How AI Can Improve Patient Flow in Healthcare

AI can help healthcare facilities to considerably improve patient flow. Read this article to get to know how hospital managers can make the most of AI and ML in this aspect.

The term “patient flow” denotes the movement of patients through the hospital from entry to discharge. This is a complex and dynamic process. When humans manage patient flow, it might take them too much time and effort. Plus, errors caused by the human factor are inevitable. Read this article to understand how AI can improve patient flow in hospitals and what medical professionals should do to make the most of this technology.

How Can AI Improve Patient Flow

Here are a few examples of how exactly AI can improve patient flow:

-Emergency departments will be less crowded and patients will receive help faster.
-The need for regular surge plans will decrease.
-Fewer surgeries will be canceled or delayed.
-Patients will stay overnight in post-operative recovery rooms only when absolutely necessary.
-Patients will be timely discharged or transferred to appropriate units based on their clinical conditions.
-Bed capacity management planning will be enhanced.
-Physicians, nurses, and staff won’t suffer from overload and burnout as much as they do now.

AI- and ML-powered solutions help human professionals by offering predictive models. Plus, they provide real-time data that humans can use to make decisions.

How to Make the Most of AI-Powered Solutions to Improve Patient Flow

It’s not enough to install AI-powered equipment or software in a hospital and explain to the staff how to use it. The managers of the establishment also need to carry out the following three steps.

Build a Data Science Team

First, they should secure support at the highest level. All the executive leaders of the healthcare facility should realize the value of data science. The dedicated data science team should collaborate with other departments and turn data into intelligence that allows better decision-making.

Create an ML Pipeline to Aggregate All Data Sources

The pipeline should contain the following elements:
-All data sources
-Visualization components

The pipeline should feature all data sources because otherwise, the predictive models would fail to identify the right areas for opportunity.

Put Together a Leadership Team to Govern Data

The team that governs data should include not only the members of the data science team but also leaders from other departments. Such an approach will enable the team to achieve two goals:

-Get various viewpoints when discussing the data science strategy
-Ensure support for data science from multiple departments

When leaders from other departments return to their teams, they should explain the significance of data science to their colleagues. All the staff of the medical facility should understand how they can benefit from the new technology.

The Necessity of Backtesting Models

Members of those teams that work with data science should regularly compare two sets of data:

-What the AI predicted
-What happened in fact

This method is known as backtesting models. It enhances transparency and allows team members to better estimate the reliability of predictive models. These models are not perfect yet, so human contribution is essential for decision-making.

When discussing the accuracy of applied models, teams might have brainstorm sessions. They can find the reasons for the inaccuracy of the previous models and come up with ideas for new ones. It’s essential to compare various models to detect the most accurate ones. All the participants of the meetings should be able to share their opinions on predictive models and voice suggestions.

The Most Efficient Way of Disseminating Information

AI-powered solutions can deliver real-time or nearly real-time data. They can generate daily reports that teams in charge of data science can examine. Yet that would be irrational. In most cases, the difference between today’s and yesterday’s data won’t be too drastic. Human professionals might be prone to so-called data fatigue: it will be hard for them to focus on the profound meaning of these numbers simply because they see them too often.

To maximize the efficiency of the reports, it would be wise to replace them with alerts. The system can notify the members of the data science team each time a certain indicator reaches an alert level. The leaders of the team can flexibly modify the alert level for each parameter. Each time the team receives an alert, they should be ready to take measures.

Humans Need to Combine ML with Their Own Expertise

Hospital patient flow is not limited to the work of one separate department. All decision-makers should have access to the data gathered and generated by AI. Different professionals might interpret the same dataset differently. To make the most of the technology, human experts should compare all the conclusions that they make from the data provided by AI.

At the initial stage of integrating AI into a hospital’s workflows, data science specialists need to guide medical professionals. The former should offer the latter the simplest models and teach them to interpret the data. Once the medical staff gets used to the process, data scientists can switch to more complicated models.

Healthcare facilities might want to integrate the Agile principles into their workflows to process the data more efficiently. This approach suggests splitting the full scope of work into short iterations. Once an iteration is over, team members analyze its results and discuss the achievements with the leaders that might not be directly involved in data science. The leaders should leverage the data to make decisions. Then, human professionals should suggest new variables for the predictive models and start the next iteration.

Final Thoughts

Hopefully, this article came in handy and now you better understand the importance and advantages of machine learning health care. AI- and ML-powered solutions can improve patient flow so that the quality of medical services rises, the staff of healthcare facilities avoid burnout, and patients report greater satisfaction. To make the most of the new technologies, hospital managers should build data science teams, create ML pipelines to aggregate all data sources, and form leadership teams to govern data. To process data with maximum efficiency, healthcare facilities might want to integrate the Agile principles into their workflows.