AI Quality Standards in Health Care: Rapid Umbrella Review.

BACKGROUND: In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies. OBJECTIVE: This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period. METHODS: We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies. RESULTS: We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications. CONCLUSIONS: Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.

Women's trust in the healthcare system in Serbia: Validation of the Women's Trust and Confidence in Healthcare System scale.

BACKGROUND: Women's role as patients is associated with power relationships embedded in society. Although trust in the health care system is a general prerequisite for positive health outcomes, practices regarding women's agency in healthcare systems in Southeastern Europe reinforce women's passivity. Most of the current psychological measures of trust have been constructed and validated in "WEIRD" (samples that are drawn from populations that are White, Educated, Industrialized, Rich, and Democratic) countries, thus having a limited application in other social contexts. OBJECTIVES: We aimed to construct an instrument for assessing women's trust in healthcare systems to describe the structure of trust: Women's Trust and Confidence in the Healthcare System scale. DESIGN: Two independent samples (N1 = 329; N2 = 333) of adult women in Serbia voluntarily completed an online questionnaire. The questionnaire comprised 20 trust-related items which were selected from an extensive collection of women's experiences in the healthcare system and evaluated by experts on a 5-point Likert-type scale. METHODS: We used exploratory factor analysis of the Women's Trust and Confidence in the Healthcare System scale to analyze the structure of trust in the first sample data set and validated it with the second sample using confirmatory factor analysis. We tested concurrent validity by exploring how women's trust in the healthcare system predicts health-related behaviors (multigroup structural equation modeling). All analyses were conducted using R statistical software. RESULTS: The Women's Trust and Confidence in the Healthcare System scale (Cronbach's alpha = 0.86) indicated a three-factor structure of trust in the healthcare system: trust in healthcare professionals, distrust in the public healthcare system, and confidence in healthcare system. This was validated using an independent sample. Interpersonal trust positively predicted women's desirable health behaviors, while trust in the system had a negative impact. CONCLUSION: The Women's Trust and Confidence in the Healthcare System scale captures women's trust in a paternalistic healthcare system, is reliable, and has a stable three-factor structure. The study's findings reveal the relationship between women's trust and health-related behavior: in paternalistic environments, trust reinforces women's passivity.

Preliminary adaptation of the systems thinking for everyday work cue card set in a US healthcare system: a pragmatic and participatory co-design approach.

INTRODUCTION: Healthcare is a highly complex adaptive system, requiring a systems approach to understand its behaviour better. We adapt the Systems Thinking for Everyday Work (STEW) cue cards, initially introduced as a systems approach tool in the UK, in a US healthcare system as part of a study investigating the feasibility of a systems thinking approach for front-line workers. METHODS: The original STEW cards were adapted using consensus-building methods with front-line staff and safety leaders. RESULTS: Each card was examined for relevance, applicability, language and aesthetics (colour, style, visual cues and size). Two sets of cards were created due to the recognition that systems thinking was relatively new in healthcare and that the successful use of the principles on the cards would need initial facilitation to ensure their effective application. Six principles were agreed on and are presented in the cards: Your System outlines the need to agree that problems belong to a system and that the system must be defined. Viewpoints ensure that multiple voices are heard within the discussion. Work Condition highlights the resources, constraints and barriers that exist in the system and contribute to the system's functions. Interactions ask participants to understand how parts of the system interact to perform the work. Performance guides users to understand how work can be performed daily. Finally, Understanding seeks to promote a just cultural environment of appreciating that people do what makes sense to them. The two final sets of cards were scored using a content validity survey, with a final score of 1. CONCLUSIONS: The cards provide an easy-to-use guide to help users understand the system being studied, learn from problems encountered and understand the everyday work involved in providing excellent care. The cards offer a practical 'systems approach' for use within complex healthcare systems.

The need for cybersecurity self-evaluation in healthcare.

