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Founded in 2015 by IIT graduates, Satish Kannan and Enbasekar Dinadayalane, MediBuddy, a prominent player in India’s digital healthcare sector, offers online and offline consultations, doctor appointments, clinics, prescription delivery, home lab tests, and more.
With over 2,000 employees, MediBuddy covers primary and tertiary care, including surgeries and second opinions, and offers preventive healthcare services. The portal boasts an alliance grid comprising over 90,000 physicians, more than 7,100 medical facilities and clinics, over 4,000 testing and examination hubs, and over 2,500 pharmaceutical outlets.
MediBuddy is backed by the likes of India Life Sciences Fund III, Quadria Capital, InvAscent and Lightrock India, among others. In February 2023, MediBuddy acquired ‘vHealth by Aetna’ to enhance its market dominance and strengthen its overall presence.
The health-tech company also expanded in rural India by acquiring Clinix, a healthcare provider with an extensive network across 20 tier-two and tier-four cities. This acquisition enabled MediBuddy to reach and serve the healthcare needs of people in remote areas of India.
MediBuddy is currently hiring data scientists (level 2 and level 3).
Inside MediBuddy’s Data Science Team
“We have a diverse tech team consisting of over 200 people, with a unique multidisciplinary data team that includes various professionals such as doctors, nurses, and clinicians besides data scientists, data engineers, analysts, product managers, lab pathologists, lab technicians, and clinical experts,” Dinadayalane told AIM in an exclusive interview.
The team also includes data annotators, who are nurses and clinical staff responsible for tagging and cleaning the data. “While we have doctors who can code, we don’t have engineers who have transitioned to become doctors,” he added jokingly.
Over the years, MediBuddy addressed various problems using AI and analytics. In 2015, the digital healthcare startup launched its app, placing digital healthcare at the forefront. Initially, they focused on online consultations, which became essential during the COVID-19 pandemic.
One of the primary impacts of the data science team can be seen in Clara, MediBuddy’s proprietary data science system developed by the company. It includes a chatbot for streamlined patient-doctor interactions, a clinical decision support system for doctors, quality control measures for improved patient experience, and an AI-based database engine for prescription accuracy. Clara also assists in providing additional healthcare services like medicine delivery, lab tests, and surgeries.
Another problem that the company is solving through AI is handwriting recognition from prescriptions which often turns out to be a deciding factor in getting the right medication. They are exploring technology solutions like AWS TextTrack and OCR-based technologies to automate interpretation for this. Another priority for MediBuddy is quality control, with robust data-based analytics and quality control engines. These engines analysed audio, video, and text data to assess interactions between doctors and patients. Critical insights were flagged and forwarded to internal clinical experts for improvement.
Taking into account India’s rich linguistic diversity, with more than 350 languages spoken, MediBuddy has undertaken a thoughtful approach to cater to the larger population who may not be proficient in English. They have implemented word embeddings to effectively tackle the unique challenges encountered in Indian English, such as the blending of Hindi words within English text.
Moreover, the MediBuddy team has dedicated efforts towards constructing models that are capable of language classification, named entity recognition, and establishing meaningful connections across various languages, leveraging extensive datasets. They recognise the significance of language teams in accurately tagging and comprehending subtle nuances within the linguistic landscape.
The company also created a doctor assistant engine, integrating AI-based auto-suggestions and recommendations. Based on chat conversations, the system suggests potential diagnoses, additional questions, and appropriate medications for doctors to review and validate. However, no decision is made without a doctor. It is just for suggestions to empower doctors to make well-informed decisions and prevent prescription errors.
To address this set of unique challenges, MediBuddy also has a diverse range of tools and frameworks.
On the data science side, Python is primarily used as the scripting language. The team employs various NLP models as their work involves chat, call, and video consultations. Speech processing, audio and video processing, and text processing are key components, as chatbots are built using NLP engines. Additionally, the team works on video quality and conversation smoothness. In terms of data modeling, the team explores emerging models like GPT-2 and GPT-3 and SVM (support vector machines) for NLP.
MediBuddy uses Superset, an open-source analytics platform, and R for data analytics tasks. Airflow is employed for data engineering and managing data pipelines. Cloud platforms such as AWS, Google, and Microsoft are used to address specific AI and healthcare requirements, including the use of TensorFlow.
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Candidates have introductory conversations to assess their fit for the role. This is followed by two or three rounds of technical interviews, consisting of in-person or take-home assignments depending on the role and problem being addressed. For higher-level positions, there may be interviews with managers to evaluate leadership potential.
“Our data science work is not limited to a separate R&D team but is integrated into everyday work across various healthcare services,” said Enbasekar.
When it comes to the skills that the company looks for in data science candidates, there are two core skill sets. Firstly, a strong understanding of fundamental modeling concepts is important. This includes expertise in mathematics, algorithms, and data science fundamentals. Secondly, candidates should have the ability to work with multiple functional teams and have a customer-oriented approach. Soft skills such as effective communication, teamwork, and customer understanding are highly valued.
From candidates, the company expects a willingness to learn and adapt to new technologies and challenges, as the field of data science is constantly evolving. MediBuddy values individuals who can bring solutions to life by collaborating with multiple teams and taking full ownership of projects.
Enbasekar describes his company’s work culture as being focused on healthcare, with a strong sense of pride and humility about the impact they can make in the field.
“We have a high-performance environment with continuous learning and problem-solving, driven by the desire to improve and serve our customers, doctors, and hospital partners”, he opined.
End-to-end ownership is a key cultural value, where employees go beyond their core roles to ensure value for the end user. MediBuddy also encourages open interaction and collaboration across teams, promoting a non-hierarchical structure where individuals freely seek help and get things done.
Why Join MediBuddy?
Joining Medibuddy offers the opportunity to have a significant impact on healthcare on a large scale. “We address unique technological challenges in healthcare, providing a valuable learning experience. Operating in both B2C and B2B sectors, MediBuddy offers exposure to various aspects of the healthcare industry,” he concluded.
So if you want to contribute to solving important challenges and ultimately benefit millions of people, making a meaningful difference in healthcare, MediBuddy is the right place for you. Check out their careers page here.
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