Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies.

BACKGROUND: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. OBJECTIVE: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. METHODS: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. RESULTS: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. CONCLUSIONS: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications.

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.

Generalizable Layered Blockchain Architecture for Health Care Applications: Development, Case Studies, and Evaluation.

BACKGROUND: Data coordination across multiple health care facilities has become increasingly important for many emerging health care applications. Distrust has been recognized as a key barrier to the success of such applications. Leveraging blockchain technology could provide potential solutions tobuild trust between data providers and receivers by taking advantage of blockchain properties such as security, immutability, anonymity, decentralization, and smart contracts. Many health technologies have empirically proven that blockchain designs fit well with the needs of health care applications with certain degrees of success. However, there is a lack of robust architecture to provide a practical framework for developers to implement applications and test the performance of stability, efficiency, and scalability using standard blockchain designs. A generalized blockchain model is needed for the health care community to adopt blockchain technology and develop applications in a timely fashion. OBJECTIVE: This study aimed at building a generalized blockchain architecture that provides data coordination functions, including data requests, permission granting, data exchange, and usage tracking, for a wide spectrum of health care application developments. METHODS: An augmented, 3-layered blockchain architecture was built on a private blockchain network. The 3 layers, from bottom to top, are as follows: (1) incorporation of fundamental blockchain settings and smart contract design for data collection; (2) interactions between the blockchain and health care application development environment using Node.js and web3.js; and (3) a flexible development platform that supports web technologies such as HTML, https, and various programing languages. Two example applications, health information exchange (HIE) and clinical trial recruitment, were developed in our design to demonstrate the feasibility of the layered architecture. Case studies were conducted to test the performance in terms of stability, efficiency, and scalability of the blockchain system. RESULTS: A total of 331,142 simulated HIE requests from accounts of 40,000 patients were successfully validated through this layered blockchain architecture with an average exchange time of 11.271 (SD 2.208) seconds. We also simulated a clinical trial recruitment scenario with the same set of patients and various recruitment criteria to match potential subjects using the same architecture. Potential subjects successfully received the clinical trial recruitment information and granted permission to the trial sponsors to access their health records with an average time of 3.07 seconds. CONCLUSIONS: This study proposes a generalized layered blockchain architecture that offers health technology community blockchain features for application development without requiring developers to have extensive experience with blockchain technology. The case studies tested the performance of our design and empirically proved the feasibility of the architecture in 2 relevant health application domains.

A Patient-Centric Health Information Exchange Framework Using Blockchain Technology.

Health Information Exchange (HIE) exhibits remarkable benefits for patient care such as improving healthcare quality and expediting coordinated care. The Office of the National Coordinator (ONC) for Health Information Technology is seeking patient-centric HIE designs that shift data ownership from providers to patients. There are multiple barriers to patient-centric HIE in the current system, such as security and privacy concerns, data inconsistency, timely access to the right records across multiple healthcare facilities. After investigating the current workflow of HIE, this paper provides a feasible solution to these challenges by utilizing the unique features of blockchain, a distributed ledger technology which is considered "unhackable". Utilizing the smart contract feature, which is a programmable self-executing protocol running on a blockchain, we developed a blockchain model to protect data security and patients' privacy, ensure data provenance, and provide patients full control of their health records. By personalizing data segmentation and an "allowed list" for clinicians to access their data, this design achieves patient-centric HIE. We conducted a large-scale simulation of this patient-centric HIE process and quantitatively evaluated the model's feasibility, stability, security, and robustness.

Development of A Blockchain Framework for Virtual Clinical Trials.

Clinical trials are essential for discovering new treatments, but there are multiple challenges to patient recruitment, patient engagement, and cost containment. Virtual clinical trials (VCT) are an innovative approach that provides potential solutions by conducting home-based, rather than site-based, clinical trials. Virtual clinical trials are still the exception rather than general practice due to technical barriers. "Blockchain," a distributed ledger technology, is a perfect match for virtual clinical trials. Its peer-to-peer design, security settings, and data transparency meet the needs of many healthcare applications. The programmable "Smart Contract" feature makes blockchain more suitable and feasible for VCT by solving computational issues. Our previous work has shown the power of applying blockchain to clinical trial recruitment. This work develops a comprehensive blockchain framework, with simulations and case studies, including patient recruitment, patient engagement, and persistent monitoring modules.

