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The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems

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Title: The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems
Authors: Ainslie Tisdale, Christine M. Cutillo, Ramaa Nathan, Pierantonio Russo, Bryan Laraway, Melissa Haendel, Douglas Nowak, Cindy Hasche, Chun-Hung Chan, Emily Griese, Hugh Dawkins, Oodaye Shukla, David A. Pearce, Joni L. Rutter, Anne R. Pariser
Source: Orphanet Journal of Rare Diseases, Vol 16, Iss 1, Pp 1-18 (2021)
Publisher Information: BMC, 2021.
Publication Year: 2021
Collection: LCC:Medicine
Subject Terms: Rare diseases, Health care costs, Diagnosis, Utilization, Medicine
More Details: Abstract Background Rare diseases (RD) are a diverse collection of more than 7–10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS). Methodology We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients. Results The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three–fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease Conclusions The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1750-1172
Relation: https://doaj.org/toc/1750-1172
DOI: 10.1186/s13023-021-02061-3
Access URL: https://doaj.org/article/bd3dbd832fd6404ca21a136660f71696
Accession Number: edsdoj.bd3dbd832fd6404ca21a136660f71696
Database: Directory of Open Access Journals
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  Data: Abstract Background Rare diseases (RD) are a diverse collection of more than 7–10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS). Methodology We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients. Results The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three–fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease Conclusions The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.
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