Advanced technology in the digital age has resulted in nearly every aspect of our lives being tracked and measured.
The number of steps we take, our heart rate, and our sleeping patterns can all be analyzed from the convenience of a smartwatch or smartphone app. This type of monitoring allows people to make smarter choices and mark progress toward their goals.
The process of data analysis is thriving in the business world as well, expanding to a variety of industries. Healthcare has become increasingly interested in data and the insights that can be unlocked through analysis. And this new-found obsession is not in vain. Data analytics has been proven to increase efficiencies and reduce costs. In healthcare, reducing costs and improving the delivery of care is paramount.
Predictive analytics is especially beneficial in healthcare and patient populations. Predictive analytics estimates the likelihood of future events based on patterns in historical data. These predictions allow clinicians and administrative staff to make more informed decisions.
Used successfully in various industries such as retail and manufacturing for over a decade, predictive analytics is growing a fan base in healthcare. One method called time series analysis can be used to analyze past data and look for trends and patterns and make a forecast of events that recur over time. Time series techniques are particularly relevant in forecasting patient in and out flows in a hospital.
In many healthcare organizations today, managers are using little more than guesswork and intuition to schedule the appropriate number and type of staff to match patient volume. Ensuring that quality clinical staff is in the right place at the right time is one of the biggest challenges for hospitals and other healthcare providers.
Staffing and scheduling problems are known to frustrate staff, and negatively impact patient care. Without proper forecasting tools, unit managers are left to trust their gut about what staffing needs will be weeks in advance. This often causes last-minute chaos of either scrambling to find resources or calling people off – both of which are major staff dissatisfiers, perpetuating a cycle of burnout and turnover.
Provider organizations that have used predictive analytics for nurse scheduling and staffing have achieved outcomes that include increased staff satisfaction scores, improved nurse retention and, reductions in their annual labor spending. Additionally, nurse managers spend less time on schedule creation and staffing tasks. This delivers more valuable time back to them to focus on patient care and staff development.
But with advanced technology comes a learning curve, especially in a naturally cautious, evidenced based industry such as healthcare. For people and organizations that have done tasks such as scheduling in a certain way for so long, it can be intimidating to bring in new technology that shakes things up.
Unit and department managers often believe that no one knows their department better than them and they schedule their staff accordingly. It is a significant change to implement a technology that is based on data and statistics rather than a person’s intuition. And many organizations fail to achieve their expected ROI because people often do not trust the predictions or do not use the software as it is intended.
Change is hard, and it threatens to throw off the equilibrium that some people have worked so hard to maintain. It is much more comfortable to live in the known, even if people are not happy with their current reality. When implementing a significant change, organizations should have a clearly defined plan for rolling it out well in advance of a go-live date.
When change comes about, many people get caught up with what is happening and lose sight of the reason why it is happening. Leaders should clearly communicate everyone’s role in the new initiative and articulate the benefits of the change. Connecting each individual to their impact on the project gives them personal accountability to see it through.
Overcoming change is a process and requires new habits to align with organizational objectives. When it is a new technology, people like to see proof of its effectiveness before they jump onboard. While there is a learning curve that comes with every novel concept, managers and leaders must have faith in the system to achieve the expected outcomes.
Predictive analytics is not a hands-off technology. The predictions are made with organization-specific data, and it takes nuanced local information like room closures or new overflow capacity to make predictions even more accurate. A provider organization and a vendor must partner and communicate about what’s happening on each and every unit to ensure that the predictions are accurate, and become even more accurate over time.
Predictive analytics and advanced technologies are finding a home in healthcare. With its ability to churn raw data into meaningful and actionable insights, predictive analytics allows hospitals and health systems to stay ahead of the curve. Healthcare data analytics can help hospitals and health systems work smarter, analyzing the metrics they have readily available. Advanced analytics delivers beneficial outcomes in a variety of areas within healthcare, ultimately enhancing patient care. It just takes a lot of data and a little bit of faith.