Maryam Y. Garza

Background: The African American population is disproportionately impacted by congestive heart failure (CHF). The impact includes a hospitalization rate that is 2.5 times higher and a hospital stay that is, on average, a quarter of a day longer compared with Caucasians. Notably, nursing care has been associated with nearly a 30% decrease in hospitalizations and readmissions. Previous studies have demonstrated that registered nurses (RNs), working in conjunction with electronic health record systems to conduct care tasks, may optimize length of stay in African Americans with CHF. Objective: This study aimed to identify the needs of RNs who performed sociotechnical tasks, the perceived importance of these sociotechnical tasks, and the perceived performance of these tasks by RNs, in relation to the length of stay of their African American patients with CHF. Methods: The study used an observational, cross-sectional survey design in RNs who were randomly selected from a total population of 3498 RNs who provided care to 22,703 African American patients with CHF within 113,543 heart failure hospitalizations between January 1, 2015, and January 1, 2024. The RNs were retrospectively stratified into 2 groups based on EHR data: those whose African American patients had a mean length of stay of 10 days or less (Group A) and those whose mean length of stay was greater than 10 days (Group B). Descriptive statistics, Cohen d , and a 2-sided unpaired t test were used to analyze the data. Results: The total sample of 200 RNs responded to the survey (100% survey completion rate). Group A (100 RNs) reported the least important task as drawing conclusions about how to use the EHR to care for African American patients (mean 4.66, SD 1.82). The least important task in Group B (100 RNs) was reading published research on African American patients (mean 4.88, SD 1.70). Group A reported performing best in caring for African American patients (mean 5.61, SD 1.44). Group B reported performing best at caring for all patients (mean 5.86, SD 1.04). A total of 17 significant sociotechnical needs were identified among groups. In total, 2 sociotechnical needs were unique to group B: caring for patients (ie, the full scope of social and technological processes in nursing care; Cohen d =0.32, 95% CI 0.04-0.59; P =.04) and working with information related to a patient’s CHF in the EHR (eg, laboratory results, discharge summaries, or radiographic images) to care for the patient (Cohen d =0.33, 95% CI 0.05-0.61; P =.03). Conclusions: Lengths of patient stay may be reduced by identifying and addressing sociotechnical needs through targeted training, nursing care interventions, and RN-led risk stratification guidelines for working with EHRs to reduce lengths of stay in those who are disproportionately impacted by CHF.

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.

The United States (US) 21st Century Cures Act of 2016 was developed to accelerate development of new therapeutics. The use of real-world data (RWD) in regulatory submissions for therapeutic development in the US is increasingly supported by US Food and Drug Administration (FDA) guidance. Registries are a recognized source of RWD for regulatory submissions to the FDA. Drug and Biologics submissions to the FDA must use the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) format. However, designed for clinical trials, the ability of SDTM to represent RWD, including data from registries has been questioned. To evaluate the expressiveness of SDTM for registry data, the American Burn Association, Burn Care Quality Program (BCQP) Registry v4.1 as an exemplar was systematically mapped to the CDISC SDTM v3.3 standard. All BCQP Registry data elements were representable in the SDTM with 93% mapping to standard SDTM domains, and the remaining 7% mapped to SDTM Supplemental Qualifiers.

As pressure to increase operational efficiency in clinical trials grows, organizations are looking to advances such as tools enabling extraction of Electronic Health Record (EHR) data and transmission to the study database or electronic Case Report Form (eCRF). As interest and adoption of these tools, often called EHR-to-eCRF software, grows in clinical research, consistent evaluative data on key outcomes, such as data accuracy, remains limited. This study compared the accuracy of data collected from EHRs using EHR-to-eCRF technology to traditional methods of clinical trial data collection. The accuracy of the EHR-extracted data was significantly higher than that of data collected through the traditional approach. These results suggest that EHR-based data collection could substantially enhance data quality in clinical trials.

Clinical data registries provide a rich source of real-world data that can be leveraged by clinicians, researchers, and public health professionals to address some of the current health challenges faced in society today. The relative usefulness of a registry depends on the ability to gather data and the overall quality of the data. To assess existing datarelated processes, including barriers and facilitators to data collection and submission, we conducted an observational case study to evaluate sites submitting data to a prominent state-based trauma registry. A mixed-methods approach was undertaken to evaluate existing processes and barriers to data collection for the Arkansas Trauma Registry (ATR). A series of interviews and observations were conducted with trauma registry personnel from level I–IV trauma centers across the state of Arkansas to collect data on current data-related processes. To facilitate observations, a think-aloud protocol was used to gather keystroke-level modeling (KLM) data. Additional observational data (qualitative) were collected regarding site processes and workflows pertaining to the collection and submission of registry data to the ATR. Following the observations, informal, semi-structured interviews were conducted to assess the participants' perspectives on current data-related processes, potential barriers to data collection or submission, and any recommendations for improvement. All sessions were recorded, and de-identified transcripts and session notes were used for analysis. Quantitative analyses were performed on the KLM data derived from observations to determine time spent performing end-to-end registry-related activities. Qualitative data from interviews were reviewed and coded by 2 independent reviewers. The qualitative codings were adjudicated by the reviewers using a consensus-driven approach. Themes were then extrapolated to generate the final set of results. Seven unique staff members (trauma registrars, coordinators, supervisors, and directors) participated in the study, having completed both observation and interview sessions. These participants were from 5 unique trauma centers (one level I, one level II, two level III, and one level IV). Through the observations, we were able to characterize the typical operational flow for level I–IV trauma centers participating in the ATR, confirming a primarily manual process was used across all sites. Furthermore, the KLM analysis demonstrated that, on average, site staff would need close to 26 total hours to identify, abstract, and transcribe a single, relatively complex trauma registry case (meaning all 288 registry data elements were captured). Results from interviews further emphasized the exhaustive nature of the current data collection processes across sites, regardless of trauma level classification. Five common themes were identified across all 7 interviews: data quality; manual processes; resources and technology; site environment and staffing; and training. Across each theme, interviewees provided their perspectives on various activities and procedures at their site, as well as on the overarching trauma registry program. Many positive perspectives were shared, and several negative perspectives and perceived issues were noted. Interviewees also provided recommendations for improving internal site processes and streamlining the way in which data are collected and submitted to the ATR. Automation of data entry, improved training resources, and adequate staffing are critical areas of improvement for efficiently abstracting registry data. By streamlining manual inputs into the registry, registrars could minimize time spent consolidating and abstracting recurring data. Conclusion: These results illuminate existing data-related practices of sites participating in the ATR and the outcomes measures for process improvement efforts. These challenges are not unique to the trauma domain and are encountered by registries across a variety of therapeutic areas. Thus, attempts to develop integrated and interoperable solutions to streamline and improve data collection would benefit all registries.

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. 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 I2 statistic. 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). 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.

While pilots and production use of software based on the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard are increasing in clinical research, we lack consistent evaluative data on important outcomes, such as data accuracy. We compared the accuracy of EHR collected, FHIR® extracted data (called EHR-to-eCRF data collection) to traditional clinical trial data collection. The accuracy rate for EHR-collected data was significantly higher than for the same data collected through traditional methods. It is possible that EHR-collected (FHIR® extracted) data can substantially improve data quality in clinical studies while decreasing the burden on study sites.

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.