63/100
influencer
You have a sharp, authentic voice and a legitimate insight—that clinical pathway alignment is fundamentally a multiplayer problem AI cannot solve alone. This resonates because it's true. But you're *stating* rather than *proving* it. The piece reads like a well-delivered opinion from an insider, not a substantive argument grounded in evidence. Your biggest weakness is evidence quality (2/5 rubric score)—you assert without supporting why, making this influencer-tier content rather than thought leadership. The secondary issue is nuance: you've created a false binary (AI vs. humans) that ignores AI-assisted hybrid approaches, which weakens your credibility with sophisticated readers.
Dimension Breakdown
📊 How CSF Scoring Works
The Content Substance Framework (CSF) evaluates your content across 5 dimensions, each scored 0-20 points (100 points total).
Dimension Score Calculation:
Each dimension score (0-20) is calculated from 5 sub-dimension rubrics (0-5 each):
Dimension Score = (Sum of 5 rubrics ÷ 25) × 20 Example: If rubrics are [2, 1, 4, 3, 2], sum is 12.
Score = (12 ÷ 25) × 20 = 9.6 → rounds to 10/20
Why normalize? The 0-25 rubric range (5 rubrics × 5 max) is scaled to 0-20 to make all 5 dimensions equal weight in the 100-point CSF Total.
Quantitative data weakness (3/5 rubric). GitHub stats cited but unsourced; SeamlessMD timeline presented as benchmark without comparative data or methodology explanation.
Evidence quality critical (2/5 rubric). Claims about AI limitations, stakeholder dynamics, and trust-building are asserted without empirical support or structured analysis of causal mechanisms.
Synthesis weak (3/5 rubric). Single-player vs. multiplayer framing is sound but underdeveloped. Lacks exploration of why this distinction matters mechanistically or whether it holds across contexts.
Reasoning depth and nuance both low (3/5, 2/5 rubrics). False dichotomy: presents human facilitation and AI tools as mutually exclusive rather than exploring AI-assisted scenarios or complementary roles.
Hedge avoidance moderate (4/5 rubric). Conversational voice is strong (5/5) but softening phrases ('might even draft,' 'could ask') dilute confidence. Minor AI patterns detected; strategic all-caps risks aggressive tone on rereads.
🎤 Voice
🎯 Specificity
🧠 Depth
💡 Originality
Priority Fixes
Transformation Examples
AI can't read the room. It doesn't know how to navigate social politics, build genuine trust, or figure out how to tailor a strategy to the specific needs and resource limitations of a local community.
Current AI models fail at pathway alignment for three reasons: First, they lack real-time access to institutional politics—the history of tension between disciplines, budget constraints, or past failed initiatives. Second, they can't dynamically adjust communication style based on stakeholder response; they generate static content, not interactive facilitation. Third, they can't hold accountability for outcomes—clinicians won't trust a system with no skin in the game. SeamlessMD solved this by assigning a human SME who brings institutional memory, reads emotional resistance, and owns implementation risk. The question isn't whether AI is metaphysically incapable of trust—it's whether current AI architecture can integrate the local context and accountability that clinical teams require. We believe the latter requires human judgment, at least for now.
How: Distinguish between technical constraints (current models can't integrate real-time local data, stakeholder preferences) and claimed ontological impossibilities (AI fundamentally cannot build trust). Test whether the problem is *what AI is* or *how we're using it*. Ask: What specifically about 'reading the room' is irreducible—emotional intelligence? Power dynamics recognition? Contextual knowledge gaps? Institutional memory? Then provide evidence for which are permanent vs. solvable constraints.
You could ask AI to create the perfect care pathway, and it might even draft something reasonable in minutes—BUT coming up with plausible pathway content is the easy part. The really hard part is getting stakeholder alignment.
Ask AI to create a care pathway, and it will generate a plausible draft in minutes. The content is technically sound. But that's not the work. The work is stakeholder alignment—getting medicine, nursing, pharmacy, and administration to agree on 50 interdependent decisions while honoring local constraints, legacy workflows, and professional autonomy. That's where six months disappears.
- Removed 'might' and 'even'—confidence suits this argument. Changed to 'will generate' and 'plausible draft,' which is factual without hedging.
- Replaced 'reasonable' with 'technically sound'—specific and clinical, not vague.
