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Smart Hospital - The Future of Healthcare

A cloud-based patient monitoring, management and treatment platform for COVID-19 patients

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I propose a connected hospital consisting of nodes in the form of modules mounted on patient beds that are able to log and control essential equipment data, collect blood samples, monitor patient health and raise alarms when necessary. The beds would form a BLE mesh network and connect to a central station that would send notifications to medical staff over a WiFi network using AWS IoT.
The beds would be equipped with microphones and speakers for the doctor-patient interaction. The nodes would have a panel of buttons for the patients to ask for certain essentials like food or water. The panel would also include a button for summoning hospital staff in case of an emergency.
Patient Logs are uploaded to the cloud and if an error occurs, the doctor is alerted via text or email. The doctor then takes control of life-support or treatment remotely. The logged data is analysed by the doctor for further treatment decisions.
The entire setup is based on Cypress PSoC 6 dev kit and AWS.

THE PROBLEM
The coronavirus pandemic has brought the world to its knees. The only reason is the highly contagious nature of the virus. Healthcare systems worldwide are not competent enough to deal with this due to several reasons like-
- Lack of equipment 
- Lack of doctors
- Lack of protective equipment
Our last line of defence - the doctors, put in every effort to treat their patients. However, more often than not they contract this deadly disease in the bargain. Their exists no method to directly treat patients without coming in contact with them even for the treatment of coronavirus, which is not very complicated except in extreme cases. The key interactions of the medical staff with the patients include - 
- Cleaning of wards
- Giving food and medicines
- Body temperature monitoring
- Collecting blood samples
- Giving instructions 
- Essential equipment monitoring
While solutions to the first two problems exist already as hospitals at many places have incorporated ground robots for this task. Taking temperature samples can easily be done by medical staff from a safe distance and there is no scope for innovation on that front.
No viable solutions exist for the other three problems, which is why everyday hundreds of doctors are getting affected. In areas where the number of doctors is less, this is a really serious issue. Due to the multitudes of patients turning up at hospitals, doctors are being forced to work overtime which is extremely stressful for them. Cases of suicide by such doctors have been reported.

THE SOLUTION
I propose a connected hospital consisting of nodes in the form of modules mounted on patient beds that are able to log and control essential equipment data, collect blood samples, monitor patient health and raise alarms when necessary. The beds would form a BLE mesh network and connect to a central station that would send notifications to medical staff over a WiFi network.
The beds would be equipped with microphones and speakers for the doctor-patient interaction. The nodes would have a panel of buttons for the patients to ask for certain essentials like food or water. The panel would also include a button for  summoning hospital staff in case of an emergency. There would be a provision for the monitoring essential devices like the ventilator, the ECG, the EKG etc. The data from all these devices would be sent to the central station where it would be analysed using complex data analysis techniques based on the TensorflowLite library and if a problem arises the central system publishes the anomaly along with the patient ID to the AWS IoT cloud. Important notifications are directly sent to the doctor incharge in the form of in-app notifications, text messages or calls. The doctor may then take preventive measures autonomously by changing settings on the life-support devices through the app that connects to AWS cloud. If the assigned doctor is not available, the web service automatically sends notifications to doctors connected to the network who can then volunteer and take life-saving decisions, everything autonomously.
This might sound far-fetched but the impact such a system can have is enormous. A web application would enable the doctors full control over life-support devices of the patient. The data can be collected on demand and collected for medical research. The doctor may also use the cloud dashboard for manual analysis of critical patient data and intervene life-support devices remotely.
The beds would also have a provision for the attachment of a non-intrusive sample collector and drug injector that has to be installed manually but can be operated remotely by means of robotic arms controlled by servo motors. This would definitely  improve the condition of medical staff, keeping them safe, reducing their workload, allowing real-time monitoring, allowing control of life support devices remotely and saving many unnecessarily lost lives by taking quick decisions remotely.

THE IMPLEMENTATION...

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  • 3 × NRF52832 BLE development board For nodes
  • 1 × Cypress PSoC 6 Pioneer development kit for central station
  • 1 × Servo Motors
  • 1 × Temperature and Humidity sensor
  • 1 × Keypad

View all 7 components

  • 16/05/2020

    Meghdoot Ghosh05/16/2020 at 08:45 0 comments

    16/05/2020

    Planned the basic design and got all the components together. Submitted the project for the PSOC Design Challenge.

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  • 1
    The Implementation

    THE IMPLEMENTATION
    I would make nodes based on the NRF52832 boards which support BLE. The node would have connectors for inputs from various life-support devices. A robotic arm with 6DOF from a previous project would serve for control of  sample collector devices. The central station would be made using the Cypress PSoC 6 Pioneer Development kit and act as a gateway between the nodes and the cloud. 
    Patient logs are sent directly to the central station which uploads everything to the cloud over the hospital`s WiFi network. Temperature and humidity would also be monitored and windows  opened and closed automatically.
    The data sent to the central station would be monitored  and analysed based on predefined parameters on a per patient basis using the TensorflowLite library. If an anomaly is found, the concerned medical staff is immediately informed and the doctor takes control of life-support equipment. If any instruction is to be given, the doctor speaks to the patient through a speaker on the bed. The doctor can then interact with the patient via voice over the cloud. The many interactions would create some latency but time is not of the essence in this case.
    The patient can key in certain instructions to the hospital staff with the help of a panel of buttons with pre-defined functions. An alert button is also present for the patient to call out for help. These functions would be implemented as interrupts in the control station code. If an injection is to be given or a sample is to be collected, hospital staff would fix the device on the robotic arm. It would then be controlled by the doctor remotely by means of the app which connects to cloud.
    The webservice is made on NodeRed and acts as a link between the central station, the doctors and Amazon Web services. The doctors are able to interact with the network with the help of an app made on the MIT App Inventor. It provides a panel for monitoring of patients directly using AWS IoT Things Graph and to control the sample collector and other life-support devices via AWS IoT Events. It also has the option to interact with the patient by voice over the cloud. The webservice also sends notifications directly to doctors and other medical staff which can be received on their copy of the application. If the assigned doctor is not available, the web service automatically sends notifications to doctors connected to the network who can then volunteer and take life-saving decisions, everything autonomously. The triggering is done using AWS IoT Events. 
    AWS IoT Greengrass Core services are hosted on the central stations to communicate amongst each other and for data analysis on the cloud and at the edge. If a station loses connectivity it relays the data to another station which then sends it to the cloud offering end-to-end data security. Using AWS IoT Device Management the central station would be maintained and OTA updates would be pushed when necessary. IoT Analytics over the cloud help manage a large number of patients and decide future treatment plans and can even help with medical resaearch.

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Jon wrote 06/21/2020 at 07:53 point

I am pretty sure that your platform is based on Python. For those who don’t know, Python is the most popular programming language for data science projects. It has also become the number one choice for many entrepreneurs who want to purchase ML-based systems or add them to their existing software products. The secret is simple - many machine learning solutions are made using Python because it helps to develop high-quality models, quickly implement them in production and get results. I Recommend You https://light-it.net/blog/top-10-python-libraries-for-machine-learning/ .

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ripsacusta wrote 06/21/2020 at 19:44 point

Right on the money!!

  Are you sure? yes | no

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