Harnessing the power of electronic health records and open natural language data mining to capture meaningful patient experience during routine clinical care

Danielle R Larrow; Orna Katz Kadosh; Shannon Fracchia; Marcella Radano; Christopher J Hartnick

doi:10.1016/j.ijporl.2023.111698

Harnessing the power of electronic health records and open natural language data mining to capture meaningful patient experience during routine clinical care

Int J Pediatr Otorhinolaryngol. 2023 Oct:173:111698. doi: 10.1016/j.ijporl.2023.111698. Epub 2023 Aug 17.

Authors

Danielle R Larrow¹, Orna Katz Kadosh², Shannon Fracchia³, Marcella Radano⁴, Christopher J Hartnick⁵

Affiliations

¹ Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, USA; Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA.
² Department of Otolaryngology-Head and Neck Surgery, Dana-Dwek Children's Hospital, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
³ Department of Pediatric Pulmonology, Massachusetts General Hospital, Boston, USA.
⁴ Department of Pediatric Gastroenterology, Massachusetts General Hospital, Boston, USA.
⁵ Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, USA; Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA. Electronic address: christopher_hartnick@meei.harvard.edu.

PMID: 37597315
DOI: 10.1016/j.ijporl.2023.111698

Abstract

Introduction: Electronic health records (EHR) are a rich data source for both quality improvement and clinical research. Natural language processing can be harnessed to extract data from these previously difficult to access sources.

Objective: The objective of this study was to create and apply a natural language search query to extract EHR data to ask and answer quality improvement questions at a pediatric aerodigestive center.

Methods: We developed a combined natural language search query to extract clinically meaningful data along with International Statistical Classification of Diseases (ICD10) and Current Procedural Terminology (CPT) code data. This search query was applied to a single pediatric aerodigestive center to answer key clinical questions asked by families. Data were extracted from EHR data from first clinic visit, operative note, microbiology lab report, and pathology report for all new patients from 2020 to 2021. Included as three queries were: 1) if I bring my child to a pediatric aerodigestive center, how often will my child obtain a medical diagnosis without needing an intervention? 2) if my child has a diagnostic procedure, how often will a diagnosis be made? 3) if a diagnosis is made, can it be addressed during that endoscopic intervention?

Results: For the 711 new patients coming to the pediatric aerodigestive center from 2020 to 2021, only 26-32% required an interventional triple endoscopy (rigid/flexible bronchoscopy with esophagoduodenoscopy). Of these triple endoscopies, 75.7% resulted in a positive finding that enabled optimization of that child's care. Of the 221 patients who underwent diagnostic triple endoscopies, 40.7% underwent intervention at the same time for laryngeal cleft (injection or suture, dependent upon age).

Conclusion: Here we created an effective model of open language search query to extract meaningful metrics of patient experience from EHR data. This model easily allows the EHR to be harnessed to create retrospective and prospective databases that can be readily queried to answer clinical questions important to patients. Such databases are widely applicable not just to pediatric aerodigestive centers but to any clinical care setting using an EHR.

Keywords: Bronchoscopy; Electronic health records; Endoscopy; Quality improvement; natural language processing.

Published by Elsevier B.V.

MeSH terms

Bronchoscopy*
Child
Data Mining
Electronic Health Records*
Humans
Patient Outcome Assessment
Retrospective Studies