Addressing distress management challenges: Recommendations from the consensus panel of the American Psychosocial Oncology Society and the Association of Oncology Social Work.

Distress management (DM) (screening and response) is an essential component of cancer care across the treatment trajectory. Effective DM has many benefits, including improving patients' quality of life; reducing distress, anxiety, and depression; contributing to medical cost offsets; and reducing emergency department visits and hospitalizations. Unfortunately, many distressed patients do not receive needed services. There are several multilevel barriers that represent key challenges to DM and affect its implementation. The Consolidated Framework for Implementation Research was used as an organizational structure to outline the barriers and facilitators to implementation of DM, including: 1) individual characteristics (individual patient characteristics with a focus on groups who may face unique barriers to distress screening and linkage to services), 2) intervention (unique aspects of DM intervention, including specific challenges in screening and psychosocial intervention, with recommendations for resolving these challenges), 3) processes for implementation of DM (modality and timing of screening, the challenge of triage for urgent needs, and incorporation of patient-reported outcomes and quality measures), 4) organization-inner setting (the context of the clinic, hospital, or health care system); and 5) organization-outer setting (including reimbursement strategies and health-care policy). Specific recommendations for evidence-based strategies and interventions for each of the domains of the Consolidated Framework for Implementation Research are also included to address barriers and challenges.

The Safety of Inpatient Health Care.

BACKGROUND: Adverse events during hospitalization are a major cause of patient harm, as documented in the 1991 Harvard Medical Practice Study. Patient safety has changed substantially in the decades since that study was conducted, and a more current assessment of harm during hospitalization is warranted. METHODS: We conducted a retrospective cohort study to assess the frequency, preventability, and severity of patient harm in a random sample of admissions from 11 Massachusetts hospitals during the 2018 calendar year. The occurrence of adverse events was assessed with the use of a trigger method (identification of information in a medical record that was previously shown to be associated with adverse events) and from review of medical records. Trained nurses reviewed records and identified admissions with possible adverse events that were then adjudicated by physicians, who confirmed the presence and characteristics of the adverse events. RESULTS: In a random sample of 2809 admissions, we identified at least one adverse event in 23.6%. Among 978 adverse events, 222 (22.7%) were judged to be preventable and 316 (32.3%) had a severity level of serious (i.e., caused harm that resulted in substantial intervention or prolonged recovery) or higher. A preventable adverse event occurred in 191 (6.8%) of all admissions, and a preventable adverse event with a severity level of serious or higher occurred in 29 (1.0%). There were seven deaths, one of which was deemed to be preventable. Adverse drug events were the most common adverse events (accounting for 39.0% of all events), followed by surgical or other procedural events (30.4%), patient-care events (which were defined as events associated with nursing care, including falls and pressure ulcers) (15.0%), and health care-associated infections (11.9%). CONCLUSIONS: Adverse events were identified in nearly one in four admissions, and approximately one fourth of the events were preventable. These findings underscore the importance of patient safety and the need for continuing improvement. (Funded by the Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.).

Illuminating the dark spaces of healthcare with ambient intelligence.

Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

LifeTime and improving European healthcare through cell-based interceptive medicine.

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.

Issues in assessing analytical performance specifications in healthcare systems assembling multiple laboratories and measuring systems.

Analytical performance specifications (APS) are usually compared to the intermediate reproducibility uncertainty of measuring a particular measurand using a single in vitro diagnostic medical device (IVD MD). Healthcare systems assembling multiple laboratories that include several IVD MDs and cater to patients suffering from long-term disease conditions mean that samples from a patient are analyzed using a few IVD MDs, sometimes from different manufacturers, but rarely all IVD MDs in the healthcare system. The reproducibility uncertainty for results of a measurand measured within a healthcare system and the components of this measurement uncertainty is useful in strategies to minimize bias and overall measurement uncertainty within the healthcare system. The root mean squares deviation (RMSD) calculated as the sample standard deviation (SD) and relative SD includes both imprecision and bias and is appropriate for expressing such uncertainties. Results from commutable stabilized internal and external control samples, from measuring split natural patient samples or using big-data techniques, are essential in monitoring bias and measurement uncertainties in healthcare systems. Variance component analysis (VCA) can be employed to quantify the relative contributions of the most influential factors causing measurement uncertainty. Such results represent invaluable information for minimizing measurement uncertainty in the interest of the healthcare system's patients.

