Health Care Research Articles

Students’ perceptions regarding the mental health education of college school – a qualitative study

PurposeSchools play a crucial role in the psychological development of adolescents. However, the education is inadequate for many students. This study aims to understand students' views and suggestions on the implementation of mental health education in schools and to help schools find the target point of the implementation of mental health education from the perspective of students' needs.Design/methodology/approachThe qualitative method of semi-structured individual interviews to explore college students' perceptions and suggestions about their schools in terms of mental health education.FindingsThree themes and six sub-themes emerged from forty-three interviews. Most students express their hope that the school can conduct more professional mental health education courses and diverse practical activities. They also emphasize the need to standardize the qualifications of teachers conducting these courses and to introduce more professional mental health personnel. Additionally, it is crucial to prioritize the protection of student privacy during mental health care, enabling more students, teachers and parents to recognize psychological issues and actively address their mental challenges.Originality/valueThis study, from the perspective of students, provides valuable insights into recommendations for schools to improve mental health education. It can significantly contribute to continuous enhancement of mental health education in schools and better protection and promotion of students' psychological well-being.

Developing generic skills for future health professionals: student and faculty perceptions of a recovery college curriculum and courses

Purpose As health-care systems worldwide grapple with complex challenges such as limited resources, qualified personnel shortages and rapid technological advancements, there is an urgent need for educational transformation in health-care professions. This urgency arises from the necessity for health-care professionals to evolve beyond traditional roles and acquire essential generic skills such as adaptative, epistemic, relational, ethical and citizenship skills – areas identified as gaps in conventional university curricula. This study aims to investigate the potential of the recovery college (RC) model, integrated into a Canadian university’s health-care curriculum, to address these gaps. Design/methodology/approach Through qualitative group interviews with eight students and three faculty members and subsequent descriptive content analysis, the authors explored the perceived outcomes of this model. Findings The authors discerned 15 themes within the five core categories of generic skills (epistemic, ethical, relational, adaptative and citizenship skills), with “experiential knowledge acquisition” central to the training input and other significant themes including “ethical sensitivity,” “collaborative communication,” “self-care” and “open-mindedness to diversity.” The findings highlight the RC model’s potential in fostering these crucial skills among future health-care professionals and challenging prevailing epistemic injustices in health care. Research limitations/implications Further investigations are needed to understand the long-term effects of this model on health-care practice and to explore its potential integration into wider health-care education programs. Originality/value This study enriches understanding of the RC model’s role in health-care education, thereby proposing a significant shift toward more inclusive and effective health-care professional training.

Association Between Health Care Discrimination and Medical Mistrust Among Black Assigned Female at Birth Adults with Minoritized Sexual and Gender Identities in the United States.

Purpose: Black sexually and gender minoritized (SGM) people who were assigned female at birth (AFAB) experience compounding health care inequities, barriers to equitable care, and disproportionately adverse health outcomes. Given prior literature indicating that both experienced and/or anticipated reported health care discrimination and medical mistrust may shape these health care experiences of Black SGM AFAB people, we sought to investigate the specific interplay between these two factors to bolster understanding of their relationship. Methods: In January and February 2023, we conducted a cross-sectional online survey of 156 Black SGM AFAB adults in the United States (U.S.) assessing their reported lifetime experiences of all-cause and gender-, race/ethnicity-, and weight-based discrimination in health care settings, in addition to their ratings of medical mistrust on the Medical Mistrust Index (MMI). Univariate statistics, analysis of variance, post hoc pairwise tests, and multivariable linear regression were conducted to assess measures of health care discrimination, medical mistrust, and covariates and their associations among the analytic sample (n = 130). Results: Most participants reported prior experiences of health care discrimination. Adjusting for demographic, socioeconomic, and health care factors, we identified an association between experiencing any-cause-, race/ethnicity-, or weight-based discrimination and significantly higher MMI scores. The association for gender-based discrimination was not statistically significant. Conclusion: Black SGM AFAB people who experience any-cause-, race/ethnicity-, or weight-based discrimination may be more likely to experience higher levels of medical mistrust. Identifying interventions and pathways to tackle health care discrimination and the systemic and structural drivers of medical mistrust will be critical to augmenting health care outcomes and experiences of Black SGM communities.

Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study.

The rapid advancement of deep learning in health care presents significant opportunities for automating complex medical tasks and improving clinical workflows. However, widespread adoption is impeded by data privacy concerns and the necessity for large, diverse datasets across multiple institutions. Federated learning (FL) has emerged as a viable solution, enabling collaborative artificial intelligence model development without sharing individual patient data. To effectively implement FL in health care, robust and secure infrastructures are essential. Developing such federated deep learning frameworks is crucial to harnessing the full potential of artificial intelligence while ensuring patient data privacy and regulatory compliance. The objective is to introduce an innovative FL infrastructure called the Personal Health Train (PHT) that includes the procedural, technical, and governance components needed to implement FL on real-world health care data, including training deep learning neural networks. The study aims to apply this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer and present the results from a proof-of-concept experiment. The PHT framework addresses the challenges of data privacy when sharing data, by keeping data close to the source and instead bringing the analysis to the data. Technologically, PHT requires 3 interdependent components: "tracks" (protected communication channels), "trains" (containerized software apps), and "stations" (institutional data repositories), which are supported by the open source "Vantage6" software. The study applies this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer, with the introduction of an additional component called the secure aggregation server, where the model averaging is done in a trusted and inaccessible environment. We demonstrated the feasibility of executing deep learning algorithms in a federated manner using PHT and presented the results from a proof-of-concept study. The infrastructure linked 12 hospitals across 8 nations, covering 4 continents, demonstrating the scalability and global reach of the proposed approach. During the execution and training of the deep learning algorithm, no data were shared outside the hospital. The findings of the proof-of-concept study, as well as the implications and limitations of the infrastructure and the results, are discussed. The application of federated deep learning to unstructured medical imaging data, facilitated by the PHT framework and Vantage6 platform, represents a significant advancement in the field. The proposed infrastructure addresses the challenges of data privacy and enables collaborative model development, paving the way for the widespread adoption of deep learning-based tools in the medical domain and beyond. The introduction of the secure aggregation server implied that data leakage problems in FL can be prevented by careful design decisions of the infrastructure. ClinicalTrials.gov NCT05775068; https://clinicaltrials.gov/study/NCT05775068.

Retrospective review of assisted peritoneal dialysis: The Alberta, Canada experience.

Assisted peritoneal dialysis (PD), where trained health care providers assist individuals with PD in their home, allows individuals, who would otherwise be ineligible, to pursue home dialysis. Alberta Kidney Care South started an assisted PD program in 2011 using licensed practical nurses (LPNs) and switched to health care aids (HCA) July 2018. A retrospective chart review to describe characteristics of assisted PD patients and their outcomes for each of the models of health care. The primary outcome was the duration of assisted PD from initiation to exit. Secondary outcomes included reasons for exit and the proportion of patients who performed independent PD. A total of 135 patients received assisted PD, mean age 70.7 ± 11.2 years and 44.4% (60/135) women. The average time to exit from assisted PD was 366.2 ± 378.1 days. There was no difference between time in PD between LPN (1.89 [1.02, 3.85] years) and HCA (2.09 [0.89, 3.26] years), p = 0.98. Main reasons for exit from assisted PD included death 30.4% (41/135) and switching to hemodialysis 26.7% (36/135). Total of 29 patients (21.5%) continued PD independently for an additional 495 ± 399 days. There was no difference in reasons for exit, p = 0.90 or peritonitis rates between the two care models, p = 0.60. Assisted PD allows patients to maintain independence in the community and facilitates the uptake of independent PD in a significant proportion. Utilizing an HCA model offers a cost-effective alternative while still providing high-quality care.

Health and well-being of staff in substance use services: The case of Scotland.

