Surgical Scheduling Errors During Manual Data Transfer

Retrospective studies examining errors within a surgical scheduling setting do not fully represent the effects of human error involved in transcribing critical patient health information (PHI). These errors can negatively impact patient care and reduce workplace efficiency due to insurance claim denials and potential sentinel events. Previous reports underscore the burden physicians face with prior authorizations which may lead to serious adverse events or the abandonment of treatment due to these delays. This study simulates the process of PHI transfer during surgical scheduling to examine the error rate of experienced schedulers when manually transferring PHI from surgical forms into electronic health records (EHR).

Participants (n = 50) manually input PHI from four surgical scheduling forms into a simulated EHR form. Eight critical data points were identified and defined as data that delay claim approvals and payments. Subjects were randomly assigned to either a control (18 minutes) or experimental (10 minutes) group. Transcription errors were flagged to measure the percentage of incorrectly inputted data fields. Two-tailed t-tests were used to determine statistical significance ( P < .05).

100% of subjects in both cohorts had at least one or more errors in every form. The 10-minute cohort had a higher average "critical errors" rate than the 18-minute cohort ( P = .03). Of the 200 forms completed, 171 forms contained 1 or more "critical errors," resulting in a potential 85.5% delay or denial in authorization or payments. The highest incidence of critical errors across all fields occurred with ICD-10 codes, CPT codes, authorization number, procedure, and insurance ID number. As critical errors fields of authorization number and insurance ID often lead to automatic denials, not only are they more susceptible to transcription error due to alphanumeric values but more indicative of delays in treatment.

These findings reveal a clear "pain point" in the routine scheduling process that leads to authorization and payment denials. With various touch points of manual data transfer in surgical scheduling, data degradation due to human error may compound at each step. Health care institutions should consider adopting digital solutions and investing in training programs to optimize clinical practice efficiency and reduce the possibility of inaccurate manual PHI transfer. Future case studies on denied payments will help further elucidate the economic impact on practices, as well as inform strategic decisions by those who directly handle health care management.