How a $3M Healthtech Startup Stopped Chasing the Next Model
BrightPath Diagnostics launched in 2020 with a clear proposition: speed up radiology triage using off-the-shelf AI models. By 2023 the team had trialed seven different models, paid for three specialized APIs, and reworked the pipeline four times. Each swap came with a promise that "this one finally understands our cases." Instead, the product produced brittle decisions, engineers spent 30% of their time on integration, and clinicians complained about inconsistent recommendations.
BrightPath was not unique. Many teams assume the fix for poor AI output is a different tool. They chase an elusive model that "gets it" and treat disagreement as noise to be smoothed out. BrightPath took a different route: they instituted a Consilium expert panel model - a structured process where disagreement was not a flaw but a required signal. The results offer precise lessons about how to stop tool-hopping and produce repeatable, defensible clinical decisions.
The Real Problem: Why Switching Models Was Making Decisions Worse
What was the core failure? It was not that models were bad. It was that BrightPath's process rewarded agreement and punished dissent. If any two models agreed, the system accepted the decision. Engineers prioritized throughput over traceability. Clinicians saw outputs that looked reasonable some days and dangerous other days. Key issues were:
- Decision drift: identical inputs yielded different outputs depending on the chosen model and its version. False confidence: automated agreement between models gave false reassurance when all models shared the same blind spot. Operational friction: each swap required three weeks of integration, testing, and retraining rules. Accountability gaps: no single human expert was required to argue against a model's output, so mistakes slipped into care pathways.
BrightPath's data showed an error rate of 18% in urgent-read classifications, a clinician override rate of 32%, and developer hours lost to integration estimated at 600 hours per year. Those numbers made it clear the problem was structural, not solely technical.
A Structured Panel Instead of a Single Consensus: The Consilium Decision Model
BrightPath designed a Consilium panel: a small, mixed group of subject experts and specialized models that were forced to disagree and justify positions. The central idea? Disagreement surfaces edge cases and reduces the chance of a shared blind spot creating a consistent falsehood.
Key elements of the Consilium model they used:
- Mixed composition: three radiologists, one data scientist, one ethicist, and two specialized AI models (one for pattern recognition, one for risk estimation). Structured dissent: at least one panel member must present a counterargument to any majority recommendation. Argument logs: every position required a succinct rationale with supporting evidence. AI outputs had to cite image features and confidence intervals; humans had to reference prior cases or guidelines. Meta-evaluation: a rotating adjudicator reviewed panel disagreements and recorded whether follow-up changes were needed in the pipeline.
Why this matters: forcing a dissenting voice prevents quiet consensus on visible but irrelevant patterns. It also provides an audit trail for clinical review and regulatory scrutiny. BrightPath set a rule: no model decision could be deployed without at least one explicit dissent either resolved or escalated.
Implementing the Consilium Model: A 60-Day Rollout
How do you move from tool-hopping to a panel process without stalling product development? BrightPath used a 60-day pilot with three phases.
Days 1-10 - Formation and Rules
Assembled the panel: contracted two radiologists (one in-house, one external), a healthcare data scientist, and an ethicist available 10 hours/week. Defined decision thresholds: established when the panel must meet (e.g., any case with model confidence under 0.85 or >15% change from prior reading). Built the log format: a one-paragraph rationale plus structured tags - features cited, confidence, historical precedent, suggested action.Days 11-30 - Integrating Models and Rules
Kept two AI models in the pipeline rather than swapping to a new single model. One focused on lesion detection, the other on outcome risk. Programmed the system to flag cases failing internal consistency checks for panel review. Ran 1,200 retrospective cases through the panel process to train workflow and timing.Days 31-60 - Live Pilot with Required Dissent
Deployed the panel on live triage for 10% of incoming studies. Panel members reviewed remotely, adding rationales into the log. Enforced the dissent rule: if panel reached a majority decision, at least one dissenting argument had to be recorded and either resolved or escalated. Tracked time-to-decision, override rates, and clinician trust via weekly surveys.Operational details they tuned during rollout: how to prioritize panel workload, when to escalate to rapid-response review, and how to present dissent to frontline clinicians so it helped rather than confused them.
