Sentiment analysis, Patient experience & Healthcare analytics

In 2022 the global healthcare industry is estimated to be worth almost 12-trillion dollars. Yet at the center of this expansive domain sits “the patient”, who must experience the manner in which their illness is treated by a vast network of people, processes, and touchpoints, from their initial appointment to their final discharge. Over the last decade, there has been a major mind shift in healthcare from simple patient satisfaction to the more involved idea of the patient experience. This change is symptomatic of an industry trend toward a more patient-centered, patient centered healthcare delivery. Patient-focused medical treatment puts the patient at the top of healthcare’s hierarchy of values and at the center of its mission to achieve a higher degree of safety, higher quality care, and improved patient experience.

What is Patient Experience?

Healthcare strives to Improve the health of the population. It does this through improving health outcomes, investing in medical research, engaging frontline staff, and improving the patient experience. In doing so, the healthcare system attempts to achieve a more patient-centric approach to caregiving that can only be enhanced by listening more closely to patient feedback, stories, and perceptions.

If you have ever been ill or taken care of a person who is, you have first-hand knowledge of patient experience. You probably know it’s a journey from start to finish, filled with mixed emotions and various twists and turns at various phases. There are multiple simultaneous mentals, sensory and tactile impressions across the different touch points along the patient journey, starting with the diagnosis. These impressions are compounded within the patient to create a “patient experience”. Improving patient experience is essential to the success of evolving a more patient-centered or patient-focused healthcare system. Yet improvement in this area can only be achieved if its outputs, or data points, can be appropriately measured, managed, and analyzed. This is where text analytics and sentiment analysis have proven themselves essential.

Healthcare leaders and managers often refer to “patient satisfaction”, and mistakenly confuse it with patient experience. Satisfaction involves discovering whether a patient’s expectations about a health encounter were met. Patient satisfaction is very subjective and is predicated upon the divergent suppositions that patients use to measure their contentment with healthcare services delivery. Patient experience is a deeper, more complex phenomena than patient satisfaction. It includes a greater degree of variables if it is to be accurately measured and managed.Healthcare feedback creates a lot of unstructured text data that requires text analytics and sentiment analysis to identify, extract, and score relevant, high-value topics, themes, and entities. The purpose of adding sentiment analysis to your healthcare analytics mix is to pinpoint those areas of patient experience that require improvement and then target them strategically and operationally for effective tactics for change. The objectives of Px change should be to reduce suffering, decrease anxiety, remove friction, and provide a seamless, positive continuum of care. This is where text analytics and sentiment analysis are essential to establishing Patient Voice, the cornerstone of enhancing a person’s medical experience.

What is Voice of Patient?

Patient voice is the practice of collecting and examining patient feedback by the healthcare industry. Many organizations such as clinics, hospitals, and doctor’s offices take this data and analyze it to improve safety, quality of care, and patient experience. Patient opinions and feelings are captured using sources such as post-appointment surveys, in-clinic questionnaires, feedback web forms, and phone calls. Some wellness vendors have even started using social media listening and opinion mining to gather patient insights from Twitter and Google reviews to better gauge their progress in offering a more positive, holistic patient experience.

Healthcare providers understand the importance of patient feedback analysis and how it can enhance their experience of care efforts. Yet, a lot of organizations are relying on slow and imprecise methods to extract the data they need to better understand what their patients are trying to say. By measuring experience with new Patient Voice methods, communities such as hospitals and even health insurance companies can gain valuable insights into where in their organization they are doing a good job, and where they might need some work. In addition, they can also use analytics tools to unlock the “why” they are performing well or not. This patient insighting doesn’t have to be manually tedious. Automated, machine-learned text analytics tools can be used to extract relevant content, perform sentiment analysis on the feedback, and then classify the results according to topics, aspects, and/or entities.

Learn more about analyzing healthcare data.

Why is Patient Voice important?

