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How to Use Text Analysis for Healthcare Reputation Management?

By May 5, 2021 No Comments
healthcare reputation management with text analysis

The healthcare industry is widely regarded as a place to improve one’s health. Patients, on the other hand, seek hospital and physician reviews in order to find the best hospital. In fact, 9 out of 10 patients read online reviews, and some even choose a hospital or physician beyond their insurance coverage for the best care possible.

While hospitals are health-enhancing institutions, they are also businesses whose first priority is to get more customers (patients). Online reviews now play a prominent role in decision-making and are a highly regarded source for information by consumers. Feedback from other users can provide information about the quality of services, facilities, physicians, and more. As a business, you can also use the feedback data to identify the shortcomings in your services. Although, manually analyzing all the reviews posted by customers on various platforms is just not possible.

However, the recent advances in text analysis have made it possible to analyze large volumes of unstructured text data. This piece explores how text analysis solutions can assist healthcare businesses in online reputation management.

Introduction

Hospitals record a ton of patient data such as vital signs, procedures, medications, test reports, diseases, immunity, family history, demographics, and more. Healthcare databases are growing exponentially and it doesn’t seem as if that would change anytime soon. While there is a lot of patient data available, the size and nature of the information make it difficult to interpret. In fact, you may even have a hard time finding the data you require.

Although advances in the data storage and management systems have made it possible to leverage this data to enhance the overall experience for the patients by reducing the margin for errors and increasing efficiency. Machine learning and NLP (natural language processing) based text analysis solutions can simplify analyzing extensive volumes of unstructured textual data, which can significantly reduce the required time for examining patients as well as the margin for error.

Text analysis solution can also be applied to analyze the reviews and suggestions of users that can guide you towards areas of focused improvement, thus enhancing the overall experience for the patient along with your institution’s reputation.

Healthcare Industry: Reputation Management with Text Analysis

When many people see a hospital, they see it as a health-enhancing institution. However, hospitals are also a business with specialized marketing teams and customers. And like every other business, getting patients in through the door is a must. To get more business or patients, healthcare institutions have to be trustworthy, reliable, and welcoming. All of these attributes directly co-relate with the reputation of your institution in the industry. The more reputable and trustworthy a business, the more customers they have walking in through the door.

To be honest, nobody likes to visit a doctor, I have yet to see a person dancing with joy for a chance to see a doctor. But when they need a doctor, they will probably check the reviews and opinions from patients for both the medical institution as well as the physicians. The more positive feedback, the more the chances of getting patients in the door. Over 48% of patients choose doctors with favorable reviews even if they are out of their insurance network. Another study states that over 93% of people in the US check reviews of hospitals as well as physicians to get the best care possible. Hospitals and physicians with 3 or more, star ratings, are more preferred by patients. 62% of patients look for online reviews if they want to consult another doctor. 73% of patients trust medical institutions with more positive reviews. According to the Harvard Business Review, a 1-star increase in overall Yelp ratings can increase business by approximately 10%.

While hospitals are known for saving lives, they are still not an exception to online business reputation management. Like any other business, hospitals must consistently monitor and analyze the conversations related to their institutions as well as physicians for all locations. It will help them analyze what users are saying about their medical institution and identify areas that need to be improved. You can also identify positive reviews of customers that can be leveraged for marketing purposes.

The Impact of Positive and Negative Reviews in Healthcare

analyze customer feedback with text analysis

The first place that users visit for information on hospitals and doctors is the internet and reviews play a major role in deciding the hospital or physician for treatment.

Negative Reviews

  • Negative reviews can impact your brand’s online visibility. Search engines like Google and Bing often consider reviews to benchmark businesses. Any business that has a lot of negative reviews will be moved to the bottom tier of the search results. And as the saying goes, any brand that does not rank on the 1st page of search engines is as good as dead. Over 90% of internet users do not go past the 1st page of search results.
  • Negatively reviews if gone unrecognized can damage your brand reputation. The negative reviews of patients with unsatisfied user experiences can impact the choices of other patients looking for a reliable medical institution or physician for treatment.
  • Once you have been pushed to the bottom tier of the search results and your reputation damaged, it’s difficult to regain the trust of customers.

