Healthcare Processes Research Articles

Разработка интеллектуальной системы поддержки принятия врачебных решений для прогнозирования хронической болезни почек у детей.

Introduction. Chronic kidney disease is often diagnosed too late. Currently, the diagnostic accuracy is 44.1%, which highlights the urgent need to improve diagnostic methods. The purpose of the study: to develop a model – a system of support of medical decisions to predict chronic kidney disease in children. Materials and methods. А one-center retrospective cohort study (2011–2022) of children with chronic kidney disease of 1–4 stages in the age from 1 to 17 years. To construct a predictive model for diagnosing chronic kidney disease in children, an ensemble learning method was used, using which the models obtained by machine learning algorithms were combined: multi-factor logistic regression and decision tree. The models use five variables: asthenic physique in a child, loss of protein and red blood cells with urine, ESR and blood serum sodium. Results. The study involved 158 patients. The main group includes 128 children with chronic kidney disease of stage 1–4 aged 1–17. The comparison group was 30 children with no diagnosed kidney pathology aged 1 to 17. The children of the two groups did not differ statistically by sex and age. A model has been obtained to predict chronic kidney disease in children on a test sample with an accuracy of 93.5% [87.1; 100.0]%; a sensitivity of 92.0% [82.1; 100.0]; a specificity of 100.0% [100.0; 100.0.0]; ROC-AUC = 98.7%;100.0].0. Obtained model of excellent quality (>90%). The model describes 90.3% [83.8; 96.1]% variance. Conclusion. The proposed model demonstrates excellent predictive ability and may be of important clinical importance for predicting the chronic process in primary health care, where symptoms associated with the risk of chronic kidney disease, may be overlooked. Predicting and developing early nephroprotective strategies can lead to better treatment outcomes and prolong life.

Development of a novel humidity sensor for breath pattern recognition using large dislocation hollow core fiber infused with nano-carbon quantum water gel microfiber

This study presents a humidity sensor based on a large-core-offset hollow core optical fiber (LD-HCF) combined with polyvinyl alcohol carbon quantum dots (PVA-CQDs) nanocomposite microfiber for monitoring human respiratory. PVA-CQDs was introduced into the hollow core structure of LD-HCF to develope the respiratory sensor. The sensor has been integrated into a respiratory monitoring mask, exposed to ultraviolet (UV) light and demonstrated with a humidity sensitivity of 0.1721 nm/%RH as well as response and recovery times of 0.232 s and 0.252 s, respectively. The mask provides the real-time monitoring of human respiratory rates during various activities, including walking, standing, coughing, running, apnea 1 time and apnea 3 times. The typical respiratory patterns have been classified with a remarkable accuracy of 97.62 % using a WOA-1D CNN-LSTM neural network. The respiratory monitoring device is proposed by optical fiber, offering a promising potential in the real-time monitoring for healthcare process.

Experiences and ethical issues during shared decision-making in healthcare at rural health facilities in Uganda: An exploratory qualitative study.

Shared decision-making in healthcare is a collaborative process where patients are supported to make informed decisions according to their preferences. Healthcare decisions affect patients' lives which necessitates patients to participate in decisions concerning their health. This study explored experiences and ethical issues related to shared decision-making in a rural healthcare setting. An exploratory qualitative study was conducted at Budumba Health Centre III and Butaleja Health Centre III in rural Eastern Uganda. In this study, 23 in-depth interviews were conducted among 12 healthcare providers and 11 patients. Data was analyzed thematically using NVivo-12 plus software. Four themes emerged which included: experiences, challenges, strategies for improvement, and ethical issues. Patients at both facilities expressed the need to be involved in decision-making processes. However, many stressed that they are not engaged in decision-making about their health. Many healthcare providers noted that shared decision-m aking could improve patient prognosis but are faced with challenges related to low male involvement and the influence of cultural and religious practices, including myths and patriarchal attitudes which impact effective patient engagement.Ethical issues included concerns about informed consent, privacy and confidentiality, deception, and harm. This study highlighted the need for better sensitization of patients and comprehensive training for HCPs to minimize and resolve ethical issues that emerge during shared decision-making processes. There is a need for targeted interventions to enhance decision-making processes in rural healthcare including but not limited to developing shared decision-making manual and continuous training of healthcare providers to ethically engage patients. Further research is needed to explore larger facilities with a bigger scope including patients under 18 years of age and and their surrogates.

