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Improving Lives through Analytics based decisions

The endpoint of analytics is not a report or a vigilant. The endpoint is a decision. Often those decisions are cognate to your business and you make them minimize peril, amend engendered, or gratifying customers.

In health care, however, the decisions made with analytics can be a matter of life or death.

Consider the life-transmuting decisions that affected these patients:

  • Heather is an adolescent mother and math edifier. After suffering lassitude for months that she attributed to her hectic schedule, she conclusively found answers in a DNA test. The test, conducted through a population health program at Renown Health Systems, revealed a genetic condition that causes lung and liver disease. Now she gets monthly treatments that are preserving her life.
  • Reagan is an adolescent girl who suffered a stroke afore she was born. Medicos provided her parents with genetic insights during the gravidity and immediately after Reagan’s birth. The analyses availed the family to make decisions about Reagan’s treatment path and about their long-term plans for a sizably voluminous family. As a result, Reagan commenced daily physical therapy so she could learn to ambulate. And afore long, Reagan became an astronomically immense sister to three younger siblings.
  • Albert, a heart surgery patient, is convalescing in his hospital bed after recent surgery. He develops mild pyrexia and an incremented heart rate. Are these early designators of an infection? Or are they mundane variations after the surgery? Auspiciously for Albert, he’s at a hospital in the Region of Southern Denmark that analyses these and other indicators and alerts doctors of a vigorous likelihood that Albert’s symptoms are caused by an infection, so they can commence him immediately on lifesaving antibiotics.

Physicians are utilizing analytics to examine types and volumes of data that they simply cannot assimilate and ascertain on their own. A machine learning model, however, can do that for them. Utilizing data amassed over time, teams of data scientists, IT specialists, and medical professionals develop machine learning models and other analytics techniques that can avail diagnose disease, treat patients, and staff medical facilities.

Analytics has proven to be consequential and efficacious in this setting. But did analytics engender a better physician? Or an AI doctor?

No. AI and analytics made these physicians preponderant, providing them with data-driven clinical decision support systems.

Decisions are the ‘Why’ behind analytics?

AI and analytics ameliorated these lives by fortifying the decision-making process:

  • For Heather, DNA analysis ameliorated the physician’s diagnosis to fortify a better treatment plan predicated on her genetics.
  • For Reagan, genetic testing availed her parents to make crucial decisions about her health and about their orchestrations to have more children.
  • For Albert, predictive analytics amended his life by identifying a silent killer afore trained professionals could detect it.

They might never ken that analytics and AI were at work. They might never have auricularly discerned of SAS® software or about the hospitals’ digital transformation initiatives.

And that is OK.

The hospital administrators, the IT department, and the data science teams ken that AI is contributing to better decisions. They ken about the art of the possible. They ken the paramountcy of data-driven decisions. And they ken these programs are preserving lives.

Even if you are not in the health care industry, the work you are doing with analytics and AI leads to better decisions that ameliorate your organization and the people that your organization fortifies or accommodates. Those people might be utility customers, grocery shoppers, or denizens. But your work influences decisions that can ameliorate their lives.


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