Junie

EIS solves the problem of elder agitation, lack of caregiver communication, and well-being tracking in memory care. 90% of elders living with Alzheimer’s, experience behavioral and psychological symptoms that affect themselves and their caregivers. A 2018 JAMDA study found that agitation symptoms increase informal care costs by 17% per month in healthcare settings, creating a substantial impact on costs in the community care setting. This is a $50 billion dollar problem in long-term care alone, according to the BMJ Open 2015. 

Apart from financial burdens, this issue contributes to healthcare provider burnout/turnover and diminished healthcare outcomes for patients. Bullying and other forms of verbal abuse are particularly prone to underreporting because of lack of communication, but many healthcare workers accept it as "part of the job." However, there are no estimates that provide a true insight into the financial and emotional toll elder agitation and care coordination takes on elders, caregivers, and families. Many of these instances go unreported because current methods of communication and documentation between caregivers involve physical notebooks- which are not legible and often misplaced.

Our software is tailored for caregivers, to collect ADLs of the residents/patients. We collect biomarkers such as location, hygiene, mood, activities, and meals daily. We are also adding a communication transfer log, family portal (to check their loved ones wellbeing ), and reporting on all levels of well-being tracking, compliance, and a quantitative care need scaling system.

Once all phases of our software are installed, we will introduce existing IoT devices into each location. These IoT devices will collect physiological biomarkers that will create predictions of early stage agitation and create an alert that will be depicted through a notification service on our software.Once a prediction is made, caregivers are notified via the application, which will let them take proactive and clinically proven care approaches. This provides healthcare workers with a proactive solution to the systemic problem of patient agitation. By identifying early stage agitation, we can improve the overall experience for residents while also improving safety and working conditions for nurses and caregivers.

We offer our services to assisted living and memory care communities. Initially, we are concentrating on privately-owned, upscale establishments with a resident count ranging from 30 to 70 individuals. These facilities are often operated as part of a larger network consisting of 6 to 30 locations. Our strategy for gaining customers primarily involves participating in industry conferences and forming collaborations with organizations dedicated to addressing issues and expanding solutions within the aging care market. As we expand, our objective is to transition from working with smaller companies to integrating or licensing our offerings into the systems of medium and large networks.

Krishan has worked as a management consultant in energy, financial services, and disaster relief. He was inspired to work on EIS from our patient advisor,  Jeff Borghoff, an advocate of Alzheimer's Disease awareness, due to his diagnosis of Younger Onset Alzheimer's Disease at the age of 51. Nathalya Ramirez, worked as a high school science teacher, has experience in nanofabrication and advanced statistical analysis and will help with caregiver training and prototype/algorithm development. Nathalya was inspired to work on this solution due to the passing of her grandmother and the lack of proper documentation of care for her during COVID. Our CTO and Co-Founder, Rohan Patil, has experience in developing deep learning algorithms/predictive models. Rohan was motivated to work on this solution because his grandfather had to retire early due to the injuries he sustained from agitation outbreaks in multiple healthcare settings. Frank Hogan, our sales and implementation lead, was inspired to work on this solution due to his uncle residing in a memory-care community. We were able to work with this community and build a solution that embeds direct feedback from all levels of management. 

When we release a new feature or redesign a solution, we assess user compliance, user friendliness, and time savings. We want to ensure that staff are using a fast, accurate, and reproducible system to track ADLs. We try to make sure this all gets completed in a pilot program before communities need to work on budgeting for the year as well.

EIS offers a unique value proposition to assisted living and memory care communities by enabling caregivers to monitor residents' Activities of Daily Living (ADLs) in real-time, identifying potential health and well-being issues before they escalate, and improving resident outcomes. Additionally, it predicts bed occupancy based on residents' ADL history, allowing communities to optimize their resources and staffing to increase revenue potential. With embedding certain predictive models with proven return on investment, we can help communities innovate while also creating a safe working environment for staff and residents.

EIS, directly addresses several aspects of UN Sustainable Development Goal 3: Good Health and Well-Being, particularly in the context of elderly individuals living with conditions like Alzheimer's and other forms of dementia. EIS aims to improve the well-being of elders in memory care by addressing issues of agitation and behavioral symptoms often associated with dementia. This is crucial for ensuring good mental health and overall well-being in this vulnerable population. Elder agitation can be emotionally taxing for caregivers. By providing a means of tracking and addressing these symptoms, EIS can help reduce caregiver stress and burnout, contributing to their own well-being. EIS also tackles the problem of underreporting of bullying and verbal abuse in healthcare settings. By offering a more efficient and reliable means of communication and documentation, it contributes to the goal of ensuring access to quality healthcare and well-being for all.

