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

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.

Recommendations from the IRDiRC Working Group on methodologies to assess the impact of diagnoses and therapies on rare disease patients

“…This can lead to increased healthcare expenditures impacting not only patients, but also HCS and providers, thus reducing the efficiency of care. In addition, the increased burden on health providers, long diagnostic odysseys, the need for specialized care, and the high price of approved treatments can result in disproportionately high health care costs [ 18 , 19 ]. These costs are often borne by the patient/families, health insurers or in countries where social medicine is provided, the government.…”

A guide for the diagnosis of rare and undiagnosed disease: beyond the exome

“…Splinter et al [ 25 ] compared the health care cost before and during the diagnosis evaluation period and found the latter to be only 6–7% of the total cost. Recently, Tisdale et al [ 307 ] performed a pilot study on 14 rare diseases within four different healthcare system databases to estimate direct medical costs. They found that per patient direct medical costs of rare diseases are about 3–5 times higher than age matched controls, highlighting the urgent need for early and accurate diagnosis for rare disease patients that may reduce the costs associated with misdiagnosis or missed opportunities for intervention at an appropriate time.…”

Applications of Artificial Intelligence to Eosinophilic Esophagitis

“…The future of EoE management will require personalized care which can be delivered by AI. Preventative measures are possible with an AI that flags patients at risk using data such as birth and family history, biomarker profiles, as well as genome and microbiome data, and such models have proven useful in application to other rare disorders [10,46]. Regression models have been used to predict features missing from shallow biopsies, and AI will likely decrease the percent of equivocal biopsies [47].…”