Pareto Tree

1. We are committed to fast tracking the process of detection and prediction of health deterioration. Globally, delayed detection of inpatient health deterioration is one of the major causes of loss of life especially in developing nations where 37% less lives would be lost if this were fixed.

2. A wearable vital signs monitor coupled with a SaaS enables possibilities of remote monitoring of the inpatients with: 

   1. Continuous and non-invasive monitoring

   2. Automated and Accurate detection of health deterioration

   3. Prediction of adverse and critical cardiovascular and respiratory events 

3. In the realm of a global rollout of such a solution, it will shift the paradigm of healthcare from a reactive to a predictive and preventive approach; which will not only save millions of lives but also reduce the cost and burden on healthcare systems (both public and private) globally. Global implementation of this can potentially save upto 20 million lives annually.

Our core belief continues to be one of predictive and preventive healthcare systems. It is essential that the global healthcare community explores and invests more into affordable solutions providing real-time insights and proactive monitoring. Pareto Tree is doing just that by focusing on preventative and mitigation measures that strengthen access to affordable primary healthcare systems and enhance disease surveillance systems. In India alone, more than 600,000 inpatients die each year due to delayed detection of health deterioration. 46% of these deaths are attributed to Human Related Monitoring Failures. Globally an estimated 20 million deaths are attributed to delayed detection of patient health deterioration.

The following factors cause delayed detection of health deterioration in hospitals:

• Non-continuous monitoring in wards

• Shortage of Staff

• Communication Lags

• Physical monitoring using multiple devices

40% of unanticipated deaths happen in the general ward due to delayed detection of patient health deterioration. 70% of doctors agree that there is a significant lag in communicating patient deterioration events.

Adding to this, the current pandemic has highly stressed upon the already existing need for remote monitoring of patients. There is  constant burden on healthcare professionals who are facing difficulties in monitoring multiple patients safely and remotely at a time.

Our solution is a hardware and software as a service combination solution; where the hardware is an affordable wireless wrist wearable medical device that monitors all vital signs (Heart Rate, Respiratory Rate, Non-invasive Blood Pressure, Oxygen Saturation and Body Temperature) with clinical-grade accuracy. This non-invasive device allows remote monitoring of multiple patients at once reducing time taken by on ground staff in monitoring and updating a patient’s status. The SaaS component of the solution is an artificial intelligence driven platform that allows scalable deployment of predictive algorithms for various diseases and critical adverse events.

Our wearable device, the Pareto Monitor, measures all vital signs and an amplitude of other parameters like Skin Conductance (GSR), Heart Rate Variability, Cardiac Output, Stroke Volume, Atrial Stiffness, Activity and Posture, and has the option to measure a wireless 12-lead ECG. Pareto Monitor evaluates these parameters using our proprietary photoplethysmography (PPG) sensor, an innovative solution designed to bring accuracy to the method.   

In our artificial intelligence driven SaaS solution, the Taru Platform, we are working towards predicting cardiac arrest prior in hospital inpatients. Taru leverages multiple machine learning models and algorithms to successfully predict critical adverse events.

Our solution will serve hospital inpatients and onground healthcare professionals. The solution is designed for developing countries starting with India as the key market for now. A country with a sizable population of over 1.4 billion people, 4.72 million healthcare professionals,102.3 million annual patients and 196,312 hospitals.

We have conducted extensive primary research and have interviewed over 100 doctors, nurses and patients to understand their unmet needs and challenges. We are also constantly involving doctors, nurses and patients in the design process of our solution by interviewing and testing prototypes. 

Through our interviews, we learned about the numerous challenges faced by doctors and other stakeholders in hospitals. With heavy working hours, and the need to monitor multiple patients at a time, they feel overburdened which can affect the health of the patients. Not to forget, most hospitals are understaffed, where the doctor patient ratio is low. In order to make their work more efficient, easier and also to eliminate human error, we are working towards designing an equipment that helps them check vital signs and patient deterioration for them with the least possible intervention. 

India is currently ranked number four in the world based on the number of COVID-19 cases; the burden on India’s slowly developing healthcare system is huge. It already faces shortage of staff, medical devices, skills and infrastructure. In the near term, our solution aims to reduce the burden on the healthcare system by allowing healthcare professionals to safely and remotely monitor patients, along with detecting patient health deterioration early on by automating these tasks entirely. In the long-term, we envision our solution playing a key role in preventing and mitigating future pandemics by using predictive analytics.

