Using Data Analytics to Fight Healthcare Fraud Allegations in Court

Using Data Analytics to Fight Healthcare Fraud Allegations in Court

Healthcare fraud is a big problem. The government says it costs taxpayers $68 billion a year. But sometimes doctors and hospitals get accused of fraud when they didn’t do anything wrong. This article talks about how data analytics can help prove you’re innocent if someone says you committed healthcare fraud.

What is healthcare fraud?

Healthcare fraud is when doctors, hospitals, or other healthcare providers intentionally bill for services that weren’t provided. Or they bill for more expensive services than were actually given. For example, a doctor might bill Medicare for an hour-long office visit when the patient was only there for 15 minutes. That’s fraud.

But healthcare billing rules are really complicated. Doctors and hospitals often make innocent mistakes. The government knows this, so they don’t prosecute every little error. But if a whistleblower reports you, the government might start an investigation. You could end up facing criminal charges or a civil lawsuit.

How can data analytics help prove your innocence?

Advanced data analytics can dig deep into your billing records. The algorithms can benchmark your billing patterns against other practices in your specialty and region. This makes it easy to spot true outliers – the real cases of fraud.

Analytics can also prove you billed appropriately. For instance, it can show that patients who got a certain code had documented symptoms supporting that diagnosis. Or it can calculate the average time you spent with patients getting a certain procedure. This evidence can refute allegations you billed for services that weren’t medically necessary.

Statistical analysis

Statistical analysis looks at all your billing data to spot anomalies. For instance, it can flag if you billed for a certain code much more often than your peers. This might indicate upcoding. Upcoding is when you bill for a more expensive service than the one you actually provided.

But there could be good reasons for a statistical outlier. Maybe you specialize in complex cases that require those codes. Statistical analysis by itself can’t prove fraud. But it helps identify areas worth investigating.

Linking datasets

Linking datasets compares your billing codes to other data like medical records. It can validate that your billing matched the treatment. For instance, if a patient’s chart doesn’t mention certain symptoms, services related to those symptoms might be questionable.

Chart reviews are manual and time-consuming. But software can instantly highlight inconsistencies between codes and records. This quickly identifies high-risk claims to examine further.

Peer comparisons

Peer comparisons put your billing up against other providers. Significant deviations from your peers could mean fraud. But there are often good reasons for differences:

• You have sicker patients who need more care.
• Your clinic focuses on complex conditions that require more services.
• You spend more time with patients explaining diagnoses and care plans.

Quality analytics can factor in patient health, demographics, and visit complexity. This gives a more accurate comparison to your peers. If your billing still looks unusual after adjusting for these variables, fraud becomes more likely.

Proving medical necessity

One of the most common fraud allegations is billing for services that weren’t “medically necessary.” This is tricky to prove or disprove. Medical necessity involves a lot of clinical judgment. But data can give strong evidence that your services met coverage rules.

Clinical documentation

Thorough clinical notes are your first defense. Documentation should clearly justify why you ordered tests, procedures, and other services. It needs to explain the medical necessity based on symptoms, history, and exam findings.

Analytics can validate documentation supports the codes you billed. But notes with lots of copied or boilerplate text undermine your case. It suggests you might be billing first, then writing generic notes to cover yourself later.

Utilization review

Utilization review looks at whether services followed clinical guidelines. It checks that tests, procedures, and referrals were evidence-based for the patient’s condition. Analytic tools can instantly measure utilization against guidelines. This evidence shows your care plans were necessary by medical standards.

Peer comparisons

Peer comparisons are also useful for medical necessity. If your utilization rates fall within norms for your specialty, it supports the necessity of your services. Outlier utilization suggests potential overtreatment and unnecessary billing.

Proving time-based billing

Some services depend on how much time you spent with a patient. For instance, office visits and psychotherapy codes are chosen based on time. If an audit shows the patient was only there half as long as you billed for, it can be evidence of fraud.

Analytics gives hard proof about time spent with patients. Electronic health record (EHR) data can validate appointment start and end times. You can also pull security camera footage or parking garage records to prove time onsite.

Time estimates in your notes help too. But they aren’t as strong as data recordings. Make sure time-based billing matches objective time evidence. This protects against allegations you exaggerated time to bill higher codes.

Using analytics in your defense

Advanced data analytics makes a powerful defense against fraud charges. But smart strategies are key to leverage analytics successfully in court:

1. Keep billing data organized

Courts want to see clean, comprehensive billing records. Disorganized data raises red flags. Proper documentation also ensures you have the data needed for analytics.

2. Start analytics early

Don’t wait until you’re under investigation to run analytics. Regularly monitor data for outliers and inconsistencies. This lets you catch issues early and self-correct.

3. Hire analytics experts

Courts give more weight to analytics done independently by specialists. Skilled experts also know how to prepare data and present analysis to make the strongest case.

4. Explain anomalies

If analytics uncovers anomalies, have a good reason ready. Be prepared to show why unusual patterns don’t mean fraud in your specific circumstances.

5. Use multiple analytic methods

No single analytics technique is foolproof. Using multiple approaches strengthens your defense. It also protects against errors in any individual analysis.

Analytics can keep you out of court

The best outcome is avoiding fraud allegations altogether. Many innocent providers get caught up in fraud investigations because they failed to monitor data proactively. Advanced analytics makes staying compliant easy by quickly detecting problems. This lets you fix issues before they lead to legal trouble.

Analytics is the future of healthcare compliance. Providers who embrace data analysis will stay ahead of regulators. They’ll also have the evidence to fight back if allegations do arise.

Key Takeaways

• Healthcare fraud allegations can destroy careers and practices, even if providers are innocent.
• Data analytics can benchmark billing patterns to prove codes were properly supported.
• Linking billing data to medical records validates services were clinically appropriate.
• Peer comparisons identify true outliers versus normal practice variations.
• Time-tracking data verifies accurate time-based billing.
• Proactive analytics monitoring enables self-correction before issues lead to legal trouble.

Sources:

https://oig.hhs.gov/oei/reports/oei-03-15-00290.asp

https://journalofethics.ama-assn.org/article/how-data-analytics-fighting-health-care-fraud/2018-02

https://revcycleintelligence.com/features/how-data-analytics-can-combat-healthcare-fraud-and-abuse