Applications of data analytics in healthcare.

An overview of Data Mining in Healthcare

Data mining in healthcare has great potential to play a significant role in the healthcare industry. This is mainly due to the fact that electronic health records of patients are increasingly getting popular among healthcare providers.

Doctors and physicians usually work with patients’ health data recorded in paper-based forms. With improved access to a large volume of patient data, it has become a big challenge for healthcare providers to shift to an efficient computerized data management system which would analyze and transform this mass of data into useful information most accurately and efficiently. This is where the use of healthcare data analytics has come to play a significant role. Healthcare data mining has the capability to discover hidden patterns or relationships among the objects in medical data.

A key first step in this process is data selection and acquisition. Here, specific healthcare datasets and relevant parameters are carefully identified from the vast array of medical information available—often from sources such as electronic health records (EHRs), electronic medical records (EMRs), aggregated documents, or even specialized systems like Cardiology EHR or Mental Health EMR. By targeting the right data sources, healthcare organizations can ensure that their analysis draws from accurate, comprehensive, and meaningful data sets.

This foundation is crucial, as it allows data mining techniques to work effectively—uncovering trends, predicting disease outbreaks, improving patient outcomes, and supporting evidence-based medical decisions.

Here in this post I have given an overview of applications of data analytics in healthcare in major areas and also highlighted the limitations of data mining in healthcare. But before that let’s first explain what is data mining healthcare and how it works.

What is healthcare data mining?

The data mining process is defined as the process of extracting useful information from the patterns of a large volume of stored data sets and using that information to build predictive models. The data mining process uses algorithms drawn from the field of statistics, machine learning and database management systems. The foundation of data mining encompasses three intertwined scientific disciplines – statistics, artificial intelligence (AI) and machine learning (ML).

Beyond uncovering hidden patterns in massive healthcare datasets, data mining offers practical benefits that are shaping the future of healthcare management. By leveraging these advanced techniques, healthcare providers can not only extract actionable insights for clinical decision-making but also cut operational costs and drive innovation within their organizations. As the fields of AI and data mining continue to evolve, opportunities are expanding for healthcare organizations to use data-driven methods to improve patient outcomes, enhance the quality of customer service, and achieve significant cost savings.

The most basic definition of healthcare data mining is – extracting useful information from the patterns of a large volume of stored healthcare data sets and using that information to build predictive models. Simply put, the goals of data mining in healthcare are – prediction, modelling, and inference.

Stages of the Clinical Data Mining Process

Now, let’s break down the typical workflow when it comes to clinical data mining in healthcare. Whether you’re dealing with hospital EHRs, specialized clinical databases, or a sprawling network of healthcare information systems, the path to meaningful insights generally follows a set of core stages:

• Data Selection and Collection: The process begins by pinpointing the relevant datasets needed for analysis. This could mean anything from comprehensive electronic health records (EHRs), aggregated administrative reports, lab results, to condition-specific databases like oncology registries.
• Data Cleaning and Preparation: Once the raw data is gathered, it must be formatted and “cleaned” to weed out inconsistencies, correct errors, and ensure compatibility. Think of this step as prepping your ingredients before cooking—messy, inconsistent data leads to unreliable outcomes.
• Analytical Processing: Here, the magic of data mining truly unfolds. By applying a mix of statistical methods, machine learning algorithms, or AI-driven tools, healthcare data is examined for patterns, correlations, and emerging trends. The specific technique depends on the goals at hand, whether predictive modeling or uncovering hidden associations.
• Interpretation and Insight Generation: Finally, the results of this analytical process are interpreted to draw out actionable insights. These findings can then inform clinical strategies, policy decisions, or even spur innovation in patient care protocols.

It’s worth noting that many of these steps can now be automated with the help of specialized data analytics software, allowing health data analysts to focus more on extracting value and less on manual data wrangling.

