In the event of public health crises such as the COVID-19 pandemic, it is imperative that governments are able to make quick policy decisions in short amounts of time. However, many of these policy decisions must be informed by quality medical research and, in order to conduct this research, medical institutions must first obtain grants to fund it. Seeking grant funding can add a significant barrier to the research process, as the grant application process is often reliant upon a variety of time-consuming factors, including providing evidence-based analyses and detailed budget plans.
While healthcare providers store many years’ worth of patient data on diagnoses and treatments, healthcare data is typically spread across – and siloed in – legacy systems that don’t communicate well with each other. In order to apply for grants, medical institutions need to be able to connect this data to rapidly develop patient cohorts and adapt them for use in medical research.
Shravan Kethireddy, Systems Director at Medical Intensive Care for Northeast Georgia Health System (NGHS), explained in the webinar “Research On-Demand: A Cloud Platform for Collaborative Research and Automation” that NGHS’s investment in IPC Global and Qlik’s research on-demand data platform has made it possible for lengthy data processes to be done immediately, which has helped drive medical research.
Research on-demand is a data network model that can grab data from a multitude of sources and integrate it seamlessly into a single platform. For medical institutions, this means the ability to combine data across legacy systems as well as outside networks to create an integrated data platform that’s ready for analyses in real-time.
NGHS wanted to conduct a study on secondary illnesses developed after COVID-19, but they first needed to locate a solid study sample – specifically, they needed real examples of patients that had contracted diverticulitis following COVID-19 hospitalization. With IPC Global and Qlik’s research on-demand platform, NGHS was able to narrow down their patient population of over 36,000 patients across over 40 million encounters to find an appropriate cohort for this study.
NGHS drilled this sample down by factors such as admission year, diagnoses, lab test results, medications, discharge position, and allergies. Once they established their sample, research on-demand made it possible to instantly transform the cohort into a Research Study Overview dashboard, where researchers could easily view all related data, as well as use it to interact with other data. For example, to provide accurate budgeting and cost analyses, NGHS needed to reference financial info that did not yet exist in the data model. The research on-demand platform made it possible to integrate information from non-data sources, such as CMS. With this ability, they were able to quickly assess the variable cost per patient per day in their cohort.
What used to be a lengthy process could be done in less than a day, and a research on-demand data network model helped NGHS procure funding to produce a completed and usable research publication within a year.
To learn more about how research on-demand helped NGHS speed up their research process, watch the webinar “Research On-Demand: A Cloud Platform for Collaborative Research and Automation”.
