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.

Hospital-acquired malnutrition: point prevalence, risk identifiers and utility of a digital Dashboard to identify high-risk, long-stay patients in five Australian facilities.

BACKGROUND: There are limited hospital-acquired malnutrition (HAM) studies among the plethora of malnutrition literature, and a few studies utilise electronic medical records to assist with malnutrition care. This study therefore aimed to determine the point prevalence of HAM in long-stay adult patients across five facilities, whether any descriptors could assist in identifying these patients and whether a digital Dashboard accurately reflected 'real-time' patient nutritional status. METHODS: HAM was defined as malnutrition first diagnosed >14 days after hospital admission. Eligible patients were consenting adult (≥18 years) inpatients with a length of stay (LOS) >14 days. Palliative, mental health and intensive care patients were excluded. Descriptive, clinical and nutritional data were collected, including nutritional status, and whether a patient had hospital-acquired malnutrition to determine point prevalence. Descriptive Fisher's exact and analysis of variance (ANOVA) tests were used. RESULTS: Eligible patients (n = 134) were aged 68 ± 16 years, 52% were female and 92% were acute admissions. HAM and malnutrition point prevalence were 4.5% (n = 6/134) and 19% (n = 26/134), respectively. Patients with HAM had 72 days greater LOS than those with malnutrition present on admission (p < 0.001). A high proportion of HAM patients were inpatients at a tertiary facility and longer-stay wards. The Dashboard correctly reflected recent ward dietitian assessments in 94% of patients at one facility (n = 29/31). CONCLUSIONS: HAM point prevalence was 4.5% among adult long-stay patients. Several descriptors may be suitable to screen for at-risk patients in future studies. Digital Dashboards have the potential to explore factors related to HAM.

Effects of COVID-19 on Irish general practice activity from 2019 to 2021: a retrospective analysis of 500,000 consultations using electronic medical record data.

BACKGROUND: General practice (GP) is crucial to primary care delivery in the Republic of Ireland and is almost fully computerised. General practice teams were the first point of contact for much COVID-19-related care and there were concerns routine healthcare activities could be disrupted due to COVID-19 and related restrictions. AIMS: The study aimed to assess effects of the pandemic on GP activity through analysis of electronic medical record data from general practice clinics in the Irish Midwest. METHODS: A retrospective, descriptive study of electronic medical record data relating to patient record updates, appointments and medications prescribed across 10 GP clinics over the period 2019-2021 inclusive. RESULTS: Data relating to 1.18 million record transactions for 32 k patients were analysed. Over 500 k appointments were examined, and demographic trends presented. Overall appointment and prescribing activity increased over the study period, while a dip was observed immediately after the pandemic's arrival in March 2020. Delivery of non-childhood immunisations increased sixfold as a result of COVID-19, childhood immunisation activity was maintained, while cervical smears decreased in 2020 as the screening programme was halted. A quarter of consultations in 2020 and 2021 were teleconsultations, and these were more commonplace for younger patients. CONCLUSIONS: General practice responded robustly to the pandemic by taking on additional activities while maintaining routine services where possible. The shift to teleconsulting was a significant change in workflow. Analysing routinely collected electronic medical record data can provide valuable insights for service planning, and access to these insights would be beneficial for future pandemic responses.

Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.

Background: Electronic medical records store extensive patient data and serve as a comprehensive repository, including textual medical records like surgical and imaging reports. Their utility in clinical decision support systems is substantial, but the widespread use of ambiguous and unstandardized abbreviations in clinical documents poses challenges for natural language processing in clinical decision support systems. Efficient abbreviation disambiguation methods are needed for effective information extraction. Objective: This study aims to enhance the one-to-all (OTA) framework for clinical abbreviation expansion, which uses a single model to predict multiple abbreviation meanings. The objective is to improve OTA by developing context-candidate pairs and optimizing word embeddings in Bidirectional Encoder Representations From Transformers (BERT), evaluating the model's efficacy in expanding clinical abbreviations using real data. Methods: Three datasets were used: Medical Subject Headings Word Sense Disambiguation, University of Minnesota, and Chia-Yi Christian Hospital from Ditmanson Medical Foundation Chia-Yi Christian Hospital. Texts containing polysemous abbreviations were preprocessed and formatted for BERT. The study involved fine-tuning pretrained models, ClinicalBERT and BlueBERT, generating dataset pairs for training and testing based on Huang et al's method. Results: BlueBERT achieved macro- and microaccuracies of 95.41% and 95.16%, respectively, on the Medical Subject Headings Word Sense Disambiguation dataset. It improved macroaccuracy by 0.54%-1.53% compared to two baselines, long short-term memory and deepBioWSD with random embedding. On the University of Minnesota dataset, BlueBERT recorded macro- and microaccuracies of 98.40% and 98.22%, respectively. Against the baselines of Word2Vec + support vector machine and BioWordVec + support vector machine, BlueBERT demonstrated a macroaccuracy improvement of 2.61%-4.13%. Conclusions: This research preliminarily validated the effectiveness of the OTA method for abbreviation disambiguation in medical texts, demonstrating the potential to enhance both clinical staff efficiency and research effectiveness.

