Healthcare Research Research Articles

Navigating the intersection of psychiatry and ophthalmology: A comprehensive review of depression and anxiety management in glaucoma patients.

Glaucoma, a prevalent and debilitating eye disease, has long been associated with vision impairment and blindness. However, recent research has shed light on the often-underestimated psychological dimensions of this condition. Anxiety and depression, two pervasive psychiatric comorbidities, have been increasingly recognized among glaucoma patients. This comprehensive review aims to explore the intricate relationship between psychiatry and ophthalmology, in the context of managing depression and anxiety in glaucoma patients. By meticulously examining peer-reviewed literature, we synthesize current knowledge on the prevalence, risk factors, and underlying mechanisms of anxiety and depression in glaucoma. The evidence reveals that glaucoma patients face an elevated risk of experiencing these mood disorders. Factors such as progressive vision loss, complex medication regimens, and the fear of further visual deterioration contribute to their vulnerability. Moreover, we delve into the bidirectional relationship between glaucoma and mood disorders, shedding light on the complex interplay between ocular and emotional health. Our review investigates the implications of anxiety and depression on glaucoma management, including their potential impact on treatment adherence, disease progression, and overall quality of life. We also explore the neurobiological pathways linking glaucoma and mood disorders, providing a foundation for future research and potential therapeutic interventions. In conclusion, recognizing the psychological burden carried by glaucoma patients is essential for holistic and patient-centered care. This review underscores the pressing need for integrated approaches that bring together ophthalmological and psychiatric expertise to optimize the well-being of individuals facing the challenges of glaucoma. By addressing anxiety and depression in glaucoma care, healthcare providers can enhance the overall quality of life for these patients, ultimately leading to improved outcomes and a brighter future for those affected by this condition. This review offers valuable insight for healthcare practitioners and researchers, providing a concise overview of key topics and research in the field of managing depression and anxiety in glaucoma patients.

The Swiss Sleep House Bern—A New Approach to Sleep Medicine

Sleep is essential for health, well-being, creativity, and productivity. Sleep loss and sleep–wake circadian disorders (SWCDs) affect at least one in three individuals but are underdiagnosed and undertreated for different reasons: First, the importance of sleep health and, second, the burden of sleep loss and SWCDs are underestimated. Third, education in sleep medicine is insufficient and health care-related sleep research is underdeveloped. Fourth, the validation and implementation of tele-sleep medicine approaches and novel devices to monitor SWCDs are still insufficient. Fifth, the reimbursement of sleep medicine in most countries is inadequate and the availability of specialized care is limited to a few centers. The Swiss Sleep House Bern (SSHB) was founded in 2022 to address these challenges and eventually promote better care for patients with SWCDs and improve sleep health for the broader population. The interdisciplinary and interprofessional team of the SSHB, which is integrated in the Bernese Interdisciplinary Sleep-Wake-Epilepsy Center, links sleep specialists with primary care providers to offer a rapid and accessible triage and first-level management of sleep complaints and SWCDs. The SSHB also promotes awareness and offers educational programs on sleep health and SWCDs, performs health care research, and fosters the implementation of new technologies, data science, and telemedicine into clinical routine.

Artificial Intelligence in Biology

This article provides an in-depth analysis of the use of Artificial Intelligence (AI) in various aspects of biology, including healthcare, agriculture, and environmental monitoring. It highlights AI's ability to mimic human intelligence and analyze large datasets for predictions and tasks. The article also discusses its integration into Chinese medicine, where AI-guided diagnostic and therapeutic systems optimize clinical treatments and health management. AI is also used in disease management, analyzing data on diseases and pests, predicting their impact on ecosystems, and implementing preventative measures. The article also highlights the role of integrated information systems in environmental monitoring. Artificial intelligence (AI) has significant potential in healthcare research and chemical discoveries. Pharmaceutical companies are using AI to improve drug development by utilizing computational biology and machine learning systems to predict molecular behavior and the likelihood of finding a useful drug. This saves time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources for drug development. Strong AI systems can analyze extensive data sets in pharmaceutical and medical research. This review focuses on integrating knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in cancer precision drug discovery.

