Health Care Model Research Articles

Impact of post-COVID-19 changes in outpatient chronic patients’ healthcare-seeking behaviors on medical utilization and health outcomes

IntroductionThis study comprehensively investigates the changes in healthcare utilization among chronic patients with regular outpatient visits to hospitals after the occurrence of Covid-19. The research examines whether patients altered their originally regular medical attendance frequencies due to the pandemic and explores potential negative impacts on the health conditions of those irregular attendees post-pandemic.MethodsData for this study were sourced from a database at a medical center in Taiwan. The subjects were chronic patients with regular hospital outpatient visits before the Covid-19 outbreak. The study tracked medical utilization patterns from 2017 to 2022 for different patient characteristics and outpatient behaviors, employing statistical methods such as Repeated Measures ANOVA and Generalized Estimating Equation to analyze changes in healthcare utilization and health status during the post-pandemic period.ResultsThe results reveal that, compared to the regular group, chronic patients with irregular outpatient visits during the post-pandemic period exhibited a decrease of 5.85 annual outpatient visits, a reduction of NT$20,290.1 in annual medical expenses, and a significantly higher abnormality rate in average biochemical test results by 0.9%.ConclusionsThe findings contribute to understanding the impact of the Covid-19 pandemic on healthcare utilization and health conditions among outpatient chronic disease populations. In response to the new medical landscape in the post-pandemic era, proactive suggestions are made, including providing telemedicine outpatient services and referral-based medical care to meet the needs of the target population, ensuring a continuous and reassuring healthcare model for chronic patients, and mitigating the operational impacts of public health emergencies on hospitals.

Lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses

BackgroundThe integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards.MethodsWe developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings.Resultslab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm’s efficacy in handling large-scale data sets.Conclusionslab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management.Graphical

Conceptualizing person undergoing rehabilitation care in the light of Axel Honneth’s theory of recognition

Objective: To reflect on the concept of person, in the context of rehabilitation, in the light of Axel Honneth’s Theory of Recognition. Methods: Theoretical-philosophical essay. Results: The concepts of person, patient, user or client have given rise to central debates in health care models, especially in the area of rehabilitation. In this context, the concept of person stands out, from an anthropological and theological perspective to the inclusion of the complexity of social interactions in this meaning. Axel Honneth adds to this view by reflecting on the individuation of the being through the foundation of recognition, sustained by the need for self-confidence, self-respect and self-esteem, making it possible to draw up a collaborative care plan that strives for the good life of the person undergoing rehabilitation. Conclusion: We can conclude that conceiving of care centered on people with disabilities is much more coherent nowadays than perpetuating other debatable expressions in terms of health. Regardless of the term to be used, recognizing all the dimensions inherent in the concept of person is fundamental in terms of establishing the integral dialogical relationship that is intended between health professionals and the person-patient-user-client undergoing rehabilitation.

Incident Response and Threat Intelligence in Healthcare: A Study on Cybersecurity Incident Management

India and other Asian nations are seeing an unparalleled pace of advancement in the modernisation of their healthcare systems. In this endeavour, information technology is crucial. Though the healthcare industry has made great progress, information security is still lagging behind the protection requirements attained in technologically developed nations such as the United States and the United Kingdom. This study is an honest attempt to pinpoint vulnerabilities and dangers in the field of cybersecurity and offers a few targeted remedies in three main domains: risks, vulnerabilities, and IoMT. Using a qualitative research methodology, this research culminates in the creation of a security maturity model for Indian healthcare. Furthermore, in light of these attacks, the well-known National Institute of Standards and Technology (NIST) risk assessment system and its guiding principles are examined. Evaluating the risks intrinsic to these hacks analytically becomes crucial given the comparatively low information risk management maturity levels in Asian healthcare organisations. While several nations in Asia and throughout the world are battling the COVID-19 outbreak, cybercriminals have been attempting to spread misinformation about vaccines. Additionally, some people and organisations are attempting to undermine specific vaccines in order to promote their COVID-19 treatments. Hacking research data, virus testing, and clinical studies that reveal side effects or possible issues are of particular interest to profit-seeking businesses. The secure methodology for identifying network attacks is suggested by this study.