Background: Addressing high rates of drug and alcohol mortality requires a sustained commitment to supporting a resilient and skilled substance use workforce. Employee well-being in wider health and social care has been the subject of much empirical research, however there is a dearth of evidence specific to professionals working in substance use services.Methods: Data were derived from a cross-sectional survey of publicly-funded drug and alcohol services operating in Scotland during autumn 2021. Nested semi-log regression models estimated the associations between workplace characteristics and workforce composition, and the main outcome measure: non-Covid sickness absence. Qualitative data were analysed using thematic analysis.Results: 88 services, representing 43% of Scotland's drug and alcohol services, were included in the study. Factors significantly and positively associated with non-Covid sickness absence in multivariable analysis included average caseload per whole-time equivalent and number of whole-time equivalent clinical roles. Descriptive statistics show that vacancy rates and caseloads are comparatively higher in statutory services, and are also higher for clinical than non-clinical positions.Conclusion: This study revealed that and burnout leading to sickness absence are serious issues for frontline staff in drug and alcohol services, and these worsened during the Covid-19 pandemic. This research provides a foundation for validating the current findings via future large-scale longitudinal studies, and improving the well-being of people delivering health and social care services in substance use settings.

Family Coping Among Patients With Heart Failure: A Qualitative Study

Heart failure is a severe and terminal stage of various heart diseases characterized by complex clinical syndromes. With regard to the management of HF, the patient’s family is the most common and essential resource. The manner in which the family copes with the disease can lead to effective or ineffective management. It is necessary to explore how families of patients with heart failure as a whole are coping with the disease. Our qualitative descriptive study involved 18 families of patients with HF, including 18 patients and 13 family members. Based on the trichotomy of coping strategies proposed by Weiten, the text of the interviews was analyzed via content analysis. Four categories emerged from the data: (1) problem-oriented coping, (2) appraisal-oriented coping, (3) emotion-oriented coping, and (4) family dynamic–oriented coping. Nine subcategories were extracted: fighting the disease during an episode; managing the disease in daily life; embarking on a journey for support; understanding the disease in a diverse way; accepting the disease or not, optimistic; worried and powerlessness; alternating role responsibilities; and adjusting family interactions. The identification of the predominant category of ‘family dynamic–oriented coping’ in our study has significant implications for health care professionals. By recognizing and addressing the unique dynamics of family interactions and coping mechanisms, health care providers can develop targeted strategies to improve overall outcomes for both patients and their families impacted by HF.

Evaluating the fidelity of AI-generated information on long-acting reversible contraceptive methods.

Artificial intelligence (AI) has many applications in health care. Popular AI chatbots, such as ChatGPT, have the potential to make complex health topics more accessible to the general public. The study aims to assess the accuracy of current long-acting reversible contraception information provided by ChatGPT. We presented a set of 8 frequently-asked questions about long-acting reversible contraception (LARC) to ChatGPT, repeated over three distinct days. Each question was repeated with the LARC name changed (e.g., 'hormonal implant' vs 'Nexplanon') to account for variable terminology. Two coders independently assessed the AI-generated answers for accuracy, language inclusivity, and readability. Scores from the three duplicated sets were averaged. A total of 264 responses were generated. 69.3% of responses were accurate. 16.3% of responses contained inaccurate information. The most common inaccuracy was outdated information regarding the duration of use of LARCs. 14.4% of responses included misleading statements based on conflicting evidence, such as claiming intrauterine devices increase one's risk for pelvic inflammatory disease. 45.1% of responses used gender-exclusive language and referred only to women. The average Flesch readability ease score was 42.8 (SD 7.1), correlating to a college reading level. ChatGPT offers important information about LARCs, though a minority of responses are found to be inaccurate or misleading. A significant limitation is AI's reliance on data from before October 2021. While AI tools can be a valuable resource for simple medical queries, users should be cautious of the potential for inaccurate information. ChatGPT generally provides accurate and adequate information about long-acting contraception. However, it occasionally makes false or misleading claims.