E-cigarettes and Youth: The Known, the Unknown, and Implications for Stakeholders.

Despite the decline in the prevalence of e-cigarette use among youth during the coronavirus disease 2019 pandemic, more than 2.5 million of US high and middle schoolers are still using e-cigarettes. Furthermore, those who use e-cigarettes are starting at a younger age and are using them more intensely, reflecting, at least in part, a high addiction liability of modern e-cigarettes. Beyond addiction, accumulating evidence suggests that, in the short-term, e-cigarettes are associated with cardiovascular and pulmonary effects, whereas the long-term effects of e-cigarette use are yet to be established. The aim of this review is to synthesize current knowledge on e-cigarette use among youth, including established and potential risks and efforts to date to curb youth exposure to e-cigarettes. In addition, we provide recommendations for health care providers, researchers, and other stakeholders to address this significant public health issue.

Of Hearth and Home: Professional Wellness After Hours

Burnout is a prevalent problem in the contemporary practice of medicine. Defined by the Agency for Healthcare Research and Quality as, “a long-term stress reaction marked by emotional exhaustion, depersonalization, and a lack of sense of personal accomplishment,” this multifactorial condition has significant implications for the clinicians who suffer it, their patients, and families. Neurologists suffer some of the highest rates of burnout. Burnout research on interventions often focus on the work environment. In this article, we will focus on burnout’s effects on home life and features of home life that can impact resiliency, specifically sleep hygiene.

Mechanical Stapling Devices for Soft Tissue Repair: A Review of Commercially Available Linear, Linear Cutting, and Circular Staplers

Stapling devices have emerged as a widespread and effective option for soft tissue surgery, offering promising outcomes for patients by reducing complication rates and surgery time. This review aims to provide an exhaustive analysis of commercially available alternatives in the market, incorporating insights from market analysis, patent landscape, and the existing literature. The main focus lies in identifying and evaluating the most widely adopted and innovative stapling devices, including linear, linear cutting, circular, and powered staplers. In addition, this review delves into the realm of bioabsorbable staples, exploring the materials utilized and the surgical fields where these advanced staples find applications. To facilitate easy comprehension, the gathered information is presented in tables, highlighting the essential parameters for each stapling device. This comprehensive research about stapling devices is intended to aid healthcare practitioners and researchers in making informed decisions when choosing the most appropriate instrument for specific surgical procedures.

Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics

Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale analysis of literature in this field based on quantitative approaches. This study performed a bibliometric and topic modeling examination on 683 articles from 2002 to 2022, focusing on research topics and trends, journals, countries/regions, institutions, authors, and scientific collaborations. Results showed that, firstly, the number of articles has grown from 1 in 2002 to 220 in 2022, with a majority being published in interdisciplinary journals that link healthcare and medical research and information technology and AI. Secondly, the significant rise in the quantity of research articles can be attributed to the increasing contribution of scholars from non-English speaking countries/regions and the noteworthy contributions made by authors in the USA and India. Thirdly, researchers show a high interest in diverse research issues, especially, cross-modality magnetic resonance imaging (MRI) for brain tumor analysis, cancer prognosis through multi-dimensional data analysis, and AI-assisted diagnostics and personalization in healthcare, with each topic experiencing a significant increase in research interest. There is an emerging trend towards issues such as applying generative adversarial networks and contrastive learning for multimodal medical image fusion and synthesis and utilizing the combined spatiotemporal resolution of functional MRI and electroencephalogram in a data-centric manner. This study is valuable in enhancing researchers’ and practitioners’ understanding of the present focal points and upcoming trajectories in AI-powered smart healthcare based on multimodal data analysis.

