HAD(A)

Imagine Guatemala without delays in medical diagnosis and an ideal treatment service, this sample analysis machine linked to Artificial Intelligence and servers could give us all that!   

Guatemala has one of the worst health systems in the world. The COVID-19 pandemic only further weakened this system. More than half of the Guatemalan population lives in poverty or extreme poverty (this information can be reviewed here) so most cannot afford a private hospital, and the service in a public one is not always the best. In addition, health centers in rural Guatemala do not meet all the needs of people and do not have state-of-the-art technologies. The high number of patients to be treated in this country and the limited number of staff and medical technologies mean that the diagnosis and treatment processes are not as fast or efficient. 

People die every moment; time is an enemy in the Guatemalan health system, but it can be battled with technology that speeds up and improves people's well-being. 

HAD(A) is a sample analysis machine (such as blood, urine, or saliva) that can collect data and send it to specific servers and to a mother server so that they can find a quick and appropriate diagnosis and treatment for the patients. 

We will summarize the process in 5 steps: 

1. The machine will analyze the sample provided.

2. The machine will send all the analyzed data about the sample to a series of specific servers (located in different hospitals or health centers in Guatemala). 

3. The specific servers will have a database with the diseases diagnosed from 2017 to the current year in each hospital. They will apply matchmaking technology to compare the sample data with diseases that present similar characteristics. If they find that match, then the doctor or the patient themself will know which disease to treat and how to treat it. 

4. If the specific servers do NOT find that match, then they will send the sample data to a mother server (located in the United States), which functions as a global database and will most likely find diseases with which it shares characteristics. If the mother server does NOT find the match EITHER, then these are diseases that are not yet registered in its database.  

5. Both the specific servers and the mother server will have an Artificial Intelligence, which will be in charge of sending to the sample analysis machine 3 possible diseases and 9 possible treatments (1:3, 1 disease, 3 treatments) that the doctor or the patient themself (if the patient buys their personal machine) will have to evaluate.

Our solution serves both patients in hospitals and medical staff in routine or extraordinary review processes. Since the entire population (technically) is a user of the health system, be it public or private, it would end up benefiting practically everyone. Our main objective is the Guatemalan population, emphasizing rural areas. All of them are affected by extremely poor health services, shortage of biomedical machinery, and lack of workers in the medical health area with high-level professional titles. However, with the advance of time and development, it can have a global projection.

It will impact their lives because, for many, it will prevent late diagnoses that put them in critical condition. It will make the testing process easier for medical staff by making it more automated. Likewise, since an AI is in charge of the solution, human error would be omitted.

Juan Ignacio

• Background: I graduated from high school, Liceo Javier, in November 2022. I live in a family of four, and I like art and science as well. 

• Skills: Leadership, Creativity, Responsibility, Problem Solving, Innovative Thinking, and Teamwork. 

• Important experiences: The reason why I decided to focus this solution on people is that I have been with the ones in need and I know how it must feel for them to live under inhumane conditions. My school has taken me to community services in places like nursing homes or San Juan de Dios Hospital which have made me realize the problems that exist outside of my house and motivated me to solve them. 

Sergio: 

• Background: I graduated from high school in November 2022. I'll be majoring in Mechatronics Engineering at Galileo University. My favorite hobbies are: playing the piano, learning French and Japanese, and practicing karate. 

• Skills: Creativity, analytical thinking, teamwork, and self-discipline. 

• Experiences: I realized how long it takes for my parents to be diagnosed and treated when they go to a hospital or clinic. It's so frustrating to wait so long. Also, I talked to my uncle, who is a doctor, and he told me how exhausting his job is in this country with so few medical resources to help so many people.

Regina: 

• Background: I just graduated from high school in early November. I'm going to major in Biology at the University of San Carlos of Guatemala. I live in a family of five. I like science and helping the environment and people in need. 

• Skills: Empathic, teamwork, conflict resolution, adaptability, work ethic, communication, situational awareness. 