Metaverse for mental health disorders: Opportunities and challenges

Several articles on the mental health impact of the metaverse and the need to balance its potential benefits with the risks of metaverse use has recently published. The metaverse consists of a combination of immersive technologies and artificial intelligence algorithms. The metaverse differs from the preceding digital psychiatric interventions due to its complex structure and interactions between components. The diverse functions of the metaverse ensure that it may have a substantial impact on mental health. However, the evidence for its efficacy in treating mental health disorders is limited to a few trials. The mental health benefits of immersive technologies are well-documented and suggest that metaverse-based psychiatric treatment may be similarly efficacious. The mental health risks of the metaverse are largely unknown, and it is not clear whether they will be greater than other digital psychiatric interventions. Much more research is needed to determine whether metaverse-based psychiatric treatment will meet the standards of appropriate mental healthcare.

The AFFIRM Framework for gender-affirming care: qualitative findings from the Transgender and Gender Diverse Health Equity Study

Abstract Background Transgender, nonbinary, and gender diverse (TGD) people experience stigma in healthcare settings impacting healthcare utilization, including avoidance of care due to anticipated discrimination. Gender-affirming care refers to care for medical gender affirmation, such as gender-affirming hormones and surgery, as well as general care that affirms and respects TGD patients. This study sought to explore the experiences of TGD adults to inform gender-affirming care delivery and develop an actionable framework for practice. Methods Between May–October 2021, one-time individual in-depth interviews were conducted with 27 TGD adults receiving any healthcare in the greater Boston Massachusetts area to gather data about gender-affirming care. Interviews were semi-structured, explored prior and current experiences in healthcare and ideal gender-affirming care models, and conducted virtually via a secure Zoom platform. Analyses were conducted using immersion crystallization and reflexive thematic analysis; interview transcripts were double coded by two coders. Results Participants had a mean age of 28.5, ranging 18–45 years, and were: 7 transgender men, 6 transgender women, 8 nonbinary, 3 genderqueer, 1 agender, and 2 gender not specified. Themes about gender-affirming care coalesced into the acronym AFFIRM: (1) Affirms in individual interactions: Participants called for affirmation of TGD identity, lived expertise, and competent TGD providers and staff. (2) Flexible and accessible: Participants expressed the need for gender-affirming care to be available beyond urban population-specific clinics, in a timely fashion without long wait lists, and in a community-centered manner such as offering non-traditional times and settings. (3) Fights systemic oppression: Participants emphasized the need for providers and health systems to eliminate gatekeeping practices for gender-affirming care and create care models that resist intersecting oppressive systems such as racism and cisgenderism. (4) Interacts with community: Patients desired intentional interaction with TGD community to holistically address health and unmet gender affirmation needs. (5) Retains patients in care: Patients shared the need to collaboratively identify and problem-solve obstacles to gender-affirming care with providers and healthcare systems to optimize TGD-specific retention strategies. (6) Multidisciplinary: Patients called for interdisciplinary teams with co-located services such as primary care and mental healthcare with letter-writing for surgical care, and incorporation of peer navigators to meet the broader social, health, and well-being needs of TGD people. Conclusions Findings from this study and the AFFIRM Framework which emerged from TGD patient narratives can be applied to improve current care and set benchmarks for high-quality gender-affirming care delivery and practice.

Culturally-Relevant Predictors of Thwarted Belongingness Among College Students of Asian Descent

ABSTRACT Suicide is one of the most prevalent causes of death among college students in the United States, and the leading cause of death among Asian American college-aged adults. Given these concerning rates, further investigation is essential to understanding the current levels of suicide risk among this population, including culture-specific risk factors, to influence mental health interventions that support this population. This study aims to explore the impacts of Asian culture-specific factors on thwarted belongingness, a proximal risk factor of suicide desire, among college students of Asian descent guided by the Interpersonal Theory of Suicide. Participants (n = 218) completed a battery of validated measures to assess predictors of thwarted belongingness, which included shame, discrimination, ethnic identity, and perfectionism. Nearly half of participants met clinical cutoff criteria for suicide desire. Further, ethnic identity commitment, perfectionism discrepancy, and external shame were statistically significant predictors of thwarted belongingness among this population. These findings underscore the need for tailored interventions that address these culture-specific factors on college campuses to reduce the risk of suicide among students of Asian descent. Additional implications for college-student serving mental health professionals and future research directions are discussed.

