Improvements to PTSD quality metrics with natural language processing

Abstract

Rationale aims and objectives As quality measurement becomes increasingly reliant on the availability of structured electronic medical record (EMR) data, clinicians are asked to perform documentation using tools that facilitate data capture. These tools may not be available, feasible, or acceptable in all clinical scenarios. Alternative methods of assessment, including natural language processing (NLP) of clinical notes, may improve the completeness of quality measurement in real‐world practice. Our objective was to measure the quality of care for a set of evidence‐based practices using structured EMR data alone, and then supplement those measures with additional data derived from NLP. Method As a case example, we studied the quality of care for posttraumatic stress disorder (PTSD) in the United States Department of Veterans Affairs (VA) over a 20‐year period. We measured two aspects of PTSD care, including delivery of evidence‐based psychotherapy (EBP) and associated use of measurement‐based care (MBC), using structured EMR data. We then recalculated these measures using additional data derived from NLP of clinical note text. Results There were 2 098 389 VA patients with a diagnosis of PTSD between 2000 and 2019, 72% (n = 1 515 345) of whom had not previously received EBP for PTSD and were treated after a 2015 mandate to document EBP using templates that generate structured EMR data. Using structured EMR data, we determined that 3.2% (n = 48 004) of those patients met our EBP for PTSD quality standard between 2015 and 2019, and 48.1% (n = 23 088) received associated MBC. With the addition of NLP‐derived data, estimates increased to 4.1% (n = 62 789) and 58.0% (n = 36 435), respectively. Conclusion Healthcare quality data can be significantly improved by supplementing structured EMR data with NLP‐derived data. By using NLP, health systems may be able to fill the gaps in documentation when structured tools are not yet available or there are barriers to using them in clinical practice.

Association between post-traumatic stress disorder severity and death by suicide in US military veterans: retrospective cohort study

“…20 Scores abstracted from structured data and from NLP of clinical notes were integrated into a single data-set, which has been described in detail elsewhere. 21 The two versions of the PCL were aligned to the DSM-IV and DSM-5, and we will call them the PCL-IV and the PCL-5. 17,22 Validation work shows a correlation of 0.87 between PCL versions in a large sample of veterans.…”

“…24 The mean baseline score for VA patients starting PTSD treatment in this PCL dataset is approximately 50. 25 Based on these thresholds, we created exposure groups using four PCL score ranges: minimal (0-18), low (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), moderate (31-49) and high (≥50).…”

Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing

“…• improvement of the diagnosis of this kind of disorders thanks to NLP methods [74]; • identification of the temporal evolution and stage of mental disorders in order to improve their treatment [16]; • detection of self-harm and suicidal data [31,75]; • link of mental health with other disorders, like COVID-19 [70,76], HIV [77], cancers [78], or drug abuse [79]; • analysis of psychedelic session narratives in order to predict changes in substance use [80]; • creation of online support systems for people suffering from mental disorders [78,81].…”

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

“…More specifically, unstructured and short-text fields describe the patient context, including sociodemographic, risk behaviors and allergies, patient experience and interactions with the provider, and rational for the health care decisions that were made, which can inform disease surveillance and research [ 8 ]. Text analytics and, more specifically, natural language processing (NLP) of text data in the EMR can identify symptoms and variable interactions across multiple tables within data holdings [ 9 - 15 ]. Mining text data from health records typically includes refining procedures and knowledge extraction, aggregation, abstraction, and summarization of EMR information to transform text data into actionable insights such as inform phenotyping, disease prognosis and management, and disease surveillance [ 9 , 16 , 17 ].…”

“…Due to this limitation, previous studies have relied on small data sets or a small number of institutions, preventing evidence of transferability of the models [ 17 ]. Primary care EMR short diagnostic text fields, more widely available than free-text data, have been suggested as a method for supplementing diagnostic definitions when free-text is unavailable [ 15 , 19 , 20 ]. Supplementation of free-text data with short-text fields, matched with refined processes for annotation and classification can support the use of EMR data in research [ 17 ].…”