Generally speaking the number of layers in the deep learning network is primarily population dependent as each patient (psychiatric patients, intoxicated patients, etc.) possess unique and varied physiological features upon agitated/violent outbreak. However, since we are studying a reasonably uniform population, we will use a model with a fixed number of layers for the population we are studying. 

We will use a Receiver operating characteristic (ROC) curve to assess our model’s overall performance. We will also use each model’s F1 score to evaluate our model’s precision. We will use these measures to determine improvement across each iteration for the prediction model. At the end of the experiment, we will use the model with the largest area under the curve in the software’s architecture. 

We are using a temporal analysis of agitation biomarkers and will use LSTM Networks to develop our prediction model. We will compare the efficiency of these networks based on computational time to train the network and time to make relevant predictions. 

They way we acquire our data now is through our own software as the data is extremely accurate and collected as caregivers are caring for their residents vs at end of shift document which is where we see the core problem of the lack of good quality data to run proper analysis.

At EIS we make AI systems transparent by providing clear information about how they work and what data they use. Transparency builds trust and allows users to understand the technology better. We also actively address bias in AI systems. Regularly audit AI models and data for biases, and take measures to mitigate and correct any biases that are identified. Also, with continuous monitoring and incorporating our team members in the oversight of deployments, we intend to reduce the risk of incorporating AI to ensure it is used ethically, safe, and where senior living operators can see a return on investment. 

For over a decade, we personally witnessed families and other dedicated caregivers. Unfortunately, ineffective methods and the absence of technological solutions resulted in negative health consequences. Drawing from our personal experience and combining it with our backgrounds as concerned family members, we were driven by curiosity and determination to look into the complexities of identifying, predicting, and preventing patient agitation. 

Driven by this passion, our company aims to lead the way in early-stage agitation detection, with the goal of preventing adverse health events or outcomes within the next five years. We seek to lay the groundwork for safer and healthier environments benefiting both residents and caregivers. This vision includes the implementation of standardized proactive protocols that will bring about positive transformations in senior living communities over time.

3 Full-Time staff

6 Part-time staff

5 years 

In addition to contributing to the relatively low representation of Latina CEOs, particularly within the senior living sector, my Latina background empowers me to amplify the perspectives and journeys of Latina caregivers in senior living communities. We are committed to developing software that aligns with both linguistic and cultural aspects, specifically designed for caregivers of Latino older adults. This will elevate the well-being and the standard of care provided to grant dignity to the aging journey of seniors.

Our team is multidisciplinary and diverse to support growth and the roadblocks for supporting growth. Krishan leads our business development and growth efforts by attending industry events and cultivating relationships with key partners and early adopters. Nathalya mainly works on onboarding, customer success, and design work for our software solutions. This ensures that all feedback we receive from all levels of the organization will be embedded into future releases. Rohan handles customer services, predictive modeling development, and software development. Our advisors have established expertise in the gaps of our management while also supporting our roles. For example, if Krishan is attending a large 5000+ attendee conference, our growth advisor will also attend the conference to support. We have proven that we can handle multiple communities on our platform. 

We have only one investor, Techstars, and all our other funds have been acquired through non-dilutive funding (~80K) and customer revenue including a UBS grant, over 5 pitch competition winnings, and a NSF I-corps program grant. We’ve also participated in accelerators including Masschallenge and have been finalists at MIT Solve, AWS University Competition, and Cisco Global Problem Solver Challenge. In the future, we will continue to bootstrap the business as much as possible through pitch competitions and grants. However, as we continue to scale, we will be raising a $300K round in Q1 of 2024 to continue to scale our solution to more communities.

Our current operating costs are around $5k a month for 2023. However, with our current backlog of signed pilots and expansions, we expect our operating cost to grow to around $12k per month in 2024. 70% of which will be human capital expenses. 

We would request $90,000. We would use the $40,000 towards product development, $40,000 towards human capital, and $10,000 for traveling and customer acquisition. 

For Product we want to develop an accessible administrative dashboard that includes advanced data analytics is needed as our users are collecting data by the minute. Right now we are pulling information as needed but it is time consuming and we can't quantify the customers' ROI using this approach. This product feature would drive our main KPIs for our clients so, being able to afford that on the development side within the next 6 months is highly needed. As we continue growing our sales pipeline by attending conferences, going to senior living related events, and participating in different alliances within the industry. The funding for this part would allow us to cover most of the conferences for next year where most of our leads are generated from.

We were members of our University's co-working space and we took advantage of everything they had to offer in Washington, DC. Krishan, our Co-Founder, is acutely based right in Manhattan and will be using the space quire frequently. One of our favorite aspects of a co-working space is being able to connect with other entrepreneurs and helping each other out with current roadblocks. Krishan LOVES to network so we will 100% be making frequent use of the space.