DOMAS: a data management software framework for advanced light sources.

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF).

Survey on electronic visual field data transfer practices among Japan Glaucoma Society board members.

BACKGROUND: Visual field (VF) testing in combination with a specialized VF analysis software is critical for characterizing and monitoring visual loss in glaucoma. Although performing glaucoma progression analysis requires original VF data rather than printouts or image files, extent of VF data transfer between referring and referred ophthalmologists is unclear. Here, we surveyed glaucoma specialists who belong to the Japan Glaucoma Society (JGS). METHODS: An internet survey of daily practice patterns regarding electronic VF data transfer at the time of glaucoma referrals (referring/referred) was sent to all 50 JGS board members. The survey consisted with 11 questionnaires, and the response rate was 100%. RESULTS: The respondents included 33 university hospital ophthalmologists (66%) (Q1), and those scattered throughout Japan (Q2). All respondents used Humphrey Visual Filed Analyzer (HFA) (Q3) and at least one of a VF progression analysis software (Q4). Ten respondents (20%) actively transferred electronic VF data, while 40 (80%) did not (Q5). The major reasons for not actively transferring data electronically were that there was no support for data transfer by neighboring (n = 26, 65%) and/or own (25, 63%) institutes (Q6). All 40 inactive respondents responded that electronic data transfer is ideal (Q7). All 10 active respondents transferred data using USB flash memory (Q8). Of the 10 active respondents, seven (70%) reported that the percentage of referral letters accompanying electronic VF data in a format that allows for progression analysis from the beginning was less than 25% (Q9). When the referral letters did not accompany the electronic VF data, four (40%) reported that they further requested the data transfer in < 25% of cases (Q10). When the 10 active respondents were requested to transfer data, six (60%) had experienced rejection due to various reasons (Q11). CONCLUSION: An internet survey showed that 80% of the JGS board members were not actively transferring VF data mainly because of the absence of a system in place at institutions for sending and receiving data, although they feel that the electronic VF data transfer is ideal. The results provide basic data for future discussions on the promotion of the VF data transfer.

The role of messaging services in day-to-day practice in pediatric neurosurgery, advantages of a bubble network, and an international survey.

INTRODUCTION: Messaging services (MS) are used widely worldwide. The implications of MS usage in daily hospital patient care have not been investigated. In this study, we discuss the extensive usage of MS in our Pediatric Neurosurgical Department, introduce our bubble algorithm, and provide additional input from an international survey. METHODS: WhatsApp activity in the department of pediatric neurosurgery at Dana Children's Hospital, Tel Aviv, Israel, was analyzed. We designed a graphic representation of the content of the different conversation bubbles and how they interact. We also described a survey evaluating MS use in daily neurosurgical practice was sent to 25 neurosurgeons around the world. Collected data included details on the usage of MS, the type of information being transferred, and the participants' opinion of the potential risks and benefits of these systems. We began collecting messaging data November 2018, before the COVID pandemic era. We continued to collect data over the course of almost 3 years. RESULTS: We identified a bubble network structure that reflects a logical method of communication between different segments of pediatric neurosurgical care in our institution. Additionally, we analyzed 22 survey responses, received from 14 different countries. The vast majority of centers with "department groups" use messaging services to transfer multimedia files of patient-related data. Nineteen responders believe that MS significantly improve overall patient care. CONCLUSION: MS has revolutionized and improved the patterns of communication in our department. The great benefits of quick, simple access to information strongly outweigh formality and the potential for medicolegal disadvantages (e.g., HIPAA).

An ethical code for collecting, using and transferring sensitive health data: outcomes of a modified Policy Delphi process in Singapore.

One of the core goals of Digital Health Technologies (DHT) is to transform healthcare services and delivery by shifting primary care from hospitals into the community. However, achieving this goal will rely on the collection, use and storage of large datasets. Some of these datasets will be linked to multiple sources, and may include highly sensitive health information that needs to be transferred across institutional and jurisdictional boundaries. The growth of DHT has outpaced the establishment of clear legal pathways to facilitate the collection, use and transfer of potentially sensitive health data. Our study aimed to address this gap with an ethical code to guide researchers developing DHT with international collaborative partners in Singapore. We generated this code using a modified Policy Delphi process designed to engage stakeholders in the deliberation of health data ethics and governance. This paper reports the outcomes of this process along with the key components of the code and identifies areas for future research.

