Lately, the Critical Pathway(CP) of Electronic Medical Record(EMR) is used to the guideline for a treatment in the public hospital. We propose a healthcare promotion service using disease pattern with lifestyle risk factors. We classify a medical historical patient data with disease codes with lifestyle risk factors (hypertension, diabetes, smoking, overweight, excessive alcohol intake, and low physical activity) to make the lifestyle risk factors through the classification. We finally make the clusters of disease code with lifestyle risk factors using the medical historical data based on EMR's electronic discharge summary data. As the result of that, we do a healthcare recommending service based on the disease pattern with lifestyle risk. We can build a medical help desk of a public hospital to support people as we check into the public hospital; how to get the procedure of curing, the desired curing clinical method for the healthcare promotion service by each disease code, and how to be better our healthcare. We evaluate the performance of the proposed system by experimenting with the datasets collected at the medical center to measure performance and report some experimental results.

Innovation development in healthcare has paved the path towards the improvement in system efficiency, patient care and cost-effective healthcare services, increasing the overall efficiency of the healthcare system in a significant manner. Electronic medical record system has fostered interoperability and collaboration in healthcare departments by enabling different systems to exchange and use patient data. This trend has been accelerated dominantly by the pandemic, leading to increased use of telemedicine, referring specifically to the use of electronic communication to provide healthcare services remotely. Moreover, an increased prevalence towards the use of artificial intelligence and machine learning in healthcare has contributed effectively towards the rigorous and informed analysis of large amounts of data as well as to identify patterns or trends that may be useful for improving care delivery or identifying potential issues. A unified electronic medical record System that will generate competitive opportunities and success prospects with reference to enhanced interoperability in US healthcare departments, can contribute towards the improvement in the quality and efficiency of care by making it easier for healthcare providers to access and share patient information as well as reducing the risk of errors in effective patient care.

Background: Malnutrition has negative consequences in hospitals, like poor disease recovery, increased mortality, length of hospitalization, readmission, and health care costs.Aims: This review was designed to determine malnutrition prevalence in hospitals located in eastern Mediterranean region (EMR).Study design: To conduct this systematic review, papers, including original articles, related references and conference proceedings on the prevalence of malnutrition in adult hospitalized patients published in international journals and scientific databases from 1 Jan 2017 to 31 Jan 2022 will be included. The following keywords and their alternatives will be used in the literature search: malnutrition, patient, and EMR. The protocol has been registered at PROSPERO (code: CRD42022339426).Methods: After initial screening based on inclusion and exclusion criteria, the full text articles will be read carefully and eligible documents will be selected according to the defined objectives. Afterward, the selected papers will be reviewed for methodological quality and the required information will be extracted. Finally, in case of sufficient data, a meta-analysis will be performed. Screening, selection, quality assessment, and data extraction will be done by at least two independent reviewers.Results: This review is ongoing and will be completed until the end of year 2023.Conclusion: This review aims to provide comprehensive evidence about the prevalence of malnutrition among adult inpatients in EMR hospitals. This can provide regional authorities, health managers and policy makers good information regarding current status to make appropriate decisions for promoting the health status in hospitals.

Objective: To evaluate the effect of propofol combined with psychological intervention on pain and emotional evaluation in painless colonoscopic colonic polypectomy patients undergoing endoscopic mucosal resection (EMR).Methods: A total of 80 patients who underwent painless colonoscopic colonic polypectomy with EMR treatment between June 2020 and June 2022 were selected in order of admission and divided into two groups. The control group received conventional analgesia, while the observation group received propofol combined with psychological intervention. Pain visual analog scale (VAS) and emotional scale were used to evaluate pain and emotions in both groups of patients. Adverse reactions and clinical efficacy were also compared between the two groups.Results: The two groups of patients were generally comparable with no significant differences (P > 0.05). The VAS score in the observation group was significantly lower than that in the control group (P < 0.05), and the emotional scale score was also improved compared to the control group (P < 0.05). The number of patients with adverse reactions in the observation group was less than that in the control group. The clinical effective rate in the observation group was significantly higher than that in the control group (P < 0.05).Conclusion: Propofol combined with psychological intervention can effectively alleviate pain and improve emotional status in painless colonoscopic colonic polypectomy patients undergoing EMR treatment, thereby increasing the comfort and success rate of the surgery. The number of patients with adverse reactions in the observation group was less than that in the control group, and the clinical effective rate in the observation group was significantly higher than that in the control group (P < 0.05).

At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

The expanding use of devices emitting Pulsed Telecommunication Signals (PTS) has launched a serious debate over the possible effects of electromagnetic radiation (EMR) on living organisms. Our previous work has indicated that PTS exposure alters Amyloid Precursor Protein (APP) and alpha-synuclein (α-syn) metabolism in human cells of neural origin, providing a possible connection between exposure and neurodegeneration. This investigation aimed to reveal, in vitro in human non-neural cells (HEK293), the aftermath of the same exposure on the processing of APP and α-syn. Data presented here, indicate changes in APP metabolism, acquisition of different cellular topologies of the newly generated APP fragments, changes in monomeric α-syn accumulation and multimerization, indicating that APP and α-syn processing is possibly altered in the periphery by EMR. These effects are accompanied by a substantial increase in the levels of Reactive Oxygen Species (ROS). Further investigation is required in order to provide insights into the interaction of PTS with non-neural cells affecting the peripheral systemic functional stability. This is necessary because nowadays whole body human exposure from various EMR sources is a fact in normal life with the valid estimation that they may be increased in view of the forthcoming 5G telecommunications network implementation.