What Is Bed Blocking, And How Can AI Help Hospitals Manage It?
Bed blocking occurs when hospital beds are occupied unnecessarily. AI aids in optimizing patient flow, facilitating real-time decision-making, extending care coordination to home settings, and implementing hospital-wide tracking systems to mitigate bed blocking effectively.
In most cases, such situations arise when a patient is well enough according to the specialist doctor to be discharged home, but due to some unavoidable reasons, the discharge gets delayed. This type of situation in hospitals is termed as ‘bed blocking’.
It may also cause a delay in the treatment of other patients or the patients couldn’t get admitted to the hospital on time.
The bed blocking situations may arise due to the following reasons;
- Incomplete discharge paper before relieving the patient
- Delay in prescribing medicine and checking the patient before allowing the discharge
- The patient may require some special facility or vehicle and its unavailability causes a delay
These are some of the common factors that hamper the flow of patients in the hospital. Apart from that, lack of resources – staff, equipment and more could be the factor that causes bed blocking.
How can AI help hospitals manage bed blocking?
AI in healthcare reduces the risk of errors and improves patient happiness by streamlining patient flow, enabling networked decision-making, extending care coordination to home settings, and implementing a tracking system that spans the entire hospital.
Most Efficient Ways To Manage Bed Blocking with AI in Healthcare
1. Improves Patient Flow and Reduces Stay Duration
Every day, the hospitals witness a flow of thousands of patients. Thus, with a centralized system, it’s easier to manage patient flow and gain all the operational insights over patients.
The effective utilization of AI in hospital management systems can promote predictive analytics. Additionally, it becomes easier to manage the patient flow actively. The availability of detailed health data of patients makes it easier to study and predict their health condition.
Ultimately, such a detailed study helps to define the patients’ stay in the hospital. All these processes become much easier and more convenient with AI in healthcare which helps in studying data and making relevant decisions.
2. Enables Networked Decision-Making in the Hospital
To carry out enterprise-wide management of patient flow requires more than a centralized system that can monitor and manage patient capacity and patient flow. Along with the automated system, it also requires senior’s affirmation to act in real-time and anticipate the patient flow across departments.
Considering both aspects together helps to deduce a reliable forecast and helps to avoid any bottleneck situation and provides suitable insights.
To facilitate patient management all the patient details should be available to all the team members and in actionable form. The use of AI in healthcare helps to study all the data and make predictions accordingly. The predictive algorithm helps to analyze early symptoms that help specialists to make decisions accordingly and within time, which can help to save lives.
3. Extends Care Coordination from Hospital to Home
Using remote diagnosis and monitoring, the centralized care system can be extended to home care as well. In this context, predictive analytics in healthcare could also make a difference. These remote diagnosing tools are equipped with Artificial Intelligence and Machine Learning.
For instance, when a patient is under remote monitoring at their home, then the monitoring machines analyze their vital sign and biometrics. If there is any concerning fluctuation in vitals, it will alert the concerned healthcare specialists for further clinical evaluation. This will help to avoid any emergency later on.
The remote monitoring system integrated with AI in healthcare has demonstrated its worth in managing chronic diseases or any long-term disease.
4. Implements a Hospital-wide Patient Tracking System
A hospital-wide tracking system provides a detailed understanding of the patient’s current presence at the place or department of the hospital. All these details are critical in decision-making for the hospital staff regarding bed allotment, transfers, and discharge planning.
Without a proper automated tracking system, the patient may have to experience issues or disruptions in continuing the treatment. But, the integration of AI in healthcare application systems can help to track the entire management system and avoid bed-blocking issues.
Importance of Hospital Bed Management
Bed management highlights the requirement of which department in the hospital requires further support and from where the required need can be fulfilled. It is evident that the bed requirement has increased but the bed supply can’t be increased instantly.
The nature of hospital services and admission is changing as more people are admitted but not for a longer duration, this increases the in-flow and out-flow of patients. As a result, it is important to monitor their progress and maintain the patient’s ins and outs properly to serve more patients in a minimum time duration with the help of AI in healthcare applications.
Thus, for the well-being of the patients, the bed manager should be the responsible source for hospitals to ensure smooth hospital operation by hospital bed management software, which keeps patients and staff satisfied and the entire process well-organized.
- Avoiding the scenario of bed blocking provides a much better and safer environment for patients. Promoting coordination between all other hospital branches, departments, and their wards. This reduces delays in patient assistance and improves their satisfaction with the care offered.
- Proper allocation of beds helps staff to focus on other important tasks. The overworked or overloaded staff are at great risk of committing mistakes and it may risk any patient’s life as well. Thus, hospitals demand effective management along with managing and blocking for better services.
- Proper monitoring and communication within the staff through AI-equipped management software ease the bed allocation process as well. A smooth system prevents staff to deal with too many patients at a time and manage bed-blocking issues as the demand arises.
Key Takeaways:
- Bed blocking in hospitals results from various factors like incomplete discharge processes and resource shortages.
- AI integration enhances patient flow, facilitates networked decision-making, extends care coordination to home settings, and implements a hospital-wide tracking system.
- Proper bed management ensures efficient hospital operations, enhances patient satisfaction, and reduces the risk of errors.
Conclusion
Bed management is a critical activity for any hospital to avoid any possible bed-blocking situation and serve patients effectively. The most convenient way to manage bed blocking is by automating the process through a centralized bed management system and integrating AI into healthcare applications. It will optimize the patient flow and avoid overcrowding on the premises and in the patient rooms too.
Thus, to facilitate your patients in the best possible manner and streamline the entire hospital system the use of AI in healthcare can provide the most feasible solution. To do so, you require an efficient AI development company having expertise in building hospital management systems.
Codiant – an expert healthcare web and app development company can help you to deploy a tailor-made solution to cater to your specific requirements. We devise suitable strategies and integrate advanced technologies like Artificial Intelligence and Machine Learning to automate your healthcare operations. For more details and discussion, you can connect with our experienced business analysts and get your working solution.
