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COVID-19 health care response boosted by mature analytic structure

SAS, Intel share insights from global health care leaders

As COVID-19 struck, one of the most critical decisions governments and health care providers around the world faced was how to best allocate limited medical resources, including intensive care unit (ICU) beds and ventilators. Health organizations responding to coronavirus who already had a mature analytics structure fared better than those that didn't.

SAS, the global leader in analytics, and Intel recently sponsored an Economist-hosted webinar Beyond the Coronavirus, gathering global health care leaders for a spirited discussion that revealed some timely lessons regarding the role of analytics in each organization's response to the crisis.

1. Nurture a culture of analytics – before a crisis hits

"Beyond any single analytics capability or software, many health care and government organizations that have so far excelled in their response to COVID-19 have nurtured a culture of analytics. For those who are earlier in their analytics journey, this may seem daunting – but it shouldn't," said Steve Bennett, Ph.D., Director of SAS' Public Sector Practice. "The crisis we are facing today has presented an opportunity for health care and government leaders to refocus and accelerate their analytics efforts."

2. Articulate which decisions are most important

For any kind of analytics, the starting point should always be the decision. Ask: What insights do I need to make this decision better, faster or cheaper? Start with the end in mind, and the technology will fall into place. Start with technology, and your organization could be at risk of creating an advanced capability that ultimately gathers dust.

It's also important to remember that any new technology capabilities will require a human touch. "Technology isn't meant to replace the human interaction – it's meant to augment and hasten medical research, perform faster analysis, and speed up testing and trials," said Rachel Mushahwar, Vice President and General Manager of Enterprise, Government and Next-Wave Cloud Sales at Intel.

3. Build models focused on the most important questions

A crisis such as a pandemic changes the urgency, impacts and scenario options that surround analytical models, but not necessarily the decision needs those models serve. Those already accustomed to using analytics to help make resource allocation decisions were better prepared for the same types of decisions in the context of a pandemic.

Cleveland Clinic created a range of models that help forecast patient volume, bed capacity and ventilator availability. The models provide timely, reliable information for hospitals and health departments to optimize health care delivery for COVID-19 and other patients and to predict impacts on the supply chain, finance and other critical areas.

4. Accelerate transformation when in crisis mode

Several health organizations have reported that they executed long-planned, multi-year digital transformation plans in a matter of weeks or even days due to the unique, acute challenges presented by COVID-19. These organizations embraced change because they simply had no choice. And it's reasonable to assume that more transformative change is on the way, especially in areas such as virtual care and remote consultations.

Glenn Gutwillig, Managing Director at Accenture focusing on global public health, said that one health care client "literally, within the first few weeks, had a 3,000% surge in their use of telehealth." Nobody could have anticipated that rate of change in such a short time, but those ready for change were able to make it happen.


Source: SAS



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