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Taking A Stand In Uncertain Times With AI

Taking A Stand In Uncertain Times With AI

Despite the difficulties created by Covid-19, leadership teams should stay the course for the good of their own business while making an effort to guide their customers, and the global community at large, by pursuing solutions that may help the world push past this pandemic.

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SwissCognitiveThis year ushered in a period of unpredictability; individuals, businesses, industries and economies were suddenly battling Covid-19. Few were exempt from the challenges presented by this pandemic, and many are seeking solutions that can help.

This moment in time has uncovered just how crucial solutions can be for the future of healthcare. Rapid changes have made it difficult to manage the pandemic’s spread and determine what the industry will look like after coming through the other side. Regardless of complications, it’s still the responsibility of leadership teams to use every tool at their disposal to manage the pandemic and be better prepared for the future. It matters that legitimate attempts are made by all — for the good of all — to pursue pioneering solutions in the face of this global challenge.

As the CEO of an software company moving into the healthcare vertical, I quickly realized this move needed to be expedited as Covid-19 presented itself. In collaboration with the CEO of a medical facility, my company produced a Covid-19 predictive model in conjunction with their superlative medical knowledge. I learned during this experience how quickly this pandemic has shortened the transition from ideation to utilization, and how well positioned is to aid advancements in healthcare.

Artificial Intelligence: A Driving Force For Healthcare

Critical industries like healthcare require sophisticated methods for increasing visibility across operations. From cutting-edge wearables providing more insight into individual health to expert support via reliable prognostic exploration, the medical community is realizing the benefits of numerous solutions helping drive dynamic advancements for healthcare. 

Examples of solutions taking a stand in the face of this pandemic include predictive models such as Penn Medicine’s CHIME, a Covid-19 capacity planning tool, and Washington State’s predictive dashboard, another pandemic risk assessment tool.

The predictive model my team built in partnership with renowned medical professionals focuses on the domain of expert knowledge. It works with multiple layers of heterogeneous and highly dimensional data categories to evaluate the consequences of social mobility on infection and hospitalization rates while estimating resource allocation supply and demand. Integrating multiple sources of time-series and spatial data into a model with learning capabilities was essential for more accurate decision-making.

This process has shown that when it comes to these predictive tools, it’s vital to understand that in order to utilize all of this data effectively, most conventional data processing, classification, pattern recognition, modeling and forecasting tasks must be automated and streamlined. This is key to delivering near real-time models with learning capabilities.

Such advancements in data science and can offer significant value in terms of creating data-driven, evidence-based tools that improve predictive performance over time. Codifying medical domain expertise in epidemiology with advanced data science techniques into models has the potential to protect millions of people from the risks of future pandemics.

Additional examples of integration into healthcare working in the fight against Covid-19, include chatbots and telehealth initiatives that utilize conversational , as well as -based drug and vaccine discovery and medical insurance fulfillment/billing optimization solutions. 

The Secrets To Successful Adoption And Implementation

adoption and implementation are the two biggest challenges in this technology becoming more commonly used in healthcare. […]

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