Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 144
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 144
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 212
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3106
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Objective: Patient-reported outcome measures (PROMs) are utilized to assess surgical success but are limited by data collection, response bias, and subjectivity. The large volume of digital healthcare data offers a new method to utilize healthcare utilization as a longitudinal, individualized, and objective proxy for health needs among surgical patients. This study aimed to design and evaluate a novel resource utilization in spine healthcare (RUSH) clustering method that complements PROMs in evaluating postoperative patient outcomes.
Methods: This retrospective cross-sectional study conducted at a large, tertiary healthcare system included all adult patients undergoing cervical or lumbar surgery between 2014 and 2020 with at least 3 months follow-up. Postoperative healthcare utilization was analyzed using clinic visits, inpatient encounters, telephone encounters, MyChart messages, opioid use, physical therapy, injections, and imaging. Latent profile analysis determined RUSH clusters and changes in PROM Information System Physical Health (PROMIS-PH) scores preoperation and 12-months postoperation.
Results: This study included 5602 surgeries (mean age 61.3 ± 13.1 years, 49.9% female). Four RUSH groups were identified: low utilizers (21.5%), moderate utilizers without advanced imaging (34.7%), moderate utilizers with advanced imaging (10.7%), and high utilizers (33.1%). Utilization patterns varied by surgery type, with lower-utilization patterns among non-fusion procedures and a consistent sub-population of high utilizers across all surgery types. High RUSH utilizers had the lowest pre-operative PROMIS-PH scores and the worst average postoperative change.
Conclusions: RUSH clustering provides a novel, data-driven approach to measure surgical success, complementing traditional PROMs, and leveraging big data to monitor and respond to surgical outcomes.
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http://dx.doi.org/10.1016/j.wneu.2024.10.019 | DOI Listing |
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