Prescription drug fraud
A bitter pill to swallow
Prescription drugs by the numbers
The Canadian Institute for Health Information (CIHI) estimated that the combined public and private Canadian health expenditure in 2018* would reach $235.5 billion or, $6,839 per capita. This represents approximately 11% of Canada's gross domestic product (GDP). What is more telling is that drug expenditure was almost 16% of total spend; and is expected to continue increasing more than other areas of health spending.
How much of that $235.5 billion can be allocated to fraudulent activities? The Canadian Medical Association Journal (CMAJ) estimated drug fraud to be anywhere between 2% and 10% of overall healthcare expenditures in North America. Other sources claim drug fraud could be up to 20% of total North American healthcare spend.
Up to $4 Billion of drug product expenditures estimated to have been lost to fraud and misuse in Canada in 2018*.
If we use these fraud estimates, that represents $250 million to $1.2 billion of annual Canadian private sector drug costs lost to fraud. For the public sector the losses are between $550 million and $2.75 billion respectively. Combined that's a staggering $800 million to $4 billion annually.
Most of us are unaware of the substantial impact of this problem on healthcare affordability. We rarely look beyond our own healthcare costs.
*2018 final figure not yet available
Following the scripts
Prescription drug fraud has seemingly flown under the radar for quite some time. A recent investigation by journalists from the Toronto Star and Global News show it to be much more widespread than imagined. They interviewed an insider helping pharmacists cover up prescription drug fraud who said that "it's easy to hide the evidence", and coaches pharmacists on how to fool the auditors.
With prescription drug fraud seemingly easy to commit, we look at the challenges facing the current healthcare system to combat this fast growing issue:
1. Most patients have no visibility into claims made in their name
The vast majority of drug claims are submitted via the Electronic Data Interchange (EDI). This means that the entities adjudicating these claims (e.g. health benefit plans and other service providers) do not see a real script or receipt before they approve and pay the pharmacy.
99% of prescription drug claims are electronic. The
adjudicator sees no receipt or prescription.
2. Fraud detection is not the programs' focus
"The cost of fraud is often accepted as part of the cost of doing business, without actually measuring its impact on
the bottom line. Claims validations are done primarily on the patient, pharmacy, program eligibility and price, but not on abnormal patterns that could reveal false claims made in the name of an unsuspecting patient.
This is often the case for off-patent branded drugs covered under a pharmaceutical Patient Assistance or Support Program (PAP/PSP), which typically views an increase in patient claims as successful patient retention.
3. Inadequate resources to investigate fraud
Where there are programs focusing more on fraud detection, such as private and public health plans, there is often a lack of resources and tools to perform comprehensive reviews and follow ups. The Ontario Ministry of Health and Long Term Care (MOHLTC) had 10 inspectors in 2016 and 2017 who undertook 286 inspections out of a then total of 4,260 pharmacies. At that rate, a pharmacy could anticipate an inspection approximately once every 15 years.
Only 6.7% of Ontario pharmacies were inspected in 2016 and
Source: Auditor General of Ontario, 2017 Annual Report
4. Lack of evidence to convict
It is a challenge to determine the full extent of prescription drug fraud in Canada because there is no dedicated federal oversight body. Because each province is responsible for its own investigations into healthcare fraud, this leads to inconsistencies in reporting and reduced pressure to prosecute. As well, where fraud is detected, frequently there is no criminal prosecution or significant fines to act as a deterrent for future fraudulent activity.
For example, although the Ontario Provincial Police (OPP) have a health fraud division, they cannot act unless a case is referred by the MOHLTC. Those who are prosecuted rarely end up being convicted because of the lack of proof.
With no set standards for punishment of fraudulent activities in their governing laws, the pharmacy sector by and large polices itself. For example:
Health benefit plan adjudicators can exclude pharmacies from reimbursement, but often there is no repayment of damages.
Provincial Colleges of Pharmacists do impose temporary suspensions for misconduct (including fraud) which can last for several months, but fines are small in comparison. And once the suspension period has elapsed, the culprit is then free to practice as a pharmacist and, in some cases, become a recidivist.
In the case of pharmaceutical PAP/PSPs, frequently there is inadequate continuous monitoring of Government cases or College of Pharmacist hearings to flag and exclude pharmacists with misconduct from reimbursement under the program. This allows fraudulent claims to continue unabated for significant periods of time.
5. Increasing involvement of organized crime
Fraud is constantly evolving and is becoming more sophisticated. Typically, fraudsters follow the path of least resistance. With the tightening of controls in other areas such as credit or debit card fraud, organized crime rings in Canada have targeted medical fraud as "easy money". There is a growing incidence of organized medical fraud involving patients, who are often in collusion with providers (doctors, pharmacists) and are sharing the proceeds of crime.
Time for some strong medicine
Acknowledging that prescription drug fraud exists is the first step, knowing what to do to prevent it is the next stage.
With this in mind, we developed a prescription drug anomaly detection model that uses a number of techniques and data sources to uncover errors, fraud and misuse in any reimbursement program, be it a public health plan or a pharmaceutical PAP/PSP. The model is tailored to each program's specifics and product indications for accuracy and relevance.
Using a rules-based approach combined with unsupervised machine learning techniques, i.e. let the artificial intelligence decide what constitutes an anomaly, we can significantly reduce the false positive rates and increase our confidence in the detected anomalous claims.
In a dashboard example below, the rules based and machine learning risk scatter allow us to zoom into the riskiest pharmacies and patients, (top right quadrant of the scatterplot charts) and see how these high-risk claims have evolved over time and by products (bar graph).
$34 worth of findings for every $1 spent on assessment, on
This model has been successful in the detection of millions of lost dollars per program across scores of pharmacies and thousands of claims. Despite the lack of evidence to convict without a physical inspection, which is often not viable when a large volume of pharmacies and claims are being flagged as highly suspicious, we have experience helping program managers successfully mitigate future revenue leakage through a number of other recourse scenarios, including:
Immediately blocking payments to flagged pharmacies until an audit is completed Filing an insurance claim Filing a fraud complaint Controls assessment and improvements with the claims adjudication and/or program management Strengthening contract terms (e.g. for pharmaceutical PAP/PSP claims adjudication, formulary
While there are regulatory measures and controls that need to be put in place to help manage the problem, using data and analytics with the right model can help you identify the offenders and stem the flow of illegal claims, potentially saving you millions of dollars annually.
In our opinion being proactive and taking preventative measures can significantly and materially reduce prescription drug fraud and discourage those who think that pharmaceutical companies are an easy target.
For a deeper discussion on prescription drug misuse and fraud, please contact one of our practice specialists.
Ven Adamov Director, One Analytics email@example.com +1 416 814 5743
Otto Akkerman Managing Director, Pharma & Life Sciences Otto.firstname.lastname@example.org +1 416 815 5310
Rob Shull Managing Director, Deals Forensics email@example.com +1 416 814 5829
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