Promoting Learning Health System Cycles by Optimizing EHR Data Clinical Concept Encoding Processes.

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.

Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults.

Importance: The Deterioration Index (DTI), used by hospitals for predicting patient deterioration, has not been extensively validated externally, raising concerns about performance and equitable predictions. Objective: To locally validate DTI performance and assess its potential for bias in predicting patient clinical deterioration. Design, Setting, and Participants: This retrospective prognostic study included 13 737 patients admitted to 8 heterogenous Midwestern US hospitals varying in size and type, including academic, community, urban, and rural hospitals. Patients were 18 years or older and admitted between January 1 and May 31, 2021. Exposure: DTI predictions made every 15 minutes. Main Outcomes and Measures: Deterioration, defined as the occurrence of any of the following while hospitalized: mechanical ventilation, intensive care unit transfer, or death. Performance of the DTI was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Bias measures were calculated across demographic subgroups. Results: A total of 5 143 513 DTI predictions were made for 13 737 patients across 14 834 hospitalizations. Among 13 918 encounters, the mean (SD) age of patients was 60.3 (19.2) years; 7636 (54.9%) were female, 11 345 (81.5%) were White, and 12 392 (89.0%) were of other ethnicity than Hispanic or Latino. The prevalence of deterioration was 10.3% (n = 1436). The DTI produced AUROCs of 0.759 (95% CI, 0.756-0.762) at the observation level and 0.685 (95% CI, 0.671-0.700) at the encounter level. Corresponding AUPRCs were 0.039 (95% CI, 0.037-0.040) at the observation level and 0.248 (95% CI, 0.227-0.273) at the encounter level. Bias measures varied across demographic subgroups and were 14.0% worse for patients identifying as American Indian or Alaska Native and 19.0% worse for those who chose not to disclose their ethnicity. Conclusions and Relevance: In this prognostic study, the DTI had modest ability to predict patient deterioration, with varying degrees of performance at the observation and encounter levels and across different demographic groups. Disparate performance across subgroups suggests the need for more transparency in model training data and reinforces the need to locally validate externally developed prediction models.

A serial cross-sectional study of trends and predictors of prescription controlled substance-related traumatic injury.

Pre-injury drug use is a key contributor to traumatic injury. However, limited research has examined trends and predictors of controlled substance-related trauma. The present study aims to provide better clarity on the specific role of prescription-controlled substances (PCS) in traumatic injury events. The data source was the American College of Surgeons National Trauma Data Bank. Trends by injury mechanism and intent for patients with PCS and no-confirmed substances were compared from 2007 to 2014. Logistic regression models were also performed to examine the association between substance use and injury mechanism and intent for data across the study period. Of 405,334 trauma patients, 328,623 (81.1%) had no-confirmed substances and 76,711 (18.9%) had PCS detected. The majority of events in the PCS and no-confirmed substance groups were classified as unintentional. Motor vehicle traffic (MVT), falls, other transport, and cut/pierce injuries accounted for approximately 80% of all injuries. From 2007 to 2014, the proportion of injuries with PCS increased for all injury mechanisms and injury intents. The injury mechanisms of fire/burn, firearm, machinery, poisoning, and other transport were significantly more likely to have PCS relative to MVT injuries. For injury intent, self-harm was more likely to have a toxicology test positive for PCS, while assault was less likely to have a toxicology test positive for PCS compared to unintentional injuries. PCS-related traumatic injuries increased significantly over time and across injury mechanisms and intents. These findings can be used to inform prescribing and understand risk factors to reduce the likelihood of PCS-related traumatic injury.

Transnasal Sphenopalatine Ganglion Block for Postdural Puncture Headache in an Adolescent: A Case Report.

We present a pediatric patient with postdural puncture headache after a lumbar puncture, who was successfully treated with a sphenopalatine ganglion block. An uneventful autologous epidural blood patch had been placed 2 days before, but the patient reported a recurrence of symptoms after about 5 hours. Sphenopalatine ganglion block is well described in the treatment of postdural puncture headache for the obstetric population, but examples of its use in the pediatric population are not described. To our knowledge, this is the first pediatric case of sphenopalatine ganglion block for postdural puncture headache reported in the literature.