Overview
Executive Summary
As a Business Analyst with the Penn Graduate Consulting Club, I contributed analytical work to an engagement focused on a conversational AI platform for early pregnancy care. The analytical work centered on building a structured business case, evaluating care delivery pathways, and communicating economic findings to both technical and nontechnical stakeholders.
The engagement required translating a complex healthcare technology question into a structured economic analysis: how does a conversational AI platform change outcomes and costs across different care pathways, and what does that mean for different customer and payer segments? The analytical approach used scenario and sensitivity frameworks to make findings interpretable despite uncertainty.
This case study documents the analytical structure and methodology. Specific client details, financial projections, and engagement recommendations remain confidential per consulting engagement standards.
Central Question
Research Question
“How can economic modeling across care delivery pathways, combined with payer and customer segment analysis, produce a structured business case for a conversational AI platform in an early-stage healthcare setting?”
Background
Project Context
Motivation
Healthcare AI platforms face a common adoption challenge: the organizations most likely to benefit are often uncertain about the financial and operational case for deployment. Building a credible business case requires translating clinical and operational hypotheses into quantified economic scenarios that decision-makers can interrogate and stress-test.
Background
Early pregnancy care involves a mix of acute, ambulatory, and care coordination touchpoints across payers, health systems, and patients. Conversational AI platforms targeting this space can potentially reduce reliance on high-cost care settings, improve care coordination, and extend provider reach. Evaluating this requires comparing pathway economics across scenarios, identifying which customer segments capture the most value, and communicating uncertainty clearly through sensitivity analysis.
Contributions
My Role
Contributed as a Business Analyst to the engagement through the Penn Graduate Consulting Club. Work focused on building the economic model, running scenario and sensitivity analyses, producing structured written deliverables, and adapting findings for stakeholders at different levels of technical familiarity.
Build an economic model comparing care delivery pathways and document modeling assumptions for decision-makers
Evaluate payer and enterprise customer segments and translate value drivers into prioritized findings
Run sensitivity analyses across key economic variables including unit costs, utilization rates, adoption rates, and care diversion rates
Produce client-ready written reports and presentation decks communicating findings across technical levels
Structure analytical outputs to support stakeholder decisions rather than to assert definitive answers
Inputs
Analytical Inputs
Engagement type
Graduate consulting club
Domain
Early pregnancy care
Analytical output
Business case with scenario modeling
Deliverable format
Deck and written report
Published Healthcare Cost and Utilization Literature
Period
2024
Scale
Published benchmarks and estimates
Economic modeling used published ranges for healthcare unit costs and utilization patterns in early pregnancy care settings. No proprietary client cost data is disclosed or reproduced in portfolio materials.
Payer and Market Structure Research
Period
2024
Scale
Segment-level analysis
Customer and payer segment analysis drew on publicly available market research, payer structure information, and healthcare AI adoption frameworks. Client-specific market assumptions and engagement findings are confidential.
Engagement-Specific Inputs
Period
2024
Scale
Not disclosed
Inputs provided by the client organization during the engagement are confidential. Portfolio materials present the analytical structure and methodology only. Specific financial projections, client recommendations, and proprietary modeling assumptions are not disclosed.
Pipeline
Data Engineering Workflow
Economic modeling used published ranges for healthcare unit costs and utilization patterns in early pregnancy care settings. No proprietary client cost data is disclosed or reproduced in portfolio materials.
Translate the client's business context into a structured analytical question: which care pathway and customer segment combination produces the most credible economic case for the platform.
Map the care pathways most relevant to the platform's potential value: emergency department presentations, ambulatory care visits, and care coordination touchpoints. Document cost and utilization assumptions for each pathway.
Evaluate payer and enterprise customer segment types, identifying which segments have the greatest alignment between platform capabilities and value capture, and which present the most viable near-term go-to-market path.
Run structured sensitivity analyses across key variables - unit costs, utilization rates, adoption curves, and care diversion rates - to communicate how findings change under different assumptions and to identify which variables matter most.
Produce client-ready decks and written reports adapting findings for different stakeholder audiences. Emphasis on communicating analytical uncertainty clearly rather than asserting false precision.
Methods
Analytical Methodology
Expand each method for description and purpose.
Analysis Outputs
Selected Output Previews
This case study presents the analytical structure and methodology of a consulting engagement conducted through the Penn Graduate Consulting Club. Client identity, financial projections, proprietary modeling assumptions, and engagement recommendations are confidential and are not disclosed. Portfolio visuals are illustrative representations of the analytical frameworks used.
A comparative model showing how cost and utilization patterns differ across high-cost emergency department presentations and lower-cost ambulatory care pathways. The model documents assumptions transparently and uses ranges rather than point estimates to communicate uncertainty. Specific financial values are confidential.
A segment prioritization framework mapping payer and enterprise customer types by their value capture potential and adoption feasibility. Segments are evaluated against platform capabilities and near-term market access. Client-specific segment data and recommendations are confidential.
A structured sensitivity analysis showing how business case conclusions respond to variation in key economic variables: unit costs, utilization rates, adoption rates, and care diversion rates. The framework identifies which variables matter most and communicates outcome ranges rather than single-point projections.
Rigor
Data Quality & Methodological Safeguards
Published benchmarks used for economic modeling, with sources documented and ranges reported rather than point estimates
Sensitivity analysis across key variables to characterize uncertainty rather than assert false precision
Assumptions documented explicitly so decision-makers can interrogate and update the model
Client-specific data and recommendations held confidential per consulting engagement standards
Responsible Interpretation
Limitations
Understanding these limitations is essential for correctly interpreting results.
Engagement Confidentiality
The full analytical work, financial projections, and client recommendations from this engagement are confidential. Portfolio materials present the methodology and framework structure only.
The analytical approach and problem-solving structure are documented in sufficient detail to demonstrate capability without disclosing confidential content.
Inherent Modeling Uncertainty
Economic models in early-stage healthcare AI settings involve significant uncertainty in adoption rates, clinical utilization shifts, and payer behavior. No model output should be interpreted as a definitive prediction.
Sensitivity analysis was used throughout the engagement to communicate the range of outcomes under different assumptions, rather than asserting point estimates.
Single Engagement Context
This work reflects one specific engagement context in early pregnancy care. Analytical frameworks used here may not generalize directly to other care settings or AI platform types.
The methodological approach - economic pathway modeling, segment analysis, sensitivity frameworks - is transferable even where the specific context differs.
Stack
Technology Stack
Analytical Tools
Deliverable Formats
Frameworks
Reflection
Lessons Learned
Communicating uncertainty clearly is more useful to decision-makers than presenting false precision - scenario and sensitivity frameworks are essential tools in early-stage healthcare AI evaluation
Payer and customer segment analysis requires understanding not just who captures value but who has the organizational capacity and motivation to act on it in a near-term timeframe
Economic models are most valuable when assumptions are made explicit and interrogable, not when they produce clean numbers that appear more certain than they are
Adapting the same analytical findings for audiences at different technical levels requires deliberate structure in how information is layered and framed
Forward
Next Steps
Apply economic modeling and scenario frameworks to other healthcare AI adoption contexts across different care settings and patient populations
Develop more sophisticated customer segment analysis methods that incorporate payer market structure and contracting dynamics
Build experience with healthcare technology market access strategy across different payer mix environments