Conversational AI for Safety Event Detection in Healthcare

The FDA estimates that 90-99% of adverse drug events go unreported. This underreporting isn’t due to a lack of diligence. Rather, it reflects the overwhelming challenge organizations face. The challenge of manually tracking, reviewing, reporting, and auditing the volume of healthcare conversations.

Pharmaceutical manufacturers are required to monitor and report safety events to protect patient safety. This process—from detection and documentation to evaluation and follow-up—must optimize patient outcomes and meet regulatory standards. However, when safety events go uncaptured, they not only create compliance risk for manufacturers but, more critically, threaten patient safety and product efficacy.

Challenges of Reporting Safety Events

Daily interactions that weave between healthcare providers, patients, and pharmaceutical companies represent a key to strategy, action, and improved reporting. They are a goldmine of feedback and of potential risk indicators. But without the right tools, various safety events may go unreported.

The challenge lies in the manual analysis of patient conversations. Conversations are an unstructured data source that are hard to organize and aggregate at a large scale. The complexities and emotions of conversations make it difficult for humans to listen—nevertheless, to listen at scale. Paired with the sheer volume of these conversations flowing through large enterprise organizations, subtle indications of side effects or safety risks are often difficult to detect, resulting in missed safety events.

The gap between reported safety events and the actual number of events occurring can seem unknowable, especially if you are thinking about the entire team of analysts and patient safety professionals your organization has dedicated to this very issue. These teams work tirelessly to ensure positive patient outcomes and keeping attuned to non-compliance. It’s not so much an issue of oversight, but maintaining oversight across all sources of data and conversations happening around a specific treatment. Simply, the challenge is on navigating high volumes of data effectively and efficiently. There is a need for increased automation to help monitor and detect safety events.

When exploring AI solutions for any task, it’s crucial to select a solution purpose-built for achieving business objectives. Not every AI tool will work if it isn’t built to understand the nuances and complexities of healthcare conversations. So, AI that is developed and trained to have healthcare knowledge, navigate dynamic conversational context, and identify indicators of these safety events offers the opportunity to expand beyond traditional reporting in a way that is reliable and scalable. 

How AI Transforms Safety Event Detection

The traditional methods of monitoring and reporting safety events rely on agent consistency. Consistency requires time and is difficult to scale with headcount alone with hundreds of hours of call time being recorded daily. These agents deal with many complex conversations, which can create a lapse in analysis because of the labor-intensive approach on the contact center.

AI excels in analyzing large amounts of unstructured conversation data, allowing organizations to process and interpret feedback in near-real time to identify patterns and risks. AI can help provide benefits by:

·   Enhancing patient safety: Healthcare organizations can respond to safety concerns quickly to improve overall patient safety outcomes by aggregating recurrent topics and trends.

·   Reducing risk: AI minimizes the likelihood of missed incidents and ensures quicker intervention by enabling faster and more accurate detection of potential safety events across a high volume of data.

·   Focusing resources: Healthcare teams can concentrate their efforts on the most critical interactions to save time and reduce the manual workload burden by automatically identifying high-risk conversations with AI.

Consider this example, a life sciences company used AI to monitor compliance and analyze for quality across all of their patient services call centers (about 45 service lines). With AI, identifying potential risk, escalating critical errors, and getting the context behind each interaction became streamlined. Within the year, the company reduced compliance observations by half and increased call monitoring by 45%. Not only did AI help refine requirements, communication, and processes, but it also improved their quality scores.

The landscape around AI is shifting constantly. And this shifting makes the legal and ethical deployment of AI in healthcare seem even more daunting due to the rapid changes and regulatory momentum. Using AI for faster and more accurate detection keeps healthcare organizations ahead of risks and ensures timely responses that protect patients and regulatory compliance.

With the automation of detection and analysis of unstructured patient conversations, AI has the power to overcome the limitations of traditional, manually intensive, escalation methods. When healthcare leaders embrace these tools, they can ensure that patient safety will always remain the top priority.