Positive Reviews

  • Positive search engines can boost your search visibility and push you to the top of organic search results. This can tremendously increase your exposure and help attract more patients.
  • Positive reviews can also be leveraged to enhance your brand’s social media reputation. The more positive reviews you have, the more chances your medical institution being preferred as the optimal choice for treatment.

The Positive Side of Negative Feedback 

Negative reviews can damage your brand reputation and decrease conversions, but there are also some benefits. Negative reviews can shed light on the shortcomings in your services and guide you towards what needs to be improved. Often, time customer reviews also provide suggestions to enhance the end-user experience.

How to analyze your hospital’s reputation?

Online reputation management involves scraping reviews and feedback from all over the internet.

Customer Feedback Data collection

The compiled data mainly consists of feedback data from the following sources:

  • Online Reviews and industry-specific review websites
  • Business Listings
  • Customers Surveys
  • Forums
  • Social Media

Text analysis solutions then process the data and remove all the noise or unwanted information from the unstructured text.

Unstructured Review/Feedback Data Processing:

The textual data processing involves:

text analysis unstructured text processing

Text Cleanup: This step involves removing all unwanted information such as ads, unwanted characters, or standardizing data converted from binary sources.

Tokenization: The next process involves splitting text into white spaces. It breaks down the text into words or sentences, also known as tokens.

Part of Speech Tagging: Part of speech tagging involves tagging every word from the textual data with the right part of speech.

Customer Feedback Data Analysis Models

These are the most commonly used text analysis models for analyzing extensive volumes of customer feedback data.

Topic Labeling

topic labeling with text analysis

Topic labeling is a text analysis model that is often used to analyze customer feedback data. This model can classify unstructured feedback data into labels. Each label has a set of pre-defined keywords, the analysis model detects the presence of these keywords from textual data and analyzes the theme of the text. The textual data is then categorized as per these pre-defined labels.

This can help you identify the key issues discussed by the patients, similarly, you can also identify the reasons and practices that draw positive reviews from customers.

Sentiment Analysis

text analysis to analyze customer feedback sentiment

Sentiment Analysis is a machine learning technique that can classify reviews based on the type of sentiments expressed by the author in any given text. It can be used to classify reviews into three categories.

  • Positive
  • Neutral
  • Negative

It can help you access the market reputation of your brand. By analyzing the scale of positive, negative, and neutral reviews, you can easily evaluate where you stand in terms of a reputed and reliable healthcare institution.

Emotion Analysis

analyze emotions with text analysis

Emotion Analysis is a machine learning and NLP technique that can help you analyze the complex emotions expressed in the text. Using this you can classify the reviews posted by users based on the type of emotion expressed in the textual data.

The BytesView emotional analysis classifies text as per the following emotions,

  • Happy
  • Sad
  • Anger
  • Fear
  • Love
  • Neutral

This can easily help businesses analyze which customers are satisfied with your services as well as the ones that need to be retained. This can also help you evaluate the lifetime value of your customers. The most satisfied patients will most probably seek your services again in need and vice-versa.

Other Applications of Text Analysis

Physicians Notes and EHR Data 

analyze unstructured text from physicians notes with text analysis

Doctors and physicians often record a lot of patient details while examining them. But all of this data is locked away in physician’s notes, the sheer volume of this data makes it unusable as it would require a heck lot of time to interpret and make sense of. Similarly, EHR’s also contains a lot of patient details which can be useful in treating patients.

This data would go unutilized due to the lack of efficient solutions to interpret and analyze it. Text analysis solutions have enabled medical institutions to leverage this data and to further enhance their services.

Example:  The Union General Hospital uses text analysis to analyze patient histories and physicians’ notes which helps them draw insights, thus improving patient care and reducing the chances of readmission.

Treatment Accuracy

enhance treatment accuracy with text analysis

As discussed earlier, the healthcare industry generates a lot of data. If we just talk about treating a patient, a physician often has to look into their history, previous treatment, medications, family history, immunization, allergies, and much more before suggesting a treatment. This can require a lot of time if done manually.

