Increasing Prior Authorization (PA) Submissions with IQVIA LAAD Claims Data
The primary goal of any healthcare system is to provide patients with access to necessary medication. However, the Prior Authorization (PA) process can make it difficult for patients to obtain the latest branded medication, as they are often required to go through older generic alternatives. The PA process is complex, involving communication among payers, pharmacy benefit managers, pharmacies, and healthcare providers. Despite efforts by companies like CoverMyMeds (CMM) to streamline the PA process, it remains cumbersome and time-consuming for providers and pharmacists.
Currently, approximately one out of every two new patients with a prescription for branded medication is unable to fill their prescription, primarily due to Prior Authorization. CMM data shows that about 4 out of 10 PAs are not even submitted by healthcare providers. However, CMM provider surveys indicate a positive trend of physicians starting the PA process when they become aware that their patients need a PA.
Using Hilary Scannapieco 艾昆纬 's LAAD claims dataset, we can perform a simple analysis to identify physicians who have a high volume of claims rejected due to the PA process, as shown in Table 1 for a specific brand, X. Among 1,000 high-volume doctors, about 40% of claims are rejected, leaving many patients without critical medication.
These high-volume doctors may require a "white glove" treatment from pharmaceutical manufacturers, given the concentration of rejected claims. Here are some initiatives that manufacturers can undertake:
Manufacturers can generate a weekly report that tracks rejected and paid claims for each doctor, along with the top three payers for each doctor. Reimbursement specialists can use this report to streamline their efforts by provider and facilitate greater patient access to the drug. The report can be used to identify physicians who have experienced significant improvements in PA approvals, shaded in green, as well as those with a marked rise in PA rejections, shaded in red. After reviewing the report, reimbursement specialists can determine which payers require their attention for the red shaded doctors, and take proactive steps to address the issue.
领英推荐
If these initiatives can reduce PA rejections by 11%, it will result in a 25% increase in new patients accessing treatment, and it will also be financially sustainable for the manufacturer in the long run. This is shown below.
We can run what-if analysis that shows %NBRx lift and breakeven cost/claim for a 1% reduction in reject rate. As shown below, if we decrease reject rate by 1%, we get a 2.3% NBRx lift and can afford an additional $45/claim to break even.
In conclusion, the utilization of IQVIA's LAAD claims data can help manufacturers identify high-volume doctors with a high rate of PA rejections, and they can implement various initiatives such as partnering with pharmacy chains, identifying payers with high rejection rates, and providing healthcare providers with training. These initiatives not only improve patient access to medication but also lead to better health outcomes. As a chief commercial officer of a major pharmaceutical corporation once stated, "If we prioritize the patient's well-being, everything else including revenue and profits will follow." By prioritizing patient care and working towards reducing PA rejections, manufacturers can achieve both financial sustainability and positive health impacts.
The views expressed are those of the author and do not represent the views of any of the companies the author has worked for or mentioned in this article. All data presented in the article are illustrative.
Vince Angotti?Kristen Harrington-Smith?Tsveta Milanova?Scott Weintraub?James Bilotta?Mara Molinello MBA, MS?Mark Soued?Murdo Gordon?Susan Sweeney?Ian Thompson?Andy Boyer?Drew Young?Adam Townsend?Joe Kelly?Phil Tennant?Mark Reisenauer?John Demaree?Chatrick Paul?Mohit Manrao?Shannon Faught?Erin Liberto?Reese Fitzpatrick?Jennifer Restivo?Christine Roth?Josh Neiman?Jeremy Graff?Matt Winton?Humaira Serajuddin?Jeff Ajer?Chris Boerner?Eric Vandal?Jeff Del Carmen?Peter Leopardi?Bill Campbell?Dan Switzer?Herm Cukier?Brent Ragans?John Keating?Anthony Mancini?Anil Kapur?Sandrine Piret-Gerard?Dawn Liu Winn?Patti Naumann?Barry Flannelly?Drayton Wise?David Daya?Linda Richardson?Richard R. Smith?Lisa French?Joseph Scalia?Gail Horwood?John Trizzino?Doug Langa?Tifani McCann?Louis Allesandrine?Jason Tardio?Angela Hwang?Lidia Fonseca?Martin Gilligan?Nicole Sweeny?Dawn Bir?David Snow?Gaurav Shah, MD?Terri Young?Margaret Borys, CPA, MBA?Lisa Mullett?Malene Haxholdt?Michelle Kehily?Mark Niemaszek?Alana Darden Powell?Cheryl Schwartz?Steve Schaefer?Kevin Harris?Brian Hilberdink?Dimitris K. Agrafiotis?Philippe Sauvage?Jonathan Witt?Stewart Campbell?Catherine Owen?Dallan Murray?Dawn Bir?Gary Zieziula?Kiernan Seth PhD, MBA?Will Kane
Great share, Vijay!
MBA, Engineer | Enterprise AI | Advanced Analytics | GTM Strategy | World's First Arbor Essbase Post-Sales Consultant
1 年Thank you for sharing Vijay!
Healthcare Practice Lead
2 年Very valid leveraging of data to improve prior authorization along with business value shown clearly. Can this be a valid use case for FHIR based implementation or is that still quite far off? In the ideal state the entire prior authorization process must be fully automated without any human intervention. Do we have any insights in to what is the volume of e-prescriptions in this data set? That can possibly tell us if we are closer to the ideal state.