As “decision intelligence” is applied to everything from reporting dashboards to general-purpose AI, Lyric today released Defining Healthcare Decision Intelligence, a paper that sets the standard for what the term should mean in healthcare, proposing three conditions any system must meet to qualify: specialized healthcare expertise built into the software; recommendations that can be traced to the policy and data behind them; and human accountability for the final determination.

Written by Chief Technology Officer Yevgeny German, Chief Product Officer Brian Berkowitz, and SVP, Engineering Alex Ulyanov, the paper argues that healthcare decision intelligence requires more than sophisticated technology and is defined by healthcare depth: clinical, payment, and policy expertise encoded in the software and maintained as evidence and regulation change. That depth, which the authors argue general-purpose technology cannot build quickly, is what allows AI to augment human decision-making, taking on the work around a decision while a person remains accountable for the determination.

The stakes are concrete. In fiscal 2025, CMS estimated that $28.83 billion in Medicare fee-for-service payments were paid improperly, a figure CMS notes is not a measure of fraud. Unlike a credit decision, the paper notes, a wrong healthcare decision reaches an individual directly: a patient waiting on care, a clinician acting on incomplete information, or a provider relationship built over years.

“Healthcare holds clinicians, coding, and payment policy to rigorous standards. The systems now shaping those decisions deserve the same,” said German. “We wrote this paper to raise that bar. A demanding, shared definition protects health plans, providers, and patients, and it gives everyone building in this space something common and real to be measured against.”

A standard built around three conditions

The paper offers health plans a pragmatic way to evaluate any system that claims the healthcare decision intelligence label:

  • The expertise is in the software. Does the system encode clinical, payment, and policy expertise, and keep it current as evidence and requirements change?

  • Traceable at decision speed. Does the system deliver each recommendation while a decision can still be changed, and trace it to the policy, data, and logic in effect when it was made?

  • Human judgment. Does the organization operating the system configure its rules and thresholds, with a person responsible for decisions about care, coverage, and payment?

Applied honestly, the authors write, the standard excludes much of what is currently described as decision intelligence. Dashboards offered as the decision mechanism fall short on timing. Automation that cannot identify which policy version applied falls short on traceability. General-purpose AI without encoded healthcare expertise falls short on expertise.

Automate the work. Keep people accountable for decisions.

The authors draw a distinction between the work surrounding a healthcare decision and the determination itself.

Finding records, assembling evidence, checking which policy applies, routing cases, and identifying discrepancies are often time-consuming tasks well suited to automation. Deciding what those findings mean for an individual’s care, coverage, or payment is different.

“Much of an expert’s day is spent gathering and assembling information, not deciding,” said Ulyanov. “That’s where AI creates the most value, bringing thousands of unstructured elements into well-defined structured data that can be presented for expert review. The determination itself belongs with someone who can be accountable for it, keeping the human in the loop in all of this.”

Under the proposed standard, AI can help experts build and maintain decision logic, track policy changes, summarize complex records, and prioritize cases that need attention. The logic governing an outcome remains deterministic, tested, and versioned before deployment. Human oversight is designed into the workflow, not added as a final approval step after an automated process has already framed the decision.

“Oversight placed at the end of an automated process is oversight in name only,” said Berkowitz. “By the time a case reaches a person, the framing is already set. Designing for human judgment means cases that need judgment arrive with the evidence and rationale assembled.”

Built on decades of healthcare expertise

The standard draws on more than 35 years of Lyric’s clinical, payment, and policy expertise. More than 100 health plan clients, including nine of the top 10 U.S. health plans, use Lyric’s healthcare decision intelligence technology to validate more than 10 billion claims annually. Lyric42, Lyric’s healthcare decision intelligence platform, is designed around the principles the paper describes, augmenting human decision-making rather than replacing it: health plans configure their own rules and thresholds, see the decision lineage behind each recommendation, and make the final determination.

“In healthcare, AI is only as good as the expertise behind it,” said Halsey Wise, Lyric CEO. “Our clients answer for their decisions, so each one needs to come with its decision lineage: the policy, the evidence, and the reasoning behind it. That depth is what more than 100 health plan clients rely on, and it’s what our technology team builds into Lyric42 every day.”

The paper identifies payment accuracy as healthcare’s first proving ground for decision intelligence, given its high transaction volumes, standardized data, codified reimbursement policies, and measurable outcomes. Over time, the authors expect the same principles to extend across the healthcare ecosystem, to decisions made before, during, and after care, as the technology and underlying data mature.

The full paper, Defining Healthcare Decision Intelligence, includes the history of the discipline, case studies from credit, supply chain, and insurance, the regulatory landscape, and the complete standard. The State of Healthcare Decision Intelligence in Payment Accuracy applies the standard function by function across payment accuracy. Read the full announcement in the Lyric newsroom.

About Lyric

Lyric is the trusted leader in healthcare decision intelligence. Built on more than 35 years of proven results, Lyric combines responsible AI with clinical, payment, regulatory and policy expertise to deliver transparent, auditable insights in milliseconds across real-time claims workflows. Lyric augments human decision-making so health plans can make faster, more accurate payments while maintaining full control. Today, Lyric supports 200 million lives, with nine of the top 10 U.S. health plans relying on its platform to reduce waste, improve efficiency, and support accurate payments. To learn more, visit lyric.ai.

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