Assessing Resident Cataract Surgical Outcomes Using Electronic Health Record Data

Area of Science:

• Ophthalmology
• Health Informatics
• Surgical Outcomes Research

Background:

• Cataract surgery outcomes are crucial for patient vision and quality of life.
• Evaluating surgical outcomes traditionally involves manual data collection, which can be time-consuming and prone to errors.
• Electronic Health Records (EHR) offer a potential solution for automated outcome assessment.

Purpose of the Study:

• To demonstrate the feasibility of using Electronic Health Record (EHR) data for automated evaluation of cataract surgery outcomes.
• To compare surgical outcomes between resident and attending surgeons using EHR data.
• To identify predictors of postoperative visual acuity (VA) in cataract surgery.

Main Methods:

• Retrospective analysis of 14,537 cataract surgery cases from the Johns Hopkins Wilmer Eye Institute.
• Extraction of preoperative and postoperative visual acuity (VA) and unplanned return to operating room data from EHR.
• Categorization of cases by surgeon (resident vs. attending) and multivariable regression analysis to identify outcome predictors.

Main Results:

• No statistically significant difference in the proportion of cases with improved postoperative VA between resident and attending surgeons.
• Attending surgeons had a higher proportion of postoperative VA better than 20/40, but this was influenced by poorer baseline VA in resident cases.
• Unplanned return to operating room rates were similar between resident (1.8%) and attending (1.2%) surgeons.
• Preoperative VA, American Society of Anesthesiologists (ASA) score, patient age, and surgeon status were significant predictors of postoperative VA.

Conclusions:

• EHR data can be effectively utilized for automated, ongoing evaluation and monitoring of cataract surgery outcomes.
• Preoperative visual acuity, ASA classification, and surgeon experience level are key predictors of postoperative visual outcomes.
• This automated approach using EHR data can inform interventions to improve surgical training and patient care.