Transforming healthcare performance monitoring - A cutting-edge approach with generalized additive profiles: GAMs for healthcare quality monitoring.

Recent findings indicate a growing trend in data analysis within healthcare using statistical process control. However, the diversity of variables involved necessitates the expansion of new process control methodologies. This study examined control chart applications in cardiology by using generalized additive models (GAMs) to construct profiles while involving multiple healthcare variables (08). Two distinct statistics: deviation (D), and Hotelling (T2) were employed for constructing control charts: a commonly used single-variable statistic for nonparametric profiles and an innovative multivariate statistic that assesses the contribution of each element to process changes. These statistics were tested for monitoring ischemic and hemorrhagic strokes in 1-year acute stroke (369) patients at the Faisalabad Institute of Cardiology. Demographic parameters (age, gender), vascular risk factors (diabetes, family history, sleep), socioeconomic variables (smoking, location), and blood pressure are included in the model. The research includes the computation of zero-state average run length (ARL) for assessing the performance of control charts. The characteristics of the proposed profile were analyzed, such as the T2 control chart, performing better than the D chart for medium-to-large shifts (δ ≥ 0.50). On the other hand, for small δ = 0.25, the D control chart produces smaller ARL values but more significant standard deviations. While both statistics contribute to profile monitoring, T2 is more effective at identifying and tracing medium and large shifts. In conclusion, such handy tools may aid healthcare performance monitoring, especially for complicated predictor-response relationships. Monitored profiles demonstrated that GAMs are useful for healthcare analysis and process monitoring.

The Digital Twin Immersive Design Process and Its Potential Disruption to Healthcare Design through a User-Centered Approach

This applied research proposes a solution to the static government design process for Thai healthcare architecture to better serve the needs of its elderly society. In its place, a novel real-time design process, termed the Digital Twin Immersive Design Process (DT-IDP), repurposes aspects of digital twin and virtual reality technologies into a ‘unitary’ immersive design system. This system accesses ‘experiential’ user-centered data, helping enhance the design of Thai healthcare space beyond a standardized government response. This text builds a rationale for departing from the current design process by describing the formation and advantages of the DT-IDP process. To test its credibility, the DT-IDP process is used to build and compare two digital versions of an existing healthcare space. In these spaces, participants are immersed (elderly patients n = 30; nursing staff n = 5; government healthcare architects n = 5) to assess visitor experiences based on daylighting, artificial lighting, and views of nature. Following immersion, government healthcare architects are interviewed in-depth to evaluate the process’s efficacy and their willingness to adopt it. Results confirm the potential for this process to capture ‘user-centered’ insights, otherwise unobtainable without immersion. Consequently, healthcare architects express a unanimous preference for DT-IDP, acknowledging its unique capacity to bridge a market gap through an experiential component that could better assist them in creating a superior final product. Ultimately, they assert that demand for these features could have a disruptive impact to the current healthcare design process, helping to re-envisage the design of future Thai healthcare space.

Polycystic Ovary Syndrome: A Comprehensive Exploration of Diagnosis Experience in Saudi Women.

Background/Objectives: Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder among women of reproductive age, characterized by symptoms such as menstrual irregularities, hyperandrogenism, and polycystic ovaries. This study aimed to explore the diagnostic experiences of women with PCOS in Saudi Arabia, evaluating the timeline to diagnosis, the adequacy of information provided, and overall patient satisfaction with the healthcare process. Methods: A cross-sectional online survey was conducted with 1182 women diagnosed with PCOS across Saudi Arabia. The survey collected data on sociodemographic characteristics, the timeline from symptom onset to diagnosis, the number of healthcare visits required for diagnosis, and satisfaction with the information and support provided during the diagnostic process. Statistical analyses, including linear regression, were performed to identify factors influencing patient satisfaction. Results: The study found that 43.2% of participants sought medical attention within a year of symptom onset, yet significant delays in diagnosis were common, with 28.6% of women waiting six months or more after seeking medical care. Only 42.7% of women reported receiving adequate information at diagnosis, and satisfaction levels varied across different aspects of care. Key predictors of lower satisfaction included marital status and longer time since diagnosis, while quicker diagnosis and more healthcare visits before diagnosis positively influenced satisfaction. Conclusions: The findings highlight critical gaps in the diagnostic process and patient education for PCOS in Saudi Arabia. The widespread dissatisfaction with the information provided underscores the need for improved patient-centered care, comprehensive education, and standardized diagnostic protocols. Addressing these issues could enhance patient satisfaction and lead to better management of PCOS, both in Saudi Arabia and globally.

