Medical Device Data Research Articles

Real-world data to support post-market safety and performance of embolization coils: evidence generation from a medical device manufacturer and data institute partnership

BackgroundRecognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils.MethodsThrough a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils’ use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana’s largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints.ResultsA total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method.ConclusionsGenerating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.

Advancing Healthcare Research Through Live-Data Integration: Implementing IEEE 11073 and FHIR in Clinical Environment.

The efficient direct integration of real-time medical device data is a promising approach to improve patient care enabling a direct and eminent intervention. This study presents a comprehensive approach for integrating real-time medical device data into clinical environments using the HL7® FHIR® standards and IEEE 11073 Service-Oriented Device Connectivity (SDC). The study proposes a conceptual framework and an opensource proof-of-concept implementation for real-time data integration within the Medical Data Integration Center (MeDIC) at UKSH. Key components include a selective recording mechanism to mitigate storage issues and ensure accurate data capture. Our robust network architecture utilizes Kafka brokers for seamless data transfer in isolated networks. The study demonstrates the selective capturing of real-time data within a clinical setting to enable medical device data for a down-stream processing and analysis.

Ethics, design, and implementation criteria of digital assistive technologies for people with dementia from a multiple stakeholder perspective: a qualitative study

BackgroundDementia impairs the ability of people with dementia to be autonomous and independent. They need support from third parties, who should ideally respect their autonomy and independence as much as possible. Supporting people with dementia can be very burdensome for caregivers and numbers of patients increase while numbers of potential caregivers decline. Digital assistive technologies (DATs) that directly support patients or their caregivers may help bridging the increasing gap between need of support and available resources. DATs have the potential to preserve the autonomy and independence of people with dementia and promote their abilities, if they are properly designed in close interaction with future users. In our study, we focused on ethical concerns, technological requirements, and implementation criteria for DAT in general and specifically to support outdoor mobility of people with dementia.MethodsWe applied a qualitative approach and conducted a World Café (2 tables, n = 7) and an online focus group (n = 6) with people with dementia, relatives, healthcare professionals, scientists, ethics experts, and experts for digitally-assisted medical care. We descriptively analyzed the data using a content analysis approach.ResultsThe participants reported technological (e.g., lack of Wi-Fi), financial (e.g., expensive devices or lack of budget for DATs), political (e.g., legal hurdles such as the European Medical Device Law or data protection regulations) as well as user-related hurdles (e.g., lack of digital competence) for the implementation of DAT in dementia care. Among the issues discussed were the importance of autonomy, independence, safety, privacy, and questions of decision making capacity in DAT’s use. Participants identified opportunities and benefits in self-learning, situation-aware DATs and wished for dementia-friendly communities. They emphasized the value of personal interaction that should not be replaced, but rather supported by DAT.ConclusionThe results revealed multiple hurdles and ethical concerns for DAT use and provided recommendations for designing and implementing DATs. Further investigations are needed on the impact of DAT on personal interactions in caregiving and the role of DAT in dementia-friendly communities.

Blockchain-Enabled Security Solutions for Medical Device Integrity and Provenance in Cloud Environments