Weather conditions and legionellosis: a nationwide case-crossover study among Medicare recipients.

Legionellosis is a respiratory infection caused by Legionella sp. that is found in water and soil. Infection may cause pneumonia (Legionnaires' Disease) and a milder form (Pontiac Fever). Legionella colonizes water systems and results in exposure by inhalation of aerosolized bacteria. The incubation period ranges from 2 to 14 days. Precipitation and humidity may be associated with increased risk. We used Medicare records from 1999 to 2020 to identify hospitalizations for legionellosis. Precipitation, temperature, and relative humidity were obtained from the PRISM Climate Group for the zip code of residence. We used a time-stratified bi-directional case-crossover design with lags of 20 days. Data were analyzed using conditional logistic regression and distributed lag non-linear models. A total of 37 883 hospitalizations were identified. Precipitation and relative humidity at lags 8 through 13 days were associated with an increased risk of legionellosis. The strongest association was precipitation at day 10 lag (OR = 1.08, 95% CI = 1.05-1.11 per 1 cm). Over 20 days, 3 cm of precipitation increased the odds of legionellosis over four times. The association was strongest in the Northeast and Midwest and during summer and fall. Precipitation and humidity were associated with hospitalization among Medicare recipients for legionellosis at lags consistent with the incubation period for infection.

COVID-19 Vaccination Response in Patients with Multiple Sclerosis Treated with Ofatumumab in the United States: A Medical Record Review.

INTRODUCTION: Real-world data are required to provide a greater understanding of the impact of ofatumumab on the ability to mount an effective immune response following the receipt of approved COVID-19 vaccinations. This retrospective real-world analysis aimed to describe the humoral immune response to COVID-19 vaccination during ofatumumab treatment in patients with multiple sclerosis (MS). METHODS: Data from patients with MS treated with ofatumumab who were fully vaccinated against COVID-19 infection were abstracted from medical charts at four clinical sites in the USA. Patient characteristics and humoral response were summarized descriptively. Differences in humoral response were documented on the basis of vaccination status during ofatumumab treatment (i.e., after full vaccination and after booster vaccination) and prior disease-modifying treatment (DMT) exposure (i.e., DMT naïve, prior anti-CD20/sphingosine 1-phosphate [S1P] therapy, prior non-anti-CD20/S1P therapy). The sample size precluded formal statistical analysis. RESULTS: Thirty-eight patients were included. The mean (standard deviation) duration of ofatumumab treatment upon data collection was 20.4 (4.6) months (treatment ongoing for 35 [92%] patients). Definitive humoral response after full vaccination was documented for 34 patients, of whom 20 (60%) were seropositive. Definitive humoral response after booster vaccination was documented among five patients, of whom three (60%) were seropositive. Among patients who were DMT naïve prior to ofatumumab (n = 15), 73% were seropositive; among patients exposed to prior anti-CD20/S1P therapy (n = 14), 33% were seropositive; and among patients exposed to prior non-anti-CD20/S1P therapy (n = 9), 56% were seropositive. Patients naïve to DMT had been living with an MS diagnosis for a shorter duration than those experienced with DMTs. CONCLUSION: Patients with MS receiving ongoing treatment with ofatumumab can mount a positive humoral response to a COVID-19 vaccination. Prior treatment with anti-CD20 or S1P DMTs may be a risk factor for lower humoral response.

Risk factors for wrong-patient medication orders in the emergency department.