Steps Involved in Preparing Healthcare Data for Mining and Analysis

Before healthcare data can be analyzed effectively, it must go through several crucial preparation stages to ensure accuracy and usefulness. Here’s a look at the essential steps typically involved in getting healthcare data ready for mining and analysis:

• Collecting data from diverse sources: Patient information is often scattered across multiple systems such as hospital databases, diagnostic labs, electronic health records, and sometimes even wearable devices. The first step is to gather all relevant data into one centralized location, enabling a more comprehensive view.
• Data cleaning and standardization: Healthcare data is notorious for inconsistencies—errors in patient records, missing values, different measurement units, and various formats are commonplace. The cleaning process involves correcting errors, filling in missing data where possible, and ensuring that all datasets follow a consistent structure.
• Structuring and integrating data: Once data is cleaned, it must be organized into structured formats, such as relational databases or data warehouses. Integration ensures that different data sources can be linked and accessed cohesively, breaking down silos between departments or institutions.
• Selecting relevant variables: Not every piece of collected data contributes to every analysis. For example, predicting hospital readmissions may require demographic information, medical history, test results, and treatment records. This step involves selecting and transforming only the relevant variables needed for a particular study or predictive model.
• Employing data mining algorithms: After the data is prepared and organized, advanced algorithms—often originating from the fields of machine learning and statistics—are applied to detect patterns, trends, and potential risk factors within the dataset.
• Presenting actionable results: The outcomes of the analysis must be translated into practical formats, such as visual dashboards, alerts for clinicians, or summarized reports, so decision-makers and care providers can take informed actions quickly.

By following these preparation steps, healthcare organizations can transform raw, unstructured data into actionable insights, ultimately supporting better patient care, streamlined operations, and more effective use of resources.

How does association or relationship analysis work in medical data mining?

Association or relationship analysis in medical data mining centers on discovering meaningful connections between various events, conditions, or attributes within large sets of healthcare data. This method actively searches for patterns that frequently occur together—such as particular symptoms routinely correlated with certain treatments or medications.

For instance, imagine a scenario where patient records reveal that individuals experiencing specific allergy symptoms often receive the same type of medication each spring. By identifying this recurring pattern, healthcare providers and pharmacists are better equipped to anticipate medication demand and optimize inventory accordingly.

These insights allow healthcare organizations to not only tailor patient care and improve outcomes but also enhance operational efficiency by predicting trends and supporting informed decision-making throughout the year.

How is data preprocessing and transformation performed in healthcare data mining?

Data preprocessing and transformation are essential steps before meaningful data analysis can take place. In healthcare, this means taking raw patient data—which can be messy, inconsistent, and spread across multiple sources—and preparing it for analysis by following well-established procedures.

This process typically involves:

• Cleaning the data: Removing duplicate or incorrect entries, fixing typing errors, and standardizing the formats of data points such as dates or measurement units.
• Handling missing information: Filling in missing values using statistical methods, or in some cases, omitting certain records if the missing data can’t be reliably recovered.
• Normalizing values: Converting numerical values onto a common scale to ensure that measurements (like blood pressure or cholesterol levels) are comparable, regardless of units or devices used at different clinics or labs.
• Structuring the data: Reorganizing information so that all data records follow a consistent structure, making it easier for machine learning algorithms or statistical models to work effectively.

By carefully preprocessing and transforming healthcare data, we improve both the quality and reliability of any insights discovered during subsequent analysis. This, in turn, paves the way for deeper descriptive and predictive analytics that inform better clinical decision-making and resource management.

What is healthcare data analytics?

The processes of healthcare data analytics are categorized into descriptive and predictive. The descriptive analysis mines a huge amount of data generated by healthcare transactions and provides the latest information on past and recent events. On the other hand, predictive analysis plays a significant role in the prediction and diagnosis of diseases and in improved decision-making, by discovering patterns and trends from this large healthcare database. Since processing and analysis of such a large collection of healthcare data are too complex and voluminous by traditional methods, advanced data mining techniques are used today which analyze and transform this mass of data into useful information most accurately and efficiently. The application of data mining algorithms plays a significant role in these advanced techniques in the prediction and diagnosis of diseases and in decision-making.

A key advantage of leveraging these analytics is the enhancement of clinical decision-making. By analyzing large patient datasets, healthcare providers can discern patterns and associations among multiple health factors—enabling them to predict potential health risks and make more accurate clinical decisions. This, in turn, ensures greater precision in patient care. Additionally, the ability to analyze vast amounts of data leads to increased diagnosis accuracy. Predictive models built with data mining techniques help doctors identify diseases more quickly and accurately, potentially saving lives by facilitating timely interventions.

Ultimately, healthcare data analytics not only streamlines the process of extracting meaningful information from complex datasets but also directly supports improved diagnostic accuracy and more informed clinical decisions, paving the way for better patient outcomes.

