Patient Medical Information Research Articles

Critical Analysis and Cross-Comparison Between English and Chinese Websites Providing Online Medical Information for Patients With Adenoid Hypertrophy: Cross-sectional Study.

In the information era, patients can easily be misled by inaccurate internet content, thus making not well-informed decisions about medical issues. Adenoid hypertrophy, one of the most common causes of chronic upper airway obstruction in children and adolescents, may lead to serious complications, including sleep apnea and craniofacial change. There have been no critical studies about the quality of websites on adenoid hypertrophy, posing a challenge for users without a medical background to determine which website offers more reliable information. Moreover, the blockage of access to internet search tools such as Google, Yahoo, and others has created an isolated internet environment for the enormous user population in mainland China. Differences in internet legislation, the commercial environment, and culture are also likely to result in varied quality of online health information inside and outside mainland China. To date, no study has compared the quality difference between mainland Chinese and English websites. The aims of this study were to (1) analyze the quality of websites about adenoid hypertrophy accessible by patients, (2) investigate the quality differences between Chinese and English websites, (3) determine which type of website (eg, government-sponsored, health care provider) is more reliable in terms of medical information, and (4) determine whether the blockage of foreign websites is hindering users' accessibility to better-quality websites in mainland China. The first 100 websites (excluding advertisements) displayed on the top three search engines worldwide and in mainland China for the key search term "enlarged adenoids" were collected as the data source. The websites were evaluated based on accessibility, accountability, interactivity, structure, and content quality (accuracy, content coverage, and objectivity). Cohen κ was calculated, and one-way ANOVA and the Kruskal-Wallis test were performed to compare the results between groups and subgroups. The mean score for the content quality of English websites was significantly higher than that of Chinese websites (6.16 vs 4.94, P=.03 for Google, Bing, and Yahoo; 6.16 vs 4.16, P<.001 for Baidu, Sougou, and Bing China). Chinese users who are not influenced by the Internet Censorship System are more likely to access higher-quality online medical information (4.94 vs 4.16, P=.02). In within-group Student-Newman-Keuls q posthoc analysis, professional organization and government-sponsored websites were generally of better quality than other websites for both Chinese and English websites (P<.05). Generally, the English websites on adenoid hypertrophy are of better quality than Chinese websites; thus, Chinese users residing outside of the Chinese mainland are less influenced by inaccurate online medical information.

Breast cancer classification using Deep Q Learning (DQL) and gorilla troops optimization (GTO)

Breast cancer (BC) is a primary reason for death among the female population around the world. Early identification can aid in decreasing the mortality rates associated with this disease all over the world. The application of big data in healthcare not only saves lives but also saves time and money. Hospital records, patient medical information, and medical exam results are big data sources in the healthcare sector. In this research, a big data-based two-class (i.e., Benign or Cancer) BC classification model is developed using the Deep Reinforcement Learning (DRL) method. The model stages are big data collection, preprocessing, feature selection, classification, and explanations. In the preprocessing step, the data is normalized, and the missing values are replaced. The gorilla troops optimization (GTO) algorithm is employed for the feature selections. Deep reinforcement learning-based Deep Q learning (DQL) is used for the classification, and LIME is used to explain the predicted output. Three different datasets such as WBCD, WDBC, and WPBC from the UCI repository, are used for evaluation. We verify the proposed model against the radial basis function ensemble boosting learning method (RBF-ELB), the Particle Swarm Optimization Multilayer Perceptron (PSO-MLP), and the Genetic Algorithm Multilayer Perceptron (GA-MLP). The results of the experiments show that our method outperforms the traditional methods. The proposed GTO-DQL model achieves 98.90% accuracy for the WBCD dataset, 99.02% for WDBC, and 98.88% for the WPBC dataset, respectively.

Blockchain-Assisted Cybersecurity for the Internet of Medical Things in the Healthcare Industry

The Internet of Medical Things (IoMT) plays an important role in strengthening sustainable healthcare systems. IoMT significantly influences our healthcare because it facilitates monitoring and checking patient medical information before transferring the data to a cloud network for future use. The IoMT is a big-data platform which is growing rapidly, so it is critical to maintain all data safely and securely. In this study, Blockchain-Assisted Cybersecurity (BCCS) for the IoMT in the healthcare industry is proposed. Blockchain is a decentralized digital ledger that allows end-to-end communication and provides interaction between untrustworthy persons. BCCS uses a conventional in-depth approach and blockchain to create a procedure for collecting medical information from the IoMT and integrated devices. The proposed system utilizes blockchain to record and extract the accumulated information in a secure and distributed manner within a closed environment suitable for healthcare professionals, such as nursing homes, hospitals, and the healthcare industry where data exchange is needed. The experimental outcomes show that the proposed system has a high security rate of 99.8% and the lowest latency rate of 4.3% compared to traditional approaches. In all, the reliability of the proposed system gives the highest rate of 99.4%.

