How do you access the millions of data points and analytics when it pertains to your patient’s cancer?
With only 30 or 40 commonly used oncology drugs available two decades ago, clinical decision making was less complex. The available treatments may have covered several diagnoses. Oncologists treated “breast cancer,” not what today’s diagnosis with hormone receptors, tumor grades, and gene expression testing done. “In the last several years, we’ve seen an explosion in the knowledge base of what we can do for our patients – not just genomics, but proteomics, transcriptomics, metabolomics – there are many considerations to keep in mind,” noted Susan Weidner, senior vice president, IntrinsiQ Specialty Solutions in a recent presentation.
With the influx of 70 new drug approvals this past year and 40 more anticipated in the coming year, providers need to distill mountains of data and protocols to understand how to personalize the cancer care of their patients.
Adding to that difficulty is the lack of standards and policies from either payers or health agencies around molecular testing, as sometimes the coverage and determination can vary from state to state or payer to payer.1
Additionally, there are thousands of potential rules that emerge when looking at NCCN and ASCO-based guidelines. Providers being able to stay on top of the most current testing and which patient might be eligible during a specific window is time consuming.
How can providers streamline some of those care decisions to not spend hours researching current data and research around treatment protocols?
Understanding that artificial intelligence (AI) can assist the progression toward precision medicine is the first step. Armed with specific patient data from your EHR and PM systems, there are services that can provide clear interpretations of molecular profile testing along with a list of treatment options. This type of data and analytics are essential to support clinical decisions, particularly with the patient’s payer.
Data and its interpretation with AI can consider protocols, pathways and understand the rules for reimbursement, as well as provide a content library to support your decisions.
A few years ago, Specialty Physician Services formed a collaboration with VieCure, a cloud-based AI platform which integrates the EMR with the precision oncology infrastructure necessary to assist oncologists in making evidence-based, payer aligned decisions for patients. The impetus, according to Weidner, was to enable a more structured order process for testing – taking away the need for providers to scroll through long documents to find treatment considerations, especially when providers might see a particular type of cancer in their patient every year or so.
Having access to data helps providers understand the journey more completely.
It’s no longer telling a patient for example, they have lung cancer, and after surgery, you’ll come in once a week for chemotherapy. How do I explain this to my patients?
It is complex enough for providers – ensuring that everyone within the practice understands the testing recommendations and requirements and how the results are interpreted for the right therapy. Now providers must help the patient understand why certain tests should be completed, and how their results will help determine the method of therapy. They could be in online forums or talking to friends where different therapies are mentioned – questioning your decisions.
Building a custom educational piece will help the patient understand why their cancer may be different than others. And why it is important that an oral therapy may be more effective in their particular case.
Having that platform integrated into your EMR which can provide specific education and monitor adherence and toxicities will help with educating your patient along the way. An integrated platform with the ability to track patients by stage and biomarkers open up opportunities into clinical trials.