Such benefits aren’t just appealing to practitioners. According to research conducted last year by the Deloitte Center for Health Solutions, 84 percent of the 56 health system executives surveyed said analytics will be important to their organizational strategies in the next few years.
Likewise, a March survey conducted by the Society of Actuaries found 89 percent of health care executives plan to use predictive analytics in the next five years — 4 percent more than 2018.
Partnerships Boost Data Collection for Oncology Predictive Analytics
To glean greater insights that can inform care via machine learning, an immense amount of data is needed.
A major boost came in 2013, when ASCO launched CancerLinQ, a subsidiary created to give oncologists a robust monitoring system that collects and analyzes data from all patient encounters. Built using SAP’s HANA platform, the system uses data lake infrastructure to store high-volume data in a centralized repository for Big Data and real-time analytics.
With more than 100 organizations sharing data, the effort “is rapidly evolving into the largest and most robust source of real-world evidence ever assembled in oncology for comparative effectiveness research and discovery,” Dr. Robert Miller, the medical director of CancerLinQ, told the Journal of Clinical Pathways in 2018.
And earlier this year, the Food and Drug Administration made news after announcing it would partner with Syapse, a provider of cloud-based software for precision medicine, for a multiyear research project using real-world evidence to support regulatory decision making in cancer care.
The joint effort will evaluate multiple sources — including clinical data from EHRs and molecular data from testing labs — to explore conclusions beyond the findings of retrospective clinical trials, which stakeholders say can be limited in context and patient diversity.
It aims to “make prospective trials more efficient and more reflective of how care is delivered in the real world,” former FDA commissioner Dr. Scott Gottleib said in a January 2019 speech.
How Analytics Improve the Treatment Experience for Cancer Patients
Not only can predictive analytics help guide a course of cancer treatment, they can streamline workflows to boost staffing efficiencies and improve the patient experience.
The oncology infusion center at Wake Forest Baptist Health in North Carolina uses analytics tools to anticipate peak utilization times and adjust scheduling accordingly. That enables “a much easier way to work than the steep incline and decline we used to deal with,” Karen Craver, a clinical practice administrator for the health system, tells HealthITAnalytics.com.
A similar notion is in play at Stanford Health Care. Several years ago, the busy medical complex turned to iQueue, a predictive analytics platform, to better understand factors that affect traffic.
“We look at extensive modeling of patient types and impose real-world constraints,” LeanTaaS president Sanjeev Agrawal, whose company developed iQueue, explained to MedCity News. “How many doctors are available? How many nurses? How long is a shift? Are they using these beds for something outside of a chemo unit?”
After one year using iQueue, Stanford was able to accommodate 25 percent more patients without sacrificing quality or safety, Agrawal says. It also saw wait times decrease by 30 percent at peak hours.
READ MORE: Learn how mobile devices are improving patient experience and quality of life.
Challenges of Predictive Analytics in Oncology
Still, the practice of predictive analytics isn’t yet widespread, especially among oncologists.
Compared to analytics-driven strides to address heart failure, pediatric asthma and other conditions, oncologists’ use remains “in the middle of the pack,” says Patt, who cites a number of hurdles that must be addressed.
Among them: HIPAA laws, which she says prevent “optimal data sharing.” Patt also cites the potential of incomplete patient data — a platform might contain outpatient data but not hospitalizations — and EHR tools that lack clinical decision support functionality.
Healthcare organizations must also support the necessary infrastructure to put predictive analytics into practice, says Dr. Parsa Mirhaji, director of the Center for Health Data Innovations at the Albert Einstein College of Medicine and Montefiore Medical Center in New York.
It’s “analytics that can consume these kinds of data service lines and workflow processes that are informed by a combination of care providers as well as automated systems collaborating with each other,” Mirhaji recently told HealthTech.
Patt considers the tools to be valuable for all specialties, even if startup costs seem daunting.
“Sometimes these investments in data infrastructure that are critical to improve in care delivery may be delayed because of the challenge in demonstrating return on investment,” she says, adding that resulting efficiencies and error reductions may satisfy those concerns.
“At the end of the day, as healthcare organizations, we all want to take terrific care of patients.”
