Routine Health Care Practice Research Articles

Effectiveness of cognitive interventions in mitigating mental fatigue among healthcare professionals in hospital settings: A systematic review

Mental fatigue poses a significant challenge to healthcare professionals in hospital settings, impairing cognitive performance, emotional well-being, and job satisfaction. This systematic review evaluates the effectiveness of cognitive interventions, including mindfulness programs, online psychological interventions, and telehealth cognitive-behavioral therapy (CBT), in mitigating mental fatigue among healthcare workers. A comprehensive literature search across PubMed, b-on, Web of Science, and Scopus identified six studies published between 2016 and 2023. These studies reported that cognitive interventions significantly reduced burnout and stress levels, with up to a 30% decrease in emotional exhaustion and improved coping mechanisms. In addition, participants demonstrated enhanced emotional well-being, reflected in increased mindfulness and life satisfaction scores of as much as 25%. Mindfulness-based programs and telehealth CBT were particularly effective in fostering resilience and improving overall cognitive function. However, the review also highlighted the need for future research to explore the long-term effects of these interventions, their cost-effectiveness, and innovative methods to improve accessibility and scalability, such as digital platforms. Integrating cognitive interventions into routine healthcare practice can enhance staff well-being, improve patient outcomes, and contribute to a more supportive work environment.

MHealth Apps for Hypertension Self-Management: Interview Study Among Patient-Users.

Hypertension is a major risk factor for cardiovascular disease, affecting over a billion people worldwide. Mobile health (mHealth) apps have emerged as effective tools for managing hypertension, offering capabilities for monitoring blood pressure, fostering lifestyle changes, and improving treatment adherence. This study aimed to explore patient-users' perspectives on the hypertension care mHealth app Hypertension.APP, focusing on its accessibility, expected benefits, potential risks, and role in hypertension management in Germany. A qualitative study was conducted involving semistructured interviews with 20 patient-users of a hypertension care mHealth app, Hypertension.APP. Participants were recruited between January and June 2023 using purposive sampling. Verbatim transcripts were analyzed using qualitative content analysis. Participants primarily discovered the app independently, driven by recent hypertension diagnoses and insufficient information from health care professionals regarding effective self-management strategies for their blood pressure. They valued the app for its continuous monitoring and feedback capabilities, aiding in understanding their condition and making lifestyle adjustments. Risks were perceived as minimal, mainly concerning data privacy and potential overreliance on the app. The app became integral to patient-users' hypertension management by offering consistent information and support. The integration into formal health care was limited, as patient-users felt that health care professionals did not accept the use of the technology or might have even felt intimidated to use it. Among the sample studied, mHealth apps like Hypertension.APP were valued for their continuous monitoring and educational content, aiding in hypertension management. The findings suggest potential benefits of mHealth apps for effective hypertension care among patients who are health- and digitally literate as well as self-effective. There is a critical need for better integration of these apps into routine health care practices, as perceived by the app users. Given the small and specific sample of this qualitative study, further quantitative research with a broader and more varied participant group is necessary to validate these findings. Deutsches Register Klinischer Studien DRKS00029761; https://tinyurl.com/r33ru22s.

Owner-reported health and disease in U.S. senior horses.