Development and Assessment of an Information Technology Intervention to Improve the Clarity of Radiologist Follow-up Recommendations

It is challenging to ensure timely performance of radiologist-recommended additional imaging when radiologist recommendation language is incomplete or ambiguous. To evaluate whether voluntary use of an information technology tool with forced structured entry of recommendation attributes was associated with improved completeness of recommendations for additional imaging over time. This cohort study of imaging report data was performed at an academic quaternary care center in Boston, Massachusetts, and included consecutive adults with radiology examinations performed from September 12 to 13, 2019 (taxonomy validation), October 14 to 17, 2019 (before intervention), April 5 to 7, 2021 (1 week after intervention), and April 4 to 7, 2022 (1 year after intervention), with reports containing recommendations for additional imaging. A radiologist scored the 3 groups (preintervention group, 1-week postintervention group, and 1-year postintervention group) of 336 consecutive radiology reports (n = 1008) with recommendations for additional imaging. Final implementation on March 27, 2021, of a voluntary closed-loop communication tool embedded in radiologist clinical workflow that required structured entry of recommendation attributes. The a priori primary outcome was completeness of recommendations for additional imaging, defined in a taxonomy created by a multidisciplinary expert panel. To validate the taxonomy, 2 radiologists independently reviewed and scored language attributes as present or absent in 247 consecutive radiology reports containing recommendations for additional imaging. Agreement was assessed with Cohen κ. Recommendation completeness over time was compared with with 1-sided Fisher exact tests and significance set at P < .05. Radiology-related information for consecutive radiology reports from the 4 time periods was collected from the radiology department data warehouse, which does not include data on patient demographic characteristics or other nonimaging patient medical information. The panel defined 5 recommendation language attributes: complete (contains imaging modality, time frame, and rationale), ambiguous (equivocal, vague language), conditional (qualifying language), multiplicity (multiple options), and alternate (language favoring a different examination to that ordered). Two radiologists had more than 90% agreement (κ > 0.8) for these attributes. Completeness with use of the tool increased more than 3-fold, from 14% (46 of 336) before the intervention to 46% (153 of 336) (P < .001) 1 year after intervention; completeness in the corresponding free-text report language increased from 14% (46 of 336) before the intervention to 25% (85 of 336) (P < .001) 1 year after the intervention. This study suggests that supplementing free-text dictation with voluntary use of a structured entry tool was associated with improved completeness of radiologist recommendations for additional imaging as assessed by an internally validated taxonomy. Future research is needed to assess the association with timely performance of clinically necessary recommendations and diagnostic errors. The taxonomy can be used to evaluate and build interventions to modify radiologist reporting behaviors.

Common clinical blood and urine biomarkers for ischemic stroke: an Estonian Electronic Health Records database study

BackgroundIschemic stroke (IS) is a major health risk without generally usable effective measures of primary prevention. Early warning signals that are easy to detect and widely available can save lives. Estonia has one nation-wide Electronic Health Record (EHR) database for the storage of medical information of patients from hospitals and primary care providers.MethodsWe extracted structured and unstructured data from the EHRs of participants of the Estonian Biobank (EstBB) and evaluated different formats of input data to understand how this continuously growing dataset should be prepared for best prediction. The utility of the EHR database for finding blood- and urine-based biomarkers for IS was demonstrated by applying different analytical and machine learning (ML) methods.ResultsSeveral early trends in common clinical laboratory parameter changes (set of red blood indices, lymphocyte/neutrophil ratio, etc.) were established for IS prediction. The developed ML models predicted the future occurrence of IS with very high accuracy and Random Forests was proved as the most applicable method to EHR data.ConclusionsWe conclude that the EHR database and the risk factors uncovered are valuable resources in screening the population for risk of IS as well as constructing disease risk scores and refining prediction models for IS by ML.

