For cancer patients and their loved ones, days, weeks and months matter. In fact, anyone who has eagerly awaited test results or the start of treatment can attest that hours, if not minutes, will drag on with emotional heaviness. Also from a clinical point of view, the time to treatment has a significant impact on the results. All suspected cancers should be ruled out or confirmed with a diagnosis so that treatment can begin as soon as possible. Likewise, patients with an incidental finding – masses or lesions detected by an imaging test performed for an unrelated reason – who require follow-ups should not fall through the cracks of the system.
Unfortunately, in a complex and overburdened healthcare environment, cancer patients often struggle to navigate the unknown, leading to unnecessary delays in care. These delays can lead to poorer outcomes, more complicated and expensive care, and reduced life expectancy. Patients today need insight, attention, follow-up, support, collaboration, guidance, communication and more, while the healthcare industry, like others, lacks of human labor. Despite the evolution of clinical solutions that successfully treat or manage many common forms of cancer, most navigation challenges that lead to poor outcomes cannot be solved by manually adding more workers or resources. Fortunately, artificial intelligence (AI) applications are disrupting persistent patterns of delays and flagging patients who need follow-up care and streamlining those recently diagnosed with cancer to follow the optimal treatment path immediately. .
Surface, resurface patients
Patients who undergo imaging and have a suspicious abnormality unrelated to their CT scan often require follow-up to track the lesion or mass over time. Unfortunately, only about 30% of these patients follow these recommendations in a timely manner. Some may not be informed of the results, while others are unclear about next steps. There is great uncertainty in the clinical field about who is responsible for tracking the progression of these results. Realistically, there is not enough human labor or even infrastructure to review and track every patient’s radiology report with an incidental finding in a central location.
With missed patient follow-ups, potential cancers have time to grow and get worse. In addition, disparities in health equity often exacerbate the impact of “forgotten” incidental findings. For people who are struggling with access to care, health literacy, family support, or financial hardship, following through on non-emergency health care recommendations can be challenging. AI applications can also identify these patients to ensure that the significance of the discovery has been correctly communicated. From there, a trusted clinician or navigator can guide the patient through the next important steps in care.
Similarly, patients experiencing the trauma of a cancer diagnosis may be overwhelmed with the shock of the news and then the sheer number of tasks to perform. AI helps by automatically reviewing structured data in patient records, identifying those most at risk for rapid disease progression, and can categorize cancer subsets to triage for faster and more timely onset of treatment. efficient.
Coordinate care
Fortunately, 9 out of 10 suspicious lesions turn out to be benign. Of course, clinicians must evaluate each of these potential cancers, carefully reviewing the pathology report and categorizing the cancer, if it is malignant. It takes the clinician 1.5 to 2 minutes to review each report. Over time, this accumulates into thousands of hours of work resources. For breast, lung and gastrointestinal cancer patients, nurse navigators historically spend the majority of their hours or 65% of their time mining data to identify new patients for navigation.
Clinicians must also assess the severity of the cancer and, consciously or not, they decide each patient’s acuity and disease, relative to others who present to initiate next steps of care. The patient’s possible paths will vary greatly based on these global identifications and assessments, and it takes a long time for humans to make such decisions on a large scale. Will a particular patient see a surgical oncologist, medical oncologist or radiation oncologist first depending on the diagnosis? Will another patient start a blood test, or an MRI or CT scan?
Eliminating the tedious nature of this manual decision is possible by applying AI to the identification and classification of cancer patients. Using AI, patients positive for malignancy are flagged, their cancer designated as one of the top 20 types, and then referred to a cancer type navigation clinician who can drive the process immediately. Through the identification of multiple data elements in the diagnosis, AI can also flag candidates for specific treatments or clinical trials, putting them on a much earlier path to treatment.
Realize gains
Patients aren’t the only ones reaping the benefits of AI applications. Healthcare providers benefit from better patient and staff retention through the use of AI in healthcare delivery. Patients who are identified as requiring next steps of care carry out recommendations through targeted, guided support, improving survival and provider loyalty. Thanks to AI, clinical staff, especially nurses and nurse navigators, realize a reduced workload. Instead of combing through pathology reports from entire patient populations to identify positive patients and their cancer characteristics, AI performs review of results in a fraction of a second, highlighting patients needing immediate attention and informing treating physicians. Nurses can spend much more one-on-one time with established patients, providing them with emotional support and guidance than working on identification and data mining tasks. Staff satisfaction remains high with the application of AI as it allows nurses to devote more energy to meaningful interactions with patients.
In a tight labor market, technology has the potential to provide relief and streamline inefficiencies to create smoother care journeys for patients. AI-based solutions are changing the face of cancer care and complementing the value of clinicians’ strengths to ensure the right care is delivered to the right patient at the right time.
About Chris Cashwell
Chris Cashwell is the CEO and Founder of Azra AI, a digital health company that interprets unstructured data to identify and triage oncology patients who need treatment and follow-up care.
For more than 20 years, Chris Cashwell has led healthcare information technology companies large and small. Most recently, he served as Managing Director and Senior Vice President of Digital Reasoning Health and previously as Managing Director of Lincor Solutions, where he was responsible for strategy and operations for global digital patient engagement technology solutions.
Chris received his BSc in Finance and Economics from Lipscomb University. He is GE Certified in Six Sigma as a Black Belt, Socratic Selling and Situational Negotiations. He is a member of the Nashville Healthcare Council, the Association of Cancer Executives and a member of the TCU Chancellor’s Advisory Board.
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