Healthcare has experienced rapid growth in the demand for data analytics as more organizations use healthcare analytics...
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tools to gain insights into their operations. Big data also continues to grow because it is now far more accessible to healthcare organizations with small IT departments. Big data cloud vendors have expanded their operations to take on new customers, allowing small providers to buy their support and skip having to build their own infrastructure internally.
For many, analytics starts with sifting through current patient billing information and promises to grow with the changes that will come with the industry's impending conversion to the ICD-10 code set. Using healthcare analytics tools has become as simple as uploading a few data sets to the provider and letting them discover and share any valuable findings they uncover.
Analytics comes to medical billing
Many proven cases of tangible results are derived from the analysis of medical billing information. Small and large hospitals, as well as health plan providers, are a few of the healthcare organizations that have made significant investments in analytics to solve some of their business challenges and increase their operational efficiency.
In the case of insurers, examining claims data is a great opportunity for them to control their expenses and costs. Most of today's health plan providers will continue to look for ways to effectively control expenses, avoid unnecessary costs and remain competitive in pricing their patient care. For this reason, many providers have looked into the data hosted in their servers to identify and eliminate costly security errors, such as fraud. Fraud has plagued both private and public payers over the years. They've lost millions of dollars to entities submitting fake medical claims.
By using healthcare analytics tools, payers now have the ability to detect certain patterns in submitted health claims data and are able to weed out the fraudsters. Unlike how past systems were used, insurers can now leverage analytics to detect early signs of possible fraud. Some of the systems available today can detect fraud before any payments are made, which helps stop a false claim before it gets pushed for final reimbursement.
Hospitals test predictive analytics
In addition to insurers, hospitals can benefit from data analytics. In the face of reimbursement cuts, as well as pressure to control patient readmission rates to avoid financial penalties, many hospitals are turning to predictive analytics tools. By processing patients' clinical and billing information, hospitals are able to identify high-risk patients, meaning they are likely to be readmitted if they fail to follow their doctor's post-discharge instructions. Hospitals have also made use of different analytics tools to identify billing patterns that cause claim rejections, or non-payments on certain claims.
The changeover to ICD-10 codes is set for Oct. 1. By increasing the number of diagnosis codes from 13,000 under ICD-9 to 68,000 under ICD-10, all analytics and reporting results will be significantly more detailed. Early adopters of analytics tools have seen a dramatic growth in the value returned from mining their data.
Examples of how hospitals have used available analytics tools range from the review and analysis of hospital productivity, performance evaluations of treatment plans, charting drug abuse, and patient's risk factors. All of these are areas in which big data initiatives can provide the tools to help transform healthcare, improve patient care and reduce costs.
About the author:
Reda Chouffani is vice president of development at Biz Technology Solutions Inc., which provides software design, development and deployment services for the healthcare industry. Let us know what you think about the story; email firstname.lastname@example.org or contact @SearchHealthIT on Twitter.
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