Utilization of electronic health record sex and gender demographic fields: a metadata and mixed methods analysis

OBJECTIVES

Despite federally mandated collection of sex and gender demographics in the electronic health record (EHR), longitudinal assessments are lacking. We assessed sex and gender demographic field utilization using EHR metadata.

MATERIALS AND METHODS

Patients ≥18 years of age in the Mass General Brigham health system with a first Legal Sex entry (registration requirement) between January 8, 2018 and January 1, 2022 were included in this retrospective study. Metadata for all sex and gender fields (Legal Sex, Sex Assigned at Birth [SAAB], Gender Identity) were quantified by completion rates, user types, and longitudinal change. A nested qualitative study of providers from specialties with high and low field use identified themes related to utilization.

RESULTS

1 576 120 patients met inclusion criteria: 100% had a Legal Sex, 20% a Gender Identity, and 19% a SAAB; 321 185 patients had field changes other than initial Legal Sex entry. About 2% of patients had a subsequent Legal Sex change, and 25% of those had ≥2 changes; 20% of patients had ≥1 update to Gender Identity and 19% to SAAB. Excluding the first Legal Sex entry, administrators made most changes (67%) across all fields, followed by patients (25%), providers (7.2%), and automated Health Level-7 (HL7) interface messages (0.7%). Provider utilization varied by subspecialty; themes related to systems barriers and personal perceptions were identified.

DISCUSSION

Sex and gender demographic fields are primarily used by administrators and raise concern about data accuracy; provider use is heterogenous and lacking. Provider awareness of field availability and variable workflows may impede use.

CONCLUSION

EHR metadata highlights areas for improvement of sex and gender field utilization.

Development and implementation of a clinical decision support system tool for the evaluation of suspected monkeypox infection

Monkeypox virus was historically rare outside of West and Central Africa until the current 2022 global outbreak, which has required clinicians to be alert to identify individuals with possible monkeypox, institute isolation, and take appropriate next steps in evaluation and management. Clinical decision support systems (CDSS), which have been shown to improve adherence to clinical guidelines, can support frontline clinicians in applying the most current evaluation and management guidance in the setting of an emerging infectious disease outbreak when those guidelines are evolving over time. Here, we describe the rapid development and implementation of a CDSS tool embedded in the electronic health record to guide frontline clinicians in the diagnostic evaluation of monkeypox infection and triage patients with potential monkeypox infection to individualized infectious disease physician review. We also present data on the initial performance of this tool in a large integrated healthcare system.

Clinical decision support improves blood culture collection before intravenous antibiotic administration in the emergency department

OBJECTIVE

Surviving Sepsis guidelines recommend blood cultures before administration of intravenous (IV) antibiotics for patients with sepsis or moderate to high risk of bacteremia. Clinical decision support (CDS) that reminds emergency department (ED) providers to obtain blood cultures when ordering IV antibiotics may lead to improvements in this process measure.

METHODS

This was a multicenter causal impact analysis comparing timely blood culture collections prior to IV antibiotics for adult ED patients 1 year before and after a CDS intervention implementation in the electronic health record. A Bayesian structured time-series model compared daily timely blood cultures collected compared to a forecasted synthetic control. Mixed effects models evaluated the impact of the intervention controlling for confounders.

RESULTS

The analysis included 54 538 patients over 2 years. In the baseline phase, 46.1% had blood cultures prior to IV antibiotics, compared to 58.8% after the intervention. Causal impact analysis determined an absolute increase of 13.1% (95% CI 10.4-15.7%) of timely blood culture collections overall, although the difference in patients with a sepsis diagnosis or who met CDC Adult Sepsis Event criteria was not significant, absolute difference 8.0% (95% CI -0.2 to 15.8). Blood culture positivity increased in the intervention phase, and contamination rates were similar in both study phases.

DISCUSSION

CDS improved blood culture collection before IV antibiotics in the ED, without increasing overutilization.

CONCLUSION

A simple CDS alert increased timely blood culture collections in ED patients for whom concern for infection was high enough to warrant IV antibiotics.

Coronavirus Disease 2019 (COVID-19) Diagnostic Clinical Decision Support: A Pre-Post Implementation Study of CORAL (COvid Risk cALculator)

BACKGROUND

Isolation of hospitalized persons under investigation (PUIs) for coronavirus disease 2019 (COVID-19) reduces nosocomial transmission risk. Efficient evaluation of PUIs is needed to preserve scarce healthcare resources. We describe the development, implementation, and outcomes of an inpatient diagnostic algorithm and clinical decision support system (CDSS) to evaluate PUIs.

METHODS

We conducted a pre-post study of CORAL (COvid Risk cALculator), a CDSS that guides frontline clinicians through a risk-stratified COVID-19 diagnostic workup, removes transmission-based precautions when workup is complete and negative, and triages complex cases to infectious diseases (ID) physician review. Before CORAL, ID physicians reviewed all PUI records to guide workup and precautions. After CORAL, frontline clinicians evaluated PUIs directly using CORAL. We compared pre- and post-CORAL frequency of repeated severe acute respiratory syndrome coronavirus 2 nucleic acid amplification tests (NAATs), time from NAAT result to PUI status discontinuation, total duration of PUI status, and ID physician work hours, using linear and logistic regression, adjusted for COVID-19 incidence.

