Created By Doctors
Created and supervised by doctors, our AI medical chatbot provides reliable and verified assistance. Because trust is our top priority, Mediktor's precision is based on rigorous medical quality processes that validate our content. Our medical team's mission is to professionally supervise our evaluator and keep raising our standards.

A Doctor-created Database
A vast amount of medical information is supervised and updated continuously.
Here's an overview of our data in numbers.
Verified Medical Content
Mediktor's database undergoes rigorous internal and external verification processes.
This is how we do it.
Internal Verification
Our Medical Department carries out strict internal evaluation procedures to test the accuracy of our solution.
Reviewed by Third-Party Experts
Our database is created by professionals and reviewed by specialists. Experts analyse our content to offer personalised and inclusive assistance.

Tropical Diseases
Mediktor's database has been enhanced to accurately detect the most prevalent diseases in tropical areas.

Sensory, Physical, and Intellectual Disabilities
We've updated and reviewed our database to personalise the assistance for individuals with disabilities.

Mental Health
We provide reliable mental health assistance that has been comprehensively analysed by experts.

Gynecology and Obstetrics
All the G&O information and content have undergone rigorous examination and approval processes.
Scientific Trials
Mediktor collaborates with relevant third-party organisations to conduct clinical trials involving real patients.

Experience of Mediktor®: a new symptom checker based on artificial intelligence, in patients treated in an A&E department
Evaluation of Mediktor's accuracy compared with physicians in diagnosing low-complexity pathologies. It assesses the solution's performance in the hospital's A&E.
Hospital Clínic de Barcelona
Barcelona, Spain. 2018

Evaluation of a diagnostic decision support system for the triage of patients in a hospital emergency department
Comparison between Manchester triage, final diagnoses made by the ED, and Mediktor's triage and pre-diagnosis. It tests the potential of the technology to complement and assist triage processes.
Hospital Clínico San Carlos
Madrid, Spain. 2018

Experience with Mediktor® (artificial intelligence) in Patients Attended in an Emergency Department in San Ignacio University Hospital in Bogotá, Colombia.
A study to evaluate the concordance between Mediktor and an emergency doctor in the diagnosis and laboratory tests. It proved Mediktor to be a reliable tool to help diagnose the most prevalent diseases in the A&E.
Hospital Universitario San Ignacio
Bogotá, Colombia. 2019

Validation of a neurological triage tool in the general population based on the use of artificial intelligence algorithms
A trial to evaluate the effectiveness of Mediktor in detecting early symptoms of ictus in the Neurology Department. It demonstrates the software's efficiency in specialised neurological triage.
Hospital Vall d'Hebrón
Barcelona, Spain. 2021. In progress.
Related Articles
Hospital Universitario Arnau de Vilanova (HUAV) is a healthcare provider in Lleida, Alto Pirineo y Arán, and parts of La Franja in Aragón, Spain. Healthcare professionals were struggling with overcrowded waiting rooms, and the HUAV was looking for a digital solution to help manage urgent care effectively.
Serving approximately 400,000 people, the hospital’s Emergency Department often experiences overcrowding, particularly during high-demand seasons like autumn and winter. “In winter, we have a really hard time because we triple or quadruple the real capacity,” explained Nuria Amador, Nurse of the HUAV’s Emergency Department.
In recent years, the HUAV has undergone a significant demand increase at the ED, with nearly 50% of emergency visits being non-urgent (IV and V triage levels). This rise in non-urgent visits is often caused by a lack of public awareness about alternative public healthcare services more suitable for patients with low-complexity needs, resulting in unnecessary congestion in the central hospital emergency department.
In response to this challenge, the HUAV sought a digital solution to manage urgent care cases more effectively. The answer was a reverse referral model designed to identify non-urgent patients in the emergency waiting room and redirect them to Lleida’s Urgent Primary Care Center (CUAP). “What we were looking for by adding artificial intelligence with Mediktor was to give an extra point of quality to the reverse referral process,” said Oriol Yuguero, Head of the Emergency Department.
Mediktor’s AI-driven software helped streamline the reverse referral model by meeting the hospital’s strict security standards and seamlessly integrating into its existing processes. Patients identified as having lower urgency levels (IV and V) through a traditional nurse triage were given the option to assess their symptoms using Mediktor’s software. This second AI-based assessment helped identify patients who could be seen at the CUAP and received a recommendation to leave the ED.
The results of this innovative approach were enlightening. Among those patients that Mediktor recommended to attend the CUAP, an impressive 90.9% left the Emergency Department. When patients receive and acknowledge Mediktor’s recommendation, they realize that their cases can be solved outside urgent care. This not only reduces their wait times but also empowers them to take control of their healthcare decisions. Most of these patients proceeded to the CUAP, where they received timely care and were subsequently discharged.
The implementation of Mediktor’s AI-driven reverse referral process has yielded significant benefits. For patients, it meant receiving appropriate care more quickly and efficiently. Those with non-urgent conditions were successfully redirected to CUAP, reducing their wait times and ensuring they received the care they needed without unnecessary delays.
The AI-powered reverse referral process not only benefited the patients but also had a positive impact on healthcare professionals. It helped alleviate the burden on HUAV’s emergency department, allowing for better management of time and resources. Nurses, in particular, played a crucial role in this process, actively engaging in patient evaluation and discharge. The model also contributed to better patient education, helping individuals understand the healthcare system and navigate it more effectively. “The aim is that services can be optimised and used in a correct way and, at the same time, provide health education”, expressed Sílvia Solís i Vidal, Director at CUAP Prat de la Riba of Lleida.
The successful collaboration between HUAV and Mediktor stands as a testament to the transformative power of technology in healthcare delivery. By leveraging AI to manage urgent care demand, HUAV has not only improved patient outcomes but also enhanced operational efficiency. This success story underscores the hospital’s dedication to innovation and excellence, setting a new standard for healthcare in the region and beyond.