Driving Optimal Outcomes through the Use of Artificial Intelligence and Real World Data
AI is driving one of the great breakthroughs in medicine: improving the lives of patients by improving outcomes, driving faster diagnosis and leading to more effective treatment, all while lowering costs. Be they undiagnosed rare disease patients, patients who would benefit from therapy line advancements or those at risk of non-adherence, this new capability is changing the way healthcare is delivered.
Over 7,000 rare diseases have been identified and the life sciences industry continues to innovate in its quest to develop life saving treatments for as many as possible. In 2019, 63 drugs were launched. Half of those were designated as orphan disease therapies; a third were first-in-class; and 46% launched with trials of less than 500 people. This environment leads to a major challenge: how can physicians be expected to understand and diagnose even a fraction of rare diseases, much less stay abreast of the constant innovations in treatments affecting so many small patient populations?
While treatments advance, it often falls on the manufacturers to educate and inform medical professionals not only about the treatment, but also the symptoms of the specific rare disease itself. General physician education is an option, but for the vast majority of physicians who do not and never will encounter a patient with a particular rare disease, carving out enough of an awareness foothold to support patients who need diagnosis is nearly impossible. This leaves individuals who suffer from symptoms that present like a variety of more common conditions in a vicious circle, bouncing from their general practitioner to a line of specialists for an unspecified amount of time until they hopefully come across a physician familiar with their rare condition.
But technology can break this cycle. Patient finding utilizing machine learning and artificial intelligence can help connect undiagnosed patients to the treatment they desperately require. Algorithms can generate findings that shorten the process that typically leads to a rare disease diagnosis. In one instance, IPM.ai collaborated with a biotech company to help predict undiagnosed patients with an extremely rare, typically fatal genetic disorder. With less than 6,500 estimated patients in the US, the likelihood of a physician recognizing this disease was extremely low. But by leveraging the understanding of the manufacturer about the 100 existing diagnosed patients, these patients’ d-identified health data and our technology, a model was developed that resulted in patients being more likely to receive the correct diagnosis and therapy much earlier. Thanks in large part to IPM.ai, the patient population increased from 100 to over 500.
Improvement of patient outcomes does not end with rare diseases. Algorithms can discover the most esoteric of patterns that on the surface may not make logical sense, thus helping predict any number of diagnoses both rare and common. In another case, we leveraged our platform and healthcare data pool to identify patients with MS who were close to failing first-line therapy. By helping our client coordinate both direct-to-consumer and healthcare professional outreach programs, we were able to assist a subset of patients suffering from a debilitating disease to transition from a treatment path that was ending to one more appropriate for their particular situations.
As technology advances, we must remember that the goal is to improve patient outcomes. Health care is a business, but one with a laudable purpose: to improve lives, and to shorten the path between illness and recovery. Life sciences organizations innovate to commercialize new therapies, but educating the right physicians and patients on these new treatments is a challenge that machine learning and artificial intelligence can certainly help solve.