Blockchain Papers

Leah Houston

The healthcare system is expensive, inefficient and frustrating due to unnecessary third parties and friction filled manual processes that get between patients and their doctors. Through decentralized identityby and credentialing we can create a distributed network of practicing doctors that connect connect securely, directly, privately with each other and with the patients they care for. We are building the future essentialized healthcare revolution and we can’t wait to build it with you.

B K Nandeesh, Afzal Ahmed Pinjar, Shrinivas Dixit, Sahil L Naikwadi

Blockchain technology has the potential to completely transform how electronic medical records are exchanged and maintained by giving healthcare practitioners safer ways to communicate medical data while safeguarding it across a decentralised peer-to-peer network. A systematic literature search was carried out to examine the existing literature on blockchain and healthcare and to identify existing challenges and open questions arising from the process, guided by the emergence of research questions related to Electronic Health Records (EHR) in a Blockchain, in order to support and facilitate understanding of this distributed ledger technology. In recent years, hackers have been interested in healthcare data. Decentralization can reduce the damaging consequences of health data. Decentralized ownership is made possible via peerto-peer (P2P) networks, which let several parties store data and carry out computations while maintaining.

James Scheibner, Marcello Ienca, Effy Vayena

Abstract Background Increasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorphic encryption allows computations to be performed on data without this data ever being decrypted. Therefore, homomorphic encryption represents a potential solution for conducting feasibility studies on cohorts of sensitive patient data stored in distributed locations. Distributed ledger technology provides a permanent record on all transfers and processing of patient data, allowing data custodians to audit access. A significant portion of the current literature has examined how these technologies might comply with data protection and research ethics frameworks. In the Swiss context, these instruments include the Federal Act on Data Protection and the Human Research Act. There are also institutional frameworks that govern the processing of health related and genetic data at different universities and hospitals. Given Switzerland’s geographical proximity to European Union (EU) member states, the General Data Protection Regulation (GDPR) may impose additional obligations. Methods To conduct this assessment, we carried out a series of qualitative interviews with key stakeholders at Swiss hospitals and research institutions. These included legal and clinical data management staff, as well as clinical and research ethics experts. These interviews were carried out with two series of vignettes that focused on data discovery using homomorphic encryption and data erasure from a distributed ledger platform. Results For our first set of vignettes, interviewees were prepared to allow data discovery requests if patients had provided general consent or ethics committee approval, depending on the types of data made available. Our interviewees highlighted the importance of protecting against the risk of reidentification given different types of data. For our second set, there was disagreement amongst interviewees on whether they would delete patient data locally, or delete data linked to a ledger with cryptographic hashes. Our interviewees were also willing to delete data locally or on the ledger, subject to local legislation. Conclusion Our findings can help guide the deployment of these technologies, as well as determine ethics and legal requirements for such technologies.

