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Flow state

Adaptive charting captures spoken bedside assessments in real time, converts them into structured clinical flowsheet entries, and verifies that flowsheet data is complete, correct, and current—while keeping the clinician

Core trick

Maps spoken bedside assessments to structured flowsheet fields, then scores each entry for completeness, correctness, and currency.

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Description

Adaptive charting captures spoken bedside assessments in real time, converts them into structured clinical flowsheet entries, and verifies that flowsheet data is complete, correct, and current—while keeping the clinician in control.
Dimex08/Voice-to-flowsheet-quality-capture-agent★ 00 forksPythonCreated Jul 18, 2026 · last push Jul 18, 2026

README excerpt

# Voice-to-Flowsheet Quality Capture Agent

This project focuses on improving nursing workflow through a voice-to-flowsheet system that captures ambient clinical information, maps it to nursing flowsheet assessments, and evaluates the quality of that documentation in real time.

## Project Overview

We are developing a potentially scalable system that captures ambient clinical information, maps it to nursing flowsheet assessments, and evaluates the quality of that documentation in real time. The goal is to improve how nurses document at the point of care while also monitoring the completeness, correctness, and currency of the mapped observations.

## Problem Statement

Evidence suggests that electronic health record (EHR) user interface features influence the quality of clinical data capture. Nurses typically document in a flowsheet, which is a structured template used for entering bedside clinical assessments. Because this workflow often requires manual clicks and navigation, it may not support real-time documentation. Delayed documentation can reduce recall and negatively affect the completeness, correctness, and currency of nursing assessments.

Research also shows that nurses commonly split documentation between partial point-of-care entry and later completion, a workaround that compromises all three data quality dimensions simultaneously.

## Evidence Base

This project is grounded in findings from our published integrative review:

> Madandola OO, Bjarnadottir RI, Yao Y

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