Healthcare systems are increasing the use of preauthorization requirements to verify the medical necessity and cost effectiveness of a service prior to its delivery. While these procedures attempt to manage cost, they often result in logjams, multifaceted administrative tasks, and added complexity which slows down care delivery. Patients don’t need care due to waiting times set by clinical reasoning; instead, they must wait due to inefficient, nontransparent, and fragmented systems. These delays are bound to hinder clinical outcomes and lower the satisfaction rates of patients and providers. Despite the significance of this problem, healthcare systems lack adequate understanding of why delays and denials occur, especially due to existing systems that keep no track of process-level information. This initiative applies data engineering to automate the analysis and optimization of preauthorization workflows. We created a comprehensive ETL pipeline for data integration and cleansing from diverse sources, including EHRs, payer communications, and insurance claims. We utilized time series analysis, denial classification techniques, process mapping, and other methods aimed at identifying systemic inefficiencies and recurring failure hotspots. Gaps in documentation and communication between the provider and payer were identified as the primary culprits for delays in data entry. Denial analysis based on cohorts also highlighted the fact that more than 40 percent of denials could have been avoided through better validation and follow-up procedures. System simulations showcased that many of them could be eliminated through flagging and automation.