The current period of medicine using digital technology for patient care presents a new level of integration of monitoring devices with the cloud computing environment that enables the collection, storage and access to data in ways that were never possible earlier. As the obvious part of this development, it is worth noting that the objective of such innovation is mostly on the integrity of data, provenance and security. Data integrity from as well as security of the Internet connected healthcare devices should be assured in the first place to keep patient safety and protect data privacy along with improve data-based decision-making. The centralized system and crowded nature of the current equipment are susceptible to single point of failure, data breach and potential manipulations of data, which raise questions and create doubts with regards data management processes pertaining to medical device systems. This work is addressed to the analysis of a novel security system based on blockchain that guarantees the implementation of a high performance with the solution of two medical device integrity and provenance safety issues in the cloud ecosystem. Fundamentally differentiating from the centralized systems that exist today, blockchain technology that is based on distributed database architectures, immutable logs, and consensus mechanisms provides for a new way to bring reliability and traceability to the entire medical device data chain. The suggested procedure is based on properties of blockchain technology. Such a solution can help to provide a clear and secure audit trail for medical devices. Storing, securing and accessing the device data can be carried out credibly, maintaining these data’s integrity and provenance. Ultimately, the solution, rely on the implementation of smart contracts, cryptocurrency processes, and the confidentiality and privacy of data, can be the answer which make up the practice of secure data sharing, data accessing and complying with regulations. The journal creates a modular system combining Medical devices, a cloud platform, and Blockchain solution. The architecture is intended to display the blockchain network's essential components, data validation and access control, and secure data storage mechanisms. Furthermore, the recommended solution implies state-of-the-art security tools, such as data encryption, access control, and abidance by regulatory systems, including HIPAA and GDPR. Implementation of an actual scenario of the proof-of-concept and performance evaluation are done to show the efficiency and performance of the blockchain-based solution provided. The results suggest that the proposed solution can establish the data reliability level, record all the various versions of modifications, and strengthen the security and transparency of medical device data processing in cloud computing. Through the exploration of the applications of blockchain for medical data management that this study proposes, we are laying the foundations of a future healthcare environment, which is expected to be more secure and trustworthy, where the sensor data of medical devices can be reliably controlled and accessed without jeopardizing the patient's safety or data privacy. To a great extent, the suggested solution can contribute to building trust in the digital tools utilized in health care, leading to more well-informed clinical decisions and ultimately improving the patients' results.

Blockchain-Enabled Security Solutions for Medical Device Integrity and Provenance in Cloud Environments

The current period of medicine using digital technology for patient care presents a new level of integration of monitoring devices with the cloud computing environment that enables the collection, storage and access to data in ways that were never possible earlier. As the obvious part of this development, it is worth noting that the objective of such innovation is mostly on the integrity of data, provenance and security. Data integrity from as well as security of the Internet connected healthcare devices should be assured in the first place to keep patient safety and protect data privacy along with improve data-based decision-making. The centralized system and crowded nature of the current equipment are susceptible to single point of failure, data breach and potential manipulations of data, which raise questions and create doubts with regards data management processes pertaining to medical device systems. This work is addressed to the analysis of a novel security system based on blockchain that guarantees the implementation of a high performance with the solution of two medical device integrity and provenance safety issues in the cloud ecosystem. Fundamentally differentiating from the centralized systems that exist today, blockchain technology that is based on distributed database architectures, immutable logs, and consensus mechanisms provides for a new way to bring reliability and traceability to the entire medical device data chain. The suggested procedure is based on properties of blockchain technology. Such a solution can help to provide a clear and secure audit trail for medical devices. Storing, securing and accessing the device data can be carried out credibly, maintaining these data’s integrity and provenance. Ultimately, the solution, rely on the implementation of smart contracts, cryptocurrency processes, and the confidentiality and privacy of data, can be the answer which make up the practice of secure data sharing, data accessing and complying with regulations. The journal creates a modular system combining Medical devices, a cloud platform, and Blockchain solution. The architecture is intended to display the blockchain network's essential components, data validation and access control, and secure data storage mechanisms. Furthermore, the recommended solution implies state-of- the-art security tools, such as data encryption, access control, and abidance by regulatory systems, including HIPAA and GDPR. Implementation of an actual scenario of the proof-of-concept and performance evaluation are done to show the efficiency and performance of the blockchain-based solution provided. The results suggest that the proposed solution can establish the data reliability level, record all the various versions of modifications, and strengthen the security and transparency of medical device data processing in cloud computing. Through the exploration of the applications of blockchain for medical data management that this study proposes, we are laying the foundations of a future healthcare environment, which is expected to be more secure and trustworthy, where the sensor data of medical devices can be reliably controlled and accessed without jeopardizing the patient's safety or data privacy. To a great extent, the suggested solution can contribute to building trust in the digital tools utilized in health care, leading to more well-informed clinical decisions and ultimately improving the patients' results.

