Electronic Data Research Articles

Postpartum Acute Care Utilization in a Health Care System in the Southeastern United States.

Introduction: Postpartum acute care utilization (PACU), including visits to an emergency department, obstetric triage, or urgent care ("outpatient"), and hospital readmissions, may indicate medical complications and signal unmet health needs. Methods: We estimated the incidence of PACU and examined patterns by sociodemographic factors, pregnancy and birth characteristics, time since discharge from the birth hospitalization, and medical indications. We constructed a retrospective cohort of people aged ≥18 years who delivered ≥1 liveborn infant >20 weeks of gestation from July 1, 2021, to December 31, 2022, using electronic health record data from a quaternary maternity hospital in the Southeastern United States PACU data throughout the health care system were collected through March 31, 2023. We excluded people with a hospital stay >6 days (n = 29). Results: In this cohort of 6,041 birthing people, 11.3% had ≥1 outpatient encounters(range 0-6) and 3.2% had ≥1 hospital readmissions (range 0-4) within 12 weeks of discharge from the birth hospitalization. Median time to first outpatient PACU was 10 days post-discharge and 6 days for first hospital readmission. Among encounters for the top five medical indications, time to first postpartum acute care encounter varied by medical indication (log-rank test of equality over strata Chi-square = 69.93, degrees of freedom = 4, p < 0.0001). Complications specified during the puerperium (n = 234) and hypertension and hypertensive-related conditions complicating the puerperium (n = 87) were the two most frequent indications. Conclusion: These findings can inform efforts to direct health resources to improve postpartum health care and health outcomes.

Examining Whether Patient Portal and Video Visit Use Differs by Race and Ethnicity Among Older Adults in a US Integrated Health Care Delivery System: Cross-Sectional Electronic Health Record and Survey-Based Study

Background Health care systems are increasingly encouraging patients to use patient portals and participate in video visits. However, there is limited information about how portal use differs among older adults. Objective This study aimed to understand how patient portal and video visit use differed by age, race, and ethnicity among older adult patients with access to the same digital health resources. Methods This cross-sectional study used electronic health record and survey data for adults aged 65 to 85 years who were members of a large Northern California health care delivery system throughout 2019 and 2020. The electronic health record cohort (N=471,152) included 320,686 White, 35,892 Black, 44,922 Latino, 20,786 Chinese, 28,732 Filipino, 8473 South Asian, 6716 Japanese, 2930 Vietnamese, and 2015 Korean adults. Racial and ethnic group and age group (65 to 75 years vs 76 to 85 years) differences in having a patient portal account by December 2020, the performance of 2 portal activities (sending ≥1 message to a clinician in 2019 or 2020 and viewing ≥1 laboratory test result in 2020), and having ≥1 video visit during 2020 were examined. Modified log-Poisson regression was used to examine prevalence ratios for portal and video visit use, comparing racial and ethnic groups to White adults and Asian ethnic groups to Chinese adults after adjusting for sex and age. Data from a 2020 member survey were used to compare internet use factors among 2867 White, 306 Black, 343 Latino, 225 Chinese, and 242 Filipino adults. Results Black, Latino, and Filipino adults were less likely to have a patient portal account than White adults, and Filipino adults were less likely to have a patient portal account than Chinese adults. Black, Latino, Filipino, Korean, Vietnamese, and South Asian adults were less likely to have sent messages and viewed test results than White adults, while Chinese and Japanese adults’ use of these features was similar to that of White adults. Filipino, Vietnamese, and Korean adults were less likely to have performed the aforementioned activities than Chinese adults. Video visit use was lower among Black and Latino adults and higher among Chinese and South Asian adults compared with White adults (aged 76 to 85 years) and lower among Filipino, Korean, and Vietnamese adults compared to Chinese adults. Survey data suggested that underlying differences in internet use may partially explain the lower use of messaging by Black, Latino, and Filipino adults compared with White and Chinese adults. Conclusions Patient portal and video visit use differed by race, ethnicity, and age group among older adult patients with access to the same patient portal. Internet use factors may contribute to these differences. Differences in patient portal and video visit use across Asian subgroups underscore the importance of disaggregating use data by Asian ethnicity.