Potential Legal Risk During Crisis: A Case Study on A Private Hospital in Malaysia

This is a case study on bomb threat and evacuation process at KPJ Seremban Specialist Hospital (KPJSSH) in Negeri Sembilan, Malaysia. This study identifies potential legal risks arising from bomb threat and evacuation process in a private hospital. Bomb threat is a crisis situation that need to be managed by the hospital. Bomb threat and the evacuation process can create legal liabilities to the hospital. To identify the potential legal risks, in‐depth content analysis was conducted on KPJSSH incident report, HICS guideline and legal judgment obtained from legal journal for case laws. The study found eight potential legal risks. Among the potential legal risks are failure to provide safe escape route and leak of patient medical information during patient transfer. Failure to provide safe escape route falls under Occupier’s Liability (Law of Tort). Leak of patient medical information during patient transfer falls under Breach of Medical Confidentiality (Law of Tort). The study finds that it is not sufficient for a hospital to identify legal risks. Knowledge of relevant branch of law is critical to understand hospital legal liabilities during crisis.

Is Heterotopic Ossification Associated With Surgical Approach in Total Hip Arthroplasty?

Although risk factors for heterotopic ossification (HO) have been defined, the effect from surgical approach is not fully understood. The primary objective of our study was to evaluate the effect that surgical approach has on the risk for developing severe HO after total hip arthroplasty (THA) and compare this with other known risk factors. We hypothesized that there would be no difference in HO formation based on the surgical approach. We retrospectively reviewed all patients who underwent primary THA at our hospital between March 2011 and March 2021. Patients with HO documented in the radiology reports were cross-referenced with our THA data set and manually reviewed to determine Brooker classification. Patient demographics, medical comorbidities, surgical details, and medication information were collected from the electronic medical record and compared. Of 3,427 patients who underwent THA, 677 (19.8%) developed HO postoperatively. A multivariable analysis confirmed that surgical approach was independently associated with increased odds for HO development. The anterolateral (odds ratio [OR], 3.43; P < 0.001) and posterior (OR, 2.24; P < 0.001) approaches had increased odds for developing HO compared with the direct anterior approach. However, only the anterolateral approach (OR, 1.85; P = 0.033) demonstrated an increased association with the development of severe HO (Brooker 3, 4) postoperatively. Although the use of the direct anterior approach had the lowest overall OR for developing HO after THA, this is likely only clinically notable when compared with the anterolateral approach. III.

Comparison of the improvement in gastric mucosal tissue after Helicobacter pylori eradication between young and elderly people

Background and study aimsHelicobacter pylori (H. pylori) infection has been clearly shown to be a cause of gastric cancer, and the incidence of gastric cancer has been shown to decrease with eradication. However, few reports have described the utility of eradication therapy in elderly people. Thus, an investigation focusing on how much actual histological improvement is obtained with eradication therapy in elderly people was conducted. Patients and methodsThis was a retrospective study conducted using medical information of patients diagnosed with H. pylori-associated gastritis and who underwent eradication therapy. The histological improvement was assessed based on changes in the atrophy and intestinal metaplasia scores of the Updated Sydney system from before to after eradication. We investigated the rates of histological improvement in atrophy and intestinal metaplasia one year after and long term more than five years after H. pylori eradication in an elderly group and a younger group. ResultsThis study included 221 patients (elderly group 123, younger group 98). In histological atrophy, higher rates of improvement were seen in the corpus than in the antrum, and the rates of cure in the antrum were lower in elderly group than in younger group (p = 0.0282). With regard to intestinal metaplasia, the rates of improvement in the antrum were lower in elderly group than in younger. In long term observation, although the rates of cure in the antrum were lower in elderly, improvements were seen in atrophy scores in most of the patients and intestinal metaplasia scores in about half of patients. ConclusionThough there is more obvious improvement in the gastric mucosa when H. pylori eradication therapy is performed at a young age, some mucosal improvement can be expected in about half of patients after eradication, even in elderly people.