Larger models yield better results? Streamlined severity classification of ADHD-related concerns using BERT-based knowledge distillation

This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural language processing (NLP) applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task—classifying severity levels of Attention Deficit Hyperactivity Disorder (ADHD)-related concerns from social media text data. Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. On the General Language Understanding Evaluation (GLUE) benchmark, comprising paraphrase identification, sentiment analysis, and text classification, the student model maintained strong performance across many tasks despite this reduction. The model was also used on a real-world ADHD dataset with an accuracy of 85%, F1 score of 85%, precision of 85%, and recall of 85%. When compared to DistilBERT (66 million parameters) and ClinicalBERT (110 million parameters), LastBERT demonstrated comparable performance, with DistilBERT slightly outperforming it at 87%, and ClinicalBERT achieving 86% across the same metrics. These findings highlight the LastBERT model’s capacity to classify degrees of ADHD severity properly, so it offers a useful tool for mental health professionals to assess and comprehend material produced by users on social networking platforms. The study emphasizes the possibilities of knowledge distillation to produce effective models fit for use in resource-limited conditions, hence advancing NLP and mental health diagnosis. Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. Especially using readily available computational tools like Google Colab and Kaggle Notebooks. This study shows the accessibility and usefulness of advanced NLP methods in pragmatic world applications.

Response to Letter Regarding Cordray et al. (2023) and Brief Commentary on the Ankyloglossia Debate

Purpose: Ankyloglossia (tongue tie) is common, and its clinical relevance is hotly debated among multidisciplinary health care providers including pediatricians, lactation consultants, otolaryngologists, and speech-language pathologists. The literature focuses on breastfeeding symptoms; less evidence exists to clarify the spectrum of possible symptoms after infancy. We conducted a scoping review of potentially related symptoms in pediatric patients who presented for clinical evaluation of untreated ankyloglossia, with the aim of mapping symptoms that providers may want to evaluate and identifying targets for further research. Kummer (2025) has alleged “erroneous statements without evidence” about “the effect of ankyloglossia on speech,” “inaccurate citations,” and “errors of omission” in our scoping review (pp. 1, 2). This letter provides a rebuttal of her statements. Results: Based on results from 20 primary studies, our review drew a provisional conclusion that ankyloglossia may be associated with speech difficulty in a subset of patients; we did not assert any causal relationship. Kummer took issue with a citation in one sentence of our review; we note that some of her concerns were inaccurate, though we appreciate her clinical expertise on speech sound production. The omissions she perceived were in fact present in our original review article. Conclusions: Kummer's letter does not warrant an erratum to our scoping review. However, her letter prompts us to urge the multidisciplinary community of providers involved in studying and caring for patients with ankyloglossia to engage in a collaborative and open-minded discussion. We must reduce professional antagonism around this controversy and work together on patient-centered research to support children whose experiences may not fit the typical clinical picture.

Unwinding of Continuous Medicaid Coverage Among Pediatric Community Health Center Patients.

