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Every year, the Food and Drug Administration (FDA) clears approximately 3,000 medical devices for marketing via the 510(k) pathway. These constitute 99% of all devices approved for human use and includes the premarket review of many devices incorporating newer technology such as artificial intelligence (AI), machine learning (ML), and other software. As the complexity of these novel technologies and the number of applications is expected to increase in the coming years, statutory changes such as the 2016 21st Century Cures Act, regulations, and guidance documents have increased both the volume and complexity of device review. Thus, the ability to streamline the review of less complex, low-to-moderate risk devices through the 510(k) pathway will maximize the FDA's capability to address other important, future-oriented regulatory questions. For over twenty five years, third party review organizations have served a defined function to assist with the review of 510(k) applications for a set of enumerated device classes. This paper reviews the history of FDA device regulation, the evolution of the 510(k) review pathway, and the recent history of the 510(k) third party review program. Finally, the paper addresses policy concerns from all stakeholders – including the FDA – along with policy suggestions to improve the third party review program and FDA device regulation writ large. [ABSTRACT FROM AUTHOR]

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology. [ABSTRACT FROM AUTHOR]

Objectives: The World Health Organization has adopted two main strategies to improve the quality of maternal health: increasing the number of deliveries by skilled birth attendants and increasing access to emergency obstetric care. Despite increased access to care, there continue to be high rates of maternal morbidity and mortality in part due to quality of care. This study aims to identify and summarize existing frameworks for measuring quality of maternal care at a facility-level. Methods: PubMed, Health Systems Evidence, Embase, Global Health, OVID Healthstar, OVID Medline, PsycINFO, and Web of Science were searched for frameworks, tools, theories, or components of frameworks relevant to maternal quality of care in facility-level settings. Title/abstract and full-text screening were completed by two independent reviewers and conflicts resolved through consensus or a third reviewer. Results: An initial search resulted in 3182 studies. Fifty-four studies were included in the qualitative analysis. A best fit framework analysis was done using the updated Hulton framework as the conceptual framework. A facility-based maternal quality of care framework is proposed including the following components, separated into provision and experience of care: (1) human resources; (2) infrastructure; (3) equipment, supplies and medicine; (4) evidence and information; (5) referral and networks of care; (6) cultural competence; (7) clinical practice; (8) financing; (9) leadership and governance; (10) cognition; and 11) respect, dignity, equity, and emotional support. Significance: Despite increased institutional access to care, there continue to be high rates of maternal morbidity and mortality in some low- and middle-income countries. This is in part due to the quality of maternal care once care has been accessed. Several frameworks have been proposed for understanding the complexities surrounding quality of care in maternal health but there has not been a single framework consistently used in the literature for facility-based quality of maternal care. This paper identifies and summarizes existing frameworks and tools, and uses a best fit framework analysis to propose a comprehensive framework for assessing facility-based maternal quality of care. [ABSTRACT FROM AUTHOR]

This paper aims to highlight how to reduce medication errors through the implementation of human factors science to the design features of medication containers. Despite efforts to employ automation for increased safety and decreased workload, medication administration in hospital wards is still heavily dependent on human operators (pharmacists, nurses, physicians, etc.). Improving this multi-step process requires its being studied and designed as an interface in a complex socio-technical system. Human factors engineering, also known as ergonomics, involves designing socio-technical systems to improve overall system performance, and reduces the risk of system, and in particular, operator, failures. The incorporation of human factors principles into the design of the work environment and tools that are in use during medication administration could improve this process. During periods of high workload, the cognitive effort necessary to work through a very demanding process may overwhelm even expert operators. In such conditions, the entire system should facilitate the human operator's high level of performance. Regarding medications, clinicians should be provided with as many perceptual cues as possible to facilitate medication identification. Neglecting the shape of the container as one of the features that differentiates between classes of medications is a lost opportunity to use a helpful characteristic, and medication administration failures that happen in the absence of such intentional design arise from "designer error" rather than "user error". Guidelines that define a container's shape for each class of medication would compel pharmaceutical manufacturers to be compatible and would eliminate the confusion that arises when a hospital changes the supplier of a given medication. [ABSTRACT FROM AUTHOR]

