• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Why Healthcare Needs New Data and Analytics Solutions Before the Next Pandemic

February 28, 2021   TIBCO Spotfire
TIBCO Data in Healthcare scaled e1614362426384 696x366 Why Healthcare Needs New Data and Analytics Solutions Before the Next Pandemic

Reading Time: 3 minutes

Despite an increase in healthcare funds, hospitals are still unprepared for major global crises. To improve patient care and better weather the next pandemic, healthcare providers can invest in better data and analytics. 

Healthcare Data, An Untapped Opportunity

According to the World Economic Forum, hospitals on average produce 50 petabytes of data per year, which equates to 50 million gigabytes or the space of 50 million CDs. That’s a lot of data. Yet hospitals waste about 97 percent of this data when they could transform it to drive better healthcare outcomes. 

How can healthcare providers unlock this data through predictive models and unified data platforms so they can improve patient care and save more lives? 

What are some of these data-related opportunities?

  • With patient data spanning numerous diagnostic and care systems, greater data alignment and sharing can provide the complete picture of the patient that doctors require. This can accelerate diagnosis and treatment, and yield healthier outcomes
  • With better access and insights from their data, providers can better personalize patient care, saving everyone time and money.
  • And to stay ahead of outbreaks, healthcare analysts can leverage data and analytics to predict and prepare for future healthcare situations.

Intercepting Medical Emergencies Before They Happen 

One example of data at work is the University of Chicago Medicine’s (UCM) predictive models. UCM implemented an eCART solution that uses streaming analytics and a predictive algorithm to predict when a cardiac arrest is most likely to occur. Before a patient goes into cardiac arrest, a doctor is notified and can administer care to avoid a serious complication. UCM has been able to reduce the number of cardiac arrests in the hospital by an estimated 15 to 20 percent. UCM also uses TIBCO solutions to streamline its data for better data management between healthcare and insurance providers.

Predictive Healthcare Analytics Saves Lives

Post-surgical infections cause rehospitalization, often leading to increased healthcare costs, complications, and patient death. The University of Iowa Hospital and Clinics, using accessible analytics, reduced surgical infections by 74 percent. Combining patient care and historical data, the hospital developed a predictive model in a real-time environment, enabling faster and accurate decision-making, reducing costs by $ 2.2 million.  

Predictive analytical models like the ones featured above are critical during a mass pandemic. Due to COVID, many clusters of the population are going through monthly testing, which is time-sensitive and costly. The estimated annual cost of COVID-19 diagnostic tests was at least $ 6 billion, and in the case of a mass testing scenario, the costs jumped to $ 25 billion. By predicting the rates and likelihood of infection, hospitals can better prepare testing sites where they are needed, reducing the number of unneeded tests and the associated costs.

Currently, COVID testing is not always efficient or accurate enough for optimal diagnosis and care. According to a recent survey by the Annals of Internal Medicine, on the day of symptom onset, the median false-negative rate was 38 percent. It decreased to 20 percent on the third day and then rose to 66 percent in the following days. False-negatives result in sick individuals infecting more of the population. More data and better analytical models can help improve testing efficacy before the next health crisis.

Transforming Data Ahead of Future Global Crises

Serving seemingly endless demand for high-quality care will always be top of the list for healthcare providers. Investments in more unified patient data and more intelligent analytics can significantly improve patient care, every day and in times of crisis. 

By predicting the rates and likelihood of infection, hospitals can better prepare testing sites where they are needed, reducing the number of unneeded tests and the associated costs. Click To Tweet

To learn more about TIBCO’s role in making greater data-driven insight a reality for healthcare providers, check out this recent webinar. And to learn more about data’s role in patient care, stay tuned for an upcoming blog from Dennis MacLaughlin.

Let’s block ads! (Why?)

The TIBCO Blog

analytics, Before, data, Healthcare, Needs, Next, pandemic, Solutions
  • Recent Posts

    • Accelerate Your Data Strategies and Investments to Stay Competitive in the Banking Sector
    • SQL Server Security – Fixed server and database roles
    • Teradata Named a Leader in Cloud Data Warehouse Evaluation by Independent Research Firm
    • Derivative of a norm
    • TODAY’S OPEN THREAD
  • Categories

  • Archives

    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited