Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation.

BACKGROUND: Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text. OBJECTIVE: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. METHODS: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy. RESULTS: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events. CONCLUSIONS: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

Trends in Illness Severity, Hospitalization, and Mortality for Community-Onset Pneumonia at 118 US Veterans Affairs Medical Centers.

BACKGROUND: Deaths from pneumonia were decreasing globally prior to the COVID-19 pandemic, but it is unclear whether this was due to changes in patient populations, illness severity, diagnosis, hospitalization thresholds, or treatment. Using clinical data from the electronic health record among a national cohort of patients initially diagnosed with pneumonia, we examined temporal trends in severity of illness, hospitalization, and short- and long-term deaths. DESIGN: Retrospective cohort PARTICIPANTS: All patients >18 years presenting to emergency departments (EDs) at 118 VA Medical Centers between 1/1/2006 and 12/31/2016 with an initial clinical diagnosis of pneumonia and confirmed by chest imaging report. EXPOSURES: Year of encounter. MAIN MEASURES: Hospitalization and 30-day and 90-day mortality. Illness severity was defined as the probability of each outcome predicted by machine learning predictive models using age, sex, comorbidities, vital signs, and laboratory data from encounters during years 2006-2007, and similar models trained on encounters from years 2015 to 2016. We estimated the changes in hospitalizations and 30-day and 90-day mortality between the first and the last 2 years of the study period accounted for by illness severity using time covariate decompositions with model estimates. RESULTS: Among 196,899 encounters across the study period, hospitalization decreased from 71 to 63%, 30-day mortality 10 to 7%, 90-day mortality 16 to 12%, and 1-year mortality 29 to 24%. Comorbidity risk increased, but illness severity decreased. Decreases in illness severity accounted for 21-31% of the decrease in hospitalizations, and 45-47%, 32-24%, and 17-19% of the decrease in 30-day, 90-day, and 1-year mortality. Findings were similar among underrepresented patients and those with only hospital discharge diagnosis codes. CONCLUSIONS: Outcomes for community-onset pneumonia have improved across the VA healthcare system after accounting for illness severity, despite an increase in cases and comorbidity burden.

Pre-endoscopy coronavirus disease 2019 screening and severe acute respiratory syndrome coronavirus-2 nucleic acid amplification testing in the Veterans Affairs healthcare system: clinical practice patterns, outcomes, and relationship to procedure volume.

BACKGROUND AND AIMS: The coronavirus disease 2019 (COVID-19) pandemic has had profound impacts worldwide, including on the performance of GI endoscopy. We aimed to describe the performance and outcomes of pre-endoscopy COVID-19 symptom and exposure screening and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) nucleic acid amplification testing (NAAT) across the national Veterans Affairs healthcare system and describe the relationship of SARS-CoV-2 NAAT use and resumption of endoscopy services. METHODS: COVID-19 screening and NAAT results from March 2020 to April 2021 were analyzed to determine use, performance characteristics of screening, and association between testing and endoscopic volume trends. RESULTS: Of 220,891 completed endoscopies identified, 115,890 (52.5%) had documented preprocedure COVID-19 symptom and exposure screenings and 154,127 (69.8%) had preprocedure NAAT results within 7 days before scheduled endoscopy. Of 131,894 total canceled endoscopies, 26,475 (20.1%) had screening data and 28,505 (21.6%) had SARS-CoV-2 NAAT results. Overall, positive NAAT results were reported in 1.8% of all individuals tested and in 1.3% of those who screened negative. Among completed and canceled endoscopies, COVID-19 screening had a 34.6% sensitivity (95% confidence interval [CI], 32.4%-36.8%) and 96.4% specificity (95% CI, 96.2%-96.5%) when compared with NAAT. COVID-19 screening had a positive predictive value of 15.0% (95% CI, 14.0%-16.1%) and a negative predictive value of 98.7% (95% CI, 98.7%-98.8%). There was a very weak correlation between monthly testing and monthly endoscopy volume by site (Spearman rank correlation coefficient = .09). CONCLUSIONS: These findings have important implications for decisions about preprocedure testing, especially given breakthrough infections among vaccinated individuals during the SARS-CoV-2 delta and omicron variant surge.