The use of Machine Learning in Predicting Disease Progression

Machine learning (ML) has emerged as a powerful tool in the healthcare sector, especially in predicting disease progression. This paper examines the application of various ML algorithms, including supervised, unsupervised, and reinforcement learning, in predicting disease trajectories, particularly in complex and chronic diseases like Alzheimer’s, multiple sclerosis, cardiovascular disease, and diabetes. By leveraging clinical data, genetic information, and patient history, ML models like random forests and neural networks can accurately predict the time to disease progression. This has profound implications for early diagnosis, personalized treatment, and patient management. However, the integration of these models into clinical practice faces challenges related to data quality, interpretability, and deployment in real-world settings. Despite these limitations, the case studies reviewed demonstrate the transformative potential of machine learning in enhancing decision-making processes in healthcare. Keywords: Machine learning, Disease progression prediction, Healthcare, Supervised learning, Chronic diseases.

Perceived Ageism in Health Care: A Hybrid Concept Analysis.

Identifying perceived ageism is a critical step in eliminating ageism. This hybrid concept analysis aims to describe the concept of perceived ageism in health care. In the theoretical phase, a scoping review was conducted. In the field phase, semi-structured interviews were conducted with nine community-dwelling older adults. In the final analytical phase, results from the theoretical phase were integrated with those from the fieldwork phase. The perceived ageism in health care includes four dimensions: self-internalization (self-denial, a sense of unease, and passivity of behavior in seeking health care), interpersonal interaction (perceived negative perceptions, neglected attitudes, and unfair care behaviors), organization system (perceived strangeness caused by unfamiliar technology, unsupportive environment, and inadequate resources), and procedure policy (perceived uniformity of healthcare process and lack of targeted practice policies). Research related to this concept has contributed to developing measurement tools for assessing perceived ageism in healthcare practice and subsequent research.

Impact of electronic medical records on healthcare delivery in Nigeria: A review.

Electronic medical records (EMRs) have great potential to improve healthcare processes and outcomes. They are increasingly available in Nigeria, as in many developing countries. The impact of their introduction has not been well studied. We sought to synthesize the evidence from primary studies of the effect of EMRs on data quality, patient-relevant outcomes and patient satisfaction. We identified and examined five original research articles published up to May 2023 in the following medical literature databases: PUBMED/Medline, EMBASE, Web of Science, African Journals Online and Google Scholar. Four studies examined the influence of the introduction of or improvements in the EMR on data collection and documentation. The pooled percentage difference in data quality after introducing or improving the EMR was 142% (95% CI: 82% to 203%, p-value < 0.001). There was limited heterogeneity in the estimates (I2 = 0%, p-heterogeneity = 0.93) and no evidence suggestive of publication bias. The 5th study assessed patient satisfaction with pharmacy services following the introduction of the EMR but neither had a comparison group nor assessed patient satisfaction before EMR was introduced. We conclude that the introduction of EMR in Nigerian healthcare facilities meaningfully increased the quality of the data.