PENERAPAN CAESAR CHIPER DAN LEAST SIGNIFICANT BIT UNTUK MENGAMANKAN DATA REKAM MEDIS

Technological developments in data security systems in maintaining information security have grown rapidly. Medical record is a document that contains personal data and medical history and medical treatment history of a patient at a hospital. Medical records are sensitive and confidential so that not just anyone can read the information in the patient's medical record. This study aims to secure patient medical record information by encrypting it with caesar cipher cryptography, the name of the patient's disease, then from the results of the encryption, the process of inserting the encrypted information into a digital image uses the least significant bit (LSB) steganography, namely replacing 1 digit behind the binary number in every 1 bit. The results of the study show that the application created is capable of encrypting and encoding patient medical record data information and embedding this information into digital images.

Business System Information Analysis Process Outcome in Hospital General Bengkayang Area

In the application of the outpatient information system business process at the Bengkayang Regional General Hospital, there are still problems faced, namely the occurrence of incompatibility of patient medical data information in the examination section / poly section, resulting in delays in the patient handling process. This is the background for the author to analyze the business process of outpatient system information at the Bengkayang General Hospital using the Business Process Management Life Cycle method. This study aims to determine how effective it is in managing the outpatient information system business process at the Bengkayang Regional General Hospital. The analytical technique used is BPMN, value chain, business area analysis and the method used is the BPM life cycle. The results of this study can support the current business processes for the better. With the addition and improvement of optimal technology, with existing information system technology to obtain a more detailed and complete procedural flow chart. The resulting procedure flow chart will integrate the data in each part of the service unit in the outpatient department, so that the data can be used together between departments so that data duplication can be avoided.

Informational Needs for Dental-Oriented Electronic Health Records from Dentists' Perspectives.

Information technology is vital to support dental care services and is yet to be thoroughly investigated. This study aims to assess the dentists' needs and requirements for health records from dental care providers' perspectives. In-depth interviews were conducted with dentists during clinic practices. This qualitative research method involves exploring the information and functions dentists use to obtain information from EHR. The target population is the dental staff interacting with the patients and accessing the electronic health records in the government and private sectors. Thirty-five dentists were interviewed directly after the treatment session and asked four pre-defined questions, the dentists' needs were collected, and the met and unmet needs were presented. The interview results revealed 42 needs (15 were met and 27 were unmet), with an average of 1.17 needs per session. The information needs were categorized into foreground and background information needs and reported in nine main themes. The interviews were analyzed, and as a result, nine themes were generated: chief complaints and symptoms, medical and health history, medications, visual representations of the problem, treatment procedures, X-ray services, needs related to advanced features, needs related to insurance coverage, and finally, information needs related to the treatment environment. The required information and functions mentioned by dentists in the study emphasize the need for integrated modules for oral and medical care services. Generally, it is evident that dentists have substantial unmet needs, and the desired EHR should have functions that cover all dentists' needs. The study's findings demonstrate gaps between current and desired EHR to serve dentists' needs. Dentists need better access to patient history and medical information, progress notes, and X-rays to provide visualization tools for problems and patient charts. Moreover, essential needs were related to messaging capability, educational tools, availability of tutorial videos, and accessing external resources. Information needs were described and should be considered when designing EHR to meet all dentists' needs.

Optimizing Cardiovascular Disease Diagnosis with Machine Learning: An Analysis

Cardiovascular disease (CVD) is a major global health problem, responsible for over 17 million deaths each year. Early and accurate diagnosis of CVD is critical to effective treatment and management, and machine learning algorithms have shown promise in this area. In recent years, machine learning models have been developed and applied to various healthcare problems, and have shown significant potential in the prediction and diagnosis of CVD. The purpose of this study is to evaluate the performance of a stacked machine learning model in the diagnosis of CVD. In particular, we aim to compare the performance of this model with that of recent studies in this area, and to assess the reliability and accuracy of machine learning algorithms in CVD diagnosis. The study used a publicly available dataset of patient data, including demographic, lifestyle, and medical history information, to train and test the stacked machine learning model. The model was trained using several different machine learning algorithms, including decision trees, random forests, and support vector machines, and the performance of the model was evaluated using metrics such as accuracy, Matthews correlation coefficient (MCC), and F1 score. The results of the study showed that the stacked machine learning model achieved a high level of accuracy in CVD diagnosis, with an accuracy of 0.7776785714285714, an MCC of 0.5613383240343626, and an F1 score of 0.7764597470054544. These results are more reliable than the results of a recent study, which reported an accuracy of 0.75 and an MCC of 0.5. Future research could extend the scope of this study by exploring the use of other machine learning algorithms, such as deep learning or neural networks, in the diagnosis of CVD. Additionally, the study could be extended to include a larger and more diverse patient population, in order to better understand the generalizability of the results.

