Healthcare Experts Research Articles

A review of the challenges and opportunities in implementing health informatics in rural healthcare settings

This paper reviews the challenges and opportunities associated with implementing health informatics in rural healthcare settings. It examines the unique barriers rural communities face, such as infrastructure limitations, workforce shortages, and financial constraints, which can impede the adoption and utilization of health informatics solutions. However, it also explores the promising opportunities afforded by health informatics, including telemedicine, electronic health records (EHRs), and mobile health technologies, to overcome these challenges and improve healthcare access, quality, and efficiency in rural areas. By addressing these challenges and leveraging the opportunities presented by health informatics, stakeholders can work towards achieving equitable healthcare outcomes for rural populations. By addressing these challenges and leveraging the opportunities presented by health informatics, stakeholders can work towards achieving equitable healthcare outcomes for rural populations. Health informatics holds immense potential to revolutionize healthcare delivery, particularly in rural settings where access to quality care is often limited Health informatics holds immense potential to revolutionize healthcare delivery, particularly in rural settings where access to quality care is often limited. Health informatics holds immense potential to revolutionize healthcare delivery, particularly in rural settings where access to quality care is often limited. Telemedicine, for instance, enables remote consultations and diagnostic services, breaking down geographic barriers and providing rural patients with access to specialists and healthcare expertise they may not have locally. Electronic Health Records (EHRs) streamline administrative processes, improve care coordination, and enhance patient safety by ensuring that vital health information is readily accessible to healthcare providers regardless of location. Mobile health technologies, including smartp hone apps and wearable devices, empower rural residents to monitor their health, track chronic conditions, and engage in self-management activities from the comfort of their homes.By embracing these innovations and overcoming infrastructure challenges, stakeholders in rural healthcare can significantly improve healthcare access, quality, and efficiency. Additionally, leveraging health informatics can help address workforce shortages by enabling healthcare providers to deliver more effective and integrated care, even in remote areas. Ultimately, by harnessing the opportunities presented by health informatics while addressing the unique challenges of rural healthcare, stakeholders can work towards achieving equitable healthcare outcomes for rural populations. Keywords: Health Informatics, Rural Healthcare, Electronic Health Records (EHRS), Mobile Health, Challenges.

Evaluation of Education Methods for the Public During Epidemics of Communicable Diseases from the Perspective of the Public: A Cross-Sectional Study

Background. In recent decades, epidemics of various infectious diseases have occurred. Despite the health authorities' educational efforts to provide people with adequate information, there is not enough evidence regarding the evaluation and effectiveness of educational methods. Therefore, the present study aimed to evaluate the effectiveness of educational methods provided for the public during epidemics of infectious diseases from people's perspective. Methods. This cross-sectional study was conducted in Iran in 2023. The participants included individuals who were older than 15 years and were living in Iran during the recent epidemics. We used a tailored questionnaire consisting of 14 questions to gather information. The validity of the questionnaire was ensured by calculating the content validity index and ratio. Furthermore, its reliability was confirmed through a pilot study. The participants were selected using convenience sampling. In order to collect data, the questionnaire was distributed to the target group both electronically and in person. The collected data were analyzed using SPSS software (version 24) with normality tests conducted, and any P-value below 0.05 was considered significant. Results. A total of 278 individuals participated in the study, with the majority being women (62.3%). The average age of the participants was 33.9 years old. Most of the participants had a bachelor's degree (43.2%). According to the results of the study, social networks were identified as the most commonly used source for receiving educational materials during infectious disease epidemics (76.8%), and they were also considered to be the most effective source (87.6%). Additionally, based on the participants' opinions, social networks outperformed other information sources (78%). Face-to-face education provided by healthcare workers was identified as the most trusted educational source (81.6%), and the participants recommended this method to others more than any other method. (82.4%). Interviews with healthcare experts and in-person education provided by healthcare workers were perceived as the most effective educational methods by the people surveyed (60.4%). Furthermore, the results indicated that as individuals age, their reliance on sources of information like the Internet, social networks, and newspapers tends to decrease. Conclusion. The results of the current study can provide valuable information for policymakers on educating the public during epidemics of communicable diseases, as well as help them make quick, effective, and evidence-based decisions.

