Real-time context awareness: Enhancing customer service with AI

Busy medical practices either succeed or fail by the quality of the conversations they hold every day — whether that’s a patient asking why a claim is still pending or a nurse clarifying dosage instructions before discharge. Yet most offices devote staggering amounts of human time to tasks the patient never sees. Canadian data shows that physicians lose 18.5 million hours each year to paperwork that adds no clinical value.

The gap between what patients need and what staff can realistically deliver is widening. In 2025, only 19% of US medical groups report using a chatbot or virtual assistant, leaving 81% of practices to field every call and communicate with patients the old-fashioned way. Real-time, context-aware AI won't solve every operational headache, but it can absorb the repetitive, high-volume interactions that keep physicians glued to screens instead of focusing on patients.

Instant, relevant support

Round-the-clock availability has shifted from a “nice-to-have” feature to a baseline expectation. Roughly two-thirds of consumers prefer to try self-service online before picking up the phone. Context-aware virtual agents recognize returning callers, surface the last appointment notes, and answer routine questions — no hold music required.

Behind the scenes, the same system routes edge-case queries straight to a live agent with the entire conversation history attached. In doing so, practices can cut abandoned calls and portal backlogs without adding headcount, while patients experience the 24/7 access they increasingly demand.

Personalized interactions

Generic reminders like “don’t forget your follow-up” rarely move the needle. Context-driven AI mines the patient’s EHR for diagnosis codes, recent vitals, and even sentiment-insecure messages to craft communications that feel human, such as “Your A1C looked great at last week's visit; let's keep the momentum by logging breakfast in the portal.”

Large language models further simplify care plans by translating specialist jargon into plain English, ensuring patients understand next steps before they leave the parking lot. That clarity reduces avoidable callbacks and improves adherence, a win for both outcomes and operating margins.

Reduced friction

Scheduling ranks among the most frustrating parts of the patient journey. Adding to this frustration, hidden context in cancellation policies or insurance prerequisites slows everyone down. According to Kyruus’s 2023 Benchmark study, 61% of patients report that they would switch providers for easier online scheduling.

AI schedulers ingest payer rules and physician preferences in real time, allowing patients to see open slots that actually fit their needs. For practices, front-desk staff reclaim hours once spent shuffling calendars and playing phone tag, while no-show rates fall thanks to automated, context-aware reminders that adjust tone and channel to each patient's history.

Proactive problem-solving

Context awareness isn't only reactive. AI engines can monitor postoperative notes, wearable feeds, and prescription refill gaps to spot red flags before they escalate into a frantic after-hours call. For example, an early alert of a patient's home BP readings trending upward for three days lets a nurse intervene with coaching rather than an ER referral.

Market forecasts anticipate that conversational AI in health care will quintuple to nearly $49 billion by 2030, primarily driven by proactive population health features like these. That translates into fewer acute escalations, lower readmissions, and steadier cycles for practices.

Smarter agent assistance

Even the most advanced chatbot hands off complex issues. What matters is the quality of that hand-off. Modern AI agent copilot tools capture prior interactions, suggest evidence-based responses, and pre-populate chart fields, allowing clinicians to focus on empathy rather than typing.

Ambient AI scribes take the concept further, converting exam-room dialogue into structured notes. Kaiser Permanente’s Northern California rollout of AI scribes saved clinicians the equivalent of 1,794 workdays in a single year and measurably improved visit satisfaction. Less time documenting means more eye contact, and that human connection is often what patients remember long after the visit ends.

Implementation guardrails

Innovation never absolves oversight. Data flowing through context-aware systems almost always qualifies as protected health information, so HIPAA’s Security Rule still governs access, storage, and audit trails. Encryption at rest and in transit, role-based permissions, and rigorous vendor due diligence remain table stakes.

Equally important is patient comfort. Transparent disclosures about when an AI assistant — not a human — handles a request build trust, as does offering an easy path to escalate to staff.

Finally, bias audits should be baked into any deployment plan. Algorithms that learn from skewed datasets can exacerbate disparities unless they are routinely checked.

Guardrails aren’t bureaucracy for its own sake. They’re the foundation that lets practices scale AI safely. By pairing robust privacy controls with continuous model monitoring, leaders can achieve efficiency gains without compromising the covenant of confidentiality that defines the medical profession.

Context-aware AI will not erase the art of medicine, but it can give physicians and their teams the gift of time to educate, reassure, and heal. Practices that align technology with empathy will find that customer service becomes more than a departmental metric; it becomes a competitive advantage that patients can feel in every conversation.

Leaders ready to pilot real-time context tools don’t need a rip-and-replace budget. They need a clear use case, a multidisciplinary team, and a tight feedback loop. Start where the friction is loudest — after-hours triage, claims follow-up, refill reminders — and expand only after staff and patients can point to concrete relief. By treating AI rollouts as living quality improvement projects rather than one-off software installations, practices can keep ethics, economics, and patient trust moving in the same direction.

Dev is the CEO/Founder at QueryPal. He was previously on the founding team at GLMX, one of the largest electronic securities trading platforms in the money markets, with over $3 trillion in daily balances. He was also CTO/Founder at Wavefront (acquired by VMware) and a Senior Engineer at Google, where he helped develop the back-end for all financial processing of Google ad revenue. He previously served as the Manager of Business Operations Strategy at PayPal, where he defined requirements and helped select the financial vendors for tens of billions of dollars in annual transactions. He also launched eBay's private-label credit line in association with GE Financial. Dev received a dual-degree B.S. in Mathematics and B.A. in Psychology from Stanford. In conjunction with research teams at Stanford and UCSF, he has published six academic papers in medical informatics and mathematical biology. Dev has been featured in American Banker, Marketwatch, Benzinga, and many more!