Data Mining in Healthcare

Data mining is a powerful tool that can be used to improve healthcare in many ways. In this article, we will provide an overview of the different data mining methods used in healthcare, as well as examples of how data mining is being used to improve patient care and outcomes.

It’s impossible to overstate the importance of data since it is one of the most valuable assets for all businesses. The enormous amounts of data generated from the rise of the IoT have resulted in many data collection and processing challenges. However, data mining in healthcare has become more mainstream, despite the challenges of collecting and processing healthcare information.

Healthcare data exchange issues resulted in the broad adoption of RESTful API in healthcare and stimulated the creation of innovative IT solutions. In a previous article, we discussed the future of healthcare data management and FHIR APIs and how they transform the approach to data exchange and the healthcare industry in general. 

The healthcare industry requires advanced data collecting, storing, managing, and extracting tools to make sense of these huge volumes of healthcare data and data mining is one of the best tools for that. According to MarketsandMarkets’ data mining tools market report, it is expected to grow from $591.2 million in 2018 to $1,039.1 million by 2023.

In this article, we will discuss the use of data mining and try to find the solution for overcoming challenges of data mining in healthcare. 

What is data mining in healthcare?

Data mining in the healthcare industry refers to identifying patterns and trends in analyzed data to help the healthcare decision-making process and improve population health management. 

Data mining allows for interpreting data from large blocks of information and using the extracted data to enhance healthcare quality. There are many benefits of data mining in healthcare and several techniques that are particularly useful when mining healthcare data.

What are the top healthcare data mining methods?


Classification is a data mining technique used for constructing classification models to target categories or classes. This method compares the classes, defines differences, and applies other data mining algorithms to the classified data. 

The classification data mining technique can lead to improved outcomes. For example, this method can help analyze patient data, group patients by symptoms or other characteristics, and predict which patients risk facing certain medical conditions.


Clustering in data mining refers to grouping similar objects into clusters based on their attributes. Clustering can be used to group people by demographic characteristics or symptoms to develop a specific treatment plan for patients with similar features. It promotes early intervention and proactive implementation of preventive measures, for example, to minimize the impact of a pandemic.


The association is a technique used for discovering co-occurrences of items in a dataset. This technique in healthcare can help analyze large datasets of patient data to find patterns in onsets of diseases, outcomes, and other factors that affect patients’ health. 

For example, by analyzing the co-occurrences on EHRs, healthcare professionals can identify more effective drugs and procedures for certain conditions, thus providing more tailored healthcare services.

Outlier detection

Outlier detection can help healthcare providers and payers with medical errors and insurance fraud identification. This data mining technique refers to detecting and analyzing data points that are abnormally different from other data points in the dataset.

Outlier detection techniques can help with medical errors and suspicious behavior investigations. For instance, outlier detection can help detect providers that bill patients for procedures that were not performed or detect outliers in a new treatment study to determine if it was beneficial for a specific disease.

Benefits of data mining in healthcare

By harnessing the power of data mining, healthcare professionals can achieve greater precision, efficiency, and improved patient care. From tailored treatments to lower healthcare costs – data mining brings many benefits to clinicians, insurers, and patients. 

Personalized Treatment

Medical data mining empowers healthcare providers with essential insights into patient records and allows them to predict treatment responses. These insights facilitate personalized medicine and interventions based on a patient’s genetic makeup, lifestyle, and specific health conditions, consequently improving patient outcomes. 

Patient care improvement

The use of clinical data mining allows for potential health risk prediction. It refers to using advanced data science methods, such as machine learning algorithms, to support evidence-based decision-making by providing correct information to clinicians. Thanks to the deep analysis of patient data, clinicians can choose the best treatment and tailor it to a particular patient’s needs and conditions.   

Another significant benefit of healthcare data mining is early disease detection. The analyzed data detect ambiguous symptoms of complex diseases for early intervention and proper treatment. Medical data mining can help reduce healthcare costs by avoiding spending on unnecessary procedures.

Treatment accuracy

Ill-conceived drug and food interaction can lead to harmful consequences. Here we discussed regulatory compliance in healthcare, including compliance with Food and Drug Administration (FDA) regulations in the United States. Manufacturers of food, drug, and medical devices must pass tests and evaluations to sell their products.

Healthcare data mining can speed up the evaluation process by analyzing big chunks of data. For example, it can analyze food and drug data repositories for the chemical composition of a drug or an EHR’s data to avoid harmful combinations and define the best treatment scheme for patients who must take multiple medications.

Advanced insurance fraud and abuse detection

In one of our previous articles, we discussed why healthcare data security solutions are important. Preventive security measures, such as employee training, access control, and potential risk assessment, are as necessary as data protection regulatory compliance. Data mining allows for detecting fraud and abuse, such as fraudulent medical claims or inappropriate prescriptions. 