During the COVID-19 pandemic public health emergency, states provided continuous Medicaid coverage to enrollees. In April 2023, states began to unwind this continuous coverage, prompting concern about the impact of this on pediatric patients. To examine loss of coverage during the unwinding of continuous Medicaid coverage among pediatric patients seen at community-based health care organizations. This cohort study with statistical analysis in November 2024 included pediatric patients from a multistate network of community-based health care organizations. Participants were aged up to 17 years at both their last Medicaid-insured visit during the continuous coverage period and at their first visit during the unwinding period (April 1, 2023, to March 31, 2024). Age, sex, race and ethnicity, language, and medical complexity. The main outcome was Medicaid disenrollment to uninsured status during the unwinding period. Logistic regression was used to estimate the odds of ever being uninsured during unwinding, and Cox proportional hazards regression models were used to examine the time to uninsured status from the start of unwinding in each patient's state of residence through the end of March 2024. Associations between age, sex, race and ethnicity, language, and medical complexity and the outcome were assessed. Among 450 146 pediatric patients, mean (SD) patient age was 8.11 (5.07) years and 50.1% were male. Overall, 8.7% were disenrolled from Medicaid to uninsured status. Patients aged 12 to 17 years had the highest estimated disenrollment among age groups (10.5%), but after adjustment, all other age groups had lower odds of disenrollment compared with those younger than 1 year. Females had higher odds of disenrollment (adjusted odds ratio [AOR], 1.15 [95% CI, 1.13-1.18]; adjusted hazard ratio [AHR], 1.14 [95% CI, 1.12-1.17]) than males. American Indian or Alaska Native patients had higher odds of disenrollment (AOR, 1.95 [95% CI, 1.81-2.09]; AHR, 1.81 [95% CI, 1.05-3.13]) than White patients, with estimated disenrollment of 17.1% vs 9.4%. Compared with patients with low medical complexity, those with either chronic but noncomplex (AOR, 1.83 [95% CI, 1.79-1.88]; AHR, 1.80 [95% CI, 1.44-2.27]) or complex chronic (AOR, 1.95 95% CI, 1.89-2.00]; AHR, 1.92 [95% CI, 1.67-2.21]) medical complexity had higher odds of disenrollment. In this cohort study of previously Medicaid-insured pediatric patients seen at community-based health care organizations, a meaningful proportion of patients were disenrolled to uninsured status during the unwinding of continuous Medicaid coverage, with associated differences by demographic and clinical characteristics. This could impact access to care and health outcomes for the patients.

Adaptive Capacity Within the Homecare Setting: The Importance and Value of a Shared Perception of Risk.

Patients receiving homecare are living their everyday lives in their own homes. Adapting care to individual needs, preferences, risks, and family situations is seen as essential. The overall aim of this study was to develop empirical knowledge of risk perception in homecare services. The research question that guided the study was: How is a shared perception of risk between health care professionals and leaders a part of providing high-quality care within a homecare setting? The research design was a multiple-embedded case study including 3 single cases. Participants were recruited from 3 Norwegian municipalities. Two focus group interviews were conducted in each municipality. In total, 19 informants participated, including 11 homecare department leaders and 8 health care professionals. The data were analyzed using thematic analysis according to Braun and Clarke's 6-phase thematic analysis. Two higher-order themes were developed from the data: "Risk perception is a collective understanding" and "A flawed system requires adapting just to make it work." The study showed that, in the homecare departments, leaders' and health care professionals' opinion, flexibility and professional discussions are key to enhancing the system's adaptive capacity. High-quality care is always perceived as person-centered and adaptive. Overgeneralizations and the use of guidelines were perceived as demanding. The findings also showed that making sense of and managing risks within a homecare setting is complex. Bridging the gaps by conceptualizing and acting upon risks in the homecare setting constitutes a fundamental part of adaptive capacity at a system level. However, better alignment of system demands to local risks is needed for sustainable and safe services.

Redefining the approach to rare diseases: the experience of "Casa dos Raros" in Brazil.

Rare diseases include 6,000-8,000 different conditions, over 70% of them having a genetic cause. Most cases have early manifestations (in childhood and adolescence), and just a small fraction (around 5%) has specific therapies available. Nevertheless, appropriate management measures contribute to improve the quality of life of patients and families. They affect up to 3.5-5.9% of the world's population and are recently attracting attention from international agencies such as the United Nations and the World Health Organization. In Brazil, a condition is considered rare when there are no more than 65 people affected in each 100,000 inhabitants and it is estimated that around 12million people in the country may present one of these conditions, which represents a significant burden to the family and to the health care system. Despite concrete advances observed in the last decades, there are still significant unmet needs for persons living with rare diseases in Brazil. With the main aim of shortening the journey of patients with rare diseases in Brazil, we envisioned a model that involves comprehensive clinical and laboratorial multiprofessional evaluations, with intensive use of telemedicine and genomics. The model includes a strong activity in education, training and research, and has several parallel initiatives (biobank, registry, undiagnosed disease program, information services, extramural diagnostic support), in addition to strategic partnerships, that make the overall project stronger. This report describes the system in place at the pilot unit of Casa dos Raros (that started activities in 2023, in Porto Alegre, Brazil) and the stimulating preliminary results, which indicate a significant reduction in the diagnostic journey. This model, that operates as a charity and does not charge any fees to patients and families, will be replicated in other regions of Brazil, with the opening of a second unit planned to occur in the near future in Sao Paulo.

Technology Readiness Level and Self-Reported Health in Recipients of an Implantable Cardioverter Defibrillator: Cross-Sectional Study

Background Approximately 200,000 implantable cardioverter defibrillators (ICDs) are implanted annually worldwide, with around 20% of recipients experiencing significant psychological distress. Despite this, there are no ICD guidelines addressing mental health as part of rehabilitation programs, which primarily focus on educating patients about their condition and prognosis. There is a need to include elements such as emotional distress, social interactions, and the future use of technologies like apps and virtual communication in ICD rehabilitation, without increasing the burden on health care professionals. Objective This study aimed to demonstrate how data from the Readiness for Health Technology Index (READHY), combined with sociodemographic characteristics and exploratory interviews, can be used to construct profiles of recipients of an ICD, describing their ability to manage their condition, their need for support, and their digital health literacy. This aims to enhance health care professionals’ understanding of different patient archetypes, serving as guidance in delivering personalized services tailored to the needs, resources, and capabilities of individual recipients of ICDs. Methods Overall, 79 recipients of an ICD participated in a survey assessing technology readiness using the READHY. The survey also collected sociodemographic data such as age, sex, and educational level. Self-reported health was measured using a Likert scale. Cluster analysis categorized participants into profiles based on their READHY scores. Correlations between READHY scores and self-reported health were examined. In addition, qualitative interviews with representatives from different readiness profiles provided deeper insights. Results Four technology readiness profiles were found: (1) profile 1 (low digital health literacy, insufficient on 5 dimensions), (2) profile 2 (sufficient on all dimensions), (3) profile 3 (consistently sufficient readiness on all dimensions), and (4) profile 4 (insufficient readiness on 9 dimensions). Participants in profile 4, characterized by the lowest readiness levels, were significantly younger (P=.03) and had lower self-reported health (P<.001) than those in profile 3. A correlation analysis revealed that higher READHY scores were associated with better self-reported health across all dimensions. Qualitative interviews highlighted differences in self-management approaches and the experience of support between profiles, emphasizing the essential role of social support toward the rehabilitation journeys of recipients of an ICD. Two patient vignettes were created based on the characteristics from the highest and lowest profiles. Conclusions Using the READHY instrument to create patient profiles demonstrates how it can be used to make health care professionals aware of specific needs within the group of recipients of an ICD.

Safety and accuracy of dating unwanted pregnancies and detecting ectopic pregnancy with the RCOG decision aid without routine ultrasound - a retrospective analysis form a large abortion service.

Evidence suggests routine pre-abortion ultrasound is unnecessary for women with regular menstrual cycles and without symptoms or risk factors for ectopic pregnancy in early gestation. We evaluated the safety and accuracy of using the decision aid for early medical abortion without ultrasound developed by the RCOG Abortion Care group in a Finnish setting. Using gestational age (GA) expected by last menstrual period (LMP), cycle irregularities, use of hormonal contraceptives, breastfeeding and self-reported symptoms indicative of possible ectopic pregnancy, women were classified into 'no ultrasound needed' and 'ultrasound needed' with a cut-off of 10 + 0 weeks or 70 days. Findings on US were then evaluated to assess classification accuracy. Between September and December 2023, 494 women attending the abortion clinic at the Helsinki University Hospital were assessed. Correct classification of the necessity of an ultrasound was made in 491/494 (99.4%) cases before the women had a scan. The remaining three cases were one woman with an unexpected GA just above 10 + 0, one asymptomatic ectopic pregnancy and one asymptomatic pregnancy of unknown location with high plasma hCG, diagnosed as a partial molar pregnancy on pathology after diagnostic vacuum aspiration. Use of the structured flowchart developed by the RCOG highly accurately identifies women needing an ultrasound examination. The few cases that would have been undetected highlight the importance of informing women and health care providers about symptoms of ectopic or abnormal pregnancy similar to practices in wanted pregnancies not routinely examined and dated before late first trimester.