Automated mapping of electronic data capture fields to SDTM

Objective The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches. Methods Siamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field. Results With a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods. Conclusion Leveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials.

Critical factors influencing electron and phonon thermal conductivity in metallic materials using first-principles calculations.

Understanding the thermal transport of various metals is crucial for many energy-transfer applications. However, due to the complex transport mechanisms varying among different metals, current research on metallic thermal transport has been focusing on case studies of specific types of metallic materials. Ageneral understanding of the transport mechanisms across a broad spectrum of metallic materials is still lacking. In this work, we perform first-principles calculations to determine the thermal conductivity of 40 representative metallic materials, within a range of 8-456 W/mK. Our predicted values of electrical and thermal conductivity are in good agreement with available experimental results. Based on the data of separated electron and phonon thermal conductivity, we employ a statistical approach to examine nine factors derived from previous understandings and identify the critical factors determining these properties. For electrons, although a high electron density of states around the Fermi level implies more conductive electrons, we find it counterintuitively correlates with low electron thermal conductivity. This is attributed to the enlarged electron-phonon scattering channels induced by substantial electrons around the Fermi level. Regarding phonons, we demonstrate that among all the studied factors, Debye temperature plays the most significant role in determining the phonon thermal conductivity, despite the phonon-electron scattering being non negligible in- sometransition metals. Correlation analysis suggeststhat Debye temperature has the highest positive correlation coefficient with phonon thermal conductivity, as it corresponds to a large phonon group velocity. Additionally, Young's modulus is found to be closely correlated with high phonon thermal conductivity and contribution. Our findings of simple factors that closely correlate with the electron and phonon thermal conductivity provide a general understanding of various metallic materials. They may facilitate the discovery of novel materials with extremely high or low thermal conductivity, or be used as descriptors in machine learning to accurately predict the thermal conductivity of metals in the future.&#xD.

Patient Health Record Protection Beyond the Health Insurance Portability and Accountability Act: Mixed Methods Study

Background The security and privacy of health care information are crucial for maintaining the societal value of health care as a public good. However, governance over electronic health care data has proven inefficient, despite robust enforcement efforts. Both federal (HIPAA [Health Insurance Portability and Accountability Act]) and state regulations, along with the ombudsman rule, have not effectively reduced the frequency or impact of data breaches in the US health care system. While legal frameworks have bolstered data security, recent years have seen a concerning increase in breach incidents. This paper investigates common breach types and proposes best practices derived from the data as potential solutions. Objective The primary aim of this study is to analyze health care and hospital breach data, comparing it against HIPAA compliance levels across states (spatial analysis) and the impact of the Omnibus Rule over time (temporal analysis). The goal is to establish guidelines for best practices in handling sensitive information within hospitals and clinical environments. Methods The study used data from the Department of Health and Human Services on reported breaches, assessing the severity and impact of each breach type. We then analyzed secondary data to examine whether HIPAA’s storage and retention rule amendments have influenced security and privacy incidents across all 50 states. Finally, we conducted a qualitative analysis of textual data from vulnerability and breach reports to identify actionable best practices for health care settings. Results Our findings indicate that hacking or IT incidents have the most significant impact on the number of individuals affected, highlighting this as a primary breach category. The overall difference-in-differences trend reveals no significant reduction in breach rates (P=.50), despite state-level regulations exceeding HIPAA requirements and the introduction of the ombudsman rule. This persistence in breach trends implies that even strengthened protections and additional guidelines have not effectively curbed the rising number of affected individuals. Through qualitative analysis, we identified 15 unique values and associated best practices from industry standards. Conclusions Combining quantitative and qualitative insights, we propose the “SecureSphere framework” to enhance data security in health care institutions. This framework presents key security values structured in concentric circles: core values at the center and peripheral values around them. The core values include employee management, policy, procedures, and IT management. Peripheral values encompass the remaining security attributes that support these core elements. This structured approach provides a comprehensive security strategy for protecting patient health information and is designed to help health care organizations develop sustainable practices for data security.

