Journal articles: 'Medical device validation'

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Klein, Devorah E., and Matthew J. Jordan. "Methods of Assessing Medical Devices." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 23 (September 2002): 1890–94. http://dx.doi.org/10.1177/154193120204602305.

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While designing and validating any complex system has challenges, the medical domain has specific requirements which must be considered for a system or device to be successful. The environments, communities of use, and interactions are varied, unpredictable, uncontrolled, and ever-changing. Given the environments, communities of use, and interactions involved with medical devices, successful early and late validation of the device must be informed by the context of use itself. Building “frameworks” which represent the context of use for the device can focus validation goals, methods, and criteria and ensure that validation is directed and appropriate. In this paper we present a process and associated methods for defining the frameworks in which medical devices can be successfully assessed. The phases of the process include Phase1: Definition in which a framework of understanding is built which represents the environment of use, community of users, and the interactions between systems and users for the medical device in development. In Phase 2: Validation the framework which defines the environment of use, community of users, and the interactions between systems and users is used to develop a validation approach and criteria. The developing device is then validated against the framework itself.

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Jones, Randy D., David L. Stalling, Jon Davis, Patrick Jurkovich, and Kirk LaPointe. "Software validation for medical device manufacturing." Quality Assurance Journal 7, no. 4 (2003): 242–47. http://dx.doi.org/10.1002/qaj.245.

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Gagliardi, John. "Medical Device Software: Verification, Validation and Compliance." Biomedical Instrumentation & Technology 45, no. 2 (March 1, 2011): 95. http://dx.doi.org/10.2345/0899-8205-45.2.95.

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Desain, C., and C. Vercimak Sutton. "Validation for Medical Device and Diagnostic Manufacturers." Journal of Clinical Engineering 21, no. 1 (January 1996): 30–31. http://dx.doi.org/10.1097/00004669-199601000-00010.

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標準委員会. "Guideline for Steilization of Medical Device Validation." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 68, no. 9 (September 1, 1998): 399–406. http://dx.doi.org/10.4286/ikakikaigaku.68.9_399.

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標準委員会. "Guideline for Sterilization of Medical Device Validation." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 68, no. 7 (July 1, 1998): 295–311. http://dx.doi.org/10.4286/ikakikaigaku.68.7_295.

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Zhao, Yincheng, Kuangjie Sheng, Zheng Wang, Xilin Zhang, HengyiYang, and Rui Miao. "Process Validation and Revalidation in Medical Device Production." Procedia Engineering 174 (2017): 686–92. http://dx.doi.org/10.1016/j.proeng.2017.01.207.

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McEvoy, Brian, Stacy Bohl Wiehle, Ken Gordon, Gerry Kearns, Paulo Laranjeira, and Nicole McLees. "Advancing the Sustainable Use of Ethylene Oxide through Process Validation." Biomedical Instrumentation & Technology 55, s3 (March 1, 2021): 35–44. http://dx.doi.org/10.2345/0899-8205-55.s3.35.

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Abstract Based on excellent material compatibility and ability for scale, ethylene oxide (EO) sterilization constitutes approximately 50% of single-use medical device sterilization globally. Epidemiological considerations have elevated focus toward optimization of EO processes, whereby only necessary amounts of sterilant are used in routine processing. EO sterilization of medical devices is validated in accordance with AAMI/ANSI/ISO 11135:2014 via a manner in which a sterility assurance level (SAL) of 10−6 is typically achieved, with multiple layers of conservativeness delivered, using “overkill” approaches to validation. Various optimization strategies are being used throughout the medical device industry to deliver the required SAL while utilizing only necessary amounts of sterilant. This article presents relevant experiences and describes challenges and considerations encountered in delivering EO process optimization. Thus far, the results observed by the authors are encouraging in demonstrating how EO processing can be optimized in the delivery of critical single-use medical devices for patient care.

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Clark, Shannon E. "Training Decay Selection for Usability Validation." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 5, no. 1 (June 2016): 76–83. http://dx.doi.org/10.1177/2327857916051018.

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When conducting usability validation testing, representative users must use the device in the expected conditions of use in the field. There is usually a period of time—days or weeks—between the point in time a user is trained, and the moment they use the device for the first time. For this reason, the FDA acknowledges the need for “training decay” as part of usability validation testing, but manufacturers face challenges simulating real-time decays. In response to challenges associated with lags of days or weeks between training and usability validation testing, medical device manufacturers typically simulate shortened training decay periods. This paper discusses the theory behind the shapes of various training decay curves and the variables that drive differences between training decay curves. The author proposes to use a task-based approach for defining training decay curves in usability validation studies and sets out generalized training decay curves at a high level. Future research could reveal detailed and generalizable training decay curves. Identifying generalizable training decay curves could standardize the usability testing required for medical devices, and ultimately improve use error identification while avoiding an undue toll on manufacturer resources.

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Vogel, David A. "Medical Device Validation: How Vendors Can Assist their Customers." Biomedical Instrumentation & Technology 41, no. 6 (November 2007): 465–68. http://dx.doi.org/10.2345/0899-8205(2007)41[465:mdvhvc]2.0.co;2.

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이동찬, 조중래, and ChangsooLee. "A Case Study on the Medical Device Software Validation." Korea International Accounting Review ll, no. 46 (December 2012): 281–302. http://dx.doi.org/10.21073/kiar.2012..46.013.

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Malinauskas, Richard A., Prasanna Hariharan, Steven W. Day, Luke H. Herbertson, Martin Buesen, Ulrich Steinseifer, Kenneth I. Aycock, et al. "FDA Benchmark Medical Device Flow Models for CFD Validation." ASAIO Journal 63, no. 2 (2017): 150–60. http://dx.doi.org/10.1097/mat.0000000000000499.

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Luong, Lan, Michelle Simkins, Rachael Snyders, Kathleen Anne Gase, Carole Leone, Carol Sykora, Christine Hoehner, and Hilary Babcock. "Validation Methodology of Healthcare-Associated Infection Device Day Denominators When Switching Electronic Medical Records." Infection Control & Hospital Epidemiology 41, S1 (October 2020): s428—s429. http://dx.doi.org/10.1017/ice.2020.1090.

