Cancer and its treatment engender disabling symptoms that are frequently undertreated, and associated with needlessly increased healthcare utilization, non-adherence to oncologic treatments, and reduced survival. The key to addressing this damaging situation lies in reliably detecting symptoms and providing evidence-based care. Of note, the National Academy of Medicine has identified systematic screening with patient reported outcomes (PROs) as critical, not only to symptom control, but also to enhancing the patient centricity and effectiveness of cancer care in general. Unfortunately, experience has shown that simply presenting PRO data to over-loaded, treatment-focused clinicians has minimal or no impact on the outcomes of patients with many diseases, including cancer. On the other hand, providing these data to mid-level providers and entrusting them to initiate, monitor and adapt individually tailored symptom management plans has proven robustly effective in the control of cancer-related and other symptoms. However, the high resource requirements of this approach have heretofore limited its dissemination and scalability. Encouragingly, such resource intense care is not always required for meaningfully benefit, as attested by the effectiveness of interventions that provide patients experiencing mild or moderate with self-management education. This application recognizes the need for both low- and high-touch approaches to cancer symptom control and proposes the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) pragmatic clinical trial to test a bundled intervention that leverages EHR interface and clinical decision support functionalities to operationalize the population-level implementation of an approach that automatically triages symptomatic patients to low-touch automated self-management (Level 1), or high-touch nurse care management (Level 2), both validated, depending on PRO scores, as well as patient and clinical factors. The E2C2 intervention will target Sleep disturbance, Pain, Anxiety, Depression, and Energy deficit/fatigue, the SPADE pentad which represents the most prevalent and potentially treatable group of overlapping cancer symptoms. To rigorously assess the effectiveness of discrete E2C2 intervention components, we will initially test a Stage 1 Symptom Control Bundle for 12 months, after which we will add a Stage 2 Implementation Bundle. The trial's stepped wedge design will randomize the order of E2C2 implementation among 21 clusters. Clusters will be defined at the level of the cancer care team, and will be randomized to one of five different tranches to receive the intervention at staggered 6 month intervals. Outcomes will include SPADE symptom scores (primary), physical function, social participation, quality of life, distress, healthcare utilization, adherence to cancer treatment, and vital status which will be collected for 9-12 months during each trial phase; pre-E2C2, Stage 1 and Stage 2. A multi-stakeholder, mixed methods approach will be used to comprehensively assess the impact of the Stage 2 Implementation Bundle, as well as both Stages' impact on rurally-based and elderly patients, groups prone to disparities in symptom control.

Public Health Relevance

The Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) pragmatic clinical trial will test the population- level implementation of validated approaches to detect and effectively manage sleep disturbance, pain, anxiety, depression, and energy deficit/fatigue among patients with all types and stages of cancer. The trial is important because it will rigorously test a means of deploying finite resources to yield maximal patient benefit in lessened symptom burden and functional loss, as well as enhanced survival and quality of life.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project with Complex Structure Cooperative Agreement (UM1)
Project #
1UM1CA233033-01
Application #
9625964
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Tuovinen, Priyanga
Project Start
2018-09-21
Project End
2023-06-30
Budget Start
2018-09-21
Budget End
2023-06-30
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905