A core competency evaluation index system for the graded use of clinical nurse specialists: A Delphi study.

To describe a grading system that can be used to evaluate core competency of clinical nurse specialists (CNSs) at different levels. Evaluate core competence of CNSs at different levels reflects the quality of nursing and the development of the nursing profession. This research employed the Delphi method. The STROBE checklist for observational cross-sectional studies was followed to report this research study. This study consisted of two main phases: a literature review and semistructured interviews. Individual semistructured interviews were conducted with 11 healthcare experts and two patients. Two rounds of questionnaire surveys were administered to 21 nursing experts using the Delphi method. The CNSs were classified as primary, intermediate or advanced based on their years of work, professional titles and educational qualifications. The graded competency evaluation system consisted of five first-level indicators (clinical practice, consulting guidance and teaching, scientific research innovation, management and discipline development, and ethical decision-making), 15 second level indicators, and 40 third-level indicators. The authority coefficients (Cr) of the experts were .865 and .901. The Kendall's concordance coefficients of the three-level indicators were .417, .289 and .316 for primary CNSs; .384, .294 and .337 for intermediate CNSs; and .489, .289 and .239 for advanced CNSs. The graded use evaluation system in clinical practice initially involves a comprehensive evaluation of the core abilities of CNSs. This is a tool for cultivating and grading the abilities of specialised nurses that can promote a practical upwards spiral. The evaluation system can promote the scientific management and continuous improvement of CNSs in clinical nursing and can serve as a practical and objective reference for the effective management and development of CNSs. Patients participated in the data collection process, during which they shared their health-seeking experience with our research team.

Key stakeholders' perspectives on the development of an early dietary phosphate self-management strategy for children and young people with chronic kidney disease stages 1-3: A modified Delphi consensus process.

An early dietary phosphate intervention (EPI) can provide vital medical benefits supporting self-management of chronic kidney disease (CKD) in childhood. To utilise expert consensus to provide early modelling for an EPI to guide clinical practice across a paediatric renal network. Forty-eight statements across six domains were constructed following a systematic review and semi-structured interviews with children and young people (CYP), parents and healthcare professionals (HCP). A three-round online Delphi survey with parents and paediatric renal multi-disciplinary healthcare experts was undertaken. Twenty-one experts agreed on 56 statements over three Delphi rounds. Statements were accepted in all six domains: definition of an EPI (2), rationale (12), intended users (3), delivery (14) (when [1], where [3], who [2], how [8]), other considerations (16) and potential concerns (9). Consensus was reached on a definition and a set of guiding principles, providing some early modelling for implementation and future research on the development of an EPI strategy for CYP with CKD.

Evaluating the Energy Efficiency of Popular US Smartphone Health Care Apps: Comparative Analysis Study Toward Sustainable Health and Nutrition Apps Practices.

The emergence of smartphones has sparked a transformation across multiple fields, with health care being one of the most notable due to the advent of mobile health (mHealth) apps. As mHealth apps have gained popularity, there is a need to understand their energy consumption patterns as an integral part of the evolving landscape of health care technologies. This study aims to identify the key contributors to elevated energy consumption in mHealth apps and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mHealth apps. Through quantitative comparative analysis of 10 prominent mHealth apps available on Android platforms within the United States, this study examined factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using ANOVA, and regression analysis to examine how certain factors impact energy use and consumption. Observed energy use variances in mHealth apps stemmed from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 milliwatt-hours), highlighting the influence of user interaction and app complexity. ANOVA verified these findings, indicating the critical role of engagement and functionality. Regression modeling (energy consumption = β₀ + β₁ × notification frequency + β₂ × GPS use + β₃ × app complexity + ε), with statistically significant P values (notification frequency with a P value of .01, GPS use with a P value of .05, and app complexity with a P value of .03), further quantified these bases' effects on energy use. The observed differences in the energy consumption of dietary apps reaffirm the need for a multidisciplinary approach to bring together app developers, end users, and health care experts to foster improved energy conservation practice while achieving a balance between sustainable practice and user experience. More research is needed to better understand how to scale-up consumer engagement to achieve sustainable development goal 12 on responsible consumption and production.

