Caregivers Are the Missing Data Source for Smarter Healthcare AI

Why Tech-Enabled Services Hold the Key to AI That Actually Improves Care

Large language models have shown clear value in healthcare: faster documentation, sharper decision support, and more personalized guidance. But models plateau if they learn from the same stale inputs. To materially improve outcomes, AI needs context that reflects real life - social, environmental, and emotional factors that shape health.

That context comes from tech-enabled services where software meets human care. Community health workers, doulas, family caregivers, and other care professionals build trust, ask timely questions, and surface the high-context data that generic systems miss. This is the data that helps AI make sense of people, not just problems.

SDoH Data Is the Difference Maker

Social determinants of health (SDoH) - housing, food, transportation, safety, stress, and more - often drive outcomes more than clinical care. Patients rarely share this in a rushed visit or with a bot. They do share it with people who earn their trust.

For background on SDoH and its impact, see the CDC overview on Social Determinants of Health here.

What Caregivers Notice That Models Need

Care delivered at home or in the community exposes issues that charts never show: an empty fridge, no car, a chaotic environment. Humans also read signals that reveal hidden needs.

• Shifts in facial expression
• Avoidance of eye contact
• Guarded posture
• Changes in tone or speech rate
• Pauses or hesitation
• What goes unsaid when a question lands

These signals prompt better questions. Those questions create novel data that improves care now and trains models to serve the next patient better.

From Subtle Cue to Actionable Data: Real-World Style Scenarios

• A postpartum patient pauses when asked about meals. A gentle follow-up uncovers food insecurity. The team connects WIC support and schedules a community delivery program. Future models learn to flag similar hesitation during nutrition check-ins.
• A heart failure patient speaks softly and avoids eye contact when discussing meds. The caregiver asks about storage and routines, discovering the patient can't read small labels. A pill organizer and large-print labels cut missed doses and reduce readmissions.
• A teen with asthma mentions "it's noisy at night." A home visit reveals overcrowding and dust. The team provides air filters and coordinates housing support. The model learns to probe environmental triggers when nighttime symptoms are reported.

Build the High-Context Data Flywheel

1) Define the data you want

• Core SDoH: housing, food, utilities, transport, safety, social support, financial strain, language, digital access, caregiver stress.
• Interaction context: location (home/virtual/clinic), time of day, presence of family, trust level (simple 1-5), follow-up needs.
• Map to standards: ICD-10 Z-codes, LOINC, SNOMED, and HL7 Gravity Project value sets. Learn more about Gravity's SDoH data standards here.

2) Instrument every interaction

• Blend structured fields (checklists) with short free-text notes that capture nuance.
• Capture provenance: who collected the data, where, and when; consent status; language; interpreter used.
• Use lightweight scripts that include open prompts and conditional follow-ups.

3) Govern the data

• Clear consent flows explaining what is collected, why, and how it's used for care and model improvement.
• De-identification for model training, access controls, audit trails, and bias monitoring.
• IRB review for research use; vendor BAAs; PHI minimization by default.

4) Integrate with your EHR

• Write SDoH and observations back via FHIR (Observation, Condition, QuestionnaireResponse, Consent).
• Surface key insights in care plans and task lists so they drive action, not just documentation.

5) Close the loop with outcomes

• Link each insight to an intervention and a measurable result: reduced no-shows, med adherence, avoided ED visit, time to service connection.
• Label examples where a cue led to a need being discovered; feed those into model tuning and prompt design.

6) Evaluate for safety and equity

• Require human-in-the-loop for any care recommendation; no autonomous changes to care plans.
• Track performance across subgroups; investigate gaps; adjust data collection and prompts accordingly.

What This Means for Stakeholders

Providers and Care Organizations

• Stand up a small "context capture" program with 10 essential SDoH items and a two-minute script.
• Equip community teams with a mobile form that logs structured fields plus brief narrative notes.
• Use a retrieval-based assistant to summarize visits and propose next best actions for review by clinicians.

Investors

• Back companies with caregiver networks, standardized SDoH capture, consent-by-design, and EHR integration.
• Value the data moat: uniqueness, refresh rate, label quality, and proven links to outcomes.

Policymakers and Payers

• Reimburse community health workers, doulas, and home-based support where SDoH needs are identified and addressed.
• Encourage Z-code adoption, Gravity-aligned documentation, and data sharing that protects privacy while enabling care coordination.

Data Signals Worth Capturing

• Food, housing, utilities, transport, safety, caregiver availability
• Reading level, language preference, digital access, device type
• Home observations: clutter, fridge contents, medication storage
• Engagement signals: missed calls, response lag, visit acceptance
• Trust proxies: patient-initiated outreach, repeat sessions, willingness to discuss sensitive topics

90-Day Starter Plan

• Weeks 0-2: Pick two populations (e.g., postpartum, CHF). Define 10 SDoH fields and three open-ended prompts.
• Weeks 3-6: Train caregivers on scripts and consent. Begin capturing signals and interventions in a shared template.
• Weeks 7-10: Connect to the EHR via FHIR. Pilot an assistant that summarizes visits and drafts tasks for human review.
• Weeks 11-12: Review outcomes, refine prompts, and formalize governance and audit processes.

The Bottom Line

Tech-enabled services aren't middlemen - they are the scaffolding that lets AI read people, not just records. Trust-based conversations generate the novel, high-context data that models need to move from generic advice to care that fits real lives.

If you're building or buying AI for care delivery, prioritize teams and tools that capture this data at the point of interaction, honor consent, and close the loop with outcomes. That is how we make AI useful in the messiness of everyday health.

Further Learning

• Team upskilling on AI by role: Complete AI Training - Courses by Job