• Experiences: This year, I was part of Campamento Misión (Mission Camp in English). This is an activity organized by my school every year that consists of taking the seniors for a whole week to a small town called Santa María Chiquimula located in the department of Totonicapán. The purpose of the camp is to make students aware of the reality of Guatemala, a country where more than half of the population lives in poverty. I witnessed how Guatemalans who live in rural areas struggle so much to have quality health services, whether public or private. On the other hand, when my grandfather had to be hospitalized in Antigua Guatemala, my whole family realized how archaic everything is because of the lack of technology, biomedical equipment, and medical staff. 

Majo: 

• Background: I graduated from high school on November 12th, 2022, and received the academic excellence award for having the highest grades among the graduating class. I live in a family of four. I like science, art and I’m very interested in global problems such as climate change, and gender and social inequality, 

• Skills: Analytical thinking, self-discipline, creativity, responsibility, teamwork, management, leadership, and problem-solving.

• Experiences: A cousin of my mother just discovered he has cancer. But the doctor can no longer perform surgery because it has spread all over his torso. It was too late when they received the diagnosis and made the appointment.

We have engaged with users such as Carlos Esquit (caesquit@uvg.edu.gt), director of the mechatronics and biomedical engineering program at the University of the Valley of Guatemala. Sergio is also looking to engage with users interested in the development of this solution at his prospective university, Galileo University. 

Our solution is innovative because despite living in an era of digital transformation in which all processes (like banking, transportation, or purchasing) have accelerated, there is one that has not done so enough: healthcare. Guatemala needs technology to take care of people's health and increase their life expectancy. The effects it will bring will be amazing, and the diagnosis and medical treatment will be provided to the patient quickly and efficiently. That is what all patients want, isn't it? 

It will definitely change the market when these positive results are made public.

Our short-term goals are:

1. Seek for the necessary financing (through MIT Solv[ED]) to design, test, and produce a first model machine. 
2. Produce and distribute at a greater scale sample analysis machines in Guatemala. 
3. Request legal authorization from the Government of Guatemala or the Ministry of Health, so the machines can be used and sold to hospitals and people.
4. Achieve the acceptance of medical staff and the Guatemalan population towards this new kind of technology. 

The entire ecosystem consists of:

• HAD(A): sample analysis machines where the samples can be placed (by the doctor or the patient) and analyzed. All the data collected from the samples will be sent to the specific servers.

Both the specific servers and the mother server operate with two technologies: Matchmaking and Artificial Intelligence. 

• Specific servers for data compilation and storage. They will be located in hospitals and health centers in Guatemala. 

• Mother server for data compilation and storage at a greater scale. It will be located in the United States (preferably at MIT) for better coverage.

• Satellite Internet for the sample analysis machines and servers located in underdeveloped areas (taking in mind Starlink).

We plan to serve 55,013 people the next year. 

The types of barriers to accomplishing our goals are: 

• A financial barrier for the designing, testing, and producing of the first working model machine. 

• A legal barrier for using and selling HAD(A) in the country.

• A cultural barrier to getting acceptance in the use of HAD(A) (Guatemala is a country where most people are not used to working with technology). 

No partnerships until now, just us. 

Clients and beneficiaries are offered a new health model through a more immediate diagnostic service. They will no longer have to travel to laboratories and other centers, they would save not only time but also distances by using either the sample analysis machine that the hospital will have or the portable machine for personal use (with fewer functions, simpler and smaller). 

The machines will be sold to hospitals and to people with a manual for instructions, maintenance, cleaning, and care of the equipment. There will also be multimedia content with this same type of information. Doctors are in charge of using it in their patients when it is needed (for blood samples, for example) and they will analyze the results of possible diseases and their respective treatments. It is worth mentioning that the machine will have separate sections depending on the type of sample (blood, urine, or saliva) to avoid contamination of those. 

In the beginning, patients that decide to go to hospitals will pay for the analysis services. However, in the long term, it will be possible to reduce costs and the fee of these because there won't be any workers to pay salaries to. The only person that will be paid is the doctor for their services. This will make health system services more accessible in a developing country like Guatemala. Poverty or delay often causes many lives to be lost and rights not fulfilled.

Optional donations, loans, and service contracts to governments and private hospitals.