Electronic Medical Record Information Research Articles

Social determinants of health and disparities in spine surgery: a 10-year analysis of 8,565 cases using ensemble machine learning and multilayer perceptron

BACKGROUND CONTEXTThe influence of SDOH on spine surgery is poorly understood. Historically, researchers commonly focused on the isolated influences of race, insurance status, or income on healthcare outcomes. However, analysis of SDOH is becoming increasingly more nuanced as viewing social factors in aggregate rather than individually may offer more precise estimates of the impact of SDOH on healthcare delivery. PURPOSEThe aim of this study was to evaluate the effects of patient social history on length of stay (LOS) and readmission within 90 days following spine surgery using ensemble machine learning and multilayer perceptron. STUDY DESIGNRetrospective chart review. PATIENT SAMPLE8,565 elective and emergency spine surgery cases performed from 2013 to 2023 using our institution's database of longitudinally collected electronic medical record information. OUTCOMES MEASURESPatient LOS, discharge disposition, and rate of 90-day readmission. METHODSEnsemble machine learning and multilayer perceptron were employed to predict LOS and readmission within 90 days following spine surgery. All other subsequent statistical analysis was performed using SPSS version 28. To further assess correlations among variables, Pearson's correlation tests and multivariate linear regression models were constructed. Independent sample t-tests, paired sample t-tests, one-way analysis of variance (ANOVA) with post-hoc Bonferroni and Tukey corrections, and Pearson's chi-squared test were applied where appropriate for analysis of continuous and categorical variables. RESULTSBlack patients demonstrated a greater LOS compared to white patients, but race and ethnicity were not significantly associated with 90-day readmission rates. Insured patients had a shorter LOS and lower readmission rates compared to non-insured patients, as did privately insured patients compared to publicly insured patients. Patients discharged home had lower LOS and lower readmission rates, compared to patients discharged to other facilities. Marriage decreased both LOS and readmission rates, underweight patients showcased increased LOS and readmission rates, and religion was shown to impact LOS and readmission rates. When utilizing patient social history, lab values, and medical history, machine learning determined the top 5 most-important variables for prediction of LOS —along with their respective feature importances—to be insurance status (0.166), religion (0.100), ICU status (0.093), antibiotic use (0.061), and case status: elective or urgent (0.055). The top 5 most-important variables for prediction of 90-day readmission—along with their respective feature importances—were insurance status (0.177), religion (0.123), discharge location (0.096), emergency case status (0.064), and history of diabetes (0.041). CONCLUSIONSThis study highlights that SDOH is influential in determining patient length of stay, discharge disposition, and likelihood of readmission following spine surgery. Machine learning was utilized to accurately predict LOS and 90-day readmission with patient medical history, lab values, and social history, as well as social history alone.

Cloud storage auditing and data sharing with data deduplication and private information protection for cloud-based EMR

In the cloud-based electronic medical records (EMRs) system, the doctors upload EMRs to the cloud for storage and sharing. However, the integrity of EMRs stored in the cloud cannot be guaranteed and sharing EMRs may leak the patients’ private information. Furthermore, shared EMRs contain a lot of duplicate data, leading to data redundancy. To solve the above issues, we propose a cloud storage auditing and data sharing scheme with data deduplication and private information protection for cloud-based EMR. In our proposed scheme, we introduce the sanitizer to sanitize the data blocks containing the private information in EMR, and transform these blocks’ authenticators into the authenticators of sanitized EMR. The authenticators are utilized to check the data integrity. Using the above method, the EMR stored in the cloud can be shared with researchers while ensuring the privacy of private information and the integrity of EMR. In addition, our scheme can achieve block-level deduplication, in which only one copy of the deduplicated blocks in EMR needs to be stored. The conducted security analysis and performance evaluation affirm the security and efficiency of the proposed scheme.