Application of the Delphi method to the development of common data elements for social drivers of health: A systematic scoping review.

Collaborative data science requires standardized, harmonized, interoperable, and ethically sourced data. Developing an agreed-upon set of elements requires capturing different perspectives on the importance and feasibility of the data elements through a consensus development approach. This study reports on the systematic scoping review of literature that examined the inclusion of diverse stakeholder groups and sources of social drivers of health variables in consensus-based common data element (CDE) sets. This systematic scoping review included sources from PubMed, Embase, CINAHL, WoS MEDLINE, and PsycINFO databases. Extracted data included the stakeholder groups engaged in the Delphi process, sources of CDE sets, and inclusion of social drivers data across 11 individual and 6 social domains. Of the 384 studies matching the search string, 22 were included in the final review. All studies involved experts with healthcare expertise directly relevant to the developed CDE set, and only six (27%) studies engaged health consumers. Literature reviews and expert input were the most frequent sources of CDE sets. Seven studies (32%) did not report the inclusion of any demographic variables in the CDE sets, and each demographic SDoH domain was included in at least one study with age and sex assigned at birth included in all studies, and social driver domains included only in four studies (18%). The Delphi technique engages diverse expert groups around the development of SDoH data elements. Future studies can benefit by involving health consumers as experts.

A Series of Suicides and Homicides by Cyanide in Türkiye: Exploring the Role of Media Reports and the Copycat Effect

Objectives: This paper focuses on the intentional deaths by cyanide that occurred in Turkey and aims to discuss the effect of internet and the media on these cases. Methods: Five highly circulated daily newspapers were selected and the term “siyanür”, which means “cyanide” in Turkish, were searched within their online search function between 2017-2021. A Google Trends analysis was performed with “siyanür+siyanur” as the keyword, “Turkey” as the region, and “1/1/2017-12/31/2021” as the custom time range. Results: Twenty different incidents and news reports of cyanide related suicides and/or homicides with 29 total deaths were found. In Google Trends analysis, seven out of total 8 periods of increased search interest coincided with the news reports related to cyanide deaths. Conclusions: The authors observed a surge in cyanide-related incidents following a highly publicized case in May 2019, with a peak in November 2019. The study highlights the influence of media coverage on subsequent incidents, with newspapers often using sensational headlines and providing detailed information on suicide methods. Reporting a news article on suicide requires great care and caution. Thus, the guidelines on reporting suicide related news should be implemented nationwide with a collaboration between media professionals and health-care experts.

2023 Cardiac Society of Australia and New Zealand Expert Position Statement on Catheter and Surgical Ablation for Atrial Fibrillation

Catheter ablation for atrial fibrillation (AF) has increased exponentially in many developed countries, including Australia and New Zealand. This Expert Position Statement on Catheter and Surgical Ablation for Atrial Fibrillation from the Cardiac Society of Australia and New Zealand (CSANZ) recognises healthcare factors, expertise and expenditure relevant to the Australian and New Zealand healthcare environments including considerations of potential implications for First Nations Peoples. The statement is cognisant of international advice but tailored to local conditions and populations, and is intended to be used by electrophysiologists, cardiologists and general physicians across all disciplines caring for patients with AF. They are also intended to provide guidance to healthcare facilities seeking to establish or maintain catheter ablation for AF.

