Clinical Research
Clinical Research
Our clinical research teams investigate the safety, clinical effectiveness, and patient outcomes of AI-delivered care—so that artificial intelligence expands access to high-quality healthcare without ever compromising patient safety.
Case Study: Improving Pre-Charting for Medicare Wellness Visits at OhioHealth
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Case Study: Improving Pre-Charting for Medicare Wellness Visits at OhioHealth
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Therapeutic Inertia in Pharmacologic Dose Optimization: Prevalence, Clinical Consequences, and Economic Burden Across Sixteen Chronic Conditions
Failure to titrate medications to guideline-recommended target doses is widespread across sixteen chronic conditions and carries substantial clinical and economic costs.
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Hidden Functional Decline: How Conversational Voice AI Detects Early Mobility and ADL Impairment in Older Adults with High Self-Rated Health
Nearly half of the older adults who described their own health as good or better showed two or more mobility or daily-living impairments when assessed by an AI care agent.
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Transforming Healthcare AI Education Through Micro-Learning: A Novel Partnership Model for Nursing Workforce Development
An academic-industry partnership delivering 11 short AI courses to 478 nursing students and faculty produced measurable knowledge gains alongside satisfaction scores averaging 4.58 out of 5.
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Multi-Call Memory in an AI Care Agent for Chronic Care Management Among Older Adults: Retrospective Observational Study
Each memory the AI care agent carried over from a previous call was associated with roughly 2.5 more minutes of conversation with older adults in chronic care management.
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Using a Multilingual AI Care Agent to Reduce Disparities in Colorectal Cancer Screening for Higher Fecal Immunochemical Test Adoption Among Spanish-Speaking Patients: Retrospective Analysis
Spanish-speaking patients called by a multilingual AI care agent opted in to colorectal cancer screening at 2.6 times the rate of English-speaking patients.
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Real-World Evaluation of Large Language Models in Healthcare (RWE-LLM): A New Realm of AI Safety & Validation
When 6,234 licensed clinicians reviewed more than 307,000 real patient calls, the rate of clinically correct guidance rose from about 80% before Polaris to 99.38% under Polaris 3.0.
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