Type 2 Artificial Intelligence Medical Billing
Our super helpful artificial intelligence is of the Type 2 capacity, comprised of custom designed machine learning and pattern analysis models around pre-programmed high-volume data sets.
The Claimocity A.I. not only generates ultra-quick data processing capacities and highly efficient smart functionalities into point of care proactive analysis and automated full-cycle error resolution but will steadily improve your results in every capacity over time.
Big Data Medical Analytics
Big data is the use of huge, highly complex data sets to garner unrivaled insights to complex matters and effectively problem-solve issues for value, veracity, efficiency, and precision.
Continuously gathering and processing a wide variety of high volume, low-density, unstructured data, our software is able to tap into the intrinsic value of both complex analysis and the data-driven decision-making power that comes from informed pattern recognition and predictive behavior.
Proprietary Rules Engine Software
Our proprietary rules engine is not only the driving force behind our intelligent software, but top an absolute powerhouse of its own right, enabling smart filters and PM enhancements.
From error identification and resolution functions that enable our billing team to catch and resolve issues as they are made, to smart filters for your practice that allow easy scheduling, patient management, and a variety of smart census and management features, our rules engine exceeds expectations.
“One of the most amazing things about Claimocity is the ability to know what a claim is worth before it is paid.”
“The software speed is remarkable. No down or lag times. Everything works so quickly and easily. Our team is able to move through their day from one task to another without any issues.”
“The Practice IQ feature is absolute gold. All of the doctors in my group talk about how helpful it is to track different financials, and the comparative rankings insights have trickled into better level coding and competitive goals that are driving the group revenue to new highs.
Smart Medical Technology: The Practical Value
Three advanced technology advantages with overlapping spheres of influence:
- Artificial Intelligence (AI) Medical Billing: Claimocity integrates AI-enhanced systems into nearly every facet of our software and RCM billing processes.
- Customizable Rules Engine: Claimocity uses layered rules engines including client engines customizable for provider and/or practice.
- Machine Learning (ML) for Medical Billing: Our dedicated machine learning uses client data and industry data to improve suggestions and resolutions.
- Significantly faster processing, syncing, and saving
- Historical data analysis of industry averages
- Higher quality control standards
- Proactive error identification
- Missing data pattern identification
- Improved efficiency trendlines
- Streamlined provider and managerial process timelines
- Individual and bulk workflow automation
- Data-driven systemic improvements
- Dynamic personalization and conditional templating
What is AI/ML and Why Does it Matter to Your Billing, Coding, and Efficiency?
What is an AI/ML system?
AI/ML (Artificial Intelligence/Machine Learning) supported systems are computer systems or software programs that are designed to use machine learning algorithms and artificial intelligence techniques to perform various tasks. These systems are capable of learning from data and making predictions or decisions based on that learning.
Examples of AI/ML supported systems include:
- Support bots: These bots use natural language processing and machine learning algorithms to interact with users in a conversational way and provide structured support.
- Recommender systems: Recommender systems use machine learning algorithms to analyze user behavior and make personalized recommendations within a defined framework and rules engine.
- Error detection systems: Error detection systems use machine learning algorithms to analyze patterns of missing or erroneous data points in a pattern and run them through follow up quality control measures or checks.
- Image and speech recognition systems: These systems use machine learning algorithms to analyze and interpret images or speech, making it possible for computers to recognize objects, faces, or speech patterns.
- Predictive maintenance systems: Predictive maintenance systems use machine learning algorithms to analyze sensor data from machines and predict when maintenance is needed to prevent equipment failure.
- Autonomous systems: Autonomous systems use a combination of AI and machine learning algorithms to navigate processes and make probability generated decisions.
Overall, AI/ML supported systems are increasingly being used to automate tasks, improve decision-making, and enhance user experiences in various industries, including healthcare, finance, transportation, and entertainment.
How this applies to our customers?
We utilize all of the above to help improve your performance while handling big chunks of your redundant data and administrative burdens to free doctors and practice managers up for more important responsibilities.
Specialized QA/QC bots assist our RCM and billing teams.
Recommender systems and error detection systems are the front line software for erroneous and missing data identification and resolution suggestions before they flow into the QA/QC team hands.
Image and speech recognition systems are built into our custom OCR and image uploading systems that auto-populate form fields with information from face-sheets, charts, census lists, and other documents. Speech recognition gives doctors the ability to use voice-to-text for their note generator and charge capture workflows instead of typing.
Predictive maintenance systems are specifically designed for our internal quality control to monitor our AI, ML, rules engine, and system chains. We constantly run over 100+ internal analyses and metrics, ensuring that everything processing active data is operating at the highest levels.