Construction and validation of clinical prediction model of acupuncture and moxibustion for Bell's palsy

To establish and validate a clinical prediction model of acupuncture and moxibustion for Bell's palsy so as to provide a tool for predicting the effect of acupuncture and moxibustion on Bell's palsy. A total of 269 patients with Bell's palsy were collected from department of acupuncture, moxibustion and tuina, Shengli Oilfield Central Hospital, neurology department, Shenxian County Central Hospital and department of rehabilitation medicine, Dongying Municipal Hospital of TCM from June 2018 to June 2023. All of these cases were treated with acupuncture and moxibustion. Of them, 182 cases, from department of acupuncture, moxibustion and tuina, Shengli Oilfield Central Hospital and neurology department, Shenxian County Central Hospital, were randomized into a training group (128 cases) and an internal validation group (54 cases); 87 cases from department of rehabilitation medicine, Dongying Municipal Hospital of TCM were assigned to an external validation group. The clinical data of all of the cases were extracted from the electronic medical record information platform. Using SPSS25.0 and R4.2.3, through univariate and multivariate Logistic regression analysis, the independent factors influencing the effects of acupuncture and moxibustion on Bell's palsy were identified. By means of internal and external validations, the receiver operating characteristic curve (ROC), the goodness-of-fit curve (GFC) and the decision curve analysis (DCA) were plotted. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the model were calculated; and its comprehensive performance was evaluated. The results of the multivariate Logistic regression analysis showed that the independent factors for the unsatisfactory effect on Bell's palsy were advanced age, severe symptoms before treatment, no use of steroids within 72 h of onset, and lack of acupuncture-moxibustion therapy during the acute phase or single acupuncture-moxibustion protocol (P<0.05, P<0.01). Based on these factors, nomogram model and online columnar plot prediction tool (https://bmuchen.shinyapps.io/dynnomapp/) were established. The area under the ROC curve of the model was 0.921 (95% CI: 0.877, 0.966), 0.876 (95% CI: 0.787, 0.966), and 0.846 (95% CI: 0.766, 0.926) in the training group, the internal validation group, and the external validation group, respectively, indicating good predictive value. The model showed a satisfactory calibration curve alignment. The decision threshold in the range of 0 to 0.8 provided clinical benefits for participants. The model exhibited the sensitivity from 65.9% to 88.0%, the specificity ranging from 77.3% to 90.7%, the accuracy from 77.8% to 85.9%, the positive predictive value from 83.3% to 90.1%, and the negative predictive value from 70.8% to 78.7%. The comprehensive evaluation indicated a satisfactory clinical application value of the model. The clinical prediction model of acupuncture and moxibustion for Bell's palsy is valuable in its practice and promotion to a certain extent. The predicted results are conductive to clinicians' judgement of the effect of acupuncture and moxibustion for this disease and making effective and high-quality clinical decisions, as well as formulating the optimal therapeutic regimen.

Development and validation of a risk prediction model and prediction tools for post-thrombotic syndrome in patients with lower limb deep vein thrombosis

PurposeOur research aims to compare the predictive performance of decision tree algorithms (DT) and logistic regression analysis (LR) in constructing models, and develop a Post-Thrombotic Syndrome (PTS) risk stratification tool. MethodsWe retrospectively collected and analyzed relevant case information of 618 patients diagnosed with DVT from January 2012 to December 2021 in three different tertiary hospitals in Jiangxi Province as the modeling group. Additionally, we used the case information of 212 patients diagnosed with DVT from January 2022 to January 2023 in two tertiary hospitals in Hubei Province and Guangdong Province as the validation group. We extracted electronic medical record information including general patient data, medical history, laboratory test indicators, and treatment data for analysis. We established DT and LR models and compared their predictive performance using receiver operating characteristic (ROC) curves and confusion matrices. Internal and external validations were conducted. Additionally, we utilized LR to generate nomogram charts, calibration curves, and decision curves analysis (DCA) to assess its predictive accuracy. ResultsBoth DT and LR models indicate that Year, Residence, Cancer, Varicose Vein Operation History, DM, and Chronic VTE are risk factors for PTS occurrence. In internal validation, DT outperforms LR (0.962 vs 0.925, z = 3.379, P < 0.001). However, in external validation, there is no significant difference in the area under the ROC curve between the two models (0.963 vs 0.949, z = 0.412, P = 0.680). The validation results of calibration curves and DCA demonstrate that LR exhibits good predictive accuracy and clinical effectiveness. A web-based calculator software of nomogram (https://sunxiaoxuan.shinyapps.io/dynnomapp/) was utilized to visualize the logistic regression model. ConclusionsThe combination of decision tree and logistic regression models, along with the web-based calculator software of nomogram, can assist healthcare professionals in accurately assessing the risk of PTS occurrence in individual patients with lower limb DVT.

