Inspiration

Hearing about the problems that face healthcare providers across the industry due to fragmented data suggested to our team the potential that could be unlocked by synthesizing disparate data sources. Richer data than ever before exists and is being used for drug management, personal wearables, genetic analysis, and the medical industry. Layering this together with self-assessments and personal goals can enable the creation of a compelling (because it's deeply personal) and intelligent set of suggestions for preventive actions. At the same time, it enables a dashboard view of many aspects of a patient's personal medical history. An app with a national scope can help solve for this problem where health records traditionally governed at the state level have not.

What it does

Our app strikes a light and playful tone while collecting relevant health data from five sources: Self-assessment, wearables, prescription usage, medical visits, and genetic screening. The data all ends up in a consolidated database, from which we pull out a summary health view and also suggest preventive screenings and health goals based on this data, which the patient can elect whether to commit to.

How we built it

The team brainstormed possibilities based on the intersection of four major factors: online social networking, drug companies, vendors, and the patient herself. We came up with more than a dozen potential features and functions that could support better health, then agreed on the opportunities to pursue.

We then sketched the data schema that would be needed to support our functionality & created wire-frame mockups of the progression through the app. This data schema was created in Access. It included 20+ fields and consisted of 10,000 unique patients. We focused in on four characters that we created - fleshed them out with details to make - sure that their path through the app was logical and fruitful.

Tem built out the core functionality in Java using springboot, with gradle and bootstrap add-ons. The app follows the model-viewer-controller framework.

Challenges we ran into

We had a challenge winnowing down the feature set and utilizing a larger data set as a part of the app.

Accomplishments that we're proud of

We used json to manipulate the raw data, which makes the output more portable to other apps: we built an API that serves raw data to another service or system.

What we learned

We learned that one of the biggest challenges in the new world of digital healthcare is deciding which opportunity to go after first, given the wealth of new connections that are possible once you start to go down the path of synthesizing different datasets. We also learned that it is important to document and agree on a core set of functionality. Our team members appreciated the opportunity this experience provided to think differently about the opportunities in front of our companies. This also provided insight on the steps and connections that get us from data to recommendation. It helped us think about what how we could want the world to work.

What's next for Caccophony

We're excited to see what's possible with the intersection of healthcare and technology. We believe there is further potential for health-nut to improve lives by exploiting opportunities in the social networking space and through daily health reminders. Tracking over time will help us also arrive at the journey to focused, managed care at your fingertips.

We will certainly take back insights gained on the challenges and opportunities in the digital health world to our individual organizations.

Built With

Share this project:

Updates