Distribution shifts remain a problem for the safe application of regulated medical AI systems, and may impact their real-world performance if undetected. Postmarket shifts can occur for example if algorithms developed on data from various acquisition settings and a heterogeneous population are predominantly applied in hospitals with lower quality data acquisition or other centre-specific acquisition factors, or where some ethnicities are over-represented. Therefore, distribution shift detection could be important for monitoring AI-based medical products during postmarket surveillance. We implemented and evaluated three deep-learning based shift detection techniques (classifier-based, deep kernel, and multiple univariate kolmogorov-smirnov tests) on simulated shifts in a dataset of 130'486 retinal images. We trained a deep learning classifier for diabetic retinopathy grading. We then simulated population shifts by changing the prevalence of patients' sex, ethnicity, and co-morbidities, and example acquisition shifts by changes in image quality. We observed classification subgroup performance disparities w.r.t. image quality, patient sex, ethnicity and co-morbidity presence. The sensitivity at detecting referable diabetic retinopathy ranged from 0.50 to 0.79 for different ethnicities. This motivates the need for detecting shifts after deployment. Classifier-based tests performed best overall, with perfect detection rates for quality and co-morbidity subgroup shifts at a sample size of 1000. It was the only method to detect shifts in patient sex, but required large sample sizes ( > 3 0 ′ 000 ). All methods identified easier-to-detect out-of-distribution shifts with small (≤300) sample sizes. We conclude that effective tools exist for detecting clinically relevant distribution shifts. In particular classifier-based tests can be easily implemented components in the post-market surveillance strategy of medical device manufacturers. [ABSTRACT FROM AUTHOR]

Introduction: Free clinics provide care for those who may otherwise not have access. While this care is often free for patients, it is not free to operate such clinics. This review will provide a budget and breakdown of all expenditures at a student-run free clinic along with average costs of services provided to patients. Methods: Accounting data was used to categorize all expenses and generate an annual budget. An inventory tracking system was developed to measure the costs of all medical supplies and services accurately, providing information on costs per clinic and costs per patient for each provided service. Results: The average cost per clinic was $53.55 (per patient: $2.14) for general clinic supplies, $43.74 (per patient: $7.29) for telehealth, $278.47 (per patient: $12.66) for laboratory services, $247.25 (per patient: $10.75) for pharmacy services, and $8.30 (per patient: $1.19) for social work. These costs contributed to a relative minority (< 33%) of the total costs to run a free clinic, where the highest costs were for volunteer appreciation and administrative overhead. Twelve categories of expenditures (administrative overhead, volunteer appreciation, medical and lab supplies, conferences and special projects, advertising and marketing, telehealth, pharmacy, specialty clinics, chronic care, patient transportation, social work, and accounting services) were ranked in order of necessity, and methods for cost reduction were discussed for each category. Conclusions: Categorizing costs can show where cost savings and cost-effective additions may be implemented. This study may serve as a financial and budgeting reference for other clinics. [ABSTRACT FROM AUTHOR]

Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these "Considerations" is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all. [ABSTRACT FROM AUTHOR]

Literature review reveals that adherence to prescribed therapy at hospital discharge averages around 50%. The purpose of this study was to evaluate adherence to prescribed therapy assessing the relationship between re-hospitalization rate at 30 days and degree of therapeutic adherence in a sample of elderly patients discharged from an acute geriatric ward using an Electronic Medication Packaging device, MePill. The study population (n = 56) was divided in 3 groups, a counseling group (A), a counseling + MePill device group (B) and a control group (C). Group A had 98.1% adherence to therapy, Group B had 100%, and control group had 90%. Analyzing the rate of hospitalization by type of intervention for Group A and C the hospitalization rate was 21% and 27.7%, respectively, whilst for Group B no patient was hospitalized. Participants in Group A and C had the shorter hospitalization-free survival as compared with subjects in Group B. [ABSTRACT FROM AUTHOR]

Purpose: Individuals diagnosed with cancer have a range of supportive care needs that are often unmet despite substantial evidence supporting interventions to address them. Addressing this knowledge-translation gap represents a significant opportunity to improve health outcomes. A scoping review of reviews was conducted to map the breadth of evidence for interventions, highlighting those with an existing evidence base, as well as those requiring further research. Methods: Systematic or meta-analytic reviews that examined interventions targeting supportive care needs of adults and children with cancer published between 2009 and 2019 were identified via searches in Medline, PsycINFO, CINAHL, Scopus and Cochrane. Results: Five hundred fifty-one systematic reviews, including 250 meta-analyses, of interventions addressing supportive care needs, were included. Most reviews focused on interventions to address the physical and psychosocial needs of individuals with few reviews identified to address practical needs. Interventions using mental health therapies and physical activity were most commonly used to address all supportive care needs, followed by the use of pharmaceutical and medical devices, complementary and alternative therapies, information and education resources, dietary information and specific spirituality and return-to-work interventions. Conclusion: This scoping review of reviews presents the first broad overview of the literature addressing the supportive care needs of people living with or beyond cancer. It provides a database that health service providers can search to identify appropriate interventions. Results highlight specific research gaps, particularly for practical needs, where reviews are needed. It highlights where a substantial evidence base exists that researchers and policy-makers can consider when implementing interventions. [ABSTRACT FROM AUTHOR]