How can healthcare organizations turn their big data assets into actionable clinical intelligence?
Healthcare organizations on the hunt for lower costs, better outcomes, and value-based care bonuses have invested heavily in hoarding as much big data as they can get their hands on.
From customer service call logs and clinical documentation to satisfaction surveys and patient-generated health data from the Internet of Things, providers of every size and specialty have fully accepted the notion that no scrap of information will go to waste in the era of machine learning, artificial intelligence, and semantic data lakes.
This may be true in the very near future. In just the past few years, the healthcare industry has made huge leaps forward in clinical decision support and predictive analytics.
The use cases for big data are proliferating rapidly as organizations move deeper into population health management and accountable care, and consumers are keeping pace with their growing demand for cost-effective services that leverage the convenience of their favorite apps and devices.
But despite the data-driven promises looming just over the horizon, the majority of healthcare organizations still have a great deal of work to do before they can turn their budding big data analytics competencies into truly actionable clinical intelligence.
A chronic lack of direction, exacerbated by deeply entrenched interoperability issues and a widespread inability to secure a qualified data science team, have left organizations in something of a slump. A series of industry surveys from recent months point out significant staffing gaps, frustrating health data exchange roadblocks, and organizational planning deficiencies that are keeping providers from breaking through their data doldrums.
“The point of analytics is to help make better decisions on a timelier basis,” says Dr. Danyal Ibrahim, Chief Data and Analytics Officer at Saint Francis Care. “But as we all know, there are so many times when our data ends up siloed. One component goes to the finance department, another to IT, and another to the quality improvement team.”
“So even though the data is supposed to be connected around a single patient’s story, ultimately it lands in different siloes all around the organization, and that can be a big barrier to using data to improve care.”
In order to develop a successful big data analytics initiative that can overcome every obstacle from data collection to point-of-care reporting, providers must not only understand where the challenges lie, but also what lies ahead once they overcome their issues.
What does it mean to achieve success with big data analytics, and how can healthcare providers reach their ultimate goal of extracting valuable insights from their rapidly expanding data stores?