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Although the literature on clinical trial methodology is quite robust, the voices of study staff as key influencers of this process are lacking, particularly for rural and underserved pediatric clinical trials. Using qualitative and quantitative (i.e., survey) methodology, the purpose of the current study was to gather information from study investigators and staff who served on one of the initial multi-state trials in the IDeA States Pediatric Clinical Trials Network (ISPCTN) regarding barriers and facilitators of conducting this rural clinical trial. Quantitative analysis indicated most study investigators and staff who responded (55%) were neutral about the various recruitment methods. Qualitative analyses identified 6 relevant themes: 1) Participant families felt overwhelmed with study procedures, 2) Incentives are important and should be given in a timely fashion to child as well as adult participants, 3) A personal connection is key to engagement and retention, 4) Specific recruitment materials and methods are preferred including family friendly consent forms and advertisements that clearly explain study procedures and clear expectations, 5) There was enthusiasm for the intervention and ideas for consideration in implementing future interventions of this type, and 6) Staff expressed enthusiasm for working in rural areas with rural participants and appreciated the unique aspects of working with this population. This paper provides valuable insight into the operational feasibility of a large, multi-site behavioral intervention trial and outlines lessons learned from study personnel with actionable tips for improving recruitment, retention, and other study procedures. These staff are open to various recruitment methods, and are enthusiastic about working with underserved, rural families. They report that they believe families can be overwhelmed by study procedures, and that a personal connection with families can facilitate study conduct.

African Americans experience approximately 2.5 times more heart failure hospitalizations than Caucasians and the complexity of heart failure requires registered nurses to work in collaboration with other types of healthcare professionals. The purpose of this study was to identify care team configurations associated with long lengths of hospital stay in African Americans with heart failure hospitalizations and the related effect of the presence of registered nurses on their length of hospital stay. This study analyzed electronic health record data on the heart failure hospitalizations of 2,274 African American patients. Binomial logistic regression identified the association between specific care team configurations and length of stay among subgroups of African American patients. Of the significant team configurations, a Kruskal-Wallis H test and linear regression further assessed the team composition and the specific change in days associated with a one-unit change in the number of registered nurses on a patient’s care team. Six team configurations were associated with a long length of stay among all African Americans regardless of age, sex, rurality, heart failure severity, and overall health severity. The configurations only differed significantly in the proportion of registered nurses with respect to other care team roles. An increase in one additional registered nurse on a care delivery team was associated with an increase in length of stay of 8.4 hours (i.e., 504 minutes). Identifying the full range of social and technical care delivery tasks performed by RNs, and controlling for their effect on length of stay, may be a key strategy for reducing length of stay and explaining why these six configurations and RNs are associated with long LOS. The identification of these models can be used to support decision-making that optimizes the availability of patient access to high-quality care (e.g., clinical staffing and supplies).

The care delivery team (CDT) is critical to providing care access and equity to patients who are disproportionately impacted by congestive heart failure (CHF). However, the specific clinical roles that are associated with care outcomes are unknown. The objective of this study was to examine the extent to which specific clinical roles within CDTs were associated with care outcomes in African Americans (AA) with CHF. Deidentified electronic medical record data were collected on 5,962 patients, representing 80,921 care encounters with 3,284 clinicians between January 1, 2014 and December 31, 2021. Binomial logistic regression assessed associations of specific clinical roles and the Mann Whitney-U assessed racial differences in outcomes. AAs accounted for only 26% of the study population but generated 48% of total care encounters, the same percentage of care encounters generated by the largest racial group (i.e., Caucasian Americans; 69% of the study population). AAs had a significantly higher number of hospitalizations and readmissions than Caucasian Americans. However, AAs had a significantly higher number of days at home and significantly lower care charges than Caucasian Americans. Among all CHF patients, patients with a Registered Nurse on their CDT were less likely to have a hospitalization (i.e. 30%) and a high number of readmissions (i.e., 31%) during the 7-year study period. When stratified by heart failure phenotype, the most severe patients who had a Registered Nurse on their CDT were 88% less likely to have a hospitalization and 50% less likely to have a high number of readmissions. Similar decreases in the likelihood of hospitalization and readmission were also found in less severe cases of heart failure. Specific clinical roles are associated with CHF care outcomes. Consideration must be given to developing and testing the efficacy of more specialized, empirical models of CDT composition to reduce the disproportionate impact of CHF.