Information on health care and health status of U.S. senior horses (≥15 years of age) is currently sparse. (A) Provide an overview of owner-reported (1) medical conditions, (2) management/treatment practices for equine metabolic syndrome and pituitary pars intermedia dysfunction (PPID), (3) frequencies of routine health care practices and (4) supplement and pharmaceutical use in U.S. senior horses (≥15 years of age). (B) Evaluate potential risk factors for certain medical conditions and for low routine health care. Online survey. Descriptive and inferential analysis (binomial logistic regression and ANOVA) of 2717 questionnaires from owners of U.S. senior horses. The most common owner-reported veterinary-diagnosed medical conditions were osteoarthritis (30%), dental disorders (15%), lameness (14%), PPID (12%) and ocular disorders (6%). Advancing age was found to be a risk factor for PPID (odds ratio [OR] [95% confidence interval, CI] = 1.14 [1.10-1.18]), dental (OR [95% CI] = 1.18 [1.15-1.22]) and ocular (OR [95% CI] = 1.05 [1.01-1.10]) disorders. Only 36% of horses were free of owner-reported veterinary-diagnosed medical conditions at the time of the survey. During the year prior to the survey, most routine healthcare practices (i.e., veterinary health care, dental care and anthelmintic treatment) were typically undertaken one to two times per year, while farrier visits occurred mostly every 5-6 weeks. Retired senior horses had a higher risk of no health care visits (OR [95% CI] = 2.1 [1.38-3.06]), no dental care (OR [95% CI] = 2.0 [1.31-3.00]) and low farrier attendance (i.e., ≤4 times/year) (OR [95% CI] = 2.4 [1.57-3.63]) compared with senior horses used for pleasure riding. The most frequently administered drug was firocoxib (18%) and joint supplements were the most provided supplements (41%). Potential recall, response and sampling bias. Risk factor analyses do not establish causal relationships. Medical conditions are highly prevalent in U.S. senior horses. Retired senior horses have an increased risk of low routine health care.

Body fat percentage vs body mass index in estimating basal cell carcinoma

BackgroundThe role of body mass index (BMI) in basal cell carcinoma (BCC) risk remains controversial, and limited information is available regarding the relationship between other physical measurements and BCC. Several recent studies have found a positive effect of adiposity on improved survival when obesity was determined solely by BMI (the "obesity paradox"). We hypothesize that body fat percentage (BFP) may serve as a more sensitive risk factor for BCC than BMI. MethodsThe study conducted a retrospective analysis of clinical data from two distinct centers in China. Individual patient-level data were obtained from medical record reviews spanning January 1, 2015, to December 31, 2022. Associations with outcomes were analyzed using univariate and stratified analyses and assessed using multiple logistic regression with adjustment for confounding factors. Additionally, we performed a meta-analysis to further test the observations in our study. ResultsA total of 337 patients, ranging in age from 50 to 91 years, with a mean age of 66.88 (standard deviation 10.16), were included. We observed no significant association between BMI and BCC after adjusting for confounders (OR: 0.71, 95 % CI: 0.36–1.40, P = 0.3186). There was also no convincing effect in a meta-analysis (n = 158,741) (OR: 0.99, 95 % CI: 0.93–1.06, P = 0.8). Furthermore, BFP was found to be associated with BCC (OR: 2.64, 95 % CI: 1.17–5.97, P = 0.0196), supported by strong clinical evidence. ConclusionsOur study supports the hypothesis that BFP is superior to BMI in assessing BCC risk. Multiple logistic regression analyses, coupled with meta-analysis, provided robust evidence that BFP is a sensitive risk factor for BCC, while BMI appears unrelated to risk. According to these findings, routine healthcare practices could benefit from utilizing BFP measurements. The reduction of body fat percentage in low-fat diets may be beneficial for adjuvant treatment of BCC.

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare.

Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this. Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources. AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue. This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.

QPGx-CARES: Qatar pharmacogenetics clinical applications and research enhancement strategies.

Pharmacogenetic (PGx)-informed medication prescription is a cutting-edge genomic application in contemporary medicine, offering the potential to overcome the conventional "trial-and-error" approach in drug prescription. The ability to use an individual's genetic profile to predict drug responses allows for personalized drug and dosage selection, thereby enhancing the safety and efficacy of treatments. However, despite significant scientific and clinical advancements in PGx, its integration into routine healthcare practices remains limited. To address this gap, the Qatar Genome Program (QGP) has embarked on an ambitious initiative known as QPGx-CARES (Qatar Pharmacogenetics Clinical Applications and Research Enhancement Strategies), which aims to set a roadmap for optimizing PGx research and clinical implementation on a national scale. The goal of QPGx-CARES initiative is to integrate PGx testing into clinical settings with the aim of improving patient health outcomes. In 2022, QGP initiated several implementation projects in various clinical settings. These projects aimed to evaluate the clinical utility of PGx testing, gather valuable insights into the effective dissemination of PGx data to healthcare professionals and patients, and identify the gaps and the challenges for wider adoption. QPGx-CARES strategy aimed to integrate evidence-based PGx findings into clinical practice, focusing on implementing PGx testing for cardiovascular medications, supported by robust scientific evidence. The current initiative sets a precedent for the nationwide implementation of precision medicine across diverse clinical domains.

