The MedMal Room: Ten MORE Preventable Tragedies in Labor and Delivery
The Safety Ledger - Why the same mistakes keep harming mothers and babies—and how hospitals and AI can stop them
She arrived in labor, healthy, excited, and full term. Her baby never cried.
When the chart was reviewed, the story was painfully familiar: abnormal fetal heart tracings ignored, oxytocin running unchecked, delayed response to decelerations, missing documentation, and no one clearly in charge. It wasn’t one catastrophic decision that caused the injury, it was ten small preventable ones, layered over hours, hidden in plain sight.
Below are the ten most common and preventable hospital errors on labor and delivery units, each explained with a practical fix and how artificial intelligence can help stop the cycle of preventable harm.
What Kind of AI Are We Talking About?
The artificial intelligence used in labor and delivery safety is not the generative kind that writes text or creates images.
It’s clinical AI, pattern-recognizing, data-analyzing software built into hospital systems that learns from millions of past patient records to detect risk in real time.
These tools don’t “think” or “decide.” They assist by monitoring fetal heart tracings, medication use, vital signs, and workflow timing with superhuman consistency. When used correctly, clinical AI acts like a second set of vigilant eyes, never distracted, never tired, and always ready to alert clinicians before small deviations turn into disasters.
1. Failure to Recognize Abnormal Fetal Heart Patterns
What goes wrong: Misinterpreting fetal heart rate (FHR) tracings remains the top cause of preventable neonatal injury. Too many providers see the same pattern differently.
How to fix: Standardized NICHD terminology, dual-reader verification, and regular simulation.
How AI can help: AI-driven CTG analysis can continuously flag concerning patterns, calculate baseline variability, and alert staff when decelerations meet pathological thresholds. Studies show AI-assisted FHR interpretation reduces interobserver variation and prompts earlier interventions.
2. Delayed or Absent Escalation
What goes wrong: Abnormal tracings or stalled labor often go unreported because staff “wait to see.”
How to fix: Predefined escalation triggers and time-based alert thresholds for Category II and III tracings.
How AI can help: Intelligent dashboards can monitor all active labors simultaneously, escalating alerts when criteria are met. AI-based triage systems can automatically message the on-call obstetrician when labor progress or fetal status meets critical thresholds, preventing silent deterioration.
3. Oxytocin Mismanagement
What goes wrong: Excessive dosing or failure to titrate leads to uterine tachysystole, fetal hypoxia, and rupture.
How to fix: Limit infusion parameters, empower nurses to stop oxytocin when fetal stress appears.
How AI can help: Smart infusion pumps linked to real-time FHR analytics could automatically adjust or stop oxytocin based on contraction frequency and fetal response. Machine learning could audit infusion logs and alert for unsafe patterns.
4. Failure to Diagnose Labor Arrest or Obstructed Labor
What goes wrong: Prolonged labor is normalized; the diagnosis of “arrest” is delayed or inconsistent.
How to fix: Apply clear definitions (ACOG/SMFM) and track cervical progress objectively.
How AI can help: Automated labor curve modeling can identify abnormal progress earlier by comparing real-time dilation data with thousands of validated labor trajectories. Predictive algorithms can suggest when mechanical dystocia or malposition should be suspected.
5. Inadequate Shoulder Dystocia Training and Documentation
What goes wrong: Teams panic, forget sequence, or fail to document maneuvers accurately.
How to fix: Mandatory annual simulation and post-event documentation template completion.
How AI can help: AI video analysis could be used during simulation or real events to capture time stamps and maneuver sequences, generating structured reports automatically for post-event debriefs and documentation audits.
6. Incomplete Team Communication
What goes wrong: Handoffs are informal, team members operate with different mental models.
How to fix: Structured “read-back” handoffs and interdisciplinary safety huddles.
How AI can help: Natural language–based systems can analyze handoff content in real time, flag missing key elements (“fetal heart category not stated,” “oxytocin dose not mentioned”), and generate a standardized handoff summary in the EMR. AI can also track team communication flow to identify breakdowns before they cause harm.
7. Failure to Respond to Maternal Hemorrhage or Hypertension
What goes wrong: Warning signs (tachycardia, rising BP, falling hematocrit) are missed in the noise of labor.
How to fix: Use maternal early warning systems and pre-packed hemorrhage kits.
How AI can help: AI-driven vital sign surveillance can detect early patterns of decompensation long before clinicians notice. For example, predictive models can recognize subtle BP trends suggesting impending eclampsia or PPH and trigger alerts directly to the obstetric rapid response team.
8. Documentation Gaps and Retrospective Editing
What goes wrong: Notes are incomplete or written hours later, obscuring what really happened.
How to fix: Real-time documentation and time-stamped templates for key interventions.
How AI can help: Voice-to-text tools with structured prompts can ensure that key elements (FHR, oxytocin dose, position changes) are recorded immediately. Generative AI can auto-complete missing fields, cross-check time stamps, and summarize continuous monitoring data into readable, accurate progress notes.
9. No Debrief or Post-Event Analysis
What goes wrong: Teams move on after a crisis, losing learning opportunities.
How to fix: Require a 24-hour multidisciplinary debrief after every sentinel event.
How AI can help: AI transcription and summarization tools can convert debrief recordings into structured quality reports, automatically highlight themes (communication, delay, protocol deviation), and suggest targeted training priorities for the next simulation cycle.
10. Systemic Undertraining and Lack of Simulation
What goes wrong: Staff lose critical emergency skills between rare events.
How to fix: Mandatory simulation for all staff, every 6–12 months.
How AI can help: Adaptive AI-driven simulators can create individualized virtual drills based on each provider’s documented gaps. Machine learning can track cumulative competency data and identify units at higher risk due to untrained personnel, prompting scheduling of targeted refreshers.
A Real Case: The Chain of Preventable Events
In one malpractice case, a 28-year-old woman in spontaneous labor developed repetitive late decelerations. The nurse continued oxytocin, believing the tracing was “moderate variability reassuring.” The resident was busy with another delivery. The attending was in the OR doing another surgery. Thirty minutes later, the baby was delivered by crash cesarean, profoundly acidotic and later diagnosed with hypoxic-ischemic encephalopathy.
Ten missed opportunities—each preventable, each now detectable by AI systems capable of real-time pattern recognition, escalation alerts, and decision support.
Ethical Accountability: Shared Duty Between Doctor and Patient
AI cannot replace professional vigilance, but it can restore it. It cane be our partner. The physician’s duty is to act; the patient’s duty is to speak up when something feels wrong. Every pregnant woman should know that pain, bleeding, or decreased fetal movement deserves immediate attention. When reassurance replaces assessment, a second opinion may save a life.
From Blame to Learning
AI’s greatest contribution to obstetrics may not be automation, but accountability. Properly used, it creates transparency, consistency, and memory. It doesn’t forget to escalate or to document. It can remind humans to be human—before tragedy reminds them instead.
Reflection / Closing
Every preventable death on labor and delivery is a system warning left unanswered. AI offers a mirror, not a shield. It shows us what we could have seen, said, or done sooner. The question is not whether machines will replace clinicians, but whether clinicians will accept help before another baby dies waiting for someone to act.