Reality on the ground
Help resources offering a telephone intervention line are often faced with a common reality: a large proportion of calls are received by a small segment of the population. In the academic literature, the term "frequent user" is used to refer to these callers. Perkis et al (2016) show that in some centers, frequent users represent 3% of the total clientele, but account for 60% of total call volume. This raises questions about the effectiveness of the tools available on an intervention line that is intended to be punctual for this atypical clientele. It is usually in these situations that an intervention plan is drawn up to better manage calls with frequent users, and that increased support is offered to the intervention team (Vivekananda et al., 2019).
In addition to the challenge of the large number of calls, there is also the clinical challenge that can weigh heavily on the intervention team itself. Indeed, Vivekananda et al. (2019) indicate that working with these users is more challenging and taxing for practitioners. Middleton et al. (2016) conducted interviews with frequent users and concluded that a continuity of care approach would be preferable in these situations. This means that adjustments to the ad hoc intervention model are recommended.
How can ASO help?
This unique situation is an example of where we can benefit from using a database such as ASO. Since the user file is the backbone on which everything related to each user is stored, ASO is the ideal environment for using the work that has already been completed as a lever for quickly identifying this clientele.
The frequent user module helps organizations to quickly identify people whose use would qualify as frequent according to the center's practices. The module also has the well-being of caregivers at heart, as it enables the support team to easily identify caregivers who have had a greater number of exchanges with these people.
The operation is simple and does not require any changes in the use of ASO. We continue to create a user file for each individual who contacts us, taking care to enter the details that might enable us to identify him or her in the event of future contact (name, telephone number, etc.). For each intervention completed, we indicate it, as usual, in the appropriate file. So, with the information already entered in ASO, the module displays on the home page of those who have access to it the list of frequent users currently identified, as well as the list of practitioners who have had the most interactions with this clientele in the last week. Once these people have been identified, ASO offers a variety of tools including recommendations and intervention plans to implement the recommendations of Middleton et al. (2016).
Configuration: how to optimize module efficiency
The module simply asks you to enter three parameters in its configuration so that it can then start working automatically: 1. Number of interventions: how many interventions before the status of frequent user is added to the file? 2. Period of interventions: this variable allows us to distinguish frequent users from users who use the services occasionally, but have been doing so for a long time. 3. Number of active days of status: once a person is identified, this variable determines how long they will retain this status before the counter resets to zero. Here is a concrete example. Let's use Middleton et al. (2016) as an example for the first two variables: 20 calls over a 30-day period. Therefore, for a person to be identified as a frequent user, they must have at least 20 interventions on their record over a period of 30 consecutive days. This means that a person who has called once every six months for 10 years would not be identified as a frequent user because, even though they have 20 interventions to their credit, these interventions were made on an ad hoc basis. However, a person who calls once a day would be identified after their 20th call. We could then decide that the status remains active for a period of 60 days from the last day on which they meet the criteria. This third variable is mainly used to identify the workers who have had the most contact with this clientele. Other configurations are obviously possible since everything is customizable. We could also decide that the only interventions that should be included are telephone interventions and ignore face-to-face interventions. For those who are already familiar with ASO's customization capabilities, we can let our imagination run wild to come up with an optimal configuration that represents the reality of our organization.
We leave you with a suggestion for further reading: Mishara et al. (2023). In this literature review, you'll find a synthesis of several academic studies that have been completed over the last few decades. For those who would like suggestions on how to categorize a frequent user, there are also some interesting ideas from around the world.
Bibliography
Middleton, A., Gunn, J.M., Bassilios, B., & Pirkis, J. (2016). The experiences of frequent users of crisis helplines: A qualitative interview study. Patient education and counseling, 99 11, 1901-1906,
Pirkis, J., Middleton, A., Bassilios, B., Harris, M., Spittal, M. J., Fedszyn, I., Chondros, P., & Gunn, J. (2016). Frequent callers to telephone helplines: New evidence and a new service model. International Journal of Mental Health Systems, 10, Article 43 Pirkis, J., Middleton, A., Bassilios, B., Harris, M., Spittal, M. J., Fedszyn, I., Chondros, P., & Gunn, J. (2016).
Vivekananda, C., Cuppari, K., Cuppari, A., Jenkins, T., & Usatoff, A. (2019). Exploring a model of care for frequent callers to counseling helplines. Advances in Mental Health, 19(1), 17–28.
Mishara BL, Côté LP, Dargis L. Systematic Review of Research and Interventions With Frequent Callers to Suicide Prevention Helplines and Crisis Centers. Crisis. 2023 Mar;44(2):154-167. doi: 10.1027/0227-5910/a000838. Epub 2022 Jan 28. Erratum in: Crisis. 2023 Mar;44(2):168. doi: 10.1027/0227-5910/a000869. PMID: 35086356; PMCID: PMC10102973.
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Frequent users: how ASO can help
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