The Australian healthcare sector is a complex mix of government departments, associations, providers, professionals, and consumers. Cybersecurity attacks, which have recently increased, challenge the sector in many ways; however, the best approaches for the sector to manage the threat are unclear. This study will report on a semi-structured focus group conducted with five representatives from the Australian healthcare and computer security sectors. An analysis of this focus group transcript yielded four themes: 1) the challenge of securing the Australian healthcare landscape; 2) the financial challenges of cybersecurity in healthcare; 3) balancing privacy and transparency; 4) education and regulation. The results indicate the need for sector-specific tools to empower the healthcare sector to mitigate cybersecurity threats, most notably using a self-evaluation tool so stakeholders can proactively prepare for incidents. Despite the vast amount of research into cybersecurity, little has been conducted on proactive cybersecurity approaches where security weaknesses are identified weaknesses before they occur.

What is the effectiveness of reporting systems in promoting learning in healthcare?

Patient safety in healthcare remains a top priority. Learning from safety events is vital to move towards safer systems. As a result, reporting systems are recognised as the cornerstone of safety, especially in high-risk industries. However, in healthcare, the benefits of reporting systems in promoting learning remain contentious. Though the strengths of these systems, such as promoting a safety culture and providing information from near misses are noted, there are problems that mean learning is missed. Understanding the factors that both enable and act as barriers to learning from reporting is also important to consider. This review, considers the effectiveness of reporting systems in contributing to learning in healthcare.

Development and validation of the Trust in Multidimensional Healthcare Systems Scale (TIMHSS).

CONTEXT: The COVID-19 pandemic has reignited a commitment from the health policy and health services research communities to rebuilding trust in healthcare and created a renewed appetite for measures of trust for system monitoring and evaluation. The aim of the present paper was to develop a multidimensional measure of trust in healthcare that: (1) Is responsive to the conceptual and methodological limitations of existing measures; (2) Can be used to identify systemic explanations for lower levels of trust in equity-deserving populations; (3) Can be used to design and evaluate interventions aiming to (re)build trust. METHODS: We conducted a 2021 review of existing measures of trust in healthcare, 72 qualitative interviews (Aug-Dec 2021; oversampling for equity-deserving populations), an expert review consensus process (Oct 2021), and factor analyses and validation testing based on two waves of survey data (Nov 2021, n = 694; Jan-Feb 2022, n = 740 respectively). FINDINGS: We present the Trust in Multidimensional Healthcare Systems Scale (TIMHSS); a 38-item correlated three-factor measure of trust in doctors, policies, and the system. Measurement of invariance tests suggest that the TIMHSS can also be reliably administered to diverse populations. CONCLUSIONS: This global measure of trust in healthcare can be used to measure trust over time at a population level, or used within specific subpopulations, to inform interventions to (re)build trust. It can also be used within a clinical setting to provide a stronger evidence base for associations between trust and therapeutic outcomes.

Exploring the impact of compassion and leadership on patient safety and quality in healthcare systems: a narrative review.

BACKGROUND: Patient safety and healthcare quality are considered integral parts of the healthcare system that are driven by a dynamic combination of human and non-human factors. This review article provides an insight into the two major human factors that impact patient safety and quality including compassion and leadership. It also discusses how compassion is different from empathy and explores the impact of both compassion and leadership on patient safety and healthcare quality. In addition, this review also provides strategies for the improvement of patient safety and healthcare quality through compassion and effective leadership. METHODS: This narrative review explores the existing literature on compassion and leadership and their combined impact on patient safety and healthcare quality. The literature for this purpose was gathered from published research articles, reports, recommendations and guidelines. RESULTS: The findings from the literature suggest that both compassion and transformational leadership can create a positive culture where healthcare professionals (HCPs) prioritise patient safety and quality. Leaders who exhibit compassion are more likely to inspire their teams to deliver patient-centred care and focus on error prevention. CONCLUSION: Compassion can become an antidote for the burnout of HCPs. Compassion is a behaviour that is not only inherited but can also be learnt. Both compassionate care and transformational leadership improve organisational culture, patient experience, patient engagement, outcomes and overall healthcare excellence. We propose that transformational leadership that reinforces compassion remarkably improves patient safety, patient engagement and quality.

Opportunities and challenges on hospital preparedness to handle motorcycle accidents in Busia County, Kenya: an exploratory qualitative study.

