With the increase in technology use comes a lot of data and information. With this deluge of information, the main challenge facing decision makers is how to extract value from this gold mine. After all, data is only as useful as the insights we can get from it. Efficient data analysis and workable insights gained from such data has shown to a huge increase in costs, an increase in business intelligence leading to better decision making and better clinical results.

Working with Big Data

Primary care providers, clinics and even hospitals stand to benefit from the right use of this big data. There are also several pitfalls that need to be avoided while working with such data.

-          Data does not necessarily mean better insight. Sometimes, the value from the data could be important but might not assist in improving clinical outcomes due to a wide variety of external factors.

-          Cost could be a challenge in implementation. Hospitals would be required to implement new methodologies and practices which require a large time and capital investment.

However, like all solutions there are several proven cases where big data can help reduce healthcare costs as outlined in this report. The key areas are:

-          High cost patients

-          Readmissions

-          Triage

-          Decompensation

-          Adverse events

-          Treatment optimization for diseases affecting multiple organ systems

Reducing readmissions

A large number of hospitals are increasingly affected by the growing number of readmissions. As per the new guidelines laid down by Medicare, hospitals now stand to face penalties for readmitting certain patients in less than 30 days. These cases include certain type of lung ailments, heart attacks, heart failure, pneumonia and hip and knee surgery. Research shows that 1,621 hospitals have been penalized in each of the five years of the program.

In order to lower these penalties, medical institutions are working towards ensuring that patients are given the care they need after discharge.

One reason high level of readmissions could be the different criteria used by the medical staff in different locations to determine is a patient needed to be readmitted. This problem can be resolved by implementing a standard diagnosis approach as well as regular meetings with the patient after discharge to ensure that their after- care is meeting the patient’s specific requirements.

Another way that predictive analysis can help is by analyzing patient data to evaluate which patients had a higher probability of readmissions based on their history. This will help reduce the readmission rates for moderate to high risk patients and also enable reduction in costs.

To find out more of how predictive analysis can work for you, get in touch with our team of experts.

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