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

Prediction of Healing Trajectory of Chronic Wounds Using a Machine Learning Approach.

Objective: New treatment options are emerging for chronic wounds, which represent a growing problem because of population ageing and increasing burden of chronic disease. While promising, the existing evidence for advanced modalities is commonly derived from small and/or poorly controlled studies and clear criteria for selecting patients, who are likely to benefit from these expensive options are lacking. In this study, we develop and validate a machine learning model to predict if a chronic wound, independent of etiology, is expected to heal within 12 weeks to identify cases in potential need of advanced treatment options. Approach: Retrospective analysis of electronic health record data from 2014 to 2018 covering 532 wound care clinics in the United States and 261,398 patients with 620,356 unique wounds. Prediction of 12-week healing trajectories with a machine learning model. Results: The best-performing model in a training dataset of a randomly drawn 75% subset of wounds contained variables for patient demographics, comorbidities, wound characteristics at initial presentation, and changes in wound dimensions over time, with the latter group being the most influential predictors. The final machine learning model had a high predictive accuracy with area under the receiver operating characteristic curves of 0.9 and 0.92 after 4 and 5 weeks of treatment, respectively. Innovation: A machine learning model can identify chronic wounds at risk of not healing by week 12 with high accuracy in the early weeks of treatment. Conclusions: If embedded in real-world care, the generated information could be able to guide effective and efficient treatment decisions.

Trending in the right direction: critical access hospitals increased adoption of advanced electronic health record functions from 2018 to 2023.

We analyzed trends in adoption of advanced patient engagement and clinical data analytics functionalities among critical access hospitals (CAHs) and non-CAHs to assess how historical gaps have changed. We used 2014, 2018, and 2023 data from the American Hospital Association Annual Survey IT Supplement to measure differences in adoption rates (ie, the "adoption gap") of patient engagement and clinical data analytics functionalities across CAHs and non-CAHs. We measured changes over time in CAH and non-CAH adoption of 6 "core" clinical data analytics functionalities, 5 "core" patient engagement functionalities, 5 new patient engagement functionalities, and 3 bulk data export use cases. We constructed 2 composite measures for core functionalities and analyzed adoption for other functionalities individually. Core functionality adoption increased from 21% of CAHs in 2014 to 56% in 2023 for clinical data analytics and 18% to 49% for patient engagement. The CAH adoption gap in both domains narrowed from 2018 to 2023 (both P < .01). More than 90% of all hospitals had adopted viewing and downloading electronic data and clinical notes by 2023. The largest CAH adoption gaps in 2023 were for Fast Healthcare Interoperability Resources (FHIR) bulk export use cases (eg, analytics and reporting: 63% of CAHs, 81% of non-CAHs, P < .001). Adoption of advanced electronic health record functionalities has increased for CAHs and non-CAHs, and some adoption gaps have been closed since 2018. However, CAHs may continue to struggle with clinical data analytics and FHIR-based functionalities. Some crucial patient engagement functionalities have reached near-universal adoption; however, policymakers should consider programs to support CAHs in closing remaining adoption gaps.

Current Practices for Delivering New Diagnosis Education at Children's Oncology Group Institutions: A Report From the Children's Oncology Group.

The Children's Oncology Group (COG) Nursing Discipline developed expert consensus recommendations and resources to guide the delivery of new diagnosis education across its member institutions. Current new diagnosis education practices across COG member institutions are unknown. To understand current new diagnosis education practices across COG member institutions. We conducted a cross-sectional survey of COG nurses between July and October 2023 to elicit current practices to deliver new diagnosis education to families across COG institutions. Data were collected and managed using REDCap electronic data capture tools. Individual responses from single institutions were averaged as a single institution-level response. Respondents included 146/227 (64.3%) COG institutions across 5 countries. Advanced practice nurses and nurse educators have primary responsibility for new diagnosis education at 28.1% and 26.7% of institutions, respectively. Less than half (42.4%) of institutions have a clearly defined process for delivering new diagnosis patient/family education. Almost all (88.2%) institutions use the COG Family Handbook; 64.7% use the COG New Diagnosis Guide, and 41.9% use the COG KidsCare app to support the delivery of new diagnosis education. More than half (61.5%) of institutions use checklists to facilitate the delivery of new diagnosis education, with 81.6% of large institutions versus 44.1% of small institutions reporting the use of checklists ( P = .004). There is variability in current new diagnosis education practices across COG institutions. These findings highlight opportunities for implementing standardized approaches aligned with expert consensus recommendations for the delivery of new diagnosis education.