Blockchain-Based LSTM Approach for Secure Data Transmission in IoT-Enabled Healthcare System

Blockchain technology can help with a number of issues facing the healthcare sector. Its potential as trust brokers includes the provision of incentive machines and the facilitation of innovative healthcare solutions. Additionally, its potential as trust brokers includes the facilitation of novel business models that offer fresh perspectives on the interactions between patients and providers, among other diverse healthcare stakeholders. Blockchain, for instance, makes incentive structures easier to implement and promotes decentralised trust in patient-centered healthcare models and global health information exchange (HIE). On the other hand, blockchain is characterised as a decentralised network or service that can help remove obstacles in the healthcare sector. The healthcare sector has benefited greatly from the effective resolution of problems relating to adoption and technological obstacles, interoperability and integration with current systems, cost unpredictability, scale, and regulatory compliance through the use of Blockchain technology. This work proposed discusses fault tolerance techniques in relation to sharing health-related data.

Enhancing Transparency in Healthcare Machine Learning Models Using Shap and Deeplift a Methodological Approach

This paper intends to provide a better understanding of how these models produce predictions, particularly in complex medical diagnoses, and at the same time bridging the gap between technical model outputs and clinical applications. This study addresses the critical problem of transparency of machine learning (ML) models in health care where interpretability is an essential aspect for ethical decision making and trust building.The goal of this paper is to present a clearer understanding of how these models generate predictions, especially in important fields like complex medical diagnoses, thereby bridging the gap between technical model outputs and clinical applications. The crucial issue of transparency in machine learning (ML) models within healthcare is addressed in this study where interpretability plays a vital role in ethical decision-making and fostering trust. The focus of the research is enhancing model transparency by using SHapley Additive exPlanations (SHAP) and Deep Learning Important FeaTures (DeepLIFT), two crucial methods that are designed to elucidate the decision-making processes of ML models.This mode of approach helps to have more distributed comprehension of the decision pathways by models thus aiding in knowing how each feature contributed to the last prediction. It is this method that has been employed to showcase the efficiency of predicting melanoma and also diabetic retinopathy which are two vital medical diagnostic areas. In healthcare, SHAP along with DeepLIFT has improved the models’ explainability and trustworthiness significantly and hence making them easy for those in the field. The advanced interpretability methods presented in this document enhances ML model transparency especially when dealing with health issues. As a result, interpretability becomes an even bigger issue and they are supposed to be able to use these tools for reliable and open decisions when it comes to medical specialists.

Integrated care models for youth mental health: A systematic review and meta-analysis.

To evaluate the effectiveness of integrated models of mental healthcare in enhancing clinical outcomes, quality of life, satisfaction with care and health service delivery outcomes in young people aged 12-25 years. A secondary objective was to identify common components of integrated mental health interventions. A systematic review and meta-analysis of studies published 2001-2023 that assessed clinical or health service use outcomes of integrated care, relative to treatment as usual, for any mental health condition in 12-25 years old accessing community-based care. Of 11,444 titles identified, 15 studies met inclusion criteria and 6 studies were entered in the meta-analysis. Pooled effect size found integrated care was associated with a greater reduction in depressive symptoms relative to treatment as usual at 4-6 months (standardised mean difference = -0.260, 95% confidence interval = [-0.39, -0.13], p = 0.001). Of the seven studies reporting access or engagement, all reported higher rates of both in the intervention arm. The most frequent components of integration were use of a multidisciplinary team (13/15 studies), shared treatment planning (11/15) and workforce training in the model (14/15). Integrated models of mental healthcare are associated with a small, but significant, increase in effectiveness for depressive symptoms relative to treatment as usual. Given integrated care may increase access and engagement, future research should focus on assessing the impact of integrated care in a wider range of settings and outcomes, including clinical and functional recovery, satisfaction with care and system-level outcomes such as cost-effectiveness.

Alternative payment models in Dutch hospital care: what works, how, why and under what circumstances? Protocol for a realist evaluation study

IntroductionThe predominant provider payment models in healthcare, particularly fee-for-service, hinder the delivery of high-value care and can encourage healthcare providers to prioritise the volume of care over the value of...