The Red Cross Red Crescent Health Information System (RCHIS): an electronic medical records and health information management system for the red cross red crescent emergency response units

BackgroundThe Red Cross and Red Crescent Movement (RCRC) utilizes specialized Emergency Response Units (ERUs) for international disaster response. However, data collection and reporting within ERUs have been time-consuming and paper-based. The Red Cross Red Crescent Health Information System (RCHIS) was developed to improve clinical documentation and reporting, ensuring accuracy and ease of use while increasing compliance with reporting standards.Case presentationRCHIS is an Electronic Medical Record (EMR) and Health Information System (HIS) designed for RCRC ERUs. It can be accessed on Android tablets or Windows laptops, both online and offline. The system securely stores data on Microsoft Azure cloud, with synchronization facilitated through a local ERU server. The functional architecture covers all clinical functions of ERU clinics and hospitals, incorporating user-friendly features. A pilot study was conducted with the Portuguese Red Cross (PRC) during a large-scale event. Thirteen super users were trained and subsequently trained the staff. During the four-day pilot, 77 user accounts were created, and 243 patient files were documented. Feedback indicated that RCHIS was easy to use, requiring minimal training time, and had sufficient training for full utilization. Real-time reporting facilitated coordination with the civil defense authority.ConclusionsThe development and pilot use of RCHIS demonstrated its feasibility and efficacy within RCRC ERUs. The system addressed the need for an EMR and HIS solution, enabling comprehensive clinical documentation and supporting administrative reporting functions. The pilot study validated the training of trainers’ approach and paved the way for further domestic use of RCHIS. RCHIS has the potential to improve patient safety, quality of care, and reporting efficiency within ERUs. Automated reporting reduces the burden on ERU leadership, while electronic compilation enhances record completeness and correctness. Ongoing feedback collection and feature development continue to enhance RCHIS’s functionality. Further trainings took place in 2023 and preparations for international deployments are under way. RCHIS represents a significant step toward improved emergency medical care and coordination within the RCRC and has implications for similar systems in other Emergency Medical Teams.

Knowledge and Attitude of Eye Care Workers towards Electronic Medical Records in a Tertiary Eye Facility in South-East Nigeria

Electronic Medical Records (EMR) has been an integral part of patient management in the developed world but still gradually gaining popularity in the developing world. It was met with resistance by health workers in Nigeria though this is changing in the post COVID era. However, there still appears to be some resistance to its acceptance for different and sometimes justifiable reasons. The aim is to determine the knowledge and attitude towards EMR among eye health workers in a teaching hospital in South East Nigeria. This cross-sectional quantitative study was conducted among consenting consecutive eye care workers at Guinness Eye Centre, Onitsha aged≥ 18years from July to September, 2023. A pretested semi structured self-administered questionnaire was used. Information of interest were biodata, knowledge and attitude towards EMR. They were summarized into simple frequencies and percentages. Knowledge scores were grouped into good and poor using the blooms cut off for knowledge attitude and practice. Univariate and bivariate analysis were done and results represented in tables and charts with levels of significance placed at p&lt;0.05. There was a total of 135 respondents; the mean age was 32.6 years ± 10 SD, male to female ratio of 1:1.1. The result showed good knowledge and positive attitude towards EMR (85.2% and 73.2% respectively) with younger age group, professional cadre and computer literacy found to be associated with good knowledge and positive attitude towards EMR in the population. The hospital/ clinic were the most common sources of EMR information. This study showed good knowledge and positive attitude toward EMR in the study population. It also suggested an association with younger age group, professional cadre and computer literacy.

