Reducing Healthcare Costs with the Right Data

Amanda Herriman, Marketing Manager Data Analytics

Muhammad Ali famously said, “Float like a butterfly, sting like a bee.” He was talking about boxing, but this also applies to rising healthcare costs. These float continually higher, often unobtrusively, until the point where we really feel the sting.

Implementing the right data (and data science!) allows healthcare providers to introduce optimization methods focused on improving patients’ health and reducing costs. A report from McKinsey estimates five primary ways to do this:

  1. By making information transparent and enabling its use at a higher level.
  2. Collecting accurate and detailed data, which exposes variability and improves results.
  3. Segmenting audiences more narrowly to enable precise customization of products and services.
  4. Providing sophisticated analytics to improve decision-making.
  5. Using predictive analytics to enhance future products and services, leading to reduced waste and shorter timelines.

Here are examples of how this theory could be put into practice to reduce costs.

Eradicate Upcoding

Some doctors and healthcare institutions use the fraudulent tactic of upcoding, charging for more expensive procedures than took place. Several facilities have consultants who specialize in finding ways to upcode, and if this happens at any point, it increases costs for all parties. Using data to “feed” an algorithm predicting the likelihood of upcoding based on traits such as service provider, demographic data and treatments administered could eliminate this.

Predict and Pretreat

A 2004 study showed early intervention could reduce costs of chronic kidney disease (CKD) by $12,000 per patient, by lowering their risk of comorbidities like diabetes and hypertension. With appropriate data, healthcare providers can develop algorithms to predict the risk of these complications. They could focus on controllable activities such as diet and exercise, and pretreat patients before comorbidities occur. This principle applies to a range of medical conditions that, if managed correctly, could result in lower costs to patients, insurers and institutions.

Machine Learning and Analytics Unlock Value

According to McKinsey, using big data creatively could drive cost-effectiveness, quality and value to more than $300 billion in savings per year in U.S. healthcare. For example, the American Society of Clinical Oncology (ASCO) is using its CancerLinQ solution to extract intelligence from medical records of 97 percent of cancer patients who are not participants in clinical trials. This should enable the society to make better, more data-driven decisions.

These examples show the importance for healthcare to use data, and where information is not specific, it’s probably worth it to gather more.

For more information on how to reduce healthcare costs with the right data, call Wax Custom Communications at 305-350-5700 or visit waxcom.com.

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