Using symbiotic empirical ethics to explore the significance of relationships to clinical ethics: findings from the Reset Ethics research project

BackgroundAt the beginning of the coronavirus (Covid-19) pandemic, many non-Covid healthcare services were suspended. In April 2020, the Department of Health in England mandated that non-Covid services should resume, alongside the continuing pandemic response. This ‘resetting’ of healthcare services created a unique context in which it became critical to consider how ethical considerations did (and should) underpin decisions about integrating infection control measures into routine healthcare practices. We draw on data collected as part of the ‘NHS Reset Ethics’ project, which explored the everyday ethical challenges of resetting England’s NHS maternity and paediatrics services during the pandemic.MethodsHealthcare professionals and members of the public participated in interviews and focus group discussions. The qualitative methods are reported in detail elsewhere. The focus of this article is our use of Frith’s symbiotic empirical ethics methodology to work from our empirical findings towards the normative suggestion that clinical ethics should explicitly attend to the importance of relationships in clinical practice. This methodology uses a five-step approach to refine and develop ethical theory based on a naturalist account of ethics that sees practice and theory as symbiotically related.ResultsThe Reset project data showed that changed working practices caused ethical challenges for healthcare professionals, and that infection prevention and control measures represented harmful barriers to the experience of receiving and offering care. For healthcare professionals, offering care as part of a relational interaction was an ethically important dimension of healthcare delivery.ConclusionsOur findings suggest that foregrounding the importance of relationships across a hospital community will better promote the ethically important multi-directional expression of caring between healthcare professionals, patients, and their families. We offer two suggestions for making progress towards such a relational approach. First, that there is a change of emphasis in clinical ethics practice to explicitly acknowledge the importance of the relationships (including with their healthcare team) within which the patient is held. Second, that organisational decision-making should take into account the moral significance afforded to caring relationships by healthcare professionals, and the role such relationships can play in the negotiation of ethical challenges.

Skin Lesion Analysis Towards Melanoma Detection using Deep Learning

Skin diseases, constituting 40% of global cancer cases, persist as a prevalent and potentially perilous health concern, impacting 56 lakh peoples in the preceding annum.Programmed grouping of skin injuries, an enduring challenge due to their nuanced variability, finds promise within the realm of Deep Learning. This innovative approach, particularly employing Convolutional Neural Networks, explores the intricate scene of fine-grained picture based investigation, showcasing exceptional precision.The research unfolds across three pivotal phases: hearty information assortment and expansion, precise plan of model engineering. The culmination in predictions across seven distinct categories of the skin diseases—melanocytic-nevi, basal-cell-carcinoma, dermato-fibroma,melanoma-lesion,vascular-lesions, actinic-keratoses and benign-keratosis-lesions. Building upon this foundation, an advanced Transfer Learning Approach seamlessly integrates with PyTorch.These models are prepared start to finish straight-forwardly from pictures, not only to enhance scalability but as it may, early determination can likewise really diminish costs.The essential target of this exploration paper is to present painless evaluating for skin infections into routine practice, streamlining the diagnostic process. Through the fusion of cutting-edge technologies and innovative methodologies, the study envisions a paradigm shift, ushering in a new era where efficient and accurate screening becomes an integral part of routine healthcare practices. Keywords—Melanoma, Deep Learning (DL),Convolutional Neural Networks(CNN),Batch Normalization, Visual Geometry Group-19 (V.G.G-19), Resnet-50, Densenet-121, Transfer Learning, Wide Resnet-101.

