Características clínicas de reanimações cardiopulmonares intra-hospitalares registradas em prontuário: estudo transversal / Clinical characteristics of in-hospital cardiopulmonary resuscitations recorded in medical records: a cross-sectional study / Características clínicas de las reanimaciones cardiopulmonares intrahospitalarias registradas en las historias clínicas: estudio transversal

Objetivo: identificar características clínicas das paradas cardiopulmonares e reanimações cardiopulmonares ocorridas em ambiente intra-hospitalar. Método: estudo quantitativo, prospectivo e observacional, a partir de informações de prontuários de pacientes submetidos a manobras de reanimação devido à parada cardiopulmonar entre janeiro e dezembro de 2021. Utilizou-se um instrumento baseado nas variáveis do modelo de registro Utstein. Resultados: em 12 meses foram registradas 37 paradas cardiopulmonares. A maioria ocorreu na unidade de terapia intensiva respiratória, com causa clínica mais prevalente hipóxia. 65% dos pacientes foram intubados no atendimento e 57% apresentaram ritmo atividade elétrica sem pulso. A duração da reanimação variou entre menos de cinco a mais de 20 minutos. Como desfecho imediato, 57% sobreviveram. Conclusão: dentre os registros analisados, a maior ocorrência de paradas cardiopulmonares foi na unidade de terapia intensiva respiratória, relacionada à Covid-19. Foram encontrados registros incompletos e ausência de padronização nas condutas.

Objective: identify the clinical characteristics of cardiopulmonary arrests and cardiopulmonary resuscitations in the in-hospital environment. Method: this is a quantitative, prospective and observational study based on information from the medical records of patients who underwent resuscitation maneuvers due to cardiopulmonary arrest between January and December 2021. An instrument based on the variables of the Utstein registration protocol was used. Results: thirty-seven cardiopulmonary arrests were recorded in 12 months. The majority occurred in a respiratory intensive care unit, with hypoxia being the most prevalent clinical cause. Sixty-five percent of the patients were intubated and 57% had pulseless electrical activity. The duration of resuscitation ranged from less than five to more than 20 min. As for the immediate outcome, 57% survived. Conclusion: among the records analyzed, the highest occurrence of cardiopulmonary arrests was in respiratory intensive care units, and they were related to Covid-19. Moreover, incomplete records and a lack of standardization in cardiopulmonary resuscitation procedures were found.

Objetivo: Identificar las características clínicas de paros cardiopulmonares y reanimaciones cardiopulmonares que ocurren en un ambiente hospitalario. Método: estudio cuantitativo, prospectivo y observacional, realizado a partir de información presente en historias clínicas de pacientes sometidos a maniobras de reanimación por paro cardiorrespiratorio entre enero y diciembre de 2021. Se utilizó un instrumento basado en las variables del modelo de registro Utstein. Resultados: en 12 meses se registraron 37 paros cardiopulmonares. La mayoría ocurrió en la unidad de cuidados intensivos respiratorios, la causa clínica más prevalente fue la hipoxia. El 65% de los pacientes fue intubado durante la atención y el 57% presentaba un ritmo de actividad eléctrica sin pulso. La duración de la reanimación varió entre menos de cinco y más de 20 minutos. Como resultado inmediato, el 57% sobrevivió. Conclusión: entre los registros analizados, la mayor cantidad de paros cardiopulmonares se dio en la unidad de cuidados intensivos respiratorios, relacionada con Covid-19. Se encontraron registros incompletos y falta de estandarización en el procedimiento.

Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning-Based Design Thinking Study.

