Análise dos registros perioperatórios baseados na sistematização da assistência de enfermagem perioperatória: estudo transversal / Analysis of perioperative records based on the systematization of perioperative nursing care: a cross-sectional study / Análisis de registros perioperatorios a partir de la sistematización de la atención de enfermería perioperatoria: estudio transversal

Objetivo: analisar os registros perioperatórios baseados nas etapas de sistematização da assistência de enfermagem perioperatória em um hospital regional público do agreste de Pernambuco. Método: estudo transversal, descritivo, com abordagem quantitativa, obtido com dados secundários, conduzido em um hospital público. A amostra foi composta por 276 prontuários de indivíduos que se submeteram a procedimentos anestésico-cirúrgicos, durante os meses de janeiro a maio de 2023. Utilizou-se a análise descritiva e inferencial. Resultados: a efetuação dos registros da sistematização da assistência de enfermagem perioperatória foi predominante na maioria do período perioperatório, com respectiva significância principalmente no pré-operatório. Conclusão: as atividades satisfatórias corresponderam à visita pré-operatória de enfermagem, histórico, diagnóstico e prescrição de enfermagem. Já as fragilidades identificadas destacaram-se a ausência de reservas sanguíneas, a verificação de alergia e a colocação de placa de eletrocautério, inserção de sonda vesical, a efetuação do controle de perdas sanguíneas, fisiológicas e secreção gástrica.

Objective: analyzing perioperative records based on the phases of systematization of perioperative nursing care in a public regional hospital in Pernambuco's harsh region. Method: a cross-sectional, descriptive study with a quantitative approach, using secondary data, conducted in a public hospital. The sample consisted of 276 medical records of individuals who underwent anesthetic-surgical procedures between January and May 2023. Descriptive and inferential analysis was used. Results: the recording of the systematization of perioperative nursing care was predominant in the majority of the perioperative period, with significance mainly in the preoperative period. Conclusion: the satisfactory activities corresponded to the preoperative nursing visit, history, diagnosis, and nursing prescription. The weaknesses identified were the lack of blood reserves, checking for allergies and placing the electrocautery plate, inserting a urinary catheter, controlling blood loss, physiological loss, and gastric secretion.

Objetivo: analizar los registros perioperatorios a partir de las etapas de sistematización de la atención de enfermería perioperatoria en un hospital público regional de la zona rural de Pernambuco. Método: estudio descriptivo transversal, con enfoque cuantitativo, a partir de datos secundarios, realizado en un hospital público. La muestra estuvo compuesta por 276 historias clínicas de personas sometidas a procedimientos anestésico-quirúrgicos, de enero a mayo de 2023. Se utilizó análisis descriptivo e inferencial. Resultados: el registro de la sistematización de la atención de enfermería perioperatoria predominó en la mayor parte del periodo perioperatorio, con significación principalmente en el periodo preoperatorio. Conclusión: las actividades satisfactorias correspondieron a la visita de enfermería preoperatoria, registro, diagnóstico y prescripción de enfermería. Las debilidades identificadas incluyeron falta de reservas de sangre, comprobación de alergias y colocación de placa de electrocauterio, inserción de sonda vesical, control de la pérdida de sangre, fisiológica y secreción gástrica.

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.

Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text.

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clinical narratives curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, where volumes of millions of records preclude reading records and the complexities of clinical notes limit usefulness of more "traditional" text-mining approaches. We discuss the application of various machine learning techniques ranging from simple models for identifying words and phrases with similar meanings to expand lexicons for keyword searching, to the use of more complex language models. Specifically, we describe the use of language models for record annotation, unsupervised approaches for identifying topics within large datasets, and discuss more recent developments in the area of generative models (such as ChatGPT). As these models become increasingly complex it is pertinent that researchers and clinicians work together to ensure that the outputs of these models are explainable in order to instill confidence in any conclusions drawn from them.

Patients with hidradenitis suppurativa are associated with risk of new-onset glaucoma: a propensity-score-matched cohort study.