Searchable Encryption With Autonomous Path Delegation Function and Its Application in Healthcare Cloud

Outsourcing medical data to healthcare cloud has become a popular trend. Since medical data of patients contain sensitive personal information, they should be encrypted before outsourcing. However, information retrieval methods based on plaintext cannot be directly applied to encrypted data. In this paper, we present a new cryptographic primitive named conjunctive keyword search with secure channel free and autonomous path delegation function (AP-SCF-PECKS), which can be applied in scenarios where patients want to search for and autonomous delegate their private medical information without revealing their private key. Particularly, the proposed solution allows patients to set up multi-hop delegation path with their preferences, and the delegated doctors in the path can search for and access the patient's private medical information with priority from high to low. Patients can ensure that authorized doctors are always trustworthy, and unauthorized users cannot obtain the private medical information of patients. Moreover, the scheme supports the conjunctive keyword search, secure channel free, and is secure against chosen keyword attack, chosen ciphertext attack, and keyword guessing attack. The security of proposed scheme has been formally proved in the standard model. Finally, the performance evaluations demonstrate that the overhead of proposed scheme are modest for healthcare cloud scenarios.

Comprehensive Review of Deep learning Techniques in Electronic Medical Records

A digital collection of patient’s health care data like diagnosis history of patient, treatment details, medical prescriptions are stored electronically. This electronic patient health records (EPHR) model provides huge volume of real time data and used for clinical research. Natural Language processing (NLP) automatically retrieve the patient’s information based on decision support system. NLP performs traditional techniques of machine learning, deep learning algorithms and focussing on word embeddings, classification and prediction, extraction, knowledge graphs, phenotyping, etc. By using NLP technique, extract the information from clinical data and analysis it provides valuable patient medical information. NLP based on clinical systems are evaluated on document level annotations which contains document of patient report, health status of patient, document section types contain past medical history of patient, summary of discharge statement, etc. similarly the semantic properties contain severity of disease in the aspects of positivity, negativity. These documents are developed and implemented on word level or sentence level. In this survey article, we summarize the recent NLP techniques which are used in EPHR applications. This survey paper focuses on prediction, classification, extraction, embedding, phenotyping, multilingually etc techniques.

Improving the Efficiency of Medication Reconciliation in Two Taiwanese Hospitals by Using the Taiwan National Health Insurance PharmaCloud Medication System

Medication reconciliation (MedRec) is a process to ensure complete and accurate communication of patient medication information throughout care transitions to prevent medication errors. Hospitals in Taiwan have stride to implement a universal protocol for MedRec. To establish a feasible protocol indigenously, the World Health Organization (WHO) protocol was incorporated with the Taiwan National Health Insurance (NHI) PharmaCloud patient medication profile. The efficiency and error detection capability of this modified protocol was evaluated in two hospitals. A prospective, non-randomized, unblinded, multicenter cohort study was conducted. Subjects were recruited among patients admitted for colorectal or orthopedic surgery with at least 4 or more chronic drugs. To obtain the best possible medication history (BPMH), the control group was conducted according to the WHO protocol, and the experimental group used the modified WHO protocol with the medication data from the PharmaCloud system. The time spent on the two protocols was recorded. Admission and discharge orders were reconciled against the BPMH to identify any discrepancies. Discrepancies were evaluated by appropriateness, prescribing intentions, and types of inappropriateness. The levels of potential harm were classified for inappropriate discrepancies. The mean time to obtain BPMH in the control group was 34.3±10.8 minutes and in the experimental group 27.5±11.5 minutes (P = 0.01). The experimental group had more subjects with discrepancies (87.9%) than the control (58.3%) (p < 0.001). The discrepancies in both admission and discharge orders for the experimental group (84.5 and 67.2%) were higher than those of the control (47.9 and 37.5%). Many inappropriate discrepancies were classified as the potential harm of level 2 (77.8%). Through the establishment of BPMH with the medication data from the Taiwan NHI PharmaCloud, MedRec could be achieved with greater efficiency and error detection capability in both the admission and discharge order validation processes.