Sarah Wheeler, Darci R Block, Dustin R. Bunch, Jamie Gramz · 7 authors

The American Medical Informatics Association defines biomedical and health informatics as the “science of how to use data, information, and knowledge to improve human health and the delivery of health care services.” More specifically within laboratory medicine, informatics and data analytics use multiple sources of data to improve all aspects of the clinical laboratory, from workflow and personnel to result interpretation. With increasing healthcare information complexity, integration and interoperability issues have become readily apparent between health information systems, bringing to the forefront questions about the validity of data exchange and basic data access. Most data generated within the clinical laboratory are of high quality, well annotated, and structured discretely, however turning these data into useful and actionable information can be a difficult data analytics bridge for many to cross. Instrument and laboratory information system (LIS) vendors are beginning to aid in the creation of generalized reports for common laboratories questions; however, this still falls short of the potential of the clinical laboratory to bring more actionable information to hospital leadership, clinicians, and patients. Collaboration among informaticians, information technology (IT) professionals, and the laboratorians is critical to ensure our health information systems can utilize and report laboratory data clinically, in addition to providing interoperable data streams for furthering research, education, and innovation in healthcare. To discuss these and other challenges and opportunities for informatics in laboratory medicine, we have invited several experts to share their experiences. Darci Block: The COVID-19 pandemic is a case in point for the value of informatics. It certainly was not easy, and there were many lessons learned, but the ability to monitor case rates and predict surges was all thanks to the mighty efforts of clinical laboratorians who became overnight experts of SARS-CoV-2 testing and informatics and data analytics (whether they knew it or not). We also learned that when our collective attention is focused on a single threat, the response can be very targeted and efficient in execution. We accomplished a tremendous amount in a relatively short duration because of this laser focus. Dustin Bunch: At this time, laboratory operation activities are receiving the largest benefits from laboratory data streams in the form of internal and external quality metrics. Most laboratories are monitoring turn-around-times, volumes, QC (quality control), and infection metrics either through reports and/or dashboards, and have mandatory reporting to federal, state, and local entities. David S. McClintock: I thought this would be an easy question; however, it wasn’t—clinical laboratories have benefited from an increased awareness and use of informatics for decades, with incremental changes over time leading to numerous diagnostic, operational, and quality improvements. For example, we have seen improved interfacing and coordination of laboratory instrumentation and automation, most recently in the areas of molecular testing, microbiology, and point-of-care testing. Data-rich analytics are driving laboratory operations more and more, in addition to laboratories seeing minor gains in interoperability with greater adoption of standards such as Logical Observation Identifiers Names and Codes (LOINC) and unique device identification (UDI). J. Mark Tuthill: Clearly, automation of manual processes has had the most direct impact in the clinical laboratory. The impact of business analytics is now having direct impact on the laboratory as well. Because of the use of descriptive analytics, predictive analytics, and artificial intelligence, laboratories have a much deeper understanding of their workflow and any deviations and thus can respond in ways not previously available. I believe this will continue to develop in the future into next-generation automation and laboratory efficiency. Edward Ki Yun Leung: The areas for which I have seen the most improvement by the increased use of informatics are in the core or integrated laboratory environments with total laboratory automation. Vendors are providing tools, usually at a middleware level, where data from the instruments can be analyzed and presented on dashboards for laboratory staff and management. The dashboards can be successfully used in different ways such as monitoring turnaround time, identifying bottlenecks in laboratory operation workflows, optimizing staffing resources to support changes in testing volumes throughout the day, and supporting test utilization programs. Jamie Gramz: Standardization, improved efficiency, and reduction of hands-on time are key improvements made possible with lab informatics. Informatics has accelerated the laboratory’s ability to generate, aggregate, and analyze data and has been the key enabler in operationalizing data through use of automation, while also helping labs to offset the growing shortage of laboratory professionals in the US and other countries around the world. Whether it be the transformation of tests ordered by a physician into the autonomous handling of samples throughout the preanalytic, analytic, and postanalytic processes or the use of autoverification to streamline the evaluation, review, and reporting of patient results, informatics is helping to drive the timely delivery of actionable patient information that medical laboratories provide. J. Mark Tuthill: There are multiple factors that impact the quality and interpretability of laboratory data by clinicians and patients. First, the ability to see, understand, and read these data in an easy fashion has been challenging. Most laboratory reports are very flat, textual, and are not summative. Nor is there interpretive guidance provided: “You got the number, figure it out.” This is not helpful. We need better graphical displays of laboratory data for patients and clinicians. In addition, because laboratory results produced by different laboratories may have different reference intervals or completely different result values, it can be very difficult for clinicians and patients to interpret results across different healthcare systems. So providing interoperable, interpretable results is only part of the challenge. Dustin Bunch: Foremost is data access, which is the most common barrier for those that want to do data science. As a community, we have not created a culture where it is normal to routinely access raw data. Historically, if data were available, access to those data came through distilled reports. Another issue is the number of people trained to process and/or interpret the data available in clinical laboratories. Edward Ki Yun Leung: Some of the current operational challenges we face that impact high-quality, interpretable laboratory data include decentralized databases and the need of multiple different data mining tools to extract the data. Both may have significant impact on the fidelity of the final data. In the current healthcare environment, data are stored in multiple systems such as electronic health records (EHR), laboratory information systems, clinical decision support systems, clinical operations and analytics software and systems, revenue cycle management systems, and software systems that support clinical trials and research. Each of these systems may have different levels of accuracy and refresh rates. In addition, each system may require a different data mining tool to extract the data. High skill-set requirements may be needed to effectively use these tools to extract, combine, and format the data. Jamie Gramz: Interoperability has come a long way over the past 10 years with completion of the 3 stages of meaningful use: driving EMR (electronic medical record) integration including data capture and sharing (2012), advanced clinical processes (2014), and improved outcomes (2016). And as a patient, I appreciate being able to quickly see my lab results in the patient portal app as soon as they are released from the laboratory. Providing patients with immediate and transparent access to their health information was an important achievement and great step forward. But in today’s consumer-driven society empowered by the internet, it introduces a new set of challenges as patients try to understand and interpret the meaning of their lab results, potentially even attempting to self-diagnose medical conditions. This may create new opportunities for laboratories to provide support for lab test result interpretation and expanding patient portal apps to include access to relevant and accurate information as a logical next step. Darci Block: Clinicians and patients seem most interested in having all “relevant” health record information in one place that is easy to reference and digest quickly. It makes shopping for healthcare more feasible and streamlines the experience when a mountain of paper results and records do not need to be synthesized at each stop. To that end, it seems like a simple thing to pull laboratory results into a single system or viewer from any place a patient has lab testing performed to support this endeavor. However, operationally the methods of standardizing results (via LOINC and other standards) have not completely overcome the challenge for reasons I am not altogether sure of. Additionally, results nomenclature (e.g., positive = “P,” “+,” “detected,” “reactive,” “confirmed,” “present”) remains a ripe opportunity for standardization to consolidate meaning in such collections of results. David S. McClintock: I like to think of laboratory informatics as how we best deliver the right clinical laboratory information to the right person, at the right place, at the right time, and in the right way. With that in mind, clinical laboratories are still far behind in delivering the “right” laboratory information to the right person. We still provide a single result or interpretation in a one-size-fits-all approach, with each lab formatting their results in different ways that can confuse both patients and clinicians alike. Unfortunately, our current lab information systems do not allow us to send multiple versions of results for multiple purposes (although, to be fair, downstream HIS [health information systems] can’t ingest differing versions of the same result either), which means it will be a long time before we can tailor our reports to meet the specific needs of the customer/right person (e.g., patient, primary care physician, subspecialty clinician, etc.). Jamie Gramz: A common one is the slow adoption of analytic solutions to help monitor performance, with many labs still following tedious and time-consuming steps to collect the data needed to manually generate reports. Automating this process with informatics solutions that provide real-time analytic reports to monitor the common key performance indicators that most labs measure could be a “low-hanging fruit” opportunity to help drive continuous improvement. Real-time analytics solutions can make it easier for labs to assess performance, identify inefficiencies, and drill-down to determine the root causes of problems. Whether it be to monitor internal metrics like turnaround time, throughput, and exception management or to investigate complex issues like identifying the leading sources of sample integrity issues, analytics should be a key tool used by the laboratory to help make data-driven decisions for improvement. J. Mark Tuthill: The biggest area where we are lagging in our use of data is widespread access to all varieties of data and the ease of access to that information. Once