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Utility of SanIA Chatbot to Maintain Continuity of Care and Psychological Support During COVID-19 Pandemic

Authors:
  • Sanitas Digital Hospital

Abstract and Figures

Introduction: Digital health facilitates accessible, safe and more efficient care, through technologies such as bots and artificial intelligence. Undoubtedly, its implementation has been accelerated thanks to the COVID-19 pandemic, where they have demonstrated their effectiveness, by maintaining continuity of care and facilitating early interventions, such as psychological support, that has been of vital importance in a crisis situation, where mental health problems have increased considerably. Objective and methods: Prospective observational study to describe the utility of SanIA chatbot during COVID-19 pandemic, focusing the psychological tool. Results: During 2020, we have experienced an exponential increase in the number of SanIA consultations, with 824,435 conversations attended. Within its multiple functions it has performed 60,467 confirmed appointments, 160,422 appointments canceled, 136,432 online checkins and 62,826 documents sent. One of its main functionalities is as a psychological bot that has carried out 6,915 psychological evaluations. Of these it has diagnosed 28% of costumers with depressed mood, 13% irritable mood, and 10% anxiety or panic attack disorder, doing 5,292 online mindfulness sessions via bot and generating 1,507 appointments with a specialist (psychologist, psychiatrist or neurologist). Conclusions: Chatbot SanIA has helped to fight the COVID-19 crisis, making information available and maintaining continuity of care by providing advice, including psicological assessment, to patients whenever they want it 24/7.
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Copyright@ César Morcillo Serra | Biomed J Sci & Tech Res | BJSTR. MS.ID.005474. 26287
Research Article
ISSN: 2574 -1241
Utility of SanIA Chatbot to Maintain Continuity of Care
and Psychological Support During COVID-19 Pandemic
César Morcillo Serra*1,4, Daniel Tizon Galisteo2, Soraya Bajat Pacios3, Ana Aroca Tanarro1,4,
Sonia Gutiérrez Gabriel4, Domingo Marzal Martín4 and José Francisco Tomás Martínez4
1Digital Hospital, Sanitas Hospitals, Spain
2
3Psicology Department, La Zarzuela University Hospital, Sanitas Hospitals, Spain
4Medical Direction, Sanitas Hospitals, Spain
*Corresponding author: César Morcillo Serra, Medical Direction, Digital Hospital, Sanitas Hospitals, Pg Manuel Girona 33. 08034
Barcelona, Spain
DOI: 10.26717/BJSTR.2021.33.005474
ARTICLE INFO ABSTRACT
Introduction:


, such
as         

Objective and Methods:    
SanIA chatbot during COVID-19 pandemic, focusing the .
Results: During 2020, w
SanIA consultations, with 
        
136,432 online check-ins and 62,826 documents sent. One of its main functionalities is as
  
diagnosed 
or panic attack disorder, doing and generating

Conclusion: Chatbot SanIA        making
  

Received: 
Published: 
Citation: César Morcillo Serra, Daniel
     
Aroca Tanarro, Sonia Gutiérrez Gabriel,
     
    
Support During COVID-19 Pandemic.
      
BJSTR. MS.ID.005474.
Keywords: Digital Health; Chat bot;

Introduction
        
  


in all areas of health [1]. These new digital technologies allow
building a different relationship with the patient, focused on their

     
        

   
         
       
     
          
        
digital diagnosis and monitoring of diseases, through the use of



Copyright@ César Morcillo Serra | Biomed J Sci & Tech Res | BJSTR. MS.ID.005474.
Volume 33- Issue 5 DOI: 10.26717/BJSTR.2021.33.005474
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

   
 
        
       
managing tasks with minimal human-machine interaction, or for
        
loss [4] or helping depression [5] or to deal with COVID-19 crisis
          
     
support for people who cannot afford care, who can’t communicate
        
        
         








           
       
the healthcare staff routine work [8].
Materials and Methods

of Sanitas SanIA chatbot during COVID-19 pandemic, between
        

  
       
      
         

           

         

         
       

  

     
   

Figure 1: SanIA screenshots showing its multiple functions.
Results
During 2020, w 
the number of     
      
160,422 appointments canceled, 136,432 online check-ins and
62,826 documents sent. One of its main functionalities is as
        
 Figures 2 & 3      28% of
costumers with depressed mood, 13% irritable mood, and 10%
        
        
SanIA has done 5,292 online mindfulness sessions (Figure 4and
Copyright@ César Morcillo Serra | Biomed J Sci & Tech Res | BJSTR. MS.ID.005474.
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generated      
          
correlated with the incidence of COVID-19, with two peaks that

Figure 2: Number of SanIA psychological assessments during 2020.
Figure 3: Prevalence of detected psychological problems.
Copyright@ César Morcillo Serra | Biomed J Sci & Tech Res | BJSTR. MS.ID.005474.
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Figure 4: Number of SanIA mindfulness sessions during 2020.
Figure 5: Number of appointments generated by SanIA with a specialist after psychological assessment during 2020.
Discussion
has helped to establish
a formal and secure communication channel between the citizen
 
     


      
            

         Digital health tools
    


centers, and decreased risks of COVID-19 infection for both citizens
and health professionals [10]. The COVID-19 pandemic has brought
about a sudden change in the adoption of digital health strategies,
 People respond
     
  
      






           
programmers became obsessed with passing the Turing test, the
         


created at M.I.T. In 1966, much progress has been made and the


  
 

        
computer rather than another person, such as a chatbot that can
     
 
or a person [14]. Thanks to AI, natural language processing,
       
can be addressed [15]. Chatbots can help users with depression or
      
        
          
       
        
        
instant messaging application that is platform independent and can
   
     
        
        
       
         

        
support [19].     
      
