Medical Device Software Research Articles

The Assessment of Medical Device Software Supporting Health Care Services for Chronic Patients in a Tertiary Hospital: Overarching Study.

Innovative digital health tools are increasingly being evaluated and, in some instances, integrated at scale into health systems. However, the applicability of assessment methodologies in real-life scenarios to demonstrate value generation and consequently foster sustainable adoption of digitally enabled health interventions has some bottlenecks. We aimed to build on the process of premarket assessment of 4 digital health interventions piloted at the Hospital Clinic de Barcelona (HCB), as well as on the analysis of current medical device software regulations and postmarket surveillance in the European Union and United States in order to generate recommendations and lessons learnt for the sustainable adoption of digitally enabled health interventions. Four digital health interventions involving prototypes were piloted at the HCB (studies 1-4). Cocreation and quality improvement methodologies were used to consolidate a pragmatic evaluation method to assess the perceived usability and satisfaction of end users (both patients and health care professionals) by means of the System Usability Scale and the Net Promoter Score, including general questions about satisfaction. Analyses of both medical software device regulations and postmarket surveillance in the European Union and United States (2017-2021) were performed. Finally, an overarching analysis on lessons learnt was conducted considering 4 domains (technical, clinical, usability, and cost), as well as differentiating among 3 different eHealth strategies (telehealth, integrated care, and digital therapeutics). Among the participant stakeholders, the System Usability Scale score was consistently higher in patients (studies 1, 2, 3, and 4: 78, 67, 56, and 76, respectively) than in health professionals (studies 2, 3, and 4: 52, 43, and 54, respectively). In general, use of the supporting digital health tools was recommended more by patients (studies 1, 2, 3, and 4: Net Promoter Scores of -3%, 31%, -21%, and 31%, respectively) than by professionals (studies 2, 3, and 4: Net Promoter Scores of -67%, 1%, and -80%, respectively). The overarching analysis resulted in pragmatic recommendations for the digital health evaluation domains and the eHealth strategies considered. Lessons learnt on the digitalization of health resulted in practical recommendations that could contribute to future deployment experiences.

Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol.

Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].

Economic evaluation of a vision-based patient monitoring and management system in an acute adult and an older adult mental health hospital in England

Background and aims Patients on acute adult and older adult inpatient mental health wards are at an increased risk of accidental injuries and deliberate harm to self and others. A vision-based patient monitoring and management (VBPMM) system was designed by Oxehealth Limited to support ward staff to provide better and more efficient care and to reduce incidents. The VBPMM system uses an infrared-sensitive camera, installed in a patient’s room, that works with cleared medical device software to deliver contact-free vital sign and activity insights to clinical teams. Data from two studies undertaken at an English National Health Service (NHS) mental health trust were used to inform an early economic assessment of VBPMM implementation into acute adult and older adult mental health wards. Methods A cost calculator was used to compare the introduction of the VBPMM system as an adjunct to standard care versus standard care alone. Observational data were collected at two English NHS mental health trusts. Both compared data pre- and post-VBPMM implementation using a 12-month baseline period. The model estimated cost per occupied bed day, cost per patient, annual cost per average-sized ward, and total cost to NHS mental health trusts across England. Costs were modeled from an NHS perspective over a 12-month time horizon. Scenario analysis was conducted to test the uncertainty of results using statistical significance of key inputs. Results and conclusions This early analysis indicated that the VBPMM system is likely to be cost saving within both settings examined, with an estimated cost saving of £272 per acute adult mental health patient and £4,591 per older adult mental health patient. This translates to £22.3 and £63.3 million, respectively, across NHS mental health trusts in England every year. VBPMM, therefore, has the potential to augment standard care, leading to positive clinical outcomes and monetary savings.

Key Aspects to Teach Medical Device Software Certification.

Certification of Medical Device Software (MDS) according to the EU Medical Device Regulation 2017/745 requires demonstrating safety and effectiveness. Thus, the syllabus of a course on MDS development must provide tools for addressing these issues. To assure safety, risk analysis has to be performed using a four-step procedure. Effectiveness could be demonstrated by literature systematic review combined with meta-analysis, to compare the MDS performances with those of similar tools.

