Why The Healthcare Industry Needs Data Mining Services

The increasing rate of growth of medical data is showing no signs of slowing down. The identification, extraction, and processing of valuable data from the collection of sources is a challenge. The accurate performance of those processes makes the difference between a patient’s survival and alternate outcomes. 

Healthcare centers can also face operational and legal issues without it. The solution to this is data mining. Data mining in healthcare is, thus, a much sought-after process that is only gaining traction with time. 

With healthcare data constituting 30% of the world’s data share and increasing, it pays to delve deep into healthcare data mining for your needs. For instance, patients having their health data on hand whenever required is invaluable. EHRs containing their medical history with anywhere, anytime access are a lifesaver.

Medical center administrators need data to understand the performance of their establishment and the professionals in it. Researchers need to use data mining techniques to conclude their studies that will have global impacts.

And medical authorities, especially government ones, depend on data to formulate the best policies. Telemedicine management is a whole other data management challenge that is progressing rapidly.  

More about how data mining affects healthcare are discussed below:

What Is Data Mining? 

The flow rate of enterprise data is high, getting inputs from multiple sources. It gets collected in a central repository i.e. the company’s/institute’s database. This raw data is erroneous and bloated, requiring data processing to turn it intelligible and actionable. Data mining services are one of the data management functions applied.

The term ‘mining’ is used here as the process resembles the extraction of valuable material by removing the unwanted material surrounding it. In this case, the required data is the valuable element, and the dirt is unwanted data. 

Technically, it is defined by TechTarget as “the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis”.

Possible relationships and patterns in structured data are recognized using data mining tools to help predict future trends and events. Sophisticated data analytics algorithms are employed for it by data mining experts.

They utilize Artificial Intelligence (AI)/ Machine Learning (ML) to gain insights from the pile of raw data. 

Data Mining in Healthcare

Data mining extracts valuable data from sources like EMR/EHR (Electronic Medical/Health Records), personal healthcare devices, and direct inputs from medical personnel on duty. Also included are insurance companies, CRM and other applicable operations software, testing and other equipment, etc. 

The extracted data further paves the path to:

• determine the methodology to be used for a procedure
• decide upon the technological tools for a procedure
• establish the effectiveness of a treatment provided
• prevent or minimize the effects of future ailments
• develop an efficient, safe, and profitable operational protocol by the administration
• better manage customer and support services/supplier relationships
• identify malpractice, wasteful approaches, and counterproductive behavior

Hence, data mining services play a pivotal role in the healthy functioning of the healthcare system. 

How Does Data Mining Work In Healthcare?

There are many parallels between data mining in other industries and healthcare. They all harness the power of AI to work on massive amounts of data, training them using actual data sets. 

These A.I. models are more adept at recognizing the patterns of the required data sets than classical computer algorithms. They adapt better to the changing circumstances, which is the standard in the medical field. 

The data mining process occurs through a series of steps or stages, their number varying depending on various factors related to the use case. But a few of those are common, whether you outsource data mining services or do them in-house.

1. Data Strategy Development

The medical institution should have in place a robust strategy to successfully manage the large quantities of data it receives and generates. The storage size, software used, number of sources, priority awarded to the sources, criteria to award those priorities, etc., should be in place. 

This helps set the stage for a smooth mining operation without disruptions throwing the process into disarray. Contingency plans will help deal with emergencies that arise in such a chaotic environment. The data pipeline will remain unclogged from end to end. 

2. Data Set Acquisition and Selection

Once the data starts flowing into the data pipeline, data mining experts will start their process of collecting data sets. They will sift through the incoming and present data to recognize the useful set.

Segmenting the data into sets helps with better strategy implementation. Test sets can be worked to gauge the effectiveness of the process. Shortcomings can be corrected to improve the process or the strategy itself early on. 

3. Preprocessing

Data from multiple sources tend to be in multiple formats. These formats may not be conducive to the data mining and/or data management pipeline. Reformatting the data according to the required or established format occurs during this stage. 

Data mining services also define protocols to manage missing data and the data points not in line with the time sequence. This further reduces opportunities for disruptions moving forward. 