Trends in direct healthcare utilization observed for persons with MS among teleneurology users and diverse populations

Background: The impact of teleneurology on healthcare utilization (HCU) in MS is unknown. Objective: Evaluate the association between teleneurology and HCU. Methods: A retrospective longitudinal analysis of HCU among adult MS and clinically isolated syndrome (CIS) patients residing in the Cleveland/Akron area from July 2020 to July 2022. Negative binomial regression models evaluated the association between number of laboratory and MRI orders per visit and number of emergency visits per patient across patient groups with variable proportions of teleneurology visits. Results: A total of 3208 patients completed 15,795 visits. Patients using teleneurology had more visits (rate ratio (RR) 1.707–1.719, p < 0.001). Teleneurology visits had fewer laboratory (RR 0.571) and MRI orders (RR 0.693, p < 0.001). There was no difference in emergency care utilization for teleneurology patients (p ⩾ 0.05). More emergency visits were observed in Black (RR 1.414) and Medicaid (RR 1.893) patients, regardless of visit type (p < 0.001). Conclusion: Teleneurology visits were associated with fewer orders, suggesting teleneurology may be incorporated into healthcare models without increasing utilization related to the visit. Teleneurology was also associated with increased visit volumes but no difference in emergency HCU. More studies are needed to clarify the ultimate impact of teleneurology on overall HCU. More emergency visits, regardless of visit type, were observed among at-risk populations, warranting further investigation.

The interweaving of threads in the fabric of care.

There are different motivations that induce a human being to take care of another, and it is from their intertwining, integrated into an organic fabric, that care in its entirety takes shape. The cure that is nourished by pietas, which refers us above all to the relationships that develop within the family, can be preached, but it is not possible to force the reluctant to practice it; and when it fails or takes on complex aspects, other methods of care appear on the social scene, in particular that which is inspired by a philanthropic ideal. Even in this context we cannot talk about rights and legal obligations, and it is in this context that we place volunteering, an indispensable completion of the care that has its roots in pietas. But when the limits of volunteering are outlined (difficulty in planning and precariousness) we see the emergence of care provided by professional caregivers. Those who carry out care professionally have received adequate training and undertake to keep it updated, and are also bound by a set of rules, which have the value of ethical commitments. The most important of these is not to make care depend on the human quality of those who receive it: everyone has the right to be cared on the basis of need, not merit, at least in societies that have adopted the model of universal healthcare. Care is the bond that holds together the society that has made solidarity its founding value.

Genetics Navigator: protocol for a mixed methods randomized controlled trial evaluating a digital platform to deliver genomic services in Canadian pediatric and adult populations

IntroductionGenetic testing is used across medical disciplines leading to unprecedented demand for genetic services. This has resulted in excessive waitlists and unsustainable pressure on the standard model of genetic healthcare....

Implementation of Virtual Yoga Shared Medical Appointment (VYSMA) Pilot at an Academic Medical Center Within a Mixed-Diagnosis Oncology Population.

Yoga is highly sought after by people seeking oncology care, endorsed by clinical practice guidelines, and supported by leading cancer organizations, yet barriers related to access, time, cost, and availability of quality providers remain. Shared medical appointments (SMA), a group healthcare model where patients with similar medical conditions participate in a collective appointment with healthcare providers, are associated with increased access to quality care, patient satisfaction, and clinician satisfaction. We piloted a unique insurance-covered virtual yoga SMA series to assess feasibility and acceptability in a mixed-diagnosis population. In this prospective cohort pilot, a trauma-informed Hanna Somatic Yoga instructor and an integrative medicine physician co-led yoga SMAs via live web-based conferencing. SMA content included conscious self-regulation through mind-body practices including breathing, movement, visualization, meditation, chanting, and guided relaxation. Qualitative and quantitative data were gathered to assess satisfaction with the 88 sessions offered over 33 months. Sixty-nine participants with diverse demographics attended a total of 500 visits. Class attendance ranged from 2-11 participants (mean 6 participants). Participants attended a mean of 7 sessions (range 1-63 sessions), with 63% attending > 1 session. Participants' diagnoses/symptoms included cancer (77%), anxiety/depression (38%), and pain (38%). Preseries, participants reported pain, weakness, neuropathy, lymphedema, insomnia, and fatigue. Postseries survey results suggested improvements in anxiety/fear, pain, fatigue, poor sleep, neuropathy, brain fog, isolation, weakness, inflexibility, and poor balance. Postseries, participants also reported incorporating mindfulness, breathing techniques, somatic skills, weight training, and yoga into their daily routines, with 91% reporting that their goals had been met. Participants appreciated remote delivery, learning new skills, community, and the instructors. This virtual yoga SMA series in a diverse population with mixed diagnoses was feasible, acceptable to participants, and showed promising positive impact. A larger randomized controlled trial with longer follow-up is recommended.