Comparison of machine learning approaches for positive airway pressure adherence prediction in a veteran cohort

BackgroundAdherence to positive airway pressure (PAP) therapy for sleep apnea is suboptimal, particularly in the veteran population. Accurately identifying those best suited for other therapy or additional interventions may improve adherence. We evaluated various machine learning algorithms to predict 90-day adherence.MethodsThe cohort of VA Northeast Ohio Health Care system patients who were issued a PAP machine (January 1, 2010–June 30, 2015) had demographics, comorbidities, and medications at the time of polysomnography obtained from the electronic health record. The data were split 60:20:20 into training, calibration, and validation data sets, with no use of validation data for model development. We constructed models for the first 90-day adherence period (% nights ≥4 h use) using the following algorithms: linear regression, least absolute shrinkage and selection operator, elastic net, ridge regression, gradient boosted machines, support vector machine regression, Bayes-based models, and neural nets. Prediction performance was evaluated in the validation data set using root mean square error (RMSE).ResultsThe 5,047 participants were 38.3 ± 11.9 years old, and 96.1% male, with 36.8% having coronary artery disease and 52.6% with depression. The median adherence was 36.7% (interquartile range: 0%, 86.7%). The gradient boosted machine was superior to other machine learning techniques (RMSE 37.2). However, the performance was similar and not clinically useful for all models without 30-day data. The 30-day PAP data and using raw diagnoses and medications (vs. grouping by type) improved the RMSE to 24.27.ConclusionComparing multiple prediction algorithms using electronic medical record information, we found that none has clinically meaningful performance. Better adherence predictive measures may offer opportunities for personalized tailoring of interventions.

Assessment of Information Technology in Electronic Medical Records Using the Concept Health Technology Assessment at X Jepara Hospital

&lt;div&gt;&lt;table cellspacing="0" cellpadding="0" align="left"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td align="left" valign="top"&gt;&lt;p&gt;&lt;em&gt;Hospitals as health service providers need to study the equipment used in health services, which is called a Health Technology Assessment. This research aims to determine the assessment of electronic medical record information technology using the Health Technology Assessment concept at regional General Hospital X Jepara. This research uses a qualitative method with a descriptive design. The research was conducted at the regional general hospital X Jepara which was carried out from March to June 2023. There were 13 informants in this research with 9 main informants and 3 triangulation informants. Data collection techniques use in-depth interviews, observation, and document study. Data analysis for this research is data reduction, data presentation, and conclusion. The results of this research on the concept of effectiveness can increase productivity, be more efficient, make it very easy to publish patient medical records in summary and detail, and doctors and nurses can easily access patient data by logging in using their password. Patient privacy is also maintained with limited access to open it. Service is fast and can increase satisfaction and improve hospital services from an economic perspective using health technology assessment methods. The characteristic aspect of using electronic medical records is that it makes hospital services easier. The security aspect of patient data is guaranteed because system access can only be accessed by users with an account, and the rights of each user are guaranteed. The management of the regional general hospital will easily use electronic medical records and improve the service and quality of Regional General Hospital X Jepara.&lt;/em&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt; &lt;strong&gt;&lt;em&gt;Keywords: Electronic medical record, Effectiveness, Economic, Technical characteristic, Security&lt;/em&gt;&lt;/strong&gt;

Research on Electronic Medical Record Information Sharing: A Bibliometric Analysis

Objective :This study aimed to conduct a bibliometric analysis of information sharing in electronic medical records. Use bibliometric methods to discover the research directions in this field, the spatiotemporal distribution of research results, and the temporal trends of research hotspots. Methods: This article uses bibliometric analysis methods to analyze relevant research on electronic medical record information sharing. The Web of Science (WOS) database was used to extract the publications. Through keyword searches and limiting literature sources to core journals, 1779 articles were ultimately included in the analysis. Citespace6.2.4 was used for bibliometric analysis. Results: The number of related studies shows a stable upward trend. The application and technological optimizations were the subjects analyzed in most studies. Based on the primary geographic distribution of published articles, the United States was the country that published the most articles. Conclusion: We found that research on laws, regulations, and information ethics is insufficient. These research directions are worth further exploration. In addition, the advancement of information technology is also a solution to the problems in electronic medical record information sharing.

