How Predictive Analytics Impacts Medical Billing and Ensures Timely Patient Intervention


Medical billing is often plagued with confusing and complex medical data, but new technology and software tools are making it easier for payers, providers, and consumers. Predictive analytics allows them to know what the data means in terms of their business interests and unique needs within the healthcare system.


In fact, data analytics and predictive analytics can help keep healthcare providers’ costs down. Using the power of artificial intelligence (AI) and machine learning (ML), tools like SEDGE help providers to optimally manage their part-time and full-time employee hours to remain compliant with the Affordable Care Act policies, saving them additional costs and mitigating potential penalties.


First, Let’s Explain Predictive and Prescriptive Analytics


We have written in detail on the topic of predictive analytics, so to recap: predictive analysis is a sophisticated analytical technique that goes beyond using data to understand past performance. Rather, it forecasts trends and statistical probabilities by using historical data, AI, algorithms, and ML.


Prescriptive analytics is where the AI platform not only provides a prediction of what is to come, but also provides or prescribes evidence-based measures to produce the best possible outcomes.


Predictive and prescriptive analytics are successfully used in a myriad of sectors and industries for it’s data-driven ability to predict what is most likely to happen. In this instance we will look at how predictive analytics helps reduce errors with medical coding and billing, limits fraud and waste, increases profits, and improves early patient intervention.


The Impact of Predictive Analytics on Medical Billing


Improves Outcomes


Predictive analytics’ ability to accurately forecast what is most likely to happen, based on trends and patterns in historical data, when it comes to medical billing, it is able to help hospitals to predict and avoid 30-day readmissions.


There are multiple sources of data that can be directed to the AI platform, such as:

  • EMR (electronic medical records)

  • Medicaid, Medicare, CHIP and other immunization programs

  • Laboratory Test Results

  • Patient and Provider Surveys

  • Medical and Pharmacy Insurance Claims

  • Social Media and Lifestyle Data

  • Socio-demographics

  • Claims

By analyzing these data sources, AI platforms like SEDGE can extract data from clinical visits, healthcare claims, and community-based assessments to identify the patients that are most likely to be readmitted. This can help hospital administration staff create more efficient discharge planning, post-discharge policies, and deliver optimized medical billing services.


The goal is for predictive analytics to help payers and providers lower admission risks, identify potential fraud, increase efficiency, and save lives, time, resources and money.



Facilitates Timely Interventions


Predictive analytics can ensure that timely interventions are deployed by healthcare practitioners and providers. Predictive analytics integrated with automated medical billing software can create alerts and reminders to healthcare professionals, and can help match patient needs with available services. This could be for home care, rehabilitation facilities, medical services, or diagnostic tests. Predictive analytics can also indicate how likely it is for the patient to return to the hospital within the next 30 days. Additionally, predictive analytics in medical billing can improve the accuracy of reimbursement decisions in Medicare and Medicaid, saving time and resources.



Insights into the Migration to Value-based Care


As healthcare shifts to alternative payment models (APMs) and other value-based care reimbursement models, providers and practitioners can no longer simply invoice patients and residents based on the services provided. Instead, reimbursements depend on several factors, such as the quality of care provided, the patient’s experience, and patient outcomes.


With these implicit changes come the emergence of new billing patterns and a greater risk of human error. Predictive analytics tools like SEDGE help practitioners review universal billing (UB) and other claims, identify patterns in billing errors, and highlight discrepancies between the care that was provided and the reimbursements received. Additionally, if UB-04 checking is automated, it effectively gives billing staff days back to their busy week, allowing them to focus on other core duties.



Minimizes Audit Risks and Ramps Up Revenue


Monitoring, managing, and analyzing the billing cycle makes it easier to quickly identify when the underlying minimum data set (MDS) does not support the billing, or vice versa, or highlighting if a treatment was missed on a claim that was listed in the MDS.


Predictive analytics gives healthcare practitioners the opportunity to make these corrections to claims before submission, often resulting in higher, quicker reimbursements, or prevention of an audit.


Tools like SEDGE can streamline and consolidate financial UB records, maximize reimbursements, accelerate A/R and guide medical professionals toward continuous improvements in billing and patient outcomes.



Uncovers Waste and Fraud


Thanks to the availability of big data with information spanning patient zip code to physicians’ certifications, AI-powered technology like SEDGE allows medical insurers to analyze populations and providers to quickly and automatically spot fraudulent trends.


Accurately unearthing fraudulent activity and highlighting waste requires access to masses of data, and the capabilities to process this vast data.


Training the technology to prevent false positives takes thousands of hours of work, and requires teams of highly qualified, experienced data analysts, like those available at SEDGE. The landscape of medical and patient data is always changing, however the experts in data analytics in healthcare are more than up to the challenge.



Exposes Schemes


Whereas the majority of healthcare professionals are trustworthy and transparent, it is quite common that - if there are a few bad apples in the bunch - they will work together. Big data analytics can help uproot such dishonest schemes.


By connecting the dots between fraudulent providers and staff, and working quickly and accurately through masses of data - which would otherwise be impossible to do at a human level - every procedure that is incorrectly input, or a careless data entry that is saved at a facility, can be brought into the light and exposed.



SEDGE: Optimize Medical Billing with Ease


When it comes to your medical billing, it is time to use SEDGE experts and technology that help you and your team do more, better, faster. Let us show you how.


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