Background: Ocular comorbidities of hidradenitis suppurativa (HS) has been widely evaluated; however real-world evidence was scarce. Moreover, risk of glaucoma in HS patients remained unclear. This study aimed to evaluate the 5-year glaucoma risk in HS patients. Methods: This retrospective cohort study used the TriNetX database covering 2005-2017. In total, 53,281 HS patients were propensity score matched 1:1 to controls based on demographics, including comorbidities, medications, healthcare utilization, etc. Patients were followed for 5 years post-index date. Glaucoma risks were calculated based on hazard ratios and 95% confidence intervals (95% CI). Stratified analyses by sex and age were performed. Results: After matching, baseline characteristics were similar between groups. HS was associated with a 1.25 times higher 5-year glaucoma risk (95% CI, 1.10-1.42). The risk was significant within 1 year (HR=1.37; 95% CI, 1.03-1.82), 3 years (HR=1.31; 95% CI, 1.12-1.54), and 5 years post-index. In subgroup analysis, women had a 1.28 times higher risk (95% CI, 1.10-1.49). Patients aged 18-64 years (HR=1.33; 95% CI, 1.14-1.55) and ≥65 years (HR=1.33; 95% CI, 1.05-1.67) also presented elevated glaucoma risks. Conclusion: This real-world data analysis demonstrated a significantly increased 5-year glaucoma risk in HS patients versus matched controls. Ocular complications should be concerned while managing HS patients.

lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses.

BACKGROUND: The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards. METHODS: We developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. RESULTS: lab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm's efficacy in handling large-scale data sets. CONCLUSIONS: lab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management.

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.

BACKGROUND: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. OBJECTIVE: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review. METHODS: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review. RESULTS: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76. CONCLUSIONS: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.

User Experiences of Transitioning From a Homegrown Electronic Health Record to a Vendor-Based Product in the Department of Veterans Affairs: Qualitative Findings From a Mixed Methods Evaluation.

BACKGROUND: The Department of Veterans Affairs (VA), the largest nationally integrated health system in the United States, is transitioning from its homegrown electronic health record (EHR) to a new vendor-based EHR, Oracle Cerner. Experiences of the first VA site to transition have been widely discussed in the media, but in-depth accounts based on rigorous research are lacking. OBJECTIVE: We sought to explore employee perspectives on the rationale for, and value of, transitioning from a VA-tailored EHR to a vendor-based product. METHODS: As part of a larger mixed methods, multisite, formative evaluation of VA clinician and staff experiences with the EHR transition, we conducted semistructured interviews at the Mann-Grandstaff VA Medical Center before, during, and after going live in October 2020. In total, we completed 122 interviews with 26 participants across multiple departments. RESULTS: Before the new vendor-based EHR went live, participants initially expressed cautious optimism about the transition. However, in subsequent interviews following the go-live, participants increasingly critiqued the vendor's understanding of VA's needs, values, and workflows, as well as what they perceived as an inadequate fit between the functionalities of the new vendor-based EHR system and VA's characteristic approach to care. As much as a year after going live, participants reiterated these concerns while also expressing a desire for substantive changes to the transition process, with some questioning the value of continuing with the transition. CONCLUSIONS: VA's transition from a homegrown EHR to a vendor-based EHR system has presented substantial challenges, both practical and cultural in nature. Consequently, it is a valuable case study for understanding the sociotechnical dimension of EHR-to-EHR transitions. These findings have implications for both VA leadership and the broader community of policy makers, vendors, informaticists, and others involved in large-scale health information technology implementations.

A scalable approach for critical care data extraction and analysis in an academic medical center.

BACKGROUND: Electronic health records are a valuable asset for research, but their use is challenging due to inconsistencies of records, heterogeneous formats and the distribution over multiple, non-integrated information systems. Hence, specialized health data engineering and data science expertise are required to enable research. To facilitate secondary use of clinical routine data collected in our intensive care wards, we developed a scalable approach, consisting of cohort generation, variable filtering and data extraction steps. OBJECTIVE: With this report we share our workflow of data request, cohort identification and data extraction. We present an algorithm for automatic data extraction from our critical care information system (CCIS) that can be adapted to other object-oriented data bases. METHODS: We introduced a data request process with functionalities for automated identification of patient cohorts and a specialized hierarchical data structure that supports filtering relevant variables from the CCIS and further systems for the specified cohorts. The data extraction algorithm takes patient pseudonyms and variable lists as inputs. Algorithms are implemented in Python, leveraging the PySpark framework running on our data lake infrastructure. RESULTS: Our data request process is in operational use since June 2022. Since then we have served 121 projects with 148 service requests in total. We discuss the hierarchical structure and the frequently used data items of our CCIS in detail and present an application example, including cohort selection, data extraction and data transformation into an analyses-ready format. CONCLUSIONS: Using clinical routine data for secondary research is challenging and requires an interdisciplinary team. We developed a scalable approach that automates steps for cohort identification, data extraction and common data pre-processing steps. Additionally, we facilitate data harmonization, integration and consult on typical data analysis scenarios, machine learning algorithms and visualizations in dashboards.