We are developing the first ever wearable monitoring device and predictive analytics platform for hospital environments specifically designed for the Indian Healthcare System. We identified a gap in the market where there is inadequate infrastructure to deploy predictive algorithms in the hospital premises, and we aim to bridge this gap by building the desired infrastructure (our connected platform) 

Our vital signs monitoring system is designed with a three layered approach that starts with ICU-level monitoring, then goes towards detecting patient deterioration in real-time and alerting clinicians, and in the third layer we predict cardiovascular and respiratory critical events a few hours prior. This is what distinguishes us from our competitors which include Vital Connect, Sotera Wireless, CareTaker Medical, BioBeat, and BiPs Medical. We take patient comfort seriously and hence our solution gives complete mobility to patients in order to make their hospital stay less stressful. It provides benefits to healthcare professionals as they have all the required information on one platform. It is a requirement for us to develop an affordable device that can not only be bought and used by private tertiary care hospitals, but also by public hospitals, all primary, secondary and tertiary. Keeping it economical is one of our key accomplishments.

Another point of difference is our goal which consists of introducing our product to developing nations, which could benefit from the technology more than the developed ones. 

A small yet an effective step to improve patient mortality across the countries.

The core technology that powers the solution to this problem is the integration of AI with non-invasive photoplethysmography (PPG) measurement technology. PPG utilizes a light emitting source and a light receiving source as the basis to measure a signal. When a PPG sensor is placed on the wrist it illuminates a portion of the skin, some of this light is absorbed and some of it is reflected back. Blood is known to absorb specific wavelengths of light and as blood volume changes with circulation, the amount of light absorbed by the blood changes, thus the light received by the photo receiver changes. Our in-house designed sensor gives us a much clearer reading relative to existing PPG sensors, which allows us to achieve ICU-level accuracy.

The Artificial Intelligence portion of our technology utilizes existing algorithms to denoise sensor signals and utilize them to predict adverse events. We use adaptive filtering and transformation algorithms to denoise sensor signals and use deep learning and machine learning models for predictive analytics.

Our wearable device uses photoplethysmography (PPG) to measure Heart Rate, Respiration Rate, Oxygen Saturation, Non-invasive Blood Pressure, Cardiac Output and Stroke Volume. PPG is a widely used technology, from pulse oximeters to apple watches. Pulse oximeters take a multiwavelength approach where a Red and Infrared Light sources are used for skin illumination. 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426305/

Our software as a service platform utilizes existing machine learning technologies to detect and predict health deterioration. Prediction algorithms are widely used in weather forecasting, ecommerce and retail. An example of one such algorithm we use is Support Vector Machine (SVM), below is a research paper that provides an overview of SVM:

https://link.springer.com/chapter/10.1007/978-3-642-34041-3_27

Our solution aims to mitigate the problem of timely detection of patient health deterioration. Our theory of change includes continuous monitoring of patients as an activity which brings about better and quicker detection of patient health deterioration (output). The outcome is lower inpatient mortality. It can prove to be a revolutionary innovation especially in the times of Covid or any such communicable diseases or viruses of the future as this device allows contactless monitoring of the patients. Regular vital signs updates of multiple patients increase the exposure of the treating professionals putting them at greater risk. Pareto Tree gives them access to the patient's vital signs data with increased frequency and ICU level accuracy.

We have had the opportunity to validate these links through interviews with our target population. Some of the common comments and themes that were noticed were the following: 

• Continuous monitoring would help detect patient health deterioration in a better and quicker manner

• Predictive algorithms especially for critical cardiac events and sepsis (a major cause of death in India) would end up saving thousands of lives each day

• Automated monitoring and alerting doctors would decrease the burden on them and help reduce the time to treat each patient

  • In a country like India, where high patient load is a big concern, reducing this burden on doctors and nurses would lead to them being able to treat more patients at a much more affordable cost than the current one

  • If we can detect changes in a patient’s health at an earlier stage, and even predict them to an extent, then we can overcome the need for patients to go through long painful procedures, hence saving time of the doctors as well 

• 80% of doctors suggest that they get informed about patient deterioration one hour after actual deterioration has occurred. The reasons for this include: 

  • Patients not being continuously monitored

  • Patients complain after facing the problem for 30-50 mins

  • Lag in communication between the on duty nurse and assigned doctor 

  • Lack of skilled nursing staff

Our solution is still in the prototype stage and is yet to be executed on ground. Hence, currently we are not serving any number of people. However, our aim is to reach a minimum of 10,000 beds in one year, by early 2021.  This will be increased to 1,00,000 beds in a span of five years, by the end of 2025.