BACKGROUND: The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literature, it has been reported that people die annually due to adverse events (AEs), and various methods exist for investigating and measuring AEs. However, some methods have a limited scope, data extraction, and the need for data standardization. In Brazil, there are few studies on the application of trigger tools, and this study is the first to create automated triggers in ambulatory care. OBJECTIVE: This study aims to develop a machine learning (ML)-based automated trigger for outpatient health care settings in Brazil. METHODS: A mixed methods research will be conducted within a design thinking framework and the principles will be applied in creating the automated triggers, following the stages of (1) empathize and define the problem, involving observations and inquiries to comprehend both the user and the challenge at hand; (2) ideation, where various solutions to the problem are generated; (3) prototyping, involving the construction of a minimal representation of the best solutions; (4) testing, where user feedback is obtained to refine the solution; and (5) implementation, where the refined solution is tested, changes are assessed, and scaling is considered. Furthermore, ML methods will be adopted to develop automated triggers, tailored to the local context in collaboration with an expert in the field. RESULTS: This protocol describes a research study in its preliminary stages, prior to any data gathering and analysis. The study was approved by the members of the organizations within the institution in January 2024 and by the ethics board of the University of São Paulo and the institution where the study will take place. in May 2024. As of June 2024, stage 1 commenced with data gathering for qualitative research. A separate paper focused on explaining the method of ML will be considered after the outcomes of stages 1 and 2 in this study. CONCLUSIONS: After the development of automated triggers in the outpatient setting, it will be possible to prevent and identify potential risks of AEs more promptly, providing valuable information. This technological innovation not only promotes advances in clinical practice but also contributes to the dissemination of techniques and knowledge related to patient safety. Additionally, health care professionals can adopt evidence-based preventive measures, reducing costs associated with AEs and hospital readmissions, enhancing productivity in outpatient care, and contributing to the safety, quality, and effectiveness of care provided. Additionally, in the future, if the outcome is successful, there is the potential to apply it in all units, as planned by the institutional organization. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55466.

Standardizing visual display of gastrointestinal pathology results for improved clinical interpretation.

OBJECTIVES: Pathology is an essential component of disease diagnosis and management in pediatric gastroenterology. Pathology reports have not been standardized in some areas of pediatric gastrointestinal pathology and pathology reporting varies. Development of electronic medical record (EMR) pathology synoptic report templates (PSRT) enables pathology data collection in a specific format and can help standardize pathology reporting. We developed, implemented, and evaluated EMR PSRTs for eosinophilic esophagitis (EoE) and inflammatory bowel disease (IBD). METHODS: PSRTs were developed by a multidisciplinary team of pediatric experts of allergy, gastroenterology, and pathology for both EoE and IBD based on available literature and validated scales. Likert surveys (range 1 low acceptance to 5 high acceptance) based on the Technology Acceptance Model assessed user acceptance of the developed PSRTs. The use of PSRTs was monitored via control charts. RESULTS: Overall, evaluation questionnaires achieved >80% response rates. Clinicians and pathologists reported moderate-to-high levels of Perceived Usefulness (median [interquartile range) for EoE PSRT: clinicians 4.0 (4.0, 5.0) and pathologists 3.5 (3.5, 4.0); and IBD PSRT: clinicians 4.0 (3.0, 4.0) and pathologists 4.0 (4.0, 5.0)) and Perceived Ease of Use (EoE PSRT: clinicians 4.5 (4.0, 5.0) and pathologists 4.0 (4.0, 4.0); and IBD PSRT: clinicians 4.0 (4.0, 5.0) and pathologists 4.0 (4.0, 5.0)) of the developed PSRTs. Control charts demonstrated 100% utilization by 2-5 months from launch. CONCLUSIONS: We demonstrate successful implementation of synoptic reporting for both pediatric EoE and IBD pathology. EMR synoptic reporting provides standardization of pathology reporting and improved methods of pathology data presentation, which could potentially optimize provider efficiency, clinician interpretation of pathology results and disease trajectory, patient care, and clinician satisfaction.

Medical record-keeping educational interventions for medical students and residents: a systematic review.

BACKGROUND: Medical records, encompassing patient histories, progress notes, and more, play a crucial role in patient care and treatment, healthcare communication, medico-legal matters, and supporting financial documentation. OBJECTIVE: Despite their significance, literature suggests inconsistencies in record quality and insufficient formal medical record-keeping education for medical students and residents. The study aimed to identify and evaluate the effectiveness of educational interventions by conducting a systematic review. METHOD: A literature search covering 2003-2023 and review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was undertaken. RESULTS: The literature search identified 44 relevant studies for inclusion. Educational methods, including lectures, feedback, workshops and discussions, addressed different components of the clinical record. The review revealed positive impacts on participant satisfaction, skills and attitudes related to record-keeping. However, some studies reported no significant positive outcomes, emphasising the need for higher-level evidence. Most studies adopted a single-group pretest-posttest design, presenting challenges in control group implementation. The Kirkpatrick evaluation levels were primarily at level 2, with few studies reaching level 3. The absence of studies at level 4 suggested the need for more robust evidence. Studies targeted medical residents more frequently than medical students, with a lack of interventions during the first year of medical education. CONCLUSION: Despite limitations including language bias and methodological variations, the review revealed diverse educational strategies and highlighted the necessity for more randomised controlled trials and studies providing higher-level evidence to enhance clinical record-keeping skills among medical students and residents. IMPLICATIONS: Medical record-keeping educational interventions can significantly improve the documentation skills of medical students and residents, thereby enhancing patient care, communication and medico-legal compliance.