RESULTS

Fewer PUIs underwent repeated testing after an initial negative NAAT after CORAL than before CORAL (54% vs 67%, respectively; adjusted odd ratio, 0.53 [95% confidence interval, .44-.63]; P < .01). CORAL significantly reduced average time to PUI status discontinuation (adjusted difference [standard error], -7.4 [0.8] hours per patient), total duration of PUI status (-19.5 [1.9] hours per patient), and average ID physician work-hours (-57.4 [2.0] hours per day) (all P < .01). No patients had a positive NAAT result within 7 days after discontinuation of precautions via CORAL.

CONCLUSIONS

CORAL is an efficient and effective CDSS to guide frontline clinicians through the diagnostic evaluation of PUIs and safe discontinuation of precautions.

Predicting Outcomes of In-Hospital Cardiac Arrest: Retrospective US Validation of the Good Outcome Following Attempted Resuscitation Score

BACKGROUND

Providers should estimate a patient's chance of surviving an in-hospital cardiac arrest with good neurologic outcome when initially admitting a patient, in order to participate in shared decision making with patients about their code status.

OBJECTIVE

To examine the utility of the "Good Outcome Following Attempted Resuscitation (GO-FAR)" score in predicting prognosis after in-hospital cardiac arrest in a US trauma center.

DESIGN

Retrospective observational study SETTING: Level 1 trauma and academic hospital in Minneapolis, MN, USA PARTICIPANTS: All cases of pulseless in-hospital cardiac arrest occurring in adults (18 years or older) admitted to the hospital between Jan 2009 and Sept 2018 are included. For patients with more than one arrest, only the first was included in this analysis.

MAIN MEASURES

For each patient with verified in-hospital cardiac arrest, we calculated a GO-FAR score based on variables present in the electronic health record at time of admission. Pre-determined outcomes included survival to discharge and survival to discharge with good neurologic outcome.

KEY RESULTS

From 2009 to 2018, 403 adults suffered in-hospital cardiac arrest. A majority (65.5%) were male with a mean age of 60.3 years. Overall survival to discharge was 33.0%; survival to discharge with good neurologic outcome was 17.4%. GO-FAR score calculated at the time of admission correlated with survival to discharge with good neurologic outcome (AUC 0.68), which occurred in 5.3% of patients with below average survival likelihood by GO-FAR score, 22.5% with average survival likelihood, and 34.1% with above average survival likelihood.

CONCLUSIONS

The GO-FAR score can estimate, at time of admission to the hospital, the probability that a patient will survive to discharge with good neurologic outcome after an in-hospital cardiac arrest. This prognostic information can help providers frame discussions with patients on admission regarding whether to attempt cardiopulmonary resuscitation in the event of cardiac arrest.

Network Analysis to Risk Stratify Patients With Exercise Intolerance

RATIONALE

Current methods assessing clinical risk because of exercise intolerance in patients with cardiopulmonary disease rely on a small subset of traditional variables. Alternative strategies incorporating the spectrum of factors underlying prognosis in at-risk patients may be useful clinically, but are lacking.

OBJECTIVE

Use unbiased analyses to identify variables that correspond to clinical risk in patients with exercise intolerance.

METHODS AND RESULTS

Data from 738 consecutive patients referred for invasive cardiopulmonary exercise testing at a single center (2011-2015) were analyzed retrospectively (derivation cohort). A correlation network of invasive cardiopulmonary exercise testing parameters was assembled using |r|>0.5. From an exercise network of 39 variables (ie, nodes) and 98 correlations (ie, edges) corresponding to P<9.5e-46 for each correlation, we focused on a subnetwork containing peak volume of oxygen consumption (pVo2) and 9 linked nodes. K-mean clustering based on these 10 variables identified 4 novel patient clusters characterized by significant differences in 44 of 45 exercise measurements (P<0.01). Compared with a probabilistic model, including 23 independent predictors of pVo2 and pVo2 itself, the network model was less redundant and identified clusters that were more distinct. Cluster assignment from the network model was predictive of subsequent clinical events. For example, a 4.3-fold (P<0.0001; 95% CI, 2.2-8.1) and 2.8-fold (P=0.0018; 95% CI, 1.5-5.2) increase in hazard for age- and pVo2-adjusted all-cause 3-year hospitalization, respectively, were observed between the highest versus lowest risk clusters. Using these data, we developed the first risk-stratification calculator for patients with exercise intolerance. When applying the risk calculator to patients in 2 independent invasive cardiopulmonary exercise testing cohorts (Boston and Graz, Austria), we observed a clinical risk profile that paralleled the derivation cohort.

CONCLUSIONS

Network analyses were used to identify novel exercise groups and develop a point-of-care risk calculator. These data expand the range of useful clinical variables beyond pVo2 that predict hospitalization in patients with exercise intolerance.