In 1 year 34907 lives

In 5 years 347507 lives

The next few years are extremely crucial for us, as we will advance rapidly and establish ourselves to be the change in the healthcare market.  

Our goals for year 2020 are the following

• Research and Development towards finalizing our sensor and algorithms 

• Minimum Viable Product Development

• Patent Filings

Our goals for year 2021 are the following

• Clinical Validation

• Regulatory approval for our wearable device

• Research and Development on predictive algorithms

• Distribution in India

We will work with marketing experts to package the solution offering and design the marketing strategy for distribution. We wish to host a demo session for key distributors and decision makers in hospitals to introduce the product. Starting from Tier 1 cities, and move to Tier 2 and Tier 3 cities and cover the whole country in about 15-18 months. The process of data collection of hospitals, clinics, distributors and key decision makers has already started. 

Our goals for years 2022-2025

• We envision to see every patient in India using our monitor

• Expansion Plan

  • Market Entry Singapore

  • Market Entry UAE

The road to success is not without many challenges, which we are ready to overcome. These include problems related to manufacturing, finance, talent and distribution network. 

As a young healthcare startup that is consistently at odds of fighting with the systematic healthcare system to make a proactive and preventive system.

We are constantly improving our product on the basis of feedback received from the healthcare professionals. The biggest unforeseen challenge at the moment is the adversity of the widespread pandemic. The kind of time we require for research and trails at this stage is unfortunately not available as the healthcare industry is under tremendous pressure right now. 

Manufacturing our product is a time taking process. It requires the need to look into minute details and perfect every single component and ensure safety and risk compliance. We are working on a model to achieve economies of scale to keep the affordability goal intact. 

All operations in a business require finance. Ours is no exception to that. In order to execute our vision we will need to raise funds through multiple rounds of investment. Getting some senior experienced resources on board will really help us in expediting the process.

Finding the right talent to join us in our mission without extensive funding has been a barrier for us to achieve our goals.

Finding the optimal distribution strategy for our solution is another challenge. We are validating and experimenting with different distribution models to analyze and seek the best outcome.

We plan to overcome each barrier by following the below mentioned strategies:

We plan to outsource manufacturing to a FDA approved manufacturing facility in India. This will help relieve us of some of the operational challenges associated with manufacturing medical devices.

In the near-term we are targeting angel investors as a source of funding. In the long-term we envision to raise our seed round through a venture capital firm. 

We plan to distribute a small portion of the equity in order to attract and retain the right talent. A round of funding will also help us in hiring more experienced talent to help us execute this great vision.

We plan to test two distribution strategies in order to find the most optimal one. The first strategy being hiring our own sales force and distributing our solution directly to hospitals. The second strategy is to partner with existing medical device distributors to sell our solution to hospitals.  

Aiming to get experts from Healthcare on our advisory board to ensure the decision making authorities are more receptive to our solution.

Full-Time = 7

Part-Time = 2

Thalansh Batra - Co-founder and Chief Executive Officer

Thalansh has a Bachelors in Management Science from University of California San Diego. He is an experienced strategy consultant with over 4 years of experience. Thalansh co-founded a Management Consulting firm in 2016, where he was the vice president of sales for 3 years and managed a team of 15 experienced sales professionals. In his university career, Thalansh worked with NASA, SpaceX and National Science Foundation funded projects.

Aniket Kale - AI Lead

Aniket has a Masters in Signal Processing and Machine learning with 4 years of experience in design and development of Machine Learning based applications for industrial automation, healthcare, and web development. Being the Millenial on the team, Aniket strives to work towards the betterment of human life and the environment by developing new and unique solutions to affordable technology. 

Archita Sarmah - R&D Lead

Archita has a Bachelors in Biomedical Engineering from Vellore Institute of Technology. Her past experience includes the development of medical prostheses for the mastectomy patients and developing optical sensors. Her creative and offbeat perspective to things proves to be very helpful in designing solutions. 

Kalyani Verma - Hardware Lead

A Bachelors in Biomedical Engineering from Mody University, Kalyani has been associated with various hospitals as a Biomedical Engineer. She has worked with IEEE designing and fabricating various micro-electronics projects. Her keen sense of observation and a calm composure gives her a very analytical edge over things. 

B2B Model

• Your key customers

  • Hospitals

• What products or services do you provide them?

  • We sell a wearable hardware and software as a service solution

• How do you provide these products or services?

  • One-time fee for each wearable device and a value-based per user pricing model (pay-as-you-go) for the SaaS subscription

• Why do they want or need them?

  • They need our solution to continuously monitor patients at an affordable price and increase the number of patients they can treat by minimising the time it takes healthcare professionals to monitor and detect patient health deterioration

We have been bootstrapping since 2019 and continue to do so to achieve the goals for 2020. Our path to financial sustainability is a combination of research grants, equity financing and licensing our products and services to existing organizations to existing organizations in the healthcare ecosystem. 

In 2021 the company would need outside investment (anticipated to be an equity investment), prior to conducting clinical validation studies.

From 2022 onwards, our goal is to be ‘grossly’ profitable while continuing to upgrade our investments in research and development. Based on our financial projections, we will be profitable by 2025. The business model has a huge marginal cost component which only applies when the company is selling more of its products or services, the unit economics suggest that our gross margin will quadruple as we go past 10,000 units. While initially the company is projecting to undergo a loss on the first 1,500 units sold.

Being an early stage startup which is completely bootstrapped to date, COVID-19 has impacted our business and product development timelines. This fellowship will really help us expand our network and get access to the right people in the industry for guidance and mentorship. The experienced LaunchPad network will not only help us with guidance in the right direction in building a progressive and holistic approach for our company; but also help us navigate business skillfully in these unprecedented times. The right network and connection at this moment will really have a huge impact on building a strong foundation for the startup and lead us in becoming a company we envision; a result oriented product making a significant contribution to improvement in healthcare systems worldwide.

Associating with MIT will become a certification for us which will prove very beneficial in taking the solution to the market. It will add the required credibility to our efforts in making the target audience more receptive towards us. In a market like India, there is a huge aspirational value to being associated with MIT This association will reflect positively on our overall endeavours and help us make it a success.

It is a mountainous challenge to change the healthcare system from reactive to preventive. A challenge that cannot be overcome in isolation, it is our sincere believe that SOLVE is different and would understand the importance of preventive inpatient care, just like it understands the value of public health.

Product/service distribution

We need support in analyzing the various distribution strategies that are ahead of us. We want to ensure that our distribution strategy aligns with our mission to get our technology to the maximum number of patients

Revenue model

Even though we have validated our business model through extensive interviews with key stakeholders, we want to make sure our business model is the perfect fit for the market by testing.

Talent recruitment

We are currently in need of great talent that could help us achieve our vision of every person using our monitor in India by 2030.

Advisors

We are currently looking for advisors and board members to join our startup and help us navigate challenges in our business and technology.

Legal or regulatory matters

Regulatory

We need a lot of guidance on this front, specifically quality and risk management systems and clinical validation required for regulatory approval.

We would like to partner with the MIT India Initiative and MIT faculty to guide us in the right direction to solve some of our technical and business challenges. We would also like to partner with Indian Center for Medical Research and All India Institute of Medical Sciences in India to help us conduct ethical and meaningful clinical validation studies.

Our solution based on Artificial Intelligence (Machine Learning and Deep Learning) will positively affect both patients and healthcare professionals and their relationship.

Helps Health Care Professionals by Continuous Patient Monitoring

• Improves patient monitoring by continuously providing vital signs information extracted by implementing Signal Processing and Machine Learning algorithms on the physiological signal

Saves patient's life by Detecting Adverse Events

• The Machine Learning and Deep Learning algorithms are used to predict adverse events a few hours prior and alert healthcare professionals to save their lives.

Helping nurses by addressing Alarm Fatigue 

• Alarm Fatigue causes desensitization to alarms and often critical alarms are missed due to frequent false alarms. We are reducing false alarms by designing and implementing adaptive and accurate machine learning based solutions for alerting. This will also help reduce the burden on healthcare professionals by allowing them to focus on more important jobs like managing treatment.

Helps Health Care Professionals in Decision Making

• Our use of Machine Learning and Deep Learning in detecting and predicting patient health deterioration will help healthcare professionals make better and quicker decisions in turn improving the efficiency of treatment procedures.

Empathy towards Patient

• Our solution is designed keeping patients, doctors and nurses at the center of the design process. We understand the emotional state of patients on hospital beds, we understand their pain and want to make life easier and better for them. Our wearable design not only allows for complete patient mobility, but also uses only non-invasive techniques to monitor vital signs.