Feedback, Workshop, and Random Monitoring as Quality Assurance Interventions in Improving Data Entries of Residents in Electronic Medical Records of UP Health Service for COVID-19 Teleconsultations.

Background: Medical records provide a repository of patient information, physical examination, laboratory findings, and the outcomes of interventions. The completeness of data contained in the electronic medical record (EMR) is an important factor leading to health service improvement. Quality assurance (QA) activities have been utilized to improve documentation in electronic medical records. Objective: To determine the effectiveness of QA interventions (feedback, workshop, and random monitoring system) in improving completeness of data entries in the EMR of resident physicians for COVID-19 teleconsultations. Methods: This was a before-and-after study involving EMR entries of physician trainees on health care workers (HCWs) from March to October 2022 of the COVID-19 pandemic. A chart audit was conducted against a checklist of criteria for three months before and after the interventions. QA interventions included the provision of feedback on the results of the initial chart review; conducting a QA workshop on setting of standards, chart audit, data encoding, analysis, and presentation; and random monitoring/feedback of resident charting. The change in the level of completeness from pre- to post-intervention was computed, and the percentage of charts meeting the minimum standard of 90% completeness was likewise determined. Results: A total of 362 and 591 chart entries were audited before and after the interventions. The average percentage of completeness of medical records during initial consultation improved from 83% to 95% (p>0.05). The documentation of the reason for seeking consultation significantly increased from <1% to 84%. The reporting of past exposure and level of risk decreased to 89% (p=0.001) in the initial consult and 12% (p=0.001) in the fit-to-work, respectively. Majority of the criteria for work clearance improved after the intervention. However, the average completeness of entries did not reach 90% post-intervention for fit-to-work consultations. Conclusion: Feedback, quality assurance workshop, and random monitoring of electronic medical records are effective in increasing documentation practices for the chief complaint and dates of illness duration but showed non-significant increasing trend on overall percentage of EMR completeness for COVID-19 teleconsultations.

Factors Influencing Facial Paralysis Recovery: A Retrospective Cohort Study Using Electronic Medical Records.

This study aims to report on treatment outcomes and related factors in Bell's palsy patients by analyzing Electronic Medical Records (EMR) data. To assess the impact on the duration until improvement in House-Brackmann grading post-onset, we conducted an analysis of inpatient records for Bell's palsy patients admitted to a traditional Korean medicine hospital from 2018 to 2022. A total of 1049 subjects were included in the analysis. Contributing factors to a duration of more than 14 days until improvement in HB Grade included age 40 or above, male gender, an initial period of more than 7 days from onset to admission, a history of past recurrence, and the indication for adjunctive therapy. To generalize the adaptation of adjunctive traditional Korean medicine treatment indications to treatment outcomes, larger-scale studies encompassing a broader patient population will be necessary.

Similarity Analysis of Clinical Notes for Revealing Writing Styles in an Electronic Medical Record System.

BACKGROUND: One of the disadvantages of medical documentation in Electronic Medical Record (EMR) systems is that records tend to be redundant by "copying and pasting", a writing style to duplicate and revise previous records. In this study, we analyzed the similarity between records to identify the factors affecting the writing style of clinical notes. METHOD: We analyzed 98,038 records of 4,149 patients from two years in the Department of Obstetricians and Gynecology at Kyoto University Hospital, Japan. We observed the correlation between the distribution of the record similarity and string amounts, as well as the disease codes and ratios of outpatient visit. RESULTS: The patient group with high record similarity and large number of strings was the group with reproductive medicine, followed by the group of malignant tumor follow-up or Women's Healthcare. DISCUSSION: In reproductive medicine, physicians have a demand for an overarching evaluation, and in follow-up malignancies or in Women's Healthcare, they have a demand to check for subtle differences from the last time. These facts along with our data insist that the writing style in EMR systems is related to the patient's status. CONCLUSION: We declared that the writing style in EMR systems is affected by the patient's status. The writing style of duplicating and revising is preferred (1) when there is a clinical demand for an overarching evaluation, and (2) when there is a clinical demand to check for subtle differences from the last time.

