2.2: Chapter 2 – The SMDC Case Study

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Page ID: 48776

Sukhjit "Bob" Singh
De Anza College

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Introduction: A Startup at the Edge of Innovation and Uncertainty

Self-Managed Diabetic Care Inc. (SMDC) is a fictional startup—but it feels real. That’s intentional. It was designed to simulate the exact kind of high-risk, high-impact, mission-driven environment that risk managers must navigate in the real world. It’s also the stage upon which all of your work in this practicum will unfold.

SMDC is not building a toy app or a convenience tool. It’s building a data platform for chronic care—a dashboard and repository to help people with Type 1 and Type 2 diabetes manage their health more effectively, with less confusion and more control.

Its mission is simple:

Give patients control over their data. Help clinicians support them. Bridge the gap with technology.

Its reality is far more complex.

As with many health-tech startups, SMDC is caught in a collision zone: between innovation and regulation, between business growth and patient safety, between user experience and clinical standards. It has passionate founders, great ideas, and early traction—but it also faces a long list of risks and constraints that could derail or reshape the project.

In this chapter, we’ll explore:

The startup's mission and context
Its unique risk landscape across technical, clinical, regulatory, and financial dimensions
The constraints that define its operating boundaries
Why this project provides the ideal environment to simulate risk management in practice

The Origin Story: Why SMDC Exists

Diabetes is one of the most data-intensive, patient-managed chronic conditions in the world. Millions of people—some as young as 5 or 6 years old—monitor their blood sugar levels multiple times per day, adjust their insulin doses, track their meals and exercise, and attempt to keep their body in a healthy range using fragmented tools, legacy apps, or simple guesswork.

The SMDC founders—one a software engineer, one a data scientist, and one a former healthcare policy analyst—believed there had to be a better way. Their idea: build a central platform that integrates data from multiple devices, gives patients and clinicians a shared dashboard, and uses intelligent alerts to prevent dangerous highs or lows.

But good ideas don’t make good startups. Execution does—and that’s where risk begins.

SMDC’s Project Vision

Product: A cloud-based dashboard that displays blood glucose trends, insulin delivery history, physical activity, meal logs, sleep, and more—drawn from wearable devices and manual input.
Users: Type 1 and Type 2 diabetics, caregivers, clinicians, and possibly school nurses.
Scope: MVP (minimum viable product) for 50 patients and 3 partner clinics within 6 months.
Team: 7-person core team: 3 engineers, 1 product lead, 1 clinician advisor, 1 marketing lead, 1 part-time regulatory consultant.
Funding: $250,000 in seed funding from angel investors.
Technology stack: Python backend, Google Cloud, React frontend, custom device integration APIs.

Constraints: What SMDC Can’t Change (Yet)

A startup’s freedom is an illusion. Yes, founders can move fast and make bold decisions—but they do so within a box defined by hard limits.

Here are SMDC’s early-stage constraints:

Time Constraint

The MVP must be launched within 6 months to maintain investor interest and hit clinical trial timelines.

Budget Constraint

The startup has only $250,000 in pre-seed funding.
Hiring is limited; most contributors are working for equity or reduced salaries.
Cloud computing, data storage, and legal fees eat into operating runway.

Regulatory Constraint

The product touches protected health information (PHI).
It must be HIPAA-compliant, and possibly subject to FDA regulation if it's classified as a clinical decision support tool.

Device Integration Constraint

Many diabetes devices (e.g., insulin pumps, continuous glucose monitors) are closed systems with limited APIs.
Reverse engineering or third-party adapters introduce liability and delay.

Team Capacity Constraint

The team is small and overcommitted.
They lack full-time legal, regulatory, or clinical support.
One key developer is responsible for three systems.

Data Ownership Constraint

Manufacturers often claim ownership of patient data streams.
Patients may not know how to export or use their own data.
Interoperability is a legal, technical, and ethical minefield.

Startup Risks: What Might Go Wrong

While constraints define the boundaries, risks define the terrain inside the map. Here are the major early-stage risk categories for SMDC, each of which will be explored through milestone scenarios later in the book.

Technical Risks

API instability with third-party devices
Data accuracy discrepancies from sensors
Delays in building data visualization or export tools
Poor user experience causing drop-off

Regulatory and Legal Risks

Misclassification by the FDA triggering a longer approval process
Security breaches or HIPAA noncompliance
Lawsuits from device manufacturers over data scraping

Financial Risks

Burning runway too quickly due to underestimated development costs
Failing to secure follow-on funding after MVP
Cost overruns in legal or compliance domains

Clinical and Safety Risks

False alerts leading to panic or missed interventions
Misinterpretation of glucose trends by patients or parents
Clinicians refusing to adopt tool due to liability or workload

Market and Adoption Risks

Patients rejecting the product due to complexity or fear
Clinicians resisting new tools that disrupt existing workflows
Competitors launching a similar product with more resources

Team and Organizational Risks

Key contributor burnout or resignation
Conflict between founders over product direction
Misalignment between product and patient needs due to lack of feedback loops

Why This Case Is Ideal for a Risk Practicum

SMDC is not just a theoretical case—it’s a living system filled with the same ambiguity, tradeoffs, tensions, and moral weight that real risk managers face every day.

It has constraints that are real and unavoidable.
It has risks that are dynamic, multidimensional, and interconnected.
It has stakeholders who care deeply—and who may strongly disagree.
It has momentum, but also fragility. The difference between success and failure may come down to a few early decisions.

In short, it is the perfect laboratory to learn how to:

Identify and categorize risks
Analyze impact and probability
Build stakeholder-aligned controls
Use both qualitative and quantitative analysis
Collaborate under pressure

What You’ll Do With SMDC

In the chapters ahead, you and your team will act as risk managers working alongside SMDC’s founding team. You’ll complete twelve hands-on milestone tasks that mirror the real-world risk lifecycle—from identification to control design to response planning.

You’ll simulate strategy meetings. Draft stakeholder checklists. Conduct root cause analysis. Prioritize interventions. And, ultimately, build a full risk mitigation portfolio that could serve a real startup.

Final Thought

If SMDC succeeds, it won’t be because they had no risks. It will be because they knew what they were, acted on them early, and built a culture that embraced uncertainty without being paralyzed by it.

This is your job now:

Don’t eliminate all risk. That’s impossible.
Learn how to see it. Frame it. Talk about it. Act on it.
And most importantly—help others do the same.

Let’s get started.

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