Design Decision Support for Healthcare Architecture: A VR-Integrated Approach for Measuring User Perception

Changing the physical environment of healthcare facilities can positively impact patient outcomes. Virtual reality (VR) offers the potential to understand how healthcare environment design impacts users’ perception, particularly among those with brain injuries like stroke, an area with limited research. In this study, our objective was to forge a new pathway in healthcare environment research by developing a comprehensive, six-module ‘user-centered’ design decision support approach, utilizing VR technology. This innovative method integrated patient engagement, architectural design principles, BIM prototyping, and a sophisticated VR user interface to produce realistic and immersive healthcare scenarios. Forty-four stroke survivors participated, experiencing 32 VR scenarios of in-patient bedrooms, followed by interactive in-VR questions and semi-structured interviews. The results of the approach proved to be comparatively efficient and feasible, provided a high level of immersion and presence for the participants, and effectively elicited extremely rich quantifiable response data, which revealed distinct environmental preferences. Our novel approach to understanding end-user responses to stroke rehabilitation architecture demonstrates potential to inform user-centered evidence-based design decisions in healthcare, to improve user experiences and health outcomes in other healthcare populations and environments.

Use of business model potential in Dutch academic medical centres-A case study.

Academic Medical Centres (AMCs) are large organisations with a complex structure due to various intertwined missions and (public) roles that can be conflicting. This complexity makes it difficult to adapt to changing circumstances. The literature points to the use of business models to address such challenges. A business model describes the resources, processes, and cost assumptions that an organisation makes in order to the delivery of a unique value proposition to a customer/patient. Do AMC business operations managers actually use business models to address challenges and operate in a way that enables AMCs to adapt to changing circumstances? This study explored whether the use of a business model is a starting point for bringing about change in AMC operations. A case study design was considered appropriate to explore the knowledge and experience of business models among business operations managers of Dutch AMCs. Through purposive sampling, participants were invited to participate in a questionnaire to provide in-depth and detailed information about the use of business models in AMCs. Our research showed that a business model can support the complex organisation of an AMC, but the design and use of business models varies. In general, respondents attribute more potential to the use of a business model than they experience in daily practice. The majority consider a business model to be suitable for bringing about change, but see it only sparingly used in their own AMC. This is the first study to provide some initial insights into the use of business models in Dutch AMCs. We can assume that improvements are possible in order to optimise the change potential of business models in AMCs worldwide. In order to successfully implement an innovative business model, the interpretation of the concept of a business model and the creation of a framework of preconditions should be taken into account. Healthcare providers, policy makers or researchers should explicitly identify the environment in which the model will operate. In particular, by identifying the level of readiness for change readiness at all levels of the organisation.

Exploring Quantitative Methodologies for Assessing the Environmental, Social, and Economic Impacts of Telemedicine: A Literature Review

The significant consumption of resources within the healthcare sector underscores the need to address both efficiency and sustainability concerns. Telemedicine has been identified as one of the most promising pathways for reducing the environmental impacts of the healthcare sector. However, a comprehensive sustainability assessment is still required. The main aim of the present study is to conduct a systematic literature review to explore approaches and methodologies employed for quantifying the environmental, social, and economic impacts of telemedicine. Moreover, the research seeks to determine whether the approaches focus on a single aspect or whether they allow for a comprehensive assessment including all three sustainability pillars. The searching phase was conducted in the Scopus and PubMed databases, considering last 10 years (i.e., 2013–2023). Keywords were related to remote care and sustainability impact fields. Following the PRISMA framework, out of 477 articles, 91 studies were included in the analysis. Primary findings highlighted that studies on telemedicine impacts predominantly focus on transport-related aspects, emphasizing direct emissions and associated costs that are avoided and time savings. Televisit emerged as the most investigated remote care activity. Database and conversion factors were mainly employed for analysis, while other methodologies were sporadically mentioned in the literature. Despite numerous papers addressing these issues, a standardized and comprehensive methodology still appears to be lacking. Future works should consider the entire life cycle process, including more stakeholders. A defined approach will be fundamental to move beyond theoretical discussions and provide actionable insights for healthcare practitioners, policymakers, and researchers.

Psychometric Evaluation of the German Version of the Perceived Access to Healthcare Questionnaire in a Sample of Individuals with Rare Chronic Diseases.

