Why Data Analytics is Important in Primary Healthcare

Data analytics is expected to reduce the cost of healthcare significantly. This is a welcome solution to a serious problem of increasing cost of healthcare in the United States. The cost of healthcare is a lot higher than it should be and continues to rise. This isn’t news to most people. But data and information technology can be a bigger part of the solution than most people realize, and while electronic recordkeeping still has a long way to go, it’s only part of the potential for these types of resources. Insurance companies are also trying to implement strategies that would reduce the cost of care such as switching from fee-for-service to plans that put patient outcomes and value of care first.

Fee-for-service sounds like a great, intuitive system that would reward the rational consumer. Unfortunately, instead, this payment method too often rewards doctors for using expensive and sometimes unnecessary treatments method and for treating lots of patients in a short time frame. This payment method consistently does not put the needs of the patients first or the quality of care for that matter. While managed care health systems have proven to be better care delivery model for cost control, it too has plenty of shortcomings. Especially when it comes to family medicine. Instead of seeing their primary care physician as a true family doctor that gains a holistic view of an individual’s health priorities, people tend to see these doctors as simply the gatekeepers for specialty care for which they’ve already identified their need online.  

Better Incentives and Methods for Clinical Practices

In the past, healthcare providers had no direct incentive to share patient information with one another, which had made it harder to utilize the power of analytics. The case is different now as there is a free flow of information between and within organizations. Now that more physicians and healthcare practitioners are getting paid based on patient outcomes, they have a financial incentive to share data that can be used to improve the lives of patients while cutting costs for insurance companies.

Clinical analytics and performance metrics have also helped physicians become more evidence-based. This means that they rely on large swathes of research and clinical data as opposed to solely their schooling and professional opinion. A good doctor never stops learning and catching up on the latest advances, new procedures such as non-invasive surgeries and so on.  As in many other industries, data gathering and management are getting bigger, and professionals need help in the matter. This new treatment attitude means there is a greater demand for big data analytics in healthcare facilities than ever before.

Specific Points of Emphasis

There’s no doubt that data analytics has already transformed the healthcare industry and continues to do so. The healthcare industry slowly started adopting the new technology and innovations that were flooding the industry and now the adoption rate has quickened. The adoption of new technology is what will push health and digital health to new levels. Here is a rundown of how data technologies are contributing to healthcare:

  • Predict the daily patient income to tailor staffing accordingly
  • Use Electronic Health Records (EHRs)
  • Use real-time alerting for instant care
  • Help in preventing opioid abuse in the US
  • Enhance patient engagement in their own health
  • Use health data for a better-informed strategic planning
  • Research more extensively to cure cancer
  • Use predictive analytics
  • Reduce fraud and enhance data security
  • Practice telemedicine
  • Integrate medical imaging for a broader diagnosis
  • Prevent unnecessary ER visits

Lingering Obstacles to Better Data Analytics in Healthcare

Incompatible data systems are the biggest technical challenge, as making these data sets able to interface with each other is quite a feat. Different healthcare organizations use different technology platforms to store and process their data and also use different binding techniques such as late-binding and early-binding. Thus merging data sets stored in incompatible systems always proves problematic.

Patient confidentiality issues is another big challenge. There are different laws state by state which govern what patient information can be released with or without consent, and all of these would have to be navigated. Patient pieces of information are sensitive and the wrong move could result in a huge lawsuit that would cost the healthcare organization more money and resources. In addition, institutions which have put a lot of time and money into developing their own cancer dataset may not be eager to share with others, even though it could lead to a cure much more quickly and improved healthcare for all.