Smart hospital: achieving interoperability and raw data collection from medical devices in clinical routine.

Today, modern technology is used to diagnose and treat cardiovascular disease. These medical devices provide exact measures and raw data such as imaging data or biosignals. So far, the Broad Integration of These Health Data into Hospital Information Technology Structures-Especially in Germany-is Lacking, and if data integration takes place, only non-Evaluable Findings are Usually Integrated into the Hospital Information Technology Structures. A Comprehensive Integration of raw Data and Structured Medical Information has not yet Been Established. The aim of this project was to design and implement an interoperable database (cardio-vascular-information-system, CVIS) for the automated integration of al medical device data (parameters and raw data) in cardio-vascular medicine. The CVIS serves as a data integration and preparation system at the interface between the various devices and the hospital IT infrastructure. In our project, we were able to establish a database with integration of proprietary device interfaces, which could be integrated into the electronic health record (EHR) with various HL7 and web interfaces. In the period between 1.7.2020 and 30.6.2022, the data integrated into this database were evaluated. During this time, 114,858 patients were automatically included in the database and medical data of 50,295 of them were entered. For technical examinations, more than 4.5 million readings (an average of 28.5 per examination) and 684,696 image data and raw signals (28,935 ECG files, 655,761 structured reports, 91,113 x-ray objects, 559,648 ultrasound objects in 54 different examination types, 5,000 endoscopy objects) were integrated into the database. Over 10.2 million bidirectional HL7 messages (approximately 14,000/day) were successfully processed. 98,458 documents were transferred to the central document management system, 55,154 materials (average 7.77 per order) were recorded and stored in the database, 21,196 diagnoses and 50,353 services/OPS were recorded and transferred. On average, 3.3 examinations per patient were recorded; in addition, there are an average of 13 laboratory examinations. Fully automated data integration from medical devices including the raw data is feasible and already creates a comprehensive database for multimodal modern analysis approaches in a short time. This is the basis for national and international projects by extracting research data using FHIR.

Leveraging Serverless Computing in Medical Device Data Processing Pipelines

Leveraging Serverless Computing in Medical Device Data Processing Pipelines

Design and Implementation of a Medical Device Data Dictionary System Based on Software Engineering Theory

Design and Implementation of a Medical Device Data Dictionary System Based on Software Engineering Theory

Exploration of remote management and an intelligent platform for in-hospital respiratory therapy

The construction of an intelligent remote management platform for respiratory therapy, utilizing artificial intelligence (AI) and the electronic medical record system (EMR), has significant potential to improve the management of respiratory therapy in critically ill patients. This platform includes the development of a dedicated respiratory therapy EMR, the integration of data from multiple mechanical ventilators from different vendors and models, and the utilization of AI-assisted analysis to understand the pathophysiology of respiratory diseases and the complex physiological factors that influence specific interventions, thereby supporting diagnosis, treatment guidance, and prognosis prediction. In addtion, a network will be established to provide seamless connectivity between hospitals and wards. The resulting platform enables the collection of medical device data from multiple points within the hospital, real-time data analysis, and timely alarms, thereby facilitating remote data access, centralization of information, and standardization of data. As a result, the platform enables efficient intra-hospital and inter-hospital doctor-patient management. Despite the benefits offered by this platform, certain challenges need to be addressed, including ensuring data privacy and security, as well as managing the financial and human resources required for its implementation and maintenance. Furthermore, continuous optimization of the platform is crucial, and the clinical use of the platform requires appropriate professional training.

Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges.