Background Medical record abstraction (MRA) is a commonly used method for data collection in clinical research, but is prone to error, and the influence of quality control (QC) measures is seldom and inconsistently assessed during the course of a study. We employed a novel, standardized MRA-QC framework as part of an ongoing observational study in an effort to control MRA error rates. In order to assess the effectiveness of our framework, we compared our error rates against traditional MRA studies that had not reported using formalized MRA-QC methods. Thus, the objective of this study was to compare the MRA error rates derived from the literature with the error rates found in a study using MRA as the sole method of data collection that employed an MRA-QC framework. Methods A comparison of the error rates derived from MRA-centric studies identified as part of a systematic literature review was conducted against those derived from an MRA-centric study that employed an MRA-QC framework to evaluate the effectiveness of the MRA-QC framework. An inverse variance-weighted meta-analytical method with Freeman-Tukey transformation was used to compute pooled effect size for both the MRA studies identified in the literature and the study that implemented the MRA-QC framework. The level of heterogeneity was assessed using the Q-statistic and Higgins and Thompson’s I 2 statistic. Results The overall error rate from the MRA literature was 6.57%. Error rates for the study using our MRA-QC framework were between 1.04% (optimistic, all-field rate) and 2.57% (conservative, populated-field rate), 4.00–5.53% points less than the observed rate from the literature ( p  < 0.0001). Conclusions Review of the literature indicated that the accuracy associated with MRA varied widely across studies. However, our results demonstrate that, with appropriate training and continuous QC, MRA error rates can be significantly controlled during the course of a clinical research study.

INTRODUCTION We compared and measured alignment between the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard used by electronic health records (EHRs), the Clinical Data Interchange Standards Consortium (CDISC) standards used by industry, and the Uniform Data Set (UDS) used by the Alzheimer's Disease Research Centers (ADRCs). METHODS The ADRC UDS, consisting of 5959 data elements across eleven packets, was mapped to FHIR and CDISC standards by two independent mappers, with discrepancies adjudicated by experts. RESULTS Forty‐five percent of the 5959 UDS data elements mapped to the FHIR standard, indicating possible electronic obtainment from EHRs. Ninety‐four percent mapped to the CDISC standards, demonstrating high compatibility with industry standards. DISCUSSION The study highlights the feasibility of harmonizing ADRC data with industry and clinical standards. CDISC demonstrated superior alignment with ADRC UDS data, whereas FHIR showed potential for improvement through resource maturation and enhanced standardization. Highlights Forty‐five percent of Alzheimer's Disease Research Center Uniform Data Set (ADRC UDS) data elements could be mapped to Fast Healthcare Interoperability Resources (FHIR), indicating potential electronic health records (EHRs) extraction. Ninety‐four percent of ADRC UDS data elements could be mapped to Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM), showing high industry compatibility. Identified areas for improving data standards harmonization in Alzheimer's disease and related dementias (ADRD) research. Systematic mapping method aligns ADRC UDS with Health Level Seven (HL7) FHIR and CDISC SDTM standards. Results support feasibility of data sharing across ADRC research, EHRs, and industry.

Background Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time. Methods We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal Opioid Withdrawal Syndrome. A confidence interval approach was used to calculate crude (Wald’s method) and adjusted (generalized estimating equation) error rates over time. We calculated error rates using the number of errors divided by total fields (“all-field” error rate) and populated fields (“populated-field” error rate) as the denominators, to provide both an optimistic and a conservative measurement, respectively. Results On average, the ACT NOW CE Study maintained an error rate between 1% (optimistic) and 3% (conservative). Additionally, we observed a decrease of 0.51 percentage points with each additional QC Event conducted. Conclusions Formalized MRA training and continuous QC resulted in lower error rates than have been found in previous literature and a decrease in error rates over time. This study newly demonstrates the importance of continuous process controls for MRA within the context of a multi-site clinical research study.