Statin use and Vital Organ Failure in Patients With Asthma-Chronic Obstructive Pulmonary Disease Overlap: A Time-Dependent Population-Based Study.

Objective: The effects of statins on the risk of hepatic, renal, respiratory, and heart failure among patients with asthma-chronic obstructive pulmonary disease overlap (ACO) have not been reported. Design: Time-dependent population-based study. Setting: Patient data from 2000 to 2010 were retrieved from the Taiwan National Health Insurance Research Database. Patients: We divided patients with ACO into cohorts of statin use (N = 1,211) and nonuse (N = 7,443). Measurements and Main Results: The cumulative incidence rates of hepatic, renal, respiratory, and heart failure were analyzed through Cox proportional regression analysis with time-dependent variables. After adjustment for multiple confounding factors, including age, sex, comorbidities, and medications [statins, inhaled corticosteroid (ICS), or oral steroid (OS)], the adjusted hazard ratios (aHRs) [95% confidence intervals (CIs)] for hepatic, renal, respiratory, and heart failure were 0.50 (0.40-0.64), 0.49 (0.38-0.64), 0.61 (0.27-2.21), and 0.47 (0.37-0.60), respectively. The aHRs (95% CIs) for statin use with [ICS, OS] for hepatic, renal, and heart failure were [0.36 (0.20-0.66), 0.52 (0.39-0.70)]; [0.82 (0.51-1.34), 0.46 (0.33-0.63)]; and [0.66 (0.40-1.07), 0.48 (0.37-0.64)], respectively. Conclusions: The ACO cohort with statin use exhibited lower risk of hepatic, renal, and heart failure than any other cohort, regardless of age, sex, comorbidities, or ICS or OS use. Regarding the combined use of statins and ICS, the risks of hepatic failure were lower. For the combined use of statins and OS, hepatic, renal, and heart failure were less frequent.

Long-Term Use of Tamoxifen Is Associated With a Decreased Subsequent Meningioma Risk in Patients With Breast Cancer: A Nationwide Population-Based Cohort Study.

Background: Earlier studies have indicated a relatively higher risk of occurring meningioma among female breast cancer survivors and have suggested that tamoxifen might decrease this risk. The present study evaluated whether tamoxifen use in breast cancer patients can reduce the risk of meningioma. Methods: We designed a population-based cohort study by using data from the National Health Insurance system of Taiwan to assess this issue. Between January 1, 2000, and December 31, 2008, women with breast cancer and of age ≥20 years were included. They were divided into two groups: those who had not received tamoxifen therapy and those who had. The Cox's proportion hazard regression analysis was conducted to estimate the effects of tamoxifen treatment and the subsequent meningioma risk. Results: We identified a total of 50,442 tamoxifen users and 30,929 non-tamoxifen users. Tamoxifen users had a borderline significantly lower overall risk of meningioma than non-tamoxifen users [adjusted hazard ratio (aHR) = 0.64, 95% confidence interval (95% CI) = 0.40-1.02]. A statistically significant difference was found in those patients with tamoxifen treatment duration longer than 1,500 days (aHR = 0.42, 95% CI = 0.19-0.91) or with cumulative dosage exceeding 26,320 mg (aHR = 0.44, 95% CI = 0.22-0.88). Furthermore, no statistically significant joint effect of aromatase inhibitors and tamoxifen on the occurrence of meningioma among breast cancer patients was seen. Conclusion: Tamoxifen users had a non-significantly (36%) lower risk of developing meningioma than did tamoxifen non-users; however, our data indicated that tamoxifen therapy is associated with a reduced meningioma risk for Taiwanese breast cancer patients receiving long duration or high cumulative dosage treatment with tamoxifen.

Effects of statins on anxiety and depression in patients with asthma-chronic obstructive pulmonary disease overlap syndrome.