Integrating Data Mining Tools with EHR and EMR Systems

For healthcare organizations aiming to harness the power of predictive analytics, integrating data mining tools with Electronic Health Record (EHR) or Electronic Medical Record (EMR) systems is an essential next step. By employing advanced machine learning algorithms—such as neural networks that can identify both common and previously unnoticed patterns—these tools can process the immense volume of health data generated every day.

The integration process typically involves several key stages:

• Data Selection and Aggregation: First, relevant health data is identified from sources like EHR/EMR databases, departmental systems (for example, cardiology or behavioral health records), and other clinical documentation. Tools from providers such as Epic, Cerner, or even cloud-based solutions like Microsoft Azure or Google Cloud Healthcare APIs can facilitate seamless data acquisition.
• Data Cleaning and Preparation: Before analysis can begin, the selected health records are standardized and cleansed to ensure accuracy. This includes normalizing formats, removing duplicate entries, and validating fields to create a high-quality dataset—which is critical for building reliable predictive models.
• Implementation of Data Mining Algorithms: With a clean dataset, predictive analytics engines can be layered on top of the EHR or EMR, employing techniques like clustering, pattern recognition, and statistical modeling to uncover hidden relationships and forecast outcomes. Integration might involve third-party analytics platforms that support interoperability standards such as HL7 or FHIR, enabling data to flow securely between systems.
• Results Interpretation and Clinical Application: Once the algorithms identify significant trends—such as forecasting blood glucose level fluctuations or detecting risk factors for chronic conditions—actionable insights can be presented directly in the provider’s workflow to support proactive decision-making, timely interventions, and personalized patient care.

Many EHR/EMR systems today are designed with open APIs and support for interoperability, making it straightforward to connect with sophisticated data mining and forecasting tools. Solutions like IBM Watson Health, SAS Health Analytics, and Google Health Data Engine provide the necessary flexibility to run predictive analytics, automate routine analysis steps, and scale as data volumes grow.

By carefully integrating these technologies, healthcare organizations can transform raw clinical data into meaningful predictions, supporting everything from early diagnosis to population health management—all while maintaining compliance with interoperability and privacy standards.

Why opt for a custom healthcare data mining solution?

Off-the-shelf data mining tools can only go so far when it comes to addressing the intricate requirements unique to each healthcare organization. Healthcare providers often handle a mixture of legacy systems, compliance obligations (think HIPAA in the U.S.), and tailor-made clinical workflows—none of which fit neatly into one-size-fits-all platforms from big names like Microsoft, IBM, or Oracle. Developing a custom solution allows healthcare organizations to:

• Seamlessly integrate with existing EHR or EMR platforms,
• Address strict data privacy and regulatory requirements,
• Incorporate specialized algorithms for disease prediction, patient stratification, or operational forecasting.

A bespoke approach means your data mining solution can be fine-tuned to the nuances of your patient population, research objectives, or reporting needs—giving you more relevant insights and a greater opportunity to innovate. Ultimately, tailored analytics empower healthcare providers to unlock deeper patterns from their unique data, supporting smarter decisions across the entire spectrum of care.

The major role of healthcare data analytics.

Healthcare providers find it too complex and voluminous to handle and analyze the massive amounts of electronic health records of patients and their related administrative reports by traditional methods. Healthcare data analytics has the capabilities to carry out this data analysis most efficiently and transform the large volume of stored data into useful information to predict future outcomes. Simply put, the goals of data analytics in healthcare are – prediction, modelling, and inference.

Here are four major areas of applications of data mining in healthcare examples where data analytics in healthcare plays an important role are:

Evaluation of the effectiveness of medical treatments

Data mining can be used to evaluate the effectiveness of medical treatment for a particular illness or health condition.

By comparing the symptoms, causes and courses of drug treatments of similar diseases, the data mining process can carry out an analysis to decide which remedies would work best and would be most cost-effective for the specific ailments.

For example, the results of treatments of patient groups with different drugs for the same illness or condition can be compared and analyzed to find out which drug would give the best results for the particular disease or condition and would also save money. Moreover, results from such applications of data mining techniques in healthcare for a long period can help to standardize approaches to treatments for specific ailments, making diagnosis and treatment processes faster and simpler.