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.

Experiences of UK clinical scientists (Physical Sciences modality) with their regulator, the Health and Care Professions Council: results of a 2022 survey.

BACKGROUND: In healthcare, regulation of professions is an important tool to protect the public. With increasing regulation however, professions find themselves under increasing scrutiny. Recently there has also been considerable concern with regulator performance, with high profile reports pointing to cases of inefficiency and bias. Whilst reports have often focused on large staff groups, such as doctors, in the literature there is a dearth of data on the experiences of smaller professional groups such Clinical Scientists with their regulator, the Health and Care Professions Council. This article reports the findings of a survey from Clinical Scientists (Physical Sciences modality) about their experiences with their regulator, and their perception of the quality and safety of that regulation. METHODS: Between July-October 2022, a survey was conducted via the Medical Physics and Engineering mail-base, open to all medical physicists & engineers. Questions covered typical topics of registration, communication, audit and fitness to practice. The questionnaire consisted of open and closed questions. Likert scoring, and thematic analysis were used to assess the quantitative and qualitative data. RESULTS: Of 146 responses recorded, analysis was based on 143 respondents. Overall survey sentiment was significantly more negative than positive, in terms of regulator performance (negative responses 159; positive 106; significant at p < 0.001). Continuous Professional Development audit was rated median 4; other topics were rated as neutral (fitness to practice, policies & procedures); and some as poor (value). CONCLUSIONS: The Clinical Scientist (Physical Sciences) professional registrants rated the performance of their regulator more negatively than other reported assessments (by the Professional Standards Authority). Survey respondents suggested a variety of performance aspects, such as communication and fitness to practice, would benefit from improvement. Indications from this small dataset, suggest a larger survey of HCPC registrants would be useful.

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.

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.

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.

Strengthening Linkages Between Health Care and Public Health: A Novel Framework for Public Health Action in Health Care Settings to Improve Cardiovascular Disease Risk.

The Chronic Disease Prevention and Control Program (CDPCP) at the Nebraska Department of Health and Human Services developed a novel public health framework and tools to translate public health knowledge, grant work, and terminology to a health care audience in order to inform clinical practice changes in the management of hyperlipidemia and hypertension. The CDPCP piloted the tools with 2 accountable care organizations that included 19 clinics and then funded 9 independent clinics. The project sought to empower clinics to design and implement interventions for reducing high blood pressure and high blood cholesterol focused on populations disproportionately at risk for those conditions utilizing electronic health records. A team comprising the CDPCP and evaluation specialists created a framework called CAAPIE (Capture, Assess, Action Plan, Implement, Evaluate) to provide a clinic-friendly approach to the public health-focused work. For the capture phase, baseline data were collected from clinics. To guide the assess, action plan, and evaluate phases, the team created a Scan & Plan Tool for clinics to assess practices and policies and then use results to develop an action plan. The assessment was repeated upon completion of the project to evaluate change. Interviews were conducted to assess the utility of these tools and capture information related to the implementation of the project. Clinicians reported the framework and tools provided a useful approach, aiding clinics in understanding public health terminology and intended outcomes of the project. Work resulted in the creation of new or enhanced clinical policies and procedures that led to modest improvements in the management of high blood pressure and high cholesterol. The CAAPIE framework is a novel approach for state health departments to utilize in translating public health grant work to health care professionals, promoting a working relationship between the spheres to achieve positive impacts on individual and population-based health care.

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.

Assessment of Saudi Public Perceptions and Opinions towards Artificial Intelligence in Health Care.