- Added concrete detail: '50 interdependent decisions,' 'local constraints, legacy workflows, professional autonomy'—shows you understand the actual problem, not just asserting one exists.
- Tightened logic: 'But that's not the work. The work is...' creates narrative momentum instead of 'coming up with... is the easy part. The really hard part is...' which feels repetitive.
Derivative Area: The core premise—'AI can generate content but can't build consensus'—is familiar territory in change management and organizational behavior literature. Your healthcare specificity is valuable, but the underlying insight (technical solutions fail without stakeholder buy-in) has been documented since at least 2000s enterprise software implementations.
The genuine contrarian move isn't 'AI can't do stakeholder alignment'—that's now conventional wisdom. The move is: 'AI-assisted facilitation could actually *accelerate* alignment if we redesigned the human-AI collaboration model.' This would require you to: (1) document where your current SME-led process bottlenecks, (2) test whether AI could remove those bottlenecks, (3) measure whether the hybrid model produces equivalent or better outcomes. If true, this is genuinely novel and positions you as a builder, not just a critic. If false, you have an interesting failure story ('We tried AI-assisted facilitation; here's why it didn't work'). Either way, you move from defensive positioning to research-backed insight.
- Why does *clinical* stakeholder alignment specifically resist AI facilitation compared to other domains? Is it the stakes (patient safety), the power structures (physician authority), the lack of written rules (unspoken norms), or something else? Compare to software teams, where stakeholder alignment is also complex but arguably faster.
- What if the problem isn't AI's limitation but our deployment strategy? Could AI work better if positioned as a *tool for humans* rather than a *replacement for humans*? What would facilitation look like if AI handled logistics, conflict tracking, and documentation while humans handled negotiation?
- Is the 6-month debate actually a proxy for something else—like organizational dysfunction, turf protection, or lack of clinical leadership? Some health systems probably align pathways faster. What are they doing differently?
- What role does pathway *design methodology* play? If stakeholders co-design with AI assistance vs. top-down AI generation, does outcome differ? Does the process matter as much as the content?
30-Day Action Plan
Week 1: Evidence Depth (Experience Depth dimension)
Audit SeamlessMD's project data for one clear example: either a failed AI pathway generation attempt or a successful SME-facilitated customization. Document: the disciplines involved, the specific friction point or resolution mechanism, and the measured outcome (time, adoption rate, implementation issues). Write a 250-word narrative of what happened and why it matters. This becomes your primary supporting evidence.
Success: You have a concrete, measurable example with timestamps and stakeholder details that could withstand scrutiny. It specifically illustrates why stakeholder facilitation matters more than content generation speed.Week 2: Nuance (Nuance dimension)
Rewrite the argument as a *complementarity framework* rather than a critique. Draft an outline: (1) What AI does well—rapid pathway drafting, content generation, pattern recognition. (2) What humans must own—stakeholder negotiation, accountability, contextual judgment. (3) How you've structured the collaboration at SeamlessMD—specific process, decision points, roles. (4) Quantified outcome—time, adoption, or quality metric that proves the hybrid approach works. Aim for 400 words; this becomes your core narrative.
Success: The argument shifts from 'AI can't do this' to 'here's the right role for each.' A skeptical reader can follow the logic without feeling defensive. You reference your SeamlessMD data (from Week 1) as proof.Week 3: Specificity (Specificity dimension)
Add sourcing and precision. (1) Find the source for the GitHub AI commit stat or remove it. (2) Quantify the 9-10 week benchmark: 'compared to the historical 6-9 month implementation across comparable health systems' or whatever your baseline is. (3) Define '50 elements'—is this care pathway tasks, decision points, stakeholder agreements? (4) Add 1-2 contextual details about SeamlessMD's facilitation methodology: 'Our SME-led model includes weekly alignment sessions, documented disagreements mapped to clinical literature, and explicit accountability roles.' Rewrite your core claims with these specifics.