Comparing Patient Perception and Physician's Records: Generative AI Performance Evaluation.

Patient perception involves a patient's thoughts and beliefs regarding their health status. It is also associated with medical compliance and outcomes. However, discrepancies often arise between patient perception and physicians' documentation within the medical records, resulting in misunderstanding and suboptimal doctor-patient communication. In this study, we assessed the efficacy of generative artificial intelligence (AI) in comparing the content of patient perception as recorded in patient questionnaires and physicians' records of the Department of Breast Surgery. We evaluated the precision and recall of the generative AI by comparison with human-created ground truth. Our results demonstrated the high performance of the generative AI in comprehending and contrasting symptoms and the entire content recorded differently by patients and physicians, with F1 scores ranging from 0.77 to 0.97. These results highlight the potential contribution of a generative AI to deeper mutual comprehension in healthcare scenarios.

Topical amitriptyline in burning mouth syndrome: A retrospective real-world evidence study.

OBJECTIVE: To evaluate the effectiveness, tolerability, and safety of topical amitriptyline as a potential route of administration for the management of burning mouth syndrome. BACKGROUND: Burning mouth syndrome is a complex, idiopathic, and debilitating orofacial pain disorder that impairs quality of life, with a prevalence of up to 18% in menopausal women. Available drugs to alleviate its burning sensation have inconsistent and limited efficacy. Given its physicochemical properties, excellent tolerability, and ability to target peripheral pathways, topical amitriptyline seems a promising mechanistically specific analgesic drug for burning mouth syndrome. METHODS: In this retrospective cross-sectional real-world evidence study, patients with burning mouth syndrome who were prescribed topical amitriptyline for 8 weeks were identified. Eligibility criteria stemmed from ICHD-3, ICOP, and consensus definitions. The primary outcome measure was mean daily pain intensity (on a 0-10 scale); secondary outcomes included adverse events and patient global impression of improvement. Data are given as the mean ± SD. RESULTS: A total of 15 patients fulfilling the eligibility criteria were included and analyzed. Mean daily pain was 6.7 ± 2.1 at baseline and 3.7 ± 2.3 after treatment, with a mean reduction of 3.1 ± 2.8 (p = 0.002). Half of the patients experienced a decrease in pain by at least 50% (p = 0.008). Several mild adverse events were reported, such as somnolence or dry mouth. CONCLUSIONS: Topical amitriptyline may be a safe and potent route of administration in the treatment of burning mouth syndrome, a hypothesis to be tested in further controlled trials.

Disparities in Documentation: Evidence of Race-Based Biases in the Electronic Medical Record.

Personal implicit biases may contribute to inequitable health outcomes, but the mechanisms of these effects are unclear at a system level. This study aimed to determine whether stigmatizing subjective terms in electronic medical records (EMR) reflect larger societal racial biases. A cross-sectional study was conducted using natural language processing software of all documentation where one or more predefined stigmatizing words were used between January 1, 2019 and June 30, 2021. EMR from emergency care and inpatient encounters in a metropolitan healthcare system were analyzed, focused on the presence or absence of race-based differences in word usage, either by specific terms or by groupings of negative or positive terms based on the common perceptions of the words. The persistence ("stickiness") of negative and/or positive characterizations in subsequent encounters for an individual was also evaluated. Final analyses included 12,238 encounters for 9135 patients, ranging from newborn to 104 years old. White (68%) vs Black/African American (17%) were the analyzed groups. Several negative terms (e.g., noncompliant, disrespectful, and curse words) were significantly more frequent in encounters with Black/African American patients. In contrast, positive terms (e.g., compliant, polite) were statistically more likely to be in White patients' documentation. Independent of race, negative characterizations were twice as likely to persist compared with positive ones in subsequent encounters. The use of stigmatizing language in documentation mirrors the same race-based inequities seen in medical outcomes and larger sociodemographic trends. This may contribute to observed healthcare outcome differences by disseminating one's implicit biases to unknown future healthcare providers.

