Blockchain technology is often considered as the fourth industrial revolution that will change the world. The enthusiasm of the transformative nature of blockchain technology has infiltrated healthcare. Blockchain is often seen as the much needed and perfect technology for healthcare, addressing the difficult and complex issues of security and inter-operability. More importantly, the "value" and trust-based system can deliver automated action and response via its smart contract mechanism. Healthcare, however, is a complex system. Health information technology (HIT) so far, has not delivered its promise of transforming healthcare due to its complex socio-technical and context sensitive interaction. The introduction of blockchain technology will need to consider a whole range of socio-technical issues in order to improve the quality and safety of patient care. This paper presents a discussion on these socio-technical issues. More importantly, this paper argues that in order to achieve the best outcome from blockchain technology, there is a need to consider a clinical transformation from "information" to "value " and trust. This paper argues that urgent research is needed to address these socio-technical issues in order to facilitate best outcomes for blockchain in healthcare. These socio-technical issues must then be further evaluated by means of working prototypes in the medical domain in coming years.
Open access
Electronic Health Records Systems
Mobile Health and mHealth Applications
Artificial Intelligence in Healthcare and Education
M. Saravanan, R. Shubha, Achsah Mary Marks, Vishakh Iyer
Wearable health devices, mobile apps and diagnostic tools revolutionize the medical field by introducing new assisting devices for patients in a way to create comfort, communication and augmented intelligence. Internet of Things involved in this transformation to provide an environment where a patient's vital parameters get transmitted by sensor devices via a gateway onto secure cloud-based platforms where it is stored, aggregated and analyzed. It also helps to store data for millions of patients and performs analysis in real time, ultimately promoting an evidence-based medicine system. Privacy and security are concerns in this environment. Based on the latest trends, this paper introduces a new healthcare paradigm named as SMEAD by developing an end-to-end secured system for assisting diabetic patients. It includes wearables to monitor different parameters thus observe and predict the diabetes status of the patient. The proposed system employs a MEDIBOX which is used to configure the dosage required and provides an alert to the users reminding them to take medication on time. In this case, the insulin dosage is maintained at suitable cooling conditions and is continuously monitored using the mentioned system. To keep all the data secure and to enable access to this data by the doctor and other trusted parties, a Blockchain-based disruptive technology is implemented which facilitates cryptographic security and formalized data access through smart contracts for medical communities. In case of an emergency like missing a dosage, abnormal blood sugar levels or any security lapse, an alert is sent to the caretakers via social networks like Twitter, Facebook or WhatsApp using mobile as a gateway which can continuously communicate the data over the internet that could save patients from fatal effects of the disease.
Peng Zhang, Michael A. Walker, Jules White, Douglas C. Schmidt · 5 authors
Blockchain is a decentralized, trustless protocol that combines transparency, immutability, and consensus properties to enable secure, pseudo-anonymous transactions. Smart contracts are built atop a blockchain to support on-chain storage and enable Decentralized Apps (DApps) to interact with the blockchain programatically. Programmable blockchains have generated interest in the healthcare domain as a potential solution to resolve key challenges, such as gapped communications, inefficient clinical report delivery, and fragmented health records. This paper provides evaluation metrics to assess blockchain-based DApps in terms of their feasibility, intended capability, and compliance in the healthcare domain.
BACKGROUND: Digital health technologies, including telemedicine, mobile health (mHealth), and remote monitoring, are playing a greater role in medical practice. Safe and accurate management of medical information leads to the advancement of digital health, which in turn results in a number of beneficial effects. Furthermore, mHealth can help lower costs by facilitating the delivery of care and connecting people to their health care providers. Mobile apps help empower patients and health care providers to proactively address medical conditions through near real-time monitoring and treatment, regardless of the location of the patient or the health care provider. Additionally, mHealth data are stored in servers, and consequently, data management that prevents all forms of manipulation is crucial for both medical practice and clinical trials. OBJECTIVE: The aim of this study was to develop and evaluate a tamper-resistant mHealth system using blockchain technology, which enables trusted and auditable computing using a decentralized network. METHODS: We developed an mHealth system for cognitive behavioral therapy for insomnia using a smartphone app. The volunteer data collected with the app were stored in JavaScript Object Notation format and sent to the blockchain network. Thereafter, we evaluated the tamper resistance of the data against the inconsistencies caused by artificial faults. RESULTS: Electronic medical records collected using smartphones were successfully sent to a private Hyperledger Fabric blockchain network. We verified the data update process under conditions where all the validating peers were running normally. The mHealth data were successfully updated under network faults. We further ensured that any electronic health record registered to the blockchain network was resistant to tampering and revision. The mHealth data update was compatible with tamper resistance in the blockchain network. CONCLUSIONS: Blockchain serves as a tamperproof system for mHealth. Combining mHealth with blockchain technology may provide a novel solution that enables both accessibility and data transparency without a third party such as a contract research organization.