Access to healthcare is multifaceted and poses significant challenges for individuals with chronic and rare diseases (RDs). This study aimed to conduct a psychometric evaluation of the German version of the Perception of Access to Healthcare Questionnaire (PAHQ) among individuals with RDs. We conducted an evaluation of the PAHQ using a sample of 271 adults with an RD diagnosis. The 31-item instrument underwent evaluation including a comparison of three different confirmatory factor models (CFA). Subsequent steps involved item removal, reliability analysis (computation of Cronbach's alpha), and analysis of criterion-related validity. The six-factor model showed the best fit to the data and was selected for further examination. Subsequently, six items were removed. Fit indices for the final model were acceptable. Cronbach's alpha ranged from 0.75 to 0.91 for the six subscales, except for the availability subscale which exhibited the lowest value (0.64). In terms of criterion-related validity, different skills relating to the navigation of access dimensions were significantly correlated with corresponding PAHQ subscales, thus confirming validity. The capacity of the PAHQ to guide targeted interventions and facilitate cross-population comparisons positions it as a valuable instrument for advancing healthcare access research and promoting equitable access to care, particularly for individuals with rare and chronic diseases.

Morphological aspects of bedsore healing against high-frequency electrical stimulation in patients with severe brain damage

BACKGROUND: Bedsores or decubital ulcers are significant medical and social problems in patients with serious brain damage. The majority of local treatments are ineffective and do not ensure their healing. Therefore, the search for nondrug methods as part of the complex treatment of decubital ulcers is extremely relevant.
 AIM: To assess the morphological picture of bedsores against the background of an improved method of treatment using high-frequency electrical stimulation with pulsed currents in chronically immobilised patients who survived a cerebral catastrophe.
 MATERIALS AND METHODS: Biopsies of bedsores were examined in 12 patients with severe neurological disorders that developed after a cerebral catastrophe. The initial status of all patients was a ‘chronic critical condition’ (CCS). In all patients, the bedsore defect was located in the sacral region, with a length of 7–25 cm2. The depth of the bedsore corresponded to grade III according to the classification of the Agency for Health Care Policy and Research. Two groups of patients were formed: the main group of five patients (three men + two women), with an average age of 49.0 years, who were subjected to electrical stimulation with pulsed currents of the radiofrequency range. A control group of seven patients (three men + four women), with an average age of 60.1 years, who underwent traditional treatment. The duration of the chronic wound process ranged from 2 months to 1.5 years. The immediate cause of CCS was more often a massive ischaemic stroke (8), less often a severe traumatic brain injury (3) and radical operations to remove brain tumours (1) of various prescriptions from 3 months to 1.5 years. Morphological studies were conducted in both groups to assess the complicated wound process.
 RESULTS: The complex morphological study revealed that in the presence of high-frequency electrical stimulation, an earlier appearance of foci of fibroblastic proliferation, an increase in the regenerative potential of tissues due to increased production of type I and III collagens, and myofibroblastic and endothelial growth factors were noted.
 CONCLUSION: In the case of CCS of cerebral genesis, the wound process in bedsores inevitably becomes complicated and, according to morphological research, is characterised by severe endothelial dysfunction, deep microcirculation disorders, depletion of the potential of myofibroblastic cells and deficiency of their growth factors. Local high-frequency electrical stimulation in patients with severe brain damage and decubital ulcers as a component of complex ulcer therapy significantly optimises cellular and tissue reactions in the area of bedsores.

Three different types of AI hype in healthcare

AbstractHealthcare systems are the embodiment of big data – as evident in the logistics of resource management, estate maintenance, diagnoses, patient monitoring, research, etc. – such that human health is often heralded as one of the fields most likely to benefit from AI. Yet, the prevalence of hype – both positive and negative – risks undermining that potential by distracting healthcare policy makers, practitioners, and researchers from many of the non-AI factors that will determine its impact. Here we categorise AI hype in healthcare into three types that include both utopian and dystopian narratives and plot a series of more productive paths ahead by which to realise the potential of AI to improve human healthcare.