Shared etiology of Mendelian and complex disease supports drug discovery.

BACKGROUND: Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. METHODS: In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. RESULTS: Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. CONCLUSIONS: Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Lessons learned from a pay-for-performance scheme for appropriate prescribing using electronic health records from general practices in the Netherlands.

INTRODUCTION: A nationwide pay-for-performance (P4P) scheme was introduced in the Netherlands between 2018 and 2023 to incentivize appropriate prescribing in general practice. Appropriate prescribing was operationalised as adherence to prescription formularies and measured based on electronic health records (EHR) data. We evaluated this P4P scheme from a learning health systems perspective. METHODS: We conducted semi-structured interviews with 15 participants representing stakeholders of the scheme: general practitioners (GPs), health insurers, pharmacists, EHR suppliers and formulary committees. We used a thematic approach for data analysis. RESULTS: Using EHR data showed several benefits, but lack of uniformity of EHR systems hindered consistent measurements. Specific indicators were favoured over general indicators as they allow GPs to have more control over their performance. Most participants emphasized the need for GPs to jointly reflect on their performance. Communication to GPs appeared to be challenging. Partly because of these challenges, impact of the scheme on prescribing behaviour was perceived as limited. However, several unexpected positive effects of the scheme were mentioned, such as better EHR recording habits. CONCLUSIONS: This study identified benefits and challenges useful for future P4P schemes in promoting appropriate care with EHR data. Enhancing uniformity in EHR systems is crucial for more consistent quality measurements. Future P4P schemes should focus on high-quality feedback, peer-to-peer learning and establish a single point of communication for healthcare providers.

Hypertension-Associated Expenditures Among Privately Insured US Adults in 2021.

BACKGROUND: There are no recent estimates for hypertension-associated medical expenditures. This study aims to estimate hypertension-associated incremental medical expenditures among privately insured US adults. METHODS: We conducted a retrospective cohort study using IQVIA's Ambulatory Electronic Medical Records-US data set linked with PharMetrics Plus claims data. Among privately insured adults aged 18 to 64 years, hypertension was identified as having ≥1 diagnosis code or ≥2 blood pressure measurements of ≥140/90 mm Hg, or ≥1 antihypertensive medication in 2021. Annual total expenditures (in 2021 $US) were estimated using a generalized linear model with gamma distribution and log-link function adjusting for demographic characteristics and cooccurring conditions. Out-of-pocket expenditures were estimated using a 2-part model that included logistic and generalized linear model regression. Overlap propensity score weights from logistic regression were used to obtain a balanced sample on hypertension status. RESULTS: Among the 393 018 adults, 156 556 (40%) were identified with hypertension. Compared with individuals without hypertension, those with hypertension had $2926 (95% CI, $2681-$3170) higher total expenditures and $328 (95% CI, $300-$355) higher out-of-pocket expenditures. Adults with hypertension had higher total inpatient ($3272 [95% CI, $1458-$5086]) and outpatient ($2189 [95% CI, $2009-$2369]) expenditures when compared with those without hypertension. Hypertension-associated incremental total expenditures were higher for women ($3242 [95% CI, $2915-$3569]) than for men ($2521 [95% CI, $2139-$2904]). CONCLUSIONS: Among privately insured US adults, hypertension was associated with higher medical expenditures, including higher inpatient and out-of-pocket expenditures. These findings may help assess the economic value of interventions effective in preventing hypertension.

Navigating electronic health record accuracy by examination of sex incongruent conditions.

