Learned Facility Behavior

An EMS Leader’s Key to Accurate Capacity Planning

Gil Glass

Chief Executive Officer, US
Inspired by a product that makes a difference and motivated by a talented staff, Gil has a passion for using technology to help communities.

As long as EMS agencies have used computerized dispatch systems, there has been the promise that those systems will be able to use day-to-day data to help make decisions based on real behavior. One of the key tenants of optimizing system performance is understanding the time that system resources allocate to daily tasks, including incidents, transports, post-moves and out-of-service times.

Bringing the Pieces Together

Historically, leadership has had access to elements of the data associated with a given task but lacked the tools to apply that behavioral data to benefit patients.

Capacity of a system is either over- or under-represented based on the level-of-service requested when data associated with real behaviors aren’t applied to planned tasks. As services strive to do more with less, it’s imperative that systems used for decision support are capable of accurately maximizing a system’s capacity.

Learning True Capacity…and Applying Accordingly

Let’s apply this to a typical EMS system. As a leader, I know the average time it takes to run a given level-of-service (Advanced Life Support, Basic Life Support, Critical Care, or Mobility). I also know how long it takes to pick up or drop off at a specific facility. Unfortunately, there are two critical elements that are missing:

  1. The ability to see average task data on a department-by-department basis. This metric is important because the time it takes to pick up or deliver a patient may be significantly different from an Emergency Department to a ward on the 5th floor to the ICU. It is also different based upon the level-of-service needed.
  2. The application of data to accurately schedule and anticipate task durations. Applying available data about timing and behavior allows for greater confidence in task time and – more importantly – allows for better on-time performance since system capacity is more accurately represented.

EMS Use Learned Data to Determine Resource Needs

In the video below, Kevin Irwin, Vice-President of Communications and Mobility Transportation with Logis’ partner entity, Emergent Health Providers (EHP), describes how learned facility durations have enabled EHP to schedule transportation more accurately by level-of-service. He demonstrates how the additional information allows them to confidently determine the correct number of resources needed to meet patient needs. Emergent Health Partners is a leader in the use of technology to power more efficient and effective operations, from dispatch to patient care to billing. Emergent Health’s paramedics and EMTs provide medical 9-1-1 coverage for more than 1 million Michigan residents and respond to upwards of 200,000 medical emergencies per year.

The CAD That Learns

Logis’ Intelligent Decision Support (IDS) CAD accurately captures all the times associated with tasks in a resource’s day. It then provides leadership with recommendations that apply this learned data to future tasks, anticipating task duration more accurately.

The Logis Data Intelligence Service is designed to model and modify expected duration behavior based on the learned experience of previous instances. There are three common types of learned data from common task behavior:

  • On-Scene Duration
  • Driving to Nearest Facility
  • Facility Handover Duration

When enabled, IDS utilizes the previous facility duration data for pick-ups and deliveries. These values are generated using a combination of On-Scene Duration and Facility Handover Duration along with the corresponding road-to-door times. IDS’ Data Intelligence Service calculates averages from historical handover durations for each facility (department, ward, etc.) and level-of-service.

For a given facility and its departments – such as a hospital with floors and wards – IDS learns from the actual time the crew spends walking from the resource to the patient’s bedside, loading or delivering the patient, then walking back to the resource. This data is collected and applied by level-of-service, so that the time it takes for an ALS pick-up/delivery is distinctly different from a Critical Care or Wheelchair pick-up/delivery.

Logis IDS’ Data Intelligence Service provides organizational leaders with the confidence that their EMS system is utilizing the most accurate data to make both automated real-time decisions and also provide these leaders with the data to backup that performance.