Containerization Strategies for Medical Device Software Development and Deployment

This research paper explores containerization strategies for the development and deployment of medical device software. Containerization offers a robust approach to managing the complexities of software development, ensuring consistency across environments, and enhancing scalability and security. This paper examines the principles of containerization, its benefits for medical device software, implementation strategies, case studies, challenges, and future research directions. The paper also includes best practices for integrating containerization with existing healthcare IT infrastructure.

Applying Agile Methodologies in Regulated Medical Device Software Development

This research paper explores the application of Agile methodologies in the highly regulated field of medical device software development. As the healthcare industry continues to evolve rapidly, there is an increasing need for efficient and flexible software development processes that can meet stringent regulatory requirements while delivering high-quality, innovative medical device software. This study examines the challenges of implementing Agile practices in a regulated environment, proposes strategies for adapting Agile methodologies to meet regulatory standards, and presents case studies of successful implementations. The paper also discusses best practices, potential pitfalls, and future trends in Agile medical device software development.

HTA13 Updating Nice Evidence Standards Framework for the Effectiveness and Economic Impact of Digital Health Technologies

NICE’s evidence standards framework (ESF) was published in 2019 to help innovators and commissioners understand what good levels of evidence for digital health technologies (DHTs) look like. Since then, these technologies have evolved with many now utilising artificial intelligence. Also, regulators have started to develop clearer regulation criteria for medical device software. NICE therefore is updating the ESF to reflect these developments and improve its useability. Here we present the foundations of the updated framework.

Prospects of Artificial Intelligence in Medical Devices – Benefits and Challenges

With advancements being accelerated in fields of Machine learning (ML), Deep Learning (DL), and the Internet of Medical Devices (IoMD), Artificial Intelligence (AI) has significantly evolved over the last decade. This transformation has opened doors for new possibilities of fostering AI in Medical Devices (AIMD) for applications that have not been explored before. With the current advancements in the computational capabilities, there has been a wide range of Medical Devices released in the global market, which are either supported by or driven by software to deliver its intended purpose. But with the advent of AI, software though built upon a string of codes has no restriction on the choices that it could make, since its essence lies on the deducing a decision based on a predictive analysis. This has called for rethinking our conventional approach towards the usage of Software in Medical Devices. Coupling with the rapid growth rate of these devices and their focus on delivering impeccable assistance to the facilitators who were doing things all by themselves previously, it also calls for a proper set of guidelines to regulate the usage of AIMD, considering its massive scalability and the gargantuan amounts of data that it brings along with it. This paper focuses on extrapolating the growth factors of AIMD along with the prerequisites that ought to be met by the organizations to prepare themselves for transforming and adopting this new technology. It also covers the aspects that are to be emphasized in regulating it in the forthcoming years.

Will the EU Medical Device Regulation help to improve the safety and performance of medical AI devices?

Concerns have been raised over the quality of evidence on the performance of medical artificial intelligence devices, including devices that are already on the market in the USA and Europe. Recently, the Medical Device Regulation, which aims to set high standards of safety and quality, has become applicable in the European Union. The aim of this article is to discuss whether, and how, the Medical Device Regulation will help improve the safety and performance of medical artificial intelligence devices entering the market. The Medical Device Regulation introduces new rules for risk classification of the devices, which will result in more devices subjected to a higher degree of scrutiny before entering the market; more stringent requirements on clinical evaluation, including the requirement for appraisal of clinical data; new requirements for post-market surveillance, which may help spot early on any new, unexpected side effects and risks of the devices; and requirements for notified bodies, including for expertise of the personnel and consideration of relevant best practice documents. The guidance of the Medical Device Coordination Group on clinical evaluation of medical device software and the MEDDEV2.7 guideline on clinical evaluation also attend to some of the problems identified in studies on medical artificial intelligence devices. The Medical Device Regulation will likely help improve the safety and performance of the medical artificial intelligence devices on the European market. The impact of the Regulation, however, is also dependent on its adequate enforcement by the European Union member states.

New regulation of medical devices in the EU: impact in dermatology.

