Diagnosing Medical Score Calculator Apps
Authors
"Mobile medical score calculator apps are widely used among practitioners to help make decisions regarding patient treatment and diagnosis. Errors in score definition, input, or calculations can result in severe and potentially life-threatening situations. Despite these high stakes, there has been no systematic or rigorous effort to examine and verify score calculator apps. We address these issues via a novel, interval-based score checking approach. Based on our observation that medical reference tables themselves may contain errors (which can propagate to apps) we first introduce automated correctness checking of reference tables. Specifically, we reduce score correctness checking to partition checking (coverage and non-overlap) over score parameters' ranges. We checked 12 scoring systems used in emergency, intensive, and acute care. Surprisingly, though some of these scores have been used for decades, we found errors in 5 score specifications: 8 coverage violations and 3 non-overlap violations. Second, we design and implement an automatic, dynamic analysis-based approach for verifying score correctness in a given Android app; the approach combines efficient, automatic GUI extraction and app exploration with partition/consistency checking to expose app errors. We applied the approach to 90 Android apps that implement medical score calculators. We found 23 coverage violations in 11 apps; 32 non-overlap violations in 12 apps, and 16 incorrect score calculations in 16 apps. We reported all findings to developers, which so far has led to fixes in 6 apps." https://doi.org/10.1145/3610912
Research Questions / Practical Problems
Question signals indexed for this paper.
- 80%
Ambiguity-aware AI Assistants for Medical Data Analysis
CHI '20· Explainable AI (XAI) +1
- 80%
CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis
CHI '20· Explainable AI (XAI) +2
- 80%
Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
CHI '24· Generative AI (Text, Image, Music, Video) +2
- 75%
"It depends": Configuring AI to Improve Clinical Usefulness Across Contexts
DIS '24· Explainable AI (XAI) +1
- 67%
Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System
CHI '23· Explainable AI (XAI) +2
- 67%
VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models
IUI '24· Explainable AI (XAI) +2
- 60%
ECGLens: Interactive Visual Exploration of Large Scale ECG Data for Arrhythmia Detection
CHI '18· Interactive Data Visualization +1
- 60%
“If I Had All the Time in the World”: Ophthalmologists' Perceptions of Anchoring Bias Mitigation in Clinical AI Support
CHI '23· Explainable AI (XAI) +1
- 60%
Harnessing Biomedical Literature to Calibrate Clinicians' Trust in AI Decision Support Systems
CHI '23· Explainable AI (XAI) +1
- 60%
Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows
CHI '23· Explainable AI (XAI) +1
Based on Jaccard similarity of research subtopics & professions (≥60%)