Text analysis solutions can help you extract key information from large volumes of data with the help of specific filters, thus significantly reducing the size of the data that needs to be analyzed manually. This can decrease the time required to examine a patient as well as minimize risk as you have more information to work with.

Example: G-Med is the largest online medical community with over 1.5 million verified physicians. These physicians were on the front lines, working across the border with various healthcare systems to research and test effective treatments to fight COVID-19. The community often shared findings to speed research, but the scale of the pandemic soon made them realized the need to centralize structured and unstructured data to speed up research. Text analysis helped them utilize all kinds of data to improve decision-making and outcomes.

Recruitment

speed up recruitment process with text analysis

Hiring talented healthcare workers can be a tedious job. You have to go through numerous resumes to find the right individuals for the job. Text analysis solutions can help you sort and filter through numerous resumes with ease to find the most qualified candidates. You no longer have to spend hours evaluating resumes.

Example: Hiring talented healthcare workers in Australia was difficult even before the pandemic. LiveHire, a talent engagement platform, started using text analysis to streamline and speed up the process of hiring talented healthcare workers.

Benefits of Using Text Analysis

Now that you know what text analysis is and how it works, let’s look at some of the benefits of using text analysis for healthcare reputation management.

Analyze market reputation

As I said earlier, text analysis models can help you analyze the opinions of patients from the reviews they post all over the internet. The positive and negative aspects of the reviews as well as the size of each type of review will help you evaluate the perception of your brand in the eyes of the patients.

You can also analyze the reviews of each hospital location or clinic along with the reviews of each physician. This can help you analyze the quality of your services by examining what patients say about them.

Find areas of improvement

While negative reviews are bad for your services, they do benefit you in one way. Negative reviews help you identify the key issues or problems faced by the patients, that diminish the overall user experience. Consistently monitoring and analyzing your reviews at regular time intervals can help you resolve issues experienced by the patients.

Resolving such issues as soon as possible can assist you in making sure that future customers don’t face the same issues. It also helps you ensure that your search engine rankings no longer due to bad reviews.

Attract more patients with favorable reviews

Today, 9 out of 10 patients check online reviews of hospitals as well as physicians before consulting them. The reviews posted by other users play a huge role in deciding which hospital or doctor to choose. The more positive reviews a medical institution and doctors have, the more patients are likely to choose it. Positive reviews are also a factor that boosts search engine rankings. So, the hospital with the most positive reviews will be given priority in Google or Bing’s search results, which helps them attract more customers.

Identify negative reviews to improve customer retention

Negative reviews can destroy any brand’s reputation. But in the case of healthcare, negative reviews question the ability of the hospital as well as the physicians. This can destroy any medical institution’s reputation. So, identify negative reviews and resolving them quickly should be the top priority of every hospital. Sometimes, even engaging and recognizing the issue can be beneficial too. You don’t have to provide an immediate solution, but you engaging or opening a dialog with the patient is a must.

Enhance patient experience

Consistently monitoring key issues can help you identify recurring issues and help take immediate measures to elevate the patient experience. You can easily identify the services that are just not up to the mark as well as the services, facilities, and practices most appreciated by the patients. You can easily evaluate the effectiveness and efficiency in keeping the patients satisfied.

Conclusion

The quality and quantity of EMRs and other healthcare records have improved drastically in the recent decade. Text analysis solutions can help you leverage that data to enhance your services, facilities, treatment, research, or reputation. Many pharmaceutical companies have already integrated text analysis to speed up research testing of new products.

Many hospitals are also trying to centralize all medical data so it can be accessed as and when needed. BytesView is an amazing text analysis that can compile and analyze text data with ease. The various text analysis models can extract various kinds of information and help speed up decision-making while minimizing risk.

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How to Use Text Analysis for Healthcare Reputation Management?
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How to Use Text Analysis for Healthcare Reputation Management?
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The article describes how to use text analysis for healthcare reputation management
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BytesView.com
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