Why Healthcare Should “Double-Down” on Exploring AI-powered BI for Reporting
Not understanding data often is the difference between success and failure.
- Between positive and negative health outcomes.
- Between improving and worsening patient satisfaction.
- Between increasing or decreasing revenue.
Many areas in healthcare rely not only on the collection of data but, importantly, the ability to decipher and act upon it. In that intersection, reporting was born. After the advent of reporting, healthcare organizations looked to business intelligence to understand and act more quickly on the information created by data.
A new three-part article by MedeAnalytics President Scott Hampel focuses on reporting trends in healthcare. Learn why healthcare organizations must transition from manual reporting to AI-fueled predictive analytics.
Get our take on industry trends
Combating the opioid epidemic in North America with Nobel Prize winning research
American Researchers Dr. David Julius and Dr. Ardem Patapoutian from California were awarded the Nobel Prize in medicine for their path breaking work on the development of non-opioid painkillers, showing immense promise for combating the epidemic.
Why managed Medicaid/Medicare health plans need analytics to improve outcomes
Managed care organizations that provide healthcare services to Medicare/Medicaid members are dedicated to improving the health and wellness of these underserved populations, especially those living in rural areas.
Using consumer analytics to steer health-related decisions
Companies tap into what people like to eat and drink, how we purchase consumables, where we like to shop, what shows we might like to stream, whether we vote, and so on. If you have ever created a profile on a streaming application (think Netflix or Amazon), you will receive recommended books, movies and other items just as soon as you start surfing.
Run: Bringing Data Science into your Organization
In this three-part series, we’ve been detailing a tiered approach to introducing and incorporating data science into your organization. In Part One: Crawl and Part Two: Walk, we discussed how to get started from scratch and start building out a dedicated data science program. Today, we’ll dive into the third and final phase to see how to grow quality, centralize governance, incorporate user feedback, and more.