Data analytics creates a possibility to answer complicated questions that remain beyond bounds for more straightforward analysis techniques. Among the many features of data mining, the most significant are as follows:

Automation

Even though simpler data techniques and statistics analysis use data for intelligent segregation, their capabilities don’t even come close to the complex abilities of data mining. This makes the latter far superior to conventions of statistical analysis. Through the automated nature of data mining models, the dependence on manual entries is significantly reduced, and much larger amounts of data can be used.

Data Analytics Meets Medical Billing and Coding Challenges

The healthcare industry is one that deals with data in large volumes. More and more organizations are opting for healthcare analytical tools to gain insights into their workings. Data companies are now more accessible to medical billing and coding companies, with everything from servicing to IT infrastructure being outsourced. From overcoming business challenges to increasing the efficiency of everyday workings, the benefits of data mining in healthcare remain unprecedented. We conducted research on the popular benefits of data mining for the medical billing and coding industry and below are the most prominent advantages:

Controlling Costs and Expenses

Identifying Fraud

Predictive Analysis for Reimbursement Cuts

Prescriptive Analysis for Rectification

Controlling Costs and Expenses

Through healthcare data analytics, an examination of claims is a substantial way to control costs and reduce expenses. Any additional claims expenses can be easily caught through the data analytics intelligent models.

Furthermore, the process is thoroughly beneficial towards the use of identifying associations between diagnosis and treatments and for the identification of inefficiencies within the current system as it seems through the data at an automated pace, with the reduced requirement for manual intervention.

The medical billing and coding industry is one that is faced with massive chunks of data and what better way to intelligently classifying this data but using data mining in healthcare.

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The Process

Costs and expenses are reduced through the following practical methods of using data:

Exploration of data

Preparation of meaningful analysis

Modeling of data

Evaluation through automated systems

Definition of problem areas

Future outcome analysis

Deployment of segregated data

Data mining works toward finally reinventing healthcare through transformed payment schemes that prevent critical occasions of readmissions. With the ability of data mining to predict the likelihood of readmissions with a right amount of accuracy, the health system can cut costs and keep health in check by raising the radar on people who are likely to be readmitted.

Identifying Fraud

With the ongoing instances of fraud in medical billing and coding continually rising, data mining is now being looked at to address and identify frauds and thereby eliminate expensive security blunders.

Whether it is fake claims or inaccurate ones, frauds have cost the healthcare industry dearly over the years. With the intelligent capturing capability of data mining, fraud can not only be identified, but there are provisional ways to eradicate the possibility of them taking place completely.

Through certain predictive analysis, data can be accumulated to prevent fraudsters from accomplishing their goal. Within the analytics system, data mining technology is used to gather the data through expert techniques. This data is then converted into meaningful analogies, and standard measurements, which ultimately culminate into an Enterprise Data Warehouse (EDW). EDW then works as the basis through which further data investigations occur that can identify fraud.

Through this EDW, data mining identifies health care providers whose:

Coding and billing strategies and actions vary from their regular practices

Coding and billing systems that differ significantly from their competitors

The Process

This is done through the analysis of the healthcare providers:

Area of practice

Location

Type of healthcare service offered

Frequency of billing

Size of operations

Through the above healthcare data analytics, fraudsters are identified, and due action is initiated, thereby saving expenses to an important lesson.

“In 2007, the Criminal Division of the Justice Department refocused our approach to investigating and prosecuting health care fraud cases. Our investigative approach is now data driven: put simply, our analysts and agents review Medicare billing data from across the country; identify patterns of unusual billing conduct; and then deploy our “Strike Force” teams of investigators and prosecutors to those hotspots to investigate, make arrests, and prosecute. And as criminals become more creative and sophisticated, we intend to use our most aggressive investigative techniques to be right at their heels.”

-Reported by Robert W. Liles, As Lanny A. Breuer, Assistant Attorney General of the Department of Justice’s (DOJ’s) Criminal Division.

Predictive Analysis for Reimbursement Cuts

Predictive analysis tools can go a long way to manage reimbursement cuts and control patient claims efficiently. These analytical tools will help in predicting patient behaviour and therefore increase the likelihood of efficient functioning while avoiding unnecessary financial costs. These tools also aid in identifying areas of billing errors and substantially reduce the risk of subsequent inefficiencies.

There is a noticeable increase in value that the medical billing and coding companies will notice from mining their data. The future predictions can lend coding companies to adopt strategies that will diminish the likelihood of reduced productivity and increase overall performance through intelligent evaluations. The evidence gained from predictive analysis allows medical coders and billers to incorporate strong and efficient categories into practice at an early stage.

Predictive Data Analysis uses the following information to make intelligent predictions:

A comprehensive record of bills submitted by healthcare providers

A quantum of data related to the billing and coding of each practice

Supporting documents related to a or a group of claims

An analysis of claims submitted

While it is virtually impossible to identify misdoings before they occur definitively, the use of predictive data analytics efficiently points the medical billing and coding industry in the right direction, wherein qualitative investigation can happen to minimize its susceptibility to wrongdoing.

The drastic increase in the diagnosis codes from 13,000 under ICD-9 to 68,000 under ICD-10 has made every kind of analytics and reporting results much more detailed than it used to be. Billing and coding companies that have adopted predictive analysis tools have received a considerably higher value return from mining their data.

Prescriptive Analysis for Rectification

After a predictive analysis is undertaken through data mining, the next order of business is a prescriptive analysis of the data. In layman terms, this literally means an analysis of what needs to be done about the predictions that have been made. This is a useful area and a significant component of the meaningful information that can be extracted through data mining. Considering this is a newer area of data mining, the prescriptive analysis offers actionable suggestions that work as solutions toward the predictions made by the predictive analysis feature of data mining.

Prescriptive analysis is undertaken with the use of the following tools:

Business rules

Algorithms

Machine learning

Computational modeling

data-analysis

An efficiently deployed predictive analytics platform can track the current and upcoming trends, gauge its effect on cash flow, and offer solutions for rectification. For example, if a company is reimbursing claims at a higher or lower level than required, predictive analysis catches the lapse, then displays the inefficacies and offers remedial action through complex algorithms.

The Response of the Medical Billing and Coding Industry to Data Mining

Several data mining techniques are endued with the capability of collating volumes of data to create meaningful analysis and offer predictive outcomes that can assist in increasing efficiency within the medical billing and coding industry by leaps and bounds. While there is no doubt about the advantages of these processes, there is still a wide number of industry professionals who are still to adopt these data analytics techniques. This could be attributed to a certain amount of confusion among industry professionals as to the detailed capabilities of data mining and its subsequent advantages. The hesitancy also comes from the attachment to conventional methods of audit and compliance that are undertaken through mostly manual statistical collection.

The following statistics point to the urgency of data mining adoption:

“Developed and developing economies are expected to see health care spending increase ranging from 2.4 percent to 7.5 percent between the year 2015 and 2020”, according to the report by Deloitte.

According to medicalbillersandcoders.com, “the coding system saw an upgrade in October 2015 through transition to International Classification of Disease (ICD-10). This new ICD-10 has approximately 69,823 codes & 71,924 procedure codes. Additionally, 140,000 new codes have been added to the list.”

It is a question of time before the entire healthcare industry embraces the benefits of data mining and slowly but surely, the trend seems to be setting in.

Challenges toward the Adoption of Data Mining

While the benefits are countless, there are also certain challenges that industry experts are facing when it comes to the adoption of data mining. The reliance on automated systems may subject certain providers to random audits and investigations that may not be necessary or justified. The sole dependence on data analytic techniques to identify providers who are undertaking wrong doing, purely based on exclusive data seems unfair to many professionals.

In a nutshell, the challenges toward the implementation of data mining include:

Uncertainty of data mining results due to their predictive nature

Reliance on technological statistics as opposed to manual operations

The cost involved towards unnecessary audits that may prove unrequired

The job cuts that come along with technology replacing manual statistical assembling

A basic unawareness of the benefits of data mining among professionals

Implementation of Data Mining for Medical Billing and coding

The healthcare industry is experiencing a revolution, one the likes of which have never been witnessed in the past. The crux of this revolution involves the adoption of data mining strategies to systematize the medical coding and billing industry within this panorama. Healthcare professionals no longer need to rely on manual audits and complicated procedures to identify misdoing and malpractices among the healthcare providers.

Because of data mining implementation, medical billing and coding companies will benefit in the following ways:

Cleaner systems of operation with data segregated systematically

Greater transparency from healthcare providers

Reduced costs of manual audits

Reduced expenses that result from wrongdoing investigation

Mitigation of risks of malpractice

Prediction of patterns and outcomes that increase efficiency

Prevention of waste, fraud, and abuse

While the benefits of adopting data mining techniques outweigh the challenges entirely and there is no doubt that the healthcare industry will witness an increasing reliance on data mining for its medical billing and coding purposes, it is important to remember that these techniques keep evolving. Therefore, medical experts need to make an added effort to keep up to date with the ever-changing technologies to obtain the maximum gain from them.

Source:- https://www.osplabs.com/insights/data-mining-in-medical-coding-and-billing/