To introduce developers to medical device regulatory processes and data considerations in artificial intelligence and machine learning (AI/ML) device submissions and to discuss ongoing AI/ML-related regulatory challenges and activities. AI/ML technologies are being used in an increasing number of medical imaging devices, and the fast evolution of these technologies presents novel regulatory challenges. We provide AI/ML developers with an introduction to U.S. Food and Drug Administration (FDA) regulatory concepts, processes, and fundamental assessments for a wide range of medical imaging AI/ML device types. The device type for an AI/ML device and appropriate premarket regulatory pathway is based on the level of risk associated with the device and informed by both its technological characteristics and intended use. AI/ML device submissions contain a wide array of information and testing to facilitate the review process with the model description, data, nonclinical testing, and multi-reader multi-case testing being critical aspects of the AI/ML device review process for many AI/ML device submissions. The agency is also involved in AI/ML-related activities that support guidance document development, good machine learning practice development, AI/ML transparency, AI/ML regulatory research, and real-world performance assessment. FDA's AI/ML regulatory and scientific efforts support the joint goals of ensuring patients have access to safe and effective AI/ML devices over the entire device lifecycle and stimulating medical AI/ML innovation.

Implementing GDPR Compliance in Medical Device Data Management

Implementing GDPR Compliance in Medical Device Data Management

P39: Generational Status as a Factor in Medical Device Information and Data Exchange

Background: Medical devices (MD), either implantable, such as mechanical circulatory support systems, or wearable, i.e. motion/physiologic sensors, are increasingly utilized for diagnostic/therapeutic purposes. For maximal use efficacy and efficiency, MDs require individual compliance and adherence to operating instructions and data exchange. In today’s world we have increasingly shifted from paper-based information transfer (data sheets, directions for use, etc.) to periodic downloads, and web- and social media-based wireless communication. With growth of MD use it has become evident that a disconnect exists in the tactics employed by device designers and manufacturers compared with patient end-users. This disconnect relates to the form and means of communication utilized to convey and extract information versus patient facility with computer, smartphone and social media information exchange. As a starting point in examining this problem, we studied this disconnect as it relates to individual “generational” status. We conducted an anonymous survey, to examine “generational ” familiarity, acceptance and use; focusing on wearable technology and web-based information sources. Methods: An anonymous survey was developed and distributed to individuals ranging in age from 19 to 78, equally split as to gender. Generations examined included: Silent (age 74-91); Boomers (age 55-73), Gen X (age 39-54), Millennials (age 23-38), and Gen Z (age 7-22). Participants were queried via 10 multiple-choice questions related to digital health, wearable technologies and telemedicine. Results: Eighty-seven individuals participated in the survey. As to generational breakdown, this included 4 Silent (5% of group), 12 Boomers (14% of group), 9 Gen X (10% of group), 38 Millennials (43% of group) and 24 Gen Z (28% of group). Overall younger individuals tended to utilize and believed in the greater accuracy of wearable technologies compared to older individuals. Up to 90% of individuals under the age of 40 believed in the accuracy of wearable technology. Out of the 87 individuals surveyed, 95% reported they were comfortable with their personal health data (labs, imaging, medications, etc.) being shared electronically, but not a single individual believed that digital health/telemedicine improved their quality of care when compared to in-person medical care. Conclusions: Our results reveal that although all individuals are willing to utilize wearable medical technology and web-based information sources, younger generational cohorts are significantly more comfortable with the technology and more inclined to use it. Our hope is that this data may help guide future device development to incorporate means of addressing these generational disparities. Incorporating means of enhancing familiarity, adoption and facility of digital medical devices technologies and web-based information sources, will continue to improve use and value of these devices driving precision medicine.

Optimalisasi Manajemen Pelayanan Gerai Sehat di Layanan Kesehatan Cuma-Cuma (LKC) Dompet Dhuafa Jawa Tengah

Free Health Service (LKC) Dompet Dhuafa Central Java is a health program that provides free health services for underprivileged communities. However, to ensure optimal service, it is necessary to optimize the service management of healthy outlets at LKC. The purpose of this study is to identify factors that influence the optimization of healthy outlet service management in LKC and design a theoretical framework that can help in improving the quality of health services in LKC. This study used a qualitative approach with data collection techniques through in-depth interviews. The results showed that the factors that affect the optimization of healthy outlet service management at LKC include: human resource management, drug and medical device procurement management, data management, and community relations management. From the results of the analysis, a theoretical framework was designed which includes six components, namely: planning, organizing, procurement, implementation, supervision, and assessment. This theoretical framework can help in improving the quality of health services in LKC by providing systematic and comprehensive guidance for LKC management. In conclusion, optimization of healthy outlet service management at LKC needs to be carried out continuously to improve the quality of health services. The theoretical framework designed can be used as a reference for LKC management in improving and developing health programs for underprivileged communities.

