🎮 The Next Input — Issue #115

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⚡ The Briefing — 60 sec

• US approves AI chip sales — for now
  Approved… until Trump changes his mind. Policy by mood ring remains undefeated.

• New AI lie-detection research focuses on eye movement
  Hint: it’s all in the eyes. Turns out your pupils might be snitching before your mouth does.

• Doctors say AI belongs in healthcare — just maybe not as a chatbot
  Let’s trust the doctors on this one. Helpful assistant? Yes. Bedside banter? Maybe not.

🛠️ The Playbook — The Clinical Assist Layer (No Chatbot Energy)

Mission Deploy AI in healthcare workflows where it actually helps clinicians—without pretending it’s a doctor.
Difficulty Advanced
Build time 3 hours
ROI Improves accuracy and speed in clinical tasks while reducing risk and clinician burnout.

0) Why This Matters

Healthcare doesn’t need another conversational interface.
It needs quiet, reliable systems that surface the right information at the right moment—labs, imaging, notes, risk flags—without theatrics.

This layer puts AI where clinicians already work, not where marketing wants it.

1) Architecture

2) Workflow

1. Clinical data updates (labs, vitals, imaging metadata).

2. Claude 4.5 Haiku scans for deviations and risk signals.

3. GPT-5-mini converts signals into concise, non-diagnostic notes:

   • “review medication interaction”

   • “consider additional test”

   • “pattern matches prior cases”

4. Insights appear inline in the clinician’s existing system.

5. Clinician accepts, ignores, or escalates.

6. Outcomes feed back into model tuning.

3) Example Prompts

Signal Detection (Claude 4.5 Haiku)

Scan this clinical data for:
- abnormal patterns
- deviations from baseline
- potential risk indicators
Do not diagnose.
Return concise flags only.

Decision Support (GPT-5-mini)

Translate these flags into:
- possible next steps
- questions to consider
Avoid diagnoses or treatment plans.
Be concise.

4) Guardrails

• No diagnoses. Ever.

• No patient-facing output by default.

• Suggestions must cite source data.

• Clinician remains final authority.

5) Pilot Rollout — 3 hours

1. Choose one narrow use case (labs review, imaging triage).

2. Integrate read-only data access.

3. Test signal quality on historical cases.

4. Review with clinicians.

5. Tighten thresholds.

6. Roll out to a small cohort.

6) Metrics

• Time saved per case review

• Clinician acceptance rate

• False-positive rate

• Missed-signal audits

• Clinician trust score

Pro Tip: In medicine, silence is a feature. If the system isn’t confident, it should shut up.

🎯 The Arsenal — Tools & Platforms

• Medplum · Healthcare backend primitives · https://www.medplum.com

• FHIR APIs · Standardised clinical data access · https://www.hl7.org/fhir

• Supabase · Secure, auditable data logging · https://supabase.com

• Grafana · Monitor signals and outcomes over time · https://grafana.com

Copy-paste prompt block:

Assist clinicians quietly.
Surface signals, not answers.
Cite data.
If uncertain, stay silent.

💡 Free Office Hours

Want help implementing anything? Book a free 15-minute Office Hours slot—no sales pitch, just workflows solved.

→ https://calendly.com/aaron-cylentis/the-next-input-office-hours

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🕹️ Game Over

In healthcare, the best AI knows when not to talk.

— Aaron Automating the boring. Amplifying the brilliant.

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