Healthcare Fraud Detection Market Report Explored in Latest Research
Fact.MR has announced the addition of the “Healthcare Fraud Detection Market Forecast, Trend Analysis & Competition Tracking - Global Market Insights 2018 to 2028" report to their offering.
[UKPRwire, Wed Sep 18 2019] The Healthcare Fraud Detection Market Gets Significant Impetus with AI
A startling number of healthcare fraud cases using false billings to Medicare and Medicaid are coming to the fore. A large number of these fraudulent activities entailed false bills of services which were never rendered. This situation can definitely be negated with the advent of AI. Advanced AI algorithms can be used in cases of financial fraud, insurance fraud and transaction fraud. Several trials have found that automated fraud detection systems using AI can negate financial losses, reduce auditors’ workloads and help generate greater resources for the patients. Here the machine learning program is fed with two subsets of billing data, one which is a bill generated by the physician and the other, some data of established fraudulent cases. The machine then draws out patterns and common inferences to detect fraud. Thus AI could present itself as a first line of defense and revolutionize the healthcare fraud detection market.
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Big data and data analytics is another way by which a medical institution might compile data about what constitutes ‘normal’ patient buying behavior. If a patient is using services in a way that is atypical, hospitals can create blacklists. Fraud clusters of data can be ascertained using unsupervised machine learning. Highly advanced data mining techniques can then be used on fraud tactics that are found to be perfidious. The analysis done is highly impactful and is a powerful tool giving traction to the healthcare fraud detection market.
Healthcare is an integral part of people’s lives and therefore it must be affordable. The healthcare industry is a highly complex system expanding at an expeditious pace with a wide range of moving components. Fraud in healthcare industry has become a critical problem in recent time, with misuse of medical insurance systems being the prime concern. As manual healthcare fraud detection is a strenuous task, healthcare firms are increasingly adopting healthcare fraud detection software equipped with machine learning and data mining techniques. The ability of healthcare fraud detection software to automatically identify healthcare frauds has been helping technology behind healthcare fraud detection gain immense traction.
Healthcare frauds are tough to detect and usually go unnoticed, which makes detection of these kinds of fraudulent claims paramount as they add to the burden on the society. With the adoption of healthcare fraud detection software, healthcare companies can perform error-free accounting and auditing based on predictive data methodologies. The careful account auditing feature of healthcare fraud detection can assist firms in revealing suspicious providers as well as policy holders, helping them detect potential fraudulent cases before they occur. Leading healthcare fraud detection companies are vying to provide regular innovation and cutting edge technology in their healthcare fraud detection offerings to move ahead of the counterparts, and gain a pole position in healthcare fraud detection market.
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Healthcare Fraud Detection Market- Notable Highlights
Some of the leading firms operating in healthcare fraud detection market include Optum, Verscend Technologies, Inc., DXC, Northrop Grumman, Fair Isaac Corporation, HCL Technologies Limited, LexisNexis, SAS Institute Inc., Pondera, Conduent, Inc., SCIOInspire, Corp., CGI Group Inc., Wipro Limited, IBM Corporation, McKesson Corporation, and others.
In August 2018, Verscend Technologies, a portfolio firm of Veritas Capital, purchased Cotiviti Holdings Inc, a payment accuracy and analytics-driven solution-provider primarily focused on healthcare industry. Together the two companies are operating under Cotiviti name, and are reinforced with novel capabilities across risk, payment, quality, and the combination of financial and clinical data, helping them in creating differentiated value for clients.
In June 2018, another leading player in healthcare fraud detection market, SCIO Health Analytics, got certified for Veeva CRM MyInsights. Using its Patient Personas and advanced predictive and prescriptive analytics capabilities, SCIO can offer actionable insights on patient risks, impact-ability, as well can profile the diverse patients treated within a site of care.
Healthcare Fraud Detection Market- Market Dynamics
Burgeoning Fraud Cases in Healthcare Industry Creating Opportunities
The proliferating fraudulent activities in healthcare industry in tandem with growing number of patients eying healthcare insurance, have been propelling adoption of healthcare fraud detection software and services. The healthcare fraud detection market is likely to expand on the back of factors such as increasing pressure of fraud, waste, and abuse on healthcare spending, and high returns on investments. For instance, in 2017, government agencies said that their yearly healthcare fraud takedown included 400 charged individuals in healthcare fraud schemes, which accounted to USD 1.3BN in fake billings to Medicaid and Medicare.
Prepayment Review Model
The prepayment review model has transformed the healthcare industry for the better, even Medicare claims are examined by prepayment reviews. With this model, the Center for Medicare and Medicaid Services (CMS) can save money, while alleviating burden on hospitals. This model not just triggers system-wide process improvements, but also has a considerable positive impact on the cash flow of hospitals. With this model, government hold money until the claim confirmation becomes valid.
Though this variant of healthcare fraud detection model adds some burden on hospitals as the number of claims under review will dramatically increase with time, hospitals no longer need to worry about audits after getting payment as cash flow is stopped until claims get verified. Owing to myriad benefits of prepayment review model, hospitals are increasingly making system-wide operations enhancements for error free documentation and are deploying predictive modeling techniques.
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Reluctance to Adopt Healthcare Fraud Detection Software
In the healthcare industry, healthcare fraud analytics are used to identify duplicate claims with the help of analytics. With the insurance companies reviewing hefty data such as disease history, medication history, and surgical procedures underwent etc., it has become essential for companies to adopt analytics for error free work. However, due to lack of proper IT infrastructure, technological advancements, under-developed medical record databases, and improper regulations to combat privacy concerns, companies in developing countries are highly unwilling to adopt analytics technologies used in healthcare fraud detection market. Further the deployment of such software is time consuming and they require regular upgrade, which is further making companies highly unwilling to adopt healthcare fraud detection software, which in turn is hindering growth of healthcare fraud detection market.
Healthcare Fraud Detection Market – Segmentation
The healthcare fraud detection market can be bifurcated on the basis of:
On-premise delivery models
On-demand delivery models
Insurance claims review
Private insurance payers
The healthcare fraud detection study presents reliable qualitative and quantitative insights into:
Healthcare fraud detection market segments and sub-segments
Healthcare fraud detection market trends and dynamics
Supply and demand chain in healthcare fraud detection market
Healthcare fraud detection market valuation (revenue and/or volume)
Healthcare fraud detection market key trends/opportunities/challenges
Forces defining present and estimated future state of the competitive landscape in healthcare fraud detection market
Technological developments in healthcare fraud detection market
Healthcare fraud detection market value chain and stakeholder analysis
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