Autonomous systems are crucial to our ability to reduce provider and managerial burdens by utilizing contextual information and integrations to autofill input fields from other sources where it has already been entered. Census info auto-populates and updates in real time in the app from the hospital or facility or other provider inputs. Patient demographics and new patient data auto syncs in rather than having to be manually entered. Redundant data is filled in. And so on until at every stage the workload for administrative and billing workload is completed for you.
Rising Value of AI/ML Systems
Due to it’s growing value, there is an influx of new scientific studies on the use of AI/ML systems across a wide range of applications, including healthcare, finance, manufacturing, and more. Here are a few examples:
- Published in the Journal of Financial Data Science: Data scientists examined the use of AI/ML models for predicting financial market returns. The study found that a deep learning model was able to outperform traditional models in predicting stock returns, and had the potential to improve investment decision-making.
- Published in the Journal of Manufacturing Systems: This study evaluated the use of AI/ML systems for predictive maintenance in manufacturing environments. The study found that the use of these systems resulted in improved equipment reliability, reduced downtime, and increased productivity.
- Published in Nature Medicine: Researchers used a machine learning algorithm to predict patient outcomes in intensive care units (ICUs). The study found that the algorithm was able to predict patient mortality and length of stay with high accuracy, and had the potential to improve patient outcomes and reduce costs.
Rising Value of AI/ML Systems
The use of AI/ML systems has the potential to improve decision-making and performance across a wide range of applications. However, it is important to note that the performance of these systems can be highly dependent on the quality and quantity of data available for training, and on the development of appropriate algorithms and models for the specific application. It is also important to ensure that AI/ML systems are used ethically and transparently, and that their outputs are validated and verified to ensure their accuracy and reliability.
Personalized Measurable Benefits
When combined with our custom-built proprietary rules engine, the Claimocity type 2 A.I. puts super-computing abilities in your pocket including prediction, analysis, historical evaluation, and statistical modeling that evolves with your practice to improve performance.
This allows Claimocity to align with your specific needs, increasing efficiency and adjusting to provide faster and more effective help in everything from catching common mistakes to suggesting workflow solutions that improve revenue maximization or workload reduction.
Technology in Action
Smart technology is only as good as the people who build, maintain, and utilize it.
Our team of programmers, engineers, product developers, and specialists really are second to none in their ability to not only bring the technology to life but optimize the flow and output to really super-charge the ability of the physician to get the most out of it on a daily basis.
We rotate multiple teams of experts which is how we are able to offer 24/7 around the clock support and concierge service. Our billing experts, software troubleshooters, designers, and coders are constantly working on improvements and ways to achieve even higher benchmarks.
All of our data processing, statistical analyses, predictive modeling, error resolution, proactive learning, and pre-programmed structures are designed with one goal, to help you perform medicine at an optimal level without being bogged down or hindered in your process.
Proprietary Computational Models System
A Framework that Adjusts to the Needs of Your Practice & Providers
We leave the mediocre blanket approaches to our competitors and offer automated software that is personal and hyper-focused on you and your practice.
Instead of forcing you to adjust to our systems, we created systems that adjust to you, and focus on designing a user-friendly means of setting up smart filters, practice rules, management options, and easy to access (and use) features that enhance your daily efficiency.
Rules Engine Software
What is a Rules Engine?
A rules engine is a software system that allows users to define, execute, and manage business rules and logic in an automated and efficient manner. The purpose of a rules engine is to enable non-technical users, such as business analysts or domain experts, to specify business rules in a form that can be easily understood and maintained.
A rules engine typically consists of two main components: a rule editor and a rule engine. The rule editor is used to create and modify business rules, while the rule engine is responsible for evaluating these rules against specific data and generating outcomes or actions based on the rules.
Rules engines can be implemented as standalone systems or integrated into larger enterprise applications. They are often designed to be highly configurable and customizable, so that users can adapt the system to their specific business needs.
What is a Medical Rules Engine?
A medical software rules engine is a software system that is specifically designed to manage complex medical data and logic. It is a type of rules engine that is used in healthcare applications to automate decision-making and improve the accuracy and efficiency of clinical processes.
Medical software rules engines typically include a rule editor that allows medical experts to define clinical rules and logic in a structured and understandable format. The rule engine component evaluates these rules against specific patient data, such as lab results, vital signs, and medication history, and generates outcomes or alerts based on the rules.
Medical software rules engines can be used in a variety of applications, such as clinical decision support, disease management, and population health management. For example, a medical software rules engine might be used to monitor patients with chronic conditions, such as diabetes or heart disease, and generate alerts when specific clinical indicators are outside of acceptable ranges.
The use of medical software rules engines can help healthcare organizations to improve the quality and consistency of care, reduce medical errors, and increase efficiency. They can also help to ensure that clinical decisions are based on the latest evidence and guidelines, and that care is delivered in a timely and appropriate manner.