Vicarious learning and graduates' adaptive career behaviours: the case of graduates on clinical internships

PurposeThe effect of vicarious learning during clinical or medical internships on graduates' adaptive career behaviours has attracted scant attention from healthcare researchers, particularly, in the developing world context. Drawing upon the social cognitive career theory model of career self-management (SCCT-CSM), the current study examines how vicarious learning influences the clinical graduates' adaptive career behaviours (i.e. career exploration and decision-making) via career exploration and decision-making self-efficacy (CEDSE) and career intention.Design/methodology/approachData were collected from 293 nursing graduates undertaking clinical internships in 25 hospitals across Nigeria who willingly participated in this study as they were also assured of confidentiality at two-waves. The proposed hypotheses were tested using a path analysis.FindingsThe findings showed that vicarious learning during clinical internship had a direct effect on career exploration, decision-making and career decision self-efficacy among graduate trainees. Also, the findings revealed that the effects of vicarious learning on the graduates' career exploration and career decision-making were significantly mediated by career decision self-efficacy and career intentions.Practical implicationsThe findings of this study have important practical implications for higher education institutions and industries that send and receive clinical graduates for clinical internships to gain more skills. More emphasis should be on encouraging learners to learn vicariously in addition to other forms of learning experiences available during clinical internships.Originality/valueThe study explains that the graduates' higher engagement in clinical career exploration and decision-making was based on a higher level of vicarious learning during internships. The results suggest that higher education institutions and healthcare service providers can derive greater benefits from more emphasis on promoting vicarious learning during clinical internships.

Disparities in seizure outcomes revealed by large language models.

Large-language models (LLMs) can potentially revolutionize health care delivery and research, but risk propagating existing biases or introducing new ones. In epilepsy, social determinants of health are associated with disparities in care access, but their impact on seizure outcomes among those with access remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to determine if different demographic groups have different seizure outcomes. We tested our LLM for differences and equivalences in prediction accuracy and confidence across demographic groups defined by race, ethnicity, sex, income, and health insurance, using manually annotated notes. Next, we used LLM-classified seizure freedom at each office visit to test for demographic outcome disparities, using univariable and multivariable analyses. We analyzed 84675 clinic visits from 25612 unique patients seen at our epilepsy center. We found little evidence of bias in the prediction accuracy or confidence of outcome classifications across demographic groups. Multivariable analysis indicated worse seizure outcomes for female patients (OR 1.33, P ≤ .001), those with public insurance (OR 1.53, P ≤ .001), and those from lower-income zip codes (OR ≥1.22, P ≤ .007). Black patients had worse outcomes than White patients in univariable but not multivariable analysis (OR 1.03, P = .66). We found little evidence that our LLM was intrinsically biased against any demographic group. Seizure freedom extracted by LLM revealed disparities in seizure outcomes across several demographic groups. These findings quantify the critical need to reduce disparities in the care of people with epilepsy.

The Role of Artificial Intelligence in Advancing Healthcare: A Comprehensive Review

In healthcare and clinical research, artificial intelligence (AI) has become a disruptive force that is transforming many facets of medical practice, diagnosis, treatment, and research. In order to give a thorough overview of the role of AI in healthcare, this review paper synthesizes views from a wide range of sources, including academic publications, conference papers, industry reports, and opinion pieces. It looks at how AI is being used in healthcare settings and how it may be used for jobs like patient care, diagnosis, and therapy. The article also covers the advantages of AI in healthcare, such as increased patient monitoring, individualized therapy alternatives, and higher diagnostic accuracy. The analysis does, however, also point out a number of obstacles and constraints that come with the broad use of AI, including worries about data privacy, algorithmic bias, legal restrictions, and ethical issues. The study highlights the need for ongoing research, innovation, and cooperation to achieve the full potential of AI in enhancing healthcare outcomes as it looks forward and identifies new trends and future directions in AI-enabled healthcare. In order to enable the responsible and fair deployment of AI technology, ethical aspects surrounding AI in healthcare are also covered. These considerations include informed consent, transparency, and equity. The article highlights the value of using lessonslearned from these experiences to guide future projects by providing case studies and examples of effective AI applications in healthcare settings. The overall goal of this review paper is to educate policymakers, researchers, healthcare providers, and other stakeholders about the opportunities and challenges that come with integrating AI into healthcare systems. It also aims to offer insightful information about the revolutionary impact of AI on clinical research and healthcare. Keywords: Artificial intelligence (AI), Healthcare, Clinical research, Diagnosis, Treatment

Patient Safety Indicators During the Initial COVID-19 Pandemic Surge in the United States.

