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1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection
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| Zeitschriftentitel: | Open Forum Infectious Diseases |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Open Forum Infectious Diseases, 6, 2019, Supplement_2, S. S424-S425 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Ding, Dan Stachel, Anna Iturrate, Eduardo Phillips, Michael Ding, Dan Stachel, Anna Iturrate, Eduardo Phillips, Michael |
|---|---|
| author |
Ding, Dan Stachel, Anna Iturrate, Eduardo Phillips, Michael |
| spellingShingle |
Ding, Dan Stachel, Anna Iturrate, Eduardo Phillips, Michael Open Forum Infectious Diseases 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection Infectious Diseases Oncology |
| author_sort |
ding, dan |
| spelling |
Ding, Dan Stachel, Anna Iturrate, Eduardo Phillips, Michael 2328-8957 Oxford University Press (OUP) Infectious Diseases Oncology http://dx.doi.org/10.1093/ofid/ofz360.1047 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities.</jats:p> <jats:p /> </jats:sec> <jats:sec> <jats:title>Disclosures</jats:title> <jats:p>All authors: No reported disclosures.</jats:p> </jats:sec> 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection Open Forum Infectious Diseases |
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| title |
1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_unstemmed |
1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_full |
1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_fullStr |
1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_full_unstemmed |
1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_short |
1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_sort |
1184. making pneumonia surveillance easy: automation of pneumonia case detection |
| topic |
Infectious Diseases Oncology |
| url |
http://dx.doi.org/10.1093/ofid/ofz360.1047 |
| publishDate |
2019 |
| physical |
S424-S425 |
| description |
<jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Background</jats:title>
<jats:p>Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Methods</jats:title>
<jats:p>Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Conclusion</jats:title>
<jats:p>The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities.</jats:p>
<jats:p />
</jats:sec>
<jats:sec>
<jats:title>Disclosures</jats:title>
<jats:p>All authors: No reported disclosures.</jats:p>
</jats:sec> |
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| description | <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities.</jats:p> <jats:p /> </jats:sec> <jats:sec> <jats:title>Disclosures</jats:title> <jats:p>All authors: No reported disclosures.</jats:p> </jats:sec> |
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| spelling | Ding, Dan Stachel, Anna Iturrate, Eduardo Phillips, Michael 2328-8957 Oxford University Press (OUP) Infectious Diseases Oncology http://dx.doi.org/10.1093/ofid/ofz360.1047 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities.</jats:p> <jats:p /> </jats:sec> <jats:sec> <jats:title>Disclosures</jats:title> <jats:p>All authors: No reported disclosures.</jats:p> </jats:sec> 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection Open Forum Infectious Diseases |
| spellingShingle | Ding, Dan, Stachel, Anna, Iturrate, Eduardo, Phillips, Michael, Open Forum Infectious Diseases, 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection, Infectious Diseases, Oncology |
| title | 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_full | 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_fullStr | 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_full_unstemmed | 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_short | 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| title_sort | 1184. making pneumonia surveillance easy: automation of pneumonia case detection |
| title_unstemmed | 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection |
| topic | Infectious Diseases, Oncology |
| url | http://dx.doi.org/10.1093/ofid/ofz360.1047 |