LifeLine

Inspiration

Healthcare is a rapidly developing field with large amounts of potential for improvement. Much of the database and healthcare management software used today is over a decade old, meaning that these programs lack the design, functionality, and security that modern software systems can provide. Beyond the need for refreshed software, healthcare applications can directly benefit other humans, making it one of the most visible methods of helping out others through code. Healthcare applications that directly solve an existing problem can almost instantly have a tangible effect on the lives of others, and these reasons are why we chose to pursue creating a healthcare app.

On-demand medical assistance is a field that is growing rapidly, but not necessarily benefiting hospitals and medical institutions themselves. On-demand physician house calls are becoming increasingly popular, especially for elderly citizens, but there is still nothing to replace the antiquated system of call buttons within hospital walls. We recognized that physicians and nurses in hospitals were likely wasting large amounts of time going to these calls and determining the issue before solving, rather than directly solving the issue in the first place. We also recognize that many patients within a hospital may not have much experience with using technology or applications. Because of this, we sought to create an application with as much description for common on-demand requests as possible, while ensuring enough simplicity for even the least tech-savvy hospital patients to navigate the platform.

What it does

LifeLine is an enterprise management web application that provides a form of on-demand medical and general assistance requests for patients, along with management software for staff in a hospital-like setting based on the live requests made by patients. For patients, the main menu displays a variety of options, ranging from food, water, medical assistance, and emergency 911 calls. When selecting one of these options, the user will then be prompted or shown additional information, such as their queue position when waiting for a physician for medical assistance or the available food options for whatever a patient may be craving when requesting food orders. Once a patient selects their need, the request will be sent to the administration side of the software, where the hospital staff can work the request into their workflow where they can efficiently assist the patient.

The admin option for LifeLine provides a form of task management for hospital staff for working through the requests generated by the patient LifeLine platform. All requests generated by LifeLine will be placed on a screen in a first in-first out queue format. Hospital staff will be able to work through the generated tasks based on the order they were submitted, making exceptions for more urgent requests or medical emergencies. Using LifeLine, hospital staff will be able to much more easily see what specific needs their patients currently require, allowing them to utilize their time much more efficiently to plan around these tasks.

How we built it

After brainstorming what our application should do and how it should look like on both the user's (the patient's) end and the admin's (or nurse's) end, we created a Figma prototype and developed the flow between each of the buttons and screens of our web app. We used React to set up the skeleton of the front end and start development of the UI that we planned out in Figma. When developing the backend, we utilized Express.js and MongoDB to follow with the MERN stack. The documents held a string value associated with each room number as well as the Boolean values for each alert that can be pressed by the patient. Two API routes were defined for reading and updating values in the DB. We used a patch route to simplify the action of toggling an alert for a given room since we only need to pass a partial body to the API for the correct behavior. The get request returns all fields present in the document so that the front end can update the values for each room at once on a refresh.

Challenges we ran into

The first challenges presented themselves while we were brainstorming the design and purpose of LifeLine. We had to consider accessibility constraints and design choices when determining how to design the UI so it could be simple for patients of all ages and abilities to use and for the admin (nurses or hospital staff) to understand and respond to tasks quickly. We specifically minimized the design of the UI to one-tap buttons for patients to request live assistance quickly. Additionally, we chose the colors of the buttons for each of the five types of requests in a way where patients are able to discern the type of help they need in an easier and more effective manner. After planning out our application and its functions, another challenge was picking a tech stack that best fit our needs. We chose the MERN stack to be able to develop a high quality web app in a short amount of time. Development challenges on the backend related to learning the differences between the put and patch requests in the REST API and making the connection. In general, creating a full stack web app in under 36 hours was a difficult yet enjoyable challenge that allowed for us all to learn more about how the client and server sides of web apps interact and come together.

What we learned and Accomplishments

We learned how to brainstorm and respond to use case scenarios regarding how a user may walk through and understand our web application. We were able to learn about the various features of Figma that helped us plan out and develop our front end UI. Specifically for development, we learned about how to best utilize the MERN stack for our web app. On the backend, we were able to utilize the PATCH request in REST API to simplify the bodies we pass in the request. We also explored more about the NoSQL database and discovered that mongoose makes it easy to connect to our database. In general, we were able to reach multiple milestones after developing our idea, creating a Figma prototype, and building out the backend by setting up a database and API endpoints.

What's next for LifeLine

LifeLine currently serves as a communication software between patients and hospital staff, but future additions to LifeLine could see it become more than just a static system. LifeLine has potential for artificial intelligence and machine learning usage, as systems could predict patient behavior from past actions to submit requests and alert staff of irregularities. Furthermore, LifeLine itself could expand beyond healthcare into other industries and audiences like residents of Assisted Living homes or anyone with disabilities.

Built With

• express.js
• figma
• javascript
• mongodb
• node.js
• react
• rest-api