Lipoprotein (a) Testing in Patients With Atherosclerotic Cardiovascular Disease in 5 Large US Health Systems.

Lipoprotein (a) is an independent risk factor for atherosclerotic cardiovascular disease. However, lipoprotein (a) testing remains variable and it is unclear what factors influence testing and if testing changes clinical management. A retrospective study using electronic medical record data from 5 health systems identified an atherosclerotic cardiovascular disease cohort divided into those with and without a lipoprotein (a) test between 2019 and 2021. Baseline characteristics and lipid-lowering therapy patterns were assessed. Multivariable regression modeling was used to determine factors associated with lipoprotein (a) testing. Among 595 684 patients with atherosclerotic cardiovascular disease, only 2587 (0.4%) were tested for lipoprotein (a). Those who were older or Black individuals were less likely to have lipoprotein (a) testing, while those with familial hypercholesterolemia, ischemic stroke/transient ischemic attack, peripheral artery disease, prior lipid-lowering therapy, or low-density lipoprotein cholesterol ≥130 mg/dL were more likely to be tested. Those with a lipoprotein (a) test, regardless of the lipoprotein (a) value, were more frequently initiated on any statin therapy (30.3% versus 10.6%, P < 0.001), ezetimibe (7.65% versus 0.8%, P < 0.001), or proprotein convertase substilisin/kexin type 9 inhibitor (6.7% versus 0.3%, P < 0.001) compared with those without a test. Those with an elevated lipoprotein (a) level more frequently initiated ezetimibe (11.5% versus 5.9%, P < 0.001) or proprotein convertase substilisin/kexin type 9 inhibitor (10.9% versus 4.8%, P < 0.001). Lipoprotein (a) testing in patients with atherosclerotic cardiovascular disease is infrequent, with evidence of disparities among older or Black individuals. Testing for lipoprotein (a), regardless of level, is associated with greater initiation of any lipid-lowering therapy, while elevated lipoprotein (a) is associated with greater initiation of nonstatin lipid-lowering therapy. There is a critical need for multidisciplinary and inclusive approaches to raise awareness for lipoprotein (a) testing, and its implications on management.

The early COVID-19 pandemic period and associated gestational weight gain.

The aim of this study was to investigate if gestational weight gain was altered during the early COVID-19 pandemic period. This a retrospective cohort study evaluating gestational weight gain among individuals delivering during the early COVID-19 pandemic epoch (March 10-December 31,2020) compared to temporally matched pre-pandemic (matched months in 2018 and 2019) controls using electronic medical record data from a large tertiary care hospital. The primary outcome was gestational weight gain defined as a categorical measure representing below, meeting, or above Institute of Medicine (IOM) criteria with further adjustment for gestational age at delivery. The early-pandemic exposure group was also categorized by gestational age at the start period (<14weeks' and 14-20weeks') to assess if duration of exposure affected gestational weight gain risks with the use of multinominal logistic regression. Among 5,377 individuals 3,619 (67.3 %) and 1,758 (32.7 %) were in the pre and early pandemic epochs respectively. Overall, 934 (17.4 %) individuals gained below recommended, 1,280 (23.8 %) met recommendations, and 3,163 (58.8 %) gained above IOM recommended gestational weight gain. Compared to the pre-pandemic epoch, the early pandemic period was not associated with weight gain below (OR 1.06, 95 % Cl 0.89-1.27) or above (OR 1.03, 95 % Cl 0.89-1.19) IOM recommendations. This was also true when the early pandemic group was stratified based on gestational age at the start of the early pandemic period (<14weeks' and 14-20weeks'). This study demonstrated no significant difference in maternal weight gain between pre and early COVID-19 pandemic periods.