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Background: From August 2017 to June 2018, 11 hospitals within a large healthcare system switched from multiple different electronic medical records (EMRs) to 1 EMR. At the time of this transition, the NHSN provided guidelines to validate healthcare-associated infection (HAI) denominators when switching from manual denominator collection to electronic denominator collection, but the NHSN did not give guidelines for validation when switching from 1 EMR to another. We aimed to build a validation process to ensure the accuracy of central-line and urinary catheter days reported to the NHSN after switching EMRs. Methods: Our validation process began with a statistical phase followed by a targeted manual validation phase. The statistical phase used 3 prediction methods (linear regression, time series analysis, and statistical process control [SPC] charts) to forecast device days after the EMR switch for units within hospitals. Models were developed using baseline data from the old EMR (January 2015 through the new EMR implementation). Using prespecified criteria for each method to determine discrepancies, we built a decision tree to identify units needing manual validation. Any unit that failed the statistical phase would need to participate in the manual validation phase, using a midnight census and direct visualization of devices. The manual validation process was composed of 14-day blocks. At the end of each block, if manual device days were within ±5% of EMR device days, they were considered validated. Manual validation would be repeated in 14-day blocks until 2 consecutive blocks passed within ±5%. Results: Overall, 157 units were evaluated for urinary catheter days and central-line days. Among them, 143 units passed the statistical validation test for urinary catheter days and 151 passed for central-line days. There was no specific pattern when comparing forecasted versus actual device days. The manual validation process for the 20 failing units (14 urinary catheter and 6 central-line units) is ongoing; preliminary results identified issues with missing nursing documentation in the EMR and with inaccurate manual counting of device days. There were no systematic discrepancies associated with the new EMR. Conclusions: We developed a novel validation process using statistical prediction methods supplemented with a targeted manual process. This process saved resources by identifying the units that need manual validation. Discrepancies were largely related to nursing documentation, which the infection prevention team addressed with additional training.Funding: NoneDisclosures: None

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Habukawa, Chizu, Naoto Ohgami, Takahiko Arai, Haruyuki Makata, Morimitsu Tomikawa, Tokihiko Fujino, Tetsuharu Manabe, et al. "Wheeze Recognition Algorithm for Remote Medical Care Device in Children: Validation Study." JMIR Pediatrics and Parenting 4, no. 2 (June 17, 2021): e28865. http://dx.doi.org/10.2196/28865.

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Background Since 2020, peoples’ lifestyles have been largely changed due to the COVID-19 pandemic worldwide. In the medical field, although many patients prefer remote medical care, this prevents the physician from examining the patient directly; thus, it is important for patients to accurately convey their condition to the physician. Accordingly, remote medical care should be implemented and adaptable home medical devices are required. However, only a few highly accurate home medical devices are available for automatic wheeze detection as an exacerbation sign. Objective We developed a new handy home medical device with an automatic wheeze recognition algorithm, which is available for clinical use in noisy environments such as a pediatric consultation room or at home. Moreover, the examination time is only 30 seconds, since young children cannot endure a long examination time without crying or moving. The aim of this study was to validate the developed automatic wheeze recognition algorithm as a clinical medical device in children at different institutions. Methods A total of 374 children aged 4-107 months in pediatric consultation rooms of 10 institutions were enrolled in this study. All participants aged ≥6 years were diagnosed with bronchial asthma and patients ≤5 years had reported at least three episodes of wheezes. Wheezes were detected by auscultation with a stethoscope and recorded for 30 seconds using the wheeze recognition algorithm device (HWZ-1000T) developed based on wheeze characteristics following the Computerized Respiratory Sound Analysis guideline, where the dominant frequency and duration of a wheeze were >100 Hz and >100 ms, respectively. Files containing recorded lung sounds were assessed by each specialist physician and divided into two groups: 177 designated as “wheeze” files and 197 as “no-wheeze” files. Wheeze recognitions were compared between specialist physicians who recorded lung sounds and those recorded using the wheeze recognition algorithm. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files, and evaluated the influence of age and sex on the wheeze detection sensitivity. Results Detection of wheezes was not influenced by age and sex. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 96.6%, 98.5%, 98.3%, and 97.0%, respectively. Wheezes were automatically detected, and heartbeat sounds, voices, and crying were automatically identified as no-wheeze sounds by the wheeze recognition algorithm. Conclusions The wheeze recognition algorithm was verified to identify wheezing with high accuracy; therefore, it might be useful in the practical implementation of asthma management at home. Only a few home medical devices are available for automatic wheeze detection. The wheeze recognition algorithm was verified to identify wheezing with high accuracy and will be useful for wheezing management at home and in remote medical care.

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Prince, Daniel, Jozef Mastej, Isabel Hoverman, Raja Chatterjee, Diana Easton, and Daniela Behzad. "Challenges to Validation Of a Complex Nonsterile Medical Device Tray." Biomedical Instrumentation & Technology 48, no. 4 (July 1, 2014): 306–11. http://dx.doi.org/10.2345/0899-8205-48.4.306.

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Validation by steam sterilization of reusable medical devices requires careful attention to many parameters that directly influence whether or not complete sterilization occurs. Complex implant/instrument tray systems have a variety of configurations and components. Geobacillus stearothermophilus biological indicators (BIs) are used in overkill cycles to to simulate worst case conditions and are intended to provide substantial sterilization assurance. Survival of G. stearothermophilus spores was linked to steam access and size of load in the chamber. By a small and reproducible margin, it was determined that placement of the trays in a rigid container into minimally loaded chambers were more difficult to completely sterilize than maximally loaded chambers.

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Kossack, Merrick F., and Andrew W. Gellatly. "The What and how of Medical Device Design Validation: A Human Factors Methodology." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 11 (October 2007): 750–55. http://dx.doi.org/10.1177/154193120705101131.

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To meet the FDA's Quality System Regulation, medical device manufacturers must include design validation as part of their design and development activities. However, the regulation does not specify which product requirements must be validated or what methods satisfy a proper design validation process. This paper outlines an approach that device manufacturers can follow to determine which product requirements should undergo design validation testing and what types of testing methods should be used.

