Leveraging Web Scraping and Api Integration for Efficient Medical Device Data Management

최근 증가하는 협업 연구는 원격 의료 데이터 공유 및 외부 기관의 데이터 접근을 요구하고 있다. 본 논문에서는 융통성 있고 효율적인 의료 데이터 관리를 위해 웹서비스를 이용하여 그리드와 PACS 간의 상호운용성 프레임워크를 제안하고 구현하였다. DICOM 표준은 PACS와 이미지 데이터베이스 사이에 의료 이미지 데이터의 교환과 전송을 정의한다. 그러나 병원들 간 의료 데이터 교환은 신뢰할 수 있는 기관들 사이에서만 제공된다. 더군다나 DICOM은 의료 데이터 관리 기능을 제공하지 않고 그리드 미들웨어는 DICOM 데이터에 접근 가능한 표준 툴킷을 제공하고 있지 않고 있다. 본 논문에서는 이러한 문제를 해결하기 위하여 PACS 간의 연동과 의료 데이터 관리를 가능하게 하는 웹 서비스기반의 그리드 서비스 중계자(Web Services-based Grid Service Mediator, WGSM)를 개발하였다. WGSM은 압축 중계자, GridFTP 중계자, RFT 중계자, MyProxy 중계자, MDS 중계자, RLS 중계자 등으로 구성된다. 제안한 웹서비스 기반의 프레임워크는 협업 환경에서 PACS 간 사용자 인증 및 안전한 데이터 접근을 제공한다. 특히, WGSM은 일반 사용자들이 그리드 미들웨어에 대한 어떠한 지식이 없어도 간단하고 효율적인 방법으로 원격 PACS를 접근할 수 있게 한다. Recently the increasing collaborative research requires the remote medical and clinical data sharing and access of external institutions. In this paper, an interoperability framework between Grid and PACS using Web services is proposed and implemented in order to provide flexible and efficient medical data management. The Digital Imaging and Communications in Medicine(DICOM) standard defines medical image data exchange and transfer between PACSs and image databases. However, medical data exchange between hospitals is limited within the trusted and static environments. Moreover, DICOM does not provide medical data management and the Grid middleware does not include standard toolkit to access DICOM data. To address this issue, a Web services-based Grid Service Mediator (WGSM) which provides PACS integration and medical image data management is developed. The WGSM consists of several service mediators such as compress mediator, GridFTP mediator, RFT mediator, MyProxy mediator, MDS mediator, and RLS mediator and others. The proposed Web services-based framework provides user authentication and secure data access between PACSs in collaborative environments. In particular, the WGSM allows ordinary users to access remote PACS data in a simple and efficient manner without any the knowledge about underlying Grid middleware.

On September 24, 2023, the US Food and Drug Administration (FDA) and other stakeholders marked the 10-year anniversary of the publication of the FDA Unique Device Identifier (UDI) Final Rule. The infrastructure for the UDI system is in place—UDI is on the label of medical devices for which UDI requirements apply, unless an exception or alternative applies, and key device identification information, including the device identifier portion of the UDI, is available in the Global Unique Device Identification Database. The focus now is on adoption and implementation of UDI throughout the healthcare ecosystem to realize the intent of the UDI Rule, which is to enhance patient safety through accurate device identification. To that end, this article provides an example of the utility of the UDI for evaluating device performance as a device evolves over time. The example demonstrates that UDI can facilitate identification of the specific version or model of a device that has malfunctioned and/or contributed to an adverse event by enabling more accurate data aggregation compared with other identifying information, potentially leading to key safety insights.

Due to the development of information communication technologies (ICT), the number of medical devices (MDs) with telemetric possibilities is rising, so the concept of homecare is gaining importance. Also, new generation medical devices are equipped with artificial intelligence that is able to perform real-time analysis of measurement result and provide diagnosis prediction. This is the Industry 4.0 happening now. However, there is still traditional approach in management of medical devices. As medical devices have been sophisticated, management systems should improve so they can encompass all the important aspects regarding safety of patients and quality of care. This chapter presents how the technology of Industry 4.0 can be used to improve medical device maintenance systems by application of artificial intelligence (AI). Clinical engineering and health technology management departments benefit from such systems in terms of increase of safety and quality of patient diagnosis and treatments, and cost optimization in medical device management.

Demand for health products in Indonesia is increasing, with an estimated annual growth of 11.9% until 2024. This is driven by population growth and public awareness of the importance of health. Indonesian government believes developing medical devices industry is essential in supporting health resilience in Indonesia. The development and marketing of medical devices does not only focus on the benefits of a product but requires planning and control so that the product can be safe and effective for users, such as by paying attention to quality standardization, patient safety, regulation, and market licensing. This exploratory qualitative research approach yields results in the form of patterns that can increase the availability of safe and quality medical devices, namely 1) research and development of medical devices, 2) regulation of medical devices, 3) assessment of medical devices (health technology evaluation), and 4) management of medical devices (health technology management). The results of this trial will provide valuable information on the process of developing medical device products to choose an approach that suits your needs and conditions.