Objectives: This paper investigates the risk factors for wrong-patient medication orders in an emergency department (ED) by studying intercepted ordering errors identified by the "retract-and-reorder" (RaR) metric (orders that were retracted and reordered for a different patient by the same provider within 10 min). Materials and Methods: Medication ordering data of an academic ED were analyzed to identify RaR events. The association of RaR events with similarity of patient names and birthdates, matching sex, age difference, the month, weekday, and hour of the RaR event, the elapsed hours since ED shift start, and the proximity of exam rooms in the electronic medical record (EMR) dashboard's layout was evaluated. Results: Over 5 years (2017-2021), 1031 RaR events were identified among a total of 561 099 medication orders leading to a proportional incidence of 184 per 100 000 ED orders (95% CI: 172; 195). RaR orders were less likely to be performed by nurses compared to physicians (OR 0.54 [0.47; 0.61], P < .001). Furthermore, RaR pairs were more likely to have the same sex (OR 1.26 [95% CI 1.10; 1.43], P = .001) and the proximity of the exam rooms was closer (-0.62 [95% CI -0.77; -0.47], P = .001) compared to control pairs. Patients' names, birthdates, age, and the other factors showed no significant association (P > .005). Discussion and Conclusion: This study found no significant influence from factors such as similarity of patient names, age, or birthdates. However, the proximity of exam rooms in the user interface of the EMR as well as patients' same sex emerged as risk factors.

A Texting- and Internet-Based Self-Reporting System for Enhanced Vaccine Safety Surveillance With Insights From a Large Integrated Health Care System in the United States: Prospective Cohort Study.

BACKGROUND: SMS text messaging- and internet-based self-reporting systems can supplement existing vaccine safety surveillance systems, but real-world participation patterns have not been assessed at scale. OBJECTIVE: This study aimed to describe the participation rates of a new SMS text messaging- and internet-based self-reporting system called the Kaiser Permanente Side Effect Monitor (KPSEM) within a large integrated health care system. METHODS: We conducted a prospective cohort study of Kaiser Permanente Southern California (KPSC) patients receiving a COVID-19 vaccination from April 23, 2021, to July 31, 2023. Patients received invitations through flyers, SMS text messages, emails, or patient health care portals. After consenting, patients received regular surveys to assess adverse events up to 5 weeks after each dose. Linkage with medical records provided demographic and clinical data. In this study, we describe KPSEM participation rates, defined as providing consent and completing at least 1 survey within 35 days of COVID-19 vaccination. RESULTS: Approximately, 8% (164,636/2,091,975) of all vaccinated patients provided consent and completed at least 1 survey within 35 days. The lowest participation rates were observed for parents of children aged 12-17 years (1349/152,928, 0.9% participation rate), and the highest participation was observed among older adults aged 61-70 years (39,844/329,487, 12.1%). Persons of non-Hispanic White race were more likely to participate compared with other races and ethnicities (13.1% vs 3.9%-7.5%, respectively; P<.001). In addition, patients residing in areas with a higher neighborhood deprivation index were less likely to participate (5.1%, 16,503/323,122 vs 10.8%, 38,084/352,939 in the highest vs lowest deprivation quintiles, respectively; P<.001). Invitations through the individual's Kaiser Permanente health care portal account and by SMS text message were associated with the highest participation rate (19.2%, 70,248/366,377 and 10.5%, 96,169/914,793, respectively), followed by email (19,464/396,912, 4.9%) and then QR codes on flyers (25,882/2,091,975, 1.2%). SMS text messaging-based surveys demonstrated the highest sustained daily response rates compared with internet-based surveys. CONCLUSIONS: This real-world prospective study demonstrated that a novel digital vaccine safety self-reporting system implemented through an integrated health care system can achieve high participation rates. Linkage with participants' electronic health records is another unique benefit of this surveillance system. We also identified lower participation among selected vulnerable populations, which may have implications when interpreting data collected from similar digital systems.

Understanding Patient Portal Uses and Needs: Cross-Sectional Study in a State Fair Setting.

This study identified 22 features that are used and the needs for desired features/data in patient portals that enable online access to medical records. Data collected at a Midwestern state fair indicates that while most participants used patient portals, use and desirability of specific features varied widely. Identified needs for enhanced data access, portal functionality, and usability can be used to inform effective patient portal design.

Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review.

Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders. Objective: This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones. Methods: A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results: A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms. Conclusions: Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general.

Strategy for scheduled downtime of hospital information system utilizing third-party applications.

