Harnessing the Power of Artificial Intelligence to Improve Patient Flow in Hospitals

Hospitals around the world face major challenges in managing high patient flow in critical departments. Especially during unexpected surges in patient admissions, healthcare institutions struggle to use the limited resources effectively. However, predictive data modeling is emerging as a powerful tool for hospitals to accurately forecast demand and allocate resources more effectively. It can also help hospitals achieve better patient outcomes, reduce costs, and create a more efficient healthcare system.
Harnessing the Power of Artificial Intelligence to Improve Patient Flow in Hospitals

Healthcare as an industry is faced with a multitude of operational challenges that involve staffing, workload, infrastructure and patient inflow among many others. It holds true especially in a country like the United States which has an increasing elderly population and limited healthcare resources. Many hospitals fail to meet daily patient demands due to inadequate staffing, poor utilization of resources or inefficient process flows. All these factors have put a great deal of strain on the healthcare ecosystem.

Have you faced a similar scenario during your visit to the hospital? Did you have to endure long lines for admission and delays in treatment?

When you consider the situation from the hospital’s perspective, healthcare professionals are overburdened with high patient flow and limited resources. The negative impact of these challenges can not only affect the patient’s healthcare journey, but it can also cause burnout among the hospital staff due to reduced efficiency and job satisfaction.

Therefore, many large healthcare organizations are looking to develop organizational models that can predict future challenges and reassess current operations. One of the biggest challenges that hospitals are facing today is maintaining patient flow. Since a patient’s journey often involves multiple clinical departments, hospitals need seamless coordination to function at optimal levels. As a result, different methodologies have been implemented to address these issues over many decades. However, the challenge of managing limited resources to maximum efficiency still seems to be a daunting task.

Assessing bottlenecks in the Emergency Department

Individuals working within the healthcare industry understand that not all departments face high patient flow while some departments handle the bulk of the workload. A few departments, like the Emergency Department (ED), handle more patients and play a critical role in the entire process.

Now, it can be tempting to think that adding a few more rooms or beds might solve the issue of overcrowding in the ED. This might however add an undue burden on the financial health of the hospital and result in underutilization of resources when the patient volume is low. Therefore, the focus needs to be on how to better manage available beds and put them to good use.

Predicting patient traffic, wait time and appointment delays can help in optimizing hospital resources and increasing patient satisfaction. In doing so, one might be faced with questions such as:

  • How to go about smoothly managing patient flow?
  • How can we anticipate patient flow? What variations do we look out for?
  • What can be done to ensure that necessary resources are available when the need arises?
Achieving optimal patient flow through an interconnected, interdependent system

Optimal hospital-wide patient flow translates to providing the right care at the right time. Any bottleneck scenarios in patient flow can lead to suboptimal care and result in negative patient experiences. Bottleneck scenarios, especially in the ED, can have a ripple effect through the entire healthcare system. Improvement of patient flow is quickly becoming a priority in most healthcare settings.

Hence, improving hospital-wide patient flow and enhancing the patient’s experience require an appreciation of the healthcare unit as an interconnected, interdependent system.

To better manage patient flow, healthcare organizations need to address questions like:

  • How many admissions does the hospital have to plan for the day?
  • When does the emergency department witness high patient flow?
  • How to better manage arrival/discharge of patients?
  • Would a patient need post-acute placement at discharge?
  • How many procedures can the surgical staff perform in a day?

Today, all hospitals have adopted digital systems to record and store patient data. With a myriad of valuable information made available at the touch of a button, healthcare organizations can use it to gain valuable insights that could become the foundation of an efficient strategy to manage patient flow. By harnessing the potential of AI-powered data algorithms, healthcare institutions can create a roadmap to achieving optimal patient flow and experience.

Predicting patient flow through Machine Learning

In most hospitals, machine learning solutions have been adopted to some degree, but it has always leaned towards clinical questions of individual patients. The scenario, however, is changing quickly. Many industry leaders in healthcare services are utilizing data models to develop a predictive analysis that can help optimize overall operations. The idea is to go beyond simple heuristics that make short-term forecasts.

Healthcare organizations are looking to implement an operational model powered by Artificial Intelligence (AI) and Machine Learning (ML). Compared to completely overhauling existing operations, the predictive modeling approach is very feasible to target and solve specific challenges. Hospitals are now partnering with leading data analysis companies to develop AI and ML models that predict patient flow through healthcare institutions. With machine learning, hospitals can implement models that learn from past patient flow and predict variability in hospital traffic.

Predictive analytics systems powered by artificial intelligence can forecast whether an ED could experience surges in traffic and alert hospitals on shortage of critical care beds. Any variability that needs attention can be flagged by a predictive data model. However, it is not just limited to predicting patient inflow. It can predict the Admissions, Discharges and Transfers (ADT) volume for the operations team and build data sources and dashboards for Urgent Care and Emergency Department teams. AI application models can learn from every data point. Such an approach can be more precise in its prediction and therefore allow hospitals to manage different departments with ease. It can also aid in enhancing patient experience as they journey through the healthcare system.

Do you want to implement data predictive models to improve the efficiency of your process flows? Factspan Analytics works with hospitals and pharmaceutical companies to provide artificial intelligence and machine learning solutions to optimize their operational capabilities.

Get in touch with our data scientists to see how you can deploy AI/ML solutions to enhance the efficiency of your business. Contact us

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