Desperate diseases seek desperate remedies. It now emphasizes urgency to deal with this healthcare scenario considering the seemingly insurmountable- resource allocation challenges in healthcare – triggering extraordinary measures to surmount it. Whether it is about grappling with the unknowns or dealing the demand vs supply imbalance, healthcare organizations are using machine learning for resource allocation in healthcare.
How do healthcare organizations use machine learning for resource allocation in healthcare?
Machine learning for resource allocation in healthcare
Let us look at resource allocation in healthcare in terms of operating rooms. There’s this block scheduling leveraged by surgeons when it comes to blocking time for carrying out their procedures. It becomes more complex when we consider the unvarying tenure of the sessions (procedures) carried out by a surgeon, cumbersome rescheduling process as when a session needs to be rescheduled, coordination required to ensure medical equipment and supplies are available for the rescheduling, when it is done. Machine learning comes to the aid in terms of resource allocation optimization in healthcare wherein demand patterns of the operating rooms can be unearthed from relevant data using machine learning algorithms.
Connecting patient demand prediction to hospital staffing optimization
Healthcare organizations face an uphill task of promoting efficient allocation of resources. It boils down to data-driven decisions triggered by the ability to forecast patient volumes and make the right staffing decisions that can help render top quality care and better patient outcomes. Taking historical data encompassing patient demand data into consideration, demand forecasting can be made possible through machine learning algorithms wherein staffing needs can be adapted based on the patient demand forecasting performed through the exercise.
Predictive analytics for resource allocation in healthcare emergency
Emergency healthcare demands emergency responses augmented by robust ambulance services. There’s lot of uncertainties involved in terms of demand enveloping incidents and emergency calls and responses to the emergency calls. One such uncertainty is the travel time uncertainty wherein ambulance response optimization raises in relevance. The key to breaking the travel time uncertainty lies in leveraging GPS-based ambulance location data.
- Time-series algorithm for finding resource allocation answers in healthcare
Let’s take the case of ambulance responding to emergency calls. Presuming that there are fixated locations of ambulance, say at 16 points in a city, we get initiated with our resource allocation optimization exercise.
The past data has rich account of the locations of ambulance, incident location, time of the incident, day and month of the incident, distance between the ambulance and the incident reported. This now develops into a time-series problem wherein historical data feed can be used to find answers to queries that matter most.
- Which time of the day corresponds to incidents being reported on a larger scale?
- Is there proper utilization of resources to respond to incidents reported?
- Are there resources that are not being utilized well being placed at a particular location?
- How to optimize resource allocation to respond to incidents that happen at a particular time, month, season etc.?
This sets the course for future prediction of where incidents could happen and allotment of resources to cater to those incidents.
- Predicting incidents and optimizing resource allocation in healthcare
Predicting where incidents could happen and optimizing resource allocation is also possible by making the best use of historical data. For instance, consider a simple problem of incidents being reported from a single location. It pays to gear up for the future in terms of allocating more resources to respond to medical emergencies that are forecasted to be reported from these locations. In a complex scenario wherein there are many locations in a big city, good network of ambulances catering to medical emergency needs, historical data can lead the way to predict incidents and optimize resource allocation to make the right use of resources at the right time in ensuring good clinical outcomes.
Here is a sample resource allocation optimization mooted through incident prediction – Let us say that we have got three months of data to work on, covering incidents – The prime data feed for machine algorithms would include incident data, date of the incident, time of the incident and distance from the ambulance locations to the incident location. By predicting incidents from a specific location, healthcare organizations can optimize resource allocation in terms of using ambulances to promote successful clinical outcomes and maximize value from every ambulance.
When a leading healthcare organization sought the expertise of Saksoft to address their resource allocation and route planning challenges (ambulance service), it called for the collation of data to a single source – call data, clinical data and ambulance data – and the use of machine learning for resource allocation in healthcare. Leveraging statistical modelling and embedded analytics, Saksoft helped the healthcare organization optimize resource allocation to promote good clinical outcomes and take real-time decisions in terms of making resource allocation.