It also could help detect outliers (unusual values in databases that can distort results of analysis), identify relationships between different healthcare actors, assess them on the subject of fraudulent network participation, and identify providers that provide unnecessary services.

Operational Efficiency Enhancement

By identifying patterns in hospital admissions, resource utilization, and patient flow, data mining allows for streamlining administrative processes, resource allocation, and workflow management that lower administrative costs and better efficiency.

The Challenges of Data Mining in Healthcare

  • Data Quality and Integration: Integrating diverse datasets from various sources that adhere to their unique format and standards can negatively impact data quality, further complicating the analysis process. Moreover, the accurate analysis of complex healthcare data requires sophisticated techniques and tools are essential. 
  • Resource Limitations: Maintaining data mining can be challenging due to the soaring costs of healthcare and budget limitations. Adopting innovative solutions will help ensure valuable healthcare resources are being put to best use while accurately identifying trends and patterns in healthcare data.   
  • Privacy, Security, and Ethical Concerns: All stakeholders must ensure regulatory compliance in healthcare to protect sensitive data during exchange and analysis. Failure to adhere to regulations like HIPAA may result in monetary penalties. Moreover, balancing extracting valuable insights while respecting patient rights is an ongoing challenge that needs handling.

To get all the benefits of healthcare data mining, stakeholders should find the balance between fostering innovation, ensuring regulatory compliance, and the ethical use of patient data.


Here are the data mining in healthcare examples: 

Data mining application for healthcare fraud detection

A group of scientists performed the analysis to develop a new data mining model for fraud detection. The research involved the study of patterns of fraudulent behavior in 183 hospitals using a two-step clustering method.

The method divides data into clusters based on specific characteristics — the first step groups data according to its similarities. During the second step, auditors identify outliers within these clusters.


With the help of clustering according to the characteristics and behavior of hospitals when treating a specific disease, they identified six clusters of hospitals and 10 outliers among all research participants. 

The final stage of the study included a human-decision support system that helped auditors with cross-validating outliers and analyzing them on fraud-related variables. As a result of the cross-validation of outliers, one public hospital was justified, and two private healthcare providers were suspected of fraud.

A data mining approach to the prognosis of COVID-19

Given the potential fatality of COVID-19 and its rapid worldwide spread, researchers tried to find a new model of early diagnosis and prognosis using hospital data mining techniques.

This research involved datasets from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. The dataset included 16 laboratory tests and demographic information of patients with positive PCR test results hospitalized between February 19 and May 12, 2020.


During this research, 8,621 data instances were processed. Researchers developed new models based on seven data mining algorithms and compared these models on four selected laboratory tests. 

As a result, the two methods showed the highest accuracy, about 85%. This research predicted at-risk patients using data mining methods by analyzing lab tests and demographic features. 

As with data mining examples in healthcare, analysis of healthcare data is important for effective treatment, accurate billing, and disease prediction. Data mining applications in healthcare can reduce healthcare spending, create more tailored treatment plans, identify patients at risk of complex diseases, and improve outcomes. 

Clinical data mining is a complex process that requires specific tools for high-quality data analysis. A data warehouse can significantly improve data mining. It integrates data from different resources and stores them in one place for further analysis.

How Kodjin Can Help You to Improve Data Mining Quality

One of the best features of the  Kodjin FHIR server in the context of data mining in healthcare is its advanced validation functionality. A FHIR server validates retrieved data by FHIR profiles and defines the structure and format of data. The server stores healthcare data in a unified format, which helps to overcome the challenge of data integration and ensures the quality of healthcare data used for mining.  

The Kodjin FHIR server provides FHIR API functionality as a standard way for different systems to communicate with each other, ensuring smooth data exchange. You should also consider implementing the FHIR server interface as a solution for exchanging data in a standardized format. 

The FHIR server offers many more benefits compared to other databases for validating and mapping healthcare data. Non-FHIR databases may not have the functionality to retrieve and store data in a standardized way, which can make the data mining process less accurate and lower its efficiency.

An ONC-compliant FHIR server will solve the problem of healthcare data security threats since it meets the requirements of healthcare data privacy regulations, such as HIPAA, and ensures the highest level of sensitive data protection. The Kodjin FHIR Server is a robust base for identifying trends and patterns within complex healthcare datasets by ensuring data integrity and compliance with data privacy requirements.

For example, when partnering with Zoadigm, Edenlab used Kodjin to build an FHIR semantic layer analysis platform that empowers data analysts, clinicians, and payers to construct interactive patient cohorts, pathways, payment model analyses, and visualizations, gaining valuable insights into patient treatments and costs.We hope this article helped you learn more about the importance of data mining in healthcare. Feel free to contact us to discuss your healthcare IT project and discover more enterprise-level solutions based on the FHIR standard.

Post author

Andrii Krylov

Product Owner at Edenlab

More article about Blog about Healthcare Data

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