Navigating the Maze of Rare Disease Diagnosis: Leveraging Data for Clarity

Ashish Patel

February 29, 2024

Typically, it takes 5 years or more to receive a correct rare disease diagnosis, according to Rare diseases are often undiagnosed or misdiagnosed, leaving patients in a limbo of unexplained symptoms and ineffective treatments. This limbo is compounded as patients journey through systems, providers specialties and differences in care based on geography. In this post, we explore how a decade of administrative Medicare claims data can provide insights into diagnosing these elusive conditions.

The Puzzle of Symptoms and Misdiagnosis

Rare diseases often manifest as a mosaic of symptoms, making diagnosis a daunting task for physicians. As patients navigate from one doctor to doctor, the continuity of their medical history becomes fragmented. This is where a longitudinal analysis of claims data becomes critical. By evaluating a patient's history over extended periods we can identify patterns of complaints and common misdiagnoses (ICD codes) associated with various rare diseases. This allows us to piece together the puzzle of symptoms that often eludes diagnosis.

Data-Driven Testing for Rare Diseases

Our approach involves the constellation of ICD codes across a patient’s healthcare journey. By analyzing the common misdiagnosis codes, we can advise and train health care providers to consider specific tests that might not be immediately apparent but are crucial for diagnosing these rare conditions. This medical education strategy is not just about looking at the symptoms; it's about understanding the biology behind them and allocating training where it’s statistically most impactful.  

Learning from Diagnostic Success Stories

Another valuable perspective is examining the journeys of patients who have received accurate diagnoses. By retracing the steps of these patients - the providers they visited, the tests they underwent, and the health systems they navigated - we gain insights into effective diagnostic pathways. These success stories become blueprints for where and how other patients with similar symptoms can be correctly diagnosed and referred for appropriate treatment.

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Bridging the Gap with Epidemiological Insights

To quantify the extent of misdiagnosis and underdiagnosis, we cross-reference our findings with published epidemiological studies and prevalence rates from confirmed diagnoses. This comparison gives us a clearer picture of the diagnostic gap in the United States. It's a vital step in understanding not only who is being missed but also why.  While our specifics are behind NDAs, in cardiovascular, oncology, and neurology, the number of rare diseases with treatments in the pipeline are astounding.  

In the ongoing effort to diagnose quickly and provide effective medicines for patients, the key lies in better data used more efficiently. These areas of medicine change so quickly, by analyzing years of claims data, each month, we realign our epidemiological methods, and with our physician and life science partners, focus on closing the gap in misdiagnosis and underdiagnosis. 

Ashish Patel

Ashish is co-founder of CareSet Systems. An entrepreneur and healthcare data transparency advocate, Ashish also founded the DocGraph Journal, bringing together the Healthcare Data Science community along with Politico and ProPublica to publish data sets for scientific advancement. Ashish is currently working to decode Medicare Claims data for Pharmaceutical companies, helping analyze provider teaming, and building robust networks.

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