Improvements to PTSD quality metrics with natural language processing
Published 2021. This article is a U.S. Government work and is in the public domain in the USA..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
|---|---|
| Enthalten in: |
Journal of evaluation in clinical practice - 28(2022), 4 vom: 01. Aug., Seite 520-530 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shiner, Brian [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 21.07.2022 Date Revised 24.08.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/jep.13587 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM325781753 |
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| 100 | 1 | |a Shiner, Brian |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improvements to PTSD quality metrics with natural language processing |
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| 500 | |a Date Revised 24.08.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Published 2021. This article is a U.S. Government work and is in the public domain in the USA. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a electronic health records | |
| 650 | 4 | |a evidence-based medicine | |
| 650 | 4 | |a natural language processing | |
| 650 | 4 | |a patient reported outcome measures | |
| 650 | 4 | |a psychotherapy | |
| 650 | 4 | |a quality assurance, health care | |
| 650 | 4 | |a stress disorders, post-traumatic | |
| 700 | 1 | |a Levis, Maxwell |e verfasserin |4 aut | |
| 700 | 1 | |a Dufort, Vincent M |e verfasserin |4 aut | |
| 700 | 1 | |a Patterson, Olga V |e verfasserin |4 aut | |
| 700 | 1 | |a Watts, Bradley V |e verfasserin |4 aut | |
| 700 | 1 | |a DuVall, Scott L |e verfasserin |4 aut | |
| 700 | 1 | |a Russ, Carey J |e verfasserin |4 aut | |
| 700 | 1 | |a Maguen, Shira |e verfasserin |4 aut | |
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