Abstract P399: Defining A Negative Space: A Scoping Review of Hypertension

Study Objectives: This scoping review seeks to examine the existing body of literature to reveal consensus or absence thereof regarding the definition and management strategies for asymptomatic hypertension. Methods: The review was conducted using Joanna Briggs Institute guidelines. We searched the CINAHL (EBSCO), Scopus, Ovid EMBASE, and PubMed (MEDLINE) databases using keywords and index terms (“hypertension”, “high blood pressure”, “elevated blood pressure”, “asymptomatic”, “symptomless”, “symptom free”, “emergenc*”, “urgent”) to identify adult patients with hypertension in an emergency or outpatient setting. After review and quality assessment, we retained 35 texts for inclusion. Results: The definition of “asymptomatic hypertension” varied widely between the texts. There was significant variation in which society or academic guideline served as the reference and what the blood pressure cut offs were; all specified a lack of end-organ damage. A majority included a definition for hypertensive urgency, most commonly based on elevated blood pressure with vague symptoms. End-organ damage was poorly defined – studies instead described the absence of hypertensive emergency. The most common emergencies mentioned were myocardial infarction, renal failure, and hypertensive encephalopathy. Of the texts which provided recommendations, all recommended a thorough history and physical examination. Only one study recommended additional testing for asymptomatic patients – an ECG and creatinine test. Treatment recommendations for asymptomatic patients relied predominantly on ACEP clinical policy, with a majority of studies advocating for gradual reduction of blood pressure rather than an immediate one, as well as referral for outpatient management. Conclusion: There is a need for unified guidelines on the definition and management of asymptomatic hypertension to ensure effective and consistent patient care. Additionally, there is no consensus for further diagnostic testing in patients with a negative history and physical exam, and there is strong agreement that rapid blood pressure decrease in asymptomatic patients can be detrimental. Addressing this gap in guideline consistency and evidence base could enhance clinical outcomes and streamline healthcare processes across different settings. Future research should focus on establishing consensus and developing management strategies that are adaptable to both emergency and primary care environments.

Cutting-Edge Methodological Guidance for Authors in Conducting the Systematic Review and Meta-Analysis.

The landscape of systematic reviews and meta-analyses (SRMA) in biomedicine has expanded exponentially, driven by the growing demand for evidence-based healthcare decision-making. However, the rapid increase of SRMAs has often outpaced the development of rigorous methodological standards, resulting in variability in quality and potentially limiting their effectiveness in informing healthcare practices. This gap highlights the critical need for advanced methodological guidance to enhance the quality and impact of SRMAs. Our contribution aims to provide comprehensive methodological direction for authors to conduct robust SRMAs. By effectively integrating qualitative and quantitative evidence, SRMAs can address complex healthcare questions more thoroughly than traditional reviews. Furthermore, these step-by-step guidelines will help researchers to address the challenges of synthesizing diverse types of evidence, thereby improving the rigor, relevance, and applicability of their findings in healthcare decision-making processes.

EHealth: digital bridges in the healthcare system

Healthcare in Germany is divided into various service sectors, which differ in terms of outpatient and inpatient care as well as the legal and financial bases. The resulting breaks in consecutive patient care are to be overcome by integrating services into cross-sectoral processes (integrated care). Digitalization and the associated use of information and communication technology (ICT) play adecisive role in this. To derive implications and benefits it is necessary to classify the technical possibilities. For this purpose, eHealth is the basic generic term for all process support and direct patient applications that are based on the electronic exchange of data. Adistinction can be made between technology for process support and technology for direct application on the patient. Applications in all categories are suitable for ensuring that interfaces in the flow of information between those involved in healthcare processes are adequately managed by eHealth. Furthermore, the allocation of specialized medicine through eHealth is independent of location and sector. New possibilities for generating and using structured data for evidence development and care research are realized through eHealth and the development of existing and new care models will be promoted.

Observing the uncertainty of the Amlodipine-Candesartan Incremental Cost-Effectiveness ratio using Deterministic Sensitivity Analysis (DSA)

