Texas Health Resources Improve Health Care Experience with Predictive Analytics


The critical sector health care involves high risk and uncertainty where predictive analytics tools and patient identifiers can help in seamless and comprehensive care delivery. These tools will facilitate understanding and anticipating the patient’s needs to deliver necessary services on-time and improve the patient result.

To create an integrated care experience, Texas Health Resources created patient IDs for each of their 7 million patients and are using predictive analytics tools. With this, they were able to tract the health care journey of different patients and integrate more than 20 data sources.

With the predictive analytics tool, they can forecast patients’ needs based on historical data of similar patients’ care experience. They can improve the health care processes now and forecast whether a patient needs a procedure or not. By what time the procedure needs to be done. They can track who is missing their vaccination and who needs mammography.
In a typical health care system, inaccuracy and data gap is common. But with more data resources, it would be easier to overcome these challenges. One such example is the address of patients that does not get updated regularly while checking the address one finds a completely different person living there.

The use of advanced analytics tools developed by Texas Health Resources allowed them to allocate the scarce resources available effectively, especially in the COVID-19 pandemic era.

With the use of predictive analytics, providers were able to predict which facility will experience a higher surge of patients and prepare accordingly. The organization can also direct resources to the pace of need and avoid the staff shortage and lower the pressure on the healthcare staff.
With the use of a predictive algorithm, they were able to manage the stress of COVID-19 without breaking the system.

For the success of this system, end-users should be able to understand and take benefit of these solutions. A comparative study of data from different resources is more effective than using data from a single organization.
Referential matching becomes much convenient and faster than probabilistic matching. If we get another data containing more updated information, which in turn speeds up the process by adding more sources into the system.

It took them one week to add about 4 million records from the new source system and match and match as much as they can. It fastens the process of setting up the system and turning them around.
Texas Health Resources aims to know exactly who you are when they enter using any of their channels. Be it online or offline, and connect with you from where you have left off and apply predictive models to enhance your experience.


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