OBJECTIVE: The increasing reliance on electronic health records (EHRs) for research and clinical care necessitates robust methods for assessing data quality and identifying inconsistencies. To address this need, we develop and apply the incongruence rate (IR) using sex-specific medical conditions. We also characterized participants with incongruent records to better understand the scope and nature of data discrepancies. MATERIALS AND METHODS: In this cross-sectional study, we used the All of Us Research Program's latest version 7 (v7) EHR data to identify prevalent sex-specific conditions and evaluated the occurrence of incongruent cases, quantified as IR. RESULTS: Among the 92 597 males and 152 551 females with condition occurrence data available from All of Us and sex-conformed gender, we identified 167 prevalent sex-specific conditions. Among the 37 537 biological males and 95 499 biological females with these sex-specific conditions, we detected an overall IR of 0.86%. Attempt to include non-cisgender participants result in inflated overall IR. Additionally, a significant proportion of participants with incongruent conditions also presented with conditions congruent to their biological sex, indicating a mix of accurate and erroneous records. These incongruences were not geographically or temporally isolated, suggesting systematic issues in EHR data integrity. DISCUSSION: Our findings call attention to the existence of systemic data incongruences in sex-specific conditions and the need for robust validation checks. Extending IR evaluation to non-cisgender participants or non-sex-based conditions remain a challenge. CONCLUSION: The sex condition-specific IR, when applied to adult populations, provides a valuable metric for data quality assessment in EHRs.

Evaluation of pharmacogenetic automated clinical decision support for clopidogrel.

Aim: Clopidogrel requires CYP2C19 activation to have antiplatelet effects. Pharmacogenetic testing to identify patients with impaired CYP2C19 function can be coupled with clinical decision support (CDS) alerts to guide antiplatelet prescribing. We evaluated the impact of alerts on clopidogrel prescribing.Materials & methods: We retrospectively analyzed data for 866 patients in which CYP2C19-clopidogrel CDS was deployed at a single healthcare system during 2015-2023.Results: Analyses included 2,288 alerts. CDS acceptance rates increased from 24% in 2015 to 63% in 2023 (p < 0.05). Adjusted analyses also showed higher acceptance rates when clopidogrel had been ordered for a percutaneous intervention (OR: 28.7, p < 0.001) and when cardiologists responded to alerts (OR: 2.11, p = 0.001).Conclusion: CDS for CYP2C19-clopidogrel was effective in reducing potential drug-gene interactions. Its influence varied by clinician specialty and medication indications.

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Genetics of posttraumatic stress disorder and cardiovascular conditions using Life's Essential 8, Electronic Health Records, and Heart Imaging.

BACKGROUND: Patients with post-traumatic stress disorder (PTSD) experience higher risk of adverse cardiovascular (CV) outcomes. This study explores shared loci, and genes between PTSD and CV conditions from three major domains: CV diagnoses from electronic health records (CV-EHR), cardiac and aortic imaging, and CV health behaviors defined in Life's Essential 8 (LE8). METHODS: We used genome-wide association study (GWAS) of PTSD (N=1,222,882), 246 CV diagnoses based on EHR data from Million Veteran Program (MVP; N=458,061), UK Biobank (UKBB; N=420,531), 82 cardiac and aortic imaging traits (N=26,893), and GWAS of traits defined in the LE8 (N = 282,271 ~ 1,320,016). Shared loci between PTSD and CV conditions were identified using local genetic correlations (rg), and colocalization (shared causal variants). Overlapping genes between PTSD and CV conditions were identified from genetically regulated proteome expression in brain and blood tissues, and subsequently tested to identify functional pathways and gene-drug targets. Epidemiological replication of EHR-CV diagnoses was performed in AllofUS cohort (AoU; N=249,906). RESULTS: Among the 76 PTSD-susceptibility risk loci, 33 loci exhibited local rg with 45 CV-EHR traits (|rg|≥0.4), four loci with eight heart imaging traits(|rg|≥0.5), and 44 loci with LE8 factors (|rg|≥0.36) in MVP. Among significantly correlated loci, we found shared causal variants (colocalization probability > 80%) between PTSD and 17 CV-EHR (in MVP) at 11 loci in MVP, that also replicated in UKBB and/or other cohorts. Of the 17 traits, the observational analysis in the AoU showed PTSD was associated with 13 CV-EHR traits after accounting for socioeconomic factors and depression diagnosis. PTSD colocalized with eight heart imaging traits on 2 loci and with LE8 factors on 31 loci. Leveraging blood and brain proteome expression, we found 33 and 122 genes, respectively, shared between PTSD and CVD. Blood proteome genes were related to neuronal and immune processes, while the brain proteome genes converged on metabolic and calcium-modulating pathways (FDR p <0.05). Drug repurposing analysis highlighted DRD2, NOS1, GFAP, and POR as common targets of psychiatric and CV drugs. CONCLUSION: PTSD-CV comorbidities exhibit shared risk loci, and genes involved in tissue-specific regulatory mechanisms.