Shifting Compensation: The Transition From Traditional to Nontraditional Healthcare

Transformative shifts in healthcare delivery and provider compensation are reshaping the landscape of modern healthcare. This change is driven by the emergence of nontraditional healthcare models such as telehealth and retail clinics. These disruptors have gained significant traction, especially in recent years, as patients increasingly seek convenient and accessible avenues for receiving medical care.

S14 Physical activity promotion in healthcare settings: Designing and implementing physical activity referral schemes

Abstract Purpose This symposium will share findings, expertise and experiences from different countries on the design and implementation of physical activity referral schemes (PARS). Description Physical activity promotion by healthcare professionals is a key strategy to increase population physical activity levels. This strategy has been outlined in the World Health Organisation’s (WHO) ‘Global Action Plan on Physical Activity’ (GAPPA) and the ‘Physical activity strategy for the WHO European Region 2016-2025‘. PARS are a promising intervention that allow healthcare professionals to advocate for physical activity and integrate its promotion into routine clinical care. PARS are a heterogeneous group of interventions. Simpler PARS, for example, may consist only of a written description, while more complex PARS might include different healthcare professionals working together systematically, applying several behaviour change techniques in combination with physical activity practice, to connect patients to community resources. There is currently no consensus on the optimal PARS approach and implementation strategies. Implementers need specific knowledge, skills and methods; however, implementation is also influenced by structural and context-related factors in the healthcare system. Accordingly, a successful implementation of PARS needs co-productive capacity building within healthcare systems and proper support for healthcare professionals. The symposium includes four contributions (3 research-oriented and 1 practise-oriented) related to the design and implementation of PARS in different countries. 1) EUPAP Feasibility Study. Practice transfer of a HEPA prescription model to other nine EU countries. From theory to practice. Sebastià Mas-Alòs 2) The association of physical activity referral scheme’ components with physical activity level, scheme uptake and adherence rates: a systematic review, meta-analysis with meta-regression. Wolfgang Geidl 3) The adoption and implementation of a physical activity referral pathway integrated into a major U.S. health system. Mark Stoutenberg 4) Designing a physical activity pathway in healthcare model for Irish Health Services. Sarah O’Brien This symposium will provide guidance on the development of PARS and future successful implementation in other healthcare settings.

Predictive Analytics for Identifying High-Risk Medicare Patients: Enhancing Preventive Care

The intended purpose of this paper is to carry out an analysis of the use of predictive modeling for the early identification of high-risk Medicare patients with the aim of enhancing preventative measures. MEDI-CARE, a vital policy in the United States of America, which focuses on offering health care services to the old and people with disabilities, has been criticized over such factors as Shortcoming of Medicare hence can be attributed to factors such as the growing costs of health care and the emerging cases of chronic diseases. A new type of risk stratification: The use of predictive analytics, which incorporates ML-AI, is also in the early warning system and to monitor or follow up on the patient’s adverse health event, subsequent to which care coordination plans can be formulated. The abstract, hence, is an explanation of the roles of preventive and predictive analytics, especially in health care, with such a thing as the identification of early high-risk patients and management. They are that there is a rise in the dependence on methodologies that are data-driven for the purpose of attaining enhanced healthcare outcomes, reduction in the hospitalization quotient and the effective management of diseases which occur chronically. A few such points comprise data and information from electronic health records and other digital healthcare databases, model construction, and model application. It also addresses the various ethical pitfalls likely to arise when using the concept of predictive analytics, and these include the privacy of the patient’s data, how the collected data will be protected and filtered, as well as the prejudice that might be experienced when developing the algorithm. Current and earlier models of prediction, as well as their application in Medicare and research limitations, are described in the literature review of the article. Whereas in the methodology section, the procedures used in data collection, modeling, and validation are described, the results and discussion section put into perspective the effectiveness of the work done in enhancing Preventive care through predictive analytics. Finally, the discussion in the conclusion shall be based on the findings of the study and recommend future research directions for policy making.