Factors associated with general practitioner-led diagnosis of long COVID: an observational study using electronic general practice data from Victoria and New South Wales, Australia.

To investigate associations between sociodemographic factors, pre-existing chronic comorbidities, and general practitioner-led diagnosis of long COVID. We conducted a retrospective observational case-control study using de-identified electronic general practice data, recorded between January 2020 and March 2023, from 869 general practice clinics across four primary health networks in Victoria and New South Wales. Sociodemographic factors and pre-existing chronic comorbidities associated with general practitioner-led diagnosis of long COVID. A total of 1588 patients had a recorded general practitioner-led long COVID diagnosis. Females exhibited a higher likelihood of general practitioner-led long COVID diagnosis (adjusted odds ratio [aOR], 1.58; adjusted confidence interval [aCI], 1.35-1.85) compared with males. Patients aged 40-59 years had a higher likelihood of general practitioner-led long COVID diagnosis (aOR, 1.68; aCI, 1.40-2.03) compared with patients aged 20-39 years. The diagnosis was more likely in patients of high socio-economic status (aOR, 1.37; aCI, 1.05-1.79) compared with those of mid socio-economic status. Mental health conditions (aOR, 2.69; aCI, 2.25-3.21), respiratory conditions (aOR, 2.25; aCI, 1.85-2.75), cancer (aOR, 1.64; aCI, 1.15-2.33) and musculoskeletal conditions (aOR, 1.50; aCI, 1.20-1.88) were all significantly associated with general practitioner-led long COVID diagnosis. Female sex, middle age, high socio-economic status and pre-existing comorbidities, including mental health conditions, respiratory conditions, cancer and musculoskeletal conditions, were associated with general practitioner-led long COVID diagnosis among general practice patients. These factors largely parallel the emerging international evidence on long COVID and highlight the patient characteristics that practitioners should be cognisant of when patients present with symptoms of long COVID.

Elucidating the role of weight loss and glycaemic control in patients with type 2 diabetes.

To investigate the independent contributions of glycated haemoglobin (HbA1c) reduction and weight loss to clinical outcomes in patients with type 2 diabetes (T2D) treated with antidiabetic drugs, including glucagon-like peptide-1 receptor agonists (GLP-1RAs). This observational, retrospective cohort study used deidentified electronic health record-derived data from patients evaluated at the Cleveland Clinic (1 January 2000-31 December 2020). Cohort A included 8876 patients with newly diagnosed T2D treated with any of six antidiabetic drug classes. Cohort B included 4161 patients with T2D initiating GLP-1RA treatment. The effects of body mass index (BMI) and HbA1c reduction, variability, and durability on clinical outcomes were investigated. In Cohort A, each 1% BMI reduction was associated with 3%, 1%, and 4% reduced risk of heart failure (p = 0.017), hypertension (p = 0.006), and insulin initiation (p = 0.001), respectively. Each 1% (~11 mmol/mol) HbA1creduction was associated with 4% and 29% reduced risk of hypertension (p = 0.041) and insulin initiation (p = 0.001), respectively. In Cohort B, each 1% BMI reduction was associated with 4% and 3% reduced risk of cardiovascular disease (p = 0.008) and insulin initiation (p = 0.002), respectively. Each 1% (~11 mmol/mol) HbA1c reduction was associated with 4% and 16% reduced risk of chronic kidney disease (p = 0.014) and insulin initiation (p = 1 × 10-4), respectively. Lower BMI variability and greater BMI durability were associated with decreased risk of clinical outcomes in both cohorts. Antidiabetic medication-associated, and specifically GLP-1RA-associated, weight loss and HbA1c reductions independently reduce real-world clinical outcome risk.

Health Care Delivery Site- and Patient-Level Factors Associated With COVID-19 Primary Vaccine Series Completion in a National Network of Community Health Centers.