Medical device (MD) is a broad term that encompasses products ranging from, for example, examination gloves to digital dermoscopy systems; all of which are regulated by a new regulatory framework in the EU from May 2021. The new Medical Device Regulation (MDR) (Regulation EU 2017/745) will have a significant effect on suppliers of MD and will have subsequent effects also for dermatologists and other clinicians. Medical device software and apps are reclassified leading to more stringent requirements on documentation within, e.g. clinical evidence, as well as regulatory authority control. The changes will likely have positive effects on quality, to the benefit of patients. There will, however, be implications affecting the availability and support of existing devices and the introduction of new devices, as well as a likely price increase due to the higher costs for suppliers. Dermatologists, other clinicians and administrators need to be aware of the effects of MDR to ensure that existing devices and new purchases can be used as planned. Specifically, clinicians need to be aware of the following: (i) improved quality of MD and follow‐up of incidents can be expected. (ii) Only ‘non‐significant’ updates will be permitted after May 2021 to many existing systems and devices unless approved under the new MDR. (iii) Existing devices that do not achieve approval under the new regulation will no longer be manufactured after May 2024. (iv) New products and methods will take longer time to be approved and available. (v) Prices will likely increase. (vi) Suppliers of products that do not fulfil the new regulation will disappear, and the availability of consumables, spare parts or upgrades might be discontinued. (vii) A trend to oligopoly may appear in the market. It is therefore important to check with your suppliers as to how and when they will adhere to the new MDR regulation.

Description of a clinical decision support tool with integrated dose calculator for paediatrics

Medication errors, especially dosing errors are a leading cause of preventable harm in paediatric patients. The paediatric patient population is particularly vulnerable to dosing errors due to immaturity of metabolising organs and developmental changes. Moreover, the lack of clinical trial data or suitable drug forms, and the need for weight-based dosing, does not simplify drug dosing in paediatric or neonatal patients. Consequently, paediatric pharmacotherapy often requires unlicensed and off-label use including manipulation of adult dosage forms. In practice, this results in the need to calculate individual dosages which in turn increases the likelihood of dosing errors. In the age of digitalisation, clinical decision support (CDS) tools can support healthcare professionals in their daily work. CDS tools are currently amongst the gold standards in reducing preventable errors. In this publication, we describe the development and core functionalities of the CDS tool PEDeDose, a Class IIa medical device software certified according to the European Medical Device Regulation. The CDS tool provides a drug dosing formulary with an integrated calculator to determine individual dosages for paediatric, neonatal, and preterm patients. Even a technical interface is part of the CDS tool to facilitate integration into primary systems. This enables the support of the paediatrician directly during the prescribing process without changing the user interface.Conclusion: PEDeDose is a state-of-the-art CDS tool for individualised paediatric drug dosing that includes a certified calculator.What is Known:• Dosing errors are the most common type of medication errors in paediatric patients.• Clinical decision support tools can reduce medication errors effectively. Integration into the practitioner’s workflow improves usability and user acceptance.What is New:• A clinical decision support tool with a certified integrated dosing calculator for paediatric drug dosing.• The tool was designed to facilitate integration into clinical information systems to directly support the prescribing process. Any clinical information system available can interoperate with the PEDeDose web service.

A bright commercial future for off-the-shelf NK cell therapy

<div class="authors authors-lg"><div class="author"><div class="author-img"><img src="https://cdn.insights.bio/uploads/Jan Spanholtz 200x200.jpg" data-image="1"></div><div class="author-det"> <b>Jan Spanholtz</b> is currently CSO of Glycostem Therapeutics, a Dutch biotech company, focused on developing allogeneic cell products for cancer therapy. Jan qualified in medical biology at Radboud UMC in Nijmegen, the Netherlands, and developmental stem cell biology at Heinrich-Heine University in Düsseldorf, Germany. Jan has 20 years of expertise in stem cell research at universities and within biotech companies. Jan has experience of biotechnology scientific research development programs, leading to patent strategies around product and business development. He is author of multiple peer review research articles and inventor of various patent applications and within Glycostem responsible for research programs, patent strategy and early clinical translation, as well as coordinating several R&amp;D collaborations with international partners from academia and biotech businesses. </div></div></div><div class="authors authors-lg"><div class="author"><div class="author-img"><img src="https://cdn.insights.bio/uploads/Troels Jordansen 200x200.jpg" data-image="1"></div><div class="author-det"> <b>Troels Jordansen</b> started his career in healthcare at LEO Pharma. After four years with Johnson &amp; Johnson Orthopaedics he was one of the initial Genzyme Europe hires to focus on commercializing Carticel and Epicel in 1996. Over the past 20 years Jordansen has worked for five different cell therapy companies including Dutch IsoTis NV, Australian Clinical Cell Culture Pty Ltd. and British Azellon Ltd. where he was co-founder. His roles have covered sales, marketing and general management; for the past 15 years he has been managing director and/or chairman for private and public listed companies. Jordansen has been part of award-winning management teams that have raised over €175 million in funding. He became Chairman of Glycostem in January 2014 and CEO in July 2016. </div></div></div><div class="authors authors-lg"><div class="author"><div class="author-img"><img src="https://cdn.insights.bio/uploads/Volker Huppert 200x200.jpg" data-image="1"></div><div class="author-det"> <b>Volker Huppert</b> is a graduate bioprocess engineer from RWTH Aachen University. Among his achievements are participation in the set up of a quality system for a medical device/biotechnology company and development of several clinical-level reagents, disposable tubing sets and process software for cell separation and cell culture medical devices. He contributed to both the tubing set and process software development of a leading cell therapy-manufacturing device. Additionally, he managed projects and teams developing cell-manufacturing procedures for hematopoietic stem cells and Natural Killer cells. Volker has published 12 papers in peer reviewed journals over the past 20 years while working for a leading biotechnology company and is co-inventor of 9 patent families, including methods for T cell depletion of hematopoietic stem cell products, NK-cell transduction and NK-cell proliferation. </div></div></div>

