Dozee is India’s first AI-powered contactless Remote Patient Monitoring (RPM) & Early Warning System (EWS) for continuous patient monitoring, aiming to develop and deploy intelligent technologies, solutions, and networks to provide a sequence of services like care, early warning systems, and responses to save lives. The Government of India supports Dozee through BIRAC and is on a path to steer the trajectory of the public and private healthcare ecosystem.

Dozee aims to provide connected health in every bed. Dozee strives to improve access and prioritise patient care with its vision of “HarBedDozeeBed”. Dozee keeps track of vital parameters like Heart Rate, Respiratory Rate, Blood Pressure, Blood Oxygen Saturation, and Skin Temperature with clinical-grade accuracy and also tracks sleep quality which helps in identifying sleep apnea. The hospitals can save almost 2.5 hours of nursing time by automating patient monitoring through Dozee. Dozee was founded in October 2015 by IIT grads Mudit Dandwate and Gaurav Parchani. 

How have technologies like AI transformed traditional manual patient monitoring in hospitals?

Technologies like Artificial Intelligence (AI) have significantly transformed traditional manual patient monitoring in hospitals, which demanded nursing hours that would often lead to potential delays in identifying critical changes in a patient’s health status and limit healthcare professional’s (HCP) capacity to deal with it. Some ways AI has impacted patient monitoring include,

• Remote Patient Monitoring: Dozee’s AI-based remote patient monitoring and early warning system can track a patient’s vitals with an accuracy of 98.4%. It eliminated the need for the constant physical presence of healthcare professionals.
• Early warning system and predictive analytics: Artificial intelligence algorithms are capable of detecting patterns and identifying potential health risks in large amounts of patient data, including vital signs, medical records, lab results, and imaging reports.
• Enhanced diagnostics: Medical images, such as X-rays, CT scans, and MRIs, can be analysed by artificial intelligence to assist radiologists in detecting abnormalities and making accurate diagnoses.
• Personalised treatment plans: AI algorithms can analyse patient data like medical history and provide personalised treatment recommendations. As a result, healthcare providers can optimise patient care and make informed treatment decisions.

Can you explain the impact of Dozee on Indian public healthcare infrastructure?

In the current public healthcare setting in India, the nurse-to-patient ratio is 1:40, which significantly deviates from the World Health Organisation’s recommended ratio of 1:4. Thus, the shortage of healthcare professionals as well as ICU beds are felt very acutely across both the public and private healthcare landscape. This is a significant obstacle in delivering quality healthcare services that can be accessible to all patients.

But, with remote patient monitoring systems in place, healthcare professionals can provide enhanced patient care by converting non-ICU beds to Step-down ICUs and HDUs (High Dependency Units). Hospitals can adopt these connected beds at a very minimal cost and further drive a holistic patient-centric approach to caregiving. To enhance the public healthcare ecosystem of India, Dozee launched the Million ICU initiative to raise funds to upgrade normal beds in hospitals across India to connected beds. In keeping with this initiative, it recently partnered with British International Investment (BII) to upgrade around 6000 hospital beds in 140+ public hospitals nationwide.

How challenging is this when it comes to the availability of Dozee in the rural areas of India?

The biggest challenge that the healthcare system in rural India currently faces is the uneven distribution of medical resources. The challenges that will hinder the implementation of Dozee beds include a lack of better infrastructure and stable internet connectivity, awareness and education on remote patient monitoring technologies, and necessary healthcare resources and support.

Addressing these challenges requires a multi-faceted approach involving collaboration between technology providers, healthcare organisations, government bodies, and community stakeholders. It involves improving infrastructure and connectivity, raising awareness and providing education, making Dozee accessible, and adapting to the specific needs and context of rural communities in India.

How has AI changed the RPM and EWS with its advancements?

AI has brought significant advancements to remote patient monitoring (RPM) and early warning systems in India, revolutionising how healthcare providers detect and respond to potential health risks. This aids in personalised care and significantly reduces the risk of hospital-acquired infections for patients. AI considerably changed the RPM and EWS through its abilities like:

• Continuous monitoring and real-time alerts
• Predictive analytics for risk assessment
• Data integration and analysis
• Early detection of critical conditions
• Remote and home-based monitoring
• Machine learning for personalised care

What challenges do you face in adopting AI in RPM systems?

 Adopting AI in remote patient monitoring systems comes with several challenges, which includes:

• Data Security and Privacy: Ensuring the security and privacy of sensitive patient information is crucial. Healthcare organisations must implement robust data protection measures and comply with regulations.
• Data quality and interoperability: AI algorithms require high-quality, standardised, and interoperable data to deliver accurate results. However, healthcare systems often face challenges in terms of data quality, inconsistencies, and interoperability between different systems and devices.
• Regulatory and ethical considerations: The adoption of AI in healthcare must navigate complex regulatory frameworks and ethical considerations.
• User acceptance and adoption: The successful adoption of AI in remote patient monitoring depends on user acceptance and adoption by healthcare professionals.
• Integration with existing systems and workflows: Integrating AI-powered remote patient monitoring systems with existing healthcare systems and workflows can be complex.

Considering all these factors, it is imperative that adopting AI-based technologies such as RPM should be done responsibly, erring on the side of caution.

What makes Dozee’s RPM system more reliable than manual nursing? Can this lead to the replacement of nursing jobs?

It’s important to note that Dozee’s RPM system is designed to enhance and support nursing care, not replace it. It does away with the existing limitations in manual nursing, enhancing the overall approach towards caregiving. By continuously and remotely monitoring patients, Dozee helps streamline the workload of nurses.

Dozee’s RPM system is a valuable tool that complements and assists nursing care by reducing burnout and creating a more holistic environment for patients and caregivers, ensuring increased satisfaction for both parties involved. While the government of India recently announced the establishment of 157 new nursing colleges in its FY2023-24 budget, the primary focus now should be to equip nurses with adequate knowledge.

Can you briefly explain a few trends in RPM systems for using AI?

In recent years, the advent of artificial intelligence (AI) has brought significant advancements to remote patient monitoring (RPM). Given below are the trends highlight AI’s increasing role in enhancing RPM systems’ capabilities.

• Predictive analytics: By analysing patient data, including vital signs, medical history, and lifestyle factors, AI algorithms can predict the likelihood of certain health conditions or events.
• Remote monitoring of chronic diseases: By collecting and analysing data continuously, AI algorithms can detect early signs of disease progression and provide personalised recommendations.
• Advanced algorithms for risk stratification: By combining multiple data sources, including genetic information, social determinants of health, and environmental factors, AI-powered RPM systems can identify individuals at higher risk of developing certain conditions or experiencing complications.
• Natural language processing and voice assistants: AI is leveraged in RPM systems to enable natural language processing and voice recognition capabilities. This allows patients to interact with the RPM system using voice commands.

What are the possibilities of this technology replacing nursing jobs?

 Technology, including AI-driven systems, can enhance and support nursing care, but it can never replace nursing completely today or in the future. The need for human interaction, emotional support, hands-on care, adaptability, flexibility, legal and ethical responsibilities, collaboration, and teamwork, are just a few of the reasons.

While technology can augment nursing practice by automating certain tasks, the core aspects of nursing, such as human connection, critical thinking, complex clinical judgment, and hands-on care, remain indispensable. Healthcare will prosper when technology comes together with nursing professionals, enhancing and supporting their roles rather than replacing them.

More collaborations between healthcare providers and tech developers must be encouraged to create a more efficient, patient-centric healthcare system that improves outcomes, reduces healthcare costs, and ensures that healthcare services are accessible and affordable to all.