BACKGROUND: The widespread adoption of Hospital Information Systems (HIS) has brought significant benefits in healthcare quality and workflow efficiency. However, downtimes for system maintenance are inevitable and pose a considerable challenge to continuous patient care. Existing strategies, including manual prescription methods, are no longer effective due to increasing reliance on digital systems. METHOD: This study implemented two main strategies to mitigate the impact of scheduled downtimes. First, we created an "Emergency query program" that switches to a read-only backup server during downtimes, allowing clinicians to view essential patient data. Second, an "Emergency prescription system" was developed based on the Microsoft Power Platform and integrated into Microsoft Teams. This allows clinicians to perform digital prescriptions even during downtimes. RESULTS: During a planned 90-minute downtime, 282 users accessed the Emergency Prescription System, resulting in 22 prescriptions from various departments. Average times for prescription confirmation and completion were 8 min and 3 s, and 18 min and 40 s, respectively. A post-downtime evaluation revealed high user satisfaction. CONCLUSION: Essential maintenance-induced HIS downtimes are inherently disruptive to patient care process. Our deployment of an emergency query program and a Microsoft Teams-integrated emergency prescription system demonstrated robust care continuity during HIS downtime.

Dexamethasone Dose for COVID-19 in a Large U.S. Hospital Network From April 2020 to May 2023.

PURPOSE: While potential harm from high doses of systemic dexamethasone for clinical management of COVID-19 is an important concern, little is known about real world dexamethasone dosing in patients hospitalized with COVID-19 in the United States. METHODS: Descriptive study to assess dexamethasone daily dose in adults with COVID-19 in a large US hospital network, overall and by respiratory support requirements, extracted using semi- structured nursing notes. RESULTS: Of 332 430 hospitalizations with a COVID-19 diagnosis, 201 637 (60.7%) hospitalizations included dexamethasone administration. The mean age of recipients was 63 years, 53.0% were male, and 64.5% White. Median time from admission to dexamethasone administration was 0 day (interquartile range [IQR], 0-1 days) and median duration of use was 5 (IQR, 3-9) days. Almost 80% of hospitalizations received standard daily doses (≤ 6 mg daily), 12.7% moderately high daily doses (> 6- ≤ 10 mg daily), and 8.1% high (> 10- ≤ 20 mg daily) or very high daily dose (> 20 mg daily). Over 20% of COVID-19 hospitalizations requiring no oxygen or simple oxygen received high doses of systemic dexamethasone. CONCLUSIONS: Given the findings from the UK RECOVERY trial, and the general uncertainty around safety of higher dexamethasone doses in those requiring more intense respiratory support, standard daily dexamethasone doses of 6 mg or less for hospitalized COVID-19 requiring supplemental oxygen are recommended.

Real-world treatment patterns, survival outcomes, and health care resource utilization for locally advanced or metastatic urothelial carcinoma in Spain.

PURPOSE: Real-world evidence on locally advanced or metastatic urothelial carcinoma (la/mUC) management in Spain is limited. This study describes patient characteristics, treatment patterns, survival, and health care resource utilization (HCRU) in this population. METHODS/PATIENTS: This retrospective observational study included all adults with a first diagnosis/record of la/mUC (index date) from January 2015 to June 2020 at nine university hospitals in Spain. Data were collected up to December 31, 2020 (end of study), death, or loss to follow-up. Patient characteristics, treatment patterns, median overall survival (OS) and progression-free survival (PFS) from index date (Kaplan-Meier estimates), and disease-specific HCRU were described. RESULTS: Among 829 patients, median age at diagnosis was 71 years; 70.2% had ≥ 1 comorbidity, and 52.5% were eligible for cisplatin. Median follow-up was 12.7 months. Most (84.7%) patients received first-line systemic treatment; of these, 46.9% (n = 329) received second-line and 16.6% (n = 116) received third-line therapy. Chemotherapy was the most common treatment in all lines of therapy, followed by programmed cell death protein 1/ligand 1 inhibitors. Median (95% confidence interval) OS and PFS were 18.8 (17.5-21.5) and 9.9 (8.9-10.5) months, respectively. Most patients required ≥ 1 outpatient visit (71.8%), inpatient admission (56.6%), or emergency department visit (56.5%). CONCLUSIONS: Therapeutic patterns were consistent with Spanish guideline recommendations. Chemotherapy had a role in first-line treatment of la/mUC in Spain during the study period. However, the disease burden remains high, and new first-line treatments recommended in the latest European guidelines should be made available to patients in Spain.

Perception towards electronic health records & uptake of digital health IDs among the urban residents in northern India: A mixed methods study.

