Rare diseases are some of the most challenging to diagnose and treat. They’re arguably even harder to live with – especially for children and their families. While children make up half of all rare disease patients in the U.S., most slip through the cracks, yo-yoing between health centers and specialists in search of accurate diagnoses and treatments. Sadly, roughly 30 percent of them will die before the age of five due to limited clinical trials and therapeutic interventions.
It’s a hard pill to swallow, but one that thousands of families must do each year. So, what makes rare diseases so challenging to identify and manage? Much of it boils down to data – fragmented, unstructured, and mislabeled data, to be specific.
To learn how you can standardize this data to strengthen the connection between EHR documentation and research needs, read our latest eBook, Invisible patients, fragmented data: How gaps in information obscure pediatric rare disease.
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Invisible patients, fragmented data: How gaps in information obscure pediatric rare disease
The following excerpt highlights some of the far-reaching effects of relying on unstructured clinical data:
Downstream effects
The absence of a unified, consistently updated source of truth within EHRs has far-reaching effects, trickling into many other areas and processes. These largely fall into two camps:
Clinical: Like searching for a needle in a haystack, mining precise data within a free-text clinical note is tricky. Without a robust terminology infrastructure, insights and clinical nuances get buried, making it challenging to track the progression of rare diseases or identify patterns. In a 2024 study about EHR usability when caring for children with medical complexity (CMC), most respondents cited locating recent patient data, performing an accurate medication reconciliation, and lack of specialized documentation templates as the greatest challenges. All of this leads to poor handoffs between specialists, delayed diagnoses, and misinformed care plans.
Research: RWD comes from many sources, including EHRs, claims, and labs – each with its own terminology and code mappings. Without normalization, RWD often leads to inaccurate cohorts, flawed data sets, and increased regulatory risks. It also undermines reproducibility and drains revenue, decreasing ROI. As noted in a 2022 PubMed commentary, “RWD are particularly relevant to pediatrics because they may potentially provide an additional source of data to inform pediatric labeling and practice patterns when clinical trials have not been or cannot be conducted”. Yet, without standardization, most of that data will remain buried and unusable.
This graphic illustrates how various disease synonyms (muscular dystrophy, Duchenne and DMD), stages (Late type Duchenne muscular dystrophy), and even distinct diseases (Becker muscular dystrophy), can map to the same generic ICD-10-CM code (G71.01). This can create confusion clinically if that is the only code used. To remedy this, IMO Health adds granularity that even SNOMED CT® codes cannot capture, granting clinicians the freedom to write however they like while still enabling administrative functions in the EHR.
Explore the full eBook to learn how you can streamline the diagnostic journey for vulnerable populations and accelerate therapy innovation.
Curious how you can apply this knowledge to your team’s workflow? Book a conversation with an IMO Health expert.
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