Classification of Medical Image Notes for Image Labeling by Using MinBERT

The lack of labeled image data poses a serious challenge to the application of artificial intelligence (AI) in medical image diagnosis. Medical image notes contain valuable patient information that could be used to label images for machine learning tasks. However, most image note texts are unstructured with heterogeneity and short-paragraph characters, which fail traditional keyword-based techniques. We utilized a deep learning approach to recover missing labels for medical image notes automatically by using a combination of deep word embedding and deep neural network classifiers. Bidirectional encoder representations from transformers trained on medical image notes corpus (MinBERT) were proposed. We applied the proposed techniques to two typical classification tasks: Medical image type identification and clinical diagnosis identification. The two methods significantly outperformed baseline methods and presented high accuracies of 99.56% and 99.72% in image type identification and of 94.56% and 92.45% in clinical diagnosis identification. Visualization analysis further indicated that word embedding could efficiently capture semantic similarities and regularities across diverse expressions. Results indicated that our proposed framework could accurately recover the missing label information of medical images through the automatic extraction of electronic medical record information. Hence, it could serve as a powerful tool for exploring useful training data in various medical AI applications.

Prediction of the risk of cytopenia in hospitalized HIV/AIDS patients using machine learning methods based on electronic medical records.

Cytopenia is a frequent complication among HIV-infected patients who require hospitalization. It can have a negative impact on the treatment outcomes for these patients. However, by leveraging machine learning techniques and electronic medical records, a predictive model can be developed to evaluate the risk of cytopenia during hospitalization in HIV patients. Such a model is crucial for designing a more individualized and evidence-based treatment strategy for HIV patients. The present study was conducted on HIV patients who were admitted to Guangxi Chest Hospital between June 2016 and October 2021. We extracted a total of 66 clinical features from the electronic medical records and employed them to train five machine learning prediction models (artificial neural network [ANN], adaptive boosting [AdaBoost], k-nearest neighbour [KNN] and support vector machine [SVM], decision tree [DT]). The models were tested using 20% of the data. The performance of the models was evaluated using indicators such as the area under the receiver operating characteristic curve (AUC). The best predictive models were interpreted using the shapley additive explanation (SHAP). The ANN models have better predictive power. According to the SHAP interpretation of the ANN model, hypoproteinemia and cancer were the most important predictive features of cytopenia in HIV hospitalized patients. Meanwhile, the lower hemoglobin-to-RDW ratio (HGB/RDW), low-density lipoprotein cholesterol (LDL-C) levels, CD4+ T cell counts, and creatinine clearance (Ccr) levels increase the risk of cytopenia in HIV hospitalized patients. The present study constructed a risk prediction model for cytopenia in HIV patients during hospitalization with machine learning and electronic medical record information. The prediction model is important for the rational management of HIV hospitalized patients and the personalized treatment plan setting.

Verifiable fuzzy keyword search supporting sensitive information hiding for data sharing in cloud-assisted e-healthcare systems

Nowadays, cloud-assisted e-healthcare systems are playing a more and more important role in intelligent medical services. By uploading Electronic Medical Records (EMRs) to the cloud, users can facilitate data sharing and decrease local data management overhead. Before EMRs are outsourced to the cloud, the sensitive information in them is usually encrypted for protecting the privacy. Though the techniques of traditional searchable encryption could achieve the retrieval of ciphertext, it results in EMRs unable to be shared with external researchers. Moreover, the existing searchable encryption schemes for cloud-assisted e-healthcare systems rarely consider fault-tolerant search and result verification. To address these issues, this paper proposes a verifiable fuzzy keyword search scheme supporting sensitive information hiding for data sharing in cloud-assisted e-healthcare systems. We encrypt sensitive information and share the rest information in EMRs among all users. We improve the approaches of keyword transformation to support the type of keyword in EMRs and realize fuzzy keyword search. For purpose of accomplishing the public verification, we adopt the BLS signature algorithm to generate two kinds of verification tags. We also generate the auxiliary match information to realize condition search. To enhance search efficiency, we build a secure index based on the balanced binary tree. In addition, the proposed scheme can also achieve dynamic update in real-time. We analyze the security and implement a series of experiments for evaluating the effectiveness of this scheme. The experimental results illustrate our scheme could reach high accuracy.