تحليل مقارن لخوارزميات التعلم الآلي لتصنيف مرض السكري باستخدام تحليل مصفوفة الارتباك

يستخدم العاملين في الرعاية الصحية التعلم الآلي أكثر فأكثر في السنوات الأخيرة لتعزيز نتائج المرضى وخفض التكاليف عليهم. بالإضافة إلى ذلك، تم تنفيذ التعلم الآلي في مجالات مختلفة، بما في ذلك تشخيص الأمراض، وتصنيف مخاطر المرضى، واقتراحات العلاج المخصصة، وتطوير الأدوية. يمكن لخوارزميات التعلم الآلي أن تفحص كميات هائلة من البيانات من السجلات الصحية الإلكترونية، والصور الطبية، ومصادر أخرى لتحديد الأنماط والتنبؤات، والتي يمكن أن تدعم المتخصصين في الرعاية الصحية والخبراء في اتخاذ قرارات مستنيرة، وتعزيز رعاية المرضى، وتحديد حالة المريض الصحية. في هذا الصدد، اختار المؤلف مقارنة أداء ثلاث خوارزميات (الانحدار اللوجستي ، Adaboost ، بايزالساذجة) من خلال معدل التصنيف الصحيح للتنبؤ بمرض السكري من أجل ضمان فعالية التشخيص الدقيق. تم الحصول على مجموعة البيانات المطبقة في هذا العمل من مستودع مؤسسي بجامعة فاندربيلت وهي بيانات متاحة للحميع. استنجتت الدراسة أن ثلاث خوارزميات فعالة للغاية في التنبؤ. بشكل أساسي، كان معدل تصنيف الانحدار اللوجستي و Adaboost أعلى من 92٪ ، وحققت خوارزمية بايز الساذجة معدل تصنيف أعلى من 90٪.

Establishing New Links Between HRM Practices and Knowledge Workers’ Retention in Healthcare Sector: Knowledge Sharing Generating Moderating Effects

Knowledge sharing is the fundamental measure through which knowledge workers can significantly contribute to innovation and eventually the competitive advantage of the organization. Drawing upon the knowledge-based view of firms, the present study aims to examine the moderating role of knowledge sharing on the relationship between HRM practices and knowledge workers’ retention in the healthcare sector. After the COVID impact, healthcare knowledge workers have become a point for wide scholarly discussion and appropriate HRM practices should be implemented to amplify their probability of a longer stay in the organization. Hence, this research has incorporated PLS-SEM for empirical investigation which is based on prior discussions with some healthcare experts. The results propagate that knowledge sharing moderates HRM practices-retention linkage except training and development which is found to be statistically insignificant. The empirical evidence generated could be crucial for researchers and practitioners within the healthcare sector, thereby, extending assistance in establishing world-wide recognition.

Implementing innovations in US transplantation system

ABSTRACTCan private jets help save lives? Yes. Can short videos nudge next‐of‐kin to donate more? Also, yes. How has an outsider to the transplantation system, with no previous knowledge or expertise in (any aspect of) healthcare, managed to make a difference? Is imagination really more important than knowledge? In this invited article, I will briefly describe two innovations—OrganJet and Nudge Videos—that I have implemented in the US Transplantation System that have extended the lives of many.

Consensus recommendations on the role of nurses in headache care: A European e-Delphi study.

Nurses work at headache centres throughout Europe, and their care for migraine patients is acknowledged. However, the specific roles and tasks of nursing vary, and a unified understanding is lacking, posing challenges to knowledge sharing and research. Using an e-Delphi study method, the objective is to obtain healthcare professional headache experts' opinions on nursing-specific roles and tasks and combine this into consensus statements for nurse recommendations for migraine treatment. A three-round questionnaire study was conducted with nurses and neurologists from 18 specialised headache centres in 10 countries. In round 1, statements were compiled from a systematic examination of existing literature and expert opinions. In rounds 2 and 3, the experts rated the importance of statements (from round 1) on a 5-point Likert scale. Statements were analysed using a content analysis method, and the consensus of pre-defined statements was evaluated with gradually increased predetermined criteria using descriptive statistics. Twenty-one experts, representing all 10 countries, participated. The predetermined consensus of ≥70% agreement was reached for 42 out of the initial 63 statements. These statements formed the final recommendations within two themes: "The nurses' roles and tasks in the clinical setting" and "The nurses' roles and tasks in educating patients and colleagues." The consensus level of statements was strong, with 40% receiving unanimous agreement (100%) and 97% achieving relatively high agreement (>80%). Nursing plays a vital role with diverse tasks in migraine care. This study offers practical recommendations and a framework for nurses, equipping them with a clinical tool to enhance care and promote a coordinated approach to migraine treatment.