Success: Every quantitative claim has a source or baseline comparison. Every vague term ('reasonable,' 'complex,' 'hard') is replaced with specific detail or metric. A reader can verify or challenge each claim.Week 4: Integration and Polish (All dimensions)
Synthesize Weeks 1-3 into a revised piece: (1) Open with your Week 2 complementarity framing, anchored by your Week 1 case study. (2) Use Week 3 specifics throughout—source citations, quantified benchmarks, process details. (3) Close with an honest limitation: What *don't* you know about AI's potential in this space? What would need to be true for AI-assisted facilitation to work? This converts your piece from defensive opinion to grounded analysis. Aim for 1,200-1,500 words; this is now hybrid-zone thought leadership (CSF 35-45).
Success: The revised piece reads as research-backed analysis rather than opinion. A healthcare executive can extract actionable insight ('Here's how AI and humans should collaborate on pathways'). A researcher can identify gaps and next questions.Before You Publish, Ask:
If AI could integrate real-time institutional context and be accountable for outcomes, could it facilitate stakeholder alignment effectively?
Filters for: Whether your argument is about AI's fundamental limitations or about current technical constraints. If you answer 'maybe,' you're thinking more nuanced than your current piece suggests. If you answer 'no,' you need to explain why ontologically, not just pragmatically.What percentage of your 9-10 week SeamlessMD process is the SME actually *facilitating negotiation* vs. *waiting for busy stakeholders* vs. *managing logistics*?
Filters for: Whether you've analyzed where the actual bottleneck is. If facilitation is only 30% of the time and logistics is 70%, your argument changes—AI could eliminate logistics burden and free humans for the real work. This insight strengthens your case.Have you compared adoption/implementation success rates between health systems using SME-facilitated pathways vs. those using top-down AI-generated pathways?
Filters for: Whether you're operating from observed data or assumption. If you haven't run this comparison, your evidence is anecdotal. If you have, that's your strongest claim. This shapes your research agenda.What would need to be true about AI for you to say 'yes, AI could do significant parts of stakeholder facilitation'?
Filters for: Whether you're open to being wrong or defensive about your position. Thought leaders can articulate the conditions under which they'd change their view. This makes you seem credible, not dogmatic.Is the six-month debate actually about the complexity of clinical pathways, or is it a symptom of organizational dysfunction, turf protection, or leadership gaps?
Filters for: Whether you've done causal analysis. If you can't distinguish between 'this is inherently hard' and 'our organization does this poorly,' your recommendation might miss the root problem. This is the difference between diagnosis and treatment.💪 Your Strengths
- Authenticity (17/20 rubric voice score): Your conversational tone, rhetorical questions, and opinionated stance feel genuine. No corporate jargon; this reads like someone who actually works in healthcare tech. This is rare and valuable.
- Structural variety (5/5 rubric): You break conventional structure deliberately—mixing short punchy sentences with longer arguments, using all-caps for emphasis, rhetorical questions. It keeps readers engaged.
- Contrarian positioning (4/5 rubric originality): You're not afraid to push back on the 'AI will solve everything' narrative. This courage to disagree is thought leadership fuel.
- Domain specificity (4/5 rubric): You name disciplines, reference actual pathway work, mention SeamlessMD by name. This grounds your argument in reality, not abstraction.
- Human insight (4/5 rubric): The single-player vs. multiplayer distinction is sound and properly applied to clinical contexts. It's a useful frame, even if underdeveloped.
You're at the threshold between influencer and hybrid thought leadership. Your voice, insider knowledge, and willingness to challenge hype are genuine assets. What's missing is rigor: you need to back your arguments with evidence rather than assertion. If you add one solid case study (Week 1), reframe the argument as complementarity rather than critique (Week 2), and source your claims (Week 3), you'll move from CSF 28 to CSF 38-42. That's the zone where editors listen, health systems adopt your framework, and you stop being 'the person who says AI can't do X' and start being 'the researcher who figured out what AI and humans should each own.' The research is within reach; you have the data and perspective. The question is whether you're willing to do the analytical work to convert opinion into insight.
Detailed Analysis
Rubric Breakdown
Overall Assessment
Exceptionally authentic voice with sharp personality. Author breaks conventional structure deliberately—using rhetorical questions, fragments, and sarcasm. Rare clichés and zero corporate jargon. Conversational, opinionated, and grounded in real experience. This reads like someone who actually works in healthcare tech, not a template.
- • Strong, unhedged opinions ('AI can't read the room'). Author commits to positions rather than softening with 'might' or 'arguably.'