Objectives. To assess multilevel factors associated with variation in COVID-19 vaccination rates in a US network of community health centers. Methods. Using multilevel logistic regression with electronic health record data from ADVANCE (Accelerating Data Value Across a National Community Health Center Network; January 1, 2022-December 31, 2022), we assessed associations between health care delivery site-level (n = 1219) and patient-level (n = 1 864 007) characteristics and COVID-19 primary vaccine series uptake. Results. A total of 1 337 440 patients completed the COVID-19 primary vaccine series. Health care delivery site characteristics were significantly associated with lower series completion rates, including being located in non-Medicaid expansion states and isolated or rural communities and serving fewer patients. Patient characteristics associated with significantly lower likelihood of completing the vaccine series included being Black/African American or American Indian/Alaska Native (vs White), younger age, lower income, being uninsured or publicly insured (vs using private insurance), and having fewer visits. Conclusions. Both health care delivery site- and patient-level factors were significantly associated with lower COVID-19 vaccine uptake. Community health centers have been a critical resource for vaccination during the pandemic. (Am J Public Health. 2024;114(11):1242-1251. https://doi.org/10.2105/AJPH.2024.307773).

Constructing a health management plan for patients with obstructive sleep apnea hypopnea syndrome based on electronic medical record data mining.

Constructing a health management plan for patients with obstructive sleep apnea hypopnea syndrome based on electronic medical record data mining.

Response comment on "Clinical profile, demographic distribution, and management of Posner-Schlossman syndrome: An electronic medical record-driven data analytics from an eye care network in India".

Response comment on "Clinical profile, demographic distribution, and management of Posner-Schlossman syndrome: An electronic medical record-driven data analytics from an eye care network in India".

Massachusetts Pediatric Injury Equity Review (MassPIER): A Process to Address Injury Inequities.

Significant inequities in pediatric injury outcomes exist. We aim to develop a process to assist child death review (CDR) teams in identifying upstream factors that lead to inequitable outcomes in pediatric injuries. We spent 6 months (November 2021-April 2022) working with 3 CDR teams in Massachusetts to understand their tools and processes for CDR. During that time, we began to iteratively develop a pediatric injury equity review process and tools. Between May and October 2022, acceptability and adaptability of the resulting Massachusetts Pediatric Injury Equity Review (MassPIER) process and tools were evaluated through focus groups and a Research Electronic Data Capture survey of participants. We compared the prevention recommendations of the CDR teams before the implementation of MassPIER with those generated using MassPIER. A χ2 and Fisher's exact test assessed whether the 2 sets of recommendations differed with regard to equity. A 7-step process was developed, along with 2 tools for use during the MassPIER process. From an acceptability and adaptability standpoint, 100% of participants strongly agreed or agreed that the MassPIER process was simple to follow and adaptable to any type of injury. Ninety-five percent of participants agreed or strongly agreed that the approach could be replicated by other teams. Furthermore, the MassPIER process increased the likelihood of generating equity-focused recommendations in general (P < .05), and particularly recommendations focusing on economic inequities (P < .05). MassPIER is effective in facilitating equity-focused discussion and recommendation development. It is acceptable to team members, and adaptable to other types of injury.

Standardising health history and injury surveillance of participants in endurance events: a modified Delphi consensus statement from the AMSSM runner health consortium.

Endurance events are popular worldwide and have many health benefits. However, runners and Para athletes may sustain musculoskeletal injuries or experience other health consequences from endurance events. The American Medical Society for Sports Medicine (AMSSM) Runner Health Consortium aimed to generate consensus-based survey items for use in prospective research to identify risk factors for injuries in runners and Para athletes training and competing in endurance events. The study design employed a modified Delphi approach, with a panel comprising 28 experts, including healthcare professionals, coaches, and athletes. Potential survey items were generated by panel members who subsequently engaged in three rounds of voting using Research Electronic Data Capture. Items were graded by clarity, relevance, and importance. Items achieving 80% consensus on all three aspects were retained. The response rate was 100% in R round 1 and 96% in Rrounds 2 and 3. Of 124 initial survey items, consensus was reached on 53, 34 and 22 items during Rrounds 1, 2, and 3, respectively. Two accepted items were removed due to redundancy. Combined with 10 non-voting items, 117 items covered key domains, including training and injury history, dietary behaviours and associated factors (such as menstrual function), footwear, mental health, and specific considerations for Para athletes. The consensus-based survey items should be considered by researchers to better understand the health of runners and Para athletes who train and compete in endurance sports to identify risk factors for injury.