Introduction: motorcycles continue to be a popular mode of transport in Kenya. However, the related injuries cause significant morbidity and mortality and remain to be a major and neglected public health issue. This raised the crucial need for hospital preparedness in managing morbidities and in reducing mortalities. This formed the basis of this paper which aims to document the challenges and opportunities in the healthcare system in handling motorcycle accidents in a Kenyan border town in Busia County. Methods: we drew data from an exploratory qualitative study that was carried out in 2021. All six referral hospitals purposively included in the study. The study targeted a total of 25 top level facility managers as key informants on the facility level opportunities and challenges in handling motorcycle accidents. Descriptive data were analyzed using SPSS version 20. Results: the hospitals were not well prepared to handle motorcycle accidents. The major challenges were understaffing in critical care services; inadequate/lack of equipment to handle motorcycle injuries; inadequate/lack of infrastructure i.e. surgical wards, emergency rooms, inadequate space, functional theatre; lack/inadequate supplies; overstretched referral services arising from the hinge burden of motorcycle accidents in the area; inadequate specialized personnel to provide trauma/care services; mishandling of cases at the site of accident; inability of victims to pay related bills; inappropriate identification of victims at the facility; lack/inadequate on-job training. Some opportunities that currently exist include health system interventions which are not limited to employment of more professionals, improvement of infrastructure, provision of equipment and increase of budgetary allocation. Conclusion: the study reveals vast challenges that are faced by hospitals in managing patients. This calls for the government to step in and capitalize on the proposed opportunities by the health managers to be able to manage morbidities and bring down mortalities due to motorcycle accidents.

The management of rare disease patients from a grassroot perspective: the role of patients' organizations in the global recognition of rare diseases in Cameroon.

Introduction: rare diseases (RD) are extremely complex health conditions. Persons affected by these conditions in Cameroon are often neglected in society and health systems through the inexistence of policies and programs. In Cameroon, there exists no program or policy conceived to address their needs in terms of access to quality health care, timely and reliable diagnosis, treatments, education, etc. The consequence is that persons living with a RD (PLWRD) and their families do not participate in social life. The unique fate of PLWRD reveals that the principle of social justice and equity is flawed in Cameroon. However, patients, in order to survive in society, rely on patients' organizations (PO) to improve their quality of life (QoL) and advocate for a better consideration in the society. The aim of this paper is to highlight how initiatives from a grassroot perspective like POs can inform decision-makers to address the needs of PLWRD and their families. Methods: the study associated a systematic literature review and semi-structured interviews with parents of children suffering from a RD and who are members of a PO. Through the systematic literature review we highlighted the impact POs have in the development of research on RDs, patient literacy, patient empowerment and advocacy while semi-structured interviews brought out the needs of patients and their families. Results: findings, on the one hand show that, in Cameroon PLWRD face a number of challenges like the incurability of their condition, catastrophic medical expenses, stigmatization and marginalization, etc. and though in POs their QoL still remains poor. On the other hand, where POs are empowered they are key actors in research on RDs and help decision-makers on having a better insight into the type of RD that exists across a geographical area, the sociodemographic profile of patients, etc. for a better management of PLWRD. Conclusion: the study suggests that the ministry of public health should create a network with existing RD POs to adequately meet the needs of PLWRD.

The Risks and Challenges of Artificial Intelligence in Endocrinology.

Artificial intelligence (AI) holds the promise of addressing many of the numerous challenges healthcare faces, which include a growing burden of illness, an increase in chronic health conditions and disabilities due to aging and epidemiological changes, higher demand for health services, overworked and burned-out clinicians, greater societal expectations, and rising health expenditures. While technological advancements in processing power, memory, storage, and the abundance of data have empowered computers to handle increasingly complex tasks with remarkable success, AI introduces a variety of meaningful risks and challenges. Among these are issues related to accuracy and reliability, bias and equity, errors and accountability, transparency, misuse, and privacy of data. As AI systems continue to rapidly integrate into healthcare settings, it is crucial to recognize the inherent risks they bring. These risks demand careful consideration to ensure the responsible and safe deployment of AI in healthcare.

Patient journey mapping: emerging methods for understanding and improving patient experiences of health systems and services.

