Turning the Healthcare and Pharma Contact Center Experience into One People Actually Like
No one enjoys the contact center experience. In fact, most people dread calling them, and healthcare and life sciences executives often struggle to manage the centers’ high costs and complexity. These costs contribute to the billions spent each year on healthcare administrative expenses.
This massive annual leakage, which stems from outdated systems and inefficient communication, directly impacts healthcare and life sciences organizations’ ability to deliver cost-effective, high-quality care, medicines and devices. For executives across the industry, modernizing how contact centers operate is not merely an operational upgrade; it’s a strategic imperative that unlocks significant opportunities to cut waste, empower AI agents and fundamentally transform the patient experience across the ecosystem.
The pressing demands
The demands placed on healthcare and life sciences contact centers are uniquely profound. For patients and providers, calls to a payer, pharma or medical device contact center are inherently driven by highly personalized, critical needs — for example, trying to understand a new wellness program, checking the status of a delayed claim, finding the right provider or receiving support for a complex treatment or device. These interactions are not transactional; they are deeply important moments in a patient’s health journey.
As the custodians of sensitive health profiles, medical histories, provider visits, treatment data and device usage reports, healthcare and life sciences organizations are uniquely positioned to deliver deeply personalized experiences. Yet the reality within many contact centers often falls short of these expectations.
Growing challenges require a smarter approach
Today, the industry is grappling with navigating rapid changes and high patient expectations, including:
Patients’ demands for simplicity and personalization: Members and patients now expect a retail-like experience: easy digital enrollment, clear cost estimates and proactive, helpful guidance either with a chatbot or representative. Traditional portals and call centers often fail to meet this expectation, leading to low satisfaction and contributing to high churn.
Managing utilization rate swings and reimbursement rate changes: Post-pandemic, payers are seeing a "return of utilization," where patients are finally seeking care (especially for high-cost surgical procedures) that was deferred, leading to sudden, large spikes in claims and uncertainty in financial forecasting. Simultaneously, providers, pharma and med device companies are navigating changing reimbursement rates, which have significant downstream impacts.
Legacy systems: Many healthcare and life sciences organizations operate on decades-old and fragmented IT systems that cannot enable the widespread data interoperability required for hyperpersonal experiences and for successful use of AI and agents.
Unpacking the data challenges for agents
Several common challenges prevent agents across the healthcare and life sciences ecosystem from delivering the patient-centric experience that is both expected and essential to ensure optimal health outcomes:
Delayed information: Agents often grapple with manually intensive and time-consuming processes to retrieve information from FAQs, benefit documents and other critical resources. This directly impacts agents' overall efficiency, decreasing member satisfaction.
Lack of personalized information: Siloed interactions across various channels and limited insights from prior engagements hinder agents’ ability to provide truly personalized service. Patients are often forced to repeat information, leading to frustration and an inefficient use of time.
Lack of contextualized information: Though enterprise data teams meticulously curate rich, customer-centric data products, this wealth of data frequently goes unused in healthcare and life sciences organizations during call interactions.
Lack of proactive information: Agents often lack proactive insights, such as predicted call intent, which would allow them to prepare for queries and offer more effective, anticipatory service.
With the escalating demand for patient personalization across healthcare and life sciences, it is no longer optional for organizations to modernize their contact centers. It is an urgent requirement for delivering superior experiences that lead to improved care outcomes and much-needed cost savings.
A two-step approach to AI implementation
Healthcare and life sciences organizations can drastically improve the patient experience at their contact centers through a clear two-step approach:
Define and enable essential data products: The foundation of a modern contact center lies in adopting a modern AI data platform that makes data of all types (structured, unstructured, multimodal) accessible and shareable. This involves identifying, structuring and making readily available the critical data products that empower AI solutions and agents with comprehensive patient insights.
Identify and enable best-fit AI/ML infusion possibilities: Once the data foundation is in place, the next step is to strategically integrate AI and machine learning. These technologies, powered by the newly defined data products, can transform AI agent workflows and customer interactions.
Unlocking the data and AI advantage
The pathway to modernizing contact centers offers a clear, compelling return: cutting billions in waste, significantly elevating customer experiences and ultimately improving health outcomes. For healthcare and life sciences organizations, this evolution is a strategic necessity to build a resilient, customer-centric future. By strategically leveraging the right data and AI tools, powered by a platform such as Snowflake, organizations can enable their teams to deliver proactive, personalized care, realizing this better future today.
To learn how Snowflake can help your organization modernize your data center, join us for the webinar Data and AI for Healthcare Contact Centers: Faster, Personalized Support.
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