5 Uses for Electronic Medical Records Analytics

Feb 28, 2017

Electronic Medical Records (EMRs) are a life-saving resource for healthcare providers. In an emergency, whether in the field or at a hospital or medical center, EMTs and doctors have access to a patient’s medical records instantly. When healthcare providers have a full picture of a patient’s past surgeries, current medications, etc., they can make quick and informed decisions about immediate patient care.

Data analytics can take using EMRs to the next level. This tactic enables an enterprise-wide comparison view of consistent data from many sources. By applying algorithms to EMR data, healthcare providers can forecast trends in the health of individual patients and entire populations for a more proactive approach to preventative medicine.

Here are 5 ways predictive analytics can be applied to EMRs:

1) Recruiting for Clinical Studies

Healthcare providers rely on clinical trials to develop and test new treatments and drugs. Finding ideal research study candidates can be challenging. In the past, researchers were hampered by selection criteria that lacked specificity and clarity. They also had a limited pool of data to draw from.

Some still struggle with this aspect. EMRs certainly alleviate some of the pressure. But, according to a report by Fractal Analytics, a global strategic analytics consultant, 80% of clinical trials today still experience long delays because of the challenge of recruiting and retaining  study suitors. These delays can cost researchers $35,000 a day.

The answer is to put the data derived from EMRs to work with comparison analyzation. Predictive analytics allow medical professionals to see the big picture and streamline data. Therefore, pinpointing patients or volunteers that fit the medial criteria needed for a particular study becomes easier and more efficient.

2) Reducing Hospital Readmissions

The Affordable Care Act penalizes hospitals for excessive readmissions within 30 days of discharge for conditions such as acute myocardial infarction (AMI), heart failure, and pneumonia. The Hospital Readmissions Reduction Program reduces Medicare and Medicaid pay-outs to healthcare providers with large readmission rates.

Predictive analytics combines information about socioeconomic status with data in the patient’s EMRs to forecast the likelihood that a patient will need to be readmitted. Patients at a high risk for readmission can be given extra treatment and follow-up appointments at the hospital or local care center to prevent a relapse.

By reducing emergency room visits and hospital readmission for at-risk groups, healthcare providers can save money and reduce the number of Medicare and Medicaid claims that need to be processed.

3) Tracking the Spread of Disease

The risk of spreading disease was highlighted by the recent Zika virus outbreak. The Center for Disease Control (CDC) and the World Health Organization (WHO) worked to map the spread of Zika as it posed a specific threat to unborn children.

The opioid epidemic has contributed to the spread of HIV and Hepatitis C. The CDC has brought together leaders in big data technology like Tableau, Alpine Data, Trifacta, and Arcadia Data to use Collaborative Advanced Analytics and Data Sharing (CAADS) developed by Lockheed Martin. With CAADS, the CDC can gather and analyze disparate data needed to monitor and report the spread of disease.

Boston Children’s Hospital has combined EMR data with Google searches to track the spread of the flu. Their study shows that the hospital can use predictive analytics to forecast the movement of the virus with near-perfect accuracy in real time.

4) Personalizing Patient Care

With analytics, healthcare providers combine information from EMRs with a patient’s established genetic makeup to customize treatment plans. Personalized care using predictive analytics is part of a larger movement towards precision medicine. They allow medical care professionals to make quicker and unbiased decisions based on algorithms.

For example, EMRs include information generated from wearables that monitor vital signs, like blood pressure and blood-glucose levels. Predictive analytics can be used to forecast trends or estimate the likelihood of a patient developing a chronic disease using the data from these test results. If troubling trends emerge, a member of the patient’s treatment team can be alerted so they immediately form a preventative plan.

5) Identifying At-risk Populations

Not only can predictive analytics be used to better treat individual patients but it can be used to understand entire populations. Analytics can process information from EMRs at hospitals and medical centers across a region to identify groups of patients with the same symptoms or chronic conditions. These patients can then be tracked and given specialized treatment to prevent the worsening of their conditions.

Promoting Proactive Healthcare

Benjamin Franklin once wrote that “An ounce of prevention is worth a pound of cure.” This adage still holds true today. With EMRs and predictive analytics, medical professionals are better equipped than ever to anticipate patients’ needs so they can intervene before a problem develops.

To make use of predictive analytics, healthcare providers need to choose the right platform for their EMRs. Not every platform can withstand the performance demands of advanced analytics or the volume of data that is being stored.

IBM POWER is designed with the challenges of big data in mind. Not only does IBM POWER supply maximal data storage capacity, but it also offers the speed needed to reach actionable insights quickly so vital healthcare decisions can be made in real time. Choosing Peak Resources as your healthcare solutions provider guarantees you have access to the expertise needed to make a smooth transition to IBM POWER so you can gain maximum insight from your EMRs.

Are you missing out on the benefits of predictive analytics? Reach out to Peak and learn more about how IBM POWER can open new possibilities for your EMRs.