With the adoption of the electronic health record and mandatory reporting to regulatory agencies, healthcare research that uses “Big Data” is quickly becoming something every healthcare professional needs to understand. “Big Data” refers to enormous data sets comprised of numerous variables. For example, the Medicare and Healthcare Cost and Utilization Project includes over 100 million observations. In fact, New Zeeland, Ireland, and Singapore recently created nationwide National Health Repositories to improve the administration and organization of their health services. Healthcare services have just begun to experiment with mining these data sets for insights that might help improve treatment outcomes.

The rehabilitation industry is certainly no stranger to using data or evidence-based research in determining best practices for various conditions.  However, when it comes to gathering and manipulating “Big Data” most rehabilitation practitioners and researchers may feel like they are living in the wild west – each therapist or clinic is experimenting with the possibilities on his or her own.

“Big Data” challenges that affect rehabilitation clinics everywhere.

1. Choosing the best tools for collecting data in your clinical practice.
   All rehabilitation providers are collecting patient data in one form or another. Therapists these days are required to spend more time than ever completing Electronic Medical Records (EMR) and filling out claims management software. Deciding which EMR system is best for the size and scope of your practice is a difficult decision.
2. Developing a system that retrieves and synthesizes data from multiple sources.
   Some EMR are easy to use when it comes to inputting data, but complicated when it comes to exporting that data into useful format. Taking data from multiple sources and synthesizing it into one size fits all “data warehouse” is an important step before the data can be manipulated and used for accurate comparisons.
3. Using data to analyze your practice patterns and adapt your decision-making.
   In the past, the best way to increase your clinic’s reimbursement was to see more patients. However, in accordance with the “triple aim” of the Patient Protection and Affordable Care Act, providers are now accountable for the quality of their patient outcomes. Rehabilitation providers must show that they are improving outcomes, not just providing more services. Cutting edge rehabilitation providers are beginning to use the data they collect in their clinics to inform their treatment choices in hopes they will see better outcomes.

 

Using Big Data to Create Predictive Analytics in Rehabilitation

By definition, “predictive analytics” are the analyses of patient data (both current and historic) to predict possible future outcomes. When applied in a clinical setting, some outpatient practices are beginning to use this technique to compare an individual’s circumstances to their database and generate an estimate of the type and amount of care that might be needed for treatment success. Therapists will be able to use the national average of treatment outcomes as a benchmark by which to measure the progress of their patients. This way, they’ll know if they’re on track with a treatment plan, or if they need to adjust care to improve outcomes.

Potential advantages of using predictive analytics

Aside from providing useful benchmarks, predictive analytics may also someday help rehabilitation clinics improve the following:

• Estimating treatment time frames. Based on a patient’s profile, within what length of time should you expect a patient to complete treatment? In the past, this may have been an open-ended question. But by comparing similar patients with each other, therapists can build a rough timeline for healing.
• Comparing the skill of your therapists. When comparing your facility’s outcomes against national averages, you may begin to see a pattern. And this can be used to indicate where your therapists can benefit from continuing education or additional training.
• Increasing effectiveness. Whenever a goal becomes measurable, it becomes achievable. As big data increases the exposure of measurable outcomes in rehabilitation, therapists will begin finding creative ways to help their patients achieve their goals faster and more efficiently. So in this way, predictive analytics may challenge your therapists to get even better — and improve care efficiency.

What’s your “Big Data” challenge?

In what ways do you see Big Data affecting rehabilitation providers?  Where is your clinic when it comes to adapting to the changes brought about by EMR and the Affordable Care Act implementation? Do you have any suggestions for other therapists as they face these “big data” challenges?

Please leave your suggestions in the comments below. Thanks!