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Reimagining Medical Diagnosis with Chatbots

4 minutes, 51 seconds read

Chatbots are rapidly gaining popularity in the healthcare sector. According to research conducted by Grand View Research, the global chatbot market is expected to reach $1.23 billion by 2025 growing at a CAGR of 24.3%. The current COVID pandemic has caused a lot of stress in the healthcare sector, with hospitals getting swamped with COVID-19 patients and also handling regular consults. 

This has made medical chatbots very attractive, helping in scheduling appointments, custom support, symptom checks, providing nutrition and wellness information, mental therapy, etc. Let’s take a look at how chatbots are transforming the digital transformation in the healthcare sector.

The shift to Medical Chatbots and Telemedicine

Lockdowns and social distancing due to COVID-19 gave a significant boost to digital business models. Organizations had to find ways to keep up the operations, make business continuity plans, and engage the workforce working remotely. Even healthcare providers took to technology such as telemedicine, chatbots, and remote monitoring equipment for patients who were not able to visit doctors in person. 

Many hospitals had been trying to implement telemedicine over the last couple of years, at least for ailments that can do without in-person diagnosis and can be cured by prescribing medicines based on symptoms told by the patient. COVID-19 gave that extra push for telemedicine. 

Another tendency that people have these days is to search for information on Google for self-diagnosis. However, that may not be effective. Therefore, many people are turning towards healthcare chatbots for medical information. 

Multilingual AI chatbot with video for diagnostic services – Hitee.chat

The Role of Chatbots in Medical Diagnosis 

The entire experience from admission to discharge is one of the key differentiators for patients while choosing a healthcare provider. People want quicker services and instant answers to their queries. 

With the coronavirus outbreak, hospitals and clinics are facing additional pressure. It has created a dire need for technology such as medical chatbots to provide better patient experience. 

Currently, there are some chatbots that leverage AI and machine learning to provide diagnoses by using algorithms to run the responses through a database of medical literature available. Let’s take a look at possible situations where chatbots play a crucial role in diagnostics-

  • Reliability: Instead of using a search engine to find answers, people will find chatbots more reliable for medical information. They need to be backed by legitimate medical databases to provide better accuracy.
  • Medical History: Chatbots cannot replace the role of a doctor while diagnosing but it can be of great assistance to them in providing medical history to better diagnose the health issue.
  • Triggering Attention: There are many symptom checking apps and bots available today which are widely used to check symptoms for possible diseases. Even with the nearest possible result in hand, it triggers the patient to a doctors’ visit if the symptoms seem grave. 
  • Support for Healthcare Workers: In case of mild diseases such as common cold, indigestion, minor wounds, etc. Chatbots are of great help as they reduce the workload of health workers who can focus on critical patients. 
  • Ensure Confidentiality: In some cases, patients may not be comfortable to open up to a doctor in person, but finds it easier to answer questions by a chatbot. Especially, when it comes to mental illness. 
  • Availability: Although rare, but there can be cases when medical help is not available physically such as during curfews or lockdowns. In such situations chatbots can be of great help for immediate medical support. 

Prevailing Challenges

Chatbots can provide basic medical information or do a cursory diagnosis of a health problem. However, the biggest challenge with diagnostic chatbots is the accuracy of the output. 

Research by the National Center for Biotechnology Information (NCBI) suggests that computer-based diagnostic support tools can be very beneficial to clinicians. But the effectiveness of 23 symptom checkers reported deficits and only 34% of standard patient evaluations were achieved in the first attempt. 

Unlike actual doctors, chatbots cannot feel the pulse, check the heartbeat or blood pressure, check the body part where the issue is, etc. Patients these days tend to self-diagnose quite often but they may not understand the diagnoses. 

Medical Chatbots can provide the information but can they explain it like a doctor as well? That would be debatable. Not everyone can understand medical jargon. Another issue is the risk of error in diagnosis. Too much dependency on the diagnosis can have steep consequences putting lives at risk. 

Redefining Chatbots in Medical Diagnosis

Currently, the chatbots function primarily through text while chatting with the patient. But in the coming future, it has a huge scope of improvement when combined with videos, images, voice recognition it will provide better information to the chatbot to provide better diagnoses. 

Medical diagnosis chatbot with video – Hitee.chat

Technologies like Natural Language Processing (NLP), machine learning, AI algorithms will enable better processing of the data and help clinicians with quicker diagnosis. It is possible to increase the capability of these chatbots through broader data and technologies. NLP integrated chatbots can also cater to specially-abled patients. 

More usage of diagnostic chatbots will make people take better care of their health. Indeed, there is scope for improvement for chatbots in medical diagnosis. But at the same time, reliability on them is also gradually increasing.

Down the Road

Chatbots in medical diagnosis can act as an aid to clinicians, reduce workload for healthcare workers, provide instant answers, and in some cases, it is a cheaper medium and lesser hassle than to visit a hospital. 

Bots have huge potential to streamline diagnosis. It won’t be a surprise to see chatbots be the first point of contact for medical help. 

We’ve introduced a multilingual AI-powered video chatbot for hospitals, private clinics, and diagnostic services. It can automate appointment bookings, checking symptoms, provide information, answer FAQs and more. You can write to us at hello@mantralabsglobal.com for your specific requirements.

Website: Hitee.chat

To know more about how HealthTech is reshaping the healthcare industry in bringing hospitals to a customer’s doorstep, watch our webinar on Digital Health Beyond COVID-19

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10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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