DPlus.24

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  Solution

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  Problem Background

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  Statistics

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  Solution Impact

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  Stakeholders View

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  Problem Statements

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  Solution Synopsis

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  Appointment Calendar

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  Health Parameter Indicators

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  Dashboard 1

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Inspiration

With key focus of this hackathon being on shaping the health and care of the future together, I am presenting a solution which can solve the problem faced by doctors and the patients during past waves of covid which hit many countries really hard. Drawing your attention toward the problem background, many popular news sites have highlighted the impact of covid infections and are predicting how it will impact the lives of many people around the world. According to The New york times, US reported record infections as Europe's Omicron cases also soar. BBC has published that the WHO has warned that the pandemic not over amid Europe case records. This is just the tip of the iceberg and there many articles highlighting the rise in covid case despite high percentage of vaccinations.

What it does

The goal is to create a system which will automate the process to perform checks on vital parameters of patients at real time, saving effort for doctors and transparency to patients. The Systems can be embedded with Machine Learning Models to forecast patient recovery cycle. Regular follow-ups auto scheduled via systems to avoid last minute appointment unavailability. And lastly facilitate patients to share regular health statistics to help the doctors keep track of their progress. All this can be addressed with our solution DPlus.24. The name DPlus.24 is emphasis on Amplify doctor's reach and making the system available 24 hours each day of the week.

How we built it

The Solution Architecture of the system has been added for reference. I have indicated numbers to address the flow. Initially the patient will register themselves on the app and get the consultation from doctor. They data will be fed into the systems and algorithms will run to smartly identify the health status based on the important health parameters. The parameters can be extended to collect more data. The System will send regular reminders to patients to request them to add important health parameters into our systems daily or twice a day based on doctor's advise. Once these details are added they overall health status is calculated based on rules set by the doctors and the patients are prioritized on their health status. Automated appointments are issued for next consultation of patients who need urgent attention and those who have status set as green are sent automated notification of their health status. Machine learning model can be integration with this solution to forecast the recovery time of the patient.

The Solution is built on Saas based low code platform Quickbase. The solution can be scaled if the demand rises and can be easily modifying and deployed to the production environment. Additionally, QuickBase has strengthened its security posture by obtaining stringent compliance certifications and attestations, including SOC 1 / SOC 2, HIPAA, EU-US Privacy Shield, and DFARS.

Challenges we ran into

Integration challenges between Saas platform and capabilities/APIs from other platforms like Salesforce and Twillio were key challenges. This enforced me to think differently - developed additional layer of abstraction exposing course grained API end points that matches the workflow requirements.

We built our own automated data transformation workflow to standardize the data.

Accomplishments that we're proud of

What we learned

There is true sense of satisfaction as a technocrat if we are able to take step towards solving a real problem. Deep diving into the root cause and understanding the gaps which are creating issue for the doctors and the patients was my true learning. We all know there any many online apps and web application but there so many gaps which are be filled to help the already stressed healthcare workforce. I am really thankful for the opportunity to work on solution for this hackathon.

What's next for DPlus.24

The future of technology will be drive by Artificial Intelligence and Machine learning. I would like to train the models which can analyze complex and embed the learning of the model into this systems to make more accurate forecast of patient recovery timelines and the requirements. Also, I would like integrate the this solution with voice ecosystem entities such as Alexa, Google Home.

Built With

• api
• quickbase