HCP Perception Study: OpenEvidence Use in the US for AI Clinical Decision Support
Primary Market Research Report (n=25 HCP Respondents)
(very or moderately)
times weekly
positively
workflow improvement
About the Study
Research Methodology
To assess Healthcare Professional (HCP) perceptions, adoption behavior, clinical utility, trust, barriers, and future expectations regarding the use of OpenEvidence in clinical practice across the United States. The study specifically evaluated trends related to AI clinical decision support, healthcare AI adoption among physicians, and the growing role of AI-powered clinical evidence retrieval in modern healthcare workflows.
Respondent Profile
- Physicians, specialists, PCPs, NPs, and PAs across the United States
Study Design
- Primary qualitative and quantitative market research
- Structured interviews and online survey
- Focused evaluation of physician perception of AI in healthcare and the adoption of evidence-based medical AI tools
- Total respondents: 25 HCPs
- Interview duration: 30–45 minutes per respondent
Detailed Findings
Question-by-Question Results
- Awareness was highest among specialists and younger physicians actively exploring AI clinical decision support technologies.
- PCPs reported peer-to-peer recommendations as a major awareness driver for AI-powered clinical evidence retrieval platforms.
- NPs and PAs demonstrated moderate awareness levels regarding evidence-based medical AI tools and digital clinical assistants.
- PCPs commonly use the platform during outpatient consultations to support clinical evidence retrieval and physician productivity.
- Specialists mainly use the tool for evidence updates, literature review, and AI-generated clinical summaries.
- Some senior clinicians remain cautious about regular AI use despite growing healthcare AI adoption among physicians.
- AI clinical decision support emerged as the leading use case among respondents.
- HCPs value time savings and improved healthcare workflow optimization compared with traditional literature searches.
- Specialists use the platform heavily as an AI literature review tool for physicians and for evolving guideline interpretation.
- Strong value perception was reported in high-volume clinical settings focused on efficiency and physician productivity.
- PCPs appreciated concise and actionable summaries generated through AI-powered clinical evidence retrieval.
- Specialists valued rapid access to current evidence and real-time medical insights using AI tools for evidence-based medicine.
- HCPs distinguish OpenEvidence from general-purpose medical AI chatbots due to its focus on evidence transparency and citations.
- Citation-supported answers improve credibility and reinforce physician trust in AI tools.
- Most clinicians still independently verify critical recommendations despite increasing reliance on citation-backed medical AI.
- Most HCPs use OpenEvidence as a supplemental AI clinical decision support tool.
- Trust increases significantly when citations and supporting references are included, highlighting the value of citation-backed medical AI.
- Clinicians remain cautious about relying solely on AI-generated outputs despite improving physician trust in AI tools.
Question 07
Barriers to Adoption
What barriers limit broader adoption of OpenEvidence?
Question 08
Workflow Efficiency Impact
Has OpenEvidence improved your workflow efficiency?
Key Insights
- HCPs reported faster clinical evidence retrieval and reduced search burden.
- PCPs noted improved preparation for patient discussions and better physician productivity.
- Specialists appreciated quicker access to treatment updates and enhanced healthcare workflow optimization.
| Metric | Positive Rating | n = 25 |
|---|---|---|
| Evidence transparency | 88% | 22 |
| Clinical decision support adoption | 80% | 20 |
| Very / moderately valuable | 84% | 21 |
| High / moderate trust in outputs | 88% | 22 |
| Sig. / moderate workflow improvement | 84% | 21 |
| AI accuracy concerns (barrier) | 76% | 19 |
Question 09
Future Expectations (Next 5 Years)
Synthesis
Executive Summary — Key Outcomes
Key Outcomes 1–2
- OpenEvidence awareness is increasing among digitally engaged HCPs in the US, reflecting broader healthcare AI implementation trends.
- HCPs primarily use the platform for rapid clinical evidence retrieval, literature summarization, and guideline review.
Key Outcomes 3–4
- Citation-backed outputs significantly improve physician trust in AI tools compared with general-purpose AI chatbots, reinforcing the importance of citation-backed medical AI.
- Most HCPs use OpenEvidence as a supplemental AI clinical decision support tool rather than a replacement for physician judgment.
Key Outcomes 5–6
- Younger physicians and specialists demonstrate higher adoption frequency of AI tools for evidence-based medicine and digital clinical assistants.
- Concerns remain regarding AI accuracy, workflow integration, legal accountability, and broader healthcare AI adoption among physicians.
Key Outcome 7
- The majority of respondents expect AI evidence platforms and clinical decision support AI tools to become standard clinical support technologies within 3–5 years.
FAQ
Frequently Asked Questions
How are physicians using AI clinical decision support tools?
Physicians are increasingly using AI clinical decision support tools for clinical evidence retrieval, literature summarization, guideline review, drug information, and patient communication support. Many healthcare professionals use evidence-based medical AI tools to improve physician productivity, accelerate decision-making, and reduce the time spent on traditional literature searches during clinical workflows.
Do doctors trust AI-generated medical recommendations?
Physician trust in AI tools continues to grow, particularly when platforms provide citation-backed medical AI outputs supported by transparent evidence sources. Most clinicians still independently verify recommendations before making clinical decisions, but trust levels are significantly higher for AI-powered clinical evidence retrieval platforms that emphasize evidence transparency, clinical relevance, and healthcare AI usability.
What barriers limit healthcare AI adoption?
Key barriers limiting healthcare AI adoption among physicians include AI accuracy concerns, hallucination risks, lack of EMR integration, institutional policy uncertainty, data privacy concerns, and clinician resistance to AI implementation. Healthcare organizations are increasingly focused on governance frameworks, healthcare AI implementation strategies, and safe adoption of clinical decision support AI tools.
How does OpenEvidence improve clinical workflow efficiency?
OpenEvidence improves clinical AI workflow efficiency by enabling faster clinical evidence retrieval, rapid literature summarization, and access to real-time medical evidence. Physicians reported reduced search burden, improved healthcare workflow optimization, quicker access to evolving treatment guidelines, and better physician productivity through AI-generated clinical summaries.
Why do physicians prefer citation-backed AI tools?
Physicians prefer citation-backed medical AI platforms because evidence transparency improves credibility, trust, and confidence in AI-assisted diagnosis and clinical recommendations. Citation-supported outputs allow healthcare professionals to independently verify sources and validate medical insights before incorporating AI clinical decision support into patient care workflows.
Can AI improve evidence retrieval for clinicians?
AI-powered clinical evidence retrieval tools can significantly improve how clinicians access medical literature, treatment updates, and guideline recommendations. Many healthcare professionals reported that AI tools for evidence-based medicine help streamline research workflows, improve healthcare workflow optimization, and support faster evidence-based clinical decision-making.
What concerns do doctors have about generative AI in healthcare?
Physicians remain cautious about generative AI in healthcare due to concerns surrounding hallucination risk, inaccurate recommendations, data privacy, legal accountability, and overreliance on AI-generated outputs. Many clinicians believe healthcare AI implementation should include strong governance, validation frameworks, evidence transparency, and physician oversight.
Will AI evidence platforms become standard clinical tools?
Most healthcare professionals expect AI evidence platforms and clinical decision support AI tools to become standard components of healthcare workflows within the next 3–5 years. As healthcare AI adoption among physicians continues to increase, organizations are expected to invest more heavily in digital clinical assistants, evidence-based medical AI tools, clinical evidence retrieval systems, and healthcare workflow optimization technologies.