A Fully Automated Intelligent Medicine Dispensary System Based on AIoT

The COVID-19 pandemic has caused a high rate of infection, and thus effective epidemic prevention measures of avoiding the second spread of COVID-19 in hospitals are major challenges for healthcare workers. Hospitals, where medicines are collected, are vulnerable to the rapid spread of COVID-19. Using the remote health monitoring technology of the Internet of Things (IoT) to automatically monitor and record the basic medical information of patients, reduce the workload of healthcare workers, and avoid direct contact with healthcare workers to cause secondary infections is an important research topic. This research proposes a new artificial intelligence solution based on the IoT, replacing existing medicine stations and recognizing medicine bags through the state-of-the-art optical character recognition (OCR) model and PP-OCR v2. The use of OCR in identification of medicine bags can replace healthcare workers in data recording. In addition, this research proposes an administrator management and monitoring system to monitor the equipment and provide a mobile application for patients to check the latest status of medicine bags in real time, and record their medication times. The results of the experiments indicate that the recognition model works very well in different conditions (up to 80.76% in PP-OCR v2 and 94.22% in PGNet), which supports both Chinese and English languages.

실손의료보험 보험금청구 간소화에 관한 법적 연구

The problem has been raised that the traditional claim method, called claim for medical insurance, does not correspond to the development of Korean IT technology. In this paper, the author critically considers that the existing insurance claim procedure is not valid, and reviews the contents of the main legislation (proposal) proposed to the National Assembly. Currently, our society is rapidly changing from the analog era to the digital era. We are living in an era where various financial payments are made with just one smartphone. Nevertheless, the procedure for claiming insurance does not reflect the new era. In the insurance industry, consumer-oriented digital methods from designing or developing insurance products to contract management and insurance payment management are very important. Among these, one of the most important factors is the simplification of the claim process for medical insurance claims of policyholders. The simplification plan for indemnity insurance is based on the content that the insurance claim is made electronically when a patient makes a request to the hospital without paper documents. While focusing on literature research, this study adopts an analytical method that accommodates difficulties encountered in the working world and a review method of major legislation (proposal) proposed by the National Assembly. We favored the simplification of medical insurance claims through insurance intermediaries, but pointed out the problems that may arise when operating this method. The simplification of insurance claims is expected to dramatically improve the efficiency of insurance companies and the convenience of consumers, but the medical community opposes this because of side effects such as restrictions on the use of patient medical information and insurance subscriptions, and leakage of patient personal medical information. This appears to be the biggest obstacle to simplifying insurance claims. A few ways to solve this problem are suggested. The method of claiming insurance by issuing a paper receipt is not suitable for the future. In order to solve the waste of human resources or administrative costs, a plan for simplifying insurance claims must be implemented.

Distal radial artery approach is safe and effective for cerebral angiography and neuroendovascular treatment: A single-center experience with ultrasonographic measurement.

The transradial artery approach to cerebral angiography can reduce both patient stress following examination and the risk of major complications due to hematoma. Recently, the distal radial artery approach (DRA) has garnered attention in cardiology as a minimally invasive method. DRA is also considered applicable to neurosurgery, although concerns about procedural difficulty and complications persist. Therefore, this study aimed to evaluate the efficacy of the DRA in cerebral angiography and neuroendovascular treatment. We retrospectively selected 30 consecutive patients for whom the DRA was attempted for cerebral angiography at our hospital. The patients' age, sex, height, weight, and medical history information was collected and correlated with successful puncture and complications. The diameter of the distal radial artery (RA) was measured using ultrasonography. The median patient age was 67 years (range, 32-87 years) and 21 (70%) were men. The median diameter of the distal RA was 2.3 mm (range, 1.7-3.2 mm). Distal RA puncture was successful in 23 patients (77%) and no complications were noted; however there was no significant correlation between successful puncture and any of the patient factors. Carotid artery stenting and preoperative tumor embolization were performed via DRA in six and three cases, respectively. Although puncture site hematoma occurred in only one case, all treatments were successful, and no major complications were observed. DRA can be safely used for cerebral angiography and neuroendovascular treatment.

