Given the constant quality, financial, and legislative pressures healthcare organizations are facing, the million dollar question remains, how do we get the right care to the right patient at the right time? Many healthcare organizations have turned to business intelligence (BI) as a way to bring information from desperate systems together, in a more efficient and effective manner, to drive better business decision to start answering this question. Implementing BI is a great first step in the right direction, however, simply building a data warehouse and doing some reporting from it alone will not provide the much needed answer to this very important question.
The answer to this question will help organizations get closer to delivering optimal patient care. In order to achieve great care, organizations not only need to know the patient’s history and present condition(s), but should be able to predict, to some degree, future outcomes. BI will provide the former, and advanced analytics will provide you the later, specifically, predictive analytics.
Trevor Strom states, “There is a critical difference between simply examining historical data and reporting on previous organizational performance, versus providing 
predictive capabilities that support proactive decisions. For example, during an influenza outbreak, it is one thing to know how many cases of influenza presented to an already crowded Emergency Department last week; it is entirely different (and much more valuable) to predict, with reasonable accuracy, how many will show up tomorrow and over the next couple of days1.”
Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict trends and behavior patterns2. Imagine if you could effectively predict who was going to be hospitalized and reallocate resources to prevent unnecessary hospitalization and put those resources to use for cure rather than care3. Predictive analytics can help with this and provide healthcare organizations with numerous benefits4:
- Optimizing Resource Utilization-Predictive analytics helps healthcare organizations to better allocate nurses, clinicians, diagnostic machinery, and other resources. Patterns and trends in admissions, bed utilization, length of stay, and other metrics can be analyzed and used to predict future volumes, particularly when peaks may occur. Hospitals can be more prepared and ensure there are enough resources on hand to provide superior care.
- Enhancing Patient Care-Predictive analytics lets healthcare facilities take a more proactive approach to treatment. For example, by more precisely predicting which patients will develop chronic conditions, or which ones will respond best to certain types of medications or therapies, they can focus not only on treating existing conditions, but also on preventing recurrences.
- Improving Clinical Outcomes-Predictive analytics can improve the outcome of patient treatments. By closely analyzing which treatments work best providers can make more intelligent decisions about treatment plans, minimizing complications and patient readmissions.
- Increasing Income and Revenue-Predictive analytics provides an efficient and accurate way for healthcare organizations to stop fraudulent behavior and identify opportunities to collect missing income, including claims that are wrongfully rejected by payers or overdue monies from patients.
Though not a perfect science, predictive analytics can be the much needed crystal ball in healthcare and begin to help answer the most important question the industry faces today.
Resources for this blog post:
- http://healthcareanalytics.info/2011/02/moving-to-predictive-analytics-in-healthcare/#.Uh_yIT_RqKY
- http://www.examiner.com/article/predictive-analytics-patient-care
- http://www.healthcareglobal.com/healthcare_technology/data-analytics-improving-health-care
- http://www.informationbuilders.com/pdf/factsheets/FS_Solution_Rstat_Healthcare_2011.pdf