The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics.

Medical device manufacturers are increasingly applying artificial intelligence (AI) to innovate their products and to improve patient outcomes. Health institutions are also developing their own algorithms, to address specific needs for which no commercial product exists. Although AI-based algorithms offer good prospects for improving patient outcomes, their wide adoption in clinical practice is still limited. The most significant barriers to the trust required for wider implementation are safety and clinical performance assurance . Qualified medical physicist experts (MPEs) play a key role in safety and performance assessment of such tools, before and during integration in clinical practice. As AI methods drive clinical decision-making, their quality should be assured and tested. Occasionally, an MPE may be also involved in the in-house development of such an AI algorithm. It is therefore important for MPEs to be well informed about the current regulatory framework for Medical Devices. The new European Medical Device Regulation (EU MDR), with date of application set for 26 of May 2021, imposes stringent requirements that need to be met before such tools can be applied in clinical practice. The objective of this paper is to give MPEs perspective on how the EU MDR affects the development of AI-based medical device software. We present our perspective regarding how to implement a regulatory roadmap, from early-stage consideration through design and development, regulatory submission, and post-market surveillance. We have further included an explanation of how to set up a compliant quality management system to ensure reliable and consistent product quality, safety, and performance .

인공지능 의료기기 소프트웨어의 IEC 62304 국제표준 준수 가이드라인 개발 및 적용

의료기기 소프트웨어 개발자는 의료기기 소프트웨어 생명주기 프로세스에 대한 국제표준인 IEC 62304가 요구하는 프로세스를 수립하고, 방대한 양의 산출물을 갖추어 인허가를 받아야 한다. 최근에는 의료영상 기반 인공지능 의료기기 소프트웨어가 활발히 개발되고 있으며, 독립형 소프트웨어로 취급되기 때문에 의료기기 소프트웨어에 대한 IEC 62304를 준수하여 인허가를 받아야 한다. 인공지능 기술에 대한 국제표준은 논의 단계에 있어, 개발자는 인공지능 의료기기 소프트웨어의 생명주기 프로세스를 임의로 수립해야 하며, IEC 62304의 어느 명세를 기준으로 삼아 인공지능 제품의 성능과 안전성을 입증할지, 어떤 품질관리 기법을 사용하여 산출물을 작성할지 파악하기 어렵다. 본 논문은 인공지능 의료기기 소프트웨어에 대한 IEC 62304 준수 범위와 요구사항을 수행하기 위한 품질관리 기법을 가이드라인의 형태로 제공한다. 또한 실제 인공지능 제품에 적용하여 본 가이드라인의 활용성을 확인한다.