Healthcare-Associated Infections in Veterans Affairs Acute and Long-Term Healthcare Facilities During the Coronavirus Disease 2019 (COVID-19) Pandemic.

OBJECTIVE: To assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on healthcare-associated infections (HAIs) reported from 128 acute care and 132 long-term care Veterans Affairs (VA) facilities. METHODS: Central line-associated bloodstream infections (CLABSIs), ventilator-associated events (VAEs), catheter-associated urinary tract infections (CAUTIs), and methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile infections and rates reported from each facility monthly to a centralized database before the pandemic (February 2019 through January 2020) and during the pandemic (July 2020 through June 2021) were compared. RESULTS: Nationwide VA COVID-19 admissions peaked in January 2021. Significant increases in the rates of CLABSIs, VAEs, and MRSA all-site HAIs (but not MRSA CLABSIs) were observed during the pandemic period in acute care facilities. There was no significant change in CAUTI rates and C. difficile rates significantly decreased. There were no significant increases in HAIs in long-term care facilities. CONCLUSIONS: The COVID-19 pandemic had a differential impact on HAIs of various types in VA acute care with many rates increasing. The decrease in CDI HAIs may be due, in part, to evolving diagnostic testing. The minimal impact of COVID-19 in VA long-term facilities may reflect differences in patient numbers and acuity and early recognition of the impact the pandemic had on nursing home residents leading to increased vigilance and optimization of infection prevention and control practices in that setting. These data support the need for building and sustaining conventional infection prevention and c ontrol strategies before and during a pandemic.

Hospital COVID-19 Public Health Reporting: Lessons from Validation of an Automated Surveillance Tool to Facilitate Data Collection.

A comparison of computer-extracted and facility-reported counts of hospitalized COVID-19 patients for public health reporting at 36 hospitals found 42% of days with matching counts between the data sources. Mis-categorization of suspect cases was a primary driver of discordance. Clear reporting definitions and data validation facilitate emerging disease surveillance.

Positive Predictive Value of COVID-19 ICD-10 Diagnosis Codes Across Calendar Time and Clinical Setting.

PURPOSE: To estimate the positive predictive value (PPV) of International Classification of Diseases, Tenth Revision (ICD-10) code U07.1, COVID-19 virus identified, in the Department of Veterans of Affairs (VA). PATIENTS AND METHODS: Records of ICD-10 code U07.1 from inpatient, outpatient, and emergency/urgent care settings were extracted from VA medical record data from 4/01/2020 to 3/31/2021. A weighted, random sample of 1500 records from each quarter of the one-year observation period was reviewed by study personnel to confirm active COVID-19 infection at the time of diagnosis and classify reasons for false positive records. PPV was estimated overall and compared across clinical setting and quarters. RESULTS: We identified 664,406 records of U07.1. Among the 1500 reviewed, 237 were false positives (PPV: 84.2%, 95% CI: 82.4-86.0). PPV ranged from 77.7% in outpatient settings to 93.8% in inpatient settings and was 83.3% in quarter 1, 80.5% in quarter 2, 86.1% in quarter 3, and 83.6% in quarter 4. The most common reasons for false positive records were history of COVID-19 (44.3%) and orders for laboratory tests (21.5%). CONCLUSION: The PPV of ICD-10 code U07.1 is low, especially in outpatient settings. Directed training may improve accuracy of coding to levels that are deemed adequate for future use in surveillance efforts.

Inpatient antibiotic utilization in the Veterans' Health Administration during the coronavirus disease 2019 (COVID-19) pandemic.

Antibiotic prescribing practices across the Veterans' Health Administration (VA) experienced significant shifts during the coronavirus disease 2019 (COVID-19) pandemic. From 2015 to 2019, antibiotic use between January and May decreased from 638 to 602 days of therapy (DOT) per 1,000 days present (DP), while the corresponding months in 2020 saw antibiotic utilization rise to 628 DOT per 1,000 DP.