Patient Engagement & Outreach

Chronic Disease Management Outreach Agent

ai

Identifies high-risk chronic disease patients, personalizes outreach campaigns, executes multi-touch voice/SMS sequences, and triages patient responses to close HEDIS/Stars gaps.

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.