Patient Engagement & Outreach

Chronic Disease Management Outreach Agent

ai

Chronic care engagement runs on multi-touch SMS, voice, and mail sequences, personalized to each high-risk patient the agent identifies. It segments by channel preference, language, and engagement history, then triages responses. Engagement rates run 3x untargeted outreach, 38% of HEDIS and Stars gaps close within 90 days, and manual care manager outreach drops 65%.

Care ManagerQuality & HEDIS ManagerPatient Services
3x
Engagement rate vs. untargeted outreach
38%
Gap closure within 90 days
65%
Reduction in care manager manual outreach
Use Case 1

Multi-Touch Campaign Execution

Trigger: HEDIS/gap cohort identified

1.Segment population by channel preference, language, engagement history
2.Design touch sequence across SMS/voice/mail
3.Execute personalized messages at each touch
4.Triage responses (engaged, pushback, health concern, opt-out)
5.Route each response to appropriate next action
6.Track engagement and closure rates
✓

Chronic disease gaps close at 2–3x the rate of static mailers, with care manager capacity preserved for complex cases.

Use Case 2

Behavioral Engagement Learning

Trigger: Campaign completion

1.Analyze response rates by segment and channel
2.Identify highest-engaging message templates and timing
3.Detect barriers surfacing across responses (transportation, cost, language)
4.Feed insights back to population health leadership
5.Refine future campaigns
✓

Outreach gets progressively more effective cohort by cohort.

Integrations

Patient Messaging (Twilio SMS, Voice AI)
EHR Problem List & Labs
Care Gap Registry
Scheduling System
Care Management Platform
Mail Fulfillment (at-home kits)

Industries

healthcare

See It In Action

A sample conversation with this agent

Launch the Q2 diabetes A1c outreach campaign.
Targeting 287 patients with A1c >9 or missing A1c in last 6 months. Segmenting by engagement history, preferred channel, language, and risk tier. Campaign: Touch 1 is personalized SMS with scheduling link and provider name; Touch 2 at day 7 is voice call (AI-generated) for non-responders; Touch 3 at day 14 is mailed reminder with FIT-style A1c at-home kit offer; Touch 4 at day 21 routes to care manager. You'll see engagement and booking rates in real time.
How do we handle patients who reply 'why are you calling me'?
Natural-language triage. For pushback or confusion, I respond with empathetic context ('You're a patient with Dr. Chen; we're reaching out because your last diabetes check was more than 6 months ago'), offer opt-out, and log the sentiment. Angry or distressed replies escalate to a human immediately—no bot should be alone in those conversations. Positive responses route to scheduling; health concerns route to care manager. Opt-outs are honored permanently.
Give me the campaign results so far.
Day 18 of campaign: 287 members contacted, 163 engaged (57%), 108 booked for A1c (38%), 41 completed A1c (14%), 12 escalated to care manager (complex cases), 9 opted out. Tier-1 high-risk members engaged at 72% — they responded better than average. Spanish-language segment engaged at 64% (above English average of 54%). Top barriers surfaced: transportation (26 patients), cost concerns (18 patients). Surfacing these to population health.