NIKITA BATHEJA

NIKITA BATHEJANIKITA BATHEJANIKITA BATHEJA

NIKITA BATHEJA

NIKITA BATHEJANIKITA BATHEJANIKITA BATHEJA

Healthcare AI Control Panel

Man analyzing data dashboard on a computer screen in an office.

Product Overview

A centralized control panel designed to manage and monitor multiple AI models used across healthcare workflows, including record mapping, claim denials, and other automation systems.

The platform enabled stakeholders to configure model behavior, schedule execution, and track performance, while translating AI output into operational and financial impact through dashboards.

Problem

Multiple AI models were being used to automate healthcare workflows, but stakeholders lacked visibility into the actual value generated by these systems.


Operational teams could not easily understand:

  • How much work AI was completing 
  • How much human effort was being reduced 
  • The financial impact of automation 
  • The trade-off between automation and accuracy at different confidence thresholds 


In addition, model configuration changes required backend deployments, creating operational dependency on engineering teams.


Without transparency, configurability, and measurable impact, it was difficult for clients to fully trust or evaluate the effectiveness of AI-driven workflows.

The Ask

Design a centralized control panel that allows stakeholders to configure AI models from the UI, monitor model output, and evaluate the operational and financial impact of AI automation across workflows.

Role & Scope

Led end-to-end product design of the AI operations platform, translating model-level configurations, AI outputs, and operational metrics into a usable system for business stakeholders.


Defined the workflows for model management, threshold configuration, impact analysis, and ROI visualization across multiple AI models.


Worked closely with engineering, data science, and business teams to structure how AI performance, labour savings, and operational impact were represented within the product.

Understanding the Workflow

The platform managed multiple AI models, each designed for different healthcare workflows such as:

  • Record mapping 
  • Claim denials 
  • Automation workflows 


Each model required:

  • Scheduling and execution control 
  • Environment configuration (UAT vs production) 
  • Confidence threshold management 
  • Output monitoring 


The larger operational need, however, was to help clients understand:

  • How much work AI resolved 
  • How much work still required human review 
  • How much time and labour cost was saved through automation 


This shifted the product focus from:

model management to AI value visibility and operational decision-making

SOLUTION

1. Centralized AI Model Management

Designed


  • Centralized interface to manage multiple AI models 
  • Controls to start, stop, and schedule model execution 
  • Controls to set thresholds
  • Environment configuration (UAT vs production) directly from UI 
  • Ability to save and manage model-level settings without backend dependency

Rationale

Managing models through backend deployments created delays and limited flexibility for business users.


By bringing configuration controls into the UI, the system enabled non-technical stakeholders to manage model behavior independently, reducing reliance on engineering teams.

Result

Improved operational efficiency by enabling faster configuration changes and reducing dependency on backend deployments.

Operational Dashboard & Throughput Forecasting

2. AI Output, ROI & Labour Savings Visibility

Designed


  • Model-level dashboards displaying AI-resolved counts and Smart Assist counts 
  • Real-time visibility into the volume of work completed by AI versus work requiring human review 
  • Conversion of AI-processed work into equivalent full-time employee (FTE) effort 
  • Configurable inputs for average human processing time and hourly labour cost 
  • ROI calculations comparing labour cost savings against AI platform subscription cost 
  • Individual dashboard views for each AI model
  • Contextual tooltips explaining how each metric and calculation was derived

Rationale

One of the primary goals of the platform was to help clients clearly understand the operational and financial value generated by AI automation.

While AI models were processing large volumes of work, stakeholders lacked a tangible way to measure what that output meant in terms of time saved, workforce reduction, and cost impact.


To address this, the system translated AI-resolved work into equivalent human effort by allowing configuration of:

  • Average time required for a human to process a task 
  • Hourly labour cost for employees 


Using these inputs, the platform calculated:

  • Estimated hours saved through automation 
  • Equivalent FTE effort reduced 
  • Labour cost savings generated by AI 
  • Effective ROI after accounting for subscription costs 


This transformed AI output from abstract processing counts into measurable business impact.

Result

Enabled clients to quantify the real operational value of AI through measurable labour savings, productivity gains, and ROI visibility.


The platform improved transparency into how much work AI was completing, how much human effort was avoided, and the financial impact of automation across workflows.

3. Impact Analysis & Threshold Simulation

Designed


  • Interactive controls to modify AI confidence thresholds 
  • Real-time impact analysis showing how threshold changes affected automation levels 
  • Visualization of additional work AI could process at different confidence settings

Rationale

Clients needed a way to evaluate the trade-off between automation volume and confidence levels before making operational decisions.


By allowing threshold simulation directly within the product, stakeholders could explore different automation strategies and understand their operational implications.

Result

Enabled data-driven decision-making by allowing stakeholders to evaluate automation impact dynamically instead of relying on static reports or assumptions.

AI Value & Control Architecture

  • Layer 1: AI Models Layer (Input) Shows the modular nature of the system, where specific models (Record Mapping, Claim Denials, Automation) are treated as distinct functional engines.
  • Layer 2: Control Panel Layer (Governance) Focuses on Human-in-the-Loop control. This highlights design thinking around system governance, including confidence thresholds and deployment scheduling.
  • Layer 3: AI Output Layer (Explainability) Bridges the gap between data and action by categorizing outcomes into "AI Resolved" cases versus "Smart Assist" counts, emphasizing how the system augments rather than just replaces human effort.
  • Layer 4: Impact Calculation Layer (The ROI Engine) Demonstrates systems thinking by showing the mathematical relationship between labor costs, human processing time, and automation throughput.
  • Layer 5: Client Dashboard Layer (Intelligence) The final output where complex calculations are distilled into executive KPIs like FTE Equivalents and Labor Cost Savings.
  • Layer 6: Transparency Layer (Trust) A crucial design detail—integrated tooltips that provide formula breakdowns, ensuring stakeholders can verify the logic behind the ROI data.

Summary

  • Transformed AI workflows into measurable operational outcomes 
  • Enabled clients to evaluate AI value through real-time labour savings and ROI metrics 
  • Reduced dependency on backend deployments through UI-driven model management 
  • Improved transparency into AI vs human workload distribution 
  • Enabled interactive analysis of automation strategies through threshold simulation

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