data is available, the ability to display that data in meaningful ways, to the correct people, at the correct time, is the next challenge. Typically, the laboratory has relied on paper data outputs to respond to workflow challenges or defects/deviations in the laboratory testing process. This is true in both preanalytic, analytic, and postanalytic processes. Replacing static, paper-based reports with dynamic, real-time dashboards is still in its infancy in many laboratories, particularly for real-time dashboards that would have direct day-to-day impact on workflow and patient care activities beyond simple “turnaround times.” Darci Block: In my experience, I would say all areas of laboratory medicine lag because access to data, even the most basic data within the laboratory information system, not to mention instrument and middleware data, is limited; these systems were designed to drive and of in time and not designed to be and for also very because we the information is there but of Edward Ki Yun Leung: area where we are lagging in our use of data to drive improvements is in point-of-care testing is very different when to laboratory testing. vendors may have their software and/or system for their and a may have to 3 even different It is not for a to use a middleware to the different software and/or systems to the and/or to the clinical laboratories, there are not as many tools to extract, and the data. Another area in where we are lagging in our use of data is in testing personnel for where there can be more For each we need to the education, and This can be very and because the information may be in paper format and in multiple Dustin Bunch: The as the value clinical laboratories are lagging in all areas when it to of data science. The lab to be in the only descriptive analytics and analytics it To data analytics in the clinical laboratory, we need to into the which more with predictive will and analytics can we make David S. McClintock: labs are well with descriptive analytics in most are not to to analytics, such as analytics predictive analytics will in the lab and and analytics can we make and in the In more value from data to increasing resources and analytics integration of operational and data, including ways to both real-time awareness of and to on and analytics analytics, integration of data with increasing analytics efforts and which been a for clinical laboratories, and laboratory medicine and healthcare systems alike. David S. McClintock: we need to more in informatics people, and laboratories, and their laboratory informatics or their and middleware clinical business Additionally, clinical laboratories need to their awareness of informatics and both in their and leadership, in addition to any informatics clinicians and needs to with their information technology and clinical informatics to ensure they have a at the the labs to both understand immediate issues at and to the about and informatics Edward Ki Yun Leung: A we face in the use of informatics is support and laboratory staff may have knowledge and experience with and may have knowledge and experience in laboratory Both will be needed for the of informatics that will be useful in laboratory We will need hospital and laboratory to and the resources to support this Darci Block: To improve need to have a basic understanding of are how it and it to should and is it do and how one In my as of I have into a as of and other resources for our The is with the and of which and from and efficient and processes for the right at different while quality and I as a to a need or from the lab to that are for and/or systems to meet both I think we can overcome by within an but also between to and best and lessons learned from one Jamie Gramz: is available and where to is a common challenge. With many solutions designed to a of lab it can be difficult to determine which will best in the determine which potential improvements will have the most significant impact to the laboratory and the there to patient need to issues, such as result reporting or that help to the use of lab staff or laboratory be operations are in would real-time analytics be the next step to improve Dustin Bunch: The future of data in the clinical laboratory will have to from a single and to data from and hospital data There are many to but there have been made to make this with like common data such as the Medical from health data and better and J. Mark Tuthill: I believe the biggest the laboratory in informatics is to the of human resources with data and personnel with experience in business analytics as well as staff who the value of analytics to drive workflow we are by of time and needs to be improved is the for analytics and of clinical laboratory as well as and who support operations across the laboratory new for the clinical laboratory is the use of that measure (e.g., and laboratory testing in are that will require new informatics As these are in their we have much experience these data be integrated to the electronic medical the other of how long should data be stored and is quality accomplished with such I am questions more these may future Jamie Gramz: Most in vendors provide solutions to help patient and quality testing, but that can value in helping the lab overcome key management solutions can the and help the process. an management can help and or to lab staff to time more meaningful monitoring and solutions can and of and automation systems in multiple laboratories from a single and reporting solutions can make it easier to monitor performance, identify inefficiencies, and investigate root causes of problems. J. Mark Tuthill: to business analytics, there are vendors that will provide analytic solutions that to the laboratory information system and help support laboratories in these business processes. However, these tools on data that is readily available to these Typically, vendors do not have access to the laboratory information systems and do not have knowledge of the laboratory information systems or its they on the laboratory to provide personnel to with of these of personnel the Once analytics tools, and are they can be by the laboratory with ease and The vendors of instruments and automation are also to make such tools basic in the at it is a process of may can also be to help laboratories understand they may Darci Block: It would be great if vendors could efforts to between common systems and instruments as well as systems and great would it be if of like the could from the experience of who have this and share experience to for David S. McClintock: vendors can by tools for labs to access their data, both for internal and for to is also vendors can better meet complex laboratory informatics to and by current ways vendors can help laboratories reporting tools to allow for easier and interoperability with other basic descriptive analytics and tools within the to support lab better integration of of within their to support for and including on labs can create or apps that in to software and create new many Edward Ki Yun Leung: such as the American Association for the American for and the Association for Informatics are great resources for laboratory professionals and to about each there are and by each of these vendors can provide support and resources to these and with these vendors are providing tools that well within their however, laboratories use one for their test Vendors can help by tools that make it easier to and use the data between different systems and Dustin Bunch: Vendors could help by their software to data into their and make it easy to This may require the software to be able to with current data such as and or to to Edward Ki Yun Leung: We can best improve the clinical laboratory and the of laboratory medicine by expanding and informatics to staff at the laboratory Informatics is to laboratory Once staff are more and with informatics can be integrated into laboratory operations and their will be able to experience the benefits of provide on the tools, and to the future of the We will be able to the right information, to the right person, at the right Dustin Bunch: The lab will be better able to if we and data into our We should be able to predict instrument issues before that create to In addition, data can help improve laboratory efficiency, but this is on many of data in the lab is to data The number and of tests are which the of interpretation. the laboratory is able to this would be a for the clinicians and patients. This can also be to for patients to understand their information J. Mark Tuthill: There are several areas that we need to and in how we can best improve the laboratory informatics. and are workflow processes. informatics tools to and understand workflow and laboratory by is step Once workflow tools have been into place, laboratory information technology needs to be in an This will have direct operational impact on laboratory efficiency, and of the laboratory information system is key and remains key to laboratory testing as well as the of Once workflow has been improved and laboratory information system technology is in place, we can to use analytics and to not only assess our workflow activities and our laboratory but to these tools in For example, artificial can be used to not only help understand laboratory testing but to help understand in laboratory testing that in outcomes or of are in their past business analytics into clinical analytics is the ability to use clinical information in actionable ways that impact patient this is the next of that laboratories can This will tools such as that are able to create laboratory and on that this information from simple testing results. David S. McClintock: The future of clinical laboratory informatics in access to all laboratory data, not and that data into data that specific operational and clinical and that data with artificial both within and external to the laboratory, to operational workflows, manual and to drive for patients and provide on clinical A better understanding and improved integration is also important clinical laboratories can with to new and improve As more to the labs will have to to how instrumentation are how they access and data, and how they their and other lab Jamie Gramz: laboratory testing has over the past years to become and there has not been much in the area of clinical decision support There remains a tremendous amount of human in the process of and lab are with not only the tests for a patient but also lab test results. to provide support for lab test result test and predictive to help the identification of patients at of specific are where could Informatics solutions for and the increased use of and artificial will be key to help the laboratory the value it to beyond the reporting of test results and reference Darci Block: To see the it will a that but also makes and decisions to that will outcomes and business that are of systems and processes that can provide and that when make the best use of systems that data is and in its most meaningful that identify and issues and challenges as they and provide into and And clinical laboratories that the data be of this to have any of human health and the delivery of healthcare Interoperability on Medical on Interoperability they have to the of this paper and have the following significant to the and of data, or and interpretation of or the for final of the and to be for all aspects of the thus that questions to the accuracy or integrity of any part of the are and all the and/or potential of J. Association for of in the of and of or J. of and support for and/or for in in the presented at Informatics on and same for at on support for and/or for and support for and/or for