Automated Electronic Health Record to Electronic Data Capture Transfer in Clinical Studies in the German Health Care System: Feasibility Study and Gap Analysis.

BACKGROUND: Data transfer between electronic health records (EHRs) at the point of care and electronic data capture (EDC) systems for clinical research is still mainly carried out manually, which is error-prone as well as cost- and time-intensive. Automated digital transfer from EHRs to EDC systems (EHR2EDC) would enable more accurate and efficient data capture but has so far encountered technological barriers primarily related to data format and the technological environment: in Germany, health care data are collected at the point of care in a variety of often individualized practice management systems (PMSs), most of them not interoperable. Data quality for research purposes within EDC systems must meet the requirements of regulatory authorities for standardized submission of clinical trial data and safety reports. OBJECTIVE: We aimed to develop a model for automated data transfer as part of an observational study that allows data of sufficient quality to be captured at the point of care, extracted from various PMSs, and automatically transferred to electronic case report forms in EDC systems. This required addressing aspects of data security, as well as the lack of compatibility between EHR health care data and the data quality required in EDC systems for clinical research. METHODS: The SaniQ software platform (Qurasoft GmbH) is already used to extract and harmonize predefined variables from electronic medical records of different Compu Group Medical-hosted PMSs. From there, data are automatically transferred to the validated AlcedisTRIAL EDC system (Alcedis GmbH) for data collection and management. EHR2EDC synchronization occurs automatically overnight, and real-time updates can be initiated manually following each data entry in the EHR. The electronic case report form (eCRF) contains 13 forms with 274 variables. Of these, 5 forms with 185 variables contain 67 automatically transferable variables (67/274, 24% of all variables and 67/185, 36% of eligible variables). RESULTS: This model for automated data transfer bridges the current gap between clinical practice data capture at the point of care and the data sets required by regulatory agencies; it also enables automated EHR2EDC data transfer in compliance with the General Data Protection Regulation (GDPR). It addresses feasibility, connectivity, and system compatibility of currently used PMSs in health care and clinical research and is therefore directly applicable. CONCLUSIONS: This use case demonstrates that secure, consistent, and automated end-to-end data transmission from the treating physician to the regulatory authority is feasible. Automated data transmission can be expected to reduce effort and save resources and costs while ensuring high data quality. This may facilitate the conduct of studies for both study sites and sponsors, thereby accelerating the development of new drugs. Nevertheless, the industry-wide implementation of EHR2EDC requires policy decisions that set the framework for the use of research data based on routine PMS data.

Frequency of errors in the transfer of treatment parameters from the treatment planning system to the oncology information system in a multi-vendor environment.

BACKGROUND: Technological advancements have made it possible to improve patient outcomes in radiotherapy, sparing both normal tissues and increasing tumour control. However, these advancements have resulted in an increase in the number of software systems used, which each require data inputs to function. For institutions with multiple vendors for their treatment planning systems and oncology information systems, the transfer of data between them is potentially error prone and can lead to treatment errors. PURPOSE: The goal of this work was to determine the frequency of errors in data transfers between the Varian Eclipse treatment planning system and the Elekta Mosaiq oncology information system. METHODS: An in-house program was used to quantify the number of errors for 2700 unique plans over an 8-month period. Using this information, the frequency of the errors were calculated. A risk priority number was calculated using the calculated frequencies to determine the impact on the clinic. RESULTS: The most common errors discovered were backup timer settings (10.7%), Field label (8.5%), DRR associations (3.3%), imaging field types (3.1%), dose rate (1%), Field Id (0.8%), imaging isocenter (0.7% and SSD (0.7%). Based on the risk priority numbers, the DRR association error was ranked as having the highest potential impact on the patient. CONCLUSIONS: The results of the work show that the most effort should be focused on checking the manual steps performed in the transfer process, while items that are imported directly from DICOM-RT without modification are highly likely to be transferred accurately. The data can be used to help guide the implementation of future automated tools and process improvement in the clinic.

Method for Using IMU-Based Experimental Motion Data in BVH Format for Musculoskeletal Simulations via OpenSim.

