Mercy: Harnessing Data to Battle COVID-19
Key Takeaways
⇨ Mercy utilized an Intel-based SAP HANA solution to transform unstructured clinical notes into actionable insights, enhancing its ability to manage COVID-19 patient care and predict resource needs.
⇨ By leveraging natural language processing (NLP) and artificial intelligence (AI), Mercy was able to rapidly build models to answer critical questions about COVID-19 spread, peak, and patient outcomes, improving overall healthcare response.
⇨ The real-world evidence (RWE) platform enabled better inter-departmental communication, coordination, and collaboration with manufacturers, leading to improved treatment strategies and patient care during the pandemic.
The COVID-19 pandemic is a global event few could have imagined. The dramatic spread and lack of knowledge around the illness caused countries to close their borders and isolate their populations.
COVID-19 presented unprecedented challenges for healthcare systems responding to the novel coronavirus, medical device manufacturers having to keep up with demand for supplies like ventilators, and pharmaceutical companies as they began their rush to develop a vaccine. Little was known about the virus as cases continued to rise worldwide, overwhelming some hospitals and the healthcare industry as a whole. Critical questions needed to be answered quickly to ensure healthcare worker safety, capacity planning, and the best possible patient care.
Mercy, a regional healthcare system, was able to repurpose an analytics system that combined structured and unstructured data to create a smarter view of how COVID-19 was spreading and how it affected patients. This view gave Mercy the capacity data it needed while also gaining a better understanding of how to treat
COVID-19 patients. This system combines technologies from Intel, SAP, and others to provide a novel method to extract critical insights from clinical notes using natural language processing (NLP) and artificial intelligence (AI). This real-world evidence (RWE) platform is helping Mercy and some of its neighboring health systems, in addition to drug and medical device makers, address the needs of patients and healthcare providers, and it will continue to provide value after the COVID-19 pandemic recedes.
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The challenge of the unknown
Mercy has a long tradition of technical innovation and leadership that has led to smarter, more informed care and lower healthcare costs. As an early adopter of electronic health records (EHR) software, Mercy implemented the Epic EHR system to standardize health records. What Mercy found, though, is that while EHR systems do well at tracking patient data and billing information, the most valuable insights into patient care and outcomes were buried within clinical notes. This meant that the data that could be the most valuable in determining trends, geographical spread, co-infection and other risk factors, treatments, and outcomes were found in unstructured, oftentimes handwritten clinical notes. But how could Mercy extract and use that highly valuable source of data?
Finding answers in unstructured data
The solution to this problem was to digitize and index Mercy’s clinical notes and combine them with structured data not only from Mercy’s systems, but from governmental and other healthcare system sources. “You can’t answer in-depth questions from higher-level data sources,” says Curtis Dudley, VP of Enterprise Analytics and Data Services for Mercy.
Learn more about this Intel case study by downloading the PDF.
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