Prevalence and Severity of Drug Interactions in a Referral Pediatric Hospital: An Observational, Retrospective Study

Background: Hospitalized infants and children are usually treated with many medications in the hospital. Concurrent use of multiple drugs, known as polypharmacy, is inevitable in critically ill patients. This study aims to investigate the possible interactions as well as their type and number, and their effect on the treatment process plus the duration of hospital stay of patients.&#x0D; Methods: In this descriptive study, the medical records of 189 patients admitted to the Intensive care unit (ICU) ward of Mofid Children's Educational Hospital in Tehran were prospectively studied over six months from August 2018 to March 2019. The collected data included disease diagnosis, patient medication information, age and gender, and treatment interventions. Interactions between drugs were identified using Up-to-date database, with the results analyzed by SPSS software.&#x0D; Results: The results revealed that hospitalization increased with an increasing number of drugs. The findings also indicated a direct relationship between the number of drug interactions and the duration of hospital stay. After examining the relationship between ICU outcome and the number of drug items as well as the number of drug interactions, it was found that there is a direct relationship between the two. There was also a direct relationship between Class D plus X interactions and mortality rate along with duration of stay.&#x0D; Conclusion: This study showed a direct relationship between drug interactions and the duration of hospitalization. In other words, as drug interactions increased, so did the duration of hospital stay.

S1064 A Longitudinal Evaluation of Patients With Inflammatory Bowel Disease in Puerto Rico

Introduction: A high prevalence of inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), has been reported among Hispanics; however, its characterization is scarce. We aim to describe changes in clinical outcomes following the disease course for at least 3 years in a cohort from Puerto Rico (PR). Methods: The UPR IBD Registry has collected clinical data on patients since 1995. We used a longitudinal survey tool to assess demographic and medical information of patients with IBD with a minimum 3-year follow-up. Subjects were first interviewed between 2012-2018, and the longitudinal component throughout 2020-2022. We analyzed sociodemographic, food security, body mass index (BMI), hospitalizations and surgeries, family history, and medications. Intellectus statistics was used for data analysis. The study was approved by the MSC-IRB. Results: 91 subjects were recruited, 50.6% (45/91) were male, and 72.5% (66/91) had a CD diagnosis. Two subjects initially diagnosed with UC were later re-diagnosed with CD [74.7% (68/91)]. The majority were single (60%), mean age was 35+15 years initially and 39+16 years for the later survey. The most-reported level of education was a bachelor's degree (41%). The mean BMI in the longitudinal survey (27+6 kg/m2) was significantly higher than in the original questionnaire (25+6 kg/m2) [p< 0.001]. We found no difference between household income between timepoints; these ranged between $28K to $29K annually. 76% had hospitalizations in the original survey and 29% in the longitudinal questionnaire. 43% of participants reported undergoing surgery in the initial questionnaire, and 28% of the subjects during follow-up [p< 0.001]. We observed a decreasing trend in the following medications: aminosalicylates, corticosteroids, immunomodulators, and antibiotics. We observed increased use of integrin blockers and IL 12/23 antagonists. 27.5% of participants reported therapeutic failure in the longitudinal survey. Conclusion: We observed a significant increase in BMI and targeted therapies after a minimum of three years of follow-up with a concomitant decrease in recent hospitalizations, conventional medications, and reported surgeries. Even though an increase in IBD diagnosis has been observed in Puerto Rico, better clinical outcomes have resulted over the last few years. A longer follow-up period with a larger sample is needed to better evaluate outcomes in this special population. Table 1. - For Longitudinal Variables Variable Original Questionnaire n (%)n=91 Longitudinal Questionnaire n (%)n=91 p-value IBD Diagnosis Crohn's Disease 66 (72.5) 68 (74.7) < 0.001 Ulcerative Colitis 25 (27.5) 23 (25.3) Food Security Category Very-Low Food Security - 1 (1.10) n/a Low Food Security - 17 (18.7) Marginal Food Security - 34 (37.4) High Food Security - 37 (40.7) BMI by Category Underweight 10 (11) 7 (7.7) < 0.001 Normal 34 (37.4) 37 (40.7) Overweight 17 (18.7) 23 (25.3) Obese 17 (18.7) 24 (26.4) IBD Medication Aminosalicylates 66 (72.5) 23 (25.3) 0.003 Corticosteroids 65 (71.4) 28 (30.8) 0.891 Immunomodulators 34 (37.4) 22 (24.2) 0.041 Antibiotics 38 (41.8) 10 (11) 0.310 Anti-TNF 54 (59.3) 51 (56) < 0.001 Integrin blockers 5 (5.5) 16 (17.6) 0.003 Interleukin antagonists 5 (5.5) 23 (25.3) 0.013 Note. Due to rounding errors, percentages may not equal 100%.