Cybersecurity vulnerabilities of cardiac implantable electronic devices: Communication strategies for clinicians—Proceedings of the Heart Rhythm Society's Leadership Summit

ImportanceStrengthening premarket and postmarket surveillance of medical devices has long been an area of focus for health policy makers. The recent class I recall (the most serious of the US Food and Drug Administration [FDA] recalls) of reperfusion catheters manufactured by Penumbra, a US-based medical device company, illustrates issues of device safety and oversight that mandate attention.ObjectivesTo review the regulatory history and clinical evidence of the Penumbra JET 7 Reperfusion Catheter with Xtra Flex Technology (JET 7) and use the device recall as a case study of the challenges associated with clinical evaluation, transparency, and oversight of medical devices in the US.EvidenceRegulatory history and clinical evidence for the Penumbra medical devices were analyzed through a qualitative review of decision letters in the Access FDA database for medical devices and medical device reports in the Manufacturer and User Facility Device Experience database and a review of market data (eg, earnings calls, company communications) and clinical literature.FindingsThe JET 7 device was subjected to a class I recall following more than 200 adverse event reports, 14 of which involved patient deaths. Regulatory analysis indicated that each of the Penumbra reperfusion catheters was cleared under the 510(k) pathway (which allows devices to be authorized with limited to no clinical evidence), with limited submission of either new clinical or animal data. Clinical evidence for Penumbra devices was generated from nonrandomized, single-arm trials with small sample sizes. The regulatory issues raised by JET 7 are reflective of broader challenges for medical device regulation. Opportunities for reform include strengthening premarket evidence requirements, requiring safety reporting with unique device identifiers, and mandating active methods of postmarket surveillance.Conclusions and RelevanceThe case study of JET 7 highlights the long-standing gaps in medical device oversight and renews the impetus to build on the Institute of Medicine recommendations and reform FDA medical device regulation to protect public health.

Materials vigilance and traceability

Purpose This study aims to examine firmware vulnerabilities in medical Internet of Things (IoT) devices, focusing on their implications for healthcare policy, management and patient safety. The research aims to provide insights into developing effective mitigation strategies and enhancing regulatory frameworks to improve device security in the rapidly advancing field of medical IoT. This study underscores the critical need for identifying and addressing vulnerabilities to safeguard patient data and maintain the integrity of healthcare systems. Design/methodology/approach The research employs a qualitative multi-case analysis of firmware vulnerabilities in medical IoT devices, including insulin pumps, pacemakers and diagnostic equipment. Cases were selected based on criteria such as device diversity, severity of vulnerabilities and their impact on patient safety, as cross-referenced with the Common Vulnerabilities Exposure database. The analysis considers factors like device age, manufacturer practices, firmware update frequency, regulatory compliance and usage context to derive insights into policy and management challenges. Findings The study identifies key factors contributing to firmware vulnerabilities in medical IoT devices and examines their implications for healthcare management and policy. It provides recommendations for manufacturers and healthcare providers to enhance device security and outlines the need for robust regulatory measures. In addition, the research highlights gaps in the healthcare technology workforce and supply chain, advocating for stronger policies and management practices to mitigate risks. Originality/value This research contributes to the literature by linking technical vulnerabilities in medical IoT devices to broader policy and management concerns. The findings underscore the necessity for enhanced regulatory frameworks, proactive management strategies and industry-wide standards to ensure the security and reliability of medical IoT devices, ultimately protecting patient safety and data integrity.

BackgroundPlanning and budgeting of medical devices allow a healthcare institution to properly use funds, acquire quality and efficient medical devices, and improve healthcare service delivery. The lack of proper policy in the procurement and management of medical devices causes inappropriate usage of funds and impedes the quality of a product. This study aimed to identify the current practices and gaps in the planning and budgeting of medical devices in Ethiopian public hospitals. In this study, an assessment was conducted in all regional public hospitals to assess the current status of medical device management, identify the gaps, and provide suggestions for areas of improvement.MethodsA descriptive cross-sectional design was used for the study assessment where a structured data collection tool was utilized to collect data. A multi-stage stratified random sampling proportionate to size technique was employed for the sampling of public hospitals in all regions of Ethiopia. The collected data was analyzed using SPSS version 26 software.ResultsThe availability of medical equipment development plans, budgeting, and spare parts procurement plans were found to be below 50% in public hospitals. It was also noted that 40.3% of hospitals do not prepare medical device technical specifications during procurement orders. Moreover, the engagement of biomedical engineers/technicians in the planning and procurement of medical devices was found to be below 50%.ConclusionThis assessment showed that there is a need for improvement in the development of procurement plans and preparation of technical specifications for medical devices in Ethiopian public hospitals. Developing policies and strategies for the proper use of funds in the procurement of medical devices, involving biomedical engineering professionals in the planning, procurement and use of medical devices could help to improve the quality, optimized utilization and efficiency of medical devices and ultimately enhance healthcare service delivery.

