RCM Data - Find it, Analyze It, and Build On Its Insights for Better Revenue

As a revenue cycle professional, do you like the sound of:

• denial prevention instead of denial management?  
• pre-emptive claims correction instead of post-submission appeals?
• real-time net revenue forecasting instead of static annual budgets?
• anticipating and preparing for financial challenges instead of scrambling to rectify them?

These four proactive tactics remind us of how medicine is shifting from disease care to preventative care (otherwise known as healthcare.) Most likely, you already have the data to prevent revenue leakage and higher costs, just as consumers have the knowledge to eat better and move more. It’s the shifting of habits that’s tough. Familiarity with data (and vegetables) takes some study, but more and more RCM professionals are investing in both every day. 

Data is the lifeblood of digital transformation in healthcare, and industry leaders like McKinsey have been repeating for years that digital transformation is “critical for organizations to not only compete but survive.”

In an environment with a dwindling population of workers and surging medical demand, it will take digitial transformation technologies like artificial intelligence, machine learning, and advanced analytics to deliver care to an aging population – a population demanding the latest, greatest, most extensive treatments. These technologies feed on data. 

This article reveals which of your current software has your data, the types of analytics to run on your data, and how to use these insights to improve revenue, reduce costs to collect – all to not only survive but thrive. 

Where healthcare stands now with digital transformation and data

Health system executives have heard the call for digital transformation. Nearly 90% consider it a high or top priority. The industry is progressing, but far from complete, as 75% of respondents reported their organizations are not yet able to fully deliver on this priority due to insufficient planning or resource allocation. 

Elements of digital transformation are: patient-centric care, personalized medicine, value-based medicine, workflow optimization, operational efficiency, enhanced patient access and digital communication. All of this is achieved via the identification and analysis of data. 

Data powers:

• Artificial Intelligence (AI): AI systems in healthcare rely on vast amounts of data to learn patterns, make predictions, and assist in decision-making. For example, AI can analyze medical images to detect anomalies or predict disease progression based on patient data.
• Machine Learning (ML): ML algorithms improve their performance over time by learning from data. In healthcare, this could mean refining diagnostic accuracy or personalizing treatment recommendations based on the constant evaluation of patient outcomes data.
• Advanced Analytics: These tools process large datasets to uncover insights that might not be apparent through traditional analysis methods. For instance, population health analytics can identify trends and risk factors across patient groups.

These three technologies underlie digital transformation. 

Healthcare data in the clinic 

Data analytics is reshaping healthcare delivery, driving a shift towards more personalized, efficient, and patient-centric care.

In the realm of care delivery, data-driven insights are enabling unprecedented levels of precision and personalization. For instance, the field of precision medicine leverages genetic data to inform highly targeted therapies tailored to an individual's unique genetic profile. This approach, exemplified by initiatives like the Cancer Moonshot program, allows for more effective treatments with fewer side effects by matching patients with the most appropriate interventions based on their genetic makeup.

The patient experience is another area being transformed in the clinic by data and data analytics. 

Wearable devices and health apps not only empower patients to take a more active role in managing their health, but they also provide real-time insights and support for managing health goals and conditions. Every measurement is a data point. These technologies, combined with patient feedback mechanisms and engagement platforms, help healthcare providers to tailor the patient experience more effectively.

When the data generated via genetic testing and wearables combine with clinician notes and lab tests, patients benefit from an incredibly informed and precise approach. Some say it’s the most comprehensive ever offered – one that optimizes patient outcomes, communication, and patient satisfaction. 

How RCM data analytics optimizes the revenue cycle 

While data analytics has incredible clinical promise, society still needs a viable healthcare infrastructure to deliver these exciting futuristic treatments. That infrastructure is reinforced by a healthy, streamlined revenue cycle. 

By offering unprecedented insights and efficiencies across the healthcare organization revenue cycle, data analytics is revolutionizing healthcare operations. From claim submission to payment collection, advanced analytics tools enable healthcare organizations to optimize their financial operations, reduce errors, and improve cash flow.  

Most providers use data and data analytics most often to improve:

• Claim status and tracking:  Predictive data analytics uses historical data to determine which current claims will need the most documentation and the highest level of scrutiny. Staff can then double-check these claims, ensuring that all boxes are checked before submitting to increase the likelihood of approval and improve clean claims rate. Real-time data analytics allows tracking of claims through the adjudication lifecycle, enabling immediate identification and resolution of issues to reduce delayed payments and denials.
• Denial pattern analysis: Data analytics can analyze claim denials to identify patterns and root causes, allowing organizations to take proactive measures to minimize errors and improve claim accuracy.
•  Revenue leakage identification: Data analytics helps uncover revenue leakage points such as under-coding, missed charges, or incorrect pricing, allowing healthcare organizations to capture previously lost revenue.
• Performance metrics tracking: Data analytics allows tracking of key performance indicators (KPIs) like denial rates, clean claims rates, first pass yield, and accounts receivable days, providing insights into RCM efficiency. RCM professionals find this data in their contract management software or spreadsheets. Payer performance analytics also reveal trends in payer behavior, rank payers in terms of performance and value to the organization, list reimbursement rates by CPT code, and find denial patterns. Concrete data and stats back up your demands for better rates and terms.  

Take a quick, self-guided tour through a powerful contract performance optimization and payer underpayments identification tool:

• Process optimization: By analyzing workflow data, organizations can identify inefficiencies and bottlenecks in the revenue cycle, leading to improved operational performance. RCM professionals find this data 
• Patient payment behavior: Data analytics can provide insights into patient payment patterns, helping to optimize billing strategies and improve collection rates. If a patient is a slow payer, the earlier the better to have staff reach out to determine a repayment schedule or find other support.
• Revenue forecasting: Leveraging the most recent historical data and predictive analytics in healthcare contract modeling enables accurate revenue forecasts, helping organizations make informed financial decisions and plan budgets effectively. 

You can model exactly how proposed payer changes will impact your revenue. Experiment with your own changes via unlimited scenarios. Take a quick tour of efficient contract modeling in action here: 

• Compliance monitoring: Data analytics helps in tracking and ensuring compliance with regulatory requirements, reducing the risk of penalties and audits.
• Benchmarking: Analytics enables healthcare organizations to leverage RCM benchmarks to gauge their performance against industry achievements, identifying areas for improvement.

These points demonstrate how data-driven insights can significantly enhance revenue cycle management across various aspects, from claims processing to financial forecasting and operational efficiency.

In essence, data fuels the entire ecosystem of revenue cycle management, enabling CFOs and VPs to generate better revenue at a lower cost to collect. These outