Big Data Analytics, Precision Medicine Top Priorities in 2020. Health systems are increasing their investments in technology that will boost big data analytics strategies and precision medicine, but limited resources and lack of reimbursement for these tools are still significant barriers for organizations, according to a report from the Center for Connected Medicine and KLAS Research.
As healthcare continues to shift to value-based care, health systems are seeking to harness all available data to make more informed decisions and deliver more effective treatments.
“Data analytics, precision medicine, and patient engagement are highly connected to the shift from fee-for-service to value-base care. As provider organizations continue to shift their payment models and take on more risk, there is a greater need for data visibility so that organizations can manage their most at-risk populations and make better care decisions,” the report stated.
Through interviews with 70 leaders representing 65 unique health systems, researchers found that many are heavily investing in technologies that will help improve data analytics and aggregation efforts.
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“Effective and comprehensive data aggregation has the potential to enable better clinical decision-making and power population health management,” report authors said.
“In order to move the needle on outcomes and cost, organizations are seeking clean, normalized data from an ever-growing number of data sources. Many believe that complete data aggregation will be an ongoing pursuit.”
On average, respondents said they are 71 percent of the way to complete integration of their clinical data. Most health systems said they use a single software platform or completely integrated EHR to integrate the majority of their data sources.
Full integration, or the aggregation of clinical data with data from financial systems and other outside sources, is progressing a little slower. On average, organizations reported 61 percent completion with full integration.
Researchers also found that the size of an organization plays a role in the advancement of integration efforts.
“Compared to their small and midsize peers, large organizations prove to be slightly more advanced in both clinical and full integration. These organizations are less likely to cite resource or funding constraints as a barrier,” the report said.
EHRs are also a key element in boosting data integration and analytics. Ninety-four percent of organizations reported using their EHR vendor for help achieving their analytics and aggregation goals, while three-fourths of respondents said they use an internal analytics team.
“Larger organizations are the most likely to have such a team, which they leverage often, as there are always additional data sources to integrate. In addition to the EMR, organizations typically use two or three other analytics software tools as well as an integration engine for the connections needed,” the report said.
Most organizations reported having integrated EHRs (98 percent), imaging data (83 percent), and claims and payer data (80 percent), but note that they still face barriers to full integration.
“Imaging data and claims/payer data have historically been difficult to aggregate, so it is surprising that such high numbers of respondents report successful integration in these areas,” report authors said.
“However, even those that report success have some of the same challenges that have always existed with these data types—i.e., imaging data is reported as incomplete (organizations have integrated referential data but not diagnostic data), and claims data is often outdated and hard to ingest due to a lack of data standards.”
The most commonly cited barriers to data integration were limited resources and funding at 44 percent, poor data normalization at 44 percent, and lack of standards at 40 percent.
“The cost to integrate can be high, and it is common for organizations to lack the funding or resources needed to properly integrate and analyze their data. Additionally, aggregated data is useful only if it is accurate, and poor normalization and standards inhibit organizations from being able to trust the data they have,” the report said.
“This lack of standards, in conjunction with a lack of strategy and governance, is cited by the majority of midsize organizations as their biggest challenge. Small organizations were the only ones to report intentional data blocking as a barrier.”
In addition to data analytics and aggregation, organizations are seeking to ramp up their precision medicine efforts. While the report found that most respondents are still very early in their exploration of precision medicine, there is a lot of optimism surrounding this area of care delivery.
Nearly 70 percent of respondents report low maturity or no deployment of precision medicine efforts, and just 12 percent of health systems can be categorized as mature in their precision medicine efforts. Overall, health systems reported using precision medicine for three use cases, with the most common being oncology.
While respondents report feeling excited about the potential for precision medicine to improve patient care, many still cite reimbursement barriers as well as a lack of resources. Fifty-one percent said they’re struggling with reimbursement or return on investment (ROI), and 11 percent said they face technological challenges.
“Currently, most precision medicine efforts are being funded through fee-for-service models or out-of-pocket payments by patients. Many organizations also utilize research grants as a source of funding, but this is not seen as a viable option for accelerated or long-term growth,” the report concluded.
“Organizations expect that these funding strategies will decrease in usage as value-based payments for precision medicine increase in alignment with the general industry’s shift to value-based care.”
Source: Health IT Analytics