Read Patients. Act Before Escalation.

Patient sentiment monitoring uses AI to read case comments, emails, and interaction history on a Health Cloud record, then determine whether the patient's overall tone is positive, negative, or neutral. GPTfy automates this analysis so care coordinators don't have to parse individual messages to gauge how a patient is feeling about their care journey.

When GPTfy detects negative or deteriorating sentiment across a patient's case interactions, it surfaces that signal directly on the Health Cloud record. Care teams can use this information to prioritize outreach, escalate cases, or schedule proactive check-ins before patient dissatisfaction compounds into a formal complaint or churn event.

Beyond a sentiment reading, GPTfy's analysis identifies the likely drivers behind a patient's emotional state — such as unresolved coverage questions, delays in care plan assignment, or miscommunication around benefit eligibility. It also suggests corrective actions the care team can take to address those drivers directly.

Yes. Before any case content is sent to an AI provider, GPTfy anonymizes personally identifiable information. Patient names, contact details, and clinical identifiers are masked within Salesforce before the sanitized text leaves the org, supporting your data protection and compliance obligations.

Care coordinators, member services teams, and case supervisors benefit most. Coordinators use sentiment signals to prioritize which patients to call first. Supervisors use them to identify systemic issues across a caseload — for example, if multiple patients show negative sentiment around the same coverage category.

GPTfy integrates with Salesforce Flow, so sentiment findings can be used to trigger automated tasks, case updates, or escalation notifications. When AI detects a patient trending negative, your org can automatically create a follow-up task, reassign the case, or send a care manager notification — all without manual intervention.