Service rationing and strategic queuing

People hate standing in line, and just about everyone in the social sector loathes having to decide who should receive a particular intervention and who should not. But service rationing and queuing are facts of life, and taking a sophisticated approach to how we prioritize services can make a substantial difference in a program’s net impact.

I take an operations research approach to program effectiveness. In this framework, each program aims to maximize a social good (or minimize some harm) given a set of constraints. Constraints come in many forms, including financial and time constraints.

Most of the organizations I work with, particularly human service organizations, have to ration their programs. For example, a homeless shelter only has so many beds and might have more demand for beds than supply in any given night. Food, tutoring, vaccinations, and many other types of social interventions have to manage scarcity daily.

So how do we prioritize who gets food, or who has a place to sleep tonight and who doesn’t? Typically I see organizations adopt a first-come first served approach, but this approach doesn’t always generate the best outcomes.

For example, let’s say a homeless shelter has nine beds and ten people need a place to stay, the first nine get in and the tenth person doesn’t. Seems fair, right?

But what if the first nine people are healthy men in their twenties who have been homeless for one night, and the tenth person is an eighty year old chronically homeless man who faces the possibility of death spending one more night on the street?

Assuming this shelter in question prioritizes minimizing life lost, the rational thing to do in this case would be to abandon the first-come first-serve prioritization model and to ensure a bed for the eighty-year old, turning down one of the nine people in their twenties.

This example illustrates that primitive rationing algorithms don’t properly optimize organizations’ intended outcomes, and can have unintended consequences for more vulnerable persons.

A better way is to develop a rationing system that aims to maximize the outcomes most important to the implementing organization. If the organization in our example aims to minimize life lost due to homelessness, then its shelter waitlist should recognize this preference.

Now, the shelter could ensure that it always has beds for the most vulnerable homeless persons by always turning away younger, healthier homeless persons. But this strategy would risk going nights where the shelter has empty beds, which wouldn’t optimally use the shelter’s resources, as the shelter might prefer that a healthy homeless person occupy a shelter bed than no one at all.

So the question then is, how do we make sure we prioritize more vulnerable persons while still trying to minimize the number of empty beds at the end of each evening? If you guessed data, I must be getting predictable.

Specifically, we would use concepts from queuing theory, utilizing historical data on what types of people come during what times of the year to try to build a model that changes prioritization based on the time of year, staffing levels, and perhaps even hour of day (for example, the shelter might have lower entry thresholds later in the evening to ensure maximum occupancy). This is the same type of approach that business like restaurants and airlines use to allocate their scare resources, except instead of maximizing profits we are maximizing social outcomes.

There is a lot of talk, and plenty of nonsense, around how data is supposed to revolutionize the philanthropic sector. I think this focus on revolution is misplaced. Instead, approaches like the one outlined in this post can help us get incrementally more effective. Indeed, the real promise of data is not to show us we have been wrong all along, but instead to provide suggestions as to how we can improve. Strategic service rationing and queuing is a great place to start.