Summer 2022
Harnessing advanced data modeling can optimize specialty care for both improved clinical outcomes and reduced cost.
Using data to aid in decision support is integral for many industries, and healthcare specialties like oncology are no different. For oncology care this approach links “…clinical guidelines to all manner of data in the patient journey” and uses “…business sense to recognize and prioritize the most valuable insights.” As such, more robust and complete data sets produce more informed decisions. For oncology care, the seriousness of the disease and often high cost for treatment requires improved information and analytics, yet the nature of its fragmented treatment can result in patient information gaps.
Though data may seem abundant, accessing all the necessary data to generate meaningful metrics with actionable results is cumbersome and out of reach for many organizations. Healthcare related data such as Electronic Medical Records (EMR), claims submissions and patient reported outcomes are often disconnected. With seamless, accurate and timely information that is integrated into the care journey, oncology care can be streamlined, clinical care can be improved, and costs can be lowered.
While analytic tools can improve care (see Case Study sidebar), they also drive down costs at the same time. Not only do robust data engines facilitate treatment recommendations, but they also can identify qualified candidates for clinical trials that offer cutting edge therapies at little to no cost. In addition, these same algorithms can identify patients that would benefit from palliative care, which reduces costly hospitalizations. Moreover, patients who are eligible for specific financial assistance programs that alleviate cost burdens can be quickly identified using demographic and EMR data.
Case Study: Leveraging Data for the Patient Journey
A male patient in his mid 50s was diagnosed with colon cancer in early 2022. Data was initially used to determine appropriate treatment per National Comprehensive Cancer Network (NCCN) guidelines and evaluate for optimal efficacy, reduced toxicity and decreased cost to the patient. Utilizing a curated mix of data from multiple sources including claims and demographic sources, the patient was assessed for clinical trials eligibility, palliative care and patient support programs. The patient’s consistent use of additional resources to report side effects also notified the patient care team to follow up in a timely manner, avoiding costly ER visits. The coordinated effort of mining claims data and consistently gathering demographic information resulted in appropriate supportive programs and cost reduction.
Spotlight: Projected Cost Savings
Key Numbers
$15B-$17B Annual Savings for US Oncology
This table outlines projected cost savings for 2025 US oncology care by using data analytics.2
| Technology | Savings Range |
|---|---|
| Consumer-focused sites of care optimization | 1.1%–1.3% |
| Enhanced clinical productivity | 2.6%–3.0% |
| Variability and waste reduction | 0.3% |
| Nonclinical efficiency | 1.7%–2.0% |
| Effective care delivery | 0.9%–1.0% |
| Total | 6.6%–7.6% |
2 Based on projected 2025 US costs for cancer https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-era-of-exponential-improvement-in-healthcare and https://www.fightcancer.org/sites/default/files/National%20Documents/Costs-of-Cancer-2020-10222020.pdf
For further information, please contact:
Adam Goldston (email)
Chief Growth Officer
OPN Healthcare