Medical Treatment Process Research Articles

Guideline for medical nutritional treatment of adult patients with gastrointestinal dysfunction (2025 edition)

Gastrointestinal dysfunction is a common clinical disease. Due to abnormal gastrointestinal digestive and absorption functions, nutrients, water, and electrolytes cannot be absorbed properly, and therefore, scientific and reasonable nutritional intervention is needed. In order to further standardize medical nutrition treatment of patients with gastrointestinal dysfunction, the Chinese Society of Parenteral and Enteral Nutrition (CSPEN) organized experts and scholars in related fields in China to elaborate on the following topics based on current evidence-based medical evidence: the standard process of medical nutrition treatment for patients with gastrointestinal dysfunction, the time and mode of enteral and parenteral nutrition application, and the nutritional intervention mode for patients with gastrointestinal dysfunction in special disease state. Finally, 29 questions and 58 recommendations were formed to provide reference for the standardized application of medical nutrition therapy for patients with gastrointestinal dysfunction.

Next-gen strain sensors: Self-healing, ultra-sensitive, lightweight, and durable MWCNT-silicone rubber for advanced human motion tracking

Strain sensors that have flexible wearable forms can be applied in different aspects, such as in soft robotics, human-computer interaction, motion monitoring, and medical treatment process. The main challenge is creating lightweight, flexible sensors with high sensitivity. In this study, we developed flexible strain sensors using a simple sandwich manufacturing method. To achieve this, multi-walled carbon nanotubes (MWCNTs) were chosen for their excellent conductivity, while silicone rubber (SR) provided a flexible and durable substrate. Advanced SEM-EDX and FTIR analyses were conducted to assess the material structure. Furthermore, the electromechanical performance of the sensors was thoroughly evaluated, highlighting their potential in real-world applications. Electromechanical performance evaluation showed that the SR/0.7-MWCNT/SR sensor in the 0–90 % strain range had a sensitivity of 34.47, an increase of 53.35 %, and a linearity of 0.978, an increase of 10 % compared to the SR/0.5-MWCNT/SR sensor. In addition, the sensor has extremely fast response and recovery times of 65 ms and 60 ms, respectively. The sensor is also able to withstand up to 1.200 cycles of stretching, twisting, and bending. The sensor also exhibited good autonomous electromechanical self-healing properties against physical damage. The sensor developed in the present study are proven to be used to detect human movements such as finger bending, wrist, elbow, and knee movements.

Integrating Complaint Analysis into Hospital Management: A Comparative Study of Surgical and Non-Surgical Complaints.

Aims/Background: In an era where patient-centred care is paramount, effectively managing and analyzing hospital complaints is crucial for improving service quality and patient satisfaction. This study examines hospital complaints to enhance management practices by differentiating between surgery-related and non-surgery-related grievances. By identifying patterns in complaint types and outcomes, we aim to inform targeted quality improvement strategies that address specific patient concerns and boost operational efficiency. Methods: The study utilized data from an internal complaint management system over one year. Complaints were categorized as either surgery-related or non-surgery-related. Descriptive statistics and cross-tabulation analysis were employed to examine the data. The sample comprised 132 complaints, with 67 being surgery-related and 65 non-surgery-related. Results: The analysis revealed that surgery-related complaints frequently involved issues with 'Patient Communication' and 'Surgical Error', while non-surgery-related complaints were primarily about the 'Medical Treatment Process'. The Surgery Department received the highest number of complaints, indicating a critical area for intervention. Additionally, the correlation between complaint types and outcomes provided insights into potential areas for improvement. Conclusion: The findings highlight the need for targeted communication training and procedural enhancements in surgical departments. Non-surgical departments should focus on improving treatment protocols and transparency. These strategies can reduce complaints and improve patient satisfaction. Future research should develop and test interventions based on these insights to further enhance healthcare quality.

Assessment of Knowledge, Attitudes, and Practices of Medical Waste Management for Healthcare Providers in Two of Government Hospitals in Karbala, Iraq

Background: Medical waste is a significant health danger and environmental pollution problem due to its hazardous composition. The expertise and practice of healthcare staff in the proper removal of medical waste is essential for good medical waste management. The purpose of this study was to analyze the knowledge, attitudes, and practices related to medical waste management among healthcare workers in hospitals in Karbala/Iraq. Materials and Methods: descriptive cross-sectional study was conducted using a questionnaire distributed to health care providers in two selected hospitals in Karbala/ Iraq. Results: The results showed the good level of knowledge, attitudes and practices of healthcare providers towards medical waste. There is statistical significance between health care providers' knowledge, attitudes and practices towards medical waste, and their educational level and training courses. In addition to the recording of the statistical significance that were between knowledge with attitudes and practices. The levels can be categorized as positive but it is important to focus on increasing and improving the training of health care providers as it is the basis of the medical waste treatment process. Conclusion: increasing knowledge is one of the best tools to maintain proper workflow in isolating medical waste and maintaining the safety of employees, patients, and the environment.