Biomechanical simulation allows for in silico estimations of biomechanical parameters such as muscle, joint and ligament forces. Experimental kinematic measurements are a prerequisite for musculoskeletal simulations using the inverse kinematics approach. Marker-based optical motion capture systems are frequently used to collect this motion data. As an alternative, IMU-based motion capture systems can be used. These systems allow flexible motion collection without nearly any restriction regarding the environment. However, one limitation with these systems is that there is no universal way to transfer IMU data from arbitrary full-body IMU measurement systems into musculoskeletal simulation software such as OpenSim. Thus, the objective of this study was to enable the transfer of collected motion data, stored as a BVH file, to OpenSim 4.4 to visualize and analyse the motion using musculoskeletal models. By using the concept of virtual markers, the motion saved in the BVH file is transferred to a musculoskeletal model. An experimental study with three participants was conducted to verify our method's performance. Results show that the present method is capable of (1) transferring body dimensions saved in the BVH file to a generic musculoskeletal model and (2) correctly transferring the motion data saved in the BVH file to a musculoskeletal model in OpenSim 4.4.

Enhancing QoS of Telecom Networks through Server Load Management in Software-Defined Networking (SDN).

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users' hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased.

GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization.

BACKGROUND: Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring. RESULTS: We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations. CONCLUSION: Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience.

SALUS-a non-inferiority trial to compare self-tonometry in glaucoma patients with regular inpatient intraocular pressure controls: study design and set-up.

PURPOSE: The SALUS study aims to improve the healthcare situation for glaucoma patients in Germany. In order to detect diurnal intraocular pressure (IOP) fluctuations, inpatient monitoring of IOP in an eye hospital for a minimum of 24 h is the current standard. SALUS assesses the benefits of a new form of outpatient care, where IOP can be measured by the patients themselves at home using a self-tonometer. This approach should promote the patient's health competence and empowerment within the healthcare system while reducing treatment costs. METHODS: The SALUS study is a randomized controlled, open non-inferiority trial, alongside an economic analysis, determining whether outpatient monitoring of IOP with self-tonometry is at least as effective as current standard care and would reduce treatment costs. Participants (n = 1980) will be recruited by local ophthalmologists in the area of Westphalia-Lippe, Germany, and randomized to receive 7-day outpatient or 24-h inpatient monitoring. Participants in both study arms will also receive 24-h blood pressure monitoring. Furthermore, patient data from both study groups will be collected in an electronic case file (ECF), accessible to practitioners, hospitals, and the study participants. The primary endpoint is the percentage of patients with IOP peaks, defined as levels 30% above the patient-specific target pressure. Data will also be collected during initial and final examinations, and at 3, 6, and 9 months after the initial examination. RESULTS: The study implementation and trial management are represented below. CONCLUSION: SALUS is a pioneering prospective clinical trial focused on the care of glaucoma patients in Germany. If SALUS is successful, it could improve the healthcare situation and health literacy of the patients through the introduction of various telemedical components. Furthermore, the approach would almost certainly reduce the treatment costs of glaucoma care. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT04698876, registration date: 11/25/2020. DRKS-ID: DRKS00023676, registration date: 11/26/2020.

Bridging the European Data Sharing Divide in Genomic Science.

