MASTA is a social network that connects doctors and patients from all over the world, in order to help solve everyday health problems and build a healthier world.
Malaria kills a child every 30 seconds, and more than one million people die from it every year. It is important to note, however, that malaria and other diseases can be treated if diagnosed in a timely manner. These deaths are the result of disparities in the social determinants of health. Patients in under-resourced regions such as sub-Saharan Africa and Southeast Asia often have little access to hospitals and microscopes for diagnosis, and even in regions with adequate medical facilities, many people lack the financial resources to take advantage of these facilities.
Witnessing all these problems afflicting our continent in particular, and the world in general, has prompted us to make our contribution in a broader, long-term sense.
Development of a social network application (web and mobile) that allows patients to post their health problems in order to get suggestions from our artificial intelligence (Machine Learning Algrorithms) based on community comments and recommendations from volunteer doctors registered on the platform.
Masta offers several features such as : an automatic AI symptom checker, a meal recommendation for diet planning, a personal medical record, fundraising for health issues and many more to come
- PHILIA(φιλία) , Voice enabled Personal Medical Assistant
- PHILIA Finder , meal recommendations for special diets
- UMOJA FUNDING , CrowdFunding Separate Platform
Coordination problems are the root cause of a lot of issues in society. Care coordination (CC) is an important fulcrum for pursuing a range of health care goals. Current research and policy analyses have focused on aggregated data rather than on understanding what happens within individual cases. At the case level,CC emerges as a complex network of communications among providers over time, crossing and recrossing many organizational boundaries. Micro-level analysis is needed to understand where and how CC fails, as well as to identify best practices and root causes of problems.
This refers to current medical practice: there is insufficient data, insufficient time, insufficient context, and insufficient presence (of doctor to patient).
We don’t have all the relevant data for each when they are seen. Patients don’t even have all their data, because it is spread across many different doctors and health systems and hard to get to. And the data from one hospital or clinic is in different formats from the next.
The average clinic visit in the United States lasts only 7 minutes for an established patient or 12 minutes for a new patient. During that very limited time, there is little eye-to-eye contact because the doctor is preoccupied entering data at a keyboard. In that minimal time with distraction, diagnostic errors are all too common—over 12 million serious diagnostic errors a year in the United States. If a doctor doesn’t think of the diagnosis quickly, in the first 5 minutes, the error rate is over 70%. Unnecessary lab tests and scans are ordered way too often, leading to vast waste, with the lack of time to adequately reflect about the patient’s story and data.
Patients feel shortchanged that their doctors are not really present, not attentive, not listening, and have the sense they’re not really cared for.
Reciprocally, doctors feel rushed, unable to execute their charge of caring for their patients which is why they entered the profession, and we are at peak levels of burnout and clinical depression. And burnout leads to a doubling of medical errors, which promotes burnout, a vicious cycle that has to be broken.
Some facts:
-
Lack of medical care assistance , more serious diagnostic errors, depression, anxiety, insufficient data, insufficient time, insufficient context, insufficient presence and (of doctor to patient)
-
the failure to establish an accurate and timely explanation of the patient's health problem(s) or communicate that explanation to the patient ...
Recommendation 1:
In recognition that the diagnostic process is a dynamic team-based activity, health care organizations should ensure that health care professionals have the appropriate knowledge, skills, resources, and support to engage in teamwork in the diagnostic process. To accomplish this, they should facilitate and support:
- Intra- and interprofessional teamwork in the diagnostic process.
- Collaboration among pathologists, radiologists, other diagnosticians, and treating health care professionals to improve diagnostic testing processes.
Recommendation 2:
Health care professionals and organizations should partner with patients and their families as diagnostic team members and facilitate patient and family engagement in the diagnostic process, aligned with their needs, values, and preferences. To accomplish this, they should:
- Provide patients with opportunities to learn about the diagnostic process.
- Create environments in which patients and their families are comfortable engaging in the diagnostic process and sharing feedback and concerns about diagnostic errors and near misses.
- Ensure patient access to electronic health records (EHRs), including clinical notes and diagnostic testing results, to facilitate patient engagement in the diagnostic process and patient review of health records for accuracy.
- Identify opportunities to include patients and their families in efforts to improve the diagnostic process by learning from diagnostic errors and near misses.
PHILIA is an intelligent personal medical assistant capable of detecting behavioral tendencies and associated behavioral intents; in order to diagnose patients and help regulate the degree of frustrations or sadness by giving possible health issues and associated treatments or some advice.
A real assistant platform to track psychopathology patients' responses during voice-to-voice conversations and remote sessions.
This platform makes use of a machine learning algorithm capable of tracking and detecting facial expressions(sooner) to identify associated emotions through a camera or voice expressions through conversations. This allows the corresponding medical staff or the platform by itself to take care of their patients by creating medical records supported by the artificially intelligent system, so they can follow-up the corresponding treatments.
Inspired by how FACEBOOK has changed how lives by getting people in touch This project has been built with a lot of love, motivations to help others and empathy for died persons in lack of good health care, using:
- wit.ai , Natural Language Experiences
- alan , Conversational voice AI
- apmedic , Symptom Checker API
- React Native , mobile app version
- React js , web app version
- GraphQL , query language for the back-end APIS
- Express js, business logic back-end for basics functionalities APIS
- Python Flask, business logic back-end for machine learning APIS
- Amazon RDS for MariaDB, a real-time database
Customers or patients need personalized meal suggestions that are both in line with their health goals and their food tastes. No matter if the customer is on low sodium, low-cholesterol or on a fully personalized DNA profile-driven diet they can get the right suggestions. Is your customer in need of only recipes that are vegetarian, contain less than 600mg of sodium per serving, are high on Vitamin A but low in fiber? PHILIA Finder can help with this.
PHILIA Finder is a Personal assistant for food recommendation , that provides over 2 million recipes that are indexed, normalized, and contain full nutrition information. The assistant allows for search by nutrient quantity, 40 diet, and health labels as well as keyword searches by cuisine type (chinese, italian, indian, french,...), meal type (lunch, dinner, breakfast, snack) or dish type (soup, salad, pizza, sandwich,...).
It is not a basic or easy system, as it is developed with innovative technologies such as:
- Amazon Alexa, Amazon’s cloud-based voice service available on hundreds of millions of devices from Amazon and third-party device manufacturers.
- Edaman, Recipe Search API, Food Database API, and Nutrition Analysis API
- Python3.7 , programming language.
- AWS Route 53, a highly available and scalable cloud Domain Name System (DNS) web service.
- AWS Lambda, a computing service that runs code in response to events and automatically manages the computing resources required by that code.
- AWS CloudFront , Fast, highly secure and programmable content delivery network (CDN)
- AWS api-gateway, Create, maintain, and secure APIs at any scale
We plan to enhance the accuracy to make it more user friendly by using the following steps:-
- Try to merge best-suited recommendations based on origins and amount of nutrition diet recommendation system to get optimized results which customer like.
- increase the variety of dishes and user-specific recommendations.
Currently, we are covering certain regions' food data in recommendation an MVP so we are covering a small region(American, Asian and European) but soon we try to increase a wide variety of foods to cover whole world in our system , especially african food.
UMOJA FUNDING is a participative financing platform (crowdfunding) separately developed but integrated in masta. Beyond offering an alternative to traditional finance, UMOJA FUNDING is a place for exchange, sharing and solidarity between a community of contributors and project leaders in several sectors.