Provider data is critical for patients to access care, yet is often overlooked.
There is an ever-evolving need to keep the data accurate and up to date for insurance companies, providers, and health systems. The burden of accurate provider data is carried heavily across all stakeholders – and felt acutely by patients as they bear the cost of bad data.
Achieving Accurate Data Cost to Patients #1:
Health plans must pass on costs to plan members
When provider data is inaccurate it affects health plans and their members. Members rely on provider data directories with their health plans to connect with care providers. But, with 50% of provider data in U.S. directories flagged as inaccurate, health plans are incurring millions of dollars annually due to data compliance hurdles that impact patients. Health plans are estimated to spend about $4 billion each year to improve their provider data accuracy. Collectively, the spending on achieving accurate and updated provider data ultimately increases administrative costs and increases the risk of regulatory penalties under the umbrella of the No Surprises Act. This ultimately results in higher premiums for plan members. The impact on patients is directly felt: they are unable to find care in a timely manner and they may encounter an increase in the cost of their health plans.
Achieving Accurate Data Cost to Patients #2::
Patients receive expensive out-of-network care
Provider directories have a high number of inaccuracies making it a daunting process for patients to seek care from the right providers. Therefore, more patients are seeking care from out-of-network providers. This results in higher healthcare expenses not covered by their insurance. Let’s take a noticeably increasing condition since the COVID-19 pandemic, mental health disease. Since COVID-19, approximately 42% of U.S. adults report anxiety and depression. A patient in the U.S. seeking care for major depression on average spends $10,836 every year on treatment. But many providers of mental health care operate outside the health insurance system. Not to mention, there is also a shortage of specialists. Patients seeking mental health care encounter ghost networks, which gives them an inaccurate list of clinicians that aren’t taking new patients, are no longer in-network, or are no longer practicing. This is a frustrating barrier for patients. Not to mention this delays their ability to get the right care. Because they cannot find or contact the right provider when they need care most, they are forced to seek out-of-network providers and incur higher care costs than necessary.
Achieving Accurate Data Cost to Patients #3:
Disruption of care access to patients
According to a study published in Health Affairs, healthcare beneficiaries in California have only a 30% chance of scheduling an appointment with a physician who is in-network. This same study found that with more than 700 physicians within California, difficulties in scheduling led to instances such as:
- Physicians were no longer in practice
- Physicians with the wrong specialty were listed
- The physician’s contact information was wrong or outdated
- Physicians were no longer accepting new patients
This study revealed delays in care caused by inaccurate information leading to poor patient and member experiences. Additionally, delays may lead to worse health outcomes for patients, such as mortality. Although this study revealed a disruption of access to care within California, the provider data in the study came from insurance providers whose parent companies operated in a dozen or more other states within the U.S.
Inaccurate provider data is at the cost of the patient but this no longer has to be the status quo. We can overcome the shortcomings of achieving accurate provider data using reliable, automated software solutions. With the power of machine learning and artificial intelligence, payers, providers, and health systems can easily connect their provider data accurately and reliably. This means helping patients access the care they need affordably and more easily.
Read this blog next on how payers today benefit from automating their provider data with accuracy and reliability.