Within the life science industry, the success of clinical trials for a medical device or pharmaceutical product hinges upon a steady stream of communication between medical teams, patients, and other stakeholders.
These discussions and sentiments represent potential actionable data points. Furnished with HCP insights, clinical research organizers can fine-tune their processes, discover new geographic markets, strengthen relationships with key opinion leaders, avoid costly mistakes and delays, and carefully navigate the already expensive and laborious journey from conception to market.
But all of that information is immaterial if it’s not properly gathered, collated, and then analyzed.
For that, patient sentiment analysis is the bridge that closes the insight gap that so often separates a healthcare organization from its patient consumers.
What is sentiment analysis in healthcare?
Sentiment analysis is a method that leverages the powers of machine learning, artificial intelligence (AI), and natural language processing (NLP) to contextually mine source materials in order to better understand an individual’s or group’s sentiment regarding the clinical trial process or the product itself.
A subcategory of opinion mining, its primary function is to sift through the vast troves of data to identify and then classify texts according to their attitude and relevance.
Generally speaking, there are two primary principles for processing textual data:
- Semantic tagging – Identifies patterns and frequencies of word usage or phraseology, often for the purpose of identifying trends. So, in the case of a clinical trial, if a number of patients report that they dislike a certain aspect of the trial, that sentiment and the associated words will then be flagged by the system.
- Linguistic parsing – Attempts to decipher context to detect sentiment. For medical trials, this is useful in that it can help both identify subgroups according to sentiment and the factors that impact said feelings.
To perform this interpretation accurately, an insights management platform will typically first categorize messages according to the ambivalence of the text (positive, negative, or neutral).
Some examples might be: This medicine helped me feel better right away (Positive); I had an awful allergic reaction to this medicine (Negative); or, this medicine cleared up my acne, but it made my skin really dry (Neutral).
From there, researchers may drill down even further into the semantic analysis, ranking texts according to groups like:
- Feelings and emotions – Frustrated, enthusiastic, happy, or sad
- Intentions – motivated or uninterested
- Urgency – Urgent or inconsequential
Once categorized, this relevant information can then be extrapolated to produce general consensus and popular opinions of groups within the data sets. This provides healthcare and life science organizations with a real-time window into the patient’s perspective.
Why does sentiment analysis matter for healthcare and life science organizations?
Patient sentiment analysis helps organizations identify gaps in their patient experience. Sentiment analysis improves patient experience and drives action. What people think or feel about a certain topic can greatly influence their future behavior.
But therein lies the trick. Human communication transcends mere words. It’s subjective.
There are subtle undertones—the way something’s said, the pitch of voice, body language, etc. And then other linguistic nuances like sentence structure, sarcasm, humor, negation, and adverbial modifiers add yet another syntactical wrinkle capable of distorting the speaker’s original intent. Because of this, a positive sentiment could be interpreted as a negative sentiment and vice versa.
Even when two people speak the same language face-to-face, it’s all too easy for the desired sentiment to get lost in translation. When that happens, a communications and insights gap arises, which can be incredibly detrimental to the success of a pharmaceutical company’s pursuit.
And yet, that gap widens further still when the message is conveyed via text or social media. Without all of the rhetorical cues, accurately gleaning the meaning of something becomes a much more enigmatic task.
Multiply that issue across tens of thousands of text and voice-based communications between the various parties involved over a product’s life cycle. Surveys, questionnaires, interviews, focus groups, expert panels—the list goes on. Trying to sift through all of that information, using manual processes, over disparate systems, and then draw any type of meaningful conclusions from it is practically impossible.
However, in the healthcare industry with the help of sentiment analysis, a medical team can process huge troves of unstructured data, identify critical issues, and then establish consistent criteria to all of the datasets in order to maintain fidelity and improve the customer experience. Focusing on what is data integrity in pharma and healthcare ensures proper validation to create meaningful insights.
Types of sentiment analysis
How does sentiment analysis work?
