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Background Elderly trauma patients are at risk for undertriage, resulting in substantial morbidity and mortality. The objective of this study was to determine whether implementation of geriatric-specific trauma team activation (TTA) protocols appropriately identified severely-injured elderly patients. Methods This single-center retrospective study evaluated all severely injured (injury severity score [ISS] >15), geriatric (≥65 years) patients admitted to our Level 1 tertiary-care hospital between January 2014 and September 2017. Undertriage was defined as the lack of TTA despite presence of severe injuries. The primary outcome was all-cause in-hospital mortality; secondary outcomes were mortality within 48 hours of admission and urgent hemorrhage control. A multivariable logistic regression analysis was performed to identify predictors of appropriate triage in this study. Results Out of 1039 severely injured geriatric patients, 628 (61%) did not undergo TTA. Undertriaged patients were significantly older and had more comorbidities. In-hospital mortality was 5% and 31% in the undertriaged and appropriately triaged groups, respectively ( P < .0001). One percent of undertriaged patients needed urgent hemorrhage control, compared to 6% of the appropriately triaged group ( P < .0001). One percent of undertriaged patients died within 48 hours compared to 19% in the appropriately triaged group ( P < .0001). Predictors of appropriate triage include GCS, heart rate, systolic blood pressure, lactic acid, ISS, shock, and absence of dementia, stroke, or alcoholism. Discussion Geriatric-specific TTA guidelines continue to undertriage elderly trauma patients when using ISS as a metric to measure undertriage. However, undertriaged patients have much lower morbidity and mortality, suggesting the geriatric-specific TTA guidelines identify those patients at highest risk for poor outcomes.

Stroke is the second leading cause of mortality globally with higher burden and younger age in low‐middle income countries (LMICs) than high‐income countries (HICs). However, it is unclear to what extent differences in healthcare access and quality (HAQ) and prevalence of risk factors between LMICs and HICs contribute to younger age of stroke in LMICs. In this systematic review, we conducted meta‐analysis of 67 articles and compared the mean age of stroke between LMICs and HICs, before and after adjusting for HAQ index. We also compared the prevalence of main stroke risk factors between HICs and LMICs. The unadjusted mean age of stroke in LMICs was significantly lower than HICs (63.1 vs. 68.6), regardless of gender (63.9 vs. 66.6 among men, and 65.6 vs. 70.7 among women) and whether data were collected in population‐ (64.7 vs. 69.5) or hospital‐based (62.6 vs. 65.9) studies (all p < 0.01). However, after adjusting for HAQ index, the difference in the mean age of stroke between LMICs and HICs was not significant (p ≥ 0.10), except among women (p = 0.048). In addition, while the median prevalence of hypertension in LMICs was 23.4% higher than HICs, the prevalence of all other risk factors was lower in LMICs than HICs. Our findings suggest a much larger contribution of HAQ to the younger mean age of stroke in LMICs, as compared with other potential factors. Additional studies on stroke care quality and accessibility are needed in LMICs.

PURPOSE Accurate recording of diagnosis (DX) data in electronic health records (EHRs) is important for clinical practice and learning health care. Previous studies show statistically stable patterns of data entry in EHRs that contribute to inaccurate DX, likely because of a lack of data entry support. We conducted qualitative research to characterize the preferences of oncological care providers on cancer DX data entry in EHRs during clinical practice. METHODS We conducted semistructured interviews and focus groups to uncover common themes on DX data entry preferences and barriers to accurate DX recording. Then, we developed a survey questionnaire sent to a cohort of oncologists to verify the generalizability of our initial findings. We constrained our participants to a single specialty and institution to ensure similar clinical backgrounds and clinical experience with a single EHR system. RESULTS A total of 12 neuro-oncologists and thoracic oncologists were involved in the interviews and focus groups. The survey developed from these two initial thrusts was distributed to 19 participants yielding a 94.7% survey response rate. Clinicians reported similar user interface experiences, barriers, and dissatisfaction with current DX entry systems including repetitive entry operations, difficulty in finding specific DX options, time-consuming interactions, and the need for workarounds to maintain efficiency. The survey revealed inefficient DX search interfaces and challenging entry processes as core barriers. CONCLUSION Oncologists seem to be divided between specific DX data entry and time efficiency because of current interfaces and feel hindered by the burdensome and repetitive nature of EHR data entry. Oncologists' top concern for adopting data entry support interventions is ensuring that it provides significant time-saving benefits and increasing workflow efficiency. Future interventions should account for time efficiency, beyond ensuring data entry effectiveness.

PURPOSE Diagnosis (DX) information is key to clinical data reuse, yet accessible structured DX data often lack accuracy. Previous research hints at workflow differences in cancer DX entry, but their link to clinical data quality is unclear. We hypothesized that there is a statistically significant relationship between workflow-describing variables and DX data quality. METHODS We extracted DX data from encounter and order tables within our electronic health records (EHRs) for a cohort of patients with confirmed brain neoplasms. We built and optimized logistic regressions to predict the odds of fully accurate (ie, correct neoplasm type and anatomic site), inaccurate, and suboptimal (ie, vague) DX entry across clinical workflows. We selected our variables based on correlation strength of each outcome variable. RESULTS Both workflow and personnel variables were predictive of DX data quality. For example, a DX entered in departments other than oncology had up to 2.89 times higher odds of being accurate ( P < .0001) compared with an oncology department; an outpatient care location had up to 98% fewer odds of being inaccurate ( P < .0001), but had 458 times higher odds of being suboptimal ( P < .0001) compared with main campus, including the cancer center; and a DX recoded by a physician assistant had 85% fewer odds of being suboptimal ( P = .005) compared with those entered by physicians. CONCLUSION These results suggest that differences across clinical workflows and the clinical personnel producing EHR data affect clinical data quality. They also suggest that the need for specific structured DX data recording varies across clinical workflows and may be dependent on clinical information needs. Clinicians and researchers reusing oncologic data should consider such heterogeneity when conducting secondary analyses of EHR data.