BACKGROUND: The effects of statins on anxiety and depression in patients with asthma-chronic obstructive pulmonary disease overlap syndrome (ACOS) have not been reported. This population-based study investigated these effects. METHODS: Taiwan's National Health Insurance Research Database between 2000 and 2010. We enrolled two ACOS cohorts, one of statin users (n = 1252) and one of nonstatin users matched by age, sex, and index date (n = 7887). The cumulative incidence of anxiety and depression was analyzed using time-dependent Cox proportional regression analysis. RESULTS: After adjustment for multiple confounding factors, including age, sex, comorbidities, and medications-statins, inhaled corticosteroids (ICSs), and oral steroids (OSs)-the ACOS cohort with statin use had significantly lower risks of anxiety and depression (anxiety: adjusted hazard ratio [aHR] = 0.34, 95% confidence interval [CI] = 0.28-0.42; depression: aHR = 0.36, 95% CI = 0.25-0.53). The aHRs (95% CIs) for statin use with ICSs or OSs were 0.32 (0.13-0.78) and 0.37 (0.24-0.57), respectively. CONCLUSION: The ACOS cohort with statin use had lower risks of anxiety and depression, regardless of age, sex, commodities, or ICSs and OSs. The incidences of anxiety and depression were relatively low among users of statins with ICSs or OSs in the ACOS cohort.

Associations between statins and coronary artery disease and stroke risks in patients with asthma-chronic obstructive pulmonary disease overlap syndrome: A time-dependent regression study.

BACKGOUND AND AIMS: We aimed at determining the effects of statin use on coronary artery disease (CAD) and stroke risks in patients with asthma-chronic obstructive pulmonary disease overlap syndrome (ACOS). METHODS: We retrospectively enrolled patients with ACOS treated with (N = 916) and without (N = 6338) statins. The cumulative incidence of CAD and stroke (ischemic and hemorrhagic) was analyzed through time-dependent Cox proportional regression. After adjustment for sex, age, comorbidities, inhaled corticosteroid steroid (ICS) use, and oral steroid (OS) use, we calculated the adjusted hazard ratios (aHRs) and their 95% confidence intervals (CIs) for CAD or stroke in the statin users (long-term [>600 days] and short-term [≤600 days]) compared with the non-users. RESULTS: Among the statin users, aHRs (95% CIs) for CAD and stroke were 0.50 (0.41-0.62) and 0.83 (0.63-1.09), respectively; moreover, aHRs were 0.30 (0.09-0.99) and 0.90 (0.68-1.20) for ischemic and hemorrhagic stroke, respectively. aHRs (95% CIs) for CAD and stroke were 0.58 (0.47-0.71) and 0.93 (0.70-1.23), respectively, in the short-term users and 0.23 (0.13-0.41) and 0.42 (0.19-0.89), respectively, in the long-term users. CONCLUSIONS: CAD risk was lower in all statin users, regardless of the duration of use, whereas ischemic stroke risk was lower only in the long-term statin users. No association was observed between hemorrhagic stroke risk and statin use.

Applying Blockchain Technology to Enhance Clinical Trial Recruitment.

Patient recruitment for clinical trials is known to be a challenging aspect of clinical research. There are multiple competing concerns from the sponsor, patient and principal investigator's perspectives resulting in most clinical trials not meeting recruitment requirements on time. Conducting under-enrolled clinical trials affects the power of conclusive results or causes premature trial termination. The Blockchain is a distributed ledger technology originally applied in the financial sector. Its features as a peer-to-peer system with publicly audited transactions, data security, and patient privacy are a good fit for the needs of clinical trials recruitment. The "Smart Contract" is a programmable self-executing protocol that regulates the blockchain transactions. Given current recruitment challenges, we have proposed a blockchain model containing multiple trial-based contracts for trial management and patient engagement and a master smart contract for automated subject matching, patient recruitment, and trial-based contracts management.

Association between neurodegenerative diseases and pneumonia: a retrospective population-based study.

