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
OhioHealth partnered with Hippocratic AI to pilot a generative AI healthcare agent for pre-charting Medicare Wellness Visits, testing whether AI could match human PACT Outreach Coordinators while reducing charting burden and improving the patient experience. This study explores the results when OhioHealth deployed AI agents to administer a comprehensive pre-visit questionnaire covering lifestyle, SDOH, fall risk, general health, mental health, advanced directives, and medical equipment—improving documentation completeness ahead of primary care visits.
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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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