Advancing health equity for Indigenous peoples in Canada: development of a patient complexity assessment framework.

Indigenous patients often present with complex health needs in clinical settings due to factors rooted in a legacy of colonization. Healthcare systems and providers are not equipped to identify the underlying causes nor enact solutions for this complexity. This study aimed to develop an Indigenous-centered patient complexity assessment framework for urban Indigenous patients in Canada. A multi-phased approach was used which was initiated with a review of literature surrounding complexity, followed by interviews with Indigenous patients to embed their lived experiences of complexity, and concluded with a modified e-Delphi consensus building process with a panel of 14 healthcare experts within the field of Indigenous health to identify the domains and concepts contributing to health complexity for inclusion in an Indigenous-centered patient complexity assessment framework. This study details the final phase of the research. A total of 27 concepts spanning 9 domains, including those from biological, social, health literacy, psychological, functioning, healthcare access, adverse life experiences, resilience and culture, and healthcare violence domains were included in the final version of the Indigenous-centered patient complexity assessment framework. The proposed framework outlines critical components that indicate the presence of health complexity among Indigenous patients. The framework serves as a source of reference for healthcare providers to inform their delivery of care with Indigenous patients. This framework will advance scholarship in patient complexity assessment tools through the addition of domains not commonly seen, as well as extending the application of these tools to potentially mitigate racism experienced by underserved populations such as Indigenous peoples.

Understanding the multidimensional role of medical travel facilitators: A study on competencies and a proposed model

The recent exponential growth of medical tourism has illuminated the essential but relatively unexamined role of medical travel facilitators (MTFs). MTFs play a crucial role in the success of medical tourism by acting as a bridge between patients and healthcare providers. However, there is a lack of understanding and standardization of the competencies needed to excel in this profession. Therefore, this study aims to reveal and categorize the key MTF competencies through a professional competency model. The research methodology involved a combination of competency classification and thematic content analysis, leveraging insights from 30 healthcare experts. The study is processed through a computer-aided analysis to identify 14 distinct themes and 35 MTF competencies. These findings build up an innovative MTF competency model. This novel model extends the understanding of MTF competencies and is a practical tool for individuals aspiring for MTF roles, promoting their professional development. The findings also suggest a standard for delivering high-quality patient care and meeting the diverse needs of industry stakeholders. The research contributes to both theoretical advancements and practical improvements in the medical tourism industry, with an emphasis on enhancing patient satisfaction and upholding industry standards.

AUTOMATED PNEUMONIA IDENTIFICATION THROUGH CNN-BASED ANALYSIS

Pneumonia, a condition caused by infectious agents such as bacteria, viruses, fungi, or parasites, results in lung infections and the accumulation of pus in affected tissues. Incorrect diagnosis or improper treatment can significantly impact a patient's quality of life. This project focuses on the precise identification and categorization of pneumonia-afflicted patients through the analysis of their chest X-rays. Leveraging advancements in deep learning, healthcare experts can make more accurate diagnostic decisions for various illnesses. This study presents an innovative approach utilizing Convolutional Neural Networks (CNN) to predict and distinguish between patients affected by pneumonia and those who are not, based on chest X-ray images. The methodology involves a series of convolutional and max pooling layers activated using the ReLU activation function. Subsequently, the processed data is passed through dense layers, culminating in the activation of the output neuron through the sigmoidal function. During the training process, the model exhibits increasing accuracy and decreasing loss, demonstrating its effectiveness. To prevent overfitting, data augmentation techniques are applied before model fitting. These strategies collectively enhance the robustness and reliability of the deep learning models, resulting in accurate and persuasive detection of pneumonia from chest X-ray images. Keywords: Pneumonia, CNN, VGG-16, VGG-19, Chest X-rays, Classification, Deeep learning, Data augmenatation, ReLU, softmax.

