Getting around the copy-paste (and other) issues with EHR data: tackle data quality concerns with newer sources of RWD
In today's digital age, electronic medical records have revolutionized life sciences research by enabling efficient and unburdensome access to de-identified patient information. However, an alarming issue still remains: patient records - particularly the unstructured notes - are riddled with inaccurate data. In the recent article published on STAT titled "Bloated patient records are filled with false information, thanks to copy-paste", the detrimental effects of this practice are highlighted.
The convenience of copying and pasting has inadvertently led to redundant and inaccurate information in medical records. A study cited in the article revealed that more than half of the text in electronic medical records was duplicated. As described by the author, the resulting bloated records can lead to diagnostic errors, wasted time for clinicians, and inappropriate billing.
This is not the first or only time that issues with EHR data are discussed. Fortune and Kaiser Health News published a multi-part series regarding their investigation that found pervasive issues, including patient deaths, tied to software glitches, user errors, and mismatched patient records (according to Pew, record matches are accurate only 50% of the time). Pew also published twelve case studies that reflect major issues with EHR systems.
And, if you’ve ever accessed your own records, this may not be news to you at all: a survey by the Kaiser Family Foundation found that 1 in 5 patients have spotted errors in their records. While an ONC report found that 25% of patients reviewing their records request error corrections, a separate report by Beth Israel researchers discovered that only 57% of requests resulted in updates to patient records.
There are major implications of these issues to Real-World Data (RWD) researchers. Outdated, fake, or unnecessary information in medical records can lead to clinical errors and compromise the quality of research: the integrity of the dataset is crucial for advancing medical evidence, afterall.
At Folia Health, we recognize the challenges posed by inaccurate patient records and are dedicated to improving the quality of life sciences research through its innovative platform and algorithms, with a lot of help from those who know the most: patients and caregivers themselves. Ultimately, patients and caregivers are the only stakeholders with full knowledge of what happened and didn’t, and full context within which to understand their experience of health and care. This is why it is vitally important to capture not only clinician-reported EMR data, but complementary home-reported outcomes data, directly from patients.
Intelligent, First-Hand Data Capture and Verification:
Folia Health's platform leverages an intelligent data capture method and user verification tools to ensure the accuracy and integrity of the information captured. Folia’s users leverage their datasets during their everyday lives, creating a higher fidelity, higher accuracy dataset in comparison to EMR data.
Structured Data Templates:
In contrast to NLP abstraction of unstructured data (the most common way for researchers to leverage EMR data in RWE studies), Folia Health’s data is already structured, with user templates tailored to their specific diagnosis, with the ability to add additional structured fields to their own records. This data collection creates in hand a precise record of symptoms, flare events, and treatment utilization that is comprehensive and unique to each individual user, enhancing the efficiency of consented, de-identified research.
Data Validation and Linkage:
Folia Health facilitates the linkage of different datasets: by integrating with patient’s electronic medical records, Folia Health enables more accurate RWD research. Many of our research partners seek Folia to help validate certain aspects of the EMR data via a continuous home-reported dataset study. With Folia, study sponsors can ensure that EMR notes are contextualized, reducing the need to make inferences and inaccurate interpretations of the dataset.
The challenges posed by EMR data filled with false information require innovative solutions. Folia Health's mission is to bring what matters most to patients to the forefront of healthcare research. Through these efforts, we aim to revolutionize healthcare and improve the overall quality and efficiency of individual care and research.