Mindbowser built an iOS app, two role-based web portals, and a serverless AWS backend for a clinical outcomes startup, replacing subjective post-op check-ins with continuous activity data synced through Apple HealthKit. A proprietary machine-learning model turns steps, distance, calories, and standing time into a recovery signal clinicians can actually read.
Talk to UsA clinical outcomes startup replacing subjective post-operative assessment with objective, activity-based recovery tracking.
Patient-facing iOS app, SuperAdmin and Care Provider web portals, and the serverless AWS backend connecting all three.
iOS, HealthKit, AWS Lambda, DynamoDB, API Gateway, Cognito, Amplify, CloudWatch, Plotly JS.
Delivered
Client-side adoption and clinical outcome metrics were not available at the time of writing. What shipped is four working systems built around a single recovery data pipeline.
iOS app syncing daily steps, distance, standing time, and calories through Apple HealthKit, with surgery details and appointment reminders built in.
Role-based SuperAdmin and Care Provider portals giving structured, filterable access to patient and cohort data.
AWS Lambda, DynamoDB, and API Gateway handle capacity and scaling automatically, with no server to provision or patch.
Custom Plotly JS charts showing individual recovery trajectories and cohort-level comparisons by age, BMI, and treatment type.
The founding team paired orthopedic and financial-modeling expertise but had no engineering background, and no standardized way to see how a patient was actually recovering between visits.
Post-operative outcomes were judged subjectively, with no standardized activity data behind the call.
No objective tool existed to track a patient's recovery trajectory or flag a critical setback before the next follow-up.
Clinicians lacked detailed visualization to compare outcomes across patients by age, BMI, or treatment type.
The non-technical founding team needed a structured process to turn a clinical idea into a defined product scope and architecture.
Managing large volumes of patient activity data for graph generation raised real questions about performance before a line of code was written.
A serverless AWS backend behind a native iOS app and two web portals, with custom visualization on top.
The founding team came from clinical and financial backgrounds, not engineering. Before any build work started, we ran Design Sprint sessions in sequence, so the team could participate fully without getting lost in technical decisions they weren't equipped to make yet. By the end, everyone knew what to build, what to cut, and how the pieces fit together.
An iOS app captures daily activity data, steps, flights climbed, distance, standing time, and calories burned, synced automatically through Apple HealthKit. Patients also manage their own surgery details and appointments directly in the app, with reminders built in.
A SuperAdmin web portal handles clients, providers, patients, and surgery reference sheets, with CSV export at every level. A separate Care Provider portal gives clinicians filterable, graphical reports on patient recovery, filterable by age, BMI, and other cohort variables, so a provider can spot a pattern across dozens of patients, not just review one chart at a time.
The platform's APIs run as serverless functions on AWS Lambda, backed by DynamoDB and API Gateway, with CloudWatch for monitoring. AWS Cognito and Amplify tie authentication together across the mobile app and both web portals through a single shared user pool, so a login on one surface holds across all three.
Off-the-shelf charting wasn't built for this data. Custom visualizations in Plotly JS display patient recovery analytics in a form clinicians could act on: individual patient trajectories before and after an intervention, and cohort-level comparisons across a full patient population.
Without standing up your own server infrastructure. Talk to us about what a serverless data pipeline with role-based portals looks like for your platform.
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