Spectroscopic Properties and Reactivity of a MnIII-Hydroperoxo Complex that is Stable at Room Temperature.

Manganese catalysts that activate hydrogen peroxide have seen increased use in organic transformations, such as olefin epoxidation and alkane C-H bond oxidation. Proposed mechanisms for these catalysts involve the formation and activation of MnIII-hydroperoxo intermediates. Examples of well-defined MnIII-hydroperoxo complexes are rare, and the properties of these species are often inferred from MnIII-alkylperoxo analogues. In this study, we show that the reaction of the MnIII-hydroxo complex [MnIII(OH)(6Medpaq)]+ (1) with hydrogen peroxide and acid results in the formation of a dark-green MnIII-hydroperoxo species [MnIII(OOH)(6Medpaq)]+ (2). The formulation of 2 is based on electronic absorption, 1H NMR, IR, and ESI-MS data. The thermal decay of 2 follows a first order process, and variable-temperature kinetic studies of the decay of 2 yielded activation parameters that could be compared with those of a MnIII-alkylperoxo analogue. Complex 2 reacts with the hydrogen-atom donor TEMPOH two-fold faster than the MnIII-hydroxo complex 1. Complex 2 also oxidizes PPh3, and this MnIII-hydroperoxo species is 600-fold more reactive with this substrate than its MnIII-alkylperoxo analogue [MnIII(OOtBu)(6Medpaq)]+. DFT and time-dependent (TD) DFT computations are used to compare the electronic structure of 2 with similar MnIII-hydroperoxo and MnIII-alkylperoxo complexes.

From triage to treatment

Background: The COVID-19 pandemic has had a significant impact on healthcare systems worldwide, leading to changes in presentation types, service utilization, and admission rates to emergency departments (ED). This study examines changes in ED visit patterns and triage misclassifications during the pandemic in Alberta, Canada. Methods: We conducted a retrospective population-level time-series analysis of all patients who presented to 12 EDs in the Edmonton Alberta Zone between March 3, 2019, and March 3, 2022. Routinely collected electronic health record data were analyzed and with primary categories of reporting including Canadian Triage Acuity Scale (CTAS), age, Canadian Emergency Department Information System (CEDIS) presenting complaint, admission status, triage misclassifications, and time markers for patient care. Results: 1.24 million cases from 12 hospitals were included. When we compare the patterns of presentation to the pre-pandemic period, we found a relative increase of 12.5% in EMS volumes, a 43.2% relative reduction in the proportion of patients presenting to tertiary EDs, 17.2% relative reduction in the number of patients under the age of 18, and a global increase in acuity with the highest relative increase 19.7% coming from patients in the highest acuity level: CTAS 1. Complaint distributions during these periods demonstrated that mental health, substance use, and environmental complaints experienced 15.5%, 22.4%, and 26.7% relative increases in volume, respectively; pediatric specific complaints experienced a 56.5% relative reduction. By the end of the study period, patients spent an average of 59 minutes longer in the ED compared to the pre-pandemic period. The proportion of patients triaged using Epic increased from 7.8% of all patients triaged in the pre-pandemic period to over 66.1% during the pandemic, and there was a 22.9% and 24.2% relative reduction in high-risk triage misclassifications (22.9%) and pain related triage misclassifications (24.2%) by the end of the period compared to the before the pandemic. Conclusion: Our study adds to the pandemic-related emergency care knowledge base by describing ED visit trends, changes in presenting complaint categories and time markers for patient care over a big-data pre and post pandemic dataset. Nursing-specific ED quality indicators that have not been previously described over a three-year duration between March 3rd 2019 and March 3rd 2022. are also presented. Our study findings have significant implications for healthcare professionals and policymakers in understanding both the impact of the pandemic on ED care delivery as well as future pandemic and post-pandemic ED operations.