Background: Hypertension treatment is carried out for a long time and can cause quite large costs. This study delves into the economic evaluation of Amlodipine-Candesartan combination therapy, a prominent treatment for hypertension. While the clinical benefits of this regimen are well-documented, its cost-effectiveness remains a critical consideration for healthcare systems. Employing Deterministic Sensitivity Analysis (DSA). This study aimed to discern the uncertainty surrounding the Incremental Cost-Effectiveness Ratio (ICER) associated with Amlodipine-Candesartan through comprehensive examination, next to refine economic assessments and offer insights for healthcare policymakers and practitioners. Method: This retrospective descriptive study at Andalas University Hospital in 2021 analyzed hypertension outpatient data. Of 284 patients, 73 were included. Among them, 28.77% received Amlodipine monotherapy, while 71.23% received Amlodipine-Candesartan combination therapy. Results: The ICER for systolic blood pressure was Rp. 74,738.10 per mmHg decrease, and for diastolic blood pressure, it was Rp. 205,918.24 per mmHg decrease. Both of them are in the northeast quadrant. Next, DSA will be carried out by creating a tornado diagram by providing a change range of 20% (min-max) for the input variables in ICER. Conclusion: The use of amlodipine-candesartan requires a higher cost, but the resulting effect is also better. Our findings contribute to the broader discourse on combination therapies and inform resource allocation strategies for hypertension management. This research attempts to enhance the precision of economic evaluations, ultimately facilitating more informed and efficient healthcare decision-making processes. Keywords: Amlodipine, Candesartan, Cost-effectiveness, ICER, DSA.

A virtual visualization method for improving the manufacturing accuracy based VPP 3D printers

Compared to traditional vat photopolymerization 3D printing methods, pixel blending technique provides greater freedom in terms of user-defined lighting sources. Based on this technology, scientists have conducted research on 3D printing manufacturing for elastic materials, biologically inert materials, and materials with high transparency, making significant contributions to the fields of portable healthcare and specialty material processing. However, there has been a lack of a universal and simple algorithm to facilitate low-cost printing experiments for researchers not in the 3D printing industry. Here, we propose a mathematical approach based on morphology to simulate the light dose distribution and virtual visualization of parts produced using grayscale mask vat photopolymerization 3D printing technology. Based on this simulation, we develop an auto-correction method inspired by circle packing to modify the grayscale values of projection images, thereby improving the dimensional accuracy of printed devices. This method can significantly improve printing accuracy with just a single parameter adjustment. We conducted experimental validation of this method on a vat photopolymerization printer using common commercial resins, demonstrating its feasibility for printing high precision structures. The parameters utilized in this method are comparatively simpler to acquire compared to conventional techniques for obtaining optical parameters. For researchers in non-vat photopolymerization 3D printing industry, it is relatively user-friendly.

The Impact of Health Policy on Access to Healthcare: Utilizing Geographic Information Systems (GIS) for Detailed Access and Equity Mapping

Health policies are significant in relation to health equity and accessibility. Achieving equity in health care must understand the geographic distribution of health care and the impact of policy decisions on access Planners can use Geographic Information Systems (GIS) as an aid powerful to visualize and scrutinize these differences. The current study addresses some of the challenges of integrating multiple data sources, definitions of access and equity and the dynamic nature of health care processes. Furthermore, sophisticated analytical methods are needed to truly map all complex health care systems. Therefore this paper, describe a cross-sectional mental health treatment (C-SMHT) that uses a fuzzy inference system to assess justice and access to health care. Using ambiguous GIS data to address uncertainty and environmental variability, this approach provides a comprehensive picture of how policy change has shaped health disparities to improve. C-SMHT has many applications for policy makers as well as public health practitioners. This paper will provide a snapshot of mental health provider availability and utilization. Simulation studies to examine how the effectiveness of the C-SMHT approach can help to better identify underserved areas, better allocate resources, and deliver treatment were found to be good. A number of initiatives will be developed as part of this project to assess their impact on the availability of mental health services in defined areas or countries. In addition to providing recommendations to policy makers based on research findings, the simulation will also provide insights into how policies affect equity and access.

Financial management, efficiency, and care quality: A systematic review in the context of Health 4.0.

