Sociotechnical Needs of Registered Nurses in the Heart Failure Hospitalizations of African American Patients: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: African Americans are disproportionately impacted by congestive heart failure (CHF). The impact includes a two and a half times greater hospitalization rate and a fourth of a day longer length of hospitalization than Caucasians, of which nursing care has been associated with nearly a 30% decrease in hospitalizations and readmissions. Prior studies have demonstrated that registered nurses (RNs), working in conjunction with electronic health record systems (EHRs) to conduct care tasks, may optimize length of stay in African Americans with CHF. OBJECTIVE: The objective of the study was to identify the needs of RNs who performed socio-technical tasks, the perceived importance of these socio-technical 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 employed an observational, cross-sectional survey design in RNs who were randomly selected from a total population of 3,498 RNs who provided care to 22,703 African Americans with CHF within 113,543 heart failure hospitalizations between January 1, 2015, and January 1, 2024. The RNs were retrospectively stratified into two 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's d, and a two-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 Americans (Mean=4.66, SD=1.82). The least important task in Group B (100 RNs) was reading published research on African Americans (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 seventeen significant socio-technical needs were identified among groups. Two socio-technical needs were unique to Group B: caring for patients (i.e., the full scope of social and technological processes in nursing care) (Cohen's d=0.32, 95% CI: 0.04,0.59, P=.04) and working with information related to a patient's CHF in the EHR (e.g., laboratory results, discharge summaries, or radiographic images) to care for the patient (Cohen's d=0.33, 95% CI: 0.05,0.61, P=.03). CONCLUSIONS: Lengths of patient stay may be reduced by identifying and addressing socio-technical 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. CLINICALTRIAL: N/a.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".