Background & objectives Ayushman Bharat Digital Mission (ABDM) envisages a unique digital health ID for all citizens of India, to create electronic health records (EHR) of individuals. The present study assessed the uptake of Digital Health IDs by the patient and general population, their attitude toward EHR, and explored the barriers to digital ID and utilizing electronic health records services. Methods A concurrent explanatory mixed methods study was undertaken in Chandigarh, India, with an analytical cross-sectional design as a quantitative part and a qualitative descriptive study. The study participants were 419 individuals aged ≥18 yr who attended the urban primary healthcare centre (n=399) and the community-based screening camps (n=20) between July 2021 and January 2022. Latent Class Analysis (LCA) was undertaken to identify hidden sub-population characteristics. In-depth interviews were done to identify the barriers to health ID uptake. Results The digital health ID uptake rate was 78 per cent (n=327). Among the study participants, those who were aware of EHR, those who wanted a national EHR system, those who were confident with the government on EHR security, and those who were willing to make national EHR accessible for research showed significantly higher digital health ID uptake than their counterparts. The themes identified under barriers of uptake from the qualitative interviews were lack of awareness, technology-related (including digital literacy) and utility-related. Interpretation & conclusions Increasing EHR awareness, digital health literacy, and enacting data protection laws may improve the acceptance of the digital health ecosystem in India.

Predicting Prenatal Depression and Assessing Model Bias Using Machine Learning Models.

Background: Perinatal depression is one of the most common medical complications during pregnancy and postpartum period, affecting 10% to 20% of pregnant individuals, with higher rates among Black and Latina women who are also less likely to be diagnosed and treated. Machine learning (ML) models based on electronic medical records (EMRs) have effectively predicted postpartum depression in middle-class White women but have rarely included sufficient proportions of racial/ethnic minorities, which has contributed to biases in ML models. Our goal is to determine whether ML models could predict depression in early pregnancy in racial/ethnic minority women by leveraging EMR data. Methods: We extracted EMRs from a large U.S. urban hospital serving mostly low-income Black and Hispanic women (n = 5875). Depressive symptom severity was assessed using the Patient Health Questionnaire-9 self-report questionnaire. We investigated multiple ML classifiers using Shapley additive explanations for model interpretation and determined prediction bias with 4 metrics: disparate impact, equal opportunity difference, and equalized odds (standard deviations of true positives and false positives). Results: Although the best-performing ML model's (elastic net) performance was low (area under the receiver operating characteristic curve = 0.61), we identified known perinatal depression risk factors such as unplanned pregnancy and being single and underexplored factors such as self-reported pain, lower prenatal vitamin intake, asthma, carrying a male fetus, and lower platelet levels. Despite the sample comprising mostly low-income minority women (54% Black, 27% Latina), the model performed worse for these communities (area under the receiver operating characteristic curve: 57% Black, 59% Latina women vs. 64% White women). Conclusions: EMR-based ML models could moderately predict early pregnancy depression but exhibited biased performance against low-income minority women.

Perinatal depression affects 10% to 20% of pregnant individuals, with higher rates among racial/ethnic minorities who are underdiagnosed and undertreated. This study used machine learning models on electronic medical record data from a hospital serving mostly low-income Black and Hispanic women to predict early pregnancy depression. While the best model performed moderately well, it exhibited bias, predicting depression less accurately for Black and Latina women compared with White women. The study identified some known risk factors such as unplanned pregnancy and underexplored factors such as self-reported pain, lower prenatal vitamin intake, and carrying a male fetus that may contribute to perinatal depression.

Adoption, acceptance, and use of a decision support tool to promote timely investigations for cancer in primary care.

BACKGROUND: The complexities of diagnosing cancer in general practice has driven the development of quality improvement (QI) interventions, including clinical decision support (CDS) and auditing tools. Future Health Today (FHT) is a novel QI tool, consisting of CDS at the point-of-care, practice population-level auditing, recall, and the monitoring of QI activities. OBJECTIVES: Explore the acceptability and usability of the FHT cancer module, which flags patients with abnormal test results that may be indicative of undiagnosed cancer. METHODS: Interviews were conducted with general practitioners (GPs) and general practice nurses (GPNs), from practices participating in a randomized trial evaluating the appropriate follow-up of patients. Clinical Performance Feedback Intervention Theory (CP-FIT) was used to analyse and interpret the data. RESULTS: The majority of practices reported not using the auditing and QI components of the tool, only the CDS which was delivered at the point-of-care. The tool was used primarily by GPs; GPNs did not perceive the clinical recommendations to be within their role. For the CDS, facilitators for use included a good workflow fit, ease of use, low time cost, importance, and perceived knowledge gain. Barriers for use of the CDS included accuracy, competing priorities, and the patient population. CONCLUSIONS: The CDS aligned with the clinical workflow of GPs, was considered non-disruptive to the consultation and easy to implement into usual care. By applying the CP-FIT theory, we were able to demonstrate the key drivers for GPs using the tool, and what limited the use by GPNs.

