Digital Health: Enabling the post-COVID-19 transition in cancer care

Top among the digital solutions that can help clinicians expedite the delivery of cancer care are AI-powered decision support systems.
11:42 AM
Doctor wearing mask

COVID-19 has created unique challenges for cancer patients.

Already considered a vulnerable population with high susceptibility for infection due to an immunocompromised state, patients now also struggle with the risk of their cancer going undetected or progressing due to delays, cancellations or postponements of screening procedures or of their oncology treatment.

In many cases patients themselves have made the decision to abstain from visiting healthcare or treatment facilities for fear of contracting COVID-19. But healthcare providers have also made the decision to cancel or delay necessary surgeries and other procedures, including screening, chemotherapy and radiation therapy based on the premise that the potential risk of COVID-19 infection outweighed the benefit of pursuing cancer care.1,2

Whether it’s the patient’s or the healthcare provider’s decision, the result is a backlog of patients in need of cancer care.

In the United Kingdom, it’s estimated that 45% of oncology patients had their treatment delayed, cancelled or changed due to COVID-19.3  Across seven major countries in Europe, radiation oncology department heads noted an overall patient volume decline of 60% due to delays/deferrals, reduced referrals and staff shortage.4 A U.S study shows a sharp nationwide decline in screenings for breast cancer of 94%, colon cancer of 86%, and cervical cancer of 94% as compared to prior year.5

Consider, for example, that the five-year survival rate is 99% for tumors located within the breast, as compared to 27% once the cancer has metastasized to the lungs, liver or bones. In lung cancer the five-year survival rate drops to 5% if the cancer metastasizes. 6,7 In fact, the National Cancer Institute (NCI) estimates that tens of thousands of additional cancer deaths will occur over the next decade as a result of missed screenings, delays in diagnosis, and reductions in cancer care due to COVID-19.8

To ensure the best clinical outcomes, clinicians need to urgently re-engage in cancer care. But they will also face the accumulation of patients looking to undergo screening, re-establish their course of treatments and/or schedule needed surgeries.

Additionally, clinicians are likely to find an increase in the number of high complexity cancer cases due to disease progression, as well as recurrence of cancer in patients who were in remission prior to COVID. The combination of these factors will necessitate agile approaches and intelligent digital health solutions for managing patients while providing personalized, high-quality care.

Top among the digital solutions that can help clinicians expedite the delivery of cancer care are AI-powered clinical decision support systems that can intelligently integrate longitudinal patient data and co-relate insights from imaging, pathology, lab and genomics to facilitate diagnostic and treatment decisions along specific oncology pathways.

Additionally, intelligent clinical decision support systems help to reduce unwarranted variations in care by ensuring necessary information is available for multi-disciplinary tumor (MDT) board discussions, and by aligning clinical decisions to evidence-based guidelines, which also lead to improvements in operational efficiency and quality.9

By eliminating the time spent compiling patient data from multiple sources, preparing reports for MDT, and looking through revisions of guidelines, these sophisticated decision support systems apply AI-algorithms and automation to provide all the necessary and relevant patient information, thereby maximizing the clinician’s time. 

The number of patients in need of cancer care increases daily, but the ramifications of COVID-19 on existing and new cancer patients will be experienced for the long-term. In the transition phase, clinicians need to be optimally prepared to address the influx of patients seeking care.

AI-powered clinical decision support systems can support clinicians by expediting their diagnostic and treatment decision-making so they can spend the time providing personalized patient care. 

For more information on the benefits of AI-powered clinical decision support solutions in cancer care, go to Siemens Healthineers Digital Health Solutions.

About the Author

Liana Romero, PhD, MBA, MT (ASCP), is the Head of Global Marketing, Clinical Decision Solutions, Digital Health, for Siemens Healthineers GmbH.

References

  1. Uzzo, R.G. et al. (2020). Coronavirus disease 2019 (COVID-19): Cancer care during the pandemic. https://www.uptodate.com/contents/coronavirus-disease-2019-covid-19-cancer-care-during-the-pandemic/abstract/3,4
  2. Lewis, M.A. (2020). Between Scylla and Charybdis - Oncologic Decision Making in the Time of Covid-19. https://pubmed.ncbi.nlm.nih.gov/32267650/
  3. Davenport, L. (2020). COVID-19: UK Cancer Care Facing 'Time Bomb' Warn Charities. https://www.medscape.com/viewarticle/931804#vp_2
  4. Slotman, B.J. et al. (2020). Effect of COVID-19 Pandemic on Practice in European Radiation Oncology Centers. Radiotherapy Oncol 2020 Jun 13;150:40-42. doi: 10.1016/j.radonc.2020.06.007
  5. Cancer Treatment Centers of America. (2020). Screening delays create concerns of an impending wave of new cancer diagnoses. https://www.cancercenter.com/community/blog/2020/05/covid-screening-delays
  6. American Cancer Society. Survival Rates for Breast Cancer. https://www.cancer.org/cancer/breast-cancer/understanding-a-breast-cancer-diagnosis/breast-cancer-survival-rates.html
  7. American Lung Association. Lung Cancer Fact Sheet. https://www.lung.org/lung-health-diseases/lung-disease-lookup/lung-cancer/resource-library/lung-cancer-fact-sheet
  8. Nelson, R. (2020). More Than 10,000 Excess Cancer Deaths due to COVID-19 Delays. https://www.medscape.com/viewarticle/932858
  9. Hipp, R. et al. (2016). A primer on clinical pathways. Hospital Pharmacy. Volume: 51 issue: 5, page(s): 416-42. https://journals.sagepub.com/doi/abs/10.1310/hpj5105-416
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