Exploring health care providers’ engagement in prevention and management of multidrug resistant Tuberculosis and its factors in Hadiya Zone health care facilities: qualitative study

BackgroundEngagement of healthcare providers is one of the World Health Organization strategies devised for prevention and provision of patient centered care for multidrug resistant tuberculosis. The need for current research question rose because of the gaps in evidence on health professional’s engagement and its factors in multidrug resistant tuberculosis service delivery as per the protocol in the prevention and management of multidrug resistant tuberculosis.PurposeThe purpose of this study was to explore the level of health care providers’ engagement in multidrug resistant tuberculosis prevention and management and influencing factors in Hadiya Zone health facilities, Southern Ethiopia.MethodsDescriptive phenomenological qualitative study design was employed between 02 May and 09 May, 2019. We conducted a key informant interview and focus group discussions using purposely selected healthcare experts working as directly observed treatment short course providers in multidrug resistant tuberculosis treatment initiation centers, program managers, and focal persons. Verbatim transcripts were translated to English and exported to open code 4.02 for line-by-line coding and categorization of meanings into same emergent themes. Thematic analysis was conducted based on predefined themes for multidrug resistant tuberculosis prevention and management and core findings under each theme were supported by domain summaries in our final interpretation of the results. To maintain the rigors, Lincoln and Guba’s parallel quality criteria of trustworthiness was used particularly, credibility, dependability, transferability, confirmability and reflexivity.ResultsTotal of 26 service providers, program managers, and focal persons were participated through four focus group discussion and five key informant interviews. The study explored factors for engagement of health care providers in the prevention and management of multidrug resistant tuberculosis in five emergent themes such as patients’ causes, perceived susceptibility, seeking support, professional incompetence and poor linkage of the health care facilities. Our findings also suggest that service providers require additional training, particularly in programmatic management of drug-resistant tuberculosis.ConclusionThe study explored five emergent themes: patient’s underlying causes, seeking support, perceived susceptibility, professionals’ incompetence and health facilities poor linkage. Community awareness creation to avoid fear of discrimination through provision of support for those with multidrug resistant tuberculosis is expected from health care providers using social behavioral change communication strategies. Furthermore, program managers need to follow the recommendations of World Health Organization for engaging healthcare professionals in the prevention and management of multidrug resistant tuberculosis and cascade trainings in clinical programmatic management of the disease for healthcare professionals.

Factors influencing the organisational homeostasis in healthcare organisations

PurposeThe purpose of this study is to identify organisational homeostasis factors in the context of healthcare organisations and to develop a conceptual model for green transformation.Design/methodology/approachThe organisational homeostasis factors were determined by review of literature study and the opinions of healthcare experts. Scheduled interviews and closed-ended questionnaires are employed to collect data for this research. This study employed “TISM methodology” and “MICMAC analysis” to better comprehend how the components interact with one another and prioritise them based on their driving and dependence power.FindingsThis study identified 10 factors of organisational homeostasis in healthcare organisation. Recognition of interdependence, hormesis, strategic coalignment, consciousness on dependence of healthcare resources and cybernetic principle of regulations are the driving or key factors of this study.Research limitations/implicationsThe study's primary focus was on the organisational homeostasis factors in healthcare organisations. The methodological approach and structural model are used in a healthcare organisation; in the future, these approaches can be applied to other industries as well.Practical implicationsThe key drivers of organisational homeostasis and the identified factors will be better comprehended and understood by academic and important stakeholders in healthcare organisations. Prioritizing the factors helps the policymakers to comprehend the organisational homeostasis for green transformation in healthcare.Originality/valueIn this study, the TISM and MICMAC analysis for healthcare is proposed as an innovative approach to address the organisational homeostasis concept in the context of green transformation in healthcare organisations.

