Creating Conversion Factors from EHR Event Log Data: A Comparison of Investigator-Derived and Vendor-Derived Metrics for Primary Care Physicians.

Physicians spend a large amount of time with the electronic health record (EHR), which the majority believe contributes to their burnout. However, there are limitedstandardized measures of physician EHR time. Vendor-derived metrics are standardized but may underestimate real-world EHR experience. Investigator-derived metrics may be more reliable but not standardized, particularly with regard to timeout thresholds defining inactivity. This study aimed to enable standardized investigator-derived metrics using conversion factors between raw event log-derived metrics and Signal (Epic System's standardized metric) for primary care physicians. This was an observational, retrospective longitudinal study of EHR raw event logs and Signal data from a quaternary academic medical center and its community affiliates in California, over a 6-month period. The study evaluated 242 physicians over 1370 physician-months, comparing 53.7 million event logs to 6850 Signal metrics, in five different time based metrics. Results show that inactivity thresholds for event log metric derivation that most closely approximate Signal metrics ranged from 90 seconds (Visit Navigator) to 360 seconds ("Pajama time") depending on the metric. Based on this data, conversion factors for investigator-derived metrics across a wide range of inactivity thresholds, via comparison with Signal metrics, are provided which may allow researchers to consistently quantify EHR experience.

Quality of Care in US NICUs by Race and Ethnicity.

BACKGROUND: Summary measures are used to quantify a hospital's quality of care by combining multiple metrics into a single score. We used Baby-MONITOR, a summary quality measure for NICUs, to evaluate quality by race and ethnicity across and within NICUs in the United States. METHODS: Vermont Oxford Network members contributed data from 2015 to 2019 on infants from 25 to 29 weeks' gestation or of 401 to 1500 g birth weight who were inborn or transferred to the reporting hospital within 28 days of birth. Nine Baby-MONITOR measures were individually risk adjusted, standardized, equally weighted, and averaged to derive scores for African American, Hispanic, Asian American, and American Indian infants, compared with white infants. RESULTS: This prospective cohort included 169 400 infants at 737 hospitals. Across NICUs, Hispanic and Asian American infants had higher Baby-MONITOR summary scores, compared with those of white infants. African American and American Indian infants scored lower on process measures, and all 4 minority groups scored higher on outcome measures. Within NICUs, the mean summary scores for African American, Hispanic, and Asian American NICU subsets were higher, compared with those of white infants in the same NICU. American Indian summary NICU scores were not different, on average. CONCLUSIONS: With Baby-MONITOR, we identified differences in NICU quality by race and ethnicity. However, the summary score masked within-measure quality gaps that raise unanswered questions about the relationships between race and ethnicity and processes and outcomes of care.

Quantifying the variation in neonatal transport referral patterns using network analysis.

OBJECTIVE: Regionalized care reduces neonatal morbidity and mortality. This study evaluated the association of patient characteristics with quantitative differences in neonatal transport networks. STUDY DESIGN: We retrospectively analyzed prospectively collected data for infants <28 days of age acutely transported within California from 2008 to 2012. We generated graphs representing bidirectional transfers between hospitals, stratified by patient attribute, and compared standard network analysis metrics. RESULT: We analyzed 34,708 acute transfers, representing 1594 unique transfer routes between 271 hospitals. Density, centralization, efficiency, and modularity differed significantly among networks drawn based on different infant attributes. Compared to term infants and to those transported for medical reasons, network metrics identify greater degrees of regionalization for preterm and surgical patients (more centralized and less dense, respectively [p < 0.001]). CONCLUSION: Neonatal interhospital transport networks differ by patient attributes as reflected by differences in network metrics, suggesting that regionalization should be considered in the context of a multidimensional system.

Racial Segregation and Inequality in the Neonatal Intensive Care Unit for Very Low-Birth-Weight and Very Preterm Infants.

Importance: Racial and ethnic minorities receive lower-quality health care than white non-Hispanic individuals in the United States. Where minority infants receive care and the role that may play in the quality of care received is unclear. Objective: To determine the extent of segregation and inequality of care of very low-birth-weight and very preterm infants across neonatal intensive care units (NICUs) in the United States. Design, Setting, and Participants: This cohort study of 743 NICUs in the Vermont Oxford Network included 117 982 black, Hispanic, Asian, and white infants born at 401 g to 1500 g or 22 to 29 weeks' gestation from January 2014 to December 2016. Analysis began January 2018. Main Outcomes and Measures: The NICU segregation index and NICU inequality index were calculated at the hospital level as the Gini coefficients associated with the Lorenz curves for black, Hispanic, and Asian infants compared with white infants, with NICUs ranked by proportion of white infants for the NICU segregation index and by composite Baby-MONITOR (Measure of Neonatal Intensive Care Outcomes Research) score for the NICU inequality index. Results: Infants (36 359 black [31%], 21 808 Hispanic [18%], 5920 Asian [5%], and 53 895 white [46%]) were segregated among the 743 NICUs by race and ethnicity (NICU segregation index: black: 0.50 [95% CI, 0.46-0.53], Hispanic: 0.58 [95% CI, 0.54-0.61], and Asian: 0.45 [95% CI, 0.40-0.50]). Compared with white infants, black infants were concentrated at NICUs with lower-quality scores, and Hispanic and Asian infants were concentrated at NICUs with higher-quality scores (NICU inequality index: black: 0.07 [95% CI, 0.02-0.13], Hispanic: -0.10 [95% CI, -0.17 to -0.04], and Asian: -0.26 [95% CI, -0.32 to -0.19]). There was marked variation among the census regions in weighted mean NICU quality scores (range: -0.69 to 0.85). Region of residence explained the observed inequality for Hispanic infants but not for black or Asian infants. Conclusions and Relevance: Black, Hispanic, and Asian infants were segregated across NICUs, reflecting the racial segregation of minority populations in the United States. There were large differences between geographic regions in NICU quality. After accounting for these differences, compared with white infants, Asian infants received care at higher-quality NICUs and black infants, at lower-quality NICUs. Explaining these patterns will require understanding the effects of sociodemographic factors and public policies on hospital quality, access, and choice for minority women and their infants.