It takes on average 7 years and 10 doctor visits to properly diagnose rare disease patient. It is known that about 50% of the rare disease patients are un diagnose or miss diagnosed. 80% of the rare diseases are genetic by disorder, and 50% affect children. The development of newborn screening with the improvement in detection and diagnostic methods somehow augment the shortfall of patients’ identification. Understanding the patient journey is the key to improve the detections of rare disease patients. I’m confidence that many lives of patients and caregivers could be relieved if we could just shorten the patient journey by early and accurate rare disease detection. The key for success is having an eagle view on the patient journey with a mission to identify earlier in the process the suspected rare disease patients’ clusters. Once those suspected patients’ clusters are identified, a through tactical plan need to be applied. The approach should be a combination of applied medical knowledge with the utilization of computer science. This duet requires a close collaboration between various stakeholders (e.g., physicians, patients, sick funds, pharmaceutical companies, technological companies, and insurance companies). 

The openness towards patient data sharing, is vital for the project success. Building a tailor-made algorithm that could identify in real time the probability of undiagnosed or misdiagnosed rare disease patient is apparently less complicated than to harness the stake holders towards the mission. Different interest, budget impact, physician computer phobia, integration, lack of stakeholder collaboration are only few reasons I can quantify. A company promoting orphan disease need to have proper planning and clear execution plan to secure the project success. The applicable computer science methodologies are considering various parameters as: physiological conditions, vital signs, family history, patients historical medical records, genomics, proteomics, metabolomics, and a physical examination. The challenge lay in collecting all the above-mentioned data and organizing it in a comprehensive manner that will allow the cross referencing of the data, to draw a meaningful output. Once the data is processed, unify, and deployed, I see somehow moderate hurdles in translating it to a meaningful power algorithm. 

The revolution in computer science methodologies, had shift the challenge from AI and ML complex models to the operative field, and to the conductor to make sure the field orchestra can comply with the needed tasks. A lot was spoken on the fact that computers will replace humans sooner than later, the true is they must work side by side, since without the field collaboration and data, computer scientist can’t do much. My proposal is to map the patient journey and to break it down into small pieces, where the medical and market access team will draw the past, present, and future expected patient journey for each disease. From there a company need to position the patient in the right column and understand where the critical mass of potential patients pull is present. Identify the comorbidities and the predefine factors that cross match to the symptoms and etiology of the rare disease is the key. Quantifying the crossmatch conditions and medical parameters can manifest on the probability of the “lost” potential patients. Moving to practical lines, by training a machine learning model of false negative and false positive expected outcomes, a company can calibrate her model to hold high level of detection accuracy. The communication stage, of the suspicious diagnose with the patient and their caregivers need to be address with high sensitivity. Although patients and caregivers are keen to reveal the disorder they are suffering from, confront with your rare condition might be disturbing. There is an increase trends toward tailor made diagnostic for variety of disease. It seems that this trend will proliferate slowly but safely to the rare disease space. What will determine the tone will be the expected monetized value outcomes. As we are living in a capitalist world, creating shareholders value is what ignite new ideas towards materialism. 

A company should focus on applicable solutions that could be execute with in relatively short period of time. There is an increase of 400% y/y in the last 2 years in new publications on precision medicines. All this valuable knowledge that been collected is directed towards new ideas embracement by private companies. This circle of innovation is what secure the future novel ideas contributing for a better detection and diagnostic for rare diseases patients. The accessibility and awareness of gene tests will be impactable to the development of the field. As more valuable data become reachable as more experimental, and test could be performed in different fashions. The reunion between health companies and technological companies is the founding stone for the upcoming revolution. I can foresee an active M&A and JV in the space in the coming years. This trend will be highly supportive to advance the early detection and diagnostic of rare diseases, by shortening the patient journey and securing a safe right on time accurate diagnostic.  

* picture was taken from: NORD – National Organization for Rare Disorders

New Patient Journey Infographic Gives A Glimpse Into The Diagnostic Odyssey