As healthcare becomes increasingly more complex, nurses must maintain the competencies necessary to deliver high-quality care. This includes the ability to respond appropriately to new technologies, which may have the potential to change nursing practice. As patient advocates and frontline caregivers, nurses must ensure that new technologies do not devalue the human element in healthcare.
Predictive modeling is a process used in data analytics to create a statistical model of likely future outcomes—in other words, a method to forecast probabilities and trends. It has long been used by the life insurance industry to manage risk and balance payouts with premiums. The healthcare industry is just beginning to use predictive modeling (PM) to guide policy and practice, as more and more data is being captured by the electronic medical record (EMR).
Two other trends, both influenced by the Affordable Care Act, have also driven an interest in predictive modeling: (1) the new emphasis on population health management and (2) an increased focus on the relationship between quality and cost, rather than on one or the other alone.
Rich data streams from the EMR are the driving force behind predictive modeling efforts. Experts see PM as a potential means to predict risk, control cost, better allocate resources, prevent complications, and generate more precise diagnoses and treatment plans. Here are a few of the ways PM is currently being applied to healthcare:
- Flexible and efficient nurse staffing. A low nurse-patient ratio can have an impact on safety and quality of care, while over-staffing can raise costs. Predictive modeling can allow hospitals to forecast the flow of patients in each unit by day of the week and hour of the day, improving the scheduling process. PM can also help with hiring and retention and can increase job satisfaction among nurses, who are less likely to experience burnout when staffing is adequate. (Read a case study online here.)
- Population health management. PM can help to organize patient populations into sub-groups that can be targeted for specific interventions, increasing the ability to provide the right care at the right time. This can ultimately manage costs and improve outcomes. For example, a report in the Online Journal of Nursing Informatics described a successful effort to identify patients at increased risk of congestive heart failure (using a combination of diagnoses as a risk marker) and predict a timeframe for the onset of disease. This is exactly the type of proactive, wellness-oriented, patient-centric response to population health management that nursing should embrace.
- Personalized medicine. By assembling data on an individual’s gender, ethnic background, genome, current medical status, lab values, prescription history, and family history, researchers have been able to build an algorithm that predicts future health status, allowing certain patients to be tracked and monitored. But PM can also use historical data to help doctors decide on the treatment plan that is most likely to be effective for a specific individual. More targeted treatments can reduce harm to patients, increase the likelihood of good outcomes, and require fewer nursing resources.
- Preventing unnecessary readmissions. Clearly, predictions are most useful when they impart knowledge that can be translated into action. Efforts to use PM to reduce readmissions have been remarkably successful (read a case study here and another one here). Case managers everywhere may soon have a remarkable tool at their fingertips, in the form of an algorithm that can zero in on patients at high risk of readmission within 30 days—and identify them at the time of admission, so discharge planning can be initiated right away. Interestingly enough, nursing assessments are an important data component in these analytics. As frontline caregivers, nurses can identify social factors, mobility issues, and even financial concerns that are not captured in diagnostic codes or clinical assessments.
So what does this all mean for nurses? In summary, predictive modeling has the potential to improve the nursing work environment through better staffing and scheduling practices. As a new resource with the potential to improve patient outcomes, PM may change the tasks that bedside nurses and nurse case managers perform in various areas. And of course, as PM becomes more widespread and more sophisticated, it may increase job opportunities for nurses specializing in informatics, who can connect the dots and formulate patient-centric applications for the technology.
Online nursing degrees like American Sentinel’s MSN with an informatics specialization can make you attractive to employers, provide you with case management knowledge and skills, and give you the academic background you’ll need to pass the credentialing exam.