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Schwab, Tyson, Bernhard Fassl, and John Langell. "The Importance of Design Validation in Global Health Surgical Innovation." Surgical Innovation 25, no. 6 (November 21, 2018): 563–69. http://dx.doi.org/10.1177/1553350618814644.

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Introduction. Medical technology development requires an understanding of user needs and environmental requirements. Accurately capturing market requirements, user needs, and design specifications are multifactorial and challenging. On-site observation and design validation may lead to development of more effective solutions to improve health care. This study was designed to evaluate the value of design validation for medical devices developed to address global medical needs. Methods. Observational comparative analysis and survey studies were used to collect data involving multiple stakeholder viewpoints. User needs, market requirements, and design inputs were created using standard operating procedures in accordance with US FDA—21 CFR 820. Design requirements included user needs, product description, regulatory standards, functional requirements, performance and physical requirements, use environment, human-system interfacing, conceptual designs, and market analysis. A random population-based cohort sample in India was used to conduct a semi-longitudinal assessment of exposure-outcome relations from device prototype use and design validation. Seventy-two subjects were observed for a 4-week duration. After validation, each component of the traceability matrix was either marked “no change,” “significant change,” or “new addition” as defined in the methods section. Results. A total of 198 design requirements and specifications were evaluated for each device. Eleven percent of the final design requirements and specifications were “new additions” and 12% were “significant changes.” Conclusion. Assessment of design requirements and specifications in the global environment improves medical device design quality and safety. This study validates environmental immersion in the target use environment early to ensure validation of user needs and design specifications during design conception.

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Konstantas, D. "Section 4: Sensor, Signal and Imaging Informatics: An Overview of Wearable and Implantable Medical Sensors." Yearbook of Medical Informatics 16, no. 01 (August 2007): 66–69. http://dx.doi.org/10.1055/s-0038-1638527.

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SummaryTo give a brief, introductory overview of current developments and trends in miniaturized medical sensors which will be informative to non-specialists in the field.Summary of the different types of wearable and implantable sensors with examples of current state-of-the-art devices and systems used in medical applications.After more than a decade of intensive research and development around the world, miniaturized medical sensors are becoming commercially available, allowing increasingly rapid collection of large-scale medical data and its wireless transmission to health care centers. However, most sensor systems are not yet in routine use and still restricted to specialized sites, undergoing validation trials, mostly within research laboratories.Challenges to routine adoption of medical sensor systems often arise from a combination of lack of awareness of the technology among many medical practitioners, technological limitations of the device systems (artifacts and noise resulting from problems in garment fit or device implantation), and open issues of evaluation and validation for the very broad scope of conditions in home-care and ambient environments over which medical sensors need to operate for routine, reliable, practical use.

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Tellez, Armando, Marco Ferrone, and Juan F. Granada. "Translational Research: The Cornerstone for Medical Technology Advancement." Toxicologic Pathology 47, no. 3 (February 11, 2019): 203–4. http://dx.doi.org/10.1177/0192623318819972.

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Significant improvements in quality of life and mortality have occurred over the last century due to the giant advancements in medical innovation. Medical innovation continues to move forward, and it is expanding to areas never explored before. In particular, the advancement in big data analytics is now enabling the rapid progress in the understanding of gene influence in human diseases. The progress in medical innovation achieved until today is significant; however, the potential that future technologies have to modify patterns of disease thought to be incurable is mind-boggling. In the present issue of Toxicologic Pathology, a wide variety of devices and validation platforms are presented as a clear evidence of the multidisciplinary approach that is necessary for the progress of this field. As a clinician, scientist, and medical device innovator, I am confident that this special issue dedicated to the “Pathology of Medical Devices” will be of great value to the scientific and medical device innovation community.

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Czeropski, Sue, and Donovan Le. "Validation ROI: An HPT case study from the medical device industry." Performance Improvement 49, no. 2 (February 2010): 8–15. http://dx.doi.org/10.1002/pfi.20124.

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Frey, Justin, Allan Guan, Zhenyu Li, Steven Turtil, and K. Scott Phillips. "Hemoglobin assay for validation and quality control of medical device reprocessing." Analytical and Bioanalytical Chemistry 407, no. 22 (July 15, 2015): 6885–89. http://dx.doi.org/10.1007/s00216-015-8856-2.

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Forrey, Christopher, David M. Saylor, Joshua S. Silverstein, Jack F. Douglas, Eric M. Davis, and Yossef A. Elabd. "Prediction and validation of diffusion coefficients in a model drug delivery system using microsecond atomistic molecular dynamics simulation and vapour sorption analysis." Soft Matter 10, no. 38 (2014): 7480–94. http://dx.doi.org/10.1039/c4sm01297f.

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Diffusion of small to medium sized molecules in polymeric medical device materials underlies a broad range of public health concerns related to unintended leaching from or uptake into implantable medical devices.

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Kuhl, J., O. Sankowski, and D. Krause. "INVESTIGATION ON METHODS AND CHARACTERISTICS IN MEDICAL DEVICE DEVELOPMENT." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 1969–78. http://dx.doi.org/10.1017/dsd.2020.95.

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AbstractA targeted development of safe medical products can be supported by design methods. This paper analyses which design methods are applied in the development of medical devices and whether they are adapted for considering medical devices’ special features (legal, human and technical issues). In particular, variety management, risk assessment and user-centered design for medical devices are examined. Typically, interdisciplinary risk assessment is methodically supported. Additionally, user-centered design methods for requirements assessment, design verification and design validation are applied.

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Beneš, Oldřich, and David Hampel. "Rationale for Replacement of the Destructive Test by Non-Destructive One in Medical Devices Manufacturing." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 68, no. 6 (2020): 967–72. http://dx.doi.org/10.11118/actaun202068060967.

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Due to expanding demand for the level of testing on one side and reduction of costs on the other side, the question how to replace expensive destructive testing of medical devices without compromising the quality of final product arising urgently. This situation is common within all highly regulated industries – in this article is addressed the problem from medical device manufacturing industry. Based on real data containing testing and validation datasets, logit model and classification tree model are estimated for establishing the relationship between result of destructive test and measurements of explored device. Results point to possibility of replacing destructive test by non-destructive one in our case.

