Across the healthcare continuum today, there is growing pressure to transform the delivery system into one that achieves better outcomes at lower costs, and to do so in a more personalized and patient-friendly way. It’s a concept that’s easier said than done. Health systems are being forced to think and act differently to successfully address the challenges that surround this new way of care delivery. In an effort to achieve this goal, significant shifts are occurring along all stages of the patient journey.
One change that is inevitable is the integration of radiology, pathology, genomics, and electronic health records (i.e., clinical history), which will allow for earlier and more personalized diagnosis and treatment. Between the growing role of AI in radiology, the digitalization of pathology and the expanded use of genomics, we’re seeing all these areas evolving rapidly.
While radiology, pathology, and genomics are sometimes viewed as different disciplines today, there’s an exponential growth of data occurring within each discipline, which is leading to a greater need for integration. By integrating the abundant amount of data available in these fields, invaluable insights on diseases that may otherwise be out of reach can be unlocked, resulting in the ability to significantly improve two key junctures in a patient’s care journey–diagnosis and treatment. It’s a concept that reverts back to one of the basic tenets of medicine–that the earlier you diagnose a disease; the earlier treatment can begin, driving lower costs and an improved chance at a better outcome. With greater, more personalized insights at these two points, clinicians are able to understand specific types of diseases, so the resulting intervention is more effective for the given patient.
An accurate and definitive diagnosis plays a key role in the patient journey, as it sets the stage for treatment. Integrated data from radiology, pathology, and genomics allows clinicians to have a very precise understanding of a patient’s specific disease type.
Consider the diagnosis of breast cancer for example. By combining the data from a diagnostic scan with the information from a pathology analysis, physicians are able to not only determine that the patient has breast cancer, but that it is a breast tumor with an ER-positive mutation and activation of the HH-pathway, and cells are proliferating rapidly and have progressed beyond the breast. The earlier physicians are able to make this conclusion, the earlier they are able to select and plan the right course of treatment–which may involve molecularly targeted drugs for these specific mutations, surgical intervention to precisely remove the bulk of the tumor, and radiation therapy. Because of how precisely we can understand the particulars of this patient’s disease (instead of just all breast cancer patients involving numerous types of mutations and expressions), we can look at past patients with this precise profile and understand what has worked and what hasn’t, and in turn select the best treatment for this patient. In doing so, patient outcome not only has the potential to be significantly improved, but the cost associated is decreased because the selected therapy is much more likely to be effective.
"An accurate and definitive diagnosis plays a key role in the patient journey, as it sets the stage for treatment"
An important consideration in having a highly precise diagnosis so as to enable the selection of the optimal treatment strategy may be no treatment at all. As an example, we’re starting to gain a deeper understanding of prostate cancer, and given some of the potentially severe side effects of treatment, sometimes the best strategy in dealing with prostate cancers that are very unaggressive may be active surveillance. It has now been documented that many men who die of old age in fact had non-aggressive forms of prostate cancer, and this presence of the cancer did not affect the lifestyle of these patients. An active-surveillance strategy can strike the right balance between the benefits and risks of treatment, and precise diagnosis will allow us to make such informed decisions more intelligently, together with the patient.
To improve outcomes while reducing costs, physicians need to be able to diagnose the patient earlier and more definitively. Connecting the data across radiology, pathology, and genomics will be key in overcoming obstacles to diagnostic confidence and expediting the path to a “first time right” clinical decision-making process.
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