It depends on the method you employ and what types of healthcare data support your intended outcomes. You have the flexibility to tailor your processes and categories to suit your particular needs. That said, typically, there are four primary types of sentiment analysis commonly used by a healthcare provider:
Graded sentiment analysis
The positive to negative opinion spectrum is sometimes referred to as a sentiment polarity. While some researchers will classify polarity according to a binary system—simply positive or negative—that typically fails to provide actionable intelligence. This issue is exacerbated by the fact that people tend to express extreme feelings over average ones.
For that reason, fine-graded sentiment analysis is a much more useful, albeit more challenging, task. This breaks down sentiment analysis into five gradations:
- Strong negative sentiment
- Weak negative sentiment
- Neutral sentiment
- Weak positive sentiment
- Strong positive sentiment
Aspect-based sentiment analysis
Human language is incredibly complex. When analyzing textual sentiment, simply understanding whether the expressed opinion is generally negative, positive, or some mixture thereof is not very helpful on its own. You must also be able to distinguish what particular issues people associate with those negative or positive sentiments.
For instance, in 2021, a life science research team from Mount Sinai Hospital collected more than 30,000 online customer reviews from 500 hospitals and then performed an aspect-based analysis, comparing the hospitals according to four aspect-based ratings:
- Doctors’ services
- Staffs’ services
- Hospital facilities
The result was a useful database that patients could easily peruse to compare and contrast their options. This also enabled hospital administrators to see how their hospital stacked up against competitors across the four major categories, identifying opportunities for patient care improvement.
Emotion detection goes into further depth than even a graded sentiment analysis might delve. It analyzes sentiments to try and understand the emotions of the original poster. This can result in more granular data, seeing as human feelings are integral to properly interpreting a patient consumer’s frame of mind.
Currently, there are four standard methods for emotion detection sentiment analysis:
- Keyword-based method – Emotional keywords are flagged in the input sentence and then a matching pattern is deployed to detect and extract those specific keywords.
- Lexicon-based method – Creates emotion lexicons that focus on selecting keywords according to the probability that they’re negative or positive.
- Machine learning-based method – There are two forms of the machine learning process, supervised or unsupervised. For the former, emotions would be categorized using a classification technique. And for the latter, a clustering technique.
- Hybrid method – Will utilize two or more of the techniques listed above to classify emotions in text.
Multilingual sentiment analysis
The vast majority of sentiment analysis addresses a single language. And that process on its own is challenging enough.
But for international life science companies conducting clinical trials with participants and experts around the world, text-based communications may be in several different languages. And every language has its own sentence structure, slang, and so on.
Multilingual sentiment analysis typically employs one language as a lexical resource to create the baseline. Then you can use techniques such as noise removal, normalization, and natural language analysis to create a sentiment lexicon for the other languages.
The importance of sentiment analysis for clinical trials
While there are dozens of practical uses for patient sentiment analysis within the context of clinical trials, one of the more prominent applications is to predict deleterious behaviors and outcomes.
Some of the major hurdles in running a successful medical trial come down to patient recruitment and retention. Both under-enrollment or low retention rates can cause significant and costly delays to clinical trial timelines.
By running sentiment analysis and machine learning, a clinical team can identify factors that are likely to lead to dropouts and then flag at-risk patients. Armed with this critical information, the team can act to ameliorate issues or meet their needs before the problem can get out of hand.
Similarly, as MD Group notes, this can also be used to ensure that patients remain compliant throughout the entire trial:
“It’s estimated that 40% of patients become non-compliant 150 days into a trial, for example, missing appointments. Monitoring and predicting patient sentiment can also identify where patients are likely to lose motivation and stop following the agreed protocol, or where they might be confused, and allow the investigative team to work more effectively and compassionately with them to ensure adherence.”
If you can avoid these issues, you not only prevent wasted money and lengthy delays, but you also ensure that the data integrity of the trial isn’t rendered obsolete due to inconsistencies.
Closing the insight gap
To achieve success, life science companies must take the steps necessary to make their online conversations and patient feedback as productive as possible. Then, and only then, are they able to close insight gaps wherever they may crop up during the product development lifecycle. Want to learn more about the insight gap and how to solve it? Get the three things life science teams need now to make better, faster decisions.