The clinical and genetic spectrum of paediatric speech and language disorders.

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52 143 individuals, reconstructing clinical histories using a large-scale data-mining approach of the electronic medical records from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of 26 broad speech and language diagnoses. We used natural language processing to assess the degree to which clinical diagnoses in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be retrieved easily through ICD-10 diagnosis codes, whereas stuttering as a speech phenotype was coded in only 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and, to a lesser degree, with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our analysis of electronic medical records were STXBP1 (n = 21), PTEN (n = 20) and CACNA1A (n = 18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P = 8.57 × 10-7, 95% confidence interval = 18.62-130.39) and MYO7A with speech and language development delay attributable to hearing loss (P = 1.24 × 10-5, 95% confidence interval = 17.46-infinity). Finally, in a sub-cohort of 726 individuals with whole-exome sequencing data, we identified an enrichment of rare variants in neuronal receptor pathways, in addition to associations of UQCRC1 and KIF17 with expressive aphasia, MROH8 and BCHE with poor speech, and USP37, SLC22A9 and UMODL1 with aphasia. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies.

Analyzing Comorbidity Patterns in Patients With Thyroid Disease Using Large-Scale Electronic Medical Records: Network-Based Retrospective Observational Study.

BACKGROUND: Thyroid disease (TD) is a prominent endocrine disorder that raises global health concerns; however, its comorbidity patterns remain unclear. OBJECTIVE: This study aims to apply a network-based method to comprehensively analyze the comorbidity patterns of TD using large-scale real-world health data. METHODS: In this retrospective observational study, we extracted the comorbidities of adult patients with TD from both private and public data sets. All comorbidities were identified using ICD-10 (International Classification of Diseases, 10th Revision) codes at the 3-digit level, and those with a prevalence greater than 2% were analyzed. Patients were categorized into several subgroups based on sex, age, and disease type. A phenotypic comorbidity network (PCN) was constructed, where comorbidities served as nodes and their significant correlations were represented as edges, encompassing all patients with TD and various subgroups. The associations and differences in comorbidities within the PCN of each subgroup were analyzed and compared. The PageRank algorithm was used to identify key comorbidities. RESULTS: The final cohorts included 18,311 and 50,242 patients with TD in the private and public data sets, respectively. Patients with TD demonstrated complex comorbidity patterns, with coexistence relationships differing by sex, age, and type of TD. The number of comorbidities increased with age. The most prevalent TDs were nontoxic goiter, hypothyroidism, hyperthyroidism, and thyroid cancer, while hypertension, diabetes, and lipoprotein metabolism disorders had the highest prevalence and PageRank values among comorbidities. Males and patients with benign TD exhibited a greater number of comorbidities, increased disease diversity, and stronger comorbidity associations compared with females and patients with thyroid cancer. CONCLUSIONS: Patients with TD exhibited complex comorbidity patterns, particularly with cardiocerebrovascular diseases and diabetes. The associations among comorbidities varied across different TD subgroups. This study aims to enhance the understanding of comorbidity patterns in patients with TD and improve the integrated management of these individuals.

Electronic Discharge Summaries: Perspectives on National Implementation from Clinicians in Hospital and Primary Care.

This paper describes clinicians' views on the structure and content of an electronic discharge summary (EDS). A sample EDS template was developed by building on existing Australian guidelines to illustrate some of the proposed elements required for a high-quality clinical document. Surveys were widely disseminated to gather feedback and perspectives of hospital and primary care clinicians. A pragmatic approach to this study was underpinned by a strong evidence base and informed by implementation science methods. Key themes were identified, including variability in workflow and clinical needs, digital maturity, and digital health literacy of the clinical workforce. Understanding different workflows and priorities between hospital and primary care clinicians was a significant barrier to implementing a high-quality EDS. The strong consensus for change from both hospital and primary care clinicians, however, signaled the workforce's readiness as a potential enabler of high-quality EDS documentation.