Make better business decisions by listening to your customersby
Amy Brown provides real-life examples of how AI-powered analysis of customer conversations can transform healthcare businesses into being more patient-centric.
Many customers contact their healthcare or insurance provider when they have a problem.
We’ve all been there, right? You have a question about your insurance claims statement, so you call the company, but the voice prompt has changed, and you can’t get to the person who can answer your question. Maybe you have time to stay on the line and figure it out. Maybe you give up, figuring you’ll try again later — or another day. Regardless, you didn’t get the answer you needed.
Enter the Eddy Effect™, which happens when an obstacle disrupts a customer’s expected or desired experience. And 60% of these disruptions are attributed to status check calls on claims and general treatment protocols from a healthcare enterprise. This phenomenon not only negatively impacts callers but the business as well because unresolved obstacles can impact business outcomes.
On average, the Eddy Effect has impacted 25% of patient interactions within healthcare organisations. Nearly half of these disruptions delay or prevent care, blocking patients from what they need. Meanwhile, healthcare organisations pay about $3.8 million in average annual costs to cover agent time and resources. Compounding, it costs $726,000 on average annually per organisation to resolve disruptions in the customer experience.
The business of healthcare is affecting patient care — and not in a good way. In fact, the majority of healthcare conversations focus on business administration (68%) not the care (32%). This disparity shows that patients can’t focus on the most essential aspect of their healthcare journey: taking care of their body and well-being. And businesses are trying to help patients navigate the system.
When they listen to customer conversations at scale, however, organisations can understand the prevalence and nature of obstacles and remove them while improving customer retention. Conversational data has the power to inform strategic business decisions that support both patient outcomes and the bottom line.
A valuable source of truth: The voice of the customer
The analysis of spoken words is rapidly becoming a critical source for understanding employee response and customer intent more deeply. By listening directly to customer voices, organisations surface insights that facilitate the development of strategies needed to deliver an ideal customer experience.
The analysis of spoken words is rapidly becoming a critical source for understanding employee response and customer intent more deeply.
Listening can help businesses highlight leading indicators of attrition, identify brand value statements, measure the percentage of waste and friction and predict sources of retention. And now we have the technology to make it happen. AI technologies empower organisations using these tools to activate conversational data in meaningful ways.
Listening at scale to the volumes of data generated by hundreds of thousands, if not millions, of customer conversations each month helps identify the root causes of pain points and the origination of common issues, such as:
- Claim denials.
- Disruptive claims processes.
- Medical coding errors.
- Missing or incorrect patient information.
It takes this listening-at-scale approach to surface, quantify, and tackle the root causes of disruptions. But one person — or even a dedicated team — cannot listen to, analyse and pull insights from such a volume of data. The right technology, however, can.
Capturing customers’ voices: Success stories
The voice of the customer is at the core of healthcare provision — and this direct feedback is critical in guiding healthcare leaders to make informed decisions. However, mining meaningful insights from the vast quantity of data is impossible without the help of AI and ML-driven technology.
Consider this example — one hospital system wanted to learn more about the root causes of patient friction in prescription inquiries, which were the top call drivers to the nurse triage team. The organisation’s goal? Decreasing these administrative calls’ overall volume would enable nurses to focus on triaging and managing patient care.
An AI-driven analysis of the Eddy Effect evaluated over 21,000 calls over 90 days. The analysis found a high rate of disruption (36% on average — 14% higher than the 22% industry benchmark), identifying and quantifying the challenges patient callers experienced when trying to fill their prescriptions:
- Inaccurate pharmacy information on file.
- Medication shortages.
- Other actions required from the provider’s office.
- Physician errors.
- Refill appointment timing.
The triage team shared the call montage with the system’s leaders and physicians at a monthly meeting and used the data to generate insights for sharing understanding, implementing process improvements and enhancing communications across the healthcare provider team.
As this case study shows, customer voices have the power to impact and guide business decisions. Leveraging this data enables healthcare companies to adapt to changes in the market, address obstacles patients encounter and identify training opportunities for agents.
Listening to those patients’ stories enables healthcare organisations to become more patient-centric in the delivery of care and service by uncovering what patients expect, how they feel about their experiences, where they encounter obstacles and more. When you use those insights to inform decisions that benefit the business and the patients, everyone benefits.
Using conversations to improve outcomes and business decisions
So what can organisations within the healthcare industry do to ensure they champion patient-centricity while also delivering positive business outcomes? They can listen to the voice of their customers and use those insights to infuse perspective into the decision-making process.
To build a proactive, customer-centric and quality-focused approach, organisations should:
- Establish scorecard benchmarks to quantify quality feedback configured to align with specific business KPIs and needs.
- Listen directly to customer conversations by using machine learning (ML) to organise unstructured data with categorisation, search capabilities and tagging — and creating sharable audio libraries.
- Sample individual agent calls to identify training needs, coaching opportunities and chances for managers to provide constructive and positive feedback or notes.
- Use recorded conversations for quality assurance for providing vendor feedback to improve consistency and share insights on nuances associated with specific program requirements.
- Leverage automation, AI and ML to expand well beyond the less than 1% of total volume commonly analysed by people to evaluate more calls, aggregate data insights, and gain a more holistic view of customer frustrations affecting the business.
As technology has evolved, it’s afforded us a fantastic opportunity to listen in a different way while preserving the human touch and providing a sense of empathy. The power of listening drives patient-centricity and positive patient outcomes.
Listening to the voice of customers offers a prime opportunity to provide data and insights that help inform strategy — and improve business outcomes — by identifying root causes, addressing primary sources of customer friction, improving customer retention, stopping unnecessary waste and much more.