Study on the Area of Waiting Space in the Old Area of a Tertiary Hospital Based on AnyLogic Simulation

The waiting space is the place where patients stay the longest during the entire medical treatment process, and the adequacy of its spatial area to the number of waiting patients has a significant impact on the treatment experience. Due to the continuous expansion of the medical scale and the increasing outpatient volume, congestion in the waiting areas of the old area of tertiary hospitals in Guangzhou is common, seriously reducing patients’ medical experience. Therefore, it is necessary to determine the optimal waiting area space in the old area based on the number of consultation rooms and evaluate the current situation of the waiting area to optimize the waiting area reasonably. Taking the Third Affiliated Hospital of Sun Yat-sen University as an example, this study determines the daily outpatient volume of departments under specific consultation room numbers, uses AnyLogic to simulate the medical treatment process of patients, outputs the change in the number of patients in the first waiting area. Based on the mean and peak number of waiting patients during peak hours, the study calculates the areas required for “generally adequate” and “specially adequate” waiting space respectively. These are then compared with the current area to evaluate the adequacy of the current waiting space.

Harnessing AI, IoT and Quantum Resonance Magnetic Analysis for Digital Diagnosis and Treatment

This research presents the development and implementation of a novel healthcare ecosystem leveraging Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning and Quantum Magnetic Resonance Analysis (QMRA) technology. The aim is to streamline medical diagnostics and treatment processes by integrating a hardware device capable of conducting comprehensive body scans in minutes, coupled with an Android application for data collection and presentation. The hardware device utilizes Quantum Magnetic Resonance Analyzer for rapid and accurate body scanning, enabling users to record symptoms through voice clips and capture images of wounds or other concerns. The collected data is securely transmitted to a backend database hosted on Firebase for storage and retrieval. The Android app facilitates seamless access to patient profiles by registered doctors, empowering them to analyze scan reports, review recorded symptoms, and view images for informed diagnosis and treatment planning. By combining IoT, AI, and Quantum Magnetic Resonance Analysis, this healthcare ecosystem offers a transformative approach to medical care, improving efficiency, accessibility, and accuracy in diagnosis and treatment.

Practice Standards in International Medical Departments of Public Academic Hospitals in China: Cross-Sectional Study.

Improving health care in cities with a diverse, international population is crucial for ensuring health equity, particularly for foreigners facing challenges due to cultural and language barriers. This situation is especially relevant in China, a major destination for expatriates and travelers, where optimizing health care services and incorporating international standards in the public sector are vital. Achieving this involves understanding the operational details, cultural and linguistic nuances, and advancing medical digitalization. A strategic approach focusing on cultural competence and awareness of health care systems is essential for effectively navigating health care for foreigners and expatriates in China. The aim of this study was to perform an in-depth analysis of the subjective and objective experiences of local and international patients in public hospitals in China to provide a basis for enhancing the medical experience of all patients. A structured questionnaire was provided to patients at an international outpatient service of a top-tier university hospital in China. Qualitative analysis of the survey responses was performed to methodically categorize and analyze medical treatment, focusing on patient demand and satisfaction across four main category elements ("high demand, high satisfaction"; "high demand, low satisfaction"; "low demand, high satisfaction"; and "low demand, low satisfaction"), enabling a detailed cross-sectional analysis to identify areas for improvement. Elements falling under "high demand, high satisfaction" for both Chinese and international patients were primarily in the realms of medical quality and treatment processes. In contrast, elements identified as "high demand, low satisfaction" were significantly different between the two patient groups. The findings highlight the importance of systematic, objective research in advancing the quality of international health care services within China's leading academic medical centers. Key to this improvement is rigorous quality control involving both patients and providers. This study highlights the necessity of certifying such centers and emphasizes the role of digital platforms in disseminating information about medical services. This strategy is expected to cater to diverse patient needs, enhancing the overall patient experience. Furthermore, by developing comprehensive diagnosis and treatment services and highlighting the superior quality and costs associated with international health care, these efforts aim to foster a sense of belonging among international patients and increase the attractiveness of China's medical services for this demographic.

