Overview

Healfirst Group is a US healthcare service provider that needed to move from reactive, backward-looking reports to real-time, forward-looking insight. Operational data was scattered across patient systems, staffing records, and spreadsheets, so leadership worked off numbers that were already a cycle old by the time anyone acted on them, and there was no way to see patient volume or staffing pressure building before it hit. Reports were static exports with no drill-down, so any deeper question meant a new request to the data team rather than an answer on screen. Bacancy Technology’s data analytics services team built a unified platform that consolidates the source systems automatically, layers predictive models for volume and staffing forecasting on top, and puts it all in front of users through interactive Tableau dashboards. Leadership moved from static, delayed exports to same-day, drill-down reporting they could act on directly.

Technologies Used

python

Project Highlights

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Real-time dashboards surfacing operational healthcare metrics as they change

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Predictive models forecasting patient volume, staffing needs, and operational bottlenecks

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Automated data consolidation cutting manual reporting work

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Interactive, drill-down Tableau dashboards for executive and operational teams

Challenges & Solutions

1

Operational data lived across patient systems, staffing records, and spreadsheets with no central source of truth, so every report started with manual consolidation.

2

Reporting was purely historical. There was no way to forecast patient volume or staffing needs, so teams reacted to pressure instead of planning around it.

3

Reports were static exports. Users couldn't drill into a number or filter by department, so any follow-up question meant a new request back to the data team.

4

There was no infrastructure built to serve predictions in real time, so even if a model existed, getting its output in front of a decision-maker would have been slow and manual.

Solutions by Bacancy

1

We consolidated operational databases and spreadsheets into a single central warehouse, with automated ETL pipelines cleaning and standardizing data on a continuous basis, replacing manual consolidation with a reliable, always-current source of truth.

2

Our team built predictive models in Python and TensorFlow for patient volume, staffing, and operational bottleneck forecasting, serving them through FastAPI so predictions were available for daily planning rather than sitting in a notebook.

3

Bacancy’s Tableau developers rebuilt the reporting layer as interactive, mobile-friendly dashboards, so executive and operational teams could filter and drill into metrics directly instead of requesting new exports.

4

We deployed the model-serving layer on containerized infrastructure with Docker and AWS, with monitoring and automated failover, so real-time predictions stayed available without downtime as usage scaled.

Core Features

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Predictive models forecasting operational trends

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Centralized, automated data pipelines and ETL

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Drill-down, real-time access through Tableau dashboards

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Real-time notifications and reporting for healthcare operations

No. of Resource

06

No. of Resource

Time Frame

March 2025 to October 2025

Time Frame

Project Snapshot

healfirst-group-dashboard

Outcomes

50% shorter reporting turnaround, from consolidated exports down to same-day dashboard access

60% less manual effort spent on data consolidation after automated ETL replaced manual pulls

70% of routine operational reports now self-served through dashboards instead of new data-team requests

Staffing and patient volume forecasts now available days ahead instead of after the fact

Zero reporting downtime recorded since the containerized model-serving layer went live

Technical Stack

Predictive Modeling Python, TensorFlow
Model Serving FastAPI
Data Pipelines Custom ETL
BI / Dashboards Tableau
Infrastructure Docker, AWS

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