Background and Objectives: The healthcare system in Saudi Arabia is growing rapidly with the utilization of advanced technologies. Therefore, this study aimed to assess the Saudi public perceptions and opinions towards artificial intelligence (AI) in health care. Materials and Methods: This cross-sectional web-based questionnaire study was conducted between January and April 2024. Data were analyzed from 830 participants. The perceptions of the public towards AI were assessed using 21-item questionnaires. Results: Among the respondents, 69.4% were males and 46% of them were aged above 41 years old. A total of 84.1% of the participants knew about AI, while 61.1% of them believed that AI is a tool that helps healthcare professionals, and 12.5% of them thought that AI may replace the physician, pharmacist, or nurse in the healthcare system. With regard to opinion on the widespread use of AI, 45.8% of the study population believed that healthcare professionals will be improved with the widespread use of artificial intelligence. The mean perception score of AI among males was 38.4 (SD = 6.1) and this was found to be higher than for females at 37.7 (SD = 5.3); however, no significant difference was observed (p = 0.072). Similarly, the mean perception score was higher among young adults aged between 20 and 25 years at 38.9 (SD = 6.1) compared to other age groups, but indicating no significant association between them (p = 0.198). Conclusions: The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care. This suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.

Environmental competencies for healthcare management at a 2nd education level as a component of strategic management.

OBJECTIVE: Aim: To present the results of the analysis of educational standards and curricula of the second educational level of training of specialists, who may be managers of healthcare, on the content of the environmental component as an element of strategic management. PATIENTS AND METHODS: Materials and Methods: Content analysis 24 educational standards of the Ministry of Education and Science of Ukraine of Ukraine for 6 fields of knowledge and 200 master's curricula from 87 institutions of higher education of Ukraine. CONCLUSION: Conclusions: There is a distribution of basic leadership and management competencies both by types of these competencies and between specialties. The requirements for the inclusion of the environmental component in the framework documents are poorly expressed. The content of environmental issues in the curricula is insufficient.

[Applying consent, in healthcare and beyond]. / Appliquer le consentement, dans les soins et au-delà.

Sexual violence constitutes a form of gender-based violence, to the extent that the victims are mainly women. Other groups of vulnerable people are also more affected, in particular gender and sexual diversity persons. Sexual and gender-based violence can also occur in healthcare. To respect the legal framework and people's rights, but also to promote safety and quality in healthcare, it is essential to obtain and respect consent. Consent must be informed, explicit, freely given, and reiterated throughout the consultation. This article reviews the concept of consent and offers practical tools for its application in healthcare.

Les violences sexuelles constituent une violence de genre, dans la mesure où les victimes sont principalement des femmes et les auteurs des hommes. D'autres groupes de personnes vulnérables sont également davantage concernés, en particulier les personnes de la diversité sexuelle et de genre. Ces violences sexuelles et de genre existent également dans les soins. Afin de respecter le cadre légal et les droits des personnes, mais aussi de favoriser des soins de qualité et en sécurité, il est primordial de recueillir et respecter le consentement. Celui-ci doit être éclairé, explicite, libre et réitéré tout au long de la consultation. Cet article fait le point sur le concept du consentement et offre des outils pratiques pour son application dans les soins.

Strengthening implementation guidelines for HIV service delivery: Considerations for future evidence generation and synthesis.

Ingrid Eshun-Wilson and colleagues summarize gaps in primary HIV implementation research methods and reporting, and propose areas for future methodological development.

Equity in Health: Consideration of Race and Ethnicity in Precision Medicine.

The causes for disparities in implementation of precision medicine are complex, due in part to differences in clinical care and a lack of engagement and recruitment of under-represented populations in studies. New tools and large genetic cohorts can change these circumstances and build access to personalized medicine for disadvantaged populations.

Evolving health care through personal genomics.

With the rapid evolution of next-generation DNA sequencing technologies, the cost of sequencing a human genome has plummeted, and genomics has started to pervade health care across all stages of life - from preconception to adult medicine. Challenges to fully embracing genomics in a clinical setting remain, but some approaches are starting to overcome these barriers, such as community-driven data sharing to improve the accuracy and efficiency of applying genomics to patient care.