Copyright@ César Morcillo Serra | Biomed J Sci & Tech Res | BJSTR. MS.ID.005474.
Volume 33- Issue 5 DOI: 10.26717/BJSTR.2021.33.005474
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of these health problems. The integration of AI, through chatbots


alerting the user and the medical team of the patient’s situation.


Conclusion
Chatbot SanIA making
       
         


sessions and .

 health problems 

References
1. Morcillo C, Gonz

2.      

3. 

4.            
        

5. 

6.  


7.    
       

8.           

9.   n Galisteo D, Ferri M, González Romero J, Morcillo
          

10.         

11.            
        

12.   

13. 

14.          


15. 
   

16.          
        

17. 
         


18.         
      
      

19.            
       
        
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ISSN: 2574-1241
10.26717/BJSTR.2021.33.005474
César Morcillo Serra. Biomed J Sci & Tech Res
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Article
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Introduction: Digital health facilitates patient-centered, accessible, safe, and more efficient care, through technologies such as telemedicine, big data, bots, artificial intelligence, and other technologies. Undoubtedly, its implementation has been accelerated thanks to the COVID-19 pandemic, where they have demonstrated their effectiveness, by maintaining continuity of care and facilitating early interventions thanks to the analysis of data and the deployment of bots, telemonitoring and virtual care platforms. Objective and methods: Prospective observational study to describe the digital health solutions implemented by Sanitas hospitals, a health insurance company with around 2 million costumers, 5 teaching hospitals and many outpatient health care facilities throughout Spain, to maintain continuity of care during COVID-19 pandemic. We outline the results of using the Sanitas telemedicine platform (video consultations and Connected Health application) and chatbot. Results: During the first 2 months of the COVID-19 outbreak, we have experienced an exponential increase in the number of video consultations, coming from an average of 300 a day before the COVID-19 crisis to around 5000 a day, going from 27.058 virtual visits made during 2019, to 114.598 in the first 5 months of 2020. The Connected Health mobile phone application allowed to remote monitoring 95 patients after hospital discharge for COVID-19 infection, measuring vital signs with a connected pulse oximeter, answer health questionnaires daily, and alert the medical team who received alerts for pain from 80% of patients and a decrease in oxygen saturation in 12% of cases. Bots has also helped to fight the COVID-19 crisis, making information available by providing the best answer to patients whenever they want it 24/7. Our bot SanIA has experienced 16.858 consultations about COVID-19 during the first 2 months of the outbreak. Conclusions: Digital health, throughout video consultations, telemonitoring platform and bots, has helped to maintain continuity of care during the COVID-19 crisis. The COVID-19 pandemic has brought a sudden change in the adoption of digital health strategies, which will undoubtedly continue in the long term, and has served us, both health staff and the population, to be better prepared for this next digital age.
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Resumen Objetivos: Los trastornos mentales son la principal causa de discapacidad en el mundo y la inteligencia artificial ha demostrado mejorar la gestión de estos problemas de salud. Hemos creado un sistema de auto valoración inicial de la salud mental del usuario, validado por especialistas en psiquiatría y psicología, basado en inteligencia artificial, con el objetivo de facilitar a cualquier persona el acceso a un recurso sanitario digital inmediato y gratuito, ayudarle a identificar un problema de salud, salvando las distancias y el estigma social, y mejorar así su calidad de la vida. Material y métodos: Se ha realizado un estudio descriptivo de los 12.896 casos que han utilizado el asistente virtual durante los primeros 9 meses de uso. El acceso al asistente es anónimo, universal y gratuito a través de cualquier dispositivo con acceso a internet. El sistema de inteligencia artificial está entrenado para utilizar los mismos circuitos de preguntas que se emplean en la consulta psicológica presencial y valora si el usuario supera ciertos umbrales relativos a los problemas psicológicos habituales. En caso necesario recomienda una derivación al especialista. Si no lo precisan y tienen un adecuado bienestar emocional se ofrecen sesiones de mindfulness. Resultados: De la totalidad de los usuarios, el 75% eran mujeres y el 25% hombres. Casi la mitad tenía 18-29 años, con una disminución de los casos más marcada a medida que aumenta la edad. El 41,6% de quienes usaron el asistente presentaban rasgos de un estado de ánimo depresivo, 28,1% estado de ánimo irritable, 26,8% ansiedad y estrés, 6,97% trastorno de ansiedad generalizado, 4,82% trastorno de ataque de pánico y 3,21% fobia. Un 26,3% (3.397 del total de pacientes) realizaron sesiones de mindfulness como promoción del bienestar emocional, que el propio asistente ofrece a aquellos pacientes que lo desean. Conclusiones: Un asistente virtual psicológico basado en inteligencia artificial, utilizado mayoritariamente por mujeres jóvenes, ha permitido valorar una elevada proporción de problemas de salud mental y realizar sesiones de mindfulness. Esta herramienta digital puede ayudar, sobre todo a las nuevas generaciones, a identificar un problema de salud y mejorar así su calidad de la vida.
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Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals. Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain. Methods: We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction). Results: Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes. Conclusions: This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.
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Background: A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. Objective: This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. Methods: In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. Results: The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. Conclusions: The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.
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