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Hospitals, primary care providers and clinics stand to benefit from the existence of a healthcare management system that makes the most of big data provided from electronic health records. While information from wearables gets a lot of press, the formal and organized structure of EHRs will likely be the influencing factor in using predictive analytics as a way to improve patient care.

To what extent big data can influence methods to improve the wellness of people remains uncertain, however. With the lack of consistent practices being a major obstacle, health providers must consider what are the best uses for EHR-linked predictive analytics in order to improve patient outcomes.

A nascent technology requires careful use
While there are many potential applications for the use of big data in a healthcare situation, the current capabilities of analytics platforms and other custom BI solutions remains somewhat limited. As a result, sometimes organizations run into unexpected obstacles. Modern Healthcare cited an example of a hospital attempting to mitigate costly hospital readmissions. Often the problem is that data lacks enough completeness to address enduring concerns such as whether a provider should monitor a patient for readmission risk. Most predictive models can't tell the difference between patients based on recidivism.

EHRs remain an important part of healthcare predictive analytics. EHRs remain an important part of healthcare predictive analytics.

The key to addressing this problem lies in better use of EHRs, according to the Harvard Business Review. While there are a wide variety of file formats and information standards going around, EHRs are often the most comprehensive information available to doctors and hospitals on the state of a patient. In implementing data from these records in a specific manner, care providers are more likely to make decisions on particular matters in ways that are effective.

Consider clinical decisions. While at the basic level, the predictive models aren't sound enough to make a high-value decision, itthey can help reinforce the choice made by doctors by incorporating best practices. The Harvard Business Review cited Parkland Health and Hospital System in Dallas, Texas, which performs risk assessments on heart failure patients. With high-risk patients, doctors perform evidence-based interventions such as educating the patient about their situation, telephone support within two days of leaving the hospital, an outpatient appointment after a week and a primary-care appointment after that. This helped reduce readmissions by 26 percent. Such low-value methodology helped prevent high-cost treatments, saving the hospital money in the long term.