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How can Machine Learning Benefit Healthcare Contact Centers?

How can Machine Learning Benefit Healthcare Contact Centers?

By Licia Wolf

Patient satisfaction is an important consideration for healthcare operations, and substantial resources are being invested to attain it. Improving customer experience with the contact center is an area that was previously overlooked but is now garnering more attention. To help with analysis of agent quality, healthcare contact centers can utilize sentiment analysis of voice and transcribed text between agents, patients, and customers. Examination of recorded words, phrases, and tone of voice can uncover positive, negative, or neutral sentiment. This type of speech and text analytics can significantly compliment other quality assessment tools for improving patient satisfaction, and today’s machine learning applications can enhance the ability to extract pertinent information.

Rule-Based Sentiment Analysis Verses Machine Learning

In traditional rule-based sentiment analysis, algorithms are created that count the number of positive and negative words in the text or voice recording. If there are more positive than negative words or phrases, the sentiment is deemed positive, and if more negative words or phrases are found, it is deemed negative. In this case, the analyst will miss potentially important details of the conversation that could help improve customer interactions in the contact center. Creating increasingly complex algorithms to discern more accurate results requires adding more rules with greater operator investment, and can quickly become costly.

Newer analytics methods that use machine learning can identify multiple sentiments to ensure that all valuable information is retrieved. This is achieved with a classification problem model in which a classifier is fed text or voice and returns a corresponding category (positive, negative, or neutral). This data is then utilized in a training and prediction process, and machine learning applies classification algorithms to analyze the sentiments more accurately.

Benefits of Real-Time Analysis

With machine learning your organization can also discern topics and categories of customer concerns in near-real time and reveal action items that can prevent further patient dissatisfaction. For example, if a catastrophic accident occurs in the area and multiple patients are admitted to the hospital, a high volume of calls might come in from family members searching for their loved ones. This could result in longer wait times and high abandonment rates, with an increase in negative feedback from patients and callers. Keyword and sentiment analysis would be unable to uncover that the underlying issue was the large accident causing a temporary spike in call volume. Machine learning would be able to connect the common issues from the callers and enable immediate preventive actions such as assigning additional agents to handle incoming calls or re-routing some calls to other departments or contact centers. With this system pro-active measures can be implemented to reduce response times and improve patient satisfaction.

Machine learning applied to speech and text analytics in healthcare provides a valuable and efficient tool for improving agent quality by elucidating relevant sentiments and phrases, as well as assessing issues in near-real time and enabling quick actions to remediate them.

HigherGround provides call recording and other data capture for healthcare contact centers and other applications. For questions about our capabilities email us at info@higherground.com.

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About the Author - Licia Wolf is the Marketing and Communications Manager at HigherGround. She holds a Ph.D., and a professional background in electronics, internet marketing, and print/imaging technology. Click here for more information on the rest of the HigherGround team.

HigherGround, Inc. provides best-in-class, reliable data capture and interaction storage solutions that enable clients to easily retrieve critical information. Our interaction recording and incident reconstruction solutions transform data into actionable intelligence, allowing optimization of operations, enhanced performance, and cost reduction.

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