How to Optimize Hospital Staffing and Improve Patient Care with AI

Use Cases & Projects Nancy Koleva

Global healthcare today is facing a clinician shortage crisis. Projections indicate that by 2030 demand for health workers will rise to 80 million, but the World Health Organization estimates there will be a worldwide shortage of around 18 million, more than one in five of the people we will need.

In other words, we are hurtling towards a global workforce crisis in healthcare because of a growing and aging population which places greater needs and demands on health at just the time when the ratio of employed workers to patients has never been more challenging.

Staffing Inefficiency: Frustration and High Costs

A number of healthcare organizations have called for countries across the globe to increase health financing to address the issues. However, despite the constant increase in public health spending in recent years, the biggest issue is often the inefficient allocation of resources, and in particular of human resources: in hospitals around the world, inability to accurately predict workforce needs and allocate staffing hours leads to staff overwork, and often even worker burnout.

Unable to handle this challenge, some physicians leave the organization, resulting in a loss of $50,000 to $1 million for hospitals in training and recruiting a new one. But the biggest cost of inefficient staffing and the resulting staff burnout is a toll on both healthcare workers and patients: failure of interpersonal relationships, increased medical errors, increased risk of malpractice, reduced patient satisfaction, and a decrease in the quality of care and patient outcomes.

Inefficient staffing allocation and the problems it creates are often the result of a lack of data-driven decision making during the staffing process. Hospitals need to better anticipate patient volumes so that staffing decisions could be made in a more transparent fashion that would not undermine providers.

doctor doing arm check up on a patient

An Automated Predictive Application to Forecast Patient Demand

Technology, and AI in particular, can play a big role in crafting a comprehensive staffing strategy to improve healthcare working conditions and patient care. If technology systems are tasked with data-driven scheduling and resource allocation activities, frontline staff can be freed from tedious clerical responsibilities and focus primarily on caring for patients. What’s more, hospitals can leverage AI to improve patient flow by identifying operational challenges, modelling patient inflows, and recommending staffing schedules based on patient demand forecasting.

Demand forecasting is typically performed by looking back at historical patient demand data and projecting trend lines with a seasonal adjustment. In order to adapt their staffing needs on a daily basis, hospitals can anticipate patient demand by building and implementing a patient forecasting system application.

First, the application automatically compiles and processes internal and historical data as well as external datasets such as weather, national epidemics, holidays, and traffic. Then, a machine-learning algorithm builds a statistical model that forecasts patient demand; this prediction is continually improved as new data is incorporated into the model. Finally, an API links the predictive model to the staffing schedule system. Staffing managers therefore can update staffing suggestions in their scheduling tool based on time, date, and department.

It is important for healthcare stakeholders to realize and clearly communicate to their staff that AI techniques are designed to support and augment human labor and not replace it, by allowing health workers to focus on more valuable and gratifying patient-facing activities. Thanks to patient demand forecasting, hospitals can significantly decrease staffing costs and turnover, while improving workforce satisfaction and productivity, and delivering better patient care.

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