4. Mining

At the heart of the processing pipeline, data mining is where the extraction of the wanted data occurs. Data patterns of particular interest are acquired using a collection of methods, tasks, algorithms, techniques, etc. 

It is conducted primarily on the database once the preprocessed data is collected there. However, in real-time situations, the mining may occur directly in the input stage. The mined data then gets stored in the database or is presented to the point of demand. 

5. Examination/Interpretation

In the final stage of data mining in healthcare, interpretation is used to make sense of the data. The final user acquiring it uses a bevy of tools and techniques to understand the information being presented by the data set. They may use it directly or send it for further processing as desired.

One of the most important criteria for healthcare-related data mining is compliance. Compliance is enforced more strictly in this sector than in others due to the nature of the work. The compliance will be based on data privacy and health data standards established by regulatory bodies. 

In the US market, all healthcare-related businesses and operators must comply with HIPAA (Health Insurance Portability and Accountability Act-1996). That includes data mining services used by these places and the software used for the purpose too.

It’s best to use industry-leading data security practices to prevent data breaches and leaks. Otherwise, the patient, staff, and/or the healthcare center will have to pay a steep penalty. 

The Data Mining Techniques Used In Healthcare

A host of techniques are used to carry out the process in healthcare. They are chosen based on the quantity of data, desired results, etc. A concise look at the selection would be as such:

1. Classification

A staple of data mining across the board, classification allows for the segmentation of data sets based on established criteria. Users and data mining experts can then find relationships between these sets, such as the connections between the number of pollution particulates in the lungs and cognitive performance.

Classification occurs based on the algorithm used. The common ones include decision trees, support vector machines, Naive Bayes, Logistic Regression, and K-nearest neighbors. 

2. Association

This technique aims to establish the interconnections between different data attributes. These relationships may be within a data set or between different ones. It is primarily conducted with the aid of AI/ML. 

Thus, as the rule gets further comprehended by the system the establishment of the relationships gets simpler and quicker. The data mining technique of association finds use in instances like the establishment of the relationship between lifestyle and associated diseases. 

3. Outlier Detection

Outlier detection recognizes data portions that do not qualify the prescribed standard. These are usually abnormalities and irregularities that get discarded from the data set. 

Ignoring this data and moving ahead would mean introducing inaccuracies into the task. Please note that not every abnormality is wasteful. It could be that the particular data bit is unsuitable for the task but useful elsewhere. 

4. Clustering

The clustering algorithm is the one that comes to the rescue when there’s a lack of information about the data objects’ nature. Data mining services use this model to fission data based on similarities found between different data sets.

An advantage of this model is that it is self-learning. It starts to recognize the patterns required in the data sets and builds on them. There’s no need to enter multiple criteria early on for the entire process. The algorithm improves with time as it processes an increasing number of data sets. 

5. Prediction

This algorithm works in conjunction with the ones mentioned earlier. It is most helpful whenever outcome forecasting is needed. The data sets used for this may be from the past or recent ones. 

This algorithm can be used for data mining in healthcare to predict the effectiveness of a new medication against a defined number of pathogens. It helps give a theoretical value to compare the results of the experiment against.

Must Read – Effective Data Mining Techniques To Let Your Business Reach Heights

The Benefits of Data Mining In Healthcare

There are numerous benefits to using mining in the healthcare sector. And these benefits are not limited to the healthcare agencies alone. Society at large has benefited from the inclusion of improved data management in the sector. And data mining plays a significant role in it, as can be seen below: 

1. Improved Diagnostic Accuracy

The US has a median diagnostic error rate of 13.6% for 15 diseases from the Big Three categories of vascular events, cancers, and infections. Data mining services are on their way to reducing it further.

Accurate data mining gives the true picture of the patient’s condition. And with improved diagnostics come improved treatments that target the problem and reduce side effects. 

2. More Efficient Treatments

The type, duration, and quantity of medication determine the efficacy and quality of treatment. Providing the best treatment, thus, means having the most accurate data possible about every associated parameter. 

With medical devices providing real-time data, the treatment can be altered as and when required. 

3. Stronger Relationships

The use of CRMs in every sector has made gathering customer data and its use to grow relationships with them easy. These CRMs work best when the data fed into them is accurate. 