Implementation of the World Health Organization Minimum Dataset for Emergency Medical Teams to Create Disaster Profiles for the Indonesian SATUSEHAT Platform Using Fast Healthcare Interoperability Resources: Development and Validation Study.

Background: The National Disaster Management Agency (Badan Nasional Penanggulangan Bencana) handles disaster management in Indonesia as a health cluster by collecting, storing, and reporting information on the state of survivors and their health from various sources during disasters. Data were collected on paper and transferred to Microsoft Excel spreadsheets. These activities are challenging because there are no standards for data collection. The World Health Organization (WHO) introduced a standard for health data collection during disasters for emergency medical teams (EMTs) in the form of a minimum dataset (MDS). Meanwhile, the Ministry of Health of Indonesia launched the SATUSEHAT platform to integrate all electronic medical records in Indonesia based on Fast Healthcare Interoperability Resources (FHIR). Objective: This study aims to implement the WHO EMT MDS to create a disaster profile for the SATUSEHAT platform using FHIR. Methods: We extracted variables from 2 EMT MDS medical records-the WHO and Association of Southeast Asian Nations (ASEAN) versions-and the daily reporting form. We then performed a mapping process to match these variables with the FHIR resources and analyzed the gaps between the variables and base resources. Next, we conducted profiling to see if there were any changes in the selected resources and created extensions to fill the gap using the Forge application. Subsequently, the profile was implemented using an open-source FHIR server. Results: The total numbers of variables extracted from the WHO EMT MDS, ASEAN EMT MDS, and daily reporting forms were 30, 32, and 46, with the percentage of variables matching FHIR resources being 100% (30/30), 97% (31/32), and 85% (39/46), respectively. From the 40 resources available in the FHIR ID core, we used 10, 14, and 9 for the WHO EMT MDS, ASEAN EMT MDS, and daily reporting form, respectively. Based on the gap analysis, we found 4 variables in the daily reporting form that were not covered by the resources. Thus, we created extensions to address this gap. Conclusions: We successfully created a disaster profile that can be used as a disaster case for the SATUSEHAT platform. This profile may standardize health data collection during disasters.

Creating a Modified Version of the Cambridge Multimorbidity Score to Predict Mortality in People Older Than 16 Years: Model Development and Validation.

BACKGROUND: No single multimorbidity measure is validated for use in NHS (National Health Service) England's General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care data set created for COVID-19 pandemic research. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk, with 37 conditions defined by Read Codes. The GDPPR uses the more internationally used Systematized Nomenclature of Medicine clinical terms (SNOMED CT). We previously developed a modified version of the CMMS using SNOMED CT, but the number of terms for the GDPPR data set is limited making it impossible to use this version. OBJECTIVE: We aimed to develop and validate a modified version of CMMS using the clinical terms available for the GDPPR. METHODS: We used pseudonymized data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC), which has an extensive SNOMED CT list. From the 37 conditions in the original CMMS model, we selected conditions either with (1) high prevalence ratio (≥85%), calculated as the prevalence in the RSC data set but using the GDPPR set of SNOMED CT codes, divided by the prevalence included in the RSC SNOMED CT codes or (2) conditions with lower prevalence ratios but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development data set (n=500,000) and construct a new CMMS model, following the methods for the original CMMS study, with variable reduction and parsimony, achieved by backward elimination and the Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous data set (n=250,000) and 1-year and 5-year mortality estimates for an asynchronous data set (n=250,000). We compared the performance with that of the original CMMS and the modified CMMS that we previously developed using RSC data. RESULTS: The initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of the modified CMMS using the more extensive SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation data sets (Harrell C=0.92) and 5-year mortality was slightly lower (Harrell C=0.90). Calibration was reasonable following an adjustment for overfitting. The performance was similar to that of both the original and previous modified CMMS models. CONCLUSIONS: The new modified version of the CMMS can be used on the GDPPR, a nationwide primary care data set of 54 million people, to enable adjustment for multimorbidity in predicting mortality in people in real-world vaccine effectiveness, pandemic planning, and other research studies. It requires 17 variables to produce a comparable performance with our previous modification of CMMS to enable it to be used in routine data using SNOMED CT.