This article investigates the intersection between innovation, Health 4.0, and financial management in the healthcare industry, emphasizing the importance of operational efficiency and quality of care. The study aims to analyze how financial management processes in healthcare relate to Health 4.0 and enhance care quality. It begins with a thorough theoretical grounding, proposing a framework that connects Health 4.0 with financial management practices. A systematic review of the literature was conducted, identifying trends, challenges, and opportunities in the financial management of Health 4.0. The results highlight selected articles on responsible innovation, Health 4.0 technologies, investments in health, hospital efficiency, performance forecasting, and high-cost patient management. These articles were clustered into "Data Analysis and Machine Learning in Healthcare" and "Health Management and Sustainability," providing a categorized understanding of the topics. The study reveals that Health 4.0 offers significant opportunities for process efficiency and cost reduction without compromising service quality. It highlights strategic advantages in addressing contemporary healthcare challenges by optimizing processes, improving financial projections, and incorporating advanced technologies efficiently. The successful implementation of Health 4.0 can lead to substantial improvements in service quality, adding value to patients and driving local economic development. This article offers valuable insights for healthcare professionals and managers, emphasizing the transformative potential of Health 4.0 and outlining strategies for its effective implementation. The clustering of articles provides a clearer understanding of current research in Health 4.0, contributing significantly to the field and guiding future research directions.

A health equity monitoring framework based on process mining.

In the United States, there is a proposal to link hospital Medicare payments with health equity measures, signaling a need to precisely measure equity in healthcare delivery. Despite significant research demonstrating disparities in health care outcomes and access, there is a noticeable gap in tools available to assess health equity across various health conditions and treatments. The available tools often focus on a single area of patient care, such as medication delivery, but fail to examine the entire health care process. The objective of this study is to propose a process mining framework to provide a comprehensive view of health equity. Using event logs which track all actions during patient care, this method allows us to look at disparities in single and multiple treatment steps, but also in the broader strategy of treatment delivery. We have applied this framework to the management of patients with sepsis in the Intensive Care Unit (ICU), focusing on sex and English language proficiency. We found no significant differences between treatments of male and female patients. However, for patients who don't speak English, there was a notable delay in starting their treatment, even though their illness was just as severe and subsequent treatments were similar. This framework subsumes existing individual approaches to measure health inequities and offers a comprehensive approach to pinpoint and delve into healthcare disparities, providing a valuable tool for research and policy-making aiming at more equitable healthcare.

Māori experiences and perspectives of hospital treatment in the context of acute care.

Through exploring Māori experiences when presenting acutely to hospital, we aimed to identify specific aspects of culturally safe care that we could incorporate into healthcare professionals' training and hospital processes. Using Kaupapa Māori approaches, we undertook semi-structured interviews and thematic analysis to collect and analyse data from diverse groups of Māori participants. We interviewed 17 participants, with 16 follow-up interviews. We found a lack of trust, and perceptions of unequal treatment for Māori. Our participants highlighted the following key needs: the importance of whanaungatanga in building trusting relationships with patients and whānau; creating safe spaces for patients and whānau; improved staff cultural safety; exploring individual patient and whānau needs, avoiding assumptions about cultural requirements or stereotypical characteristics; upholding the mana of all patients with respectful interactions; and empowering Māori as partners in their care. By sharing their personal experiences, participants have highlighted specific aspects of their interactions with hospital staff and the healthcare system that could and should be improved. These insights can guide our efforts to enhance cultural safety for Māori patients and whānau through targeted staff training and structural changes.

Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review.

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as "black boxes." Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process. In this paper, we present a scoping review exploring the application of different XAI techniques in medical text classification, identifying two main types: model-specific and model-agnostic methods. Despite some positive feedback from developers, formal evaluations with medical end users of these techniques remain limited. The review highlights the necessity for further research in XAI to enhance trust and transparency in AI-driven decision-making processes in healthcare.

HCE2RNFC: An Efficient Methodology for Reusing the EHR in the Spanish National Hip Fracture Registry.

Hip fracture is a condition associated with ageing and frailty, with an associated prevalence of 7 per 10000 population in Spain. Evidence suggests that factors in the healthcare process can influence clinical outcomes, so the creation of a national registry is an opportunity to monitor and improve this process. In this regards, Electronic Health Record (EHR) can provide a large amount of data, that can be used to populate the Spanish National Hip Fracture Registry (RNFC, by its acronym in Spanish). However, this reuse of the EHR requires a prior effort in modelling and standardization to build the extraction, transformation, and loading (ETL) processes in a flexible, transparent, and scalable manner. In this work, a robust EHR reuse methodology is implemented to obtain EHR-derived data for the RNFC. The main result of this work was the design and implementation of an EHR data reuse methodology, which was able to load 1279 hip fracture cases and almost 68% of the required concepts from the RNFC.