Patient journey mapping is an emerging field of research that uses various methods to map and report evidence relating to patient experiences and interactions with healthcare providers, services, and systems. This research often involves the development of visual, narrative, and descriptive maps or tables, which describe patient journeys and transitions into, through, and out of health services. This methods corner paper presents an overview of how patient journey mapping has been conducted within the health sector, providing cardiovascular examples. It introduces six key steps for conducting patient journey mapping and describes the opportunities and benefits of using patient journey mapping and future implications of using this approach.

How to Navigate the Pitfalls of AI Hype in Health Care.

In this Medical News article, Arvind Narayanan, PhD, a professor of computer science at Princeton University, discusses the benefits of using artificial intelligence in research and clinical settings while remaining cautious of hype, biases, and data privacy issues.

Solving the Autism Puzzle: Creating a Broad-Spectrum Tool to Improve Care Delivery in the Clinical Setting.

PURPOSE: The purpose of this quality improvement (QI) project was to develop and implement an assessment tool to identify a patient's specific needs due to autism spectrum disorder (ASD). The use of an individualized plan of care related to sensory and behavioral differences correlates with improved experiences in the perioperative setting for patients with ASD. DESIGN: Mixed methods, pre-post survey, retrospective data comparison. METHODS: Metrics planned to evaluate intervention outcomes included: (1) Comparison of pre and postsurvey data obtained from perioperative staff members following ASD education, (2) Evaluation of the number of behavior response team calls made compared to retrospective data, and (3) Survey response data from families assessing the perceived experience of perioperative stay. FINDINGS: Two hundred and fifty staff members responded to the learning needs survey; 164 in the preperiod and 86 in the postperiod. The perioperative process for these patients improved from the pre- to the postperiod in its ability to meet the needs of patients with autism (P < .001). Overall, respondents rated the sensory aids and the behavioral and sensory assessment tool as very useful (Median = 5, IQR = 2) and stated that they are likely to continue to use the tools in the future when caring for patients with autism (Median = 5, IQR = 1). CONCLUSIONS: The caregivers of study patients felt they had a high level of satisfaction with their surgery or procedure experience. Health care providers also reported increased confidence working with individuals with ASD in the perioperative environment and satisfaction with the intervention program.

The Delivery Model of Perceived Medical Service Quality Based on Donabedian's Framework.

ABSTRACT: The implementation of the National Health Insurance has transformed the medical care landscape in Taiwan, rendering perceived medical service quality (PMSQ) and patient satisfaction significant focal points in medical care management. Past studies mostly focused on the technical aspects of medical care services, while overlooking the patients' perception of services and the delivery process of PMSQ in the medical care experience. This study integrated the theoretical framework of the Donabedian SPO model and the SERVQUAL questionnaire. The survey was conducted among the outpatients of three types of medical institutions in northern Taiwan: academic medical centers, metropolitan hospitals, and local community hospitals. A total of 400 questionnaires were collected, and 315 valid questionnaires remained after eliminating the incomplete ones. This study established a PMSQ delivery model to explore patients' perceptions of medical service quality. It was found that the variable, Assurance, could deliver the PMSQ and enhance the Medical outcome (MO), while improving the variable, Tangible, in medical institutions could not significantly enhance the MO. These findings emphasize the importance of healthcare institutions prioritizing the professional background, demeanor of their healthcare staff, treatment methods, and processes over tangible elements.

Effectiveness of RADAR on diabetes processes of care for First Nations communities in Alberta, Canada.

The epidemic of type-2 diabetes in First Nations communities is tragic. Culturally-appropriate approaches addressing multiple components, focusing beyond glycemic control, are urgently needed. Using an intention-to-treat framework, 13 processes of care indicators were assessed to compare proportions of patients who received care at baseline relative to 2-year follow-up. Clinical improvements were demonstrated across major process of care indicators (e.g. screening, education, and vaccination activities). We found RADAR improved reporting for most diabetes processes of care across seven FN communities and was effective in supporting diabetes care for FN communities, in Alberta Canada.

A Nationwide Network of Health AI Assurance Laboratories.

Importance: Given the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed. Observations: While there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings. Conclusion and Relevance: The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.