Thyroid Diseases After Total Knee Replacement: A Multi-center, Propensity-score-matched Cohort Study.

BACKGROUND/AIM: Thyroid diseases are prevalent endocrine disorders that significantly affect overall health. Although the impact of pre-existing thyroid dysfunction on total knee replacement (TKR) outcomes has been studied, the potential for TKR to increase the risk of developing thyroid disorders remains unexplored. PATIENTS AND METHODS: We examined electronic medical records from a large U.S. research network in the TriNetX research network. The study focused on patients with osteoarthritis, comparing those who had total knee replacement surgery (TKR) between 2005 and 2018 to a non-TKR group who did not have the surgery. Propensity score matching was employed to control for critical confounders. The hazard ratios (HRs) for the risk of thyroid diseases in TKR patients versus non-TKR controls were assessed. RESULTS: Post-matching, the TKR cohort demonstrated a significantly higher risk of developing thyroid diseases compared to the non-TKR cohort (unadjusted HR=1.218, 95%CI=1.169-1.269). This elevated risk persisted after adjusting for confounders (adjusted HR=1.126, 95%CI=1.061-1.196). Stratification analysis indicated that female TKR patients and those aged ≥65 years were at higher risk of developing thyroid diseases than their respective control groups. CONCLUSION: This study suggests a potential link between TKR and an increased risk of thyroid diseases, particularly among older adults and females. Potential mechanisms include inflammatory processes, surgical stress, autoimmune responses, and pharmacological effects. Healthcare providers should be vigilant in monitoring and managing thyroid dysfunction in TKR patients. Further research is necessary to elucidate the underlying mechanisms and develop preventive strategies.

Risk of Upper Limb Diseases in Osteoarthritis Patients: A Propensity-score-matched Cohort Study.

BACKGROUND/AIM: Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly impacts quality of life, particularly when affecting the hands. However, whether patients with OA are associated with higher risk of developing upper limb disorders, specifically trigger finger (TF) and carpal tunnel syndrome (CTS), remains unclear. This study aimed to evaluate the risk of upper limb disease in OA patients. PATIENTS AND METHODS: Using the US Collaborative Network, a subset of the TriNetX research network, we identified patients diagnosed with OA and matched them 1:1 with non-OA controls based on propensity scores. Matching covariates included age, sex, race, and comorbidities. The cohort consisted of 1,554,182 patients in each group. The hazard ratio of TF and CTS, as well as related surgical interventions, was assessed over a 5-year follow-up period. RESULTS: Patients with OA had a 1.30-fold increased risk of TF [95% confidence interval (CI)=1.27-1.33] and a 1.50-fold increased risk of CTS (95%CI=1.48-1.53) compared to controls. The hazard ratios for undergoing surgical interventions were 1.61 for TF (95%CI=1.51-1.71) and 1.97 for CTS (95%CI=1.78-2.19). These risks remained significant across various sensitivity analyses and stratifications according to age and sex. CONCLUSION: OA significantly increases the risk of TF and CTS. These findings highlight the need for vigilant monitoring and management of upper limb disorders in OA patients to improve overall patient care and outcomes. Future research is warranted to focus on pathological mechanisms of OA and their impact on upper limb health to develop targeted interventions.

Design of Interoperable Electronic Health Record (EHR) Application for Early Detection of Lung Diseases Using a Decision Support System by Expanding Deep Learning Techniques.