The Frontier Exploration of the Development Trend of Key Technologies in Digital Healthcare in Medical Big Data Analysis

With the rapid development of medical big data, digital healthcare as an interdisciplinary field is gradually transforming the traditional healthcare model, continuously advancing the realization of personalized medical services and remote health monitoring. This paper systematically reviews 138 research articles related to the development trends of key technologies in digital healthcare, exploring various critical areas including digital health and privacy security, the application of artificial intelligence and machine learning in health, remote monitoring and mobile health, medical data analysis and electronic health records, and the ethical and policy challenges in digital health. Studies indicate that the improvement of privacy security measures and efficient medical data processing technologies are the foundation for the safe promotion of digital healthcare; Artificial intelligence and machine learning technologies play an important role in enhancing the accuracy of diagnosis and disease prediction. Meanwhile, mobile health and remote monitoring technologies show great potential in improving the accessibility and continuity of medical services. In addition, this paper discusses the challenges of ethics and policies in digital health practice, such as data sharing, conflicts of interest, and respect for patient privacy. Future research must focus on refining technological solutions to address these challenges and fully integrate digital healthcare technologies into clinical practice.

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Artificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

Development and Implementation of an Aural Rehabilitation Program Using Community Health Workers in Rural Alabama

Purpose: Considering the lack of audiological care in many areas across the United States, it is crucial to develop new models of hearing health care. Described within this document is a procedure used to develop and implement an aural rehabilitation (AR) program and a community health worker (CHW) educational training program in West Central and South Alabama. The AR program and the CHW educational training program were used to assist those with hearing loss in rural Alabama communities who received over-the-counter hearing aids (OTC HAs). The work described was part of a registered clinical trial (NCT04671381) with OTC HAs in rural Alabama communities. Method: The development and implementation of the AR program and CHW training program was conducted using foundational work from experiential learning principals and from experts in AR and community outreach and engagement. Results: Over the course of 3 years from March 2021 to March 2024, the AR program and CHW training were developed and implemented. Guidance from the study personnel, consultants, and CHWs helped to refine the AR program and CHW training after implementation. Conclusion: Future studies will assess the success of the AR program through quantifying outcomes from speech perception testing and survey completion obtained from adults in rural communities who enroll in the OTC HA clinical trial.

Perspectives and guidance for developing artificial intelligence-based applications for healthcare using medical images

Artificial intelligence (AI) has significant potential to transform healthcare and improve patient care. However, successful development and integration of AI models requires careful consideration of study designs and sample size calculations for development and validation of models, publishing standards, prototype development for translation and collaboration with stakeholders. As the field is relatively new and rapidly evolving there is a lack of guidance and agreement on best practices for most of these steps. We engaged stakeholders in the form of clinicians, researchers from academia and industry, and data scientists to discuss various aspects of the translational pipeline and identified the challenges researchers in the field face and potential solutions to them. In this viewpoint, we present the summary of our discussions as a brief guide on the process of developing AI-based applications for healthcare using medical images. We organized the entire process into six major themes (i.e., The gaps AI can fill in healthcare, Development of AI models for healthcare: practical and important things to consider, Good practices for validation of AI models for healthcare: study designs and sample size calculation, Points to consider when publishing AI models, Translation towards products, Challenges and potential solutions from a technical perspective) and presented important points as a rule of thumb. We conclude that successful integration of AI in healthcare requires a collaborative approach, rigorous validation, adherence to best practices as described and cited, and consideration of technical aspects.

Multi-Objective Performance Optimization of Machine Learning Models in Healthcare.

Multi-objective optimization holds particular significance for medical applications, wherein enhancing sensitivity is crucial to avoid costly missed diagnoses, and maintaining high specificity is imperative to prevent unnecessary procedures. In particular, when optimizing machine learning architectures for clinical diagnostics, it becomes essential to balance target quality measures such as accuracy, sensitivity, and specificity. Therefore, we developed MOOF, a multi-objective optimization framework that employs NSGA-II and TOPSIS to simultaneously optimize the model parameters of three selected ML algorithms: random forest, support vector machine, and multilayer perceptron. Finally, we evaluated the performance of the optimized MOOF models compared to gold standard methods such as multi-score grid search and single objective optimizations. Our results show that MOOF generally outperforms other approaches by inherently providing optimal solutions, representing the trade-offs between the target objectives. In conclusion, the study supports the importance of multi-objective optimization in medical informatics, with MOOF as a powerful tool for precise ML models, potentially improving patient care and clinical decision support systems.