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Hsiou, David, Chenlu Gao, Natalya Pruett, and Michael Scullin. "254 Validation of a Non-Wearable Sleep Tracking Device in Healthy Adults Under Normal and Restricted Sleep Conditions." Sleep 44, Supplement_2 (May 1, 2021): A102. http://dx.doi.org/10.1093/sleep/zsab072.253.

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Abstract Introduction Polysomnography (PSG) is the gold standard for measuring sleep, but this method is cumbersome, costly, and sometimes does not reflect naturalistic sleep patterns. Leading technology companies have developed non-wearable sleep tracking devices that have attracted public interest. However, the accuracy of these devices has either been shown to be poor or the validation tests have not been conducted by independent laboratories without potential conflicts of interest. Relative to PSG and actigraphy, and under conditions of both normal and restricted sleep, we assessed the accuracy of early and newer versions of a non-wearable sleep tracking device (Beddit, Apple Inc.). Methods Participants were 35 healthy young adults (Mage=18.97, SD=0.95 years; 77.14% female; 42.86% Caucasian). We randomly assigned them to go to bed at 10:30pm (normal sleep) or 1:30am (restricted sleep) in a controlled sleep laboratory environment. Lights-on was 7:00am for all participants. Sleep was measured by the early version (3.0) or newer version (3.5) of a non-wearable device that uses a sensor strip to measure movement, heart rate, and breathing. We also measured PSG, wristband actigraphy, and self-report. For each device, we tested accuracy against PSG for total sleep time (TST), sleep efficiency (SE%), sleep onset latency (SOL), and wake after sleep onset (WASO). Results While the early version displayed poor reliability (ICCs<0.30), the newer version of the non-wearable device yielded excellent reliability with PSG under both normal and restricted sleep conditions. Not only was agreement excellent for TST (ICC=0.96) and SE% (ICC=0.98), but agreement was also excellent for the notoriously difficult metrics of SOL (ICC=0.92) and WASO (ICC=0.92). This newer version significantly outperformed clinical grade actigraphy (ICCs often in the 0.40 to 0.75 range), and self-reported sleep (ICCs often below 0.40). Conclusion Surprisingly, a non-wearable device demonstrated greater agreement with PSG than clinical grade actigraphy. Though the field has generally been skeptical of commercial non-wearable devices, this independent validation provides optimism that some such devices would be efficacious for research in healthy adults. Future work is needed to test the validity of this device in older adults and clinical populations. Support (if any) National Science Foundation (1920730 and 1943323)

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Lansdowne, Krystal, Christopher G. Scully, Loriano Galeotti, Suzanne Schwartz, David Marcozzi, and David G. Strauss. "Recent Advances in Medical Device Triage Technologies for Chemical, Biological, Radiological, and Nuclear Events." Prehospital and Disaster Medicine 30, no. 3 (April 14, 2015): 320–23. http://dx.doi.org/10.1017/s1049023x15004641.

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AbstractIn 2010, the US Food and Drug Administration (Silver Spring, Maryland USA) created the Medical Countermeasures Initiative with the mission of development and promoting medical countermeasures that would be needed to protect the nation from identified, high‐priority chemical, biological, radiological, or nuclear (CBRN) threats and emerging infectious diseases. The aim of this review was to promote regulatory science research of medical devices and to analyze how the devices can be employed in different CBRN scenarios. Triage in CBRN scenarios presents unique challenges for first responders because the effects of CBRN agents and the clinical presentations of casualties at each triage stage can vary. The uniqueness of a CBRN event can render standard patient monitoring medical device and conventional triage algorithms ineffective. Despite the challenges, there have been recent advances in CBRN triage technology that include: novel technologies; mobile medical applications (“medical apps”) for CBRN disasters; electronic triage tags, such as eTriage; diagnostic field devices, such as the Joint Biological Agent Identification System; and decision support systems, such as the Chemical Hazards Emergency Medical Management Intelligent Syndromes Tool (CHEMM-IST). Further research and medical device validation can help to advance prehospital triage technology for CBRN events.LansdowneK, ScullyCG, GaleottiL, SchwartzS, MarcozziD, StraussDG. Recent advances in medical device triage technologies for chemical, biological, radiological, and nuclear events. Prehosp Disaster Med. 2015;30(3):1-4

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Williams, Adam, Bijo Sebastian, and Pinhas Ben-Tzvi. "A Robotic Head Stabilization Device for Medical Transport." Robotics 8, no. 1 (March 25, 2019): 23. http://dx.doi.org/10.3390/robotics8010023.

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In this paper, the design and control of a robotic device intended to stabilize the head and neck of a trauma patient during transport are presented. When transporting a patient who has suffered a traumatic head injury, the first action performed by paramedics is typically to restrain and stabilize the head and cervical spine of a patient. The proposed device would drastically reduce the time required to perform this action while also freeing a first responder to perform other possibly lifesaving actions. The applications for robotic casualty extraction are additionally explored. The design and construction are described, followed by control simulations demonstrating the improved behavior of the chosen controller paradigm, linear active disturbance rejection control (LADRC). Finally, experimental validation is presented, followed by future work and directions for the research.

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Chen, Yeong-Lin. "OQ & PQ protocols development for medical device two-level process validation." Journal of Medical Engineering & Technology 43, no. 7 (October 3, 2019): 431–42. http://dx.doi.org/10.1080/03091902.2019.1692939.

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Foster, L. Bryant, Russell J. Branaghan, Dean Barker, Jen Donahue, and Dave Mitropoulous-Rundus. "What Can Medical Device Human Factors Learn from Other Industries?" Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 6, no. 1 (May 15, 2017): 183–84. http://dx.doi.org/10.1177/2327857917061039.

