What is a decision support system?

Software that supplies the information a decision needs, at the moment the decision is being made. In healthcare that splits two ways: clinical decision support, which helps a clinician choose; and financial decision support, which tells a provider what a payer is likely to approve before the claim is filed.

Why does fraud only appear at volume?

Because an individual claim rarely looks wrong. What is visible across a large claim set is the pattern: correlations between volumes that expose organised activity, and irregularities relative to a peer group. That is why analytics is the tool and a case-by-case review is not.

What does real-time insight actually change?

It lets an investigator improve their line of enquiry as it develops, rather than after the file is closed, and it surfaces suspicious claims while they can still be held. Timing is most of the value here — the same finding a week later is a recovery exercise instead of a prevention.

How do findings get back into the business?

By updating rules and operating models across the other stages of the policy life cycle. Analytics that only produces reports leaves the same claims to be caught again next month; analytics that changes the rules stops producing them.

Does this replace clinical judgement?

No. Decision support supplies the input; the clinician makes and owns the decision. The distinction is practical rather than legal — systems presented as deciding get overridden and then ignored, which wastes the capability entirely.

Data Analytics and Decision Support System

Data-driven insights that can improve patient care

As healthcare data processing improves, the potential to acquire practical knowledge from significant datasets increases. To learn how AI might transform healthcare, let’s explore the capacities of Nano Health Suite Data analytics and AI tools built for health.

Uncovering the trends with analytics

Real-time insights help resources refine their line of enquiry as needed and flag suspicious claims to investigators. When we have numerous insurance claims, analytics helps identify fraud patterns that are not easily detectable at the level of individual claims. Correlating across data volumes helps identify organized fraud and update the rules and operating models across other policy life cycle stages.

The power of NANO Health analytics is amplified with artificial intelligence which can sift through massive data sets, distinguish patterns and irregularities based on algorithms, and flag cases for investigation.

Fighting Insurance Claim Fraud

NHS Claims Fraud Analytics solution applies AI to offer a significant advantage on investment in fraud analytics as insurance companies can quickly be informed of fraud leakage.

Insurance companies are acutely aware of the impact of fraud on their profitability. By leveraging predictive modeling, geographic data mapping, social media analytics, and text mining across particular stages of a policy life cycle, businesses can effectively discover fraud and significantly reduce claims costs.

NHS Advanced Analytics helps capture the identity of the client, linkages to fraud, and other abnormal behavioral patterns. Our analytics helps claim handlers validate a more significant number of claims with greater accuracy in a shorter period.

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How this works with the rest of the suite

NANO CDSS
This is the decision-support capability in product form — pricing and approval likelihood surfaced while the decision is still open.
NANO BRAIN
The pattern detection described here is BRAIN, trained on hundreds of millions of processed claims and approvals.
NANO DDI
The analytics layer is where these questions are actually asked of the data rather than described.
NANO Reports
A finding has to reach the person who can act on it, which is a reporting problem rather than an analytical one.

Frequently asked questions

What is a decision support system?: Software that supplies the information a decision needs, at the moment the decision is being made. In healthcare that splits two ways: clinical decision support, which helps a clinician choose; and financial decision support, which tells a provider what a payer is likely to approve before the claim is filed.
Why does fraud only appear at volume?: Because an individual claim rarely looks wrong. What is visible across a large claim set is the pattern: correlations between volumes that expose organised activity, and irregularities relative to a peer group. That is why analytics is the tool and a case-by-case review is not.
What does real-time insight actually change?: It lets an investigator improve their line of enquiry as it develops, rather than after the file is closed, and it surfaces suspicious claims while they can still be held. Timing is most of the value here — the same finding a week later is a recovery exercise instead of a prevention.
How do findings get back into the business?: By updating rules and operating models across the other stages of the policy life cycle. Analytics that only produces reports leaves the same claims to be caught again next month; analytics that changes the rules stops producing them.
Does this replace clinical judgement?: No. Decision support supplies the input; the clinician makes and owns the decision. The distinction is practical rather than legal — systems presented as deciding get overridden and then ignored, which wastes the capability entirely.