From 18% Error Rate to 5%: Measurable Results in 6 Months
The numbers before and after are concrete. Over six months following full deployment, BrightPath measured:
Metric Before Consilium After 6 Months Urgent-read classification error 18% 5% Clinician override rate 32% 10% Developer integration hours/year 600 220 Average decision turnaround (triage) 48 hours 12 hours Clinician trust score (0-100) 41 78Which changes drove results?
- Disagreement forced explicit consideration of edge cases, catching systematic model blind spots. Argument logs created accountability and a teachable dataset for retraining models on misclassified patterns. Panel review reduced harmful automation by ensuring humans could question confident but flawed model outputs. Standardized escalation reduced wasted engineering time because swapping models was no longer the default fix.
3 Critical Lessons From a Panel That Required Dissent
What should you, as a product leader or clinician, walk away with?
1 - Agreement is not proof of correctness
When multiple models agree, they may be jointly wrong. Ask: do models share training data or architecture? If so, their agreement likely reflects shared blind spots. Require a dissenting view or external evidence before accepting unanimous outcomes in high-stakes areas.
2 - Test the dissent, don't silence it
Dissent must be structured. Open-ended disagreement becomes noise. BrightPath used concise rationales and tags that made every dissent actionable. Train your team to document why they disagree and what evidence would change their mind.
3 - Build the process before you swap the tool
Switching https://suprmind.ai/ tools without process treats models as quick fixes. Establish clear decision rules, logging, and escalation paths first. Tools change; a durable process prevents repeated rework and preserves institutional knowledge for model retraining.
How Your Team Can Reproduce This Without a Month-Long Shutdown
Do you need to pause product development to adopt a Consilium panel? Not necessarily. Use this lean path to test the model:
Start with a pilot on a small slice of traffic - 5-10% - and the most consequential cases. Assemble a compact panel: two domain experts and one technical reviewer are sufficient to start. Define a single dissent rule: at least one dissent required for any decision flagged as high-risk. Log rationales in a short template: claim, evidence, confidence, and suggested follow-up. Measure the basics: error rate, override rate, turnaround time, and practitioner trust.Ask yourself: where are your shared blind spots likely to be? Which high-cost errors persist despite tool swaps? Those areas are the best candidates for a panel pilot.
What Can Go Wrong - Common Failure Modes and How to Avoid Them
Panels can fail. BrightPath learned the hard way by seeing three clear pitfalls in early tests:
- Groupthink under a charismatic expert - fix by rotating the adjudicator and anonymizing initial positions. Token dissent - fix by requiring evidence tags and tracking whether a dissent produces change. Operational overload - fix by limiting panel review to cases that meet clear risk thresholds.
Would your team tolerate a slower decision pace in exchange for lower risk? That trade-off must be explicit. BrightPath accepted a temporary increase in turnaround for harder cases in exchange for significant downstream reductions in error and clinician time.
Summary: Stop Treating Tools as Panaceas - Enforce Dissent and Track Rationale
Switching AI tools is tempting but often masks a deeper governance problem. BrightPath's Consilium expert panel demonstrates that forcing structured disagreement, logging rationales, and using a small group of mixed expertise produces far better outcomes than constant tool-hopping. You do not need more models; you need a process that surfaces blind spots and turns dissent into productive data.
Final questions for you:
- Which recurring errors in your system could a structured dissent reveal? Do your current decision logs explain why an output was accepted or rejected? Can you run a 60-day pilot with a compact panel on a small slice of cases?
If you answer yes to any of these, you are ready to stop chasing the perfect tool and start building a process that protects outcomes even as models evolve. BrightPath's experience shows that disagreement, when required and structured, is not a bug - it is the safeguard that prevents confident mistakes from becoming harmful ones.
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