In the unprecedented response to COVID-19, health systems are facing new pressures and challenges and, as a result, aren’t able to operate at their full abilities. Early on in the pandemic in the U.S., health systems halted elective procedures, including cancer screenings, to conserve health system resources for COVID-19 cases. This means patients have put off critical preventive care throughout much of 2020 to avoid spreading the virus, and the consequences now loom for both health systems and patients when it comes to cancer diagnoses and care.
These changes have created new urgent needs for AI in healthcare to augment human intelligence and improve care management to deal with a backlog of cancer patients. Delayed diagnosis and care negatively impact patient outcomes, leaving health systems with ground to make up while simultaneously responding to the global pandemic.
Here are six ways health systems are impacted by delays in cancer care and how AI can help:
1) Missed opportunities for early diagnosis. Catching cancer early is associated with better outcomes and more opportunities for curative treatment. By waiting to conduct cancer screenings and other routine checkups due to COVID-19, that window of opportunity is significantly reduced. Not only are early diagnoses better for patients, but health systems with better outcomes are more successful when it comes to value-based care and alternative payment arrangements.
Since the COVID-19 pandemic began, the number of newly identified weekly cancer cases in the U.S. dropped 46.4% for six common types of cancers, according to a study from the Journal of the American Medical Association. Similar results have been reported around the world. The Netherlands found a 40% weekly decline in cancer rates during COVID-19, while in the U.K., referrals for suspected cancer cases dropped 75% since COVID-19 restrictions were put into place.
2) More deaths and complex cancer cases. Because patients are putting off health screenings, more advanced cancers are being diagnosed that can be more difficult to treat. Cancers diagnosed at a later stage typically require more complex––and expensive––care and are also linked to higher death rates and poorer outcomes, which can impact health systems on value-based measures. One estimate puts the number of excess cancer deaths in the U.S. as a result of COVID-19 at nearly 34,000.
Not only are new cancer cases being diagnosed later as a result of delayed screenings, existing cancer patients are impacted from many facilities halting surgeries and treatment delays. More than half of cancer patients and survivors in the U.S. have seen some impact on their cancer care from COVID-19, with nearly 1 in 4 reporting a delay in care or treatment, according to a survey from the American Cancer Society. With health systems at their limits responding to COVID-19, AI can offer insights alongside cancer care teams and ease some of the strain.
3) Urgent need to ramp up technology. Healthcare professionals in the U.S., U.K. and across Europe are seeing a need to triage cancer care, revealing a huge opportunity to integrate AI into care management for an accelerated care process. Natural language understanding and machine learning are already helping health systems meet the extraordinary challenges of the COVID-19 pandemic when it comes to cancer care patients by augmenting workflow in real time.
Patient Intelligence, the healthcare analytics care management software from Digital Reasoning, uses machine learning and natural language processing to read pathology and radiology reports and work behind the scenes with nurse navigators and physicians on the diagnosis and treatment process. The AI solution expands the capabilities of health systems to treat more cancer patients and work through a potential backlog of patients needing care from COVID-19.
4) Reduced cancer screenings will continue. The temporary halt on elective procedures is still ongoing as COVID-19 continues to spread, though some health systems have eased up on performing certain services. However, the return to normal isn’t likely to come before the end of 2020.
One analysis from UnitedHealth, parent company to the largest insurer in the U.S., found a decline of roughly one million mammograms, colorectal and cervical cancer screenings during the first eight months of 2020 compared to the same time period in 2019. Even as some health services return, the number of screenings has not rebounded to make up for those lost during the height of pandemic restrictions, meaning health systems should brace for below-average rates of screenings through at least the end of 2020.
5) Revenue loss response. Health systems have stepped up their response to the COVID-19 pandemic by implementing new procedures to reduce spread of the virus within their facilities, rapidly expanding testing capabilities and intensifying protections for patients and staff with personal protective equipment. At the same time, health systems have leveraged eased regulations to provide more virtual care offerings, leaning on technology such as AI. All these added expenses and challenges, coupled with the halt of elective procedures and routine care, have resulted in huge revenue losses for health systems in 2020.
From March through June 2020, U.S. hospitals and health systems lost more than $200 billion, according to an estimate from the American Hospital Association. These sudden changes have led health systems to lean on AI for cost savings and improved operational efficiency elsewhere. For example, hospital and cancer care partners of Digital Reasoning have seen 200%-250% gain in productivity across their cancer care teams from using machine learning to identify higher-risk patients and prioritize cancer diagnosis within patient populations. Nurses and doctors are also spending more time with patients and less on documentation EMR and computer work by 70% to 30% on average.
6) Loss of patient continuity. With patients avoiding routine care to stay home and limit exposure to COVID-19, patient continuity within a network is at risk. This is another source of revenue loss to health systems, but also an area where AI improves can help health systems get back on track. Machine learning and natural language processing enable oncology care teams to act more efficiently by reading pathology and radiology reports and prioritizing patients. Health systems partnered with Digital Reasoning reported a reduction in the time-to-treatment period of five days.
Accelerating care pathways can also improve the patient experience, and clinician-patient interactions have doubled from Digital Reasoning’s AI system by reducing the documentation workload on patients.
“The call to action for the cancer community is clear—get patients into screenings and treatments as quickly and safely as possible,” says Paul Clark, Digital Reasoning’s director of healthcare research. “With Digital Reasoning’s Patient Intelligence Oncology platform for accelerated diagnosis, outreach, clinical follow-up and cancer care coordination, it’s possible to make up this lost ground.”
There is no doubt health systems have been hit hard by the COVID-19 pandemic, with cancer care taking a backseat in 2020. Digital Reasoning’s Patient Intelligence system can help health systems make up lost ground from delayed diagnoses by augmenting workflow care and streamlining operational efficiencies.