1232 Automating HSAT Patient Data Capture Within EHR (Electronic Health Records System) to Improve Workflow Efficiency and Reduce Waste

Abstract Introduction Home Sleep Apnea Testing (HSAT) workflows often rely on paper-based patient questionnaires(PQs) and manual data entry, creating inefficiencies and risks for errors. Although health systems are optimizing HSAT logistics, these interventions are largely structural [delivery, prioritization and insurance processing] and rarely address data capture or report granular metrics. This clinical quality improvement project aims to assess the significant efficiency gain and the reduced waste by automating HSAT patient data capture using an EHR-integrated workflow. Methods The pre-intervention process required patients to complete a 2-step process. Offline paper forms were manually scanned, uploaded, and entered or tagged in the EHR. Online financial attestation forms were filled via a 3rd party application. At the Cleveland Clinic's Sleep Disorder Center, we implemented an automated workflow using Power-Automate and the EHR patient portal, enabling coordinators to send standardized electronic templates for patients to enter data electronically. Manual scanning and re-entry were eliminated. Process measurable outcomes included coordinator time per patient, weekly savings in terms of time, cost and paper consumption. Results These interventions reduced coordinator time by 4 minutes per patient and at a volume of 290/week HSATs, saved 19.3 labor hours/week. The associated labor cost savings were $427/week and $22,217.87 annually. Integrating the financial attestation within MyChart amounted to $51,000 in additional costs saved. The new workflow eliminated the use of 30,160 sheets of paper annually (approximately $210 in material costs), with an estimated environmental benefit equivalent to preserving 1.5 trees per year. Standardized electronic templates improved data accuracy by reducing opportunities for transcription and scanning errors. Given that this intervention was integrated into EHR, there was virtually no adoption friction on the patient end. Conclusion The literature is rich with HSAT implementation-oriented projects that focus on workflows and improving accessibility to care, but rarely focus on the administrative burden. To our knowledge, this is the first explicit attempt on quantifying and optimizing the same. Automating HSAT patient data entry through a patient portal-based workflow substantially improved the process efficiency, financial performance, paper waste and drove data standardization. This approach is generalizable to other diagnostic workflows that currently depend on paper forms and manual data entry. Support (if any)