Who needs clinician attention first? A qualitative study of critical care clinicians’ needs that enable the prioritization of care for populations of acutely ill patients

BackgroundTo adequately care for groups of acutely ill patients, clinicians maintain situational awareness to identify the most acute needs within the entire intensive care unit (ICU) population through constant reappraisal of patient data from electronic medical record and other information sources.Our objective was to understand the information and process requirements of clinicians caring for multiple ICU patients and how this information is used to support their prioritization of care among populations of acutely ill patients. Additionally, we wanted to gather insights on the organization of an Acute care multi-patient viewer (AMP) dashboard. MethodsWe conducted and audio-recorded semi-structured interviews of ICU clinicians who had worked with the AMP in three quaternary care hospitals. The transcripts were analyzed with open, axial, and selective coding. Data was managed using NVivo 12 software. ResultsWe interviewed 20 clinicians and identified 5 main themes following data analysis: (1) strategies used to enable patient prioritization, (2) strategies used for optimizing task organization, (3) information and factors helpful for situational awareness within the ICU, (4) unrecognized or missed critical events and information, and (5) suggestions for AMP organization and content. Prioritization of critical care was largely determined by severity of illness and trajectory of patient clinical status. Important sources of information were communication with colleagues from the previous shift, bedside nurses, and patients, data from the electronic medical record and AMP, and physical presence and availability in the ICU. ConclusionsThis qualitative study explored ICU clinicians’ information and process requirements to enable the prioritization of care among populations of acutely ill patients. Timely recognition of patients who need priority attention and intervention provides opportunities for improvement of critical care and for preventing catastrophic events in the ICU.

Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation

Clinical electronic medical records (EMRs) contain important information on patients' anatomy, symptoms, examinations, diagnoses, and medications. Large-scale mining of rich medical information from EMRs will provide notable reference value for medical research. With the complexity of Chinese grammar and blurred boundaries of Chinese words, Chinese clinical named entity recognition (CNER) remains a notable challenge. Follow-up tasks such as medical entity structuring, medical entity standardization, medical entity relationship extraction, and medical knowledge graph construction largely depend on medical named entity recognition effects. A promising CNER result would provide reliable support for building domain knowledge graphs, knowledge bases, and knowledge retrieval systems. Furthermore, it would provide research ideas for scientists and medical decision-making references for doctors and even guide patients on disease and health management. Therefore, obtaining excellent CNER results is essential. We aimed to propose a Chinese CNER method to learn semantics-enriched representations for comprehensively enhancing machines to understand deep semantic information of EMRs by using multisemantic features, which makes medical information more readable and understandable. First, we used Robustly Optimized Bidirectional Encoder Representation from Transformers Pretraining Approach Whole Word Masking (RoBERTa-wwm) with dynamic fusion and Chinese character features, including 5-stroke code, Zheng code, phonological code, and stroke code, extracted by 1-dimensional convolutional neural networks (CNNs) to obtain fine-grained semantic features of Chinese characters. Subsequently, we converted Chinese characters into square images to obtain Chinese character image features from another modality by using a 2-dimensional CNN. Finally, we input multisemantic features into Bidirectional Long Short-Term Memory with Conditional Random Fields to achieve Chinese CNER. The effectiveness of our model was compared with that of the baseline and existing research models, and the features involved in the model were ablated and analyzed to verify the model's effectiveness. We collected 1379 Yidu-S4K EMRs containing 23,655 entities in 6 categories and 2007 self-annotated EMRs containing 118,643 entities in 7 categories. The experiments showed that our model outperformed the comparison experiments, with F1-scores of 89.28% and 84.61% on the Yidu-S4K and self-annotated data sets, respectively. The results of the ablation analysis demonstrated that each feature and method we used could improve the entity recognition ability. Our proposed CNER method would mine the richer deep semantic information in EMRs by multisemantic embedding using RoBERTa-wwm and CNNs, enhancing the semantic recognition of characters at different granularity levels and improving the generalization capability of the method by achieving information complementarity among different semantic features, thus making the machine semantically understand EMRs and improving the CNER task accuracy.