Hemant B. Mahajan

No abstract is available for this record.

Suzanna Schmeelk, Megha Kanabar, Kevin Peterson, Jyotishman Pathak

Abstract Objective The purpose of this study was to conduct a scoping review of publications that explored blockchain technology in the context of interoperability and challenges of electronic health record (EHR) implementations. We synthesize the literature regarding standards and security, specifically regulation, regulatory operability, and conformance to standards. We review open practitioner questions that were not addressed in the studies as directions for further research. Materials and Methods We conducted a literature search in the OVID databases (Medline and Embase) on terms blockchain, implementation, interoperability, EHRs, security, and standards. The search resulted in 152 nonduplicate, peer-reviewed manuscripts, of which 15 were relevant to our objective and included for synthesis. Results Based on the search results, we analyzed the adoption of blockchain technology in the healthcare systems and challenges to EHR implementation of blockchain. From the synthesized research, we categorized and reported compelling factors of blockchain for EHR integration using current knowledge on blockchain research standardization and architectural challenges. Discussion Our research showed promise in implementing blockchain technology associated with EHRs, especially with Health Information Exchanges. The studies relevant for both EHR (n = 5) and blockchain (n = 10) reported compelling factors and limitations of the architecture. Security (n = 4) and interoperability (n = 4) features were reported as compelling requirements with lingering challenges. Standardization literature (n = 3) reported implementation challenges. Conclusion This study shows promise in implementing blockchain technology within EHR systems. The adoption is increasing; however, multiple implementation challenges remain from architectural perspectives (eg, scalability and performance), to security challenges (eg, legal requirements), and standard perspectives including patient-matching problems.

Daniel Toshio Harrell, Muhammad Usman, Ladd Hanson, Mustafa Abdul‐Moheeth · 10 authors

Objective: Clinical data in the United States are highly fragmented, stored in numerous different databases, and are defined by service providers or clinical specialties rather than by individuals or their families. As a result, linking or aggregating a complete record for a patient is a major technological, legal, and operational challenge. One of the factors that has made clinical data integration so difficult to achieve is the lack of a universal ID for everyone. This leads to other related problems of having to prove identity at each interaction with the health system and repeatedly providing basic information on demographics, insurance, payment, and medical conditions. Traditional solutions that require complex governance, expensive technology, and risks to privacy and security of the data have failed adequately to solve this interoperability problem. We describe the technical design decisions of a patient-centric decentralized health identity management system using the blockchain technology, called MediLinker, to address some of these challenges. Design: Our multidisciplinary research group developed and implemented an identity wallet, which uses the blockchain technology to manage verifiable credentials issued by healthcare clinics, banks, and insurance companies. To manage patient's self-sovereign identity, we leveraged the Hyperledger Indy blockchain framework to store patient's decentralized identifiers (DIDs) and the schemas or format for each credential type. In contrast, the credentials containing patient data are stored 'off-ledger' in each person's wallet and accessible via a computer or smartphone. We used Hyperledger Aries as a middleware layer (API: Application Programming Interface) to connect Hyperledger Indy with the front-end, which was developed using a JavaScript framework, ReactJS (Web Application) and React Native (iOS Application). Results: MediLinker allows users to store their personal data on digital wallets, which they control. It uses a decentralized trusted identity using Hyperledger Indy and Hyperledger Aries. Patients use MediLinker to register and share their information securely and in a trusted system with healthcare and other service providers. Each MediLinker wallet can have six credential types: health ID with patient demographics, insurance, medication list including COVID-19 vaccination status, credit card, medical power of attorney (MPOA) for guardians of pediatric or geriatric patients, and research consent. The system allows for in-person and remote granting and revoking of such permissions for care, research, or other purposes without repeatedly requiring physical identity documents or enrollment information. Conclusion: We successfully developed and tested a blockchain-based technical architecture, described in this article, as an identity management system that may be operationalized and scaled for future implementation to improve patient experience and control over their personal information.

