How predictive analytics can help guide operational decisions

Advanced data analysis can give facility executives better insight into complex decisions such as surge planning and workforce estimates

____simple_html_dom__voku__html_wrapper____>Most of hospital executives increasingly turn to scientific approaches for making predictions about future hospital operations. In fact, most mature industries rely heavily on science and technology to guide decision making.

Despite all of the technological capabilities and data available, some healthcare organizations still rely on a hodgepodge of disconnected data, institutional memory or intuition for making key operational decisions.

Too often, hospital decision makers rely on labor-intensive, error-prone spreadsheet analysis to make important departmental decisions. These departmental decisions are often made without the benefit of a holistic view or understanding of their ripple effect across the hospital or health system as a whole. This frequently results in negative overall consequences for the organization and patients. There are a number of contributing factors that can led to this lag in technology adoption. Traditionally, these factors have included:

• Lack of pressure to be efficient because of historically high reimbursement rates.

• Lack of formal training in operations science for leaders who rise through the ranks from clinical backgrounds.

• Lack of access to timely and reliable data.

In the face of industry demands to cut costs and improve efficiency, technology is playing a more prominent role in helping hospitals and health systems move away from a reactive approach to managing patient flow and take actions to mitigate bottlenecks before they happen. Data and analytics, combined with human process improvements, go a long way towards helping organizations make critical patient flow decisions that not only support better patient care, but create happier staff and more profitable operations.

For example, many hospitals believe patient admissions and census peaks are unpredictable, and consequently, they deal with them only after a problem occurs. This leads to delays between the time additional resources are believed or estimated to be needed rather than when they are actually added. This puts stress on hospital staff and other resources, makes patient care suboptimal and ultimately results in higher costs for the hospital. These flow issues often cause bottlenecks resulting in patients boarding, reduced access, suboptimal case and lost revenue.

Most experienced hospital managers know intuitively that census is higher on certain days of the week and at particular times of day, but they don’t have the capability to accurately anticipate severe peaks far enough in advance to take proactive measures. Technology can help address this challenge by augmenting managers’ instincts and intuition with timely and actionable data.

Operational planning, and surge planning in particular, becomes more effective with accurate demand and supply modeling, and forecasting. Using modern data science techniques such as simulation, scenario testing and other advanced forecasting methodologies can give front-line managers several days’ warning of volume spikes (or low census), enabling health systems to respond more proactively to changes in demand.

Hospital operational decision makers now have a powerful toolkit to make intelligent data-driven decisions for both short-term improvements and long-term strategic planning. This makes it possible to predict patient census and discharge dates with an actionable degree of accuracy, giving hospitals a way to anticipate the future and take appropriate action.

For example, if predicted census for tomorrow goes above a threshold that would trigger a hospital’s “surge plan,” the hospital could take steps to be ready to implement the plan quickly. These steps could include notifying on-call staff that they are will be needed tomorrow to ensure that additional beds are staffed to accommodate the higher-than-usual number of patients. In this way, hospitals can mitigate problems before they occur, and create a better care environment for patients and work environment for staff. Conversely, a prediction of low census can be used to reduce staffing more thoughtfully, resulting in higher staff satisfaction.

Hospitals no longer should rely on guesswork or labor-intensive, error-prone methodologies to staff and schedule facilities usage. Technology can harness the power of analytics coupled with institutional knowledge to obtain an accurate, holistic view throughout an entire health system. This helps ensure better care for patients, improved access, reduced costs and the opportunity to drive additional revenue opportunities for the hospital.

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