vision techniques to mobile audio​[22]​ and video​[23][24]​ data streams along with machine generated sensor data and be compared to human labelled clinical data in clinical studies over a longitudinal time frame of 7-day hospital stay and 30-day discharge period at home. A combined composite mobile and sensor score of all 9 data streams equally weighted can be compared to equivalent composite clinical score of 9 physician reported parameters and compared for correlation and predictive power in the hospital and later in patient’s home environment. Again, by crowdsourcing to BMD token developer community, the efficient optimization of superior algorithms—a world’s first true deep learning platform on the blockchain can be created not only for Wireless Medical’s multifunctional sensor, but ultimately 17
  18. 18. for other medical IoT devices generating massive amounts of 24/7 physiological data real time​[25]​ . Figure 13 - Data Published to IPFS and BMD Smart Contract The BMD token developer community can be incentivized to analyze open source community data such as Beth Israel—MIT EKG database for arrhythmias, or TI’s database for HR detection algorithms and efficiently optimize algorithms on both individual and community level. Ultimately the goal will be to generate supervised, semi-supervised and unsupervised deep learning algorithms​[26][27]​ that trains on labelled and unlabeled open source community data and individual medical IoT data to perform accurately on newly generated patient data across wide variety of settings, as that will prove a most powerful and valuable use case. Unlike previous and existing legacy systems, this open protocol allows the BMD token developer community to capture much of this value creation in the deep learning algorithms of newly generated mobile and sensor data as shown to be useful for the patient and physician. The de-identified raw sensor data on public distributed IPFS file storage network will always be kept separate from patient, physician identifiers kept on off-chain, encrypted AWS. ​One 18
  19. 19. possibility is to run Apache Spark​[28]​ on Amazon EMR, and use Terraform​[29]​ (by HashiCorp) to build a Spark and Apache Zeppelin​[30]​ cluster on Amazon EMR which is HIPAA compliant​[31]​ . This solution encrypts all data at rest and in-flight, logs all user activities, as well as satisfies many other aspects of a HIPAA compliant environment. The use of Terraform provides a high degree of management of Cluster Configuration, Data Accessibility, Scalability, Security, and Availability. (See Figure 14) Figure 14 - Running Big Data Applications in HIPAA Environment As such HIPAA compliance can be met, while allowing open BMD developer community to rapidly iterate, and test mobile and sensor data with real world clinical inputs to create a deep learning blockchain platform which can be applied not only to affect various cardiac conditions such as heart failure, cardiac arrhythmias, post heart surgery management but also other non-cardiac entities such as pulmonary diseases etc. 19
  20. 20. V. ​Machine to Machine Payments BlockMed’s 3​rd​ core innovation is to allow machine to machine payments on the open Ethereum blockchain platform, such that any set of individual and community parameters can be set in the smart contract as incentives for individual data streams and combined sensor score results. Current medical IoT devices have to go through large, expensive studies to prove positive changes in clinical outcomes worthy of reimbursement by Payers. These population-based studies provide a major hurdle to widespread medical IoT adoption as the desired outcomes measure results in lengthy binary decisions to reimburse or not based on entire population tested when certain patients may benefit at individual level. (See ​Figure 15) Figure 15 – Current Medical IoT Ecosystem The BMD open platform will allow Payer to incentivize each sensor data stream and combined score to standards on individual real-time level. For example, HR can be tracked to certain normal ranges, and deviations from that community standard can be verified on public Ethereum blockchain platform. Patients’ that have minimal deviations can then have de-identified BMD token transfers on public Ethereum blockchain that connects to Payer’s off-chain encrypted account of that individual with resulting co-pays, deductibles etc. available to adjust accordingly. Such value transfers with BMD tokens real-time from Payer to device at individual level allows for faster commercial adoption of medical IoT devices as Payers do not necessarily need to wait until binary outcomes data from large commercial adoption studies when deciding on reimbursement in remote monitoring setting. Instead, medical IoT devices can be incentivized to 20
