Implementation of Risk Management in the Medical Device Industry

This study looks at the implementation and effectiveness of risk management (RM) activities in the medical device industry. An online survey was distributed to medical device professionals who were asked to identify RM-related activities performed during the device life cycle. RM activities and techniques included Establishing Risk Acceptance Criteria, Hazard Identification, Human Factors/Usability, Fault Tree Analysis (FTA), Design Failure Mode and Effects Analysis (DFMEA), Process Failure Mode and Effects Analysis (PFMEA), Hazard and Operability Study (HAZOP), Hazard Analysis and Critical Control Point (HACCP), Risk Benefit Analysis, and Risk Assessment of Customer Complaint. Devices were identified by type (therapeutic, surgical/clinical tools, diagnostic, instrument disposable, implantable, etc.), development history (new, second, third or later generation device), and time since market release. Respondents were also asked to indicate the degree of change made to the device as a result of RM activities and to rate the effectiveness of associated RM activities for the device. Survey results indicated that RM\u27s impact and level of effectiveness on a medical device are dependent primarily on the device type and life-cycle stage (i.e., pre-market versus post-market). There is also some impact of development history and the time since the device was released to market

User involvement in healthcare technology development and assessment: Structured literature review

Purpose – Medical device users are one of the principal stakeholders of medical device technologies. User involvement in medical device technology development and assessment is central to meet their needs. Design/methodology/approach – A structured review of literature, published from 1980 to 2005 in peer-reviewed journals, was carried out from social science perspective to investigate the practice of user involvement in the development and assessment of medical device technologies. This was followed by qualitative thematic analysis. Findings – It is found that users of medical devices include clinicians, patients, carers and others. Different kinds of medical devices are developed and assessed by user involvement. The user involvement occurs at different stages of the medical device technology lifecycle and the degree of user involvement is in the order of design stage > testing and trials stage > deployment stage > concept stage. Methods most commonly used for capturing users’ perspectives are usability tests, interviews and questionnaire surveys. Research limitations/implications – We did not review the relevant literature published in engineering, medical and nursing fields, which might have been useful. Practical implications – Consideration of the users’ characteristics and the context of medical device use is critical for developing and assessing medical device technologies from users’ perspectives. Originality/value – This study shows that users of medical device technologies are not homogeneous but heterogeneous, in several aspects, and their needs, skills and working environments vary. This is important consideration for incorporating users’ perspectives in medical device technologies. Paper type: Literature review

User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner

Background: The rigorous elicitation of user needs is a crucial step for both medical device design and purchasing. However, user needs elicitation is often based on qualitative methods whose findings can be difficult to integrate into medical decision-making. This paper describes the application of AHP to elicit user needs for a new CT scanner for use in a public hospital. Methods: AHP was used to design a hierarchy of 12 needs for a new CT scanner, grouped into 4 homogenous categories, and to prepare a paper questionnaire to investigate the relative priorities of these. The questionnaire was completed by 5 senior clinicians working in a variety of clinical specialisations and departments in the same Italian public hospital. Results: Although safety and performance were considered the most important issues, user needs changed according to clinical scenario. For elective surgery, the five most important needs were: spatial resolution, processing software, radiation dose, patient monitoring, and contrast medium. For emergency, the top five most important needs were: patient monitoring, radiation dose, contrast medium control, speed run, spatial resolution. Conclusions: AHP effectively supported user need elicitation, helping to develop an analytic and intelligible framework of decision-making. User needs varied according to working scenario (elective versus emergency medicine) more than clinical specialization. This method should be considered by practitioners involved in decisions about new medical technology, whether that be during device design or before deciding whether to allocate budgets for new medical devices according to clinical functions or according to hospital department

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Trends in virtual reality technologies for the learning patient

NextMed convened the Medicine Meets Virtual Reality 22 (MMVR 22) conference in 2016. Since 1992, the conference has brought together a diverse group of researchers to share creative solutions for the evolving challenge of integrating virtual reality tools into medical education. Virtual reality (VR) and its enabling technologies utilize hardware and software to simulate environments and encounters where users can interact and learn. The MMVR 22 symposium proceedings contain projects that support a variety of learners: medical students, practitioners, soldiers, and patients. This report will contemplate the trends in virtual reality technologies for patients navigating their medical and healthcare learning. The learning patient seeks more than intervention; they seek prevention. From virtual humans and environments to motion sensors and haptic devices, patients are surrounded by increasingly rich and transformative data-driven tools. Applied data enables VR applications to simulate experience, predict health outcomes, and motivate new behavior. The MMVR 22 presents investigations into the usability of wearable devices, the efficacy of avatar inclusion, and the viability of multi-player gaming. With increasing need for individualized and scalable programming, only committed open source efforts will align instructional designers, technology integrators, trainers, and clinicians. Curriculum and InstructionCurriculum and Instructio

Information and communication technology solutions for outdoor navigation in dementia

INTRODUCTION: Information and communication technology (ICT) is potentially mature enough to empower outdoor and social activities in dementia. However, actual ICT-based devices have limited functionality and impact, mainly limited to safety. What is an ideal operational framework to enhance this field to support outdoor and social activities? METHODS: Review of literature and cross-disciplinary expert discussion. RESULTS: A situation-aware ICT requires a flexible fine-tuning by stakeholders of system usability and complexity of function, and of user safety and autonomy. It should operate by artificial intelligence/machine learning and should reflect harmonized stakeholder values, social context, and user residual cognitive functions. ICT services should be proposed at the prodromal stage of dementia and should be carefully validated within the life space of users in terms of quality of life, social activities, and costs. DISCUSSION: The operational framework has the potential to produce ICT and services with high clinical impact but requires substantial investment

Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

Reviewing and extending the five-user assumption: A grounded procedure for interaction evaluation

" © ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Computer-Human Interaction (TOCHI), {VOL 20, ISS 5, (November 2013)} http://doi.acm.org/10.1145/2506210 "The debate concerning how many participants represents a sufficient number for interaction testing is well-established and long-running, with prominent contributions arguing that five users provide a good benchmark when seeking to discover interaction problems. We argue that adoption of five users in this context is often done with little understanding of the basis for, or implications of, the decision. We present an analysis of relevant research to clarify the meaning of the five-user assumption and to examine the way in which the original research that suggested it has been applied. This includes its blind adoption and application in some studies, and complaints about its inadequacies in others. We argue that the five-user assumption is often misunderstood, not only in the field of Human-Computer Interaction, but also in fields such as medical device design, or in business and information applications. The analysis that we present allows us to define a systematic approach for monitoring the sample discovery likelihood, in formative and summative evaluations, and for gathering information in order to make critical decisions during the interaction testing, while respecting the aim of the evaluation and allotted budget. This approach – which we call the ‘Grounded Procedure’ – is introduced and its value argued.The MATCH programme (EPSRC Grants: EP/F063822/1 EP/G012393/1