Preventing harmful drug and food interactions

Data mining also plays a crucial role in identifying potential risks associated with the combination of certain medications and food items. By analyzing vast amounts of patient records and prescription histories, these advanced techniques can highlight patterns where negative interactions have occurred in the past. For instance, the system may detect that patients prescribed warfarin, a common blood thinner, who also consumed leafy green vegetables high in vitamin K, experienced compromised treatment outcomes.

By bringing such patterns to light, healthcare providers can make more informed decisions when prescribing medications or advising on dietary restrictions. This not only reduces the risk of adverse reactions but also enhances patient safety and overall treatment effectiveness.

In a healthcare landscape where more than 20,000 prescription medications are approved for use in the U.S. alone, patients—especially those with chronic conditions—often take multiple drugs simultaneously. This increases the risk of unintended interactions, from allergies to chemical incompatibilities. Data-driven tools, such as drug interaction checkers and digital food interaction models, are becoming invaluable in helping physicians and pharmacists anticipate and prevent dangerous combinations.

This is particularly important in geriatric care, where seniors frequently manage several chronic health issues at once. With the accumulation of age-related conditions and the growing number of prescriptions, monitoring for potential drug and food interactions becomes even more essential. Integrating computer-assisted data mining into clinical workflows empowers providers to offer safer, more tailored care to every patient.

Managing customer relationship

As in the case of commercial organizations, customer relationship management is also very important for healthcare providers. Data mining healthcare services can be used to recognize patient preferences and their current and future needs to improve their level of satisfaction. Based on hundreds and thousands of healthcare transaction data of a large number of patients, healthcare data analytics can identify patients who can benefit most from specific healthcare services and encourage them to access the said services. Through this process, healthcare facilities can reach the right audiences for improved health and long-term patient relationships and loyalty. Eventually, this will result in more effective and efficient communications as well as increased revenue for healthcare providers.

Detecting fraud and misuse

Data mining healthcare combines powerful analytical techniques to detect healthcare fraud and abuse related to medical and insurance claims. Data mining in healthcare first establishes norms by analyzing the mass of data generated by millions of prescriptions, operations and treatment courses and building predictive models for finding fraudulent claims. These healthcare data analytics models are then applied to future claims to identify any abnormal patterns of medical claims by clinics and labs or inappropriate prescriptions or referrals by physicians and probable fraudulent insurance claims.

Besides the above, other areas of applications of data mining in healthcare examples are,

i. Evaluating the practitioners – Data collected regarding patients’ experiences with medical practitioners can be analyzed to know the areas for their improvement;

ii Analyzing data more quickly and efficiently – Data analytics in health care can analyze data more quickly and efficiently than humans with the help of machine-learning algorithms;

iii. Predicting the spread of illness – Data analytics can predict trends in the spread of illness, allowing to take adequate precautions beforehand;

Reducing Costs and Fueling Innovation

Another significant advantage of data mining in healthcare lies in its potential to both curtail expenses and stimulate innovation. By uncovering hidden patterns and inefficiencies in patient data, treatment protocols, and operational workflows, healthcare organizations can streamline processes and allocate resources more effectively. For instance, analyzing large datasets can highlight unnecessary diagnostic tests or treatments, allowing practitioners to implement more cost-effective strategies without compromising patient care.

Beyond cost savings, the integration of data mining with emerging technologies such as artificial intelligence opens doors to entirely new ways of delivering healthcare services. Hospitals and research institutions can leverage these tools to develop predictive models for early disease detection, personalize treatment plans, and even identify areas ripe for development of new medical devices or procedures. In this way, data-driven insights not only reduce wasted expenditures but also lay the groundwork for groundbreaking advancements in patient care and healthcare delivery.

Current Trends in U.S. Healthcare Spending and the Role of Data Mining

Healthcare spending in the United States continues to climb, with recent figures from the Centers for Medicare & Medicaid Services highlighting both the scale and the rate of growth. In 2022, total expenditures reached an astonishing $4.5 trillion, accounting for a significant portion of the country’s GDP. This sustained increase places immense financial pressure on healthcare providers, patients, and the broader economy.

Addressing these rising costs calls for innovative approaches—this is where data mining steps in. By harnessing vast amounts of patient data, treatment records, and billing information, data mining helps healthcare organizations uncover inefficiencies, identify cost-saving opportunities, and improve overall patient care quality. For instance, advanced analytics can:

• Pinpoint which treatment plans yield the best outcomes while optimizing cost-effectiveness.
• Reveal patterns that lead to unnecessary spending, such as redundant tests or overprescribing.
• Detect potentially fraudulent insurance or medical claims, ensuring that resources are devoted to genuine patient needs.

