Sophia Y Wang1, Suzann Pershing2, Elaine Tran3, Tina Hernandez-Boussard4. 1. Byers Eye Institute, Stanford University, 2452 Watson Ct, Palo Alto, CA, 94303, USA. Electronic address: sywang@stanford.edu. 2. Byers Eye Institute, Stanford University, 2452 Watson Ct, Palo Alto, CA, 94303, USA; Veterans Affairs Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA, 94304, USA. 3. Byers Eye Institute, Stanford University, 2452 Watson Ct, Palo Alto, CA, 94303, USA. 4. Center for Biomedical Informatics Research, Stanford University, 1265 Welch Rd, Stanford, USA.
Abstract
OBJECTIVE: Comprehensive analysis of ophthalmic surgical outcomes is often restricted by limited methodologies for efficiently and accurately extracting clinical information from electronic health record (EHR) systems because much is in free-text form. This study aims to utilize advanced methods to automate extraction of clinical concepts from the EHR free text to study visual acuity (VA), intraocular pressure (IOP), and medication outcomes of cataract and glaucoma surgeries. METHODS: Patients who underwent cataract or glaucoma surgery at an academic medical center between 2009 and 2018 were identified by Current Procedural Terminology codes. Rule-based algorithms were developed and used on EHR clinical narrative text to extract intraocular lens (IOL) power and implant type, as well as to create a surgery laterality classifier. MedEx (version 1.3.7) was used on free-text clinical notes to extract information on eye medications and compared to information from medication orders. Random samples of free-text notes were reviewed by two independent masked annotators to assess inter-annotator agreement on outcome variable classification and accuracy of classifiers. VA and IOP were available from semi-structured fields. RESULTS: This study cohort included 6347 unique patients, with 8550 stand-alone cataract surgeries, 451 combined cataract/glaucoma surgeries, and 961 glaucoma surgeries without concurrent cataract surgery. The rule-based laterality classifier achieved 100% accuracy compared to manual review of a sample of operative notes by independent masked annotators. For cataract surgery alone, glaucoma surgery alone, or combined cataract/glaucoma surgeries, our automated extraction algorithm achieved 99-100% accuracy compared to manual annotation of samples of notes from each group, including IOL model and IOL power for cataract surgeries, and glaucoma implant for glaucoma surgeries. For glaucoma medications, there was 90.7% inter-annotator agreement. After adjudication, 85.0% of medications identified by MedEx determined to be correct. Determination of surgical laterality enabled evaluation of pre- and postoperative VA and IOP for operative eyes. CONCLUSION: This text-processing pipeline can accurately capture surgical laterality and implant model usage from free-text operative notes of cataract and glaucoma surgeries, enabling extraction of clinical outcomes including visual acuities, intraocular pressure, and medications from the EHR system. Use of this approach with EHRs to assess ophthalmic surgical outcomes can benefit research groups interested in studying the safety and clinical efficacies of different surgical approaches.
OBJECTIVE: Comprehensive analysis of ophthalmic surgical outcomes is often restricted by limited methodologies for efficiently and accurately extracting clinical information from electronic health record (EHR) systems because much is in free-text form. This study aims to utilize advanced methods to automate extraction of clinical concepts from the EHR free text to study visual acuity (VA), intraocular pressure (IOP), and medication outcomes of cataract and glaucoma surgeries. METHODS:Patients who underwent cataract or glaucoma surgery at an academic medical center between 2009 and 2018 were identified by Current Procedural Terminology codes. Rule-based algorithms were developed and used on EHR clinical narrative text to extract intraocular lens (IOL) power and implant type, as well as to create a surgery laterality classifier. MedEx (version 1.3.7) was used on free-text clinical notes to extract information on eye medications and compared to information from medication orders. Random samples of free-text notes were reviewed by two independent masked annotators to assess inter-annotator agreement on outcome variable classification and accuracy of classifiers. VA and IOP were available from semi-structured fields. RESULTS: This study cohort included 6347 unique patients, with 8550 stand-alone cataract surgeries, 451 combined cataract/glaucoma surgeries, and 961 glaucoma surgeries without concurrent cataract surgery. The rule-based laterality classifier achieved 100% accuracy compared to manual review of a sample of operative notes by independent masked annotators. For cataract surgery alone, glaucoma surgery alone, or combined cataract/glaucoma surgeries, our automated extraction algorithm achieved 99-100% accuracy compared to manual annotation of samples of notes from each group, including IOL model and IOL power for cataract surgeries, and glaucoma implant for glaucoma surgeries. For glaucoma medications, there was 90.7% inter-annotator agreement. After adjudication, 85.0% of medications identified by MedEx determined to be correct. Determination of surgical laterality enabled evaluation of pre- and postoperative VA and IOP for operative eyes. CONCLUSION: This text-processing pipeline can accurately capture surgical laterality and implant model usage from free-text operative notes of cataract and glaucoma surgeries, enabling extraction of clinical outcomes including visual acuities, intraocular pressure, and medications from the EHR system. Use of this approach with EHRs to assess ophthalmic surgical outcomes can benefit research groups interested in studying the safety and clinical efficacies of different surgical approaches.
Authors: Sally L Baxter; Adam R Klie; Bharanidharan Radha Saseendrakumar; Gordon Y Ye; Michael Hogarth Journal: J Med Internet Res Date: 2020-08-14 Impact factor: 5.428