Detection and Classification of Tumors in Brain based on the Location by using MRI

Abstract: Brain tumor is a serious disease occurring in human being. Medical treatment process mainly depends on tumor types and its location. The final decision of neuro specialist and radiologist for the tumor diagnosis mainly depend on evaluation of MRI (Magnetic Resonance Imaging) images. To overcome this, Faster R-CNN deep learning algorithm was proposed for detecting the tumor and marking the area of their occurrence with Region Proposal Network (RPN). The selected MR image dataset consists of three primary brain tumors namely glioma, meningioma and pituitary. The proposed algorithm uses VGG-19 architecture as a base layer for both the region proposal network and the classifier network.

Survey and clinical considerations of gender identity in lower primary school children.

Gender consciousness directly affects the development of gender identity, which is a continuous and lifelong process. Meanwhile, hospitalization is a part of many children's lives and has an impact on their gender development. To investigate the current situation of gender identity in lower primary school children by conducting a survey of 202 hospitalized children in the lower grades and to provide a theoretical basis and foundation for the cultivation of gender identity and medical treatment of children based on the results. This study aims to inspire clinical medical staff to scientifically and reasonably arrange hospital wards for lower primary school children and pay attention to gender protection during the medical treatment process and to help children shape a unified and clear gender identity, which will enable them to better integrate into society and promote their personality development. The gender consciousness scale for elementary and middle school students was used for the survey. Gender identity was already present in lower primary school children. The children's gender roles and gender equality consciousness were strong, exceeding the critical value, but their gender characteristics, gender identity, and gender ideal consciousness were weak. Children aged 6 had the weakest gender identity, and girls had significantly stronger gender identity than boys. Gender identity is already present in lower primary school children, providing a basis and inspiration for the cultivation of gender identity and medical treatment of lower primary school children. Clinical medical staff should be aware of and understand these results and should scientifically and reasonably arrange hospital wards for lower primary school children.

Glossary of Terms and Concepts in Medical Radiation Physics. Terminology Used in Dosimetry and Treatment Planning of Brachytherapy

Medical radiation physics supports medical diagnostic and treatment process based on the use of ionizing radiation in radiation therapy, nuclear medicine, radiation diagnostics and radiation safety of patients and personnel. However, the successful implementation of this process requires close interaction of specialists in all the above areas. The basis of such interaction is the unification of the terminology and concepts used at various stages of the diagnostic and treatment process. The article presents a glossary on dosimetry and treatment planning of brachytherapy which contains the most commonly used terms in this field and explanations for each of them. The glossary is intended for use both in professional education, including postgraduate education, and for medical physicists and radiation oncologists working in radiological medical organizations

Glossary of Terms and Concepts in Medical Radiation Physics. Terminology Used in Quality Assurance of External Radiation Therapy

Medical radiation physics supports medical diagnostic and treatment process based on the use of ionizing radiation in radiation therapy, nuclear medicine, radiation diagnostics and radiation safety of patients and personnel. However, the successful implementation of this process requires close interaction of specialists in all the above areas. The basis of such interaction is the unification of the terminology and concepts used at various stages of the diagnostic and treatment process. The article presents a glossary on quality assurance of external radiation therapy which contains the most commonly used terms in this field and explanations for each of them. The glossary is intended for use both in professional education, including postgraduate education, and for medical physicists and radiation oncologists working in radiological medical organizations.

The Phenomenon and Determinants of Healthcare Service Utilization for Older Adults With Multimorbidity in China: An Explanatory, Mixed-Method Study.

The objective of this study was to explore the phenomenon and determinants of healthcare service utilization in Chinese older adults with multimorbidity. We adopted a mixed-methods explanatory design from July 2022 to May 2023. The quantitative research was a social network analysis to explore the phenomenon of healthcare service utilization in target participants. The quantitative results were further interpreted as the participant's propensity for healthcare services and the potential for information sharing between healthcare providers through shared patients. Logistic regression was conducted to identify individual determinants for healthcare service utilization. The quantitative research was followed by qualitative interviews with stakeholders to deeply understand the phenomenon of interest from the individual, healthcare system, and societal perspectives. We recruited 321 participants for the quantitative study. They preferred using medication services from primary healthcare providers, pharmacists at private pharmacies, and hospital specialists, and preferred using other services from hospital specialists. Dense relationships arose from shared patients among healthcare providers across various professions and settings, making it possible to share patient information. Primary healthcare providers were particularly important in the process, as they were closely related to others through patient sharing. Health status was identified through logistic regression and qualitative interviews as an individual determinant for healthcare service utilization. More determinants were explored in qualitative interviews with 30 stakeholders, including trust, resource allocation, healthcare accessibility, medical treatment process, and healthcare awareness. Strategies should be proposed to intervene with patients' nonoptimal propensity toward healthcare services and promote information sharing among healthcare providers.

A Study on the Influencing Factors of Deaf Peoples Medical Behavior and Attitude in Shanghai

During an emergency medical treatment experience, one of our team members saw a deaf person with heart discomfort who could not express his condition to the doctor and felt anxious and helpless and thus began to pay attention to the medical treatment of deaf people. In the preliminary research, the team found that the medical treatment process and experience of deaf people are inconvenient. Some deaf people show more negative medical treatment behavior and attitude. Therefore, on this basis, the research question of this paper is further proposed: what are the difficulties and obstacles existing in the current medical treatment of deaf people? What factors influence the medical treatment behavior and attitude of deaf people? Taking the deaf-dumb people in Shanghai as the research target, this paper conducts a relevant investigation utilizing a questionnaire, semi-structured interview, and participatory observation. A total of 148 questionnaires were collected, and ten deaf patients, volunteers, and professionals from deaf organizations were interviewed for interviewing method. The team members also launched a participatory observation by volunteering at the deaf and hard-of-hearing outpatient clinic. This study found that some deaf people in Shanghai have negative attitudes and behaviors toward medical treatment, mainly manifested as reluctance to see a doctor in time, fear of going to the hospital, and distrust of the medical system and doctors. The main reasons for this problem are as follows: poor communication quality, low self-confidence, poor financial conditions, doctors negative attitudes, unavailability of voice services, and lack of volunteers. With the development of digital media in recent years, the difficulties in diagnosing and treating deaf patients have been alleviated.

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures.

The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services.

Discrete-Event Simulation Model for Monitoring Elderly and Patient’s Smart Beds

In this paper, we explore the development of a simulation model to assist in the decision-making process for monitoring the health conditions of elderly patients using data from their smart beds. The main objective of the proposed simulation model is to estimate the transitional condition of the health system by tracking patients’ physiological signals via a growth model. This approach allows the model to mimic the dynamics of various health-related issues faced by patients and the elderly. When combined with real-world data and practical expertise, the discrete-event simulation model can become a valuable tool for improving patient health and streamlining medical treatment processes. The proposed simulation model incorporates input from subject-matter experts and utilises statistical analysis to fit essential parameters based on patient data obtained from smart beds. However, authors should consider rephrasing this section to improve clarity and coherence.

Abstracts from the 26th Nordic Congress of Gerontology

This paper is dedicated to the research presented at the 26th Nordic Congress of Gerontology in Odense, Denmark, in June 2022, which was organized by the Danish Gerontological Society and the Danish Society for Geriatrics. The overall topics of the congress were change and continuity. Participants were invited to explore questions on change and continuity from the perspective of ageing and later life within the following congress themes: digitisation and technology; housing, generations and mobility; lifestyle, engagement and transition; education and competences in ageing societies; morbidity, medical treatment and ageing processes; and a good life and a good death. Life is about change and continuity. We experience ourselves and our surroundings differently throughout our lives. For most people, later life is characterized by major transitions, moving towards frailty and multiple losses. But it may also be a part of life with more time to engage in preferred activities, explore new possibilities, and to cope and come to terms with new challenges. The COVID-19 pandemic proved to be a challenge to the world beyond imagination, particularly for frail and vulnerable people and societies. However, it also provided valuable new insights and reminded us of the values in life. We, therefore, also welcomed presentations and discussions on societal perspectives such as citizen perspective, ethnicity, inequality, sustainability and ecology, the pandemic and lockdown. We thank the organizers, scientific committees and all participants for a great congress with high-quality presentations and discussions.

