Duplication and other errors can turn population health into a guessing game and waste time and money.
Data are among the most valuable assets for organizations in virtually every industry around the world, and healthcare is no exception. Hospitals, health systems, and payers increasingly are relying on clinical and operational data and analytics to make care decisions, reduce costs and improve efficiency.
Unfortunately, the laboratory market remains behind other healthcare sectors in recognizing the importance of data — and data quality — for providing actionable insights to improve patient outcomes and advance medical research. This is a huge missed opportunity. Most clinical decisions in healthcare are made based on test results while population health management is heavily dependent on the aggregation of both deidentified and uniquely identified test data — the kind of data that can verify the spread or control of infectious diseases such as COVID-19.
Errors in lab-generated data can give public health officials a misleading picture about the prevalence of a disease or condition within a specific population. The most common way lab data is flawed is through duplication. Up to 30% of data in a lab’s patient database is unknowingly duplicated.
Let’s take the example of a physicians group that wants to find out, as an exercise in population management, what percentage of women in a geographic cohort have had abnormal results from a PAP smear. Suppose the lab affiliated with the physicians group had a patient who came in every three months for a PAP smear, which turns out abnormal each time. If the lab does a poor job of managing and indexing patient data, it will appear to the physicians group as if four women in the population, and not one, had abnormal tests.
That is just one example of a duplication error. There are hundreds of others. When 30% of your data is inaccurate or misleading, population management is reduced to a guessing game. Modern labs desperately need clean identity data sets to participate in population management and to extract greater value from their data. To do so, they must have a master patient index (MPI).
Payers are keenly interested in clean identity resolution because they are investing in systems to analyze population risks.When signing payer contracts, labs can leverage clean datasets to negotiate better reimbursement.
An MPI can help labs and imaging centers avoid patient identification problems that lead to duplication of records and corrupt population health data. By ensuring a lab’s patient records are accurate, an MPI improves individual care by creating a reliable longitudinal health record (LHR) and population health by improving the overall quality of their data sets.
Boosting the bottom line
The benefits of an MPI to labs and imaging centers extend beyond the clinical realm to the bottom line. If a lab has a high patient data duplication rate, you can be sure that lab also is losing money through an inefficient billing and collections process. One study found that a hospital in Texas whose duplicates amounted to 22% of all patient records was costing the facility $96 per duplicate. That adds up fast.
Let’s go back to our example of the patient who comes in four times a year for a PAP smear. It’s entirely likely that the lab will send that patient four different bills because its database incorrectly recognizes her as four different people. So right off the bat the lab is wasting money on staff time as well as the cost of mailing out separate bills (rather than one aggregated bill) to a single patient. While extra mailing costs may seem relatively insignificant, they add up over time and can eat into a provider organization’s profit margin.
For patients, getting four bills can be tremendously confusing, particularly if there are differences in the bills regarding deductibles, co-pays, and other coverage details. Worse, the confused patient may not know how to resolve the problem because labs typically don’t have a strong relationship with patients, who typically have a more direct relationship with their doctors. Often the patient doesn’t even know where the doctor is going to send their test to get results; they just see a bill (or multiple bills) in the mail.
Improving collections from patient and payers
In many of these cases the confused patient simply won’t pay, leading to delays in collections that could have been avoided if the lab used an MPI that ensures a high level of patient record quality. And the confusion caused by excessive duplication rates in lab patient databases can lead to claims denials by insurers, which can cost a lab 20% to 30% of its billings. The good news is that labs increasingly are becoming aware of the impact of poor data quality on insurance denials.
By vastly reducing and even eliminating duplicate and inaccurate patient records, MPIs allow labs to conveniently present clear information to patients about test results, billing, and insurance coverage.
Offering patients this data through the convenience of an app — which is exactly what most patients now expect — avoids confusion and sticker shock on the part of the patient. Making it easy for patients to access their test and billing information digitally, while providing them with an opportunity to pay with a single click – will boost a lab’s collections rate and improve the patient experience.
Clean patient data sets not only are important for patient outcomes and population health initiatives, they are imperative to running an efficient business. Failing to protect their data against duplications and other quality issues is costing labs revenue through unpaid bills and claims denials. But an MPI provides labs with a clean data set that will improve the billing and collections process while also building a better relationship with patients. The technology to reduce lab patient record duplications to as low as 1% exists today.