Design and implementation of Internet of Things for emergency medical devices based on cloud-edge-device architecture

Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.

Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation.

The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.

Investigation and analysis of four countries’ recalls of osteosynthesis implants and joint replacement implants from 2011 to 2021

BackgroundMedical devices are used in almost all orthopedic surgical subspecialties, and the frequency of adverse events is increasing, which should not be ignored. To provide suggestions on how to avoid implant recalls from the perspective of manufacturers, medical institutions and supervisions, as well as how to respond promptly to adverse events.MethodsThe research extracted recalls of osteosynthesis implants and joint replacement implants from January 1, 2011, to June 30, 2021, in the CNMPA, FDA, HC and ATGA websites and collected the information on device name, recall time, recall class, recall manufacturer, device classification and affected areas. Moreover, the McKinsey 7S model and fishbone diagram were used to analyze recall reasons.ResultsA total of 315 cases of osteosynthesis implants and 286 cases of joint replacement implants were reported in China, the USA, Canada and Australia. The recalls number from 2016 to 2021 was more than that from 2011 to 2015 for osteosynthesis implant (p = 0.012) and joint replacement implant (p = 0.002), and both mainly focused on class II (76.19% and 78.32%). There were statistical differences in the four countries for both implants (p = 0.000), especially osteosynthesis implant between China and the USA (p = 0.000), China and Canada (p = 0.001), the USA and Australia (p = 0.002), and joint replacement implant between China and Australia (p = 0.000).ConclusionsTo avoid the recalls of such implants, manufacturers should strictly select implant materials and components, develop detailed labels and instructions, severely control the packaging process and establish the integrity of medical device data. Medical institutions should standardize procurement procedures, use qualified equipment and train medical workers. It also requires supervisions to conduct premarket safety assessments. In addition, regulators should strengthen supervision and establish reporting systems to deal with the occurrence of adverse events promptly.

Open-source Web Portal for Managing Self-reported Data and Real-world Data Donation in Diabetes Research: Platform Feasibility Study.

BackgroundPeople with diabetes and their support networks have developed open-source automated insulin delivery systems to help manage their diabetes therapy, as well as to improve their quality of life and glycemic outcomes. Under the hashtag #WeAreNotWaiting, a wealth of knowledge and real-world data have been generated by users of these systems but have been left largely untapped by research; opportunities for such multimodal studies remain open.ObjectiveWe aimed to evaluate the feasibility of several aspects of open-source automated insulin delivery systems including challenges related to data management and security across multiple disparate web-based platforms and challenges related to implementing follow-up studies.MethodsWe developed a mixed methods study to collect questionnaire responses and anonymized diabetes data donated by participants—which included adults and children with diabetes and their partners or caregivers recruited through multiple diabetes online communities. We managed both front-end participant interactions and back-end data management with our web portal (called the Gateway). Participant questionnaire data from electronic data capture (REDCap) and personal device data aggregation (Open Humans) platforms were pseudonymously and securely linked and stored within a custom-built database that used both open-source and commercial software. Participants were later given the option to include their health care providers in the study to validate their questionnaire responses; the database architecture was designed specifically with this kind of extensibility in mind.ResultsOf 1052 visitors to the study landing page, 930 participated and completed at least one questionnaire. After the implementation of health care professional validation of self-reported clinical outcomes to the study, an additional 164 individuals visited the landing page, with 142 completing at least one questionnaire. Of the optional study elements, 7 participant–health care professional dyads participated in the survey, and 97 participants who completed the survey donated their anonymized medical device data.ConclusionsThe platform was accessible to participants while maintaining compliance with data regulations. The Gateway formalized a system of automated data matching between multiple data sets, which was a major benefit to researchers. Scalability of the platform was demonstrated with the later addition of self-reported data validation. This study demonstrated the feasibility of custom software solutions in addressing complex study designs. The Gateway portal code has been made available open-source and can be leveraged by other research groups.

