Christian A Koziatek1, Emma Simon2,3, Leora I Horwitz2,3,4, Danil V Makarov2,5, Silas W Smith1, Simon Jones2, Soterios Gyftopoulos6, Jordan L Swartz1. 1. Ronald O. Perelman Department of Emergency Medicine, NYU School of Medicine, New York, NY. 2. Department of Population Health, NYU School of Medicine, New York, NY. 3. Center for Healthcare Innovation and Delivery Science, NYU School of Medicine, New York, NY. 4. Department of Medicine, NYU School of Medicine, New York, NY. 5. Department of Urology, NYU School of Medicine, New York, NY. 6. Department of Radiology, NYU School of Medicine, New York, NY.
Abstract
BACKGROUND: The assessment of clinical guideline adherence for the evaluation of pulmonary embolism (PE) via computed tomography pulmonary angiography (CTPA) currently requires either labor-intensive, retrospective chart review or prospective collection of PE risk scores at the time of CTPA order. The recording of clinical data in a structured manner in the electronic health record (EHR) may make it possible to automate the calculation of a patient's PE risk classification and determine whether the CTPA order was guideline concordant. OBJECTIVES: The objective of this study was to measure the performance of automated, structured data-only versions of the Wells and revised Geneva risk scores in emergency department (ED) encounters during which a CTPA was ordered. The hypothesis was that such an automated method would classify a patient's PE risk with high accuracy compared to manual chart review. METHODS: We developed automated, structured data-only versions of the Wells and revised Geneva risk scores to classify 212 ED encounters during which a CTPA was performed as "PE likely" or "PE unlikely." We then combined these classifications with D-dimer ordering data to assess each encounter as guideline concordant or discordant. The accuracy of these automated classifications and assessments of guideline concordance were determined by comparing them to classifications and concordance based on the complete Wells and revised Geneva scores derived via abstractor manual chart review. RESULTS: The automatically derived Wells and revised Geneva risk classifications were 91.5 and 92% accurate compared to the manually determined classifications, respectively. There was no statistically significant difference between guideline adherence calculated by the automated scores compared to manual chart review (Wells, 70.8% vs. 75%, p = 0.33; revised Geneva, 65.6% vs. 66%, p = 0.92). CONCLUSION: The Wells and revised Geneva score risk classifications can be approximated with high accuracy using automated extraction of structured EHR data elements in patients who received a CTPA. Combining these automated scores with D-dimer ordering data allows for the automated assessment of clinical guideline adherence for CTPA ordering in the ED, without the burden of manual chart review.
BACKGROUND: The assessment of clinical guideline adherence for the evaluation of pulmonary embolism (PE) via computed tomography pulmonary angiography (CTPA) currently requires either labor-intensive, retrospective chart review or prospective collection of PE risk scores at the time of CTPA order. The recording of clinical data in a structured manner in the electronic health record (EHR) may make it possible to automate the calculation of a patient's PE risk classification and determine whether the CTPA order was guideline concordant. OBJECTIVES: The objective of this study was to measure the performance of automated, structured data-only versions of the Wells and revised Geneva risk scores in emergency department (ED) encounters during which a CTPA was ordered. The hypothesis was that such an automated method would classify a patient's PE risk with high accuracy compared to manual chart review. METHODS: We developed automated, structured data-only versions of the Wells and revised Geneva risk scores to classify 212 ED encounters during which a CTPA was performed as "PE likely" or "PE unlikely." We then combined these classifications with D-dimer ordering data to assess each encounter as guideline concordant or discordant. The accuracy of these automated classifications and assessments of guideline concordance were determined by comparing them to classifications and concordance based on the complete Wells and revised Geneva scores derived via abstractor manual chart review. RESULTS: The automatically derived Wells and revised Geneva risk classifications were 91.5 and 92% accurate compared to the manually determined classifications, respectively. There was no statistically significant difference between guideline adherence calculated by the automated scores compared to manual chart review (Wells, 70.8% vs. 75%, p = 0.33; revised Geneva, 65.6% vs. 66%, p = 0.92). CONCLUSION: The Wells and revised Geneva score risk classifications can be approximated with high accuracy using automated extraction of structured EHR data elements in patients who received a CTPA. Combining these automated scores with D-dimer ordering data allows for the automated assessment of clinical guideline adherence for CTPA ordering in the ED, without the burden of manual chart review.
Authors: Arjun K Venkatesh; Jeffrey A Kline; D Mark Courtney; Carlos A Camargo; Michael C Plewa; Kristen E Nordenholz; Christopher L Moore; Peter B Richman; Howard A Smithline; Daren M Beam; Christopher Kabrhel Journal: Arch Intern Med Date: 2012-07-09
Authors: Frank S Drescher; Sharad Chandrika; Ian D Weir; Jeffrey T Weintraub; Lewis Berman; Ronald Lee; Patricia D Van Buskirk; Yun Wang; Adeshola Adewunmi; Jonathan M Fine Journal: Ann Emerg Med Date: 2010-11-02 Impact factor: 5.721
Authors: Jadranka Stojanovska; Ruth C Carlos; Keith E Kocher; Arun Nagaraju; Karen Guy; Aine M Kelly; Aamer R Chughtai; Ella A Kazerooni Journal: J Am Coll Radiol Date: 2015-10 Impact factor: 5.532
Authors: Adam L Sharp; Ganesh Nagaraj; Ellen J Rippberger; Ernest Shen; Clifford J Swap; Matthew A Silver; Taylor McCormick; David R Vinson; Jerome R Hoffman Journal: Acad Emerg Med Date: 2017-01 Impact factor: 3.451
Authors: Ali S Raja; Ivan K Ip; Luciano M Prevedello; Aaron D Sodickson; Cameron Farkas; Richard D Zane; Richard Hanson; Samuel Z Goldhaber; Ritu R Gill; Ramin Khorasani Journal: Radiology Date: 2011-12-20 Impact factor: 11.105
Authors: Arne van Belle; Harry R Büller; Menno V Huisman; Peter M Huisman; Karin Kaasjager; Pieter W Kamphuisen; Mark H H Kramer; Marieke J H A Kruip; Johanna M Kwakkel-van Erp; Frank W G Leebeek; Mathilde Nijkeuter; Martin H Prins; Maaike Sohne; Lidwine W Tick Journal: JAMA Date: 2006-01-11 Impact factor: 56.272
Authors: Safiya Richardson; Nasen Jonathan Zhang; Philippe Rameau; Marsophia Julemis; Yan Liu; Jeffrey Solomon; Sundas Khan; Thomas McGinn Journal: JMIR Form Res Date: 2022-02-28