Machine-Learning Model for Mortality Prediction in Patients with Community-Acquired Pneumonia: Development and Validation Study

Background: Pneumonia is the leading infectious cause of mortality globally. Artificial intelligence tools and techniques such as machine learning (ML) are increasingly seen as a suitable manner to increase the prediction capacity of currently available tools in infectious diseases. However, studies evaluating the efficacy of ML methods in enhancing the predictive capacity of existing scores for community-acquired pneumonia (CAP) are limited. We aimed to apply a causal probabilistic network (CPN) model (SepsisFinder) to predict 30-day mortality in patients with CAP. As secondary objective, we pursued validating the ML model using a large cohort of CAP patients. Methods: This was a derivation-validation retrospective study to test the CPN model in its prediction of 30-day mortality in patients hospitalised with CAP. The derivation cohort comprised all consecutive adults with CAP admitted between 2003 and 2016 to a university hospital in Barcelona, Spain. For the validation cohort, we included all consecutive patients with CAP admitted to a university hospital in Valencia, Spain between 2012 and 2018. Findings: The derivation cohort comprised 4,531 patients whilst the validation cohort had 1,034 patients. In the derivation cohort, the AUC of SepsisFinder, CURB-65, SOFA, PSI and qSOFA were 0.801, 0.759, 0.671, 0.799 and 0.642, respectively, for 30-day mortality prediction. In the validation study, the AUC of SepsisFinder was 0.826, concordantly with the AUC (0.801) in the derivation data (p=0.51). The AUC of SepisFinder was significantly higher than those of CURB-65 (0.764, p=0.03) and qSOFA (0.729, p=0.005). However, it did not differ significantly from PSI (0.830, p=0.92) and SOFA (0.771, p=0.14). Interpretation: Sepsisfinder shows potential for improving mortality prediction amongst patients with CAP using structured health data. Additional external validation studies should be conducted to support generalisability. Funding: CIBER de Enfermedades Respiratorias (CIBERES CB06/06/0028), and by 2009 Support to Research Groups of Catalonia 911, IDIBAPS. Declaration of Interest: Dr Cilloniz is the recipient of the SEPAR fellowship 2018, and a grant from the Fondo de Investigacion Sanitaria (PI19/00207). LW is employed on a consulting basis and is a shareholder of Treat Systems ApS. MM is CEO and a shareholder of Treat Systems ApS. Treat Systems ApS is a manufacturer of medical device software for decision support in the field of infectious diseases and clinical microbiology, including TREAT-Lab. All other authors have nothing to declare. Ethical Approval: Institutional approval was provided by the Ethics Committee of the University Hospital La Fe of Valencia (XXXXXXX) and the Comite Etic d’Investigacio Clinica of the Hospital Clinic of Barcelona (2011/0219), which waived the need for informed consent. All data set were anonymously analyzed, and the study was performed following current recommendation of the Declaration of Helsinki.

Delivery of CANTAB assessments across diverse health systems

AbstractBackgroundAssessment of cognitive function is an important component of differential diagnosis in Alzheimer’s disease and dementia and is critical to characterising the impact of the disease. Objective assessment of function provides real world evidence for assessing effectiveness of current standards of care, and evaluating interventions designed to improve outcomes. However, sensitive, reliable tools for administering standardised tests at scale have been lacking to date.MethodDigital tools, providing near‐patient assessments in the community or at home are one way to meet the demands of patient characterisation across a range of resource settings. Cambridge Neuropsychological Automated Test Battery (CANTAB) is a set of 25 computerised assessments designed to assess cognition across a broad range of domains relevant to neurological and psychiatric conditions. A number of tests from this battery have been integrated into medical device software to assess the impact of neurological and neurodegenerative disease. These tools have been deployed in community, primary and secondary health care settings in relatively resource‐rich healthcare systems (primarily UK and US). We are exploring whether such an approach can be generalised to a lower‐middle income countries/South Asia.ResultWe will present performance of 4000 healthy participants recruited across nine regions of India on CANTAB assessments including performance on episodic memory (Paired Associates Learning (PAL)), working memory (Spatial Working Memory (SWM)) and attention (Matching to Sample (MTS)). Evidence from this study supports the suitability of our platform for delivering cognitive assessments in health care systems outside the UK. We will contrast performance of healthy participants on PAL and SWM with recent normative data collections across the UK and US; demonstrating similarity of performance and the effectiveness of assessment platforms. The demographic diversity of these normative collections supports the need to partner these tools with appropriate recruitment and operational initiatives to ensure accessibility.ConclusionWe will reflect on how this evidence guides our current and future plans for making scientifically robust cognitive assessment tools globally available, both for increasing access to treatment, but also in the context of basic research and drug development.

