Life sciences and Biotechnology companies with specialty and rare disease therapies are uniquely challenged to find patients. The number of applicable patients may be small, their diagnosis may be challenging to come by, and their transit across the healthcare system is often incomplete. Further complicating this challenge is using dated, incomplete data and inefficient methods to identify these patients and their providers.
So, how can those charged with commercialization, engagement, and market access find patients and providers? Simply using raw claims data falls short of pinpointing patient-level results. Whether for assessing new therapy market potential or targeting providers for engagement, data users require a more holistic, timely, complete, and granular view to find the right patients and providers at the right time. Patient-level data helps Life Sciences and Biotech users target only the healthcare providers and organizations currently seeing rare disease patients and those most likely to care for these patients in the future. Kythera Labs delivers more accurate results by linking de-identified datasets and remastering data to enable more accurate analysis, which helps patients get the treatment they need.
Let’s look at wet macular degeneration as an example of the six steps for finding rare patients and providers in remastered real world data (RWD). This guide is a suggested approach and is not intended to be a comprehensive list of treatments, codes, etc.
Wet Age Related Macular Degeneration
Age-related macular degeneration (ARMD) is an eye disorder and the leading cause of incurable blindness in the elderly worldwide. In the United States, 11-15 million people have ARMD, and of these, 10% of patients with dry ARMD will develop wet ARMD. Approximately 200,000 new cases of wet ARMD are diagnosed each year, leading to 90% of legal blindness. Untreated, wet ARMD leads to loss of vision in the majority of patients.
Wet Age-related Macular Degeneration (Wet ARMD)
Samuel D. Hobbs; Kristine Pierce.
Using data, Life Sciences and Biotech companies can build a path to finding these patients and their providers with higher accuracy. Here are the 6 steps:
Identify patients who received a diagnosis of wet ARMD within remastered claims data during a specific lookback period ranging from last week to January 2016 using applicable ICD-10 codes.
Examine EHR data and use symptoms typically reported with wet ARMD.
Look for known risk factors associated with ARMD within claims, EHR, and other data sources.
Medical Claims Data
Diabetic Retinopathy EHR Data
Genetic Data
Look for the procedures associated with diagnosing and treating ARMD within claims data.
Diagnosis
Look for specific therapies used to treat ARMD in medical and pharmacy claims, specifically the HCPC and J codes for medically administered therapies and the site of care for that appear on the medical claim.
Providers administering these injections (PDT) often practice in multiple locations. Pinpointing the site of care where a patient encounter takes place offers unique insight into segmenting where and when services were delivered.
Not only can ARMD patients be identified within RWD, providers treating ARMD can too. Typically, patients with ARMD are seen by various healthcare providers before being referred to a retina specialist. So, primary care providers, optometrists, general ophthalmologists, and retina specialists should be included when identifying providers treating rare patients. It should be noted that in many regions of the US, retina specialists may not be accessible; therefore, widening the lens to include general ophthalmologists is essential.
In addition to specific providers, the data should let you drill into specific health care organizations down to the site of care level and segment providers by a number of variables, including:
With remastered RWD, Life Sciences and Biotech users can analyze a combination of symptoms, diagnosis, procedures, risk factors, therapies, precise site of care, and referral behaviors to create a more complete, nuanced view of diagnosed and pre-diagnosed patients, not just patient counts. This holistic, granular view more accurately identifies the targeted patient population and helps build a model to predict patients who may have a future diagnosis.
Connect with Kythera Labs to learn more about using Wayfinder, our big data platform, to access 15+ Terabytes of remastered claims data and integrate data like EHR or genetics to pinpoint your rare disease patients and their providers.
Photo by Markus Spiske on Unsplash
.png)
