When the first wave of COVID-19 hit New York, the state quickly became the epicenter of the U.S. pandemic. Hospitals were overwhelmed. Public health systems were flying blind. The State of New York needed real-time visibility into who was sick, where, and how badly.
Every minute mattered. There was no time for red tape. No room for failure.
That’s when Cloudticity got the call.
The New York State Department of Health (NYSDOH) regularly collects information from the healthcare system in the State, but at the start of the pandemic did not have a process to ingest large clinical data sets directly from from six different HIEs, referred to as “Qualified Entities.” There was an existing system for the data to be shared amongst healthcare entities, the Statewide Health Information Network for New York (SHIN-NY), but not a way for the state health department to connect to the network to support the public health need. These data were needed to help state agencies see a more complete picture of how COVID-19 impacted New York residents and what steps they could take to slow the spread.
The NYSDOH was receiving CSV files and ingesting real-time data from over 100 stakeholders across the state, from symptom trackers to hospital admission records to co-morbidity profiles. Overwhelmed by CSVs, they needed a more automated approach to integrate, consolidate, and transform clinical data on COVID-19 patients so they could perform meaningful analysis.
The department needed to aggregate COVID-19 clinical data in order to understand:
To gain real-time insight into symptoms, comorbidities, and hospital stays, NYSDOH needed a centralized data hub. But with compliance frameworks like HIPAA and HITRUST governing every move, speed couldn’t come at the expense of security. NYSDOH initially contacted AWS for guidance, and AWS brought Cloudticity onto the project 5 days later.
Working side by side with AWS and more than 118 stakeholders, Cloudticity helped build and deploy the nation’s first COVID-19 data registry in just 11 days. The system ingested clinical data from across the state, normalized it, and presented it in a way public health leaders could use to guide policy decisions in real time.
Cloudticity enabled rapid onboarding and training of staff across agencies, and within six days of our involvement, the registry had an operational data lake with data flowing as well as a platform for using it.
Cloudticity Healthcare DataHub is a next-generation data platform purpose-built to quickly and efficiently transform large scales of healthcare data into actionable insights via 100% cloud-native services and groundbreaking automation. It allows healthcare providers to ingest, normalize, analyze, and report on patient data from a wide variety of sources in a fraction of the time and cost of traditional healthcare data solutions.
With Cloudticity Healthcare DataHub, there’s no physical infrastructure or tedious data transformation required. The solution automatically parses the data, allowing you to view all your data in a common language on a single dashboard. Combine real-time and historical data analysis to predict trends, improve care, and drive long-term growth with ease.
In just 11 days, New York had a fully operational, cloud-native COVID response platform. It allowed real-time tracking of hospitalizations, symptoms, ICU capacity, and vulnerable populations. This was achieved all while meeting the strictest security and privacy standards.
As a result, the department was able to:
Innovation in healthcare has traditionally moved at a glacial pace. But the COVID crisis showed that with the right partners, it’s possible to compress years of progress into days. Because when lives are on the line, 11 days can change everything.
In a world where speed and security are often seen as trade-offs, Cloudticity delivered both. At scale. Under pressure. On time.
In just 6 weeks, New York went from being the hardest hit state in the country to the first to cross green. That’s the kind rapid and radical health system IT transformation that Cloudticity makes possible.