AI Insight
A Japanese healthcare facility replaced its traditional paper-based post-checkup health guidance system with an integrated digital workflow using Microsoft Access, robotic process automation, and web-based responses. The digital system reduced staff handling time per case from 10 minutes to 30 seconds, eliminated transcription errors, and increased health guidance completion rates from 28.3% to 39.2%. The proportion of cases taking 200 days or longer decreased from 30.5% to 20.9%.
Why it matters
This demonstrates that low-tech digital integration can substantially improve preventive healthcare delivery without requiring artificial intelligence or expensive infrastructure. The approach may be particularly relevant for healthcare systems seeking cost-effective ways to reduce administrative burden and improve patient engagement in preventive care programs.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background: Post-checkup health guidance in Japan has traditionally relied on paper-based communication and manual administrative processes. These workflows are time-consuming, prone to transcription errors, and can delay timely engagement with health guidance recipients. Objective: To assess whether replacing a paper-based workflow with an integrated digital system using Microsoft Access, robotic process automation (RPA), and web-based responses could improve administrative efficiency, operational reliability, and engagement among health guidance recipients. Methods: This single-site quality improvement initiative redesigned the existing letter-based workflow. Access served as a central interface for managing recipients and generating guidance letters. RPA (EzRobot) automated repetitive clerical and billing-related tasks. A web form accessed via a QR code enabled recipients to respond digitally. Outcomes included manual administrative handling time per case, occurrence of transcription-related errors, health guidance completion rate, and guidance duration distribution. Results: Following implementation, staff active handling time per case decreased from approximately 10 minutes to less than 1 minute (approximately 30 seconds), while automated RPA execution typically required about 4-5 minutes per case without staff input. No transcription-related errors were detected during the post-implementation observation period. Health guidance completion rates improved from 28.3% to 39.2% (chi-square test, P<0.01; R4 (FY2022) n=184, R5 (FY2023) n=536). Guidance duration distributions, calculated using the corrected method, shifted towards shorter durations: cases with >=200 days decreased from 30.5% to 20.9% and cases with >=240 days decreased from 13.6% to 8.9% (R4 n=59, R5 n=158). Conclusion: An integrated Access-RPA-Web workflow was associated with improvements in administrative efficiency and operational reliability in post-checkup health guidance while retaining human verification and exception handling. This pragmatic, non-AI-dependent approach may offer a useful model for process-level improvement in preventive care settings.