Development of the Self-Voice Program for the Digital Transformation of Voice-Related Patient-Reported Outcome Measures

Background and Objectives To develop Self-Voice, a browser-based platform for administration, scoring, visualization, longitudinal tracking, and export of voice-related patient-reported outcome measures (PROMs).Materials and Method Self-Voice was developed as a static client-side web application in which patient and assessment data are stored in the user’s browser. The platform implements the Korean Voice Handicap Index (K-VHI)-30, K-VHI-10, Korean Voice Fatigue Index (K-VFI), and Korean Voice-Related Quality of Life (K-VRQOL), preserving each instrument’s original scoring structure and direction. It calculates raw and subscale scores, 0–100 range-transformed scores, direction-aligned scores for visual display, and the instrument-standardized K-VRQOL score. Instrument-specific literature-based change references are applied only to the original score metric of the same instrument and scale. The program also supports assessment-context recording, patient-specific export, bulk research CSV export using system-generated research IDs, encrypted JSON backup, PIN-based locking, automatic locking, and audit logging. Internal verification used fictional data and predefined functional cases.Results Automated scores matched expected calculations, and 11 boundary cases for instrument-specific change references were processed as intended. No automatic change threshold was applied to K-VFI. Assessment-context information remained linked to the corresponding assessment. Bulk research export generated one row per PROM administration, retained stable research IDs across repeated assessments, and excluded direct identifiers and raw free-text information. Browser-local storage, access-control functions, encrypted backup pathways, audit logging, and version-based data migration were implemented and reviewed.Conclusion Self-Voice provides an integrated, voice-disorder-specific workflow for digital PROM administration and longitudinal data management. Range-transformed and direction-aligned scores are visual aids rather than normative or severity measures. Prospective studies with patients and clinicians are required to evaluate usability, feasibility, and clinical utility.

Authors

Institutions

Publication Details

Journal
Journal of The Korean Society of Laryngology Phoniatrics and Logopedics
Published
2026-08-26
DOI
https://doi.org/10.22469/jkslp.2026.37.2.78
Primary Topic
Voice and Speech Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of the Self-Voice Program for the Digital Transformation of Voice-Related Patient-Reported Outcome Measures

Hee Young Son, Chang Yoon Lee, Soo-Youn An
Journal of The Korean Society of Laryngology Phoniatrics and Logopedics
Voice and Speech Disorders
article

Development of the Self-Voice Program for the Digital Transformation of Voice-Related Patient-Reported Outcome Measures

Hee Young Son, Chang Yoon Lee, Soo-Youn An
article en

Abstract

Background and Objectives To develop Self-Voice, a browser-based platform for administration, scoring, visualization, longitudinal tracking, and export of voice-related patient-reported outcome measures (PROMs).Materials and Method Self-Voice was developed as a static client-side web application in which patient and assessment data are stored in the user’s browser. The platform implements the Korean Voice Handicap Index (K-VHI)-30, K-VHI-10, Korean Voice Fatigue Index (K-VFI), and Korean Voice-Related Quality of Life (K-VRQOL), preserving each instrument’s original scoring structure and direction. It calculates raw and subscale scores, 0–100 range-transformed scores, direction-aligned scores for visual display, and the instrument-standardized K-VRQOL score. Instrument-specific literature-based change references are applied only to the original score metric of the same instrument and scale. The program also supports assessment-context recording, patient-specific export, bulk research CSV export using system-generated research IDs, encrypted JSON backup, PIN-based locking, automatic locking, and audit logging. Internal verification used fictional data and predefined functional cases.Results Automated scores matched expected calculations, and 11 boundary cases for instrument-specific change references were processed as intended. No automatic change threshold was applied to K-VFI. Assessment-context information remained linked to the corresponding assessment. Bulk research export generated one row per PROM administration, retained stable research IDs across repeated assessments, and excluded direct identifiers and raw free-text information. Browser-local storage, access-control functions, encrypted backup pathways, audit logging, and version-based data migration were implemented and reviewed.Conclusion Self-Voice provides an integrated, voice-disorder-specific workflow for digital PROM administration and longitudinal data management. Range-transformed and direction-aligned scores are visual aids rather than normative or severity measures. Prospective studies with patients and clinicians are required to evaluate usability, feasibility, and clinical utility.

Journal of The Korean Society of Laryngology Phoniatrics and LogopedicsVol. 37(2)
Dongnam Institute of Radiological & Medical Sciences (KR)
Reduced inequalities
Openalex Percentile: Top 11%
Voice and Speech Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.