Why 90 percent of health care organizations are investing in advanced analytics
Most have been using the EMR for clinical data capture and financial management. However, this is starting to change, as organizations realize that the data in the EMR can be integrated into analytics solutions that could help doctors and nurses save lives. Patterns could be spotted more easily, and artificial intelligence could offer treatment options based on patient history, current symptoms, and similar cases.
The good news is that 90 percent of health care organizations either have or will upgrade or implement analytics in the next 12 months. The bad news is you might be left behind if you’re not one of them. And even those adding analytics capabilities often lack the resources, strategy, or know-how to move forward.
Here’s why more health care organizations are turning to analytics — and why it’s so important to get started.
Leveraging EMRs for analytics
Understanding advanced analytics is an important first step to demonstrating their importance. After all, health care organizations have been conducting basic analytics like reports and dashboards for years. But data sets have grown in both size and complexity, requiring increasingly sophisticated tools like machine learning and natural language processing to find insightful and actionable patterns that a human cannot.
Many organizations will start their analytics programs by analyzing structured data from EMRs, which offers a wide variety of information—height, weight, allergies, prescriptions, and vital signs, for a start. But not all important health data is necessarily structured. In fact, 70 percent of EMR data is unstructured.
The unstructured data— physician notes on a patient’s family history or medical images, for example–can be just as important as structured data, but is more difficult to extract and analyze.
Add socioeconomic and wellness data submitted by patients, such as exercise patterns (reported by wearable activity trackers), income, and environment, and you have a web of connected data with a huge number of factors that can contribute to new programs and services to improve health outcomes. The eventual goal is to have artificial intelligence capable of combining and analyzing all of these structured and unstructured data sources as part of a comprehensive analytic solutions.
Predictions would be more accurate as the data pool grows, hopefully leading to better preventative care, more specialized treatment, and better health overall.
Of course, recognizing this as the end goal is often overwhelming, and can turn medical professionals off to the overall idea of analytics in medicine. If you don’t and can’t have all the information right now, should you even try?
Turning analytics into results
Despite the urge to wait until all the data is available, starting your program and bolstering it with more data later is typically a better idea. Building an analytics program, even on smaller amounts of data, can be immensely helpful to health care and drastically increase the quality of care and the value to your patients. Just look at some of the ways analytics have been used in health care thus far.
For example, Sharp HealthCare built a predictive model using structured EMR data to better identify which patients would be at highest risk of requiring a rapid response team (RRT) intervention. With this type of insight health care organizations could better allocate limited RRT resources, increasing their efficiency and resource management.
Analytics can identify those at risk for preventable events (such as falls) and improve their safety, spot frequent hospital-acquired conditions to prevent infections, anticipate how weather might increase patient volume, and more.
With so many resources already poured into EMR implementation, using analytics to extract valuable information from them just makes sense. And with so many uses of EMR data, health care organizations have a number of places to get started.
Keeping up with the competition
EMRs implemented a certain amount of consistency in health care, and turned the field into a digital industry. But there’s still quite a way to go. The eventual goal, automation and artificial intelligence, may still be pretty far off, but analytics aren’t too much of a stretch.
The right tools can help predict illness and prevent readmissions, make sure that staff and resources are used efficiently, lower the overall cost of treatment, and improve patient health. Make sure you’re not the last to experience these benefits.
About the author
Andy Bartley is a Senior Solutions Architect in the Health & Life Sciences Organization at Intel Corporation. His focus areas are client technology and predictive analytics for healthcare providers. Andy serves as a trusted adviser to stakeholders across the care continuum to aid in the development of leading-edge technology solutions that improve the provider and patient experience and enable healthcare delivery systems to deliver new models of care.