Mustafa Abdul‐Moheeth, Muhammad Usman, Daniel Toshio Harrell, Anjum Khurshid

Background: The current healthcare ecosystem in the United States is plagued by inefficiencies in transitions of patient care between healthcare providers due in large part to a lack of interoperability among the many electronic medical record (EMR) systems that exist today. Both providers and patients experience significant frustration due to the negative effects of increased costs, unnecessary administrative burden, and duplication of services that occur because of data fragmentation in the system. Blockchain technology provides a potential solution to mitigate or eliminate these gaps by allowing for exchange of healthcare information that is distributed, auditable, immutable, and respectful of patient autonomy. Our multidisciplinary team identified key tasks required for a transition of care to design and develop a blockchain application, MediLinker, which served as a patient-centric identity management system to address issues of data fragmentation ultimately aiding in the delivery of high-value care services. Methods: The MediLinker application was evaluated for its ability to accomplish various key tasks needed for a successful transition of patient care in an outpatient setting. Our team created 20 unique patient use cases covering a diversity of medical needs and social circumstances that were played out by participants who were asked to perform various tasks as they received case across a simulated healthcare ecosystem composed of four clinics, a research institution, and other ancillary public services. Tasks included, but were not limited to, clinic enrollment, verification of identity, medication reconciliation, sharing insurance and billing information, and updating demographic information. With this iteration of MediLinker, we specifically focused on the functionality of digital guardianship and patient revocation of healthcare information. In addition, throughout the simulation, we surveyed participant perceptions regarding the use of MediLinker and blockchain technology to better ascertain comfortability and usability of the application. Results: Quantitative evaluation of simulation results revealed that MediLinker was able to successfully accomplish all seven clinical scenarios tested across the 20 patient use cases. MediLinker successfully achieved its goal of patient-centered interoperability as participants transitioned their simulated healthcare data, including COVID-19 vaccination status and current medications, across the four clinic sites and research institution. In addition to completing all key tasks designated, all eligible participants were able to enroll with and subsequently revoke data access with our simulated research site. MediLinker had a low data-entry error rate, with most errors occurring due to work-flow vulnerabilities. Our qualitative analysis of user perceptions indicated that comfortability and trust with blockchain technology, such as MediLinker, grew with increased education and exposure to such technology. Conclusions: The ubiquitous problem of data fragmentation in our current healthcare ecosystem has placed considerable strain on providers and patients alike. Blockchain applications for health identity management, such as MediLinker, provide a viable solution to stem the inefficiencies that exist today. The interoperability that MediLinker provided across our simulated healthcare system has the potential to improve transitions of care by sharing key aspects of healthcare information in a timely, secure, and patent-centric fashion allowing for the delivery of consistent and personalized high value care. Blockchain technologies appear to face similar challenges to widespread adoption as other novel interventions, namely recognition, trust, and usability. Further development and scaling are required for such technology to realize its full potential in the real world and transform the practice of modern health care.

Thiago Bulhões da Silva Costa, Lucas Shinoda, Ramon A. Moreno, José Eduardo Krieger · 5 authors

Background The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data to the respective owner. In spite of this importance, most of the works focus more on describing how blockchain models can be used in a PHR scenario rather than whether these models are in fact feasible and robust enough to support a large number of users. Objective To achieve a consistent, reproducible, and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share, and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related (net)works. Methods We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system: first, under workload, ranging from 100 to 2500 simultaneous record submissions, and second, increasing the network size from 3 to 13 peers. In both experiments, we used throughput and average latency as the primary metrics. We also created a health database, a cryptographic unit, and a server to complement the blockchain network. Results With a 3-peer network, smart contracts that write on the ledger have throughputs, measured in transactions per second (tps) in an order of magnitude close to 102 tps, while those contracts that only read have rates close to 103 tps. Smart contracts that write also have latencies, measured in seconds, in an order of magnitude close to 101 seconds, while that only read have delays close to 100 seconds. In particular, smart contracts that retrieve, list, and view history have throughputs varying, respectively, from 1100 tps to 1300 tps, 650 tps to 750 tps, and 850 tps to 950 tps, impacting the overall system response if they are equally requested under the same workload. Varying the network size and applying an equal fixed load, in turn, writing throughputs go from 102 tps to 101 tps and latencies go from 101 seconds to 102 seconds, while reading ones maintain similar values. Conclusions To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below what the traditional distributed databases achieve, indicating that the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production.

Hemant B. Mahajan, Ameer Sardar Kwekha Rashid, Aparna A. Junnarkar, Nilesh Uke · 8 authors

No abstract is available for this record.

