How to Use Machine Learning for the Healthcare Industry?

Jun 12, 2020 - 7 min read

Here at Kimola, we develop technologies for brands in many industries allowing them to know their target audience better, understand their motivations more profoundly, and get research results more quickly.

Among many sectors that we provide support in every step of data collection, data classification, and reaching insights is the healthcare industry. We make research in the industry more effective by using more in-depth categories in subject-based classifications, by associating the source-based classifications with subject-based ones, and by using machine learning in this process.

Let us examine how Kimola uses machine learning for the healthcare industry and where our Cognitive product differs from its counterparts.

We make source-based classification and analysis

Businesses operating in the healthcare sector have long been cooperating with research companies to improve their products and services. Many companies, however, focus on subject-based research whether or not they use machine learning. For instance, based on the data collected from social media or other sources, they can provide information on which data is about lung cancer, what aspects of lung cancer are frequently mentioned, etc.

However, especially in the healthcare sector, the source of the data is as important as its content. For example, the reliability of research depends on the identification of the source of a conversation about lung cancer (whether it is coming from the patients, the caregivers, or the physicians), and classification of the data accordingly.

As Kimola, we do source-based classification for the data we collect digitally with our Analytics product or the data provided by you in your healthcare industry researches. Say you have data on ulcerative colitis. Our method reveals that one of the most mentioned topics with this disease is "nutritional advice" and we also allow you to see how much of the data in the "nutritional advice" category comes from the physicians. We can reach an insight like this: “While many patients with ulcerative colitis need nutritional advice, the contents about the nutritional advice created by the physician remain relatively low.” This insight would allow the industry to realize that there’s a gap between supply and demand for nutritional advice and to develop strategies to fill it.

On the other hand, our ready-to-use models that use machine learning, such as the Patient Model in Cognitive product, quickly process high-volume data with high accuracy while classifying it based on sources such as patient and caregiver.

We use in-depth categorization when making subject-based classifications

Kimola does so much more than just classifying the data by a few topics. For instance, in a study on lung cancer, we not only gather data about this disease but also divide all data into main categories such as "Question," "Informative content," "Experience," "Comment," etc. Then we classify tens of thousands of lines of data quickly and practically using machine learning. If you need more information on machine learning and how it is used in qualitative research, please feel free to check those articles.

One of the essential features that distinguish Kimola from its peers in data classification and analysis stages is that we make in-depth classifications. We do not just classify the data according to the main categories mentioned above; we also do sub-classifications such as "Diagnosis / Treatment / Alternative Treatment Methods," etc. A healthcare analysis in Kimola consists of an average of 6 main and 30 sub-categories. With these categorizations, we reach priceless insights such as the most frequently asked questions by the patients, the main concerns of the patients, how the society perceives the disease, what people think about each type of treatment, so forth and so on.

In addition to these sub-categories, we also use the power of our Analytics product in your healthcare researches. Kimola Analytics collects social media posts of designated physicians anonymously and provides data about their lifestyle, demographics, interests, favorite TV shows, favorite news outlets, and so much more.

What kind of insights can we gain in the healthcare industry using machine learning?

Let's give some concrete examples of the classification and analysis we can provide for the healthcare industry thanks to our Analytics and Cognitive products:

When we classify our data by machine learning into categories such as "Disease," "Caregiver," and "demography," we can easily see the age group for the caregivers of the patients with a particular type of disease, and their affinity, so you can develop a strategy accordingly. Suppose that we have concluded that the care of children with Disease A is mostly carried out by their parents, while the older patients are looked after by their grandchildren. Thus, when you want to develop a strategy for the caregivers of the patients with a disease seen in older people, knowing that your target audience is young provides valuable insight about which digital channels to use to communicate with them or what language to choose.

On the other hand, when we classify our data into categories such as "Disease," "Patient," "Demography," and "Questions," we can learn what kind of experiences people with a particular disease have. For instance, let's assume that we want to find out the main concerns of Disease B patients. When we classify and analyze the data according to the categories we have mentioned, if we find out that male patients between the ages of 18-24 frequently ask questions about how the Disease B may affect them during their time in the army, we can propose a communication strategy such as providing more informative content on this issue.

As these two examples tell us when we use the proper categories together, we can not only understand your target audience correctly, but we can also notice precious hidden insights.

Kimola’s data collection, data classification, and analysis services allow you to gain the following insights:

  • Researches on Disease X show that 66% of people who expressed their opinions have a scientifically unproven belief about the ways of transmission for the disease. You should develop a strategy that focuses on providing people the correct information.
  • If a disease is not noticeable when looking at the patient, it's the patients who ask the questions about the condition. However, if the disease is noticeable, then the patient's connections, such as parents, cousins, and friends, are more interested in the treatment process. Therefore, when determining your target audience, you should consider which category that disease falls under.
  • While there is a considerable demand for information about the treatment of Disease X, informative content is scarce. People who request information mostly do so by leaving comments on the blog articles of private hospitals.
  • 80% of the complaints about Drug X are about how difficult it is to access the drug. Therefore, concerned associations might be encouraged to take the necessary steps.
  • 60% of the questions about Disease X comes from Disease Y and Z patients wondering if their existing diseases make them more likely to catch the Disease X. This means that a company that develops drugs or vaccines for Disease X should also have knowledge about Diseases Y and Z.

(The insights in the examples below have been selected only to give an idea and may not reflect the actual research results.)

Try Kimola Cognitive for Free!

If you would like to benefit from our years of experience in cooperation with biggest actors of the healthcare industry and learn more about the services we provide using machine learning, such as data collection, data classification, and data analysis, you can contact us here or request a meeting appointment via Calendly.

If you'd like to try Cognitive for free and see how its ready-to-use models, such as our Patient Model, which has an accuracy rate of 89%, allow you to classify your data practically, you can sign up here.

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