Design and Manufacturing of Non- Contact Type Trolley using IOT

Abstract: Automation is a need of time. Automation makes task easier and reliable. Robot is a reprogrammable, multifunctional gadget which is basically intended to work like human being.The Internet of Things (IoT) and robotics communities have so far been driven by different yet highly complementary objectives, the first focused on supporting information services for pervasive sensing, tracking and monitoring; the latter on producing action, interaction and autonomous behaviour. In this paper, we will mainly focus on the issues of patients as well as safety and security, specifically the effect of medical device regulation and data protection laws on robots in healthcare. First, we will examine the demand for robots in healthcare and assess the benefits that robots can provide. Second, we will look at the types of robots currently being used in healthcare, anticipate future innovation, and identify the key characteristics of these robots that will present regulatory issues. To overcome the issues like direct contact with patients in Covid-19 wards, we will develop a robot i.e., corona warrior robot which will be used to provide medicines to patients and sanitize the person when any person comes in front of the robot Keywords: Sensors, Robot, IOT, Camera, Non contact type

A Platform and Multisided Market for Translational, Software-Defined Medical Procedures in the Operating Room (OP 4.1): Proof-of-Concept Study.

BackgroundAlthough digital and data-based technologies are widespread in various industries in the context of Industry 4.0, the use of smart connected devices in health care is still in its infancy. Innovative solutions for the medical environment are affected by difficult access to medical device data and high barriers to market entry because of proprietary systems.ObjectiveIn the proof-of-concept project OP 4.1, we show the business viability of connecting and augmenting medical devices and data through software add-ons by giving companies a technical and commercial platform for the development, implementation, distribution, and billing of innovative software solutions.MethodsThe creation of a central platform prototype requires the collaboration of several independent market contenders, including medical users, software developers, medical device manufacturers, and platform providers. A dedicated consortium of clinical and scientific partners as well as industry partners was set up.ResultsWe demonstrate the successful development of the prototype of a user-centric, open, and extensible platform for the intelligent support of processes starting with the operating room. By connecting heterogeneous data sources and medical devices from different manufacturers and making them accessible for software developers and medical users, the cloud-based platform OP 4.1 enables the augmentation of medical devices and procedures through software-based solutions. The platform also allows for the demand-oriented billing of apps and medical devices, thus permitting software-based solutions to fast-track their economic development and become commercially successful.ConclusionsThe technology and business platform OP 4.1 creates a multisided market for the successful development, implementation, distribution, and billing of new software solutions in the operating room and in the health care sector in general. Consequently, software-based medical innovation can be translated into clinical routine quickly, efficiently, and cost-effectively, optimizing the treatment of patients through smartly assisted procedures.

Preliminary feasibility assessment of CDM-based active surveillance using current status of medical device data in medical records and OMOP-CDM

In recent years, there has been an emerging interest in the use of claims and electronic health record (EHR) data for evaluation of medical device safety and effectiveness. In Korea, national insurance electronic data interchange (EDI) code has been used as a medical device data source for common data model (CDM). This study performed a preliminary feasibility assessment of CDM-based vigilance. A cross-sectional study of target medical device data in EHR and CDM was conducted. A total of 155 medical devices were finally enrolled, with 58.7% of them having EDI codes. Femoral head prosthesis was selected as a focus group. It was registered in our institute with 11 EDI codes. However, only three EDI codes were converted to systematized nomenclature of medicine clinical terms concept. EDI code was matched in one-to-many (up to 104) with unique device identifier (UDI), including devices classified as different global medical device nomenclature. The use of UDI rather than EDI code as a medical device data source is recommended. We hope that this study will share the current state of medical device data recorded in the EHR and contribute to the introduction of CDM-based medical device vigilance by selecting appropriate medical device data sources.