Bader Aldughayfiq, Srinivas Sampalli

OBJECTIVE: To evaluate the attitudes of the parties involved in the system toward the new features and measure the potential benefits of introducing the use of blockchain and machine learning (ML) to strengthen the in-place methods for safely prescribing medication. The proposed blockchain will strengthen the security and privacy of the patient's prescription information shared in the network. Once the ePrescription is submitted, it is only available in read-only mode. This will ensure there is no alteration to the ePrescription information after submission. In addition, the blockchain will provide an improved tracking mechanism to ensure the originality of the ePrescription and that a prescriber can only submit an ePrescription with the patient's authorization. Lastly, before submitting an ePrescription, an ML algorithm will be used to detect any anomalies (eg, missing fields, misplaced information, or wrong dosage) in the ePrescription to ensure the safety of the prescribed medication for the patient. METHODS: The survey contains questions about the features introduced in the proposed ePrescription system to evaluate the security, privacy, reliability, and availability of the ePrescription information in the system. The study population is comprised of 284 respondents in the patient group, 39 respondents in the pharmacist group, and 27 respondents in the prescriber group, all of whom met the inclusion criteria. The response rate was 80% (226/284) in the patient group, 87% (34/39) in the pharmacist group, and 96% (26/27) in the prescriber group. KEY FINDINGS: The vast majority of the respondents in all groups had a positive attitude toward the proposed ePrescription system's security and privacy using blockchain technology, with 72% (163/226) in the patient group, 70.5% (24/34) in the pharmacist group, and 73% (19/26) in the prescriber group. Moreover, the majority of the respondents in the pharmacist (70%, 24/34) and prescriber (85%, 22/26) groups had a positive attitude toward using ML algorithms to generate alerts regarding prescribed medication to enhance the safety of medication prescribing and prevent medication errors. CONCLUSION: Our survey showed that a vast majority of respondents in all groups had positive attitudes toward using blockchain and ML algorithms to safely prescribe medications. However, a need for minor improvements regarding the proposed features was identified, and a post-implementation user study is needed to evaluate the proposed ePrescription system in depth.

Ye Seul Bae, Yujin Park, Seung Min Lee, Hee Hwa Seo · 9 authors

Health information exchange can improve health outcomes and reduce unnecessary medical expenses. An important task in health information exchange is to prove data integrity and strengthen the right to self-determination of individuals. This can be addressed using blockchain technology and dynamic consents. We aimed to develop a blockchain-based mobile platform called HealthPocket to exchange reliable health information with proven integrity through a dynamic consent system based on the HL7 FHIR standards. Through HealthPocket, subjects can selectively provide their consent to share specific medical and PHGD through proper authorization, and each response is converted into the JSON format with FHIR compatibility. We conducted a usability test to demonstrate health information exchange between primary and tertiary medical institutions. A total of 116 subjects used the HealthPocket mobile application to selectively share health information for at least one month, and conducted a questionnaire about their experience. In addition, medical staff of the institution could access the medical information shared by the participants and use it for treatment. The user surveys for patients examined the perceived usefulness, perceived ease of use, and overall service satisfaction. The mean overall satisfaction with the health information exchange service was the highest (4.67 out of 5 points). As security, personal information protection, and interoperability are main concerns related to health information exchange, blockchain technology can provide suitable solutions. For instance, when health information is exchanged using blockchain technology, it is impossible to alter data blocks. Therefore, by using a blockchain-based platform and dynamic consent system, we ensured the integrity of medical data.

Valli S. Kumar, John J. Lee, Qin Hu

Abstract With an increasing affinity towards patient‐centric care, sharing real‐time sensitive data for collaboration between multiple parties with finer access control becomes critical. Most existing studies based on the blockchain technology in the medical field discuss various application scenarios and security aspects, without focusing on data ownership, secure data sharing, or finer access control. In this work, a non‐fungible token (NFT)‐based system is proposed to implement a health record marketplace. The system leverages the NFT technology to provide dual ownership along with finer access control and efficiency in data sharing. The advantage of permissioned blockchain along with InterPlanetary File System (IPFS) are taken for off‐chain data storage to improve security and efficiency. Because price determination is critical in the market, Stackelberg game theory is utilized to determine pricing strategies for both data owners and consumers. Also, to efficiently achieve finer access control, a popularity‐based adaptive NFT management scheme using reinforcement learning is proposed. Simulation experiments are carried out to demonstrate accuracy and efficiency of our proposed schemes.

Ryan W. Seaberg, Tyler R. Seaberg, David C. Seaberg

Objective: Distributed ledger technology can be used as a transparent, shareable ledger, that can record transactions between two parties efficiently and in a more secure, verifiable, and permanent way than the current electronic prescribing systems. We studied the use of a distributed ledger electronic prescribing programme, Prescription Abuse Greatly Reduced (PAGR) Prescriptions, to examine the effect of blockchain on provider prescribing efficiency at three family medicine clinics. Design: The PAGR was installed side-by-side to the electronic health record at three family medicine practice clinics in middle Tennessee. A prospective, convenience sample of patients at all three clinics was used for analysis. Trained observers were used in each clinic to document the side-by-side use of current prescribing practice versus the use of the PAGR electronic prescribing system by the individual providers.The primary outcome was total time to write the prescription. Secondary metrics included compliance with checking the state's Physician Drug Monitoring Program (PDMP.) , accuracy of medicine reconciliation, use of patient's eligibility on insurance, prescription benefits, and change in prescription caused by benefits analysis or drug-interactions. Provider satisfaction was measure on a 4-point Likert scale.Data were analysed using two-tailed, paired Student T-tests with alpha set at 0.05. A sample size of 107 patients was calculated to have a power of 80% to detect a 50% change in the prescription writing time. Results: The primary outcome of total prescription writing time was 171 ± 41 sec for current prescribing practice versus 63 ± 15 sec for the PAGR system (p = 0.0006). All providers were extremely satisfied with the use of the PAGR programme. Conclusion: Use of the PAGR electronic prescription programme significantly saved a mean of 1 min 48 sec per written prescription at the three Family Medicine Clinics. The PAGR also provided accurate medicine reconciliation and complete PDMP checks for controlled substance prescriptions. The patient real-time benefits check and drug-drug and allergy-drug reviews resulted in the provider changing the prescription 28% of the time, enhancing safety and out-of-pocket patient expenses. Future enhancements include expanding the insurance benefits analysis and developing provider notifications when patients are non-compliant with filling their prescriptions.