Innovative healthcare organizations look to engage their patients in dialogue during various stages in the patient journey. They also use outreach to gather and listen to the stories their patients are telling about their care. This outreach can consist of marketing campaigns, text messages, emails, patient portals, and even mobile apps. Creating dialogue, and listening to patient voices, has proven to drive engagement, improve the experience of care, and deliver higher overall healthcare satisfaction.

Patients with a better experience during a particular encounter have been proven to have less healthcare utilization and fewer complications after that visit. Establishing patient voice, while measuring and tracking its progress, will assist your healthcare organization in better meeting the needs of your patients.

Unfortunately, one of the biggest challenges experienced by the healthcare industry is taking all this unstructured patient data and then quickly and efficiently, making it more analytical, tangible, and actionable. This is where Repustate’s sentiment analysis API can help.

Read more about the importance of Data Analytics in Healthcare.

How can Sentiment Analysis help you with Patient Voice?

Sentiment Analytics is the practice of using Natural Language Processing or NLP techniques to identify patient sentiments and opinions. By combining text analytics with machine learning, Repustate’s sentiment analysis API can help you to quickly and accurately establish a patient voice across strategically important topics and aspects in your healthcare experience delivery.

While patient voice surveys collect feedback from their answers to survey questions, sentiment analysis goes a step further. By gathering feedback from surveys, reviews, and social media, you can take what your patients are saying in their surveys and understand where improvements are needed by gauging whether their experiences have been positive, neutral, or negative. When you combine text analytics with patient voice, you’re gathering the emotional feedback of your patients.

Take a look at this example of sentiment analysis in healthcare.

Using patient feedback for improving patient experience

The power of a great feedback solution is well known in many industries. Couple the benefits of a patient voice tool with sentiment analytics, and you’ve now got access to insights that will make your healthcare organization more data-driven and confident in its strategic decisions to improve the patient experience.

In order to improve the patient experience, you must establish how to measure service improvement or decline. Sentiment analysis and text mining in healthcare can be understood as subsets of or complement to Big Healthcare data analytics that aim to measure, manage, and analyze large volumes of medical data, including patient experience. Using natural language processing and sentiment analytics tools enable healthcare professionals to make more effective and efficient strategic decisions required to make improvements throughout the patient journey.

Healthcare data sentiment analysis preview

Examples of insights gained from sentiment analysis can help you better understand how patients feel about various aspects of care delivery in a doctor’s office. For example, are they positive, neutral, or negative about things like:

Or, in a hospital setting, some of the insights extracted from data can be classified by the following aspects:

Here are four examples of sentiment analysis being applied to unstructured hospital reviews for Toronto’s General Hospital. Applying text analytics and sentiment analysis allows us to begin extracting and classifying vital insights and sentiment regarding the aspects of wait times, medical staff, and ER:

The doctor that I saw was great and very patient with me, but I’ve been waiting here since 2:00 pm and it’s now 9:00 pm and I’m still here. Unbelievable. These wait times are outrageous

Wait times are ridiculous, I waited 5 hours and didn’t see anyone.

I don’t normally write bad review here. But I feel that I have to let people know about this. The system of the emergency room is confusing. You have to go through a few rounds until you get to see the real doctor. That means the waiting time is forever.

Toronto General’s okay. The doctors were nice but don’t dig deep enough to find the root of the problem, rather cover up the problem with a bandaid. It’s still one of the better hospitals in Toronto, but compared to the world, it’s quite lacking in the ER department.

These types of insights can be visualized into an customer insights dashboard that facilitates the process of identifying patient experience areas of need.

Repustate’s Sentiment Analysis API can process and perform sentiment analysis on 1,000 documents/second and scales quickly and easily.

Discover More: NLP in Healthcare

GIVE YOUR PATIENTS A VOICE

Get fast, reliable, and accurate results every time, no matter what language your patients speak. Our intelligent solution understands and can effectively process information in over 23 different languages, including English, German, Spanish, and Arabic.