Improving Patient-Reported Understanding of Radiographic Reports in Spine Surgery Through Implementation of Natural Language Processing: A Randomized Controlled Study

BACKGROUND AND OBJECTIVES: Many spinal imaging reports are written above recommended health literacy levels, limiting patient comprehension at a time when rapid electronic access to radiology results is increasing. We aim to evaluate whether supplemental natural language processing (NLP)–modified radiology reports—within the context of usual patient portal access to the original report—are associated with improved patient-reported perceived understandability compared with standard reports among patients undergoing spine imaging. METHODS: This is a prospective randomized controlled study with limited participant blinding, conducted with 2 spine surgeons affiliated with a single academic medical center in New York City. Participants (18 years or older) undergoing spine imaging at initial surgical consultation were enrolled and were randomized to receive either their original radiology report or a supplemental NLP–modified version. Radiology reports were rewritten using a standardized natural language processing prompt (ChatGPT 4.0) and reviewed for accuracy by study investigators. The primary outcome was survey-based patient-reported perceived understandability. Secondary outcomes included objective readability scores (Flesch-Kincaid Grade Level, Flesch Reading Ease, Simple Measure of Gobbledygook, Gunning-Fog Index). RESULTS: Fifty-two adults were enrolled; 42 (81%) completed the postintervention survey. Among 52 randomized participants (26 women [50%]), NLP–modified reports demonstrated significantly improved objective readability across all indices compared with original reports (Flesch-Kincaid Grade Level, 5.9 ± 1.1 vs 11.0 ± 1.7; Reading Ease, 71.0 ± 10.3 vs 28.2 ± 12.5; Simple Measure of Gobbledygook, 20.1 ± 12.7 vs 23.5 ± 15.7; Gunning-Fog Index, 8.2 ± 1.1 vs 14.3 ± 1.8; all P < .01). Of 42 survey respondents, those receiving supplemental NLP-modified reports reported greater ease of understanding the report language (4.2 ± 1.2 vs 2.6 ± 1.5, mean-difference 95% CI: 0.75-2.53, P < .01, Bonferroni-corrected- P = .02), and less difficulty with medical terms (2.0 ± 1.5 vs 3.6 ± 1.4, mean-difference 95% CI: −2.51 to −0.60, P < .01, Bonferroni-corrected- P = .04). CONCLUSION: Supplemental NLP-modified radiology reports were associated with improved objective readability and higher patient-reported perceived ease of understanding in a spine surgery initial consultation setting. These findings support further investigation of NLP-generated supplemental explanations within supervised clinical workflows.

Authors

Institutions

Publication Details

Journal
Neurosurgery
Published
2026-10-22
DOI
https://doi.org/10.1227/neu.0000000000004233
Primary Topic
Health Literacy and Information Accessibility
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improving Patient-Reported Understanding of Radiographic Reports in Spine Surgery Through Implementation of Natural Language Processing: A Randomized Controlled Study

Nathan A. Shlobin, Dean Chou, Chiemela Izima, Vivian P. Le et al.
Neurosurgery
Health Literacy and Information Accessibility
article

Improving Patient-Reported Understanding of Radiographic Reports in Spine Surgery Through Implementation of Natural Language Processing: A Randomized Controlled Study

Nathan A. Shlobin, Dean Chou, Chiemela Izima, Vivian P. Le, Alex Hernandez Manriquez, Shailen G. Sampath, Shaw Qin, Christopher Pawul, Christina M Bear, Anthony Tang, Alan X. Chen, Miles L. Botkiss, Phillip Chung, Andrew K. Chan
article en

Abstract

BACKGROUND AND OBJECTIVES: Many spinal imaging reports are written above recommended health literacy levels, limiting patient comprehension at a time when rapid electronic access to radiology results is increasing. We aim to evaluate whether supplemental natural language processing (NLP)–modified radiology reports—within the context of usual patient portal access to the original report—are associated with improved patient-reported perceived understandability compared with standard reports among patients undergoing spine imaging. METHODS: This is a prospective randomized controlled study with limited participant blinding, conducted with 2 spine surgeons affiliated with a single academic medical center in New York City. Participants (18 years or older) undergoing spine imaging at initial surgical consultation were enrolled and were randomized to receive either their original radiology report or a supplemental NLP–modified version. Radiology reports were rewritten using a standardized natural language processing prompt (ChatGPT 4.0) and reviewed for accuracy by study investigators. The primary outcome was survey-based patient-reported perceived understandability. Secondary outcomes included objective readability scores (Flesch-Kincaid Grade Level, Flesch Reading Ease, Simple Measure of Gobbledygook, Gunning-Fog Index). RESULTS: Fifty-two adults were enrolled; 42 (81%) completed the postintervention survey. Among 52 randomized participants (26 women [50%]), NLP–modified reports demonstrated significantly improved objective readability across all indices compared with original reports (Flesch-Kincaid Grade Level, 5.9 ± 1.1 vs 11.0 ± 1.7; Reading Ease, 71.0 ± 10.3 vs 28.2 ± 12.5; Simple Measure of Gobbledygook, 20.1 ± 12.7 vs 23.5 ± 15.7; Gunning-Fog Index, 8.2 ± 1.1 vs 14.3 ± 1.8; all P < .01). Of 42 survey respondents, those receiving supplemental NLP-modified reports reported greater ease of understanding the report language (4.2 ± 1.2 vs 2.6 ± 1.5, mean-difference 95% CI: 0.75-2.53, P < .01, Bonferroni-corrected- P = .02), and less difficulty with medical terms (2.0 ± 1.5 vs 3.6 ± 1.4, mean-difference 95% CI: −2.51 to −0.60, P < .01, Bonferroni-corrected- P = .04). CONCLUSION: Supplemental NLP-modified radiology reports were associated with improved objective readability and higher patient-reported perceived ease of understanding in a spine surgery initial consultation setting. These findings support further investigation of NLP-generated supplemental explanations within supervised clinical workflows.

Neurosurgery
NewYork–Presbyterian Hospital (US), Columbia University (US)
Openalex Percentile: Top 8%
Health Literacy and Information Accessibility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon