As healthcare transitions from fee-for-service to value-based care, there is an emphasis on lowering costs and improving outcomes. In the midst of this transition, care teams are currently equipped with outdated tools that drive a reactive, “let’s fix it,” mentality. However, reaching these goals requires shifting the way care is delivered by being more proactive and patient-centered. This isn’t a new concept, but it’s something we’ve yet to achieve. How can we make this happen? By combining existing information in electronic health records (EHRs), vital signs, lab results, and nursing data with predictive analytics to help care teams attain this proactive mindset.
Nursing assessments have long been considered a standard nursing practice; however, combining them with the advent of clinical analytics makes them a far more powerful tool. For example, the insights generated by predictive analytics can allow care teams to determine a patient’s risk of a serious health event, with time to proactively intervene earlier. By leveraging analytics, we can transform the way we provide care.
The importance of analytics in making care proactive
More often than not, a care team’s clinical decisions are reactive. For example if a patient’s condition significantly deteriorates, the care team will assess the patient’s vitals – heart rate, body temperature, etc. – in order to determine what the most likely issue is, and therefore determine their course of action. But imagine if you already knew that a patient’s condition was rapidly deteriorating before it was necessary to activate a rapid response team (RRT) or before he or she coded? To improve outcomes, care teams need tools that allow them time to intervene and provide the right care.
Predictive analytics is an essential part of this proactive toolkit because it provides early warning signs that can change the care team’s workflow. This shift in care benefits patients and their families, in addition to healthcare providers, because it creates more time to make clinical decisions. For example, RRTs, who come to the rescue when a patient’s case worsens quickly, can benefit greatly from predictive analytics. The RRT is made up of seasoned clinicians that quickly assess a patient’s condition and make decisions around the course of action needed. However, RRTs only show up when there is enough concern for them to be called in order to assess a patient and identify areas of concern that need treatment. With predictive analytics, we may see the decline of RRTs because now care teams can be notified of the risk of a patient’s deterioration before it happens, allowing them to intervene and act on changes in a patient’s condition before he or she deteriorates. This shift in the clinical course of action will help bring value-based care to life and enable a transition to proactive care.
Connecting the data
Similar to the RRT scenario, in many response situations, data has little impact because it’s used too late. One reason for this is health data is often very siloed – accessing it and connecting the dots between relevant data is a real industry challenge. However, in order to leverage analytics, this data must be organized and made accessible. With properly organized data, the use of analytics can be used more broadly across the care team and between providers.
While predictive analytics is undoubtedly the way of the future, there will always be great value in reviewing retrospective data for insights. Analyzing and learning from how patients were cared for in the past and leveraging lessons learned across the care team will benefit both providers and patients as a more proactive and patient-centered approach is advanced. It is important to leverage both predictive analytics as well as retrospective data; however, the key is to utilize it in a timely matter.
The challenges to accessing data and leveraging analytics are not unique to healthcare; however, healthcare comes with its own set of data security and privacy laws. Although very much needed, these parameters make it difficult to share and transmit health data. Another obstacle to consider regarding the sharing of data is how much of health data is unstructured. Analysts have found that unstructured data makes up 80 percent of data within business organizations, and health data is no different. In fact, studies estimate that of the 1.2 billion clinical documents produced annually in the U.S., 60 percent contain unstructured patient data. Since this unstructured data is difficult to access and use on its own, sharing it becomes far more complicated. If patients or care providers could better access this information, it could be used to help improve patient care.
Giving care teams time to intervene sooner and save more lives
Leveraging valuable data through clinical analytics can provide care teams an early warning with time to act well before a patient’s condition deteriorates. The key is utilizing these insights in a timely matter or risk facing a missed opportunity. Successful use of analytics will allow clinicians to work smarter by prioritizing those in most need of care rather than sticking to arbitrary rounds, and will help change the healthcare mindset from reactive to proactive – not only improving patient safety and care, but ultimately saving lives.