The endpoint of analytics is not a report or an alert. The endpoint is a decision. Often those decisions are related to your business and you make them to reduce risks, improve production or satisfy customers.
In health care, however, the decisions made with analytics can be a matter of life or death.
Consider the life-changing decisions that affected these patients:
- Heather is a young mother and math teacher. After suffering exhaustion for months that she attributed to her hectic schedule, she finally 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 saving her life.
- Reagan is young girl who suffered a stroke before she was born. Physicians provided her parents with genetic insights during the pregnancy and immediately after Reagan’s birth. The analyses helped the family make decisions about Reagan’s treatment path and about their long-term plans for a large family. As a result, Reagan began daily physical therapy so she could learn to walk. And before long, Reagan became a big sister to three younger siblings.
- Albert*, a heart surgery patient, is convalescing in his hospital bed after a recent surgery. He develops a mild fever and an increased heart rate. Are these early indicators of an infection? Or are they normal variations after the surgery? Fortunately for Albert, he’s at a hospital in the Region of Southern Denmark that analyzes these and other indicators, and alerts doctors of a strong likelihood that Albert’s symptoms are caused by an infection, so they can start him immediately on lifesaving antibiotics.
In each of these cases, physicians are using 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. Using data collected over time, teams of data scientists, IT specialists and medical professionals develop machine learning models and other analytics techniques that can help diagnose disease, treat patients and staff medical facilities.
Models that predict patient health concerns can now provide information to a physician who combines these results with the skills and information she is trained to act on.
Clearly, the hospitals are seeing great results with data and analytics. The medical staff understands the power of machine learning. And even the patients are seeing the results of the digital transformation at these facilities.
Analytics has proven to be important and effective in this setting. But did analytics create a better physician? Or an AI doctor?
No. AI and analytics made these physicians better, providing them with data-driven clinical decision support systems.
Decisions are the ‘why’ behind analytics
AI and analytics improved these lives by supporting the decision-making process:
- For Heather, DNA analysis improved the physician’s diagnosis to support a better treatment plan based on her genetics.
- For Reagan, genetic testing helped her parents make crucial decisions about her health and about their plans to have more children.
- For Albert, predictive analytics improved his life by identifying a silent killer before trained professionals could detect it.
They might never know that analytics and AI were at work. They might never have heard 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 know that AI is contributing to better decisions. They know about the art of the possible. They know the importance of data-driven decisions. And they know these programs are saving 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 improve your organization and the people that your organization supports or serves. Those people might be utility customers, grocery shoppers or citizens. But your work influences decisions that can improve their lives. We hope you take pride in that and throw that energy into your work with SAS.
As data scientists and SAS users, you create the connective tissue between data and impact. That is what you do.
Improving lives through better decisions. That is why you do it.Learn more about making better decisions with AI