4 Key Uses of Analytics in Healthcare

4 Key Uses of Analytics in Healthcare

The healthcare industry generated more than 500 petabytes of data to provide value-based care to patients, reduce costs, and improve efficiency after the introduction of the Healthcare Affordable Care Act.

The healthcare analysts were presented with the unique challenge of translating this massive volume of structured and unstructured data into valuable insights to help reduce cost and improve healthcare services’ efficiency.

Enter Big Data Analytics!

But the role of data analytics in healthcare is not restricted to costs and efficiency. It does much more than this. But before we dive deeper into the critical role played by data analytics in healthcare, let’s understand what exactly healthcare data analytics is!

What is Healthcare analytics?

This branch of analysis focuses on offering valuable insights into clinical management, diagnosis, and quality of care.

Analytics uses statistical methods and technology to search through massive amounts of information, both historical and real-time, and analyzing it to predict individual patients’ medical outcomes.

The systematic data analysis allows healthcare professionals to increase their efficiency, improve patient engagement, and accelerate diagnosis while reducing costs.

According to the report, the healthcare analytics market is expected to cross $50 billion by 2024 from $14 billion in 2019, a CAGR of more than 28% during the forecast period.

Why Do We Need Healthcare Analytics?

The challenges facing the healthcare industry are compelling arguments to ascertain the need for analytics.

Healthcare providers have always been under constant pressure to improve productivity and reduce skyrocketing costs. Also, the advancement of technology compelled providers to adopt digital tools like EHRs to gain a competitive edge, improve quality of care, engage patients, and enhance business prospects.

Now, the widespread adoption of EHRs saddled healthcare professionals with tons of structured and unstructured data. Crunching these numbers and extracting meaningful knowledge was one big problem.

The need to address these challenges drove the adoption of healthcare analytics.

What is the Role of Analytics in Healthcare?

Here are some of the ways advanced analytics software is helping the healthcare industry achieve goals:

1. Predicting and Preventing Chronic Diseases

The treatment of chronic diseases is one of the highest costs for the healthcare industry. Analytics can help healthcare professionals track many causatives and indicative chronic disease factors and report the findings. The provider can use this information to perform tests to confirm specific medical conditions and initiate the appropriate action.

Analytics can also help cut costs by predicting which patients are at higher risk for chronic diseases and arrange early action before the problem worsens. This involves aggregating data related to a variety of factors, including (but not restricted to) patient’s medical history, demographic, and socio-economic profile. Medical history typically includes age, race, gender, blood group, blood pressure, pressure, family history of chronic conditions, and other factors.

By aggregating and analyzing all these forms of data, healthcare professionals can effectively allocate resources, allowing them to aggressively intervene in high-risk cases early on and prevent long-term costs.

2. Increasing the Accuracy of Diagnosis

Misdiagnosed patients are not a thing of the past. According to a published report, despite technological advancement in healthcare, almost 5% of patients are incorrectly diagnosed every year in the US. Errors during diagnostic procedures account for almost 10% of patients’ deaths.

This is where analytics can come to healthcare professionals’ rescue and help them make accurate diagnoses.

For instance, when a patient comes to the ER complaining of chest pain, it is often difficult to decide whether to hospitalize the patient. Access to the patient’s historical data with an accurate assessment of whether the patient could be sent home safely will help the doctor make the right decision. The system’s “prediction” of the patient’s health will not replace the provider’s judgment but would assist them in making the right call.

3. Reducing Cost of Treatment

The recent shift in the US healthcare system from a volume-based payment model to value-based care is transforming the way providers manage costs, get paid, and deliver care.

To ensure a value-based model’s success, eye care practices will have to reduce unnecessary variations and contain costs. This is where data analytics becomes essential. How?

Analytics can be used to predict the staffing needs by analyzing the historical data for factors like local weather trends, seasonal infections, and holidays. These insights will help the administrative team allocate staff accordingly, reduce labor costs, and ensure efficient shift management.

Another way analytics can help hospitals save costs is through pharmacy data analysis. According to a report, the cost of drugs and other supplies comprises 15% of a hospitals’ total budget. Better drug utilization and reduced stock can positively impact the bottom line.

Similarly by monitoring a patient’s health, not only the chances of readmission can be reduced, but also the cost of care associated with readmissions.

4. Improving Medical Outcomes

While the rise in patient portal adoption has made it easier for patients to access their medical data, many still struggle to understand their medical condition. Low awareness rates, complicated by medical jargon, often lead to poor medication adherence and a lack of patient engagement.

Patient engagement is directly related to medical outcomes; therefore a low patient engagement rate is less likely to yield a good medical outcome.

So, how can analytics help increase patient engagement? On example is wearable technology. The practical and informative use of data from wearable devices helps patients understand what they need to do to stay healthy. On the same data, after providers apply analytics, it helps them predict and make informed decisions around the next treatment options for individual patients.

A recent study has revealed that medication adherence is only about 50% – 60%. Healthcare providers are also leveraging analytics to discover patients who don’t adhere to medication plans. And when patients don’t adhere to medication or treatment plans, it jeopardizes the quality of care and medical outcomes and increases healthcare costs.

By leveraging analytics, providers can help patients participate in their care plans leading to improved medical outcomes and care delivery.

Conclusion

With the healthcare industry becoming increasingly patient-oriented, and patients shopping around for the best-reviewed providers, analytics will be a cog in the wheel for ensuring a healthier future, both for patients and providers. Analytics brings a lot to the table. Reducing costs is one of the top goals, but using analytics to improve patient experience and grow revenue is what will help you gain a completive edge in the long term!

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