Care4U: Integrated healthcare systems based on blockchain

During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sick people, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patients can ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT). The IoMT can monitor and transmit sensitive health data; however, it may be vulnerable to various attacks. In this paper, an efficient healthcare security system is proposed for IoMT applications. In the proposed system, the medical sensors can transmit sensed encrypted health data via a mobile application to the doctor for privacy. Then, three consortium blockchains are constructed for load balancing of transactions and reducing transaction latency. They store the credentials of system entities, doctors' prescriptions and recommendations according to the data transmitted via mobile applications, and the medical treatment process. Besides, cancelable biometrics are used for providing authentication and increasing the security of the proposed medical system. The investigational results show that the proposed system outperforms existing work where the proposed model consumed less processing time by values of 18%, 22%, and 40%, and less energy for processing a 200 ​KB file by values of 9%, 13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. From these results, it is clear that the proposed system gives a very reliable and secure performance for efficiently securing medical applications.

Cross-Department Collaborative Healthcare Process Model Discovery From Event Logs

Healthcare plays an increasingly essential role in our daily life. Modern Hospital Information Systems (HISs) record and store detailed medical treatment process information for all patients as event logs. By taking event logs as input, process mining techniques have been widely applied to extract valuable insights to improve medical treatment processes and deliver better healthcare services. However, considering the complexity of collaborations among different medical departments, existing model discovery techniques cannot be applied directly. To handle this limitation, this paper proposes a novel approach to support the discovery of Cross-department Collaborative Healthcare Process (CCHP) models from medical event logs. Specifically, an extension of classical Petri Nets with message and resource attributes is first introduced to formalize CCHPs. Then, a novel discovery algorithm is proposed to discover Intra-department Healthcare Process (IHP) models. Next, collaboration patterns among medical departments are formalized and corresponding discovery algorithms are given on that basis. Finally, a global CCHP model is obtained by integrating all discovered collaboration patterns and IHP models. By using four public medical event logs, we quantitatively compare our approach with the state-of-the-art process mining techniques in terms of model quality, and our experimental results demonstrate that the proposed approach can discover more accurate healthcare process models. <i>Note to Practitioners</i>&#x2014;The recorded medical event logs by HISs can be used to extract valuable insights for the analysis of healthcare processes. However, existing process model discovery techniques cannot be applied for the analysis directly due to the complex collaborations among different medical departments of a hospital. This paper introduces a novel approach for cross-department collaborative healthcare process model discovery from medical event logs. All proposed techniques are fully implemented and publicly available. Using four public medical event logs, we show the applicability and advantages of our approach against existing ones. The proposed techniques are applicable to the model discovery and behavior understanding of real-life operational healthcare processes.

Studi Pendekatan Bentuk pada Desain Rumah Sakit Khusus Lanjut Usia di Jakarta dengan Tema Arsitektur Biophilic

This article aims to examine the approach to the design form of a hospital specifically for senior citizens with a Biophilic Architecture approach. Elderly hospitals are based on specific care service needs for senior citizens who require special treatment both psychologically and medically. The Biophilic Architecture design approach was chosen for design oriented towards creating a close relationship between humans as users and their natural environment. Discussion in the design preparation process is based on a descriptive qualitative approach with survey techniques and literature studies. From the results of the discussion, it can be stated that the process of medical treatment for treatment and recovery is the main activity realized in the treatment room, inpatient room and interaction room. The design concept leads to healing environment conditions in the form of a feeling of relaxation, human and comfortable relationships with open spaces in voids, openings, and the unity of supporting spaces in the form of parks integrated in buildings.

Process-Aware Attacks on Medication Control of Type-I Diabetics Using Infusion Pumps

As medical infusion pumps are known to be vulnerable to cybersecurity threats, industrial reports, guidelines, and state-of-the art research have focused on securing such devices. This includes hardening a pump's network communications, wireless interfaces, and patching software flaws that can allow adversaries to compromise the device's usability and potentially lead to adverse effects on patients. Still, a very small percentage of this work has focused on securing devices against <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">process-aware attacks</i> that target the business logic behind medical treatment processes, even though it is widely known that deviations or disruptions in continuous medication administration may be harmful, even lethal. In this work, we first develop a threat model on an insulin infusion pump used for blood glucose regulation in Type-I diabetics. We then set up a generalized Simulink model of common insulin pumps used for Type-I diabetic treatment and perform a volume control assessment to investigate the probability of process-aware cyber-attacks to cause patient harm through microalterations on continuous medical administration that stay within operational limits but manage to impact a patient's health. We achieve this by manipulating the business process logic behind semiautomatic drug administration on the insulin pump model that uses continuous glucose monitoring systems. We validate attack models capable of causing adverse impact on patients through performance degradation of the drug administration processes.