PURPOSE: The association between pneumonia and neurodegenerative diseases (NDs) has never been reported in detail. We address this relationship with reference to the general population. METHODS: Using Taiwan's National Health Insurance Research Database to identify a pneumonia cohort (including the typical and atypical), we established an ND cohort of 19,062 patients and a non-ND cohort of 76,227 people. In both cohorts, the risk of pneumonia was measured using multivariable Cox proportional hazards models. RESULTS: The adjusted hazard ratio (aHR) (95% confidence interval [CI]) for the pneumonia cohort was 2.10 (1.96-2.24), regardless of age, sex, comorbidities or drug use in the ND cohort. The aHR (95% CI) for adults aged 20-49 years was 2.08 (1.58-2.75), men 2.20 (2.01-2.40). However, older subjects were at greatest risk of pneumonia, (3.41 [2.99-3.88]) if the 20-49 years age group is used as the reference. For the ND and non-ND cohorts, those with comorbidities (with the exception of hyperlipidemia) had higher risk; aHR (95% CI) 2.35 (2.30-2.52). The aHR (95% CI) for those without comorbidities is 3.28 (2.52-4.26). No significant difference was observed in incidence of pneumonia between those who were and were not using statin medications; the aHR (95% CI) was 1.03 (0.93-1.14). CONCLUSION: The ND cohort had a higher risk of pneumonia, regardless of age, sex, comorbidities or statin use. The risk of pneumonia was higher in elderly and male patients in the ND cohort.

Statin for Tuberculosis and Pneumonia in Patients with Asthma⁻Chronic Pulmonary Disease Overlap Syndrome: A Time-Dependent Population-Based Cohort Study.

We investigated the effects of statins on tuberculosis (TB) and pneumonia risks in asthma⁻chronic pulmonary disease overlap syndrome (ACOS) patients. We extracted data of patients diagnosed as having ACOS during 2000⁻2010 from the Taiwan National Health Insurance Research Database and divided them into statin users and nonusers. All study participants were followed up from the index date until death, withdrawal from insurance, or TB and pneumonia occurred (31 December 2011). The cumulative TB and pneumonia incidence was analyzed using Cox proportional regression analysis with time-dependent variables. After adjustments for multiple confounding factors including age, sex, comorbidities, and use of medications [statins, inhaled corticosteroids (ICSs), or oral steroids (OSs)], statin use was associated with significantly lower TB [adjusted hazard ratio (aHR) 0.49, 95% confidence interval (CI) 0.34⁻0.70] and pneumonia (aHR 0.52, 95% CI 0.41⁻0.65) risks. Moreover, aHRs (95% CIs) for statins combined with ICSs and OSs were respectively 0.60 (0.31⁻1.16) and 0.58 (0.40⁻0.85) for TB and 0.61 (0.39⁻0.95) and 0.57 (0.45⁻0.74) for pneumonia. Thus, statin users had lower TB and pneumonia risks than did nonusers, regardless of age, sex, comorbidities, and ICS or OS use. Pneumonia risk was lower among users of statins combined with ICSs or Oss and TB risk was lower among the users of statins combined with OSs.

Applying Blockchain Technology for Health Information Exchange and Persistent Monitoring for Clinical Trials.

"Blockchain" is a distributed ledger technology originally applied in the financial sector. This technology ensures the integrity of transactions without third-party validation. Its functions of decentralized transaction validation, data provenance, data sharing, and data integration are a good fit for the needs of health information exchange and clinical trials. We investigated the current workflow of Health Information Exchange and clinical trials; conducted design thinking processes with clinicians, trial managers, informaticians, and blockchain professionals; and implemented a private blockchain model to tackle known issues. We used coded Smart Contract regulations to simulate several scenarios in healthcare processes. This proof-of-concept work provides a feasible simulation for potential solutions to monitor clinical trials across different census regions persistently. Various levels of data access privileges have been designed to utilize a suite of customized Smart Contract settings. These settings emulate the workflow protocols for the monitoring entities, trial sponsors, clinical sponsors and participating subjects. Keywords: Blockchain, Smart Contract, Health Information Exchange, Clinical Trial, Persistent Monitoring.