Internet of Medical Things and the Evolution of Healthcare 4.0: Exploring Recent Trends

Enhanced patient care and remote health monitoring have always been important issues. Internet of Medical Things (IoMT) is a subsection of Healthcare 4.0 that uses recent technologies like mobile computing, medical sensors, and cloud computing to track patients' medical information in real-time. These data are stored in a cloud computing framework that may be accessed and analyzed by healthcare experts. IoMT and Healthcare 4.0 have immense potential for revolutionizing patient care and diagnostics, despite facing numerous complex challenges. This paper thoroughly analyzes technical, structural, and regulatory obstacles encountered by the healthcare sector. Challenges in IoMT implementation include cost considerations, network stress, interoperability issues, ethical limitations, policy intricacies, security concerns, and vulnerabilities jeopardizing patient privacy. However, amidst these challenges, the study highlights the prospective long-term benefits, including diminished medical costs and enhanced patient care. In this study, we have portrayed a comprehensive exploration of the field of IoMT and different related technologies from more than 100 papers to represent the transformation and growth in this decade. We have illustrated some of the significant findings of applications and innovations in the domain of IoMT. This paper delves into IoMT's application in dementia detection and care, improved data management, fortified cybersecurity measures, and modernizing existing healthcare systems. The study also offers valuable insights into potential mitigation strategies, offered by ongoing research and innovation to address emerging trends and challenges, propelling the trajectory of Healthcare 4.0 towards an optimized and transformative future for patient well-being. Hence future research needs to integrate more prudent technologies addressing challenges including security, privacy, interoperability, and implementation costs.

Design of a Predictor Model for Feature Selection using Machine Learning Approaches

Parkinson’s disease is a neural degenerative disease where patients’ faces various critical neurological disorders. Thus, the earlier prediction of PD helps to enhance the patients’ life. The prediction of PD in earlier stage is complex and it consumes huge time. Therefore, effectual and appropriate prediction of PD is measured to a challenging factor for the health care experts and practitioners. To deal with this issue and to accurately predict the PD in earlier stage, this work concentrates on machine learning approaches for designing a predictor system. For developing the anticipated model, L1-norm based Genetic algorithm (L1-GA) is applied for predicting PD in the earlier stage. This L1-GA is utilized for selecting the influencing features for accurate prediction. This L1-GA produces newer feature subset from UCI Machine Learning (ML) dataset for PD for measuring feature weights. For validation , this work considers k-fold cross validation (CV) is used. Also, metrics like accuracy, error rate and execution time are evaluated. The inputs are taken from The PD dataset which is available online for preceding the feature selection process. The optimal accuracy attained with these newly selected sub-sets are considered for further computation. The simulation is performed in Python environment and the experimental findings determine that this study recommends that L1-GA provides better contribution towards PD feature selection and to predict PD in earlier stage. In recent times, Clinical Decision Support System (CDSS) plays an essential role for assisting PD recognition. As well, the anticipated model lays a bridge to fill the gap encountered in feature selection using the available data. The anticipated model gives better trade-off in contrast to prevailing approaches.

Physician assistants in the German inpatient care: barriers and enablers of integration

PurposeVarious health systems are challenged by a multifaceted crisis of increased service demand and workforce shortages. The effects are devastating and may end up in decreased access to care, poor quality of patient care and extreme demands on the workforce. The introduction of the physician assistant (PA) profession provided an avenue to address such challenges in several countries. In Germany, the integration of PAs has been proceeding slowly.Design/methodology/approachTo understand the integration of the profession in Germany, we conducted 15 expert interviews to reveal how the PA profession is perceived among healthcare experts as well as the barriers and facilitators of integration in inpatient care.FindingsOur results highlight a generally positive perception of PAs, particularly in terms of workload relief and bridging the gap between healthcare professionals. Nonetheless, barriers include resistance from healthcare workers unfamiliar with the PA role, while workforce shortages and collaborative teamwork facilitate integration.Originality/valueThese findings lay the groundwork for potential integration-enhancing strategies in Germany.