<i>Medical Device Interoperability and Data Integration to Clinical Information Systems</i> : Medical Device Data Alignment

Recent safety issues involving non-active implantable medical devices (NAIMDs) have highlighted the need for better pre-market and post-market evaluation. Some stakeholders have argued that certain features of medicine safety evaluation should also be applied to medical devices. Our objectives were to compare the current processes and methodologies for the assessment of NAIMD safety profiles with those for medicines, identify potential gaps, and make recommendations for the adoption of new methodologies for the ongoing benefit–risk monitoring of these devices throughout their entire life cycle. A literature review served to examine the current tools for the safety evaluation of NAIMDs and those for medicines. We searched MEDLINE using these two categories. We supplemented this search with Google searches using the same key terms used in the MEDLINE search. Using a comparative approach, we summarized the new product design, development cycle (preclinical and clinical phases), and post-market phases for NAIMDs and drugs. We also evaluated and compared the respective processes to integrate and assess safety data during the life cycle of the products, including signal detection, signal management, and subsequent potential regulatory actions. The search identified a gap in NAIMD safety signal generation: no global program exists that collects and analyzes adverse events and product quality issues. Data sources in real-world settings, such as electronic health records, need to be effectively identified and explored as additional sources of safety information, particularly in some areas such as the EU and USA where there are plans to implement the unique device identifier (UDI). The UDI and other initiatives will enable more robust follow-up and assessment of long-term patient outcomes. The safety evaluation system for NAIMDs differs in many ways from those for drugs, but both systems face analogous challenges with respect to monitoring real-world usage. Certain features of the drug safety evaluation process could, if adopted and adapted for NAIMDs, lead to better and more systematic evaluations of the latter.

In medical services, the amount of data generated by medical devices is increasing explosively, and access to medical data is also put forward with higher requirements. Although HBase-based medical data storage solutions exist, they cannot meet the needs of fast locating and diversified access to medical data. In order to improve the retrieval speed, the recognition model S-TCR and the dynamic management algorithm SL-TCR, based on the behavior characteristics of access, were proposed to identify the frequently accessed hot data and dynamically manage the data storage medium as to maximize the system access performance. In order to improve the search performance of keys, an optimized secondary index strategy was proposed to reduce I/O overhead and optimize the search performance of non-primary key indexes. Comparative experiments were conducted on real medical data sets. The experimental results show that the optimized retrieval model can meet the needs of hot data access and diversified medical data retrieval.

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.

Objective: To determine the accuracy of stroke identification by the Emergency Medical Services (EMS) dispatch center as the first responder and to identify barriers to the accurate diagnosis. Background The accuracy of stroke identification by dispatch units is an important determinant for directing appropriate level of EMS and response times. Design/Methods: We used the 2009 National Emergency Medical Services Information System (NEMSIS) Research Dataset, which is representative of 26 states in the US. We compared the diagnosis for dispatch based on ascertainment by dispatch staff with the diagnosis made by EMS after arrival at scene for identification of stroke/cerebrovascular accident (CVA) and determined the reasons for false diagnosis. We also estimated the sensitivity, specificity, and predictive values of dispatch diagnosis of stroke/CVA. Results: Among the 58013 patients identified by the dispatch center as stroke/CVA, 35118(60.5%) were diagnosed as stroke/CVA by EMS after evaluation at scene. Among the patients with false dispatch diagnosis, the EMS diagnosis was as follows: 8401(14.5%) altered level of consciousness, 4107(7.1%), syncope/fainting, 2391(4.1%) diabetic symptoms-hypoglycemia, 917(1.6%) seizures and 7079(12.2%) other. Identification of stroke by dispatch unit showed a sensitivity of 57%, specificity of 99.03%, positive predictive value of 60.5%, negative predictive value of 98.8%, positive likelihood ratio of 59 (95% CI 58-60) and negative likelihood ratio of 0.43(95% CI 0.43-0.44). Conclusions: The complaint of stroke/CVA reported by dispatch unit to EMS seems to have low sensitivity but high specificity. Efforts are required to increase the sensitivity of dispatch unit diagnosis of stroke to ensure appropriate level of EMS care is dispatched and reduce response times. Disclosure: Dr. Jadhav has nothing to disclose. Dr. Pawar has nothing to disclose. Dr. Chaudhry has nothing to disclose. Dr. Hassan has nothing to disclose. Dr. Namasaka has nothing to disclose. Dr. Suri has nothing to disclose. Dr. Qureshi has nothing to disclose. Tuesday, April 24 2012, 07:30 am-12:00 pm

Evidence-based clinical engineering: Machine learning algorithms for prediction of defibrillator performance