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Introduction: Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format., Objective: The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format., Methods: A pneumonia-specific natural language processing (NLP) pipeline was strategically developed applying Clinical Text Analysis and Knowledge Extraction System (cTAKES) to validate pneumonia diagnoses following development of a pneumonia feature-specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: "positive," "negative," or "not classified: requires manual review" based on tagged concepts that support or refute diagnostic codes., Results: A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest X-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as "Pneumonia-positive," 19% as (15401/81,707) as "Pneumonia-negative," and 48% (39,209/81,707) as "episode classification pending further manual review." NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%)., Conclusion: The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: The International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations where pneumonia is standardly subtyped by settings, exposures, and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHRs), frequently in nonstructured formats including radiological interpretation or clinical notes that complicate electronic classification., Objective: The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR., Methods: Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for "rule of two" pneumonia-related codes or one ICD code and radiologically confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support subclassification based on features including symptomatic patient point of entry into the health care system timing of pneumonia emergence and identification of clinical, laboratory, or medication orders that informed definition of the pneumonia subclassification algorithm., Results: Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following Natural Language Processing classification of pneumonia status as "negative" or "unknown." Subtyping of 83,387 episodes identified: community-acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), health care-acquired (5%), and ventilator-associated (0.4%) cases, and 9.4% cases were not classifiable by the algorithm., Conclusion: Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored., Competing Interests: None declared., (Thieme. All rights reserved.)

Objective: To compare the catheter-associated urinary tract infections (CAUTI) standardized infection rate (SIR) before and after implementation of a multimodal intervention approach in a rural tertiary hospital., Design: Before-after analysis of a multimodal intervention to evaluate primary outcomes of the incidence of inpatient CAUTI, the SIR for CAUTI, and number of urinary catheter days., Setting: All inpatient departments of a rural 504-bed tertiary care facility in the Midwest., Participants: Patients admitted for in-patient care., Interventions: A multimodal intervention composed of: (a) physician and nurse education, (b) modification of progress note templates and daily provider reminders for the clinical necessity of catheters, (c) implementing established best practices for eliminating CAUTIs, (d) advocating for alternative toileting options, and (e) promoting aseptic techniques for insertion and removal of catheters. SIR, CAUTI, and the number of urinary catheter days were obtained before and after implementation of the multimodal intervention in 2015 and 2017, respectively., Results: After a one-year timeframe of intervention, CAUTI event rates decreased, and SIR for CAUTI was reduced by 60.2% (from 1.524 to 0.607) with P value<0.05. Urinary catheter days were also reduced from 16,195 in 2015 to 13,348 in 2017 (17.6% reduction) with P value <0.05., Conclusions: Incidence of CAUTIs were significantly reduced with a team effort involving infection control, physician and nursing education, modification of progress note and templates and daily provider reminders for the clinical necessity of catheters and appropriate usage of urinary catheter with corresponding reduction in urinary catheter days., (Copyright © 2020 Marshfield Clinic Health System.)