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The global healthcare industry faces multiple challenges, ranging from maintaining an optimal operational efficiency to determining how to adapt to the new era of value-based care. A company which provides transformational clinical analytics to empower care providers adapt to existing clinical workflows and translate data automatically into the right action, at the right time and place is Clinigence. Analytics Insight speaks to Kobi Margolin, Co-founder & CEO of Clinigence to understand how the company is helping healthcare providers derive substantial business value from clinical data for quality patient care.
Clinigence offers data analytics software-as-a-service to healthcare providers, including hospitals and physician practices. Clinigence is the company behind one of the United States' largest and most diverse clinical data integration and analytics platforms – the Clinigence Value Improvement Platform (VIP).
Over the past decade, the US healthcare industry has been undergoing a major transformation. Driven by the imperative to reduce the cost of care, the industry has been shifting provider payment from volume- to outcomes-based models. Clinigence saw early on that this shift would create the need for a new breed of analytics in healthcare — clinical business intelligence.
When your revenue is tied to patient outcomes, you must have the analytic tools to manage these outcomes at the population level. Clinical business intelligence is the analytic layer that drives population health management — an emerging segment within the US healthcare industry.
We understood from the beginning that the ultimate value of healthcare data analytics is in the application of big data technologies. With all the advancement in medicine over the past few decades, it is unconscionable to realize how little we know. The vast majority of healthcare data unfortunately remains untapped when it comes to medical research.
I remember speaking a couple of years ago to the VP of Technology Development at one of the Nation's leading cancer research and treatment institutions. He shared with me that they were struggling to use their own patient data for outcomes-based research. Imagine what we could do to advance cancer research if we could leverage patient data on a global basis.
Compared to other industries, healthcare data is unique in its variety. The biggest challenge in clinical data analytics is in aggregating and harmonizing data from numerous varied sources, such as medical claims, electronic health records, patient monitoring devices, etc. Clinigence's unique innovation is leveraging artificial intelligence (AI) and machine learning technologies to this challenge. This allows us to aggregate and harmonize huge amounts of data in the cloud more efficiently than any other platform.
In an effort to manage population health outcomes, hospitals and physician practices in the US are forging new alliances. One of our early adopters, for example, is a community hospital that partnered with primary care practices to deliver higher quality care to patients in the community. Instead of "putting out fires" when patients arrive in the emergency department or have to be hospitalized, this "accountable care organization" (ACO) focuses on preventive care, effective post-acute care and wellness.
To achieve its goals, the ACO first needed to aggregate patient data across the community. This is where we came in. Within weeks, we provided them with community-wide patient records, empowering care coordinators to see the full picture of each patient. We also enabled clinicians to see what gaps in care (i.e. deviations from established medical guidelines) a patient had before seeing him/her in the exam room.
We also provide administrators with dashboards that highlight variations in care among staff members — a powerful tool for quality improvement. Medical directors and analysts get invaluable insights into both clinical and financial outcomes. As a result, the ACO has been able to improve quality by 30% and reduce the cost of care by 12% over 3 years. Such savings could mean hundreds of billions of dollars if applied across the nation.
Our experience shows that semantic web and artificial intelligence technologies can be used for large scale, highly efficient data extraction, transformation and analytics in the cloud.
Specialize in healthcare. We are only at the beginning stages of building a "healthcare learning system" that will accelerate medical knowledge for generations to come.
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