In essence, as healthcare expenses continue their upward trend, data mining plays a crucial role in not only curbing unnecessary costs but also in supporting better healthcare delivery and more informed decision-making.

Future Prospects and Emerging Trends in Healthcare Data Mining

Looking ahead, the future of data mining in healthcare appears exceptionally promising. As both the volume and complexity of healthcare data continue to grow, advanced data analytics is poised to drive even greater advancements across the industry. We can expect to see data mining playing a central role in fostering evidence-based medicine, supporting innovative clinical research, and driving more personalized care delivery.

Emerging trends suggest that newer data mining techniques—particularly those powered by machine learning and artificial intelligence—will become even more integral in areas such as early disease detection, predictive analytics for patient outcomes, and real-time healthcare monitoring. For example, organizations like Mayo Clinic and Kaiser Permanente are already leveraging predictive models to identify high-risk patients and implement preventative measures, improving both quality of care and operational efficiency.

Moreover, as interoperability among healthcare systems improves and wearable devices generate increasingly rich streams of patient data, there will be more opportunities for mining insights that can inform public health strategies and optimize resource allocation. Collaboration between healthcare providers, researchers, and technology companies will further accelerate the adoption of data-driven practices, setting the stage for breakthroughs in diagnosis, treatment, and patient engagement.

Ultimately, the continued evolution of data mining in healthcare aligns perfectly with the industry’s broader goals: delivering higher quality care, improving patient outcomes, reducing costs, and driving continuous innovation.

Limitations of data mining healthcare

Although data mining applications in healthcare are very powerful tools, they cannot do everything by themselves. There are some limitations and challenges in the use of data mining in healthcare which creates a major obstacle to successful data mining. To be successful, data mining needs correct data and the help of an expert who can make objective conclusions from the extracted information. If there is a supply of incorrect or incomplete information, the output will be affected and the forecast will not be credible. Some of the major limitations of data mining healthcare are the reliability of medical data, data sharing across healthcare organizations and improper modelling leading to erroneous predictions.

Beyond these challenges, it’s important to recognize that the process itself is resource-intensive. Effective data mining in healthcare requires considerable computing power, robust data-warehousing capacity, advanced mathematical skills, and the expertise of data science specialists. The close relationship between Big Data and data mining means that organizations must also handle massive volumes of complex, sensitive information—often in real time. Without the right infrastructure and personnel, even the best-intentioned data mining efforts can fall short of delivering meaningful or actionable insights.

In summary, while data mining holds immense promise, its full potential can only be realized when supported by accurate data, skilled professionals, and substantial technical resources.

Challenges of Data Warehouse Compression in Healthcare

As the volume of healthcare data balloons—fueled by high-resolution imaging, detailed patient histories, and ever-growing digital records—organizations naturally turn to compression as a way to manage storage demands efficiently. However, compressing data warehouses is not without pitfalls.

A primary concern is the risk of data quality degradation. If compression techniques are applied incorrectly, especially with large, complex files like MRI or X-ray images, critical details can be lost, potentially impacting diagnoses and patient care. Lossy compression may save space but at the expense of image clarity, which is simply unacceptable when every pixel could matter.

Addressing these challenges requires a technologically savvy approach. Leveraging advanced cloud-based storage solutions, combined with more sophisticated compression algorithms, can provide much-needed scalability while maintaining the integrity of sensitive medical data. Additionally, incorporating DevOps principles helps ensure these solutions remain robust, adaptable, and secure. By continually refining compression processes and rigorously testing outcomes, healthcare organizations can balance resource efficiency with the uncompromising standards required for patient data.

Balancing Privacy Risks With Data Mining Benefits

While the advantages of data mining in healthcare are significant, privacy concerns often come to the forefront. The vast troves of patient data exchanged and analyzed understandably raise worries about personal information falling into the wrong hands. Yet, much like the calculated risks we accept in daily life—think about getting behind the wheel each day—engaging in data-driven healthcare sometimes carries manageable uncertainties.

To address these concerns, some experts recommend providing patients with choices regarding the use of their health information for data mining. This could include offering opt-in mechanisms or even incentives, like tax benefits, to encourage participation. The reasoning is that while privacy is important, the ultimate priority in healthcare is saving lives and improving outcomes. Health organizations and regulators continue to refine data handling protocols to balance risk, ensuring that robust safeguards are placed alongside data mining practices.

