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Interest in clinical AI in the health sector is surging. Many health care organizations dealing with chronic labor shortages, delayed diagnoses, diagnostic errors and care personalization challenges have become comfortable enough with the tech to incorporate it into their operations.
While this segment of health care AI has matured enough to yield positive outcomes for patients and higher productivity levels for large hospitals, clinics, nursing homes and ambulatory centers, its rapid adoption can lead to expensive, reputationally damaging regulatory compliance issues. Accreditation programs can alleviate the concerns of executives, department heads and board directors contemplating how to mitigate liability when deploying clinical AI from a third-party vendor.
How URAC Accreditation Addresses the Compliance Risks of Clinical AI
URAC has developed Health Care AI Accreditation programs for technology companies and clinical AI end users to minimize exposure to the following compliance risks.
Informed Consent
A 2023 study published in PLOS Digital Health found that 52.9% of patients resist AI diagnostic tools and prefer human doctors. Although the sentiment toward clinical AI improved when it proved accurate, physicians have an ethical obligation — and sometimes, a legal responsibility — to disclose the integration of the tech into clinical decision-making.
Discrimination
Training clinical AI models on historical data riddled with societal prejudices can perpetuate inequities. Algorithmic bias may violate the civil rights of patients from certain racial and ethnic groups.
Opacity
Deep learning models use complex steps to reach conclusions, rendering it challenging to verify the quality of insights gleaned from clinical AI-powered analyses. Accepting AI decisions without fully understanding their reasoning may leave health care organizations liable for poor patient outcomes.
Oversight
Clinical AI systems are subject to ongoing checks to ensure they function as intended. AI hallucination can go unchecked when these measures are neglected.
Patient Safety
Haphazardly implementing clinical AI can lead to misdiagnosis and incorrect treatment, endangering patients. Such medical malpractice can trigger high-profile lawsuits and blemish the reputation of clinicians who fail to exercise critical oversight.
Data Privacy
AI is highly vulnerable to cyberattacks, and rapid deployment increases that risk. Sophisticated threat actors can poison the training data, corrupting the system slowly until it becomes prone to backdoors and data leaks.
How URAC Accreditation Mitigates Liability When Deploying Clinical AI From a Third-Party Vendor
This accreditation is an impartial stamp of approval that demonstrates the conformance of clinical AI technologies with AI governance standards in health care settings that emphasize safety, fairness, transparency and accountability.
Moreover, being accredited indicates that your healthtech company passes the URAC process. This multistage procedure involves a rigorous review of documents — such as quality meeting minutes and reports — that demonstrate compliance with the independent body’s standards. Once everything looks good on paper, URAC validates whether your organization’s practices reflect policies, procedures and workflows recorded in the documents through interviews, facility tours and system assessments.
URAC does not rigidly dictate how an applicant should meet its standards. Its accreditation process is collaborative, and there is often considerable back-and-forth for clarification. The whole process takes months to complete, giving the accreditor adequate time to make a decision.
Decisions are not either approval or denial. Applicants found with deficiencies may earn conditional accreditation. This accredited status means your organization satisfies most standards and has a limited period to address inadequacies to gain full accreditation. Corrective action is a non-accredited status, requiring you to submit a plan to correct deficiencies and comply with URAC’s standards to earn accreditation.
URAC Health Care AI Accreditation does not certify regulatory compliance or AI system effectiveness or safety. Nevertheless, it can help accredited clinical AI developers stand out. This third-party recognition signifies regulatory readiness, telling health care organizations that your innovations are trustworthy. Accredited entities undergo ongoing monitoring post-accreditation to maintain their status.
Why Health Care Organizations Should Pursue URAC Accreditation When Adopting Clinical AI
URAC provides a separate accreditation path for clinical AI end users. Sourcing intelligent systems from accredited developers is not enough to ensure compliance. Health care professionals themselves must learn mitigation strategies to reduce liability when deploying clinical AI from a third-party vendor.
The accreditation body’s proven method can help health care organizations meet nationally recognized standards for patient safety, ethical governance and transparency. Accreditation stimulates quality improvement, increases adoption of evidence-based practices and strengthens key third-party partnerships. URAC’s program boosts operational confidence and proves to stakeholders that trust and accountability are at the core of a facility’s AI-enabled care.
Enhancing Readiness for Clinical AI Development and Adoption with URAC Accreditation
Liability mitigation is vital when deploying clinical AI solutions from third-party vendors. Consistently meeting regulatory frameworks is a dynamic, arduous process. Pursuing URAC’s Health Care AI Accreditation will reduce compliance risks that developers and end users face.