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Human factors has grown in popularity in the medical device industry since the introduction of FDA’s Draft Guidance Applying Human Factors and Usability Engineering to Optimize Medical Device Design in 2011. However, human factors and ergonomics has been practiced since the 1940’s and adopted by various industries, regulated and unregulated, to improve user safety and satisfaction. Over the last 70+ years, many manufacturers in industries like aerospace, aviation, automotive, and consumer goods have incorporated human factors into their product design controls. Nevertheless, many medical device manufacturers still treat human factors as a singular step (validation testing) in the regulatory submission process. Leveraging the diverse experience, we aimed to identify human factors practices that have proven successful in non-medical industries that are not commonly practiced in the medical device industry, possible reasons these practices are not used by medical device manufacturers, and recommend possible solutions. Our discussions revealed four human factors practices that are not commonly used in the medical device industry, reasons the medical device industry may be slow to adopt human factors, and potential solutions for each.

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Kumar, Anil R., Kevin Cluff, and Tim McLeroy. "Is Remote Human Factors Testing an Acceptable Approach for Human Factors Validation." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 10, no. 1 (June 2021): 152–56. http://dx.doi.org/10.1177/2327857921101083.

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Human factors (HF) validation of medical devices is vital for approval of use by the Food and Drug Administration (FDA). Historically, validation testing of medical devices has taken place within laboratory-controlled environments where conditions are controlled, and testing is executed in accordance with similar circumstance. Due to the COVID-19 pandemic, laboratory research has decreased in a wide range of disciplines or in instances continued with masks and many other COVID mitigations. As a result, medical device manufacturers who need to provide human factors validation to receive U.S. Food and Drug Administration (FDA) approval were impacted. Remote usability testing, while a fairly new phenomenon for physical devices, affords the ability to functionally test a product within naturalistic environments that are indistinguishable from the settings in which they would be used (e.g. the user’s home). However, published literature to support whether remote HF testing could potentially be an acceptable approach is rare. The objective of this study is to replicate the objectives and structure of an original in-person study, which was conducted using migraine patients who performed one unaided simulated injection using a 2-step autoinjector. The original methodology has been modified to adapt to the remote testing. This paper reports the procedures that has been developed for this ongoing endeavor.

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Weinreich, Gerhard, Jeff Armitstead, Volker Töpfer, You-Ming Wang, Yi Wang, and Helmut Teschler. "Validation of ApneaLink as Screening Device for Cheyne-Stokes Respiration." Sleep 32, no. 4 (April 2009): 553–57. http://dx.doi.org/10.1093/sleep/32.4.553.

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Sum, Kelli, Lana Sneath, Shannon Clark, and Dan Nathan-Roberts. "Training Decay Selection for Usability Validation: Pilot Study Methodology and Results for an FDA Research Grant." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 721–25. http://dx.doi.org/10.1177/1071181320641167.

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As medical devices become more technologically advanced, patients risk forgetting their training and missing critical steps. Existing literature explores ways to train patients on medical devices but does not quantify how long information is retained, which is essential for valid medical device testing before approval. The aim of the research presented is to validate a robust method of quantifying training decay research across multiple periods. Some participants were trained on an insulin pump and assigned to decay periods of one hour, one day, or one week. Additionally, an untrained cohort represented a theoretical maximum decay. Although results are not statistically significant due to a small sample size, task performance shows possible differences between time points and task types. Improvements and considerations translating this pilot study into a more extensive main study are also discussed.

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Garguilo, John J., Sandra Martinez, and Maria Cherkaoui. "Medical Device Interoperability A Standards-Based Testing Approach." Biomedical Instrumentation & Technology 45, no. 3 (May 1, 2011): 249–55. http://dx.doi.org/10.2345/0899-8205-45.3.249.

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Abstract We present a black-box messaging test approach employed to achieve a level of rigor which improves—if not assures, given no optionality—correct data exchange. In particular, we verify that physiological information derived and communicated via messaging from a source medical device (e.g., an infusion pump) or healthcare information system, to another medical device (e.g., a patient monitor) or healthcare information system, which consumes or makes use of the data, is syntactically and semantically correct. In other words, the structure of information exchanged within the healthcare system is compliant to a defined specification(s) and the information meaning conveyed and interpreted by the consumer is exactly the same and as intended by the source. Our approach for developing a test system to validate messages is based on constraining identified and recognized specifications. The test system validation performed uses codified assertions derived from the specifications and constraints placed upon those specifications. To first show conformance—which subsequently enables interoperability—these assertions, which are atomic requirements traceable by clause to the base specifications, are employed by our medical device test tools to rigorously enforce standards to facilitate safe and effective plug-and-play information exchange.

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Liu, Chong, Zide Zhang, Yuan Ma, Tuo Liang, Chaojie Yu, Zhaojun Lu, Guoyong Xu, et al. "Predicting the Failure Risk of Internal Fixation Devices in Chinese Patients Undergoing Spinal Internal Fixation Surgery: Development and Assessment of a New Predictive Nomogram." BioMed Research International 2021 (January 26, 2021): 1–13. http://dx.doi.org/10.1155/2021/8840107.

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The current study is aimed at developing and validating a nomogram of the risk of failure of internal fixation devices in Chinese patients undergoing spinal internal fixation. We collected data from a total of 1139 patients admitted for spinal internal fixation surgery at the First Affiliated Hospital of Guangxi Medical University from May 2012 to February 2019. Of these, 1050 patients were included in the spinal internal fixation group and 89 patients in the spinal internal fixation device failure group. Patients were divided into training and validation tests. The risk assessment of the failure of the spinal internal fixation device used 14 characteristics. In the training test, the feature selection of the failure model of the spinal internal fixation device was optimized using the least absolute shrinkage and selection operator (LASSO) regression model. Based on the characteristics selected in the LASSO regression model, multivariate logistic regression analysis was used for constructing the model. Identification, calibration, and clinical usefulness of predictive models were assessed using C-index, calibration curve, and decision curve analysis. A validation test was used to validate the constructed model. In the training test, the risk prediction nomogram included gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. The model demonstrated moderate predictive power with a C-index of 0.722 (95% confidence interval: 0.644–0.800) and the area under the curve (AUC) of 0.722. Decision curve analysis depicted that the failure risk nomogram was clinically useful when the probability threshold for internal fixation device failure was 3%. The C-index of the validation test was 0.761. This novel nomogram of failure risk for spinal instrumentation includes gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. It can be used for evaluating the risk of instrumentation failure in patients undergoing spinal instrumentation surgery.

