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Introducing Our GeoIntelligent-Scheduling Selves

Introducing Our GeoIntelligent Scheduling Selves

It is zero hour for the GigFlex Web site and we’ve launched with an inaugural blog post dedicated to introducing ourselves. We are GigFlex LLC, a software company steeped in algorithms focused on scheduling – specifically, the scheduling of workers performing multiple time-constrained tasks in the field within one shift. If you have a family member who receives in-home healthcare services, you’re already familiar with the type of professional our applications are designed to serve. A nurse arrives for a 20 or 30 minute visit, then departs for the home of another patient. Many factors impact if and how that nurse arrives at that home and at every other home on the day’s schedule. Managing those factors is where we come in with our specialized software and a useful new functionality called geointelligent scheduling.

GeoIntelligent Scheduling™
If you owned the home healthcare business employing the nurse mentioned in the previous paragraph, do you think you’d be concerned about your healthcare professionals getting to where they were supposed to be on time, completing their assignment in the allotted time, and then commuting to the next appointment as scheduled? Of course you would be. Why? Because the patients who hire you to provide them with medical care depend on the professionals you employ to address their medical issues in a timely manner. What you’re selling is prompt, efficient, reliable service. Performing up to that “guarantee” has never been a simple matter. So many obstacles can arise. The nurse can get sick, oversleep, be stuck in traffic, or decide to stop for coffee. How would you know unless you received a complaint call from the patient or the nurse actually called in to let you know of the delay? Geointelligent scheduling would keep you informed about your nurse’s whereabouts at all times. Even better, it would automatically initiate communication with the affected patient and seek a solution to the “tardy nurse” problem. Here’s how it works:

  • Home healthcare company has our system deployed in the home office
  • Nurses have our app on their mobile phone
  • During working hours, the system tracks their geolocation at all times*
  • Supervisors can see the geolocation of medical staff at all times in system’s interface
  • The interface displays current traffic conditions in the area
  • If a nurse is not within a certain distance from a scheduled patient at a preconfigured time, the system kicks into action and notifies the patient, the supervisor, and the nurse
  • The system can be configured to contact a different nurse to visit the patient, should one be in close proximity and able to reach the patient quickly
  • If a nurse spends more than the allotted time with a given patient, the system automatically notifies the supervisor, the nurse, and the next patient on the schedule
  • Again, a substitute nurse can be sent to that next patient, if necessary

In short, geointelligent scheduling removes the stress and headache of manually managing workers in the field. There’s much more to how the system works – e.g., we integrate with existing workforce management tools for access to certain employee data – but geointelligent scheduling is the essential component that consistently ensures the best possible service delivery.

We’ve used home healthcare as an example, but the same principle applies in any industry with “roaming” professionals. We currently offer versions of our application for three of them: home healthcare, private car and limo services, and staffing agencies. In each of these, geointelligent scheduling keeps workers going where they’re supposed to go and doing what they’re supposed to do according to their prescribed schedule. That’s good for your clients. And for your business. It’s also good for the workers you employ. We’ll address that in our next post.


*In case you’re wondering if the system tracks medical staff even when they’re off duty, the answer is no. The system automatically stops tracking workers at the end of their scheduled shift. It begins tracking them again 30 minutes before their first appointment the next day.

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