SHM Converge Scientific Abstract ePoster Gallery-Assessing the Quality of Discharge Instructions: A Review of Current Practices and Areas for Improvement

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This review evaluated the quality of hospital discharge instructions in after-visit summaries (AVSs) at UCSF Parnassus. The goal was to identify gaps in communication that could affect patient safety and recovery.<br /><br />The study analyzed 81 adult inpatient AVSs from October to November 2024 and included both attending-only and resident teams. Two independent reviewers assessed multiple quality domains, including readability, use of the institutional template, disease-specific instructions, medication accuracy, follow-up details, and translation quality. In addition, semi-structured interviews were conducted with 9 healthcare workers to better understand workflow issues and opportunities for improvement.<br /><br />Results showed that while standardized AVS templates were used in most cases (94%), several important problems remained. About 27% of AVSs contained medical jargon or were written above an 8th-grade reading level. Only 57% used patient-friendly language for the discharge diagnosis, and only 73% did so for the hospital course. Disease-specific care instructions were present in just 21% of AVSs, and only 9% included home health contact information. Medication lists were inconsistent with auto-populated lists in 37% of cases. Follow-up instructions were also incomplete: although 51% mentioned referrals, only 13% listed appointment date and time, and only 15% gave scheduling instructions. Among translated AVSs, 31% had incomplete or incorrect translations.<br /><br />Interviews highlighted recurring concerns: nurses emphasized medication changes and follow-up clarity, while physicians noted confusion around referral information.<br /><br />The authors concluded that AVSs often include required sections but still fall short in readability, language accuracy, medication reconciliation, and follow-up guidance. In response, they updated the standardized template to better reflect best practices and plan to study its implementation, including possible use of AI to draft clearer discharge instructions.

Asset Subtitle

Lina Vadlamani

Meta Tag

Author List Akhil Upneja, Alanna Williams, Alexander Conway, Ashish Gandhi, Baffour Kyerematen, Brandon Yan, Carissa Longo, Emily Siegel, Jason Silva-Rudberg, Justin C. Zhang, Lina Vadlamani, Michael Love, Michelle Tong, Monisha Bhatia, Nicholas Campalans, Nikka

Category Research

Concept After-Visit Summary

Concept Hospital Discharge Instructions

Concept Readability

Concept Medication Accuracy

Concept Accessible Language

Distinguished Non-Finalist

Presenter Organization Ucsf

Presenting Author Lina Vadlamani

Track Communication

Keywords

hospital discharge instructions

after-visit summaries

patient safety

readability

medication reconciliation

follow-up instructions

translation quality

discharge templates

UCSF Parnassus

AI drafting

After-Visit Summary

Hospital Discharge Instructions

Readability

Medication Accuracy

Accessible Language