When people say “big data” in health care, I think of the information needs of big players to solve industry-wide challenges such as Medicare trying to control billions of dollars in health care spending or how pharmaceutical manufacturers are trying to find a drug that prevents or cures a major disease. At the practice level, it's really the “little data” that can make a big difference. For physicians, little data are more actionable. Yet, the challenge for physicians is tapping into their wealth of little data for insights on how to improve their clinical and financial performance.
That challenge is particularly difficult for community-based oncology practices. While they may be swimming in clinical and financial data, they don't always have the means to extract useful information to improve patient or business results. With the transition to value-based reimbursement models, it's critical for oncology practices to adopt advanced analytics as more payers base payments on outcomes.
Actionable Clinical Insights Generated from Data
So what could the data generated by a community-based oncology practice tell their doctors and office staff?
- Data could show details about the practice's patient population (i.e. how many patients were treated for each specific type of cancer, disease stages for patients at diagnosis, treatment plans for patients and outcomes of the treatment plans for patients).
- Data could offer clinical decision support. That means looking critically at different treatment options and choosing the best possible option for patients, or staying apprised of the latest clinical trials and identifying patients eligible for those clinical trials.
Data could show the practice how its care compares with national benchmarks, accepted clinical protocols and evidence-based standards. The data could uncover any inconsistencies or variations in care and provide direction on how to improve.
In short, how do we support medical oncologists in understanding which treatment options are the best options for their patient population? That answer can come from this data.
Data Also Provides Actionable Insights on Financial Performance
Data can tell an oncology practice a lot about its financial performance as a business. The data can offer insights into both revenue and expenses. For example:
- The data can measure nurse and staff productivity. Are nurses and office staff working at their full potential and, if not, can their time be redeployed or allocated to other functions to improve operations or performance in other areas?
- The data can also measure physician productivity. Is each of the practice's doctors seeing the optimal number of patients? If not, why? Is each of the practice's doctors maximizing revenue opportunities and minimizing unnecessary expenses without sacrificing the quality and safety of patient care?
- And the data can monitor the practice's revenue cycle management performance. It can flag problem areas in coding, billing or collections which allows practices to take the appropriate actions to maximize cash flow.
Discussions about such financial topics can be delicate and sensitive. The value that data brings to those discussions is objectivity. It tells a story about actions the practice can to take to improve its financial performance.
Overcoming Barriers to Adopting Oncology Data Analytics
Data can provide actionable clinical and financial insights, and the marriage of the two through data analytics is powerful. That union can build collaborative relationships between oncology practices and payers over value-based care models. It can also build reciprocal relationships between practices and oncology drug manufacturers over research and clinical trials.
Why haven't more practices invested in analytics to harness the power of their clinical and financial data? The three primary barriers are scale, expertise and cost.
- Scale refers to a practice having access to clinical and financial data beyond its own walls. To understand whether its treatment choices are working, for example, the practice needs to compare its outcomes with that of other practices with a similar patient population.
- Expertise is about knowing how to collect the clinical and financial data, how to analyze it, how to compare the data with other data and how to derive actionable insights. Oncologists are doctors. They're not data analysts.
- And cost is obvious. For community-based oncology practices, it's often impossible to justify the investment in information technology and the staff to run it.
The solution to overcoming all three barriers is finding the right partner. That partner could be another oncology practice or related medical specialty practice willing to share and scale data for analytical purposes. That partner could be an oncology data analytics technology vendor that can provide both the scalability and the expertise that small practices need. Or that partner could be a payer willing to subsidize the cost of the technology and expertise in exchange for insights on how to achieve the best possible clinical outcomes for cancer patients at the lowest possible cost.
For community-based oncology practices, finding the right partner is worth the effort as the return on investment will be substantial as measured by better outcomes for cancer patients and better business results for the practices themselves.
Related: Learn more about McKesson's Oncology Practice Analytics Solution.