Business Case: CallBots managing medical appointments for Asisa
Asisa – a member of the HLA group – is one of the leading insurance companies in Spain, with its own healthcare network consisting of 16 hospitals and more than 10,000 subsidised centres. They needed to provide a fast and high quality response to their patients’ requests for medical appointments, which led them to choose Vozitel’s CallBots technology.
“The appointment management service had, and still has, a call curve that is very complex to cover with telephone managers alone, and in order to provide the best possible service, we needed to improve service on certain days and at certain times. As a result, we came up with the proposal to adopt a scalable, high-quality callbot solution”, says Joaquín Juridíaz, head of customer service channels at Asisa.
As previously mentioned, there were several issues that led HLA to look for an alternatives that would not only solve their challenges, but also allow them to offer a better service to their customers, some of these challenges were:
- Automation: Medical appointments management was being done entirely by their agents.
- Cost reduction: There was a high cost per appointment, associated with a manual appointment process.
- Service improvement: The agents had a high workload, and where only available during business hours.
In June 2020, Asisa approached Vozitel to explore the possibilities of solving the aforementioned challenges, and it was decided that the best solution would be to automate their appointment management process by implementing of a virtual agent or CallBot. It was necessary to reduce the cost per appointment while guaranteeing the best customer service. Asisa was specifically looking for “an end product that was closed and tested and that could be easily adapted to the requirements of the business and could be implemented quickly”.
ntil then, Asisa was managing its medical appointments through its Call Centre, and its medical centres had different IVR configurations. Vozitel proposed the integration of a virtual agent or CallBot for the management of medical appointments via voice, which would be in charge of the low-value tasks and, therefore, generate a lower cost per completed appointment. In addition, the combination of this Artificial Intelligence with the experience of the ASISA staff would help generate the best user experience.
Then, the best implementation route was determined and the integration of the virtual agent or CallBot for the management of medical appointments was continued, this was achieved through different phases:
1. Data collection and analysis (2 weeks)
A series of meetings was held with Asisa in order to understand their motivation, issues, and the expected results of the appointment automation process. The project started, by requesting Asisa for audios of the agents performing the complete appointment process and the technical manuals of the database. Crucial data for launching the project.
2. CallBot design and integration (6 weeks)
This was the most technical phase of the project, in which the following aspects were developed:
- Conversation flow maps.
- Analysis of API manuals.
- Design of a new data model to ensure the correct connection of the CallBot.
- Reduction of the number of API interactions.
- Creation of Artificial Intelligence structures so that the CallBot could vocally recognize doctors, specialists and centres.
- Tests with the first CallBot prototype.
3. Implementation (8 weeks)
The CallBot was gradually introduced in Asisa’s centres and hospitals, this phase was implemented as follows:
- First: In 3 centers with the lowest volume of appointments managed in order to affect the patient experience as little as possible.
- Second: Once the results in these centers were successful, it was implemented in a dozen centers with medium volume.
- Third: Implementation in all Asisa centers and hospitals.
4. Supervision and improvements (12 weeks)
Following the implementation of the CallBot in all the centres, weekly meetings were scheduled to assess the results, propose improvements in the flow, and solve any issues.
5. Continuous follow-up (on-going)
Meetings are still being held every two weeks to review CallBot performance and identify any improvements in the flow, reports or procedures.
This process allowed for appointments to be managed through a combination of Virtual Agents and humans, the consolidation of a single IVR that decides whether to transfer the call to the CallBot or to an agent, depending on the reason for the consultation and, finally, the integration of the appointments managed by the CallBot into its management software Green Cube via API.
3. Customer acceptance
The integration of the CallBot was a complete success and the reception and satisfaction of its customers is proof of this. Right from the outset, technology was placed at the service of patients as a solution that would provide them with a way of managing their relationship with Asisa in a faster, more efficient way.
Considering that Asisa provides its services to a large number of people, it was taken into account that certain groups would have some kind of resistance when interacting with this type of technology. Therefore, the CallBot was proposed as assistance (an option) and never as an obligation and, if required or requested, they would have the option to talk to a customer service agent.
“That said, the acceptance and its evolution over time is not only very positive but, as shown in our monitoring, it follows a growing trend”” HLA says.
Moreover, acceptance by the contact centre agents has also been positive, as they see this technology as an ally that helps them manage the calls from patients that they would not otherwise be able to handle.
Another key indicator of the success of this project has been the great results been achieved, of which we can highlight:
- 33.08% reduction in traffic referred by the IVR to ASISA.
- Agents handled 22.53% fewer calls for appointment management than in the previous period.
- 23% of the total number of medical appointments were processed outside business hours.
- Average waiting times were 0:00
- CallBot talk time per appointment was 31.60% lower than that of an agent.
In this, as in many other success stories, it has been possible to demonstrate the effectiveness of CallBots in the management and automation of low value tasks, specifically the management of appointments. Exceeding expectations, being a great ally for customer service agents, and generating great results for those companies that decide to implement them.
If you too want results like the ones in this example, do not hesitate to contact us.