A Hybrid Fuzzy Rule-Based Multi-Criteria Framework for Security Assessment of Medical Device Software

A Hybrid Fuzzy Rule-Based Multi-Criteria Framework for Security Assessment of Medical Device Software

Medical Device Apps: An Introduction to Regulatory Affairs for Developers.

The Poly Implant Prothèse (PIP) scandal in France prompted a revision of the regulations regarding the marketing of medical devices. The new Medical Device Regulation (MDR [EU]) 2017/745 was developed and entered into force on May 25, 2017. After a transition period of 3 years, the regulations must be implemented in all EU and European Economic Area member states. The implementation of this regulation bears many changes for medical device development and marketing, including medical device software and mobile apps. Medical device development and marketing is a complex process by which manufacturers must keep many regulatory requirements and obligations in mind. The objective of this paper is to provide an introduction and overview of regulatory affairs for manufacturers that are new to the field of medical device software and apps with a specific focus on the new MDR, accompanying harmonized standards, and guidance documents from the European Commission. This work provides a concise overview of the qualification and classification of medical device software and apps, conformity assessment routes, technical documentation, clinical evaluation, the involvement of notified bodies, and the unique device identifier. Compared to the previous Medical Device Directive (MDD) 93/42/EEC, the MDR provides greater detail about the requirements for software qualification and classification. In particular, rule 11 sets specific rules for the classification of medical device software and will be described in this paper. In comparison to the previous MDD, the MDR is more stringent, especially regarding the classification of health apps and software. The implementation of the MDR in May 2020 and its interpretation by the authorities will demonstrate how app and software manufacturers as well as patients will be affected by the regulation.

Standards for Mobile Health-Related Apps: Systematic Review and Development of a Guide.

BackgroundIn recent years, the considerable increase in the number of mobile health (mHealth) apps has made health care more accessible and affordable for all. However, the exponential growth in mHealth solutions has occurred with almost no control or regulation of any kind. Despite some recent initiatives, there is still no specific regulation procedure, accreditation system, or standards to help the development of the apps, mitigate risks, or guarantee quality.ObjectiveThe main aim of this study was to propose a set of criteria for mHealth-related apps on the basis of what is available from published studies, guidelines, and standards in the various areas that are related to health app development.MethodsWe used three sources of information to identify the most important criteria. First, we conducted a systematic review of all the studies published on pain-related apps. Second, we searched for health app recommendations on the websites of professional organizations. Third, we looked for standards governing the development of software for medical devices on the specialized websites of regulatory organizations. Then, we grouped and subsumed the criteria we had identified on the basis of their shared characteristics. Finally, the comprehensibility and perceived importance of the resulting criteria were evaluated for face validity with a group of 18 stakeholders.ResultsWe identified a total of 503 criteria from all sources, which, after close analysis, were grouped into eight different categories, including 36 important criteria for health apps. The resulting categories were usability, privacy, security, appropriateness and suitability, transparency and content, safety, technical support and updates, and technology. The results of the preliminary analysis showed that the criteria were mostly understood by the group of stakeholders. In addition, they perceived all of them as important.ConclusionsThis set of criteria can help health care providers, developers, patients, and other stakeholders to guide the development of mHealth-related apps and, potentially, to measure the quality of an mHealth app.

Solutions for Mitigating Cybersecurity Risks Caused by Legacy Software in Medical Devices: A Scoping Review

Cyberattacks against healthcare institutions threaten patient care. The risk of being targeted by a damaging attack is increased when medical devices are used which rely on unmaintained legacy software that cannot be replaced and may have publicly known vulnerabilities. This review aims to provide insight into solutions presented in the literature that mitigate risks caused by legacy software on medical devices. We performed a scoping review by categorising and analysing the contributions of a selection of articles, taken from a literature set discovered through bidirectional citation searching. We found 18 solutions, each fitting at least one of the categories of intrusion detection and prevention, communication tunnelling or hardware protections. Approaches taken include proxying Bluetooth communication through smartphones, behaviour-specification based anomaly detection and authenticating signals based on physical characteristics. These solutions are applicable to various use-cases, ranging from securing pacemakers to medical sensor networks. Most of the solutions are based on intrusion detection and on tunnelling insecure wireless communications. These technologies have distinct application areas, and the decision which one is most appropriate will depend on the type of medical device.