Background: Electronic health records (EHRs) are live, digital patient records that provide a thorough overview of a person's complete health data. Electronic health records (EHRs) provide better healthcare decisions and evidence-based patient treatment and track patients' clinical development. The EHR offers a new range of opportunities for analyzing and contrasting exam findings and other data, creating a proper information management mechanism to boost effectiveness, quick resolutions, and identifications. Aim: The aim of this studywas to implement an interoperable EHR system to improve the quality of care through the decision support system for the identification of lung cancer in its early stages. Objective: The main objective of the proposed system was to develop an Android application for maintaining an EHR system and decision support system using deep learning for the early detection of diseases. The second objective was to study the early stages of lung disease to predict/detect it using a decision support system. Methods: To extract the EHR data of patients, an android application was developed. The android application helped in accumulating the data of each patient. The accumulated data were used to create a decision support system for the early prediction of lung cancer. To train, test, and validate the prediction of lung cancer, a few samples from the ready dataset and a few data from patients were collected. The valid data collection from patients included an age range of 40 to 70, and both male and female patients. In the process of experimentation, a total of 316 images were considered. The testing was done by considering the data set into 80:20 partitions. For the evaluation purpose, a manual classification was done for 3 different diseases, such as large cell carcinoma, adenocarcinoma, and squamous cell carcinoma diseases in lung cancer detection. Results: The first model was tested for interoperability constraints of EHR with data collection and updations. When it comes to the disease detection system, lung cancer was predicted for large cell carcinoma, adenocarcinoma, and squamous cell carcinoma type by considering 80:20 training and testing ratios. Among the considered 336 images, the prediction of large cell carcinoma was less compared to adenocarcinoma and squamous cell carcinoma. The analysis also showed that large cell carcinoma occurred majorly in males due to smoking and was found as breast cancer in females. Conclusion: As the challenges are increasing daily in healthcare industries, a secure, interoperable EHR could help patients and doctors access patient data efficiently and effectively using an Android application. Therefore, a decision support system using a deep learning model was attempted and successfully used for disease detection. Early disease detection for lung cancer was evaluated, and the model achieved an accuracy of 93%. In future work, the integration of EHR data can be performed to detect various diseases early.

Childhood Maltreatment and Perinatal Complications: A Scoping Review of Official Health Data.

Child maltreatment (CM) poses significant risks to victims, resulting in enduring physical, psychological, and developmental consequences. Adult survivors of CM seem especially vulnerable to perinatal complications. However, existing research on perinatal outcomes presents mixed results and relies heavily on self-reported data, which may not align with official medical data. Hence, a systematic review using official health data may provide clarity on this association; it may orient future research and the provision of perinatal services. This scoping review aimed to synthesize and evaluate the quality of the literature that utilizes official health data to explore associations between CM and perinatal complications. Following Arksey and O'Malley's model, searches across four databases (PsycINFO, MEDLINE, Scopus, and ProQuest Dissertations/Thesis) produced 8,870 articles. After screening, 23 articles met the inclusion criteria (e.g., recorded perinatal complications using official health data, and peer-reviewed studies or dissertation). Evidence indicates CM survivors have less prenatal care visits, more fetal loss and preterm births, lower gestational age, and increases in emergency cesarean sections. Adults had more cervical insufficiency, lower episiotomies and sphincter ruptures, and overall pregnancy and postpartum complications while adolescents had lower Apgar scores. No associations were observed on other outcomes (e.g., vaginal bleeding, group B streptococcus, and fetal distress). Mixed findings emerged for other perinatal and maternal health concerns such as birth weight and blood pressure. CM survivors may face an increased risk of experiencing perinatal complications. Findings point to the relevance of leveraging health data for CM research and adopting trauma-informed practices in perinatal services.

Prevalence of parenthood among hospitalized adult patients with severe mental illness: a quantitative data analysis.

Introduction: In the Western world, more than one-third of the patients of productive age hospitalized for severe mental illness (SMI) are parents. Each of their offspring is exposed to several stressors related to their parent's illness and hospitalization, which puts them at an increased risk of developing mental health problems. In the Czech Republic, no statistics are currently available about the families of patients with SMI, inpatients who are parents, or data about their children (ages ≤18 years). Therefore, our research aim was to describe the prevalence of parenthood among hospitalized patients with SMI, assess the number of children and determine the extent to which offspring information was present in medical records. Methods: Quantitative data from medical records (2,768 patients, aged 18-63 years, hospitalized for SMI between 2017 and 2020) from two large inpatient psychiatric facilities were examined. Parental information, demographic characteristics, number of children, and other available data were collected. Results: The prevalence of parenthood among inpatients with SMI was 34.6%. Parenthood was most prevalent among female patients and patients with recurrent depressive and bipolar disorders. The total number of offspring in 957 patient-parents was 1781 (41.7% minors under the age of 18). Information on parenthood was available in 99.7% of cases; information on the age of offspring, custody, and sociodemographic situation varies, being included in 73% to 89.7% of the medical records (some details were more frequently recorded than others). Discussion: The data obtained may help to better understand and address the specifics of these families and thus serve as a basis for the development of prevention programs.