Baldwin C. Mak, Bryan T. Addeman, Jia Chen, Kim Papp · 9 authors

Objective: Despite the implementation of quality assurance procedures, current clinical trial management processes are time-consuming, costly, and often susceptible to error. This can result in limited trust, transparency, and process inefficiencies, without true patient empowerment. The objective of this study was to determine whether blockchain technology could enforce trust, transparency, and patient empowerment in the clinical trial data management process, while reducing trial cost. Design: In this proof of concept pilot, we deployed a Hyperledger Fabric-based blockchain system in an active clinical trial setting to assess the impact of blockchain technology on mean monitoring visit time and cost, non-compliances, and user experience. Using a parallel study design, we compared differences between blockchain technology and standard methodology. Results: A total of 12 trial participants, seven study coordinators and three clinical research associates across five sites participated in the pilot. Blockchain technology significantly reduces total mean monitoring visit time and cost versus standard trial management (475 to 7 min; P = 0.001; €722 to €10; P = 0.001 per participant/visit, respectively), while enhancing patient trust, transparency, and empowerment in 91, 82 and 63% of the patients, respectively. No difference in non-compliances as a marker of trial quality was detected. Conclusion: Blockchain technology holds promise to improve patient-centricity and to reduce trial cost compared to conventional clinical trial management. The ability of this technology to improve trial quality warrants further investigation.

Vinodhini Mani, M. Prakash

No abstract is available for this record.

Francesco Sanmarchi, F Toscano, M Fattorini, Andrea Bucci · 5 authors

Modern healthcare management and clinical practice strongly rely on data and scientific evidence. Digital technologies, tools, and services are core components of Healthcare Management and scientific Research (HMR). Data interoperability, security, privacy, and ease of sharing represent fundamental conditions for guaranteeing quality HMR. Current data management solutions in HMR are mainly built on two technological infrastructures: cloud-based (CB) or distributed ledger systems (DLTs). DLTs offer alternative and reliable alternatives for the management and sharing of data in HMR. Their use can help increase confidence and trust in the integrity of data and the resulting evidence. 
\nThe aim of this paper is to shed light on CB and DLT solutions, emphasizing the potential role of innovative digital solutions based on DLTs in creating a data-driven transformation of HMR, and to describe relevant examples and practical uses of DLT-based solutions for patients, healthcare management, and research activities. 
\nDLTs in particular can be increasingly useful for patients to truly have control over their health, for healthcare policymakers to increase the quality of organizational processes, and for research funders, editors and publishers to increase the return on investment, and the reuse and reproducibility of research. 
\nIn conclusion, harnessing the potential of digital technologies is essential to transform healthcare management and research, by enhancing data quality, reliability, and trust.

Bandar Alamri, Ibrahim Tariq Javed, Tiziana Margaria

An up-to-date personal health record (PHR) system is crucial for people's health. Achieving a reliable PHR system in the e-Health and m-Health era is still a challenge concerning data integration from different EHRs, data interoperability, and enforcing that access to data is fully under the patient's control. We address these challenges by proposing an electronic health wallet (EHW) system that uses emergent decentralized technologies like blockchain and IPFS and adopts health data interoperability standards and technologies like FHIR's APIs. The EHW stands on a GDPR-compliant framework for IoT-based PHR systems that ensures both data privacy and interoperability. The proposed conceptual framework and system architecture provide a comprehensive solution for a patient-centered IoT-based PHR system that preserves data privacy and satisfies the data interoperability needs. By encouraging patients to share their data in a controlled way, also enables health big data analytics by utilizing the IoT data in a privacy-preserving fashion.

Hyeong Joon Kim, Hye Hyeon Kim, Hosuk Ku, Kyung Don Yoo · 11 authors

BACKGROUND: ) for data privacy and participatory medicine; however, its fully decentralized architecture has come at the expense of decentralized data management and data provenance. OBJECTIVE: The introduction of blockchain and smart contract technologies to the legacy Health Avatar Platform with a clinical metadata registry remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data. METHODS: The Health Avatar Platform decentralizes patient data in appropriate locations (ie, on patients' smartphones and on physicians' smart devices). We implemented an Ethereum-based hash chain for all transactions and smart contract-based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into 3 smart contracts, in this case, a health data transaction manager, a transaction status manager, and an application programming interface transaction manager. The actual decentralized health data are managed in an off-chain manner on appropriate smart devices and authenticated by hashed metadata on-chain. RESULTS: Metadata of each data transaction are captured in a Health Avatar Platform blockchain node by the smart contracts. We provide workflow diagrams each of the 3 use cases of data push (from a physician app or an intelligent agents to a patient Avatar), data pull (request to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash-chained metadata support data element-level verification of data integrity in subsequent transactions. Smart contracts can incentivize transactions for data sharing and intelligent digital health care services. CONCLUSIONS: Health Avatar Platform and interconnected patient Avatars, physician apps, and intelligent agents provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions with smart contracts.