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Rodríguez-Salazar, Leonardo Andrés, Edna Magaly Gamboa-Delgado, Sherneyko Plata Rangel, Oscar Alberto Mantilla-Prada, Eugenio Sarmiento-Caraballo, and José Domingo Rincón-Riveros. "Clinical validation study of the SignCare Vital Signs Monitor of Fundación Cardiovascular de Colombia." Revista de la Facultad de Medicina 64, no. 3 (July 1, 2016): 459. http://dx.doi.org/10.15446/revfacmed.v64n3.49339.

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Introduction: In Colombia, due to the difficult access to health services and to geographic conditions, the implementation and innovation of telemedicine technological tools is a priority. Having a validated vital signs monitor (VSM) improves proper medical treatment and diagnosis.Objective: To design and perform clinical trials for the SignCare VSM.Materials and methods: A device for continuous monitoring of electrocardiography, respiration, oxygen saturation, temperature and noninvasive blood pressure (NIBP) was designed. This device was validated in a laboratory in order to ensure a robust prototype, close to the level of commercial medical devices. Clinical trials were performed through a cross-section study with 98 patients, whose vital signs were measured using the SignCare monitor and a commercial monitor. These two measurements were compared using Pearson’s correlation coefficients.Results: There were no statistically significant differences between the results obtained with the SignCare VSM and the commercial monitor. The highest correlations were found for the following items: heart rate by electrocardiogram (r=0.844), heart rate by oxymetry (r=0.821), body temperature (r=0.895), systolic blood pressure (r=0.780), and diastolic blood pressure (r=0.811).Conclusions: The SignCare device is as reliable as the commercial monitor in the qualitative detection of morphologic alterations of electrocardiogram records, as well as in breathing, temperature, oxygen saturation and blood pressure parameters, which makes it recommendable for clinical use in adult population.

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Segman, Yosef (Joseph), and Ella Sheiman. "Post marketing study of hemodynamic and hematological noninvasive readings in a blood bank." SAGE Open Medicine 6 (January 2018): 205031211879606. http://dx.doi.org/10.1177/2050312118796065.

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Objectives: This validation test was conducted in the Fujisan Blood Bank, Fortaleza, Brazil and evaluated the noninvasive TensorTip MTX (MTX, Cnoga Medical Ltd.) readings of hemoglobin, hematocrit, red blood cells, blood pressure, and heart rate compared to reference lab device readings. Generally, these parameters are measured from venous or capillary blood samples run on a laboratory analyzer or handheld invasive testing devices. Needle sticks are inconvenient to blood donors with relatively high exposure risks. To our vision, noninvasive determination would be of benefit to blood contributors and medical professional teams; it would be fast and painless. Methods: A total of 334 subjects were included in the Fujisan blood bank validation (65% male, 35% female). Hemoglobin, hematocrit, and red blood cells, as well as blood pressure and heart rate, were measured noninvasively using the MTX device and were compared to venous blood samples run on two laboratory hematology analyzers (Horiba ABX Micros60 and Siemens blood count analyzer), to digital sphygmomanometer (OMRON BP786) and to manual auscultation. The noninvasive measurement with the appropriate virtual arm cuff setting was performed simultaneously with the blood sample extraction of the reference devices measurement. Results: There was no statistically significant difference ( p > 0.05, paired, two-tailed t-test) between the average daily hemoglobin, hematocrit, and red blood cells measurements provided by the MTX device and the laboratory hematology analyzer. In addition, there was no significant difference between the daily blood pressure and heart rate results provided by the MTX device and the digital and manual sphygmomanometers. The error calculated between the MTX and the reference device was found to be sufficiently accurate according to the relevant standards. Conclusion: The MTX accuracy of noninvasive hemoglobin, hematocrit, red blood cells, blood pressure, and heart rate measurements satisfies the industrial standards; therefore, the device enables more accurate, efficient, and effective patient care.

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McLaughlin, Anne Collins, Patricia R. DeLucia, Frank A. Drews, Monifa Vaughn-Cooke, Anil Kumar, Robert R. Nesbitt, and Kevin Cluff. "Evaluating Medical Devices Remotely: Current Methods and Potential Innovations." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 7 (September 22, 2020): 1041–60. http://dx.doi.org/10.1177/0018720820953644.

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Objective We present examples of laboratory and remote studies, with a focus on studies appropriate for medical device design and evaluation. From this review and description of extant options for remote testing, we provide methods and tools to achieve research goals remotely. Background The FDA mandates human factors evaluation of medical devices. Studies show similarities and differences in results collected in laboratories compared to data collected remotely in non-laboratory settings. Remote studies show promise, though many of these are behavioral studies related to cognitive or experimental psychology. Remote usability studies are rare but increasing, as technologies allow for synchronous and asynchronous data collection. Method We reviewed methods of remote evaluation of medical devices, from testing labels and instruction to usability testing and simulated use. Each method was coded for the attributes (e.g., supported media) that need consideration in usability studies. Results We present examples of how published usability studies of medical devices could be moved to remote data collection. We also present novel systems for creating such tests, such as the use of 3D printed or virtual prototypes. Finally, we advise on targeted participant recruitment. Conclusion Remote testing will bring opportunities and challenges to the field of medical device testing. Current methods are adequate for most purposes, excepting the validation of Class III devices. Application The tools we provide enable the remote evaluation of medical devices. Evaluations have specific research goals, and our framework of attributes helps to select or combine tools for valid testing of medical devices.

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Wang, Zhichao, Jianfeng Zheng, Yu Wang, Wolfgang Kainz, and Ji Chen. "On the Model Validation of Active Implantable Medical Device for MRI Safety Assessment." IEEE Transactions on Microwave Theory and Techniques 68, no. 6 (June 2020): 2234–42. http://dx.doi.org/10.1109/tmtt.2019.2957766.

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Saletin, Jared, John McGeary, and Mary Carskadon. "276 The Actigpatch: validation of a novel adhesive monitor against PSG and wrist-actigraphy." Sleep 44, Supplement_2 (May 1, 2021): A110—A111. http://dx.doi.org/10.1093/sleep/zsab072.275.