When you outsource data mining services, the associated algorithms can be integrated into the CRM itself. With that, the staff can know the exact specialist a patient needs. Re-admissions can be prevented in some cases by preemptively acting on a person’s problem.

Their medical history is traceable by tracking their pharmacy purchases. Like this and many more instances, data mining can be used to develop customer relationships and trust. 

4. Insurance Fraud Detection

Medical insurance helps pay for a lot of the treatments. The data regarding the multiple providers, policies, and coverages need to be managed efficiently and accurately. 

Otherwise, it’s easy to lose track and become susceptible to fraud from either the patient’s end or the provider’s. Data mining experts can provide accurate data that helps prevent such fraudulent behavior. Misdemeanors can be flagged early on based on the data inconsistencies. 

5. Accurate Situation Prediction

Preparing for a possible occurrence in the future helps save lives and costs. When predictive data mining algorithms are combined with data analysis, they can provide such foresight. 

The more accurate data can help prepare for possible spikes in short-term, near-future rises in patients. It can also help prepare against shortages of medications and staff. Newly developed operational protocols can get implemented more easily with some effects already known. 

6. Adverse Food and Drug Reaction Avoidance

Without accurate data about the patient’s health condition and the constituents of the drugs they’re taking, they could have adverse reactions. Data mining in healthcare can prevent such mishaps by giving the required accurate data.

There will be reduced chances of drug and food combinations that don’t work well together. New drugs can also be implemented with better knowledge instead of a trial-and-error approach. 

7. Improved Clinical Decisions

Automation is making its way into the medical field in-depth. Algorithms are now participating in making clinical decisions or at least assisting those who do. These Clinical Decision Support Systems (CDSS) as they are called rely on accurate data to function. 

They work by analyzing data with machine learning algorithms or make decisions by applying rules to a knowledge base. Data mining techniques suitable for this process are crucial to determining their correct outcome.

There are also the added ones of costs and time savings that come with the availability of accurately mined data. 

Example of Data Mining In Healthcare

Data mining can be used to gather data from previously impossible or unconventional sources. 

An example of this in action is this study conducted by Jeremy Ginsberg et. al. about Detecting Influenza Epidemics Using Search Engine Query Data.

The purpose of this research was to gather data about influenza spreads, be it seasonal or epidemical, to preempt it. Older data mining techniques like patient medication purchase data and voluntary telephone calls by them to help centers were earlier used to gather the required data.

The researchers instead shifted attention to Google and other search engine queries to gather relevant data. The model they developed mined relevant data from Google Search queries conducted by US citizens over 5 years. The system processed hundreds of billions of individual searches looking for influenza-related keywords and phrases. 

This increased influenza surveillance capabilities drastically, allowing for data gathering on a regional and state-level in the U.S. The researchers hope to expand the study’s scope to include global data. It’ll then provide the same insight on a global scale, all thanks to data mining in healthcare. 

Conclusion

Data mining has been around for a very long time in some form or the other. The modern age has enhanced its capabilities with the use of sophisticated algorithms. The future is guaranteed to rely much more on it, especially in industries like healthcare. 

The vast quantities and varieties of data that are present in it make data mining a must-have for this sector. Not to forget the mitigation of harm to lives and reputation that come with it. The improving algorithms and techniques are only solidifying the effectiveness of data mining in healthcare across different functions. 

Outsource Data Mining Services To DataEntryIndia.in

DataEntryIndia.in is your ideal medical data mining partner for a host of reasons. We have a cherished past of working with the best healthcare providers for many years. It has yielded us our ISO certifications for data quality and information security management. Our experts come with all relevant certifications and skillsets required for the job. 

We use the latest technologies for easy, quick, and accurate data mining. The scale and complexity of the data won’t determine the quality and speed of our services. Our data management services will keep your database healthy so that you can do the same with your patients. 

So, outsource data mining services to us and experience its many advantages, including reduced costs, turnaround times, and errors. 

Contact Us today to get a free trial. We can also be found via our email info@dataentryindia.in or through these numbers: +1 585 283 0055 | +44 203 514 2601.