Health Gender Bias: Patient Navigation & Access Inequity

Initiative: Care Friction Mapper

What this is

The Care Friction Mapper is a system-level analysis engine that exposes where and how healthcare access breaks down for women—not because of noncompliance or personal choice, but because of structural friction embedded in care pathways.

It reframes access inequity from:

“Why didn’t the patient follow through?”
to
“Where did the system make follow-through unreasonably hard?”

The mapper treats every delay, denial, and dropout as a design signal, not a patient failure.

The core problem

Healthcare access is often discussed as a binary: care received vs not received.
In reality, access is a gauntlet of steps:

  • securing an appointment

  • obtaining referrals

  • navigating insurance approvals

  • coordinating time, childcare, and work

  • persisting through delays and denials

Women—especially those with chronic, complex, or “non-acute” conditions—encounter more friction at each step. These frictions compound until care is delayed, downgraded, or abandoned.

AI approach: mapping access as a process, not an endpoint

1) Multi-source operational data analysis

The system analyzes administrative and operational data, including:

  • appointment scheduling logs

  • wait time distributions

  • referral initiation and completion records

  • insurance prior authorization and denial data

  • follow-up adherence timestamps

Each interaction is treated as a navigation event within a care journey.

2) Gender- and condition-stratified modeling

Access metrics are stratified by:

  • gender

  • condition category

  • specialty

  • care setting

This allows the system to compare:

  • how long women vs men wait for the same type of care

  • how often referrals stall or expire

  • where insurance barriers disproportionately appear

The focus is on equivalent need, unequal effort.

3) Journey-level drop-off detection

Using sequence analysis, the mapper identifies:

  • points where women disproportionately exit the care pathway

  • steps that require repeated self-advocacy to progress

  • transitions (e.g., primary care → specialty) where friction spikes

Drop-off is interpreted as system failure, not disengagement.

What the system detects

A) Wait time asymmetries

Differences in:

  • time to first appointment

  • time to specialty referral

  • time to diagnostic testing

Particularly for conditions that require persistence rather than emergency escalation.

B) Insurance and authorization bias

Patterns where:

  • women face higher denial rates

  • approvals require more documentation

  • appeals are more frequently necessary

  • care is delayed until symptoms worsen

These are often invisible in clinical records but decisive for outcomes.

C) Referral attrition

Situations where:

  • referrals are issued but never completed

  • scheduling barriers halt progression

  • follow-up responsibility is implicitly shifted to the patient

These are key points where inequity is quietly produced.

Core outputs

1) Access inequality heatmaps

Visualizations showing:

  • where wait times diverge by gender

  • which specialties exhibit the greatest friction

  • which conditions trigger repeated access barriers

Heatmaps turn bureaucratic delays into geographic and procedural evidence.

2) Care journey drop-off maps

Step-by-step representations of care pathways highlighting:

  • high-friction transitions

  • cumulative delays

  • gender-skewed exit points

These maps make navigation failure auditable.

3) System-level intervention targets

Actionable insights for:

  • scheduling reform

  • referral automation

  • insurance policy review

  • care coordination support

The mapper identifies where fixes will matter most, not just where inequity exists.

Bias exposed (reframed clearly)

The system demonstrates that:

  • women’s care journeys require more persistence

  • access barriers masquerade as “non-adherence”

  • administrative design choices have clinical consequences

  • inequity is produced long before treatment decisions

In short:

Women do not receive worse care because they try less.
They receive worse care because the system asks more of them.

Why this matters clinically and institutionally

When access friction is unmeasured:

  • delays are normalized

  • patients are blamed

  • clinicians underestimate system barriers

  • inequity is treated as inevitable

When access friction is measured:

  • accountability shifts upstream

  • care pathways can be redesigned

  • navigation support can be targeted

  • equity becomes operational, not aspirational

The larger shift

The Care Friction Mapper reframes access from:

“Did the patient get to care?”
to
“How much resistance did the system impose along the way—and on whom?”

Because in healthcare,
friction is not neutral—it shapes who arrives early, who arrives late, and who never arrives at all.

Health Gender BiasFrancesca Tabor