Expanding the knowledge of the bat fauna of the Brazilian Caatinga: new geographical records of molossid bats (Chiroptera, Molossidae) for the Chapada Diamantina region, with taxonomic notes.

The Caatinga, an exclusive biome in Brazil, is the largest tropical dry forest area in the Americas. It is characterized by a semi-arid climate and various soils that harbor a great diversity of flora and fauna. Novel records of aerial insectivorous bat species in the family Molossidae in the Chapada Diamantina, northeastern Brazil are presented. The study is based on field sampling of 115 molossid bat specimens from six genera and 12 taxonomically confirmed species, along with four taxa requiring further evaluation for definitive species identification. All specimens were obtained using mist nets around a small freshwater lagoon surrounded by semideciduous dry forest. The verified genera were Cynomops, Eumops, Molossops, Molossus, Neoplatymops, and Nyctinomops. Our findings enhance the understanding of bat diversity in the Brazilian Caatinga, with the first records of Eumopsdelticus, E.bonariensis, and Molossuscurrentium. The most abundant species were Molossusrufus, Eumopsglaucinus, Cynomopsplanirostris, Nyctinomopslaticaudatus, and Molossusmolossus. Previously unreported morphological and morphometric variations for these Caatinga taxa were examined. Additionally, information on sexual dimorphism in craniodental characteristics of Molossopstemminckii and variations in the presence of the sagittal crest in Neoplatymopsmattogrossensis are provided. Based on the voucher specimens from this study, the recognized number of species of Molossidae known from the Caatinga has increased to 21. Our results offer new insights into the taxonomy and biogeography of Neotropical molossids, highlighting their importance as members of bat communities in dry forest ecosystems from northeastern South America.

Health Data Sciences and Cardiovascular Diseases in South Asia: Innovations and Challenges in Digital Health.

PURPOSE OF REVIEW: Health data sciences can help mitigate high burden of cardiovascular disease (CVD) management in South Asia by increasing availability and affordability of healthcare services. This review explores the current landscape, challenges, and strategies for leveraging digital health technologies to improve CVD outcomes in the region. RECENT FINDINGS: Several South Asian countries are implementing national digital health strategies that aim to provide unique health account numbers for patients, creating longitudinal digital health records while others aim to digitize healthcare services and improve health outcomes. Significant challenges impede progress, including lack of interoperability, inadequate training of healthcare workers, cultural barriers, and data privacy concerns. Leveraging digital health for CVD management involves using big data for early detection, employing artificial intelligence for diagnostics, and integrating multiomics data for health insights. Addressing these challenges through policy frameworks, capacity building, and international cooperation is crucial for improving CVD outcomes in region.

Enhancing medical students' documentation skills: the impact of an assessment and feedback program.

PURPOSE: We not only developed a clinical practice program for the assessment and feedback vis-à-vis medical students' medical records but also evaluated the effectiveness of this program via a self-assessment of medical students' competence in writing medical records pre- and post-program. METHODS: In 2022, 74 third-year medical students were divided into four groups and participated in a 2-week program. The students' medical records were graded on a scale ranging from 1 to 3 daily, and the mean scores for 2 weeks were compared. Pre- and post-program, the students' self-assessment survey was conducted. RESULTS: The mean scores increased from 1.30 in the first week to 2.14 in the second week. The mean score of self-assessment showed significant improvements, increasing from 2.43 to 4.00 for medical record, 2.64 to 4.08 for write present illness, 2.08 to 3.89 for initial orders, 2.35 to 4.34 for signature, and 2.38 to 3.97 for consent (all p<0.001). CONCLUSION: We found that providing students with real-time assessment and feedback on their medical records increased their skills and confidence in medical records writing.