Symptom Cluster Analysis in DizzyGuide, an Automated Decision-Support Triage System
Faculty Advisor Name
Erin Piker
Department
Department of Communication Sciences and Disorders
Description
DizzyGuide is a dizziness triage program that uses artificial intelligence to automate and optimize the scheduling of patients presenting with dizziness symptoms. Based on patient input from an online questionnaire, DizzyGuide delivers the symptom clusters, which are recommendations for potential dizziness and instability-related diagnoses. The clinician can then use these results to help decide what tests and/or specialists are needed for their patient.
Dizziness is the third most common complaint in primary care settings and accounts for 2-3% of emergency department demands in one year1,2. In addition to these statistics, there is growing evidence that it is not uncommon for patients with dizziness to receive misdiagnoses from emergency department care, emphasizing the need for access to specialized care and increased diagnostic precision3. Dizziness is a common and non-specific symptom often provoked by an underlying disorder or multimorbidity of disorders4,5,6. Despite numerous batteries of tests and medical imagining available, collecting a thorough and accurate case history is critical for differentiating between multiple diagnosis, or when suspecting a multiplicity of overlapping diagnoses3,7. DizzyGuide acts as a decision-support triage program using artificial intelligence to automate and optimize scheduling of patients presenting with dizziness symptoms based on patient input from an online questionnaire. The algorithm designates patients into symptom clusters, designed to reduce time and effort from the healthcare system and improve patient access to care.
Symptom Cluster Analysis in DizzyGuide, an Automated Decision-Support Triage System
DizzyGuide is a dizziness triage program that uses artificial intelligence to automate and optimize the scheduling of patients presenting with dizziness symptoms. Based on patient input from an online questionnaire, DizzyGuide delivers the symptom clusters, which are recommendations for potential dizziness and instability-related diagnoses. The clinician can then use these results to help decide what tests and/or specialists are needed for their patient.
Dizziness is the third most common complaint in primary care settings and accounts for 2-3% of emergency department demands in one year1,2. In addition to these statistics, there is growing evidence that it is not uncommon for patients with dizziness to receive misdiagnoses from emergency department care, emphasizing the need for access to specialized care and increased diagnostic precision3. Dizziness is a common and non-specific symptom often provoked by an underlying disorder or multimorbidity of disorders4,5,6. Despite numerous batteries of tests and medical imagining available, collecting a thorough and accurate case history is critical for differentiating between multiple diagnosis, or when suspecting a multiplicity of overlapping diagnoses3,7. DizzyGuide acts as a decision-support triage program using artificial intelligence to automate and optimize scheduling of patients presenting with dizziness symptoms based on patient input from an online questionnaire. The algorithm designates patients into symptom clusters, designed to reduce time and effort from the healthcare system and improve patient access to care.