- • Creative structural rule-breaking—fragments, caps, rhetorical questions, dashes—feels intentional and energetic, not accidental.
- • Insider expertise evident through specific domain knowledge (interprofessional teams, care pathway implementation, health system timelines). Reads like someone who's lived this.
- • Occasional softening ('might even draft something reasonable') dilutes confidence in an otherwise bold argument. Tighten language.
- • Heavy use of all-caps ('GOOD LUCK,' 'BUT') works for emphasis but risks feeling aggressive on rereads. Selective would be more powerful.
- • Could lean deeper into personal anecdote—a 2-3 sentence story about a failed AI pathway attempt would strengthen credibility further.
Rubric Breakdown
Concrete/Vague Ratio: 1.5:1
The content balances concrete specifics with strategic vagueness effectively. It anchors claims with GitHub data (4% to 20%), named entities (SeamlessMD, specific disciplines), and tangible examples (50 pathway elements, 9-10 weeks). However, some assertions about AI limitations lack quantified evidence. The writing prioritizes rhetorical impact over exhaustive detail.
Rubric Breakdown
Thinking Level: First-order with isolated second-order moments
The piece identifies a genuine distinction between coding and clinical pathway work—that social alignment differs from technical implementation. However, it relies on assertion rather than analysis. The core insight (multiplayer vs. single-player) is sound but underdeveloped. Missing: exploration of *why* stakeholder alignment fails, what specific mechanisms AI lacks, and whether the dichotomy holds across contexts.
- • Identifies genuine gap between technical capability and organizational readiness—non-obvious to technologists
- • Correctly prioritizes stakeholder alignment as often-ignored bottleneck in healthcare transformation
- • Concrete distinction (single vs. multiplayer) is memorable and teachable
- • Acknowledges limits of AI without dismissing technology entirely
Rubric Breakdown
Strong framing of a real implementation gap—the single-player vs. multiplayer distinction—with credible pushback against AI hype in healthcare. However, the core insight (technical solutions fail without stakeholder alignment) is familiar in change management literature. The piece gains strength through specific healthcare context but lacks deeper exploration of why alignment is specifically hard in clinical environments.
- • Single-player (coding) vs. multiplayer (clinical pathways) as the critical AI limitation distinction—properly applied to healthcare's specific constraints
- • Quantified model: 9-10 week timeline for SME-led pathway customization as a competitive benchmark against pure AI generation
- • The gap between 'plausible content generation' (easy, fast) and 'stakeholder alignment' (hard, slow) as the real healthcare implementation problem
Original Post
𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝘀𝘁: “AI can code, so creating care pathways for patients must be easy.” 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: Clinicians spend 6 months debating the nuances of a care pathway - while all an AI can do is document the debate, but not resolve it. Today 4% of all GitHub public commits are written by AI - and it’s projected to reach 20% by the end of 2026. So if AI is taking over coding, one of the highest paying knowledge work activities of the last decade, isn’t it reasonable to assume AI will do the same for clinical pathways? The short answer is “No” - the big difference is that coding is largely a “single player” activity, while developing clinical pathways is a complex, “multiplayer” exercise in diplomacy. When you code, it can just be you and your AI coding agent going back and forth until it works. But if you’ve ever been part of an interprofessional care team, you know that implementing care pathways is anything but an individual activity. You could ask AI to create the perfect care pathway, and it might even draft something reasonable in minutes - BUT coming up with plausible pathway content is the easy part. The really hard part is getting stakeholder alignment. Oh, you think you could just create a voice AI persona to lead these complex change management activities? GOOD LUCK getting AI to facilitate medicine, nursing, pharmacy, OT/PT, administration, etc. agreeing on all 50 elements in a pathway. Because let's be real: AI can’t read the room. It doesn't know how to navigate social politics, build genuine trust, or figure out how to tailor a strategy to the specific needs and resource limitations of a local community. Those are the deeply human-to-human parts of the job - the stuff that requires real communication, collaboration, and most importantly, shared accountability. It’s why the best decision we ever made at SeamlessMD for digital care journeys (e.g. pre/post-surgery patient navigation) was not only to develop template care pathway content in-house, but to provide clinical subject matter experts who can facilitate pathway customization and alignment among care teams in 9-10 weeks (lighting fast for health systems!) How could AI possibly do that?