Leveraging Data Analytics for the Value-based Care Transition
By Dr. Lucio Gordan |
Community oncology practices are disproportionately feeling the reverberations of the transition to value-based care compared to most other healthcare stakeholders. While the primary goals are the same—improving care quality, lowering costs and increasing patient satisfaction—the circumstances are a bit more challenging, thanks to some unique economic and regulatory pressures.
The Journal of the National Cancer Institute anticipates the cost of cancer care to reach $173 billion by 2020, a 39 percent increase from 2010.1 At the same time, a 2016 report by the Community Oncology Alliance indicates that, since 2008, community oncology practices have been disappearing at an alarming rate, as evidenced by:
- A 121 percent increase in cancer clinic closures
- A 172 percent increase in practices acquired by hospitals
- A 54 percent increase in practices merged with or acquired by a corporate entity2
Add in the implications of the Medicare Access and CHIP Reauthorization Act (MACRA), the Merit-based Incentive Payment System (MIPS), ever-evolving regulations and unfamiliar payment models, and community oncologist practices must find new and innovative ways to survive (and thrive) amid some rather seismic shifts and tenuous conditions.
Follow the Data
Technology is widely regarded as the most promising means through which healthcare entities can navigate this rapidly changing landscape. It’s all about data or, more specifically, leveraging technology to compile, analyze and understand data in order to unlock the vast potential it holds to help organizations compete in the value-based era. For community oncology practices, this has significant clinical and financial implications. From a clinical perspective, an effective data analytics solution can reveal key information on a practice’s population, such as:
- The number of patients with a specific type of cancer
- Disease stages for patients at diagnosis
- Line of therapy
- Toxicity grading
- Performance status
- Dose density and intensity of treatment
- Time to response to therapy, progression-free survival, actuarial overall survival
- Research opportunities, including clinical trial matching
- Tumor molecular matching with specific treatment options and phase I/II trials
Data can also provide appropriate clinical decision support to help oncologists devise the most effective treatment plan and ensure that it complies with established guidelines. Additionally, physicians can determine whether the patient is eligible for a clinical trial and/or consult with virtual tumor boards for multidisciplinary perspectives on the optimal treatment pathway.
Also, as benchmarking becomes increasingly important in value-based arrangements, effective data analytics can provide critical intelligence for points of comparison with peers and national standards. For example, oncologists can leverage data to ensure treatment meets clinical standards in order to reduce variability and improve alignment. A practice can track, measure and compare the performance of its individual providers and/or different office locations to gauge adherence with quality metrics and, if necessary, make appropriate adjustments.
Data analytics can have a similar impact on a practice’s financial performance to help streamline operations and maximize efficiencies.
With detailed insight on staff productivity per patient, for example, a practice can ensure that its resources are optimally allocated and its workforce properly configured and aligned. Similarly, data can reveal important details on physician productivity in terms of numbers of patients seen, diagnoses, billing practices and utilization — all critical factors in lowering costs and supporting lean operations.
Data analytics can also help a practice better manage its revenue cycle by enabling it to identify any abnormalities or variances in coding, collections, billing and cash flow. This is a particularly significant area for oncology practices where expensive medications, supplies and procedures are commonplace and potential associated financial losses can be significant.
To this end, data can also provide critical insights for inventory management, especially when solutions like inventory cabinets, practice management systems and specialty electronic health records are deployed and integrated. In fact, these tools can automate inventory management for buy-and-bill products and significantly reduce the administrative burden of producing reports and learning and leveraging best practices for reducing inventory leakage.
Some oncology practices have even leveraged analytics to determine real estate values by factoring patient population figures into lease agreements. This allows them to accurately determine the size and location of facilities and then right-size or prepare for anticipated growth.
Taking the Data Plunge
While the power of data analytics is readily evident, adoption among oncology practices has been slow. Considering that most providers have expressed trepidations about implementing even basic health information technology;3 delving into next-level analytics is understandably even more daunting. However, with the right approach, challenges can be overcome relatively easily.
It starts with the designation of a physician technology champion or team that can continuously interface with the necessary stakeholders—finance, information technology, senior management—to thoughtfully assess needs and carefully devise an effective plan. This person or persons should be intimately familiar with existing care delivery workflows. Their involvement will increase the probability that other clinicians will embrace the data analytics strategy and recommended action steps—including new care processes—to improve medical outcomes. In this regard, the success of analyzing and leveraging data is as much a matter of organizational culture as it is engineering design. Community oncology practices that encourage innovation can reap benefits in quality, safety, and care coordination.
From there, it is crucial to select an analytics technology partner that offers the experience necessary to collaboratively develop the right strategy. The practice should consider what the vendor offers in terms of training and support, other organizations’ and users’ satisfaction with the proposed system, and perceived relevance and ease-of-use of its applications. There is no one-size-fits-all approach. What works for a large academic medical center likely won’t work for a much smaller oncology practice with considerably less resources. The right partner will understand this and draw on its expertise to recommend an analytics infrastructure and associated plan that meets a practice’s exact needs and budgetary parameters. It should then offer the resources to remain a steady strategic collaborator that can help the practice adapt to long-term changes and evolving requirements.
Despite the unique circumstances facing them, community oncology practices can meet the challenges of value-based care and capitalize on the opportunities it will ultimately present. Maximizing the vast power and potential of data is crucial. With some thoughtful planning and a trusted, consultative partner, a practice can survive today’s turbulence and be well positioned for tomorrow’s possibilities.
2. Community Oncology Alliance. “2016 Community Oncology Alliance Practice Impact Report.” 4 October 2016. Available online at https://www.communityoncology.org/wp-content/uploads/2016/09/PracticeImpactReport-2016-Report.pdf. Accessed 11 July 2017.
3. Friedman, Emily. “The Paper Chase: Why Are Some Providers Reluctant to Embrace Health Information Technology?” Hospitals & Health Networks. 2 April 2013. Available online at http://www.hhnmag.com/articles/6406-the-paper-chase-why-are-some-providers-reluctant-to-embrace-health-information-technology. Accessed 5 July 2017.