Deploying the Red Cross Red Crescent Health Information System (RCHIS) into a Type-One Fixed EMT: How the Use of a Custom Built Application for Electronic Medical Records and Data Reporting Improves Patient Care and Mandatory Reporting.

Introduction:RCHIS is an Electronic Medical Record (EMR) and Health Information System (HIS) that has been purpose-built for use by Red Cross Red Crescent (RCRC) Emergency Response Units (ERUs), which are the equivalent of Type 1 (fixed and mobile) and Type 2 facilities in the Emergency Medical Teams (EMT) classification.Method:A three day in-person super user training was held with 13 participants: 9 first aid volunteers, 2 nurses and 2 medical doctors. Seven of the delegates had experience using an EMR. These super users served as trainers for staff at the pilot.The pilot occurred with the Portuguese Red Cross (PRC) for the Peregrinação de Fátima, where 200,000 people were in attendance. The PRC was part of a wider coordination cell with the civil defense authority, who required live reporting from the three Type 1 fixed clinics PRC had set up.Results:In total there were 77 user accounts, of which, 243 patients were consulted with and either discharged or referred throughout the four day pilot.The delegates shared feedback directly and through a survey. 88% stated that RCHIS was ‘very easy’ to use with the majority of delegates requiring less than 20 minutes of training to be using the application and inputting patient data related to their user role. Additionally, after their training 95% of delegates stated they had sufficient training to use RCHIS to its full extent. Informal feedback from delegates was hugely positive, indicating that it was improving patient care and also continuity of care when a patient returned the next day.The civil defense authorities were able to utilize the real-time reporting to assist in their operational response. The application was well received by the wider civil defense authority.Conclusion:The first RCHIS pilot was very successful from both a technical and organizational perspective.

From Two hours to Two Seconds: Using the Red Cross Red Crescent Health Information System (RCHIS) to Complete the WHO Emergency Medical Teams - Minimum Data Set Reporting

Introduction:Standardization of data collection and reporting within EMT’s is challenging. In past deployments, the Red Cross Red Crescent Type one and two facilities (Emergency Response Units- ERU’s) have collected data by hand using paper-based form and Excel spreadsheets. This process can be laborious, time consuming and often inaccurate.Method:RCHIS is both an electronic medical record (EMR) and health information system (HIS). RCHIS has been designed to produce pre-made reports including the MDS in seconds extracting data from the patient records. Through significant testing and pilot deployments in a domestic type one fixed clinic, the rapid production of reports such as the MDS has increased compliance and accuracy with reporting.Results:The utilization of an EMR and integrated HIS system for increasing compliance and accuracy with the MDS has been hugely successful. An in-depth analysis of the export data was done to confirm the 100% accuracy within the MDS report.Furthermore, feedback from users and managers within the ERU’s expressed the excitement for the ease of reporting not only to the EMTCC, but also to IFRC and back-donors. Moving forwards, this data collection will also be used to collect essential data to audit and improve the quality of care provided within the RCRC ERU’s.Conclusion:In conclusion, the utilization of RCHIS within a domestic ERU (equivalent to an EMT type one or two) has been hugely successful. The next steps will involve the deployment of RCHIS within an international deployment.

Involvement of folate and vitamin B12 deficiency in patients with normocytic anemia.

