Munson Healthcare is the largest healthcare system in Northern Michigan. They currently have more than 540,000 unique patients across 30 counties. There are nine community hospitals in this huge network. To manage such a huge customer base, having an accurate and comprehensive record of patients data had become a prime necessity for them to operate flawlessly. After all, healthcare is all about the highest quality of patient care and productivity for users. There was an asking for a new EMR to enhance operational efficiency. The motive was also to ensure compliance with increasing regulations. Complete Data Accuracy Platform from Naveego was not in their procurement list still. It was assumed that the new EMR will be effective enough to manage their newly acquired hospitals, outpatient facilities, and practice groups. But the new EMR system was not able to help in data integration. Something was seriously missing.
The data modeling process is arguably the nucleus of deploying cognitive computing technologies and applications across the enterprise today, largely because it’s a requisite for integrating those applications and their data for horizontal use cases.
Consequently, data modeling techniques have kept pace with contemporary data sources and deployments whose value, in most instances, hinges on low latency responses. In 2019, conventional relational data modeling approaches will all but lapse into obsolescence as numerous alternatives—in most cases, purpose-built for real-time use cases integrating heterogeneous data sources—surge to the fore of this facet of data management.