Clearing and Holding: A 26-Month Interrupted Time-Series Evaluation of CAMHS Waiting-List Redesign in Qatar

Waiting for help is a universal human experience, but in the world of child and adolescent mental health, the wait can become a crisis in itself. When families seek support for a young person's emotional or behavioral struggles, they often face a long line of people ahead of them. This backlog is not just a list of names; it represents a growing gap between the number of children who need care and the number of specialists available to see them. In many places, this imbalance means that by the time a child finally gets an appointment, their situation may have worsened, or they may have given up hope entirely. The core challenge for healthcare systems is not simply clearing the line once, but keeping it from filling up again when new families arrive every day. The question is whether a service can reorganize its existing staff and time to handle a massive pile of past cases without hiring more people, and then maintain that progress when demand continues to rise.

In Qatar, a team at a major pediatric medical center decided to test a new way of managing this pressure. They focused on their Child and Adolescent Mental Health Services, a department that helps children aged five to eighteen. For years, the waiting list had grown so large that it threatened to overwhelm the team of five psychiatrists and four psychologists. Instead of asking for more staff or more money, the team redesigned how they worked. They called their approach the CLEAR Access framework, a set of steps designed to sort through the backlog and keep the flow of patients moving. The process began with a concentrated effort to clear the existing queue. They merged two separate waiting lists—one for psychiatry and one for psychology—into a single line. A senior doctor took charge of deciding who needed to be seen first, ensuring that every referral was checked for current need. They called families to confirm if they still required help, and for those who did, they offered faster, face-to-face appointments. To make room for these urgent first visits, the team shortened the length of each initial appointment and temporarily reduced the time spent on routine follow-up visits for patients already in care. This intensive drive lasted for about a month in late 2024.

The results of this initial push were dramatic. Before the changes, the waiting list held hundreds of children. Within weeks, the number of people waiting dropped sharply. The researchers tracked this change using daily records over a period of more than two years. They found that the redesign was associated with a sudden drop of nearly four hundred patients from the waiting list. The number of children waiting fell from a projected level of over five hundred down to just under one hundred and thirty. The lowest point reached was thirty-one children waiting, a figure that stood in stark contrast to the hundreds waiting before the changes began. This clearance was not just a temporary dip; the team managed to keep the list small for many months afterward. Even when the number of new families asking for help began to rise again, the waiting list did not return to its old, overwhelming size.

However, the story does not end with a simple victory. The researchers were careful to note that while the list got shorter, they did not know exactly what happened to every single person who was removed from it. During the month of the intensive drive, the team personally saw and assessed one hundred and ninety-one children. This number accounted for just over half of the total reduction in the waiting list. The fate of the remaining hundreds of children was not fully recorded in the data. Some may have been redirected to local community services that could help them, some may have improved while they waited, and others may have been removed because the team could not reach them by phone. Because the study did not track these individual outcomes, the researchers could not confirm that every child who left the list had received the right kind of help. They could only say that the list itself was smaller and better managed.

The true test of the new system came after the initial cleanup, when the service had to deal with a fresh wave of demand. In the months following the clearance, the number of new referrals increased significantly, rising by more than sixty percent compared to the previous period. Despite this surge, the waiting list remained much lower than it had been before the redesign. The average number of children waiting during this busy period was still seventy-one percent lower than the pre-change average. The team had to work harder to keep it that way. They had to protect specific blocks of time in their schedules just for new assessments, ensuring that follow-up work did not crowd out the chance to see new patients. When the list began to creep up again, they intensified these protected time blocks, rotating senior staff through weeks dedicated solely to new assessments. This flexibility allowed them to absorb the extra demand without letting the queue grow out of control again.

The study also revealed that the way the team sorted patients had changed permanently. Before the redesign, most new referrals were marked as high priority, which made it difficult to tell who truly needed immediate attention. After the changes, the team became much better at distinguishing between urgent and less urgent cases. The proportion of appointments marked as high priority dropped dramatically, and the team started meeting their time targets for these urgent cases far more often. The time it took for a family to get an appointment also improved drastically. In the year before the changes, the median wait for a routine appointment was seventy days. After the redesign, that wait time fell to eighteen days, and even during periods of high demand, it stayed well below the original figure. The administrative steps to book an appointment also became much faster, shrinking from two weeks to just one day.

Despite these successes, the researchers were clear about the limits of their work. They did not hire new staff, buy new technology, or receive extra funding. The entire improvement came from rearranging how the existing team used their time. This meant that the gains came with a cost. During the month of the intensive drive, the number of follow-up visits for children already receiving care dropped by about twenty percent. The team had to pause some routine work to clear the backlog. Furthermore, because the study was conducted at a single center and relied on operational data rather than a controlled experiment, the researchers could not prove that their changes were the only reason for the improvements. They also noted that the data did not capture the long-term safety or outcomes of the children who were removed from the list without a face-to-face assessment.

The final picture is one of a service that learned how to clear a massive backlog and hold the line against rising demand, but only by making difficult trade-offs and accepting that some details remain unknown. The team proved that a waiting list could be managed without expanding the workforce, but it required a constant, disciplined effort to protect the time needed for new assessments. The structural changes, like merging the queues and having one person in charge of triage, stayed in place and worked well. But the parts of the system that required active management, like reserving time for new patients, needed to be watched closely and adjusted when demand shifted. The study suggests that for mental health services facing similar pressures, the key is not just a one-time cleanup, but a continuous balance between the flow of new requests and the capacity to see them. The waiting list can be kept under control, but it requires a system that is flexible enough to respond when the pressure rises again.