Deficiencies in folate (FA) and vitamin B12 (VB12) are causes of macrocytic anemia. However, in clinical practice, FA and/or VB12 deficiency can occur in patients with normocytic anemia. This study aimed to determine the prevalence of FA/VB12 deficiency in patients with normocytic anemia and the importance of vitamin replacement therapy in these patients. We retrospectively reviewed electronic medical record information of patients whose hemoglobin and serum FA/VB12 concentrations were measured at the Department of Hematology (N=1,388) and other departments (N=1,421) of Fujita Health University Hospital. In the Hematology Department, 530 (38%) patients showed normocytic anemia. Of these, 49 (9.2%) had FA/VB12 deficiency. Twenty of 49 (41%) patients had some hematological malignancies and 27 (55%) had benign hematological disorders. Of the nine patients who received vitamin replacement therapy, one showed a partial improvement in the hemoglobin concentration of ≥1g/dL. In the clinical setting, the measurement of FA/VB12 concentrations in patients with normocytic anemia may be useful. Replacement therapy may be a treatment option to consider in patients with low FA/VB12 concentrations. However, physicians need to pay attention to the presence of background diseases, and the mechanisms of this situation require further investigation.

Efficacy and safety of robotic-assisted versus median sternotomy for cardiac surgery: results from a university affiliated hospital.

We aimed to report the experience of robotic-assisted cardiac surgery (RACS) using the Da Vinci robotic surgical system, meanwhile its efficacy and safety was also evaluated by comparing with traditional open-heart surgery (TOHS), thus to provide evidence for a broader application of RACS in clinical practice. From July 2017 to May 2022, a total of 255 patients who underwent cardiac surgery assisted by Da Vinci robotic surgery system in the First Affiliated Hospital of Anhui Medical University, which included 134 males with an average age of 52.6±6.3 years and 121 females with an average age of 51.8±5.4 years. They were defined as the RACS group. By searching the hospital's electronic medical record information system, 736 patients with the same disease types who underwent median sternotomy and had complete data in the same period were selected as the TOHS group. Intra- and postoperative clinical results of the both groups were compared, and we focused the following indices including surgery time, reoperation rate for postoperative bleeding, length of intensive care unit (ICU) stay, postoperative hospitalization day, the number of died and withdrawing treatments, and the time of patients back to normal daily activities after discharge. In RACS group, 2 patients were scheduled to undergo mitral valvuloplasty (MVP), but they had to change to mitral valve replacement (MVR) due to unsatisfactory results; furthermore, 1 patient who received atrial septal defect (ASD) repair experienced abdominal hemorrhage because a rupture of abdominal aorta which were induced by the femoral arterial cannulation, and this patient eventually died of invalid rescue. As for the comparison of clinical results between both groups, there were no significant statistical differences in reoperation rate for postoperative bleeding, and the number of died and withdrawing treatments between both groups. However, length ICU stay, postoperative hospitalization day, and the time of patients back to normal daily activities after discharge was lower in RACS group in addition to the surgery time. Compared with TOHS, RACS is safe and effective in clinical and is worthy of promotion in an appropriate place.

Multi-Network Fusion Technology for Online Health Care

This research presents a multifunctional remote medical care structure based on multi-network fusion. The structure facilitates various processes, including entity registration, authentication, electronic medical record establishment, data acquisition, remote diagnosis, and information feedback. The user terminal consists of a smart phone and a medical sensor, while the structure server includes modules such as server communication, information management, safety, disease diagnosis, information feedback, and authentication center. The structure offers rapid response, timely detection of pathological changes, convenience, and rapidness. Additionally, it allows for easy functional expansion and significant upgrading capabilities.

Towards accurate and automatic emergency department workflow characterization using a real-time locating system

ABSTRACT This paper presents how patient throughput within an Emergency Department (ED) can be accurately and automatically characterized using a combination of data derived from a real-time location system (RTLS) and other traditional hospital IT systems such as Electronic Medical Records (EMR) and Laboratory Information Systems. Such insights can be used by a hospital to identify bottlenecks or inefficiencies and develop strategies to optimize and monitor patient flows. A descriptive, retrospective study was conducted among 1149 patients. Five KPIs, including time from arrival to triageand total length of stay, were used to evaluate ED timestamps to characterize the flow of the patient. A description of the techniques used to combine the various data sources to perform the accurate measurements is provided. The paper also describes the measurements obtained and indicates how real-time locating systems can contribute towards improving the quality of the timestamps generated compared to only using data from traditional hospital IT systems. A principial finding is that there is a large gap between the length of stay using only EMR data and the one computed combining EMR and RTLS data. Finally, the paper provides guidelines on how to deploy such a system effectively.