In this viewpoint, we argue for the importance of creating data spaces for genomic research that are detached from contexts in which fundamental rights concerns related to surveillance measures override a purpose-specific balancing of fundamental rights. Genomic research relies on molecular and phenotypic data, on comparing findings within large data sets, on searchable metadata, and on translating research results into a clinical setting. These methods require sensitive genetic and health data to be shared across borders. International data sharing between the European Union (EU) or the European Economic Area and third countries has accordingly become a cornerstone of genomics. The EU General Data Protection Regulation contains rules that accord privileged status to data processing for research purposes to ensure that strict data protection requirements do not impede biomedical research. However, the General Data Protection Regulation rules applicable to international transfers of data accord no such preferential treatment to international data transfers made in the research context. The rules that govern the international transfer of data create considerable barriers to international data sharing because of the cost-intensive procedural and substantive compliance burdens that they impose. For certain jurisdictions and select use cases, there exist practically no lawful mechanisms to enable the international transfer of data because of concerns about the protection of fundamental rights. The proposed solutions further fail to address the need to share large data sets of local and regional cohorts across national borders to enable joint analyses. The European Health Data Space is an emerging federated, EU-wide data infrastructure that is intended to function as an infrastructure bringing together EU health data to improve patient care and enable the secondary use of health-related data for research purposes. Such infrastructure is implementing new institutions to support its functioning and is being implemented in reliance on a new enabling law, the regulation on the European Health Data Space. This innovation provides the opportunity to facilitate EU contribution to international genomic research efforts. The draft regulation for this data space provides for a concept of data infrastructure intended to enable cross-border data exchange and access, including access to genetic and health data for scientific analysis purposes. The draft regulation also provides for obligations of national actors aimed at making data widely available. This effort is laudable. However, in the absence of further, more fundamental changes to the manner in which the EU regulates the secondary use of health data, it is reasonable to believe that EU participation in international genomic research efforts will remain impeded.

Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi.

A seismograph was designed based on Raspberry Pi. Although comprising 8 channels, the seismograph can be expanded to 16, 24, or 32 channels by using a USB interfacing with a microcontroller. In addition, by clustering more than one Raspberry Pi, the number of possible channels can be extended beyond 32. In this study, we also explored the computational intelligence of Raspberry Pi for running real-time systems and multithreaded algorithms to process raw seismic data. Also integrated into the seismograph is a Huawei MH5000-31 5G module, which provided high-speed internet real-time operations. Other hardware peripherals included a 24 bit ADS1251 analog-to-digital converter (ADC) and a STM32F407 microcontroller. Real-time data were acquired in the field for ambient noise tomography. An analysis tool called spatial autocorrelation (SPAC) was used to analyze the data, followed by inversion, which revealed the subsurface velocity of the site location. The proposed seismograph is prospective for small, medium, or commercial data acquisition. In accordance with the processing power and stability of Raspberry Pi, which were confirmed in this study, the proposed seismograph is also recommended as a template for developing high-performance computing applications, such as artificial intelligence (AI) in seismology and other related disciplines.

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Analysis and Comparison of Rad-Hard Ring and LC-Tank Controlled Oscillators in 65 nm for SpaceFibre Applications.

This work presented a comparison between two Voltage Controlled Oscillators (VCOs) designed in 65 nm CMOS technology. The first architecture based on a Ring Oscillator (RO) was designed using three Current Mode Logic (CML) stages connected in a loop, while the second one was based on an LC-tank resonator. This analysis aimed to choose a VCO architecture able to be integrated into a rad-hard Phase Locked Loop. It had to meet the requirements of the SpaceFibre protocol, which supports frequencies up to 6.25 GHz, for space applications. The full custom schematic and layout designs are shown, and Single Event Effect simulations results, performed with a double exponential current pulses generator, are presented in detail for both VCOs. Although the RO-VCO performances in terms of technology scaling and high-integration density were attractive, the simulations on the process variations demonstrated its inability to generate the target frequency in harsh operating conditions. Instead, the LC-VCO highlighted a lower influence through Process-Voltage-Temperature simulations on the oscillation frequency. Both architectures were biased with a supply voltage of 1.2 V. The achieved results for the second architecture analyzed were attractive to address the requirements of the new SpaceFibre aerospace standard.

Evaluation of information transfer and data transfer models of rain-gauge network design based on information entropy.

Rainfall is one of the most fundamental components of the water cycle and is one of the fundamental inputs of hydrological models. A well-designed network can not only depict the regional precipitation characteristics, but also economically yield maximum needed rainfall information. In regions where either there is limited data or data is not available, it is a common challenge to add stations. The entropy theory-based information transfer model and geostatistical interpolation techniques are two solutions to meet the challenge. In this study, we used a representative rain gauge network to do the network design. Two models, based on information transfer and data transfer, were compared for network design. Other rain gauges in the study area were used as reference ("true values") for assessing the model. Results showed that the information transfer model estimated transinformation between station pairs better than did the data transfer model. Different representative gauges were evaluated separately by the directional information transfer index (DIT). The candidate gauges selected with least information redundancy were similar for both information transfer and data transfer models. Though both models captured some least information-redundant areas, other areas may be bypassed because of model errors or estimation errors.