Background. It is reported that racial differences exist among patients with multimorbidity. However, there are no studies that have investigated racial disparities within multimorbidity-related hospitalization encounters among patients with multimorbidity in rural states such as Arkansas. Methods. Binomial logistic regression identified associations between race and hospitalization utilization. Insurance type was assessed as a potential effect modifier of the association. Results. Non-Hispanic Black, non-Hispanic Other and Hispanic patients collectively represented more than 50% of 18–34-year-old patients with multimorbidity. Compared with patients who were non-Hispanic White, Other patients were more likely to have a high length of stay. In the insurance-type stratified analysis, uninsured Hispanic patients demonstrated greater hospital length of stay during the study period. Conclusion. Results of the current study suggest that multimorbidity-related conditions differentially affect racially and ethnically minoritized, young patients. These findings highlight the need for future studies to understand the contributory factors involved in this disparity.

With the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR® ‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enable seamless exchange between the electronic medical record and public health registries. That said, in order to understand the current utility of FHIR® for supporting the public health use case, we must first measure the extent to which the standard resources map to the required registry data elements. Thus, using a systematic mapping approach, we evaluated the level of completeness of the FHIR® standard to support data collection for three public health registries (Trauma, Stroke, and National Surgical Quality Improvement Program). On average, approximately 80% of data elements were available in FHIR® (71%, 77%, and 92%, respectively; inter-annotator agreement rates: 82%, 78%, and 72%, respectively). This tells us that there is the potential for significant automation to support EHR-to-Registry data exchange, which will reduce the amount of manual, error-prone processes and ensure higher data quality. Further, identification of the remaining 20% of data elements that are “not mapped” will enable us to improve the standard and develop profiles that will better fit the registry data model.

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.

Background Coronavirus disease 2019 (COVID-19) vaccines demonstrate excellent effectiveness against infection, severe disease, and death. However, pediatric COVID-19 vaccination rates lag among individuals from rural and other medically underserved communities. The research objective of the current protocol is to determine the effectiveness of a vaccine communication mobile health (mHealth) application (app) on parental decisions to vaccinate their children against COVID-19. Methods Custodial parents/caregivers with ≥ 1 child eligible for COVID-19 vaccination who have not yet received the vaccine will be randomized to download one of two mHealth apps. The intervention app will address logistical and motivational barriers to pediatric COVID-19 vaccination. Participants will receive eight weekly push notifications followed by two monthly push notifications (cues to action) regarding vaccinating their child. Through branching logic, users will access customized content based on their locality, degree of rurality-urbanicity, primary language (English/Spanish), race/ethnicity, and child’s age to address COVID-19 vaccine knowledge and confidence gaps. The control app will provide push notifications and information on general pediatric health and infection prevention and mitigation strategies based on recommendations from the American Academy of Pediatrics (AAP) and the Centers for Disease Control and Prevention (CDC). The primary outcome is the proportion of children who complete COVID-19 vaccination series. Secondary outcomes include the proportion of children who receive ≥ 1 dose of COVID-19 vaccine and changes in parent/caregiver scores from baseline to immediately post-intervention on the modified WHO SAGE Vaccine Hesitancy Scale adapted for the COVID-19 vaccine. Discussion The COVID-19 pandemic inflicts disproportionate harm on individuals from underserved communities, including those in rural settings. Maximizing vaccine uptake in these communities will decrease infection rates, severe illness, and death. Given that most US families from these communities use smart phones, mHealth interventions hold the promise of broad uptake. Bundling multiple mHealth vaccine uptake interventions into a single app may maximize the impact of deploying such a tool to increase COVID-19 vaccination. The new knowledge to be gained from this study will directly inform future efforts to increase COVID-19 vaccination rates across diverse settings and provide an evidentiary base for app-based vaccine communication tools that can be adapted to future vaccine-deployment efforts. Clinical trials registration ClinicalTrials.gov NCT05386355 . Registered on May 23, 2022.