  21. 21. join open platform with Payers setting individual standards for data streams regarding BMD token value transfers. (See ​Figure 16) Figure 16 - BlockMed Token Ecosystem Positive reinforcement for clinical compliance as evidenced by medical IoT data that adheres to community standards can now be transferred through BMD tokens real-time, machine to machine. Payers that allow select patient community data to be de-identified and made available to open platform can benefit from crowdsourced modelling of most efficient payment incentives at individual level, similar to Dutch auction model used for deep learning algorithm development regarding accuracy of individual and composite data streams. Again, the BMD platform allows developers who create the most accurate algorithms for Payers to transfer token value can capture most of the value creation, as there is much potential cost reduction in entire healthcare system regarding prevention and management of chronic disease in remote monitoring setting. (See ​Figure 17) 21
  22. 22. Figure 17 - Network Virality Effect and BMD Circulation The medical IoT space is ideal for machine to machine payments as typical hospital re-admissions can cost Payer $10-15K, and happen 20-25% of time on all major admission categories such as heart failure, pneumonia, heart surgeries etc.​[32]​ . In addition, patient deductibles are increasingly high, such as $3-10K on all types of Payer plans. Crowdsourced deep learning models which accurately predict individual data streams at individual level, then tie combined composite score directly to Payer accounts of deductibles and premiums, all on off-chain, encrypted AWS and public Ethereum blockchain platforms offer unique opportunity to transform fundamental payment models currently in place primarily for inpatient and clinic visits. (See ​Figure 18) 22
  23. 23. Figure 18 - Enterprise and Secure Computation in BMD Network Reimbursement for medical IoT remote monitoring is still very much in its infancy due to lack of quantifiable data regarding effectiveness of various low-tech disease management approaches as well as digital therapeutic, medical IoT offerings. BMD’s open platform allows for quantified, results orientated, machine to machine micro-payments at individual level, based on medical IoT real-time data streams. VI. ​Token Economics & Governance BlockMed will issue its BMD tokens through pre-sale and later it in public sale on rolling basis until funds deemed sufficient for network development. A trusted public wallet managing the escrow account with the initial 1B tokens such as CoinBase with secure offline cold storage 23
  24. 24. capabilities will be used to safely handle the exchange of Ethereum, Bitcoin, USD, Euro into BMD tokens. Appropriate identification provided by third party vendors will be requested so Know Your Customer (KYC), Anti-Money Laundering (AML) and all other relevant regulations are complied with. A multi-sig. contract whose keys are held by trusted individuals within BlockMed will be used to handle all funds. 1 billion total BMD tokens will be issued at initial exchange rate of 1USD per BMD token. Allocations include: 20% to founding team and advisors, 30% to BMD Foundation, 50% to public. (See ​Figure 19) Figure 19 – BlockMed, BMD ERC20 Smart Contract Execution BlockMed’s initial governance will be guided by founding team, board of directors to set direction of strategy, including key internal hires and outside engineering design/ development houses to execute out private and public blockchain platforms in preparation for UCSF, Stanford clinical pilot and later commercial network Payer partners. To promote a common standard among the open protocol proposed by BlockMed, decentralized consensus may be utilized with open governance and voting among the BMD token holders, guided by BlockMed’s founding 24
  25. 25. team. Later a true decentralized autonomous organization (DAO) governance structure may be considered once the open protocol has reached sufficient community adoption. Figure 20 - Token Allocation VII.​ ​Timeline Jan.--Aug. 2018—Whitepaper Research May--Dec. 2108—Smart Contract Prototype Jan.--Dec. 2019—Rolling Raise BMD Tokens Jan.--Dec. 2019—BlockMed.AI Platform Development July--Dec. 2019—UCSF, Stanford Pilot Studies Jan.--Dec. 2020—Payer / Pharma Licensing 25
  26. 26. VIII. ​Summary Remote medical IoT data has the potential to fundamentally transform the healthcare landscape with regards to chronic care disease management, keeping patients out of the hospital and allowing more cost savings to entire system through early prediction and therapeutics. Unfortunately, the current system depends on large scale trials and pilots with payers and providers to prove cost savings on community populations before large scale deployment and reimbursement. BlockMed proposes a novel, secure, deep learning blockchain platform, based on the BMD token, whereby large population-based community trials are not a necessary precursor to individual transfer of value real time based on medical IoT data. Based on public Ethereum smart contract parameters, remote medical IoT data that meets certain algorithmic criteria can result in real-time machine to machine payments through BMD token. Such a protocol has potential to transform transmission of value throughout the entire payer, provider, patient ecosystem for medical IoT, and truly incentivize improved health outcomes and lower costs among all affected parties. IX. Acknowledgements We would like to thank our mentors, advisors, and friends who have provided invaluable advice on BlockMed. In particular, for educating, reviewing and providing feedback on this work in specific and blockchain-cryptocurrency world in general. 26
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