Tyler B. Wray, Philip A. Chan, Erik M. Simpanen, Don Operario
BACKGROUND: Men who have sex with men (MSM) are the group at highest risk for contracting human immunodeficiency virus (HIV) in the United States, but many do not test as frequently as recommended. Home-based self-testing (HBST) for HIV holds promise for promoting regular testing among these individuals, but currently available HBSTs have limited follow-up options, providing only a 1-800 number that participants can call. Failure to actively conduct follow-up counseling and referrals after HBST use could result in delays in seeking confirmatory testing and care among users receiving reactive (preliminary positive) test results. HBST also fails to connect users who test negative with other prevention services that can reduce their future risk for HIV. OBJECTIVE: The aim of our study was to use qualitative research methods with high-risk MSM to inform development of a "smart" HBST kit. The kit utilizes existing Internet-of-Things (IoT) technologies to monitor HBST use in real-time and enable delivery of timely, active follow-up counseling and referrals over the phone. METHODS: In phase 1, individual interviews (n=10) explored how participants might use HBST and their views and preferences for conducting counseling and referral after HBST. Based on these perspectives, we developed a smartphone app (iOS, Android) that uses data from light sensors on Bluetooth low energy (BLE) beacons to monitor when HBST kits are opened, facilitating timely follow-up phone contact with users. In phase 2, a usability study conducted among high-risk MSM (n=10) examined the acceptability and feasibility of this system and provided user perspectives after using the system along with HBST. RESULTS: Phase 1 themes suggested that MSM preferred HBST, that most thought active follow-up after HBST would be valuable, and that doing so over the phone within 24 h after testing was preferable. Phase 2 results showed that the eTEST system successfully detected HBST use in nearly all cases. Participant perspectives also suggested that the timing, method (ie, phone call), and duration of follow-up were appropriate and helpful. CONCLUSIONS: Using BLE beacons and a smartphone app to enable follow-up counseling and referral over the phone after HBST use is feasible and acceptable to high-risk MSM. Future research is needed to compare the effects of follow-up counseling on rates of repeat testing and receipt of referral services (eg, testing for sexually transmitted infections and initiation of preexposure prophylaxis) and to explore the acceptability of the eTEST system over longer periods of time.
BACKGROUND: Software to help control diabetes is currently an embryonic market with the main activity to date focused mainly on the development of noncomputerized solutions, such as cardboard calculators or computerized solutions that use "flat" computer models, which are applied to each person without taking into account their individual lifestyles. The development of true, mobile device-driven health applications has been hindered by the lack of tools available in the past and the sheer lack of mobile devices on the market. This has now changed, however, with the availability of pocket personal computer handsets. METHOD: This article describes a solution in the form of an intelligent neural network running on mobile devices, allowing people with diabetes access to it regardless of their location. Utilizing an easy to learn and use multipanel user interface, people with diabetes can run the software in real time via an easy to use graphical user interface. The neural network consists of four neurons. The first is glucose. If the user's current glucose level is within the target range, the glucose weight is then multiplied by zero. If the glucose level is high, then there will be a positive value multiplied to the weight, resulting in a positive amount of insulin to be injected. If the user's glucose level is low, then the weights will be multiplied by a negative value, resulting in a decrease in the overall insulin dose. RESULTS: A minifeasibility trial was carried out at a local hospital under a consultant endocrinologist in Belfast. The short study ran for 2 weeks with six patients. The main objectives were to investigate the user interface, test the remote sending of data over a 3G network to a centralized server at the university, and record patient data for further proofing of the neural network. We also received useful feedback regarding the user interface and the feasibility of handing real-world patients a new mobile phone. Results of this short trial confirmed to a large degree that our approach (which also can be known as intensive insulinotherapy) has value and perhaps that our neural network approach has implications for future intelligent insulin pumps. CONCLUSIONS: Currently, there is no software available to tell people with diabetes how much insulin to inject in accordance with their lifestyle and individual inputs, which leads to adjustments in software predictions on the amount of insulin to inject. We have taken initial steps to supplement the knowledge and skills of health care professionals in controlling insulin levels on a daily basis using a mobile device for people who are less able to manage their disease, especially children and young adults.