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to analyze high-suicide risk Veterans Affairs patients' unstructured electronic health records. Our sample included all high-risk patients that died (cases) or did not (controls) by suicide in 2017 and 2018. Cases and controls shared the same risk, location, and treatment intervals and received nine months of mental health care during the year before the relevant end date. Each case was matched with five controls. We analyzed case records from diagnosis until death and control records from diagnosis until matched case's death date. Our final sample included 218 cases and 943 controls. We analyzed the corpus using a Python-based Dynamic Topic Modeling algorithm. We identified five distinct topics, "Medication," "Intervention," "Treatment Goals," "Suicide," and "Treatment Focus." We observed divergent change patterns over time, with pathology-focused care increasing for cases and supportive care increasing for controls. The case topics tended to fluctuate more than the control topics, suggesting the importance of monitoring lability. Our study provides a method for monitoring risk fluctuation and strengthens the groundwork for time-sensitive risk measurement.

Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries.

The economic trend and the health care landscape are rapidly evolving across Asia. Effective real-world data (RWD) for regulatory and clinical decision-making is a crucial milestone associated with this evolution. This necessitates a critical evaluation of RWD generation within distinct nations for the use of various RWD warehouses in the generation of real-world evidence (RWE). In this article, we outline the RWD generation trends for 2 contrasting nation archetypes: "Solo Scholars"-nations with relatively self-sufficient RWD research systems-and "Global Collaborators"-countries largely reliant on international infrastructures for RWD generation. The key trends and patterns in RWD generation, country-specific insights into the predominant databases used in each country to produce RWE, and insights into the broader landscape of RWD database use across these countries are discussed. Conclusively, the data point out the heterogeneous nature of RWD generation practices across 10 different Asian nations and advocate for strategic enhancements in data harmonization. The evidence highlights the imperative for improved database integration and the establishment of standardized protocols and infrastructure for leveraging electronic medical records (EMR) in streamlining RWD acquisition. The clinical data analysis and reporting system of Hong Kong is an excellent example of a successful EMR system that showcases the capacity of integrated robust EMR platforms to consolidate and produce diverse RWE. This, in turn, can potentially reduce the necessity for reliance on numerous condition-specific local and global registries or limited and largely unavailable medical insurance or claims databases in most Asian nations. Linking health technology assessment processes with open data initiatives such as the Observational Medical Outcomes Partnership Common Data Model and the Observational Health Data Sciences and Informatics could enable the leveraging of global data resources to inform local decision-making. Advancing such initiatives is crucial for reinforcing health care frameworks in resource-limited settings and advancing toward cohesive, evidence-driven health care policy and improved patient outcomes in the region.

Agreement between Clinical Frailty Scale-scores based on information from patient interviews and Clinical Frailty Scale-scores based on information from medical records - a cross sectional study.

INTRODUCTION: Frailty is an age-related condition with increased risk for adverse health outcomes. Assessing frailty according to the Clinical Frailty Scale (CFS) based on data from medical records is useful for previously unassessed patients, but the validity of such scores in exclusively geriatric populations and in patients with dementia is relatively unknown. METHODS: Patients admitted for the first time to one of two geriatric wards at Örebro University hospital between January 1st - December 31st, 2021, were included in this study if they had been appointed a CFS-score by anamnestic interview (CFSI) at admission. CFS scores based on medical records (CFSR) were appointed by a single medical student, who was blinded to the CFSI score. Score-agreement was evaluated with quadratic weighted Cohen's kappa (κ). RESULTS: In total, 145 patients between the age of 55-101 were included in the study. The CFSR and CFSI scores agreed perfectly in 102 cases (0.7, 95% CI 0.65-0.77). There was no significant difference regarding age, sex, comorbidity, or number of patients diagnosed with dementia between the patients with complete agreement and the patients whose scores did not agree. Agreement between the scores was substantial, κ = 0.66, 95% CI 0.53-0.80. CONCLUSIONS: CFS scores based on information from medical records can be generated with substantial agreement to CFS scores based on in-person anamnestic interviews. A dementia diagnosis does not influence the agreement between the scores. Therefore, these scores are a useful tool for assessing frailty in geriatric patients who previously lack a frailty assessment, both in clinical practice and future research. The results support previous findings, but larger studies are warranted.