What is Scalability in Cloud Computing

What is Scalability in Cloud Computing

Digital transformation is increasingly important in today’s healthcare environment. Providers can augment and correlate their own ever-growing patient data with an abundance of publicly available information, including social media, research and IoT connected sensors, to derive extremely valuable insights. They must leverage all of this data with predictive analytics and digital technology platforms to deliver the best possible care, overcome staffing shortages, minimize wait times, and reduce emergency room readmissions. At a time when healthcare costs are rising so swiftly, providers and hospitals can’t afford to mismanage resources.

However, with the onslaught of new data and applications associated with digital transformation come even bigger critical requirements. Healthcare organizations are faced with many challenges that include:, some of which are:

  • Selecting the best hardware and software to build a solid, reliable infrastructure to ensure uptime for these life-saving applications and enable users to access data and insights quickly and easily.
  • Squeezing the most out of their resource capacity to keep costs down.
    Accurately forecasting capacity needs to effectively scale and meet demand while managing costs.
  • Having the ability to test what-if scenarios to ensure applications are going to continue meeting SLAs.
  • And more.

With proven expertise in healthcare, Pepperdata helps healthcare organizations overcome all these challenges as they embark on their digital transformation path. Philips Wellcentive is a great example.

Philips Wellcentive

Philips Wellcentive processes data on a daily basis for over 35 million patients for 60,000 managed care populations to deliver information to providers, health systems, employers, and payers. Care managers also use the Philips Wellcentive platform to continually assess the quality of care for patients and determine their qualification for certain programs and services.

The company was running a large application every day to process and analyze medical records for their entire patient population, providing analytics on thousands of patient metrics to deliver better services. It originally took eight hours a day and significant resources to support, and the continual influx of data presented a reliability challenge. Additionally, the approach created a large amount of temporary data,