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Abstract Introduction Wrist actigraphy is a gold-standard method for estimating sleep patterns in the field. Actigraphy adherence is limited when participants remove the device for daily activities (e.g., showers, exercise). Here we evaluate the validity of a novel water-resistant wearable, the “Actigpatch,” compared to polysomnography and traditional actigraphy. Methods Seven adults (4F; aged 22-54 years [m: 31.1±13.1]) slept in the laboratory for a total of 33 nights. Participants wore a Micro Motionlogger actigraphy (Ambulatory Monitoring Inc., Ardley, NY) on the non-dominant wrist and the Actigpatch—a 0.5in2 circuit board enclosed in a water-resistant adhesive (Circadian Positioning Systems, Newport, RI)—on the triceps. Both devices recorded tri-axial accelerometry, with sleep-wake estimates produced in 1-minute epochs (Sadeh algorithm). Simultaneous PSG data were reduced to 1-minute resolution favoring wake, keeping with recent recommendations. We computed epoch-by-epoch confusion matrices and derived 2 validation parameters: sensitivity (e.g., ability to detect sleep) and specificity (e.g., ability to detect wake). Finally, we compared total sleep time estimates (TST) to evaluate the bias of each device. Nested mixed models (nights within individuals) compared device performance. Results The Actigpatch demonstrated high sensitivity (.95; 95%CI: [.92 .98]) and specificity (.89; [.86, .91]) against polysomnography. Similar sensitivity (.96; [.94, .99]) and specificity (.84; [.78 .91]) were found comparing the Actigpatch to the Motionlogger. Comparing the devices’ validity with PSG, sensitivity was not statistically different between the Actigpatch and Motionlogger (b=.0041, t=0.56; p=.58); however, the Motionlogger demonstrated higher specificity (.95; [.92, .97]) compared to the Actigpatch (b=0.065, t=4.69; p<.001). To that end, TST estimates were longer (p=.016) for the Actigpatch (449min; [428, 471] relative to the Motionlogger (438min; [416, 459]). Conclusion These data indicate that the adhesive “Actigpatch” is as sensitive to detect polysomnographic-confirmed sleep as a common research-grade actigraph. The Actigpatch may be less capable of detecting wake episodes. Unlike traditional actigraphs, the Actigpatch can be worn continuously for 3 weeks without risk of water or impact damage. Participants are not responsible for remembering to wear the device. Field studies, or studies in populations struggling with adherence (e.g., children) may benefit from wearable monitors such as the Actigpatch. Support (if any) R01AA025593, Circadian Positioning Systems

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Kutafina, Ekaterina, Alexander Brenner, Yannic Titgemeyer, Rainer Surges, and Stephan Jonas. "Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection." PeerJ 8 (May 1, 2020): e8969. http://dx.doi.org/10.7717/peerj.8969.

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Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we developed a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are recorded simultaneously during 30 min long sessions in resting state. The data are collected from 22 patients with epileptiform abnormalities in EEG. In order to compare two multichannel EEG signals with differently placed references and electrodes, a novel data processing pipeline is proposed. It allows deriving matching pairs of time series which are suitable for similarity assessment through Pearson correlation. The average correlation of 0.64 is achieved on a test dataset, which can be considered a promising result, taking the positions shift due to the simultaneous electrode placement into account.

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Challener, Douglas, Priya Sampathkumar, and John O. O’Horo. "Practice Variation in Validation of Device Denominator Data for National Healthcare Safety Network Reporting." Infection Control & Hospital Epidemiology 41, S1 (October 2020): s354—s355. http://dx.doi.org/10.1017/ice.2020.973.

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Background: The NHSN is a widely used CDC program for tracking healthcare-associated infections (HAIs). The goal of the NHSN is to help healthcare organizations to identify and track the incidence of HAI and to prevent adverse events as well as to simplify mandatory quality reporting to the CMS. Healthcare organizations provide both event data for HAIs and information about the population at risk. For device-related infections, device denominator data (eg, data related to urinary or intravascular catheters, and ventilators) must be collected and reported. NHSN guidelines require that electronic reporting of device denominator numbers be validated to be within 5% of manually collected counts over a period of 3 consecutive months. Little is known about current practical application of validation practices. Methods: We surveyed members of the SHEA Research Network (SRN) to assess awareness of and compliance with the current NHSN requirements for device denominator data validation. Results: The survey was sent to 89 member institutions of the SRN from November 20, 2018, to December 12, 2018. The response rate was 35.7%, and 90% of respondents are currently using an electronic system for device denominator count reporting. All except 1 institution manually validated the data. Of the facilities that had completed validation, 31% used <90 days of manual data. Moreover, 82% of these facilities found a difference of <5% between the electronic data and manual data without a statistically significant difference between those with at least 90 days of validation data and those with <90 days. Also, 21% of facilities validated data based on a subset of units. Conclusions: Although most respondents to the survey validate electronically collected device denominator data in accordance with NHSN’s requirements, nearly one-third reported using shorter validation periods than NHSN requires. However, shorter periods were not associated with worse concordance. The NHSN should evaluate whether the burden of a 3-month validation period is justified.Funding: NoneDisclosures: None

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Kost, Gerald J. "Preventing Medical Errors in Point-of-Care Testing." Archives of Pathology & Laboratory Medicine 125, no. 10 (October 1, 2001): 1307–15. http://dx.doi.org/10.5858/2001-125-1307-pmeipo.

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Abstract Objective.—To prevent medical errors, improve user performance, and enhance the quality, safety, and connectivity (bidirectional communication) of point-of-care testing. Participants.—Group A included 37 multidisciplinary experts in point-of-care testing programs in critical care and other hospital disciplines. Group B included 175 professional point-of-care managers, specialists, clinicians, and researchers. The total number of participants equaled 212. Evidence.—This study followed a systems approach. Expert specifications for prevention of medical errors were incorporated into the designs of security, validation, performance, and emergency systems. Additional safeguards need to be implemented through instrument software options and point-of-care coordinators. Connectivity will be facilitated by standards that eliminate deficiencies in instrument communication and device compatibility. Assessment of control features on handheld, portable, and transportable point-of-care instruments shows that current error reduction features lag behind needs. Consensus Process.—Step 1: United States national survey and collation of group A expert requirements for security, validation, and performance. Step 2: Design of parallel systems for these functions. Step 3: Written critique and improvement of the error-prevention systems during 4 successive presentations to group B participants over 9 months until system designs stabilized into final consensus form. Conclusions.—The consensus process produced 6 conclusions for preventing medical errors in point-of-care testing: (1) adopt operator certification and validation in point-of-care testing programs; (2) implement security, validation, performance, and emergency systems on existing and new devices; (3) require flexible, user-defined error-prevention system options on instruments as a prerequisite to federal licensing of new diagnostic tests and devices; (4) integrate connectivity standards for bidirectional information exchange; (5) preserve fast therapeutic turnaround time of point-of-care test results; and (6) monitor invalid use, operator competence, quality compliance, and other performance improvement indices to reduce errors, thereby focusing on patient outcomes. (Arch Pathol Lab Med. 2001;1307–1315)

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Decker, Ali, Ariana Perez, and Shannon Clark. "Surgical Simulation: Simulating Realistic Use in Human Factors Validation Testing Using Study Models." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 9, no. 1 (September 2020): 248–52. http://dx.doi.org/10.1177/2327857920091010.

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When conducting simulated use human factors validation testing, the U.S. FDA requires that test conditions are sufficiently realistic to represent actual conditions of use. As part of this realistic simulation, a simulated patient, also called a “study model,” may be developed or acquired specifically for human factors validation testing of the device. The authors present a recommended process for choosing or developing a study model, using examples from past surgical device studies, and they provide a sample of study model design requirements as well as a summary of study model development considerations. This paper is meant to encourage discussion amongst medical device developers, human factors practitioners, and regulatory agencies regarding the development of study models that are sufficiently realistic for human factors validation testing.

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Stich, Manuel, Larissa Blümlein, Anne Slawig, Felix Schmidl, Karina Schuller, Richard Lösch, Matthias Hipp, Sabine Hentschel, Gregor Schaefers, and Ralf Ringler. "Development and validation of a tissue-equivalent test environment for detection of malfunctions in active medical implants caused by ionizing radiation." Current Directions in Biomedical Engineering 4, no. 1 (September 1, 2018): 153–56. http://dx.doi.org/10.1515/cdbme-2018-0038.

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AbstractMany patients in radiotherapy carry active implantable medical devices (AIMDs) such as pacemakers or cardioverter defibrillators (ICDs). The influence of the ionizing radiation can lead to failures in the device function. This study presents a tissue-equivalent test environment to investigate the influence of ionizing radiation on AIMDs. The in-vitro test environment is designed to simulate a human torso. Structures such as the heart, lungs, ribs, spinal column and soft tissue are replicated from tissue-equivalent materials to allow realistic treatment planning and to simulate the effect of ionizing radiation on active implants. CT measurements and Monte-Carlo validations have shown that Polytetrafluorethylen (bone), carrageenan (heart), Styrodur (lung) and Biresin® G27 (soft tissue) fulfill all requirements for suitable tissue surrogates. A plug-in unit integrated in the test environment has been designed specifically to allow the placement AIMDs in the phantom at typical positions for implant placement in humans. The dosimetry validation showed that the test environment is applicable in the full treatment planning process.

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Kvasnytsia, Maryna, Nele Famaey, Michal Böhm, and Eva Verhoelst. "Patient Specific Vascular Benchtop Models for Development and Validation of Medical Devices for Minimally Invasive Procedures." Journal of Medical Robotics Research 01, no. 03 (September 2016): 1640008. http://dx.doi.org/10.1142/s2424905x16400080.

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Using realistic benchtop models in early stages of device development can reduce time and efforts necessary to move the device to further testing. In this study, we propose several patient specific vascular benchtop models for the development and validation of a robotic catheter for transcatheter aortic valve implantation. The design and manufacturing of these models, and their properties are presented. Additionally, it is demonstrated that the described design process provides virtual models that are accurately linked to the physical models.

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Shireesha, P., and P. Niranjan. "A Study on Development of Processes for Verification and Validation in Medical Device Domain." Indian Journal of Public Health Research & Development 10, no. 7 (2019): 312. http://dx.doi.org/10.5958/0976-5506.2019.01585.7.

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Formosa, Gregory A., Joseph Micah Prendergast, Jinghui Peng, Donald Kirkpatrick, and Mark E. Rentschler. "A Modular Endoscopy Simulation Apparatus (MESA) for Robotic Medical Device Sensing and Control Validation." IEEE Robotics and Automation Letters 3, no. 4 (October 2018): 4054–61. http://dx.doi.org/10.1109/lra.2018.2861015.

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Peck, Jacquelin, Michael J. Wishon, Harrison Wittels, Stephen J. Lee, Stephanie Hendricks, Hector Davila, and S. Howard Wittels. "Single limb electrocardiogram using vector mapping: Evaluation and validation of a novel medical device." Journal of Electrocardiology 67 (July 2021): 136–41. http://dx.doi.org/10.1016/j.jelectrocard.2021.06.003.

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Beaurain, Florian. "GNRB (Medical Device) vs MRI on Anterior Cruciate Ligament (ACL) Tears with Arthroscopic Validation." International Journal of Research Studies in Medical and Health Sciences 5, no. 4 (2020): 21–23. http://dx.doi.org/10.22259/ijrsmhs.0504006.

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Jones, Ronald N., Nicole M. Holliday, and Paul R. Rhomberg. "Validation of a Commercial Dry-Form Broth Microdilution Device (Sensititre) for Testing Tedizolid, a New Oxazolidinone." Journal of Clinical Microbiology 53, no. 2 (November 19, 2014): 657–59. http://dx.doi.org/10.1128/jcm.02769-14.

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Tedizolid, a novel oxazolidinone antibacterial with potent activity against a wide range of Gram-positive pathogens, was recently approved by regulatory authorities for the treatment of acute bacterial skin and skin structure infections. A commercial broth microdilution device (Sensititre; Thermo Fisher Scientific) was validated using 285 selected Gram-positive isolates, and the device was documented to have 100.0% essential and categorical agreement with reference MIC results and excellent MIC endpoint reproducibility.

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Journal articles on the topic 'Medical device validation'

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