Hao Sen Andrew Fang, Teng Hwee Tan, Cheryl Yan Fang Tan, Marcus Chun Jin Tan

BACKGROUND: Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing. OBJECTIVE: This review aims to examine the current landscape, design choices, limitations, and future directions of blockchain-based PHRs. METHODS: Adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, a cross-disciplinary systematic review was performed in July 2020 on all eligible articles, including gray literature, from the following 8 databases: ACM, IEEE Xplore, MEDLINE, ScienceDirect, Scopus, SpringerLink, Web of Science, and Google Scholar. Three reviewers independently performed a full-text review and data abstraction using a standardized data collection form. RESULTS: A total of 58 articles met the inclusion criteria. In the review, we found that the blockchain PHR space has matured over the past 5 years, from purely conceptual ideas initially to an increasing trend of publications describing prototypes and even implementations. Although the eventual application of blockchain in PHRs is intended for the health care industry, the majority of the articles were found in engineering or computer science publications. Among the blockchain PHRs described, permissioned blockchains and off-chain storage were the most common design choices. Although 18 articles described a tethered blockchain PHR, all of them were at the conceptual stage. CONCLUSIONS: This review revealed that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage. Being the first systematic review on blockchain PHRs, this review should serve as a basis for future reviews to track the development of the space.

Ramya Gangula, Sri Varun Thalla, Ijeoma Ikedum, Chineze Okpala · 5 authors

Adopting and implementing the Clinical Decision Support System (CDSS) technology is a critical element in an effort to improve national quality initiatives and evidence-based practice at the point of care. CDSS is envisioned to be a potential solution to many current challenges in the healthcare sphere, which includes information overload, practice improvement, eliminating treatment errors, and reducing medical consultation costs. However, the CDSS did not manage to achieve these goals to the desired levels and provide context-appropriate alerts, although integrated with the electronic health records (EHRs) (1). Clinical decision support alerts can save lives, but frequent ones can cause increased cognitive burden to clinicians, worsen alert fatigue, and increase the duplication of tests. This ultimately increases health care costs without refining patient outcomes. Studies show that 49-96% of clinical alerts are ignored, raising questions about the effectiveness of CDSS (1). Blockchain, a decentralized, distributed digital ledger that contains a plethora of continuously updated, time-stamped, and highly encrypted virtual record, can be a key to addressing these challenges (2). The blockchain technology if integrated with the CDSS can serve as a potential solution to eliminating current drawbacks with CDSS (3). This article addresses the most significant and chronic problems facing the successful implementation of CDSS and how leveraging the Hyperledger Fabric can alleviate the clinical alert fatigue and reduce physician's burnout using patient-specific information. The proposed architecture framework for this study is designed to equip the CDSS with overall patient information at the point of care. This then empowers the physicians with the blockchain-integrated CDSS, which holds the potential to reduce clinician's cognitive burden, medical errors, and costs and ultimately enhance patient outcomes. The research study broadly discusses how the blockchain technology can be a potential solution, reasons for selecting the Hyperledger Fabric, and elaborates on how the Hyperledger Fabric can be leveraged to enhance the efficacy of CDSS.

Anjum Khurshid, Cole Holan, Cody Cowley, Jeremiah Alexander · 9 authors

OBJECTIVE: Healthcare systems suffer from a lack of interoperability that creates "data silos," causing patient linkage and data sharing problems. Blockchain technology's unique architecture provides individuals greater control over their information and may help address some of the problems related to health data. A multidisciplinary team designed and tested a blockchain application, MediLinker, as a patient-centric identity management system. METHODS: The study used simulated data of "avatars" representing different types of patients. Thirty study participants were enrolled to visit simulated clinics, and perform various activities using MediLinker. Evaluation was based on Bouras' criteria for patient-centric identity management and on the number of errors in entry and sharing of data by participants. RESULTS: Twenty-nine of the 30 participants completed all study activities. MediLinker fulfilled all of Bouras' criteria except for one which was not testable. A majority of data errors were due to user error, such as wrong formatting and misspellings. Generally, the number of errors decreased with time. Due to COVID-19, sprint 2 was completed using "virtual" clinic visits. The number of user errors were less in virtual visits than in personal visits. DISCUSSION: The evaluation of MediLinker provides some evidence of the potential of a patient-centric identity management system using blockchain technology. The results showed a working system where patients have greater control over their information and can also easily provide consent for use of their data. CONCLUSION: Blockchain applications for identity management hold great promise for use in healthcare but further research is needed before real-world adoption.

Mario Ciampi, Angelo Esposito, Fabrizio Marangio, Mario Sicuranza · 5 authors

No abstract is available for this record.

Karamo Kanagi, Cheng‐Yuan Ku, Li-Kai Lin, Wen-Huai Hsieh

BACKGROUND: While electronic health records have been collected for many years in Taiwan, their interoperability across different health care providers has not been entirely achieved yet. The exchange of clinical data is still inefficient and time consuming. OBJECTIVES: This study proposes an efficient patient-centric framework based on the blockchain technology that makes clinical data accessible to patients and enable transparent, traceable, secure, and effective data sharing between physicians and other health care providers. METHODS: Health care experts were interviewed for the study, and medical data were collected in collaboration with Ministry of Health and Welfare (MOHW) Chang-Hua hospital. The proposed framework was designed based on the detailed analysis of this information. The framework includes smart contracts in an Ethereum-based permissioned blockchain to secure and facilitate clinical data exchange among different parties such as hospitals, clinics, patients, and other stakeholders. In addition, the framework employs the Logical Observation Identifiers Names and Codes (LOINC) standard to ensure the interoperability and reuse of clinical data. RESULTS: The prototype of the proposed framework was deployed in Chang-Hua hospital to demonstrate the sharing of health examination reports with many other clinics in suburban areas. The framework was found to reduce the average access time to patient health reports from the existing next-day service to a few seconds. CONCLUSION: The proposed framework can be adopted to achieve health record sharing among health care providers with higher efficiency and protected privacy compared to the system currently used in Taiwan based on the client-server architecture.