The COVID-19 pandemic presented a challenge to inpatient safety. It is unknown whether there were spillover effects due to COVID-19 into non-COVID-19 care and safety. We sought to evaluate the changes in inpatient Agency for Healthcare Research and Quality patient safety indicators (PSIs) in the United States before and during the first surge of the pandemic among patients admitted without COVID-19. We analyzed trends in PSIs from January 2019 to June 2020 in patients without COVID-19 using data from IBM MarketScan Commercial Database. We included members of employer-sponsored or Medicare supplemental health plans with inpatient, non-COVID-19 admissions. The primary outcomes were risk-adjusted composite and individual PSIs. We analyzed 1,869,430 patients admitted without COVID-19. Among patients without COVID-19, the composite PSI score was not significantly different when comparing the first surge (Q2 2020) to the prepandemic period (e.g., Q2 2020 score of 2.46 [95% confidence interval {CI}, 2.34-2.58] versus Q1 2020 score of 2.37 [95% CI, 2.27-2.46]; P = 0.22). Individual PSIs for these patients during Q2 2020 were also not significantly different, except in-hospital fall with hip fracture (e.g., Q2 2020 was 3.42 [95% CI, 3.34-3.49] versus Q4 2019 was 2.45 [95% CI, 2.40-2.50]; P = 0.01). The first surge of COVID-19 was not associated with worse inpatient safety for patients without COVID-19, highlighting the ability of the healthcare system to respond to the initial surge of the pandemic.

Institution-Wide Retreats Foster Organizational Learning and Action at a Comprehensive Cancer Center.

Providing safe and informed healthcare for sexual and gender minority (SGM) individuals with cancer is stymied by the lack of sexual orientation and gender identity (SOGI) data reliably available in health records and by insufficient training for staff. Approaches that support institutional learning, especially around sensitive topics, are essential for hospitals seeking to improve practices impacting patient safety and research. We engineered annual institutional retreats to identify and unify stakeholders, promote awareness of gaps and needs, identify initiatives, minimize redundant projects, and coordinate efforts that promote improvements in SGM cancer care, education, and research. The 2022 and 2023 retreats employed a 4-h hybrid format allowing virtual and in-person engagement. Retreat organizers facilitated small-group discussions for brainstorming among participants. We performed descriptive statistics from retreat evaluations. The retreats engaged 104 attendees from distinct departments and roles. Participants expressed robust satisfaction, commending the retreat organization and content quality. Notably, the first retreat yielded leadership endorsement and funding for a Quality Improvement pilot to standardize SOGI data collection and clinical staff training. The second retreat provided a platform for updates on focused efforts across the institution and for receiving direction regarding national best practices for SGM care and research. We report the processes and outcomes of institution-wide retreats, which served as a platform for identifying gaps in organizational healthcare practices and research for SGM individuals with cancer. The strategies described herein may be readily scaled at other cancer hospitals seeking to learn and enact system-wide practice changes that support the needs of SGM patients and families.

The Impact of Health Care Algorithms on Racial and Ethnic Disparities : A Systematic Review.

There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities. To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities. Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023. Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension. Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques. Results are mostly based on modeling studies and may be highly context-specific. Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes. Agency for Healthcare Quality and Research.

Protocol: Prospective evaluation of feasibility, added value and satisfaction of remote digital self-assessment for mild cognitive impairment in routine care with the neotivCare app

IntroductionTimely diagnosis of mild cognitive impairment (MCI) in Alzheimer’s disease is crucial for early interventions, but its implementation is often challenging due to the complexity and time burden of required...