Validation of Diagnostic Codes to Identify Glaucoma in Taiwan's Claims Data: A Multi-Institutional Study.

Healthcare databases play a crucial role in improving our understanding of glaucoma epidemiology, which is the leading cause of irreversible blindness globally. However, the accuracy of diagnostic codes used in these databases to detect glaucoma is still uncertain. To assess the accuracy of ICD-9-CM and ICD-10-CM codes in identifying patients with glaucoma, including two distinct subtypes, primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG). We analyzed electronic medical records data from a 2% random sample of patients who newly underwent visual field examination in Taiwan's largest multi-institutional healthcare system from 2011 to 2020. The diagnosis of glaucoma was confirmed by two ophthalmologists, based on the glaucoma diagnostic criteria. The positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for ICD-9-CM codes 365.1X and 365.2X, and ICD-10-CM codes H4010X, H4011X, H4012X, H4020X, H4021X, H4022X, H4023X and H4024X for glaucoma were calculated. We randomly selected 821 patients (mean age: 56.9 years old; female: 50.5%) from the original cohort of 41,050 newly receiving visual field examination in the study. Among 464 cases with an ICD-9-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 86.5, 96.5, 91.9, and 90.9%, respectively. Among 357 cases with an ICD-10-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 87.0, 92.8, 92.2 and 87.9%, respectively. The accuracy of diagnostic codes to identify POAG and PACG remained consistent. The diagnostic codes were highly reliable for identifying cases of glaucoma in Taiwan's routine healthcare practice. These results provide confidence when using ICD-9-CM and ICD-10-CM codes to define glaucoma cases in healthcare database research in Taiwan.

Evaluation of a co‐designed Health Check‐in for adults with intellectual and developmental disabilities and family caregivers to support pandemic recovery

AbstractBackgroundThe COVID‐19 pandemic has brought about disruptions in healthcare for adults with intellectual and developmental disabilities. There is a need to explore ways to promote proactive healthcare and better prepare individuals for healthcare encounters.MethodsA co‐designed tool, the COVID Check‐in Tool, was introduced as part of a virtual health education programme to encourage proactive healthcare. Implementation of this Health Check‐in was evaluated with 36 adults with intellectual and developmental disabilities and 96 family caregivers who completed the programme using surveys, structured interviews and focus groups.FindingsForty‐four percent of participants engaged in the Health Check‐in process, resulting in many reported benefits for those who participated. However, there were also barriers to initiating the Check‐in, along with challenges using the COVID Check‐in Tool, according to both the adults with disabilities who were interviewed and the family caregivers.ConclusionsThe study underscores the importance of considering ways to integrate tools into routine healthcare practices, to facilitate improved healthcare delivery for people with intellectual and developmental disabilities during pandemic recovery efforts. As well, involving people with lived experience in the development and implementation of healthcare resources is critical.

The Nursing Practitioner Workforce: Attitudes, Preferences and Solutions towards Aging Population with Cognitive Disorder - A Systematic Literature Review

Objectives: The objective of this systematic review is to comprehensively evaluate screening practices for cognitive impairment in older adults, emphasizing the critical role of nurse practitioners. Methodology: a systematic literature search of the major databases, inclusive of PubMed, PsycINFO, and Cochrane library was carried out. The search was keywords centred on articles that dealt with cognitive impairment screening in older adults with mental health problems. Articles were carefully chosen according to the inclusion criteria such as only those articles which had been published during the last nine years and had undergone a peer-review process. Data extraction process involved identification of screening methods, participant characteristics, and major findings. The quality assessment made sure that the chosen literature was of good reliability, and data synthesis identified common factors such as similarities and differences between various studies. Results: The search resulted to 144 articles but after intensive screening 20 of them were included in the review. Results were reported from different healthcare settings, showing both the frequency and the effectiveness of screening tools for identifying cognitive impairment. The role of nurse practitioners was identified in the selected articles, more particularly in establishment screening systems, use tools for identification, and practice advanced roles. The synthesis revealed the role of patient-centred treatment, all-rounded approaches, and the opportunity of blending cognitive screening with routine healthcare practices.

Reviewing the evolution of U. S. telemedicine post-pandemic by analyzing its growth, acceptability, and challenges in remote healthcare delivery during Global Health Crises

This study provides a comprehensive analysis of the evolution, growth, acceptability, and challenges of telemedicine in the United States, particularly in the post-pandemic era. The primary objective was to understand the impact of telemedicine on healthcare delivery, technological advancements, and policy implications. Utilizing a systematic literature review and content analysis methodology, the study sourced data from electronic databases such as PubMed, Scopus, and Web of Science, focusing on peer-reviewed articles published between 2010 and 2023. Key findings indicate that telemedicine experienced significant growth during the COVID-19 pandemic, transitioning from a supplementary healthcare service to a primary mode of healthcare delivery. This growth was facilitated by advancements in digital technologies and necessitated by the need for remote healthcare services. The study identified challenges in telemedicine, including technological barriers, regulatory issues, and concerns regarding equitable access. Despite these challenges, telemedicine has shown potential in enhancing healthcare accessibility, efficiency, and patient-centered care. The study concludes that telemedicine is poised to play a crucial role in the future of healthcare, with opportunities for further integration into routine healthcare practices. However, this requires robust regulatory frameworks, stakeholder engagement, and continued technological innovation. Recommendations for healthcare leaders and policymakers include developing comprehensive telemedicine policies and investing in infrastructure to support telehealth services. Future research directions involve exploring long-term outcomes of telemedicine interventions and its economic impact on healthcare systems.

Identifying Factors to Facilitate the Implementation of Decision-Making Tools to Promote Self-Management of Chronic Diseases into Routine Healthcare Practice: A Qualitative Study.

This study, as part of the COMPAR-EU project, utilized a mixed-methods approach involving 37 individual, semi-structured interviews and one focus group with 7 participants to investigate the factors influencing the implementation and use of self-management interventions (SMIs) decision tools in clinical practice. The interviews and focus group discussions were guided by a tailored interview and focus group guideline developed based on the Tailored Implementation for Chronic Diseases (TICD) framework. The data were analyzed using a directed qualitative content analysis, with a deductive coding system based on the TICD framework and an inductive coding process. A rapid analysis technique was employed to summarize and synthesize the findings. The study identified five main dimensions and facilitators for implementation: decision tool factors, individual health professional factors, interaction factors, organizational factors, and social, political, and legal factors. The findings highlight the importance of structured implementation through SMI decision support tools, emphasizing the need to understand their benefits, secure organizational resources, and gain political support for sustainable implementation. Overall, this study employed a systematic approach, combining qualitative methods and comprehensive analysis, to gain insights into the factors influencing the implementation of SMIs' decision-support tools in clinical practice.

Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022

ObjectiveThe aim of this literature review is to yield a comprehensive and exhaustive overview of the existing evidence and up-to-date applications of artificial intelligence for knee osteoarthritis. MethodsA literature review was performed by using PubMed, Google Scholar, and IEEE databases for articles published in peer-reviewed journals in 2022. The articles focusing on the use of artificial intelligence in diagnosis and prognosis of knee osteoarthritis and accelerating the image acquisition were selected. For each selected study, the code availability, considered number of patients and knees, imaging type, covariates, grading type of osteoarthritis, models, validation approaches, objectives, and results were reviewed. Results395 articles were screened, and 35 of them were reviewed. Eight articles were based on diagnosis, six on prognosis prediction, three on classification, three on accelerated image acquisition, and 15 on segmentation of knee osteoarthritis. 57% of the articles used MRI, 26% radiography, 6% MRI together with radiography, 6% ultrasonography, and 6% only clinical data. 23% of the articles made the computer codes available for their study, and 26% used clinical data. External validation and nested cross-validation were used in 17% and 14% of articles, respectively. ConclusionsThe use of artificial intelligence provided a promising potential to enhance the detection and management of knee osteoarthritis. Translating the developed models into clinics is still in the early stages of development. The translation of artificial intelligence models is expected to be further examined in prospective studies to support clinicians in improving routine healthcare practice.

Barriers to and Facilitators of the Implementation of Digital Mental Health Interventions as Perceived by Primary Care Decision Makers: Content Analysis of Structured Open-Ended Survey Data.

Digital mental health represents a way to increase access to evidence-based psychological support. However, the implementation of digital mental health in routine health care practice is limited, with few studies focusing on implementation. Accordingly, there is a need to better understand the barriers to and facilitators of implementing digital mental health. Existing studies have mainly focused on the viewpoints of patients and health professionals. Currently, there are few studies about barriers and facilitators from the perspective of primary care decision makers, that is, the persons responsible for deciding whether a given digital mental health intervention should be implemented in a primary care organization. The objectives were to identify and describe barriers to and facilitators of the implementation of digital mental health as perceived by primary care decision makers, evaluate the relative importance of different barriers and facilitators, and compare barriers and facilitators reported by primary care decision makers who have versus have not implemented digital mental health interventions. A web-based self-report survey was conducted with primary care decision makers responsible for the implementation of digital mental health in primary care organizations in Sweden. Answers to 2 open-ended questions about barriers and facilitators were analyzed through summative and deductive content analysis. The survey was completed by 284 primary care decision makers-59 (20.8%) decision makers representing implementers (ie, organizations that offered digital mental health interventions) and 225 (79.2%) respondents representing nonimplementers (ie, organizations that did not offer digital mental health interventions). Overall, 90% (53/59) of the implementers and 98.7% (222/225) of the nonimplementers identified barriers, and 97% (57/59) of the implementers and 93.3% (210/225) of the nonimplementers identified facilitators. Altogether, 29 barriers and 20 facilitators of implementation were identified related to guidelines; patients; health professionals; incentives and resources; capacity for organizational change; and social, political, and legal factors. The most prevalent barriers were related to incentives and resources, whereas the most prevalent facilitators were related to the capacity for organizational change. A number of barriers and facilitators were identified that could influence the implementation of digital mental health from the perspective of primary care decision makers. Implementers and nonimplementers identified many common barriers and facilitators, but they differ in terms of certain barriers and facilitators. Common and differing barriers and facilitators identified by implementers and nonimplementers may be important to address when planning for the implementation of digital mental health interventions. For instance, financial incentives and disincentives (eg, increased costs) are the most frequently mentioned barrier and facilitator, respectively, by nonimplementers, but not by implementers. One way to facilitate implementation could be to provide more information to nonimplementers about the actual costs related to the implementation of digital mental health.

Child and adolescent mental health services in a devolved healthcare system: a qualitative exploration of sustainable practices

BackgroundThe transference of research evidence into routine healthcare practice remains poorly understood. This includes understanding the prerequisites of longer-term viability. The present study investigated the sustainable practices of GM i-THRIVE, a programme which reconceptualizes mental health services for children and young people (CYP) in Greater Manchester, United Kingdom. We aimed to establish whether a sustainable future was likely, and to identify areas of focus to improve that likelihood.MethodsThe NHS Sustainability Model, typically completed as a questionnaire measure, was converted into interview questions. The responses of nine professionals, from a variety of roles across the CYP mental health workforce, were explored using inductive thematic framework analysis. Selected participants completed the original questionnaire.ResultsFive themes (communication; support; barriers to implementation; past, present, and future: the implementation journey; and the nuances of GM i-THRIVE) and 21 subthemes formed the final thematic framework. Relationships with senior leaders and with colleagues across the workforce were seen as important. Leaders’ roles in providing meaning and fit were emphasized. Whilst training delivered the programme’s aims well, monitoring its dissemination was challenging. Widespread issues with dedicating sufficient time to implementation were raised. The flexibility of the programme, which can be applied in multiple ways, was discussed positively. This flexibility links to the idea of GM i-THRIVE as a mindset change, and the uniqueness of this style of intervention was discussed. To varying degrees, themes were supported by responses to the quantitative measure, although several limitations to the use of the questionnaire were discovered. Consequently, they were used to infer conclusions to a lesser degree than originally intended.ConclusionsProfessionals involved with GM i-THRIVE reported many elements that indicate a positive future for the programme. However, they suggested that more attention should be given to embedding the core concepts of the model at the current stage of implementation. Limitations relating to its use within our study are discussed, but we conclude that the NHS Sustainability Model is a suitable way of guiding qualitative implementation research. It is especially valuable for localized interventions. The constraints of our small sample size on transferability are considered.

Resident Trainee Access and Barriers to Routine Health Services in the Military Health System

Resident physician wellness has been a highly contentious topic in graduate medical education over the past 2 decades. Physicians, including residents and attending physicians, are more likely than other professionals to work through illness and delay necessary healthcare screening appointments. Potential reasons for underutilization of health care include-unpredictable work hours, limited time, concerns about confidentiality, poor support from training programs, and apprehension about the impact on their peers. The goal of this study was to evaluate access to health care amongst resident physicians within a large military training facility. This is an observational study using Department of Defense approved software to distribute an anonymous ten question survey on routine health care practices of residents. The survey was distributed to a total of 240 active-duty military resident physicians at a large tertiary military medical center. One hundred seventy-eight residents completed the survey with a 74% response rate. Residents from 15 specialty areas responded. Compared to male counterparts', female residents were more likely to miss routine scheduled health care appointments to include, behavioral health appointments (54.2% vs 28%, p < 0.01). Female residents were more likely to report that attitudes toward missing clinical duties for health care appointments impacted their decision to start or add to their family more than male coresidents (32.3% vs 18.3%, p = 0.03). Surgical residents are also more likely to miss routine screening appointments or scheduled follow ups than residents in nonsurgical training programs; (84.0-88% compared to 52.4%-62.8%) respectively. Resident health and wellness have long been an issue, with resident physical and mental health being negatively impacted during residency. Our study notes that residents in the military system also face barriers accessing routine health care. Female surgical residents being the demographic most significantly impacted. Our survey highlights cultural attitudes in military graduate medical education regarding the prioritization of personal health, and the negative impact that can have on residents' utilization of care. Our survey also raises concerns particularly amongst female surgical residents, that these attitudes may impact career advancement, as well as influence their decision to start or add to their families.

Applications, benefits and challenges of telehealth in India during COVID-19 pandemic and beyond: a systematic review

BackgroundIndia, the seventh-largest country in the world and the second-most populated faces enormous challenges when it comes to healthcare. The country’s healthcare system was close to collapse due to the detrimental effects of the COVID-19 pandemic. Telehealth, which enables treating patients remotely, played a critical role during these challenging times. This systematic review investigates in detail the role of telehealth during COVID-19 and its application beyond the pandemic.MethodsDatabase searches on PubMed, Scopus, Science Direct and Web of Science were carried out for studies published on telehealth, and articles were included if they focused on any audio or video telehealth consultation during the pandemic in India. Findings were synthesised into three main themes: applications, benefits and challenges of telehealth services. Methodological quality was assessed using JBI critical appraisal tools.ResultsThe initial search on databases yielded 1143 articles. Of those, 19 met the eligibility criteria. Findings highlight the effective utilisation of telehealth across multiple medical specialities. Although insufficient technological infrastructure and other barriers due to the virtual consultation challenge the successful implementation of telehealth in India, it has the potential to bridge the rural-urban healthcare divide with cost-effective and easily accessible services.ConclusionHigh patient/provider satisfaction underscores the need to integrate telehealth into routine healthcare practices in the country. However, the review urges the government and healthcare practitioners to address the telehealth challenges with prime importance to ensure quality healthcare throughout the nation even after the pandemic.

Survival Outcomes of Patients With Tropomyosin Receptor Kinase Fusion-Positive Cancer Receiving Larotrectinib Versus Standard of Care: A Matching-Adjusted Indirect Comparison Using Real-World Data.

Larotrectinib, a highly specific tropomyosin receptor kinase (TRK) inhibitor, previously demonstrated high response rates in single-arm trials of patients with TRK fusion-positive cancer, but there are limited data on comparative effectiveness against standard-of-care (SoC) regimens used in routine health care practice, before widespread adoption of TRK inhibitors as SoC for TRK fusion-positive cancers. Matching-adjusted indirect comparison, a validated methodology that balances population characteristics to facilitate cross-trial comparisons, was used to compare the overall survival (OS) of larotrectinib versus non-TRK-inhibitor SoC. Individual patient data from three larotrectinib trials (ClinicalTrials.gov identifiers: NCT02122913, NCT02637687, and NCT02576431) were compared with published aggregate real-world data from patients with locally advanced/metastatic TRK fusion-positive cancer identified in the Flatiron Health/Foundation Medicine database. OS was defined as the time from advanced/metastatic disease diagnosis to death. After matching population characteristics, the analyses included (1) a log-rank test of equality to test whether the two groups were similar before larotrectinib initiation; and (2) estimation of treatment effect of larotrectinib versus non-TRK-inhibitor SoC. These analyses are limited to prognostic variables available in real-world data. Eighty-five larotrectinib patients and 28 non-TRK-inhibitor SoC patients were included in the analyses. After matching, log-rank testing showed no difference in baseline characteristics between the two groups (P = .31). After matching, larotrectinib was associated with a 78% lower risk of death, compared with non-TRK-inhibitor SoC (adjusted hazard ratio, 0.22 [95% CI, 0.09 to 0.52]; P = .001); median OS was 39.7 months (95% CI: 16.4, NE [not estimable]) for larotrectinib and 10.2 months (95% CI: 7.2, 14.1) for SoC. Matching-adjusted indirect comparison analyses suggest longer OS with larotrectinib, compared with non-TRK-inhibitor SoC, in adult patients with TRK fusion-positive cancer.

Generating high-fidelity privacy-conscious synthetic patient data for causal effect estimation with multiple treatments.

In the past decade, there has been exponentially growing interest in the use of observational data collected as a part of routine healthcare practice to determine the effect of a treatment with causal inference models. Validation of these models, however, has been a challenge because the ground truth is unknown: only one treatment-outcome pair for each person can be observed. There have been multiple efforts to fill this void using synthetic data where the ground truth can be generated. However, to date, these datasets have been severely limited in their utility either by being modeled after small non-representative patient populations, being dissimilar to real target populations, or only providing known effects for two cohorts (treated vs. control). In this work, we produced a large-scale and realistic synthetic dataset that provides ground truth effects for over 10 hypertension treatments on blood pressure outcomes. The synthetic dataset was created by modeling a nationwide cohort of more than 580, 000 hypertension patient data including each person's multi-year history of diagnoses, medications, and laboratory values. We designed a data generation process by combining an adapted ADS-GAN model for fictitious patient information generation and a neural network for treatment outcome generation. Wasserstein distance of 0.35 demonstrates that our synthetic data follows a nearly identical joint distribution to the patient cohort used to generate the data. Patient privacy was a primary concern for this study; the ϵ-identifiability metric, which estimates the probability of actual patients being identified, is 0.008%, ensuring that our synthetic data cannot be used to identify any actual patients. To demonstrate its usage, we tested the bias in causal effect estimation of four well-established models using this dataset. The approach we used can be readily extended to other types of diseases in the clinical domain, and to datasets in other domains as well.