Healthcare Big Data Analytics Plays Critical Role in Quality

The healthcare industry has plenty of big data on its hands, but the ability to extract meaningful, actionable insights from this wealth of raw information will be the key to improving quality and patient outcomes across the developing learning health system, says a new white paper from the National Quality Forum (NQF).

In order for a broader spectrum of providers to see benefits from healthcare big data analytics, healthcare organizations must commit to cultural and leadership changes that promote an analytical approach to the practice of medicine.

Healthcare big data analytics challenges

The recommendations are part of a collaboration between NQF, the Peterson Center on Healthcare, and the Gordon & Betty Moore Foundation that aims to educate, encourage, and learn from healthcare stakeholders as they enter the complex world of big data analytics.

The white paper, entitled “Data for Systematic Improvement,” summarizes a recent meeting of healthcare experts who discussed how to make big data analytics a sustainable reality for more organizations.

While some pioneering providers have made significant strides towards harnessing big data for quality and operational improvements, the questionable impact of health IT systems on patient safety and provider productivity, as well as sluggish progress on health information exchange and health data interoperability that limits communication and care coordination, has left the healthcare industry floundering for immediate, actionable solutions.

“Sustained improvement requires a systems approach that takes into account the fact that multiple clinicians and healthcare workers are involved in a patient’s care, the complexity of modern diagnostics and treatments, the different settings that healthcare is delivered (such as hospitals, out-patient clinics, skilled nursing facilities, home health, and other settings), and the different determinants of a person’s health,” says the report. “For systems improvement tools to achieve their potential, they require multiple types of data, which can identify opportunities, gauge progress, and help users understand what works.”

During the meeting, participants shared the results of big data projects that have led to quality improvements within their organizations, such as leveraging predictive analytics to identify high-risk patients, implementing evidence-based protocols to reduce infections and adverse events, and highlighting the importance of patient-centered care.

“One important finding was that simply providing data feedback can drive improvement as long as the data is timely and clinically relevant,” the paper adds. “The project participants and surveyed leaders recalled multiple cases where clinicians improved their care practices once presented with trusted, accurate, and meaningfully synthesized data.”

“This occurred because feedback leverages clinicians’ intrinsic motivation as professionals to deliver high quality care. Furthermore, data are required for the success of other incentives for better care, such as payment programs, as clinicians and healthcare organizations need timely data to understand where to improve and track their progress.”

But the eagerness of healthcare organizations to provide this type of data to their clinicians has been met with some massive and stubborn roadblocks.  Stakeholders once again identified the chronic lack of health data interoperability as a major challenge for healthcare big data analytics, noting the difficulties in collecting and linking disparate data sources from across the community into a workable data pool. Providers also struggled with enacting internal improvements based on their own quality and performance data, as well as providing feedback to relevant parties in a timely way.

Healthcare organizations continue to wrestle with issues of data integrity, including the trustworthiness, completeness, and accuracy of information gathered from EHRs and other data sources. In order to create a big data platform that allows for optimal decision-making, organizations must continue to chip away at data siloes that prevent them from fully leveraging all available sources, including administrative data, claims data, patient experience feedback, and community-level information.

The issues aren’t just technical, either. Effective change management is difficult and slow to enact, the participants admitted, and changing organizational culture to become more reliant on data is often an uphill battle.

“Workforce training is required in how to apply process improvement tools, understanding the potential and limitations of data, and analyzing data. But training is not enough,” the report says. Providers must make an effort to revolutionize “the organization’s culture, the business case, leadership commitment to using data for improvement, and communication channels that share what works.”

“In addition, several participants also noted that successful initiatives depend on clear priorities, and that clinicians and healthcare professionals feel pulled in too many directions to make significant improvement in any one area.”

Healthcare organizations that hope to benefit clinically and financially from big data analytics must tackle these challenges quickly if they are to take advantage of big data’s potential. Starting small, in one or two areas of operational improvement, may help to build interest and buy-in that starts to build the case for more widespread improvements. Providers should be sure to gather input and feedback from as many internal stakeholders as possible to ensure that they are moving in the right direction as big data continues to spark innovative tools that make meaningful quality improvements a reality.

The National Quality Forum seeks public comment on this report and its findings, due by June 15, 2015. A webinar will be held on June 30 to discuss the comments and refine recommendations related to leveraging healthcare big data analytics on a larger scale for improved quality and better outcomes. sites list how to find my ip address . domain address . site rank offshore centre expidoms . domain analysis