Multi-Armed Bandits with Endogenous Learning Curves: An Application to Split Liver Transplantation

Problem Definition: Proficiency in many sophisticated tasks is attained through experience-based learning, in other words, learning by doing. For example, transplant centers’ surgical teams need to practice difficult surgeries to master the skills required. Meanwhile, this experience-based learning may affect other stakeholders, such as patients eligible for transplant surgeries, and require resources, including scarce organs and continual efforts. To ensure that patients have excellent outcomes and equitable access to organs, the organ allocation authority needs to quickly identify and develop medical teams with high aptitudes. This entails striking a balance between exploring surgical combinations with initially unknown full potential and exploiting existing knowledge based on observed outcomes. Methodology/results: We formulate a multi-armed bandit (MAB) model in which parametric learning curves are embedded in the reward functions to capture endogenous experience-based learning. In addition, our model includes provisions ensuring that the choices of arms are subject to fairness constraints to guarantee equity. To solve our MAB problem, we propose the L-UCB and FL-UCB algorithms, variants of the upper confidence bound (UCB) algorithm that attain the optimal [Formula: see text] regret on problems enhanced with experience-based learning and fairness concerns. We demonstrate our model and algorithms on the split liver transplantation (SLT) allocation problem, showing that our algorithms have superior numerical performance compared with standard bandit algorithms in a setting where experience-based learning and fairness concerns exist. Managerial implications: From a methodological point of view, our proposed MAB model and algorithms are generic and have broad application prospects. From an application standpoint, our algorithms could be applied to help evaluate potential strategies to increase the proliferation of SLT and other technically difficult procedures. Funding: The authors would like to acknowledge the support of CMU Tepper’s Health Care Initiative Funding. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/msom.2022.0412 .

Swasthya Pahal (Health for All) Using a Sustainable, Multisector, Accessible, Affordable, Reimbursable, and Tailored Informatics Framework in Rural and Urban Areas of Chennai, Tamil Nadu: Protocol for a Quantitative Study

Background Noncommunicable diseases (NCDs) require a longer period of care, for which health care systems must acquire technologically advanced solutions to enhance patient care. Swasthya Pahal (health for all) is an innovative, interactive, multilingual, stand-alone, internet-enabled computer-based program that aims to improve the self-management of NCDs. Objective This study aims to enhance the self-management of chronic NCDs (diabetes, hypertension, high cholesterol, and obesity) by determining the usefulness, acceptance, and effectiveness of the Swasthya Pahal program in hospital and community settings in both rural and urban areas of Chennai, Tamil Nadu. This objective can be met by generating risk factor profiles of individuals enrolled and enhancing their self-management of NCDs using a portable health information kiosk that uses the Sustainable, Multisector, Accessible, Affordable, Reimbursable, and Tailored (SMAART) model. Methods A quantitative study will be conducted on a convenient sample of 2800 individuals from selected hospital and community settings in rural (n=1400) and urban areas (n=1400) in Chennai, Tamil Nadu. Data will be collected on sociodemographics, health behaviors, and clinical status, as well as knowledge, attitudes, and practices. Objective assessments such as weight, blood pressure, and random blood sugar levels will be measured. In addition, the usefulness, acceptance, and effectiveness of the Swasthya Pahal program will be determined. Results Results will be summarized using descriptive analysis. Appropriate bivariate and multivariate regression analysis will be performed to determine the predictors of the outcome variables of usefulness, acceptance, and effectiveness of Swasthya Pahal in wider settings. All analyses will be performed using SAS (version 9.1; SAS Institute), and the results will be reported as 95% CI values and P<.05. Conclusions The study proposes to enhance the self-management of NCDs in both rural and urban community settings through the implementation of the Swasthya Pahal program based on the SMAART informatics framework. The study aims to understand the implementation, acceptability, and usability of Swasthya Pahal among a diverse sample of people in urban and rural settings. International Registered Report Identifier (IRRID) PRR1-10.2196/39950