Determining Wheel Forces and Moments on Aircraft Landing Gear with a Dynamometer Sensor.

This paper describes airfield measurement of forces and moments that act on a landing gear wheel. For the measurement, a wheel force sensor was used. The sensor was designed and built based on strain gage technology and was embedded in the left landing gear wheel of a test aircraft. The sensor is capable of measuring simultaneously three perpendicular forces and three moments and sends data to a handheld device wirelessly. For the airfield tests, the sensor was installed on a PZL 104 Wilga 35A multipurpose aircraft. The aircraft was towed at a "marching man" speed and the measurements were performed at three driving modes: Free rolling, braking, and turning. The paper contains results obtained in the field measurements performed on a grassy runway of the Rzeszów Jasionka Aerodrome, Poland. Rolling resistance of aircraft tire, braking friction, as well as aligning moment were analyzed and discussed with respect to surface conditions.

Transferring Health Big Data within the European Legal Framework: What Role for National Healthcare Services?

The main objective of this article is to describe the legal principles governing the selection by European public authorities, such as National Health Services (NHS) of third parties, when entering into agreements for the transfer of health data. According to Directive 2003/98/EC, and in light of the provisions of the Treaties of the European Union, the choice as to how a public authority makes its data available to third parties needs to be transparent, non-discriminatory and may not in any case benefit a specific company at the expense of others. For this reason, we maintain that a hypothetical agreement by which a public authority grants exclusive access to a large amount of health data to a private company selected with non-transparent criteria appears highly questionable. We advocate that the NHS should adopt more appropriate data policies aimed at promoting the sustainability of the NHS, following the legal framework analysed in this article.

[Current legal and jurisdictional questions from the perspective of the German Federal Association for Geriatrics : Practical report]. / Aktuelle rechtliche und rechtspolitische Fragestellungen aus Sicht des Bundesverbands Geriatrie : Praxisbericht.

The article discusses the questions of the arbitration bodies according to § 111b SGB V (Volume V of the Social Insurance Code) in the individual federal states from the perspective of geriatric rehabilitation hospitals. The content of the agreement of reimbursement between a rehabilitation hospital and health insurance will be targeted as well as the question whether the entire content of the agreements of reimbursement can be negotiated at the arbitration body. In addition, the authors describe the consequences of the jurisprudence of the Federal Social Court on § 301 I. S. 1 no. 8 SGB V and the reaction of the lawgiver. Furthermore the authors describe the effects of the jurisprudence of the Federal Social Court regarding the minimum age associated with complex geriatric treatment (OPS 8-550).

Probabilistic Assessment of High-Throughput Wireless Sensor Networks.

Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved.

Novel Method for Recruiting Representative At-Risk Individuals into Cancer Prevention Trials: Online Health Risk Assessment in Employee Wellness Programs.

Participation in cancer prevention trials (CPT) is lower than 3 % among high-risk healthy individuals, and racial/ethnic minorities are the most under-represented. Novel recruitment strategies are therefore needed. Online health risk assessment (HRA) serves as a gateway component of nearly all employee wellness programs (EWPs) and may be a missed opportunity. This study aimed to explore employees' interest, willingness, motivators, and barriers of releasing their HRA responses to an external secure research database for recruitment purpose. We used qualitative research methods (focus group and individual interviews) to examine employees' interest and willingness in releasing their online HRA responses to an external, secure database to register as potential CPT participants. Fifteen structured interviews (40 % of study participants were of racial/ethnic minority) were conducted, and responses reached saturation after four interviews. All employees showed interest and willingness to release their online HRA responses to register as a potential CPT participant. Content analyses revealed that 91 % of participants were motivated to do so, and the major motivators were to (1) obtain help in finding personally relevant prevention trials, (2) help people they know who are affected by cancer, and/or (3) increase knowledge about CPT. A subset of participants (45 %) expressed barriers of releasing their HRA responses due to concerns about credibility and security of the external database. Online HRA may be a feasible but underutilized recruitment method for cancer prevention trials. EWP-sponsored HRA shows promise for the development of a large, centralized registry of racially/ethnically representative CPT potential participants.