Ultimately, it comes down to informed decision-making—patients, providers, and policymakers must weigh the potential life-saving advancements against the importance of confidentiality. With transparency and evolving safeguards, the benefits of better diagnoses, treatments, and fraud prevention can often outweigh the inherent risks, especially when everyone involved understands and consents to the process.

Patient privacy concerns in healthcare data mining

While the advantages of data mining in healthcare are indeed significant, it’s essential to recognize the potential concerns it raises for patient privacy. Since data mining relies on vast amounts of personal health information, some worry that sensitive details might be improperly accessed or even misused.

The sheer scale of data sharing between medical facilities, insurance providers, and researchers increases the risk that a patient’s confidential records could inadvertently fall into the wrong hands. Data breaches, intentional misuse, or accidental exposure of private information are very real possibilities, making privacy a central topic in any discussion about healthcare data mining.

It’s worth noting that experts often weigh these privacy risks against the potential benefits. Many argue that, much like taking calculated risks in everyday life, individuals and organizations must find a balance between safeguarding sensitive information and enabling data-driven advancements that can save lives. Some proposals suggest giving patients more control over how their data is used—perhaps even offering incentives for those who consent to share their health information for medical research and analytics.

In summary, the most common concerns revolve around:

• Unauthorized access or leaks of sensitive patient data.
• Difficulties in ensuring data security across varied healthcare systems.
• The delicate balance between protecting individual privacy and unlocking insights that benefit public health.

Navigating these challenges is crucial to maintaining trust while still harnessing the power of data mining for improved healthcare outcomes.

Addressing Patient Privacy Concerns in Data Mining

When it comes to patient privacy, several solutions have been suggested to ensure ethical data mining in healthcare. One widely discussed approach is to allow patients to actively opt in or out of having their health information used for data mining purposes. This not only gives patients greater control over their sensitive data but also fosters trust between patients and healthcare providers.

In addition, some proposals aim to motivate participation by offering incentives—such as tax benefits—to patients who consent to data sharing for research and analytical purposes. These strategies seek to balance the valuable insights derived from health data with respect for individual privacy and autonomy.

Careful implementation of such measures, alongside robust data anonymization techniques and strong regulatory frameworks, can help alleviate concerns and promote responsible use of data mining in healthcare.

Frequently asked questions

Before I conclude let me also address some of the related queries often made by people.

How can data mining be used in the healthcare system?

Data mining can be used to help doctors to provide effective treatments and better patient care, help healthcare organizations to improve customer relationships, help healthcare insurers to detect fraud and abuse and many more.

What is big data or data mining?

Big Data refers to the collection of a large amount of healthcare data. Whereas data mining refers to the extraction of useful information from the patterns of a large volume of stored healthcare data sets and to use that information to build predictive models.

In healthcare, this data can come from a variety of sources—not just patient records, but also lab results, imaging metadata, prescriptions, billing files, insurance claims, and demographic information. Advanced health data mining even incorporates data from wearable devices (like Fitbit or Apple Watch), genomic datasets, and patient-reported outcomes. Modern data mining tools are designed to bring all these diverse sources together, transforming them into structured formats that can be efficiently analyzed. This integration allows healthcare organizations and researchers to uncover patterns and trends that simply wouldn’t be visible otherwise, making predictions more accurate and insights more actionable.

How data is used in healthcare?

In recent years, healthcare data collection has moved into the digital sphere, making analysis faster and more accurate. Healthcare data is used to help healthcare in 4 major areas as mentioned below:

1. For managing the good health of a patient;

2. For providing more accurate treatment;

3. For preventing cases before they occur;

4. For reducing prescription errors;

Conclusion

From the above discussion, it is evident that data mining in healthcare has huge potential to play a significant role in the healthcare industry. Healthcare data analytics can quickly filter massive amounts of data to find treatment options for different diseases and provide accurate solutions based on historical information. It may also provide customized solutions to unique concerns for particular patients. This is going to improve patient care and reduce medical costs in a big way.

However, as already mentioned the success of data mining techniques in healthcare depends on the availability of correct healthcare data. It is, therefore, critical to be concerned about how data can be better captured, stored, prepared, and mined. Possible advice in this context may be, sharing of data across healthcare organizations to enhance the benefits of applications of healthcare data mining.