3 Keys to Success
Every facet of our smart technology and data analytics is driven to increase the daily efficiency of our end user… you. Our physician-centric software is built within a user-friendly framework and automated so that all the hard lifting happens in the background while you just swipe, click and go about your day quickly and efficiently.
Helping doctors and practice managers do the wrong things faster is counterproductive so we spent nearly a quarter of a million man hours on R&D and evaluations to ensure that the filters, features, and smart productivity enhancements we created are not just efficient but the “right tools” to drive workflow optimization and better bottom line financial results.
All our hard work in designing and building Claimocity has been to not only save you time but generate effective revenue efficiency tools that help achieve higher levels of success in practice management, daily operations, and financial growth. We help doctors work faster and more effectively in every daily capacity.
What is an RCM Rules Engine?
In healthcare, RCM stands for Revenue Cycle Management, which refers to the financial process of managing patient care from appointment scheduling and insurance verification to claim submission, payment collection, and account follow-up. An RCM rules engine is a software system that is specifically designed to automate the financial and administrative processes associated with revenue cycle management.
An RCM rules engine typically includes a rule editor that allows users to define business rules and logic related to financial processes. The rule engine component evaluates these rules against specific data, such as patient demographics, insurance coverage, and claims data, and generates outcomes or actions based on the rules.
Why is a rules engine key to saving time?
The primary goal of an RCM rules engine is to help healthcare organizations to optimize their revenue cycle processes, reduce denials, and maximize reimbursement. RCM rules engines can be used in a variety of applications, such as claims management, eligibility verification, and denial management. For example, an RCM rules engine might be used to verify insurance coverage for a patient prior to a scheduled procedure, or to identify and correct errors in a claim before it is submitted to a payer.
The use of an RCM rules engine can help healthcare organizations to improve their financial performance and reduce administrative burden. By automating revenue cycle processes and ensuring that business rules are consistently applied, RCM rules engines can help to reduce the likelihood of errors and delays, and help organizations to collect payments in a timely and efficient manner.
Scientific Studies on the Value of an RCM Rules Engine
There have been a number of scientific studies on the use of rules engines for revenue cycle management (RCM) in healthcare. Here are a few examples:
- Journal of Healthcare Information Management: Researchers examined the impact of an RCM rules engine on the revenue cycle processes of a large academic medical center. The study found that the use of the rules engine resulted in significant improvements in denial rates and days in accounts receivable, as well as a reduction in the number of staff needed for claims management.
- Journal of Medical Systems: Evaluated the effectiveness of an RCM rules engine in improving the accuracy and efficiency of claims processing in a large hospital system. The study found that the rules engine was effective in reducing the number of claims with errors, as well as the time and resources required to manage claims.
- Journal of Health Information Management: Examined the impact of an RCM rules engine on the financial performance of a large multi-specialty physician practice. The study found that the use of the rules engine resulted in significant improvements in the accuracy and completeness of claims data, as well as a reduction in days in accounts receivable and an increase in net collections.
These studies suggest that the use of rules engines for RCM in healthcare can lead to significant improvements in revenue cycle processes, including reductions in denial rates, days in accounts receivable, and staffing requirements, as well as improvements in accuracy and completeness of claims data and financial performance.
AI in Medical Billing: Why It Matters
Time-Saving Automation Enhanced Efficiency
Claimocity uses type 2 artificial intelligence, commonly referred to as limited memory A.I., in order to enhance the abilities of our software to automate the daily workflow processes and drive better revenue metrics while saving measurable quantities of time otherwise spent on admin tasks. Ai in medical billing at an overview level frees you up to spend more time on your patients and the medicine instead of struggling with paperwork.
AI Medical Coding: The Smart Tech Advantage
We measured the difference between our software without A.I. enhancements, where the isolated variable is human efficiency growth over time against the advantages provided by our software at full capacity. The differences were significant, and one of the key areas that stood out was coding efficiency where AI medical coding showed a significantly higher level of optimization, exceeding national standards.
From the onset the software improves AI medical coding performance scores, compared as relative value units (encompassing a combination of time cost, revenue generation, and mistakes made). Over time the gap grows as the human efficiency factor begins to show higher levels of a diminished margin of returns.
Machine Learning for Medical Billing: The Long Approach
We use highly-advanced modern computational opportunities to process and transform huge sets of complex interconnected data down to a set of simple mobile tools that are easy to understand and even easier to use.
The value over the short term is more of the AI generated value, where speed and efficiency are keys. The value over the long term is in the machine learning as it is able to take nuanced data, user patterns, industry context, and past performance to predict optimal changes towards the current situation. The result is that over time, instead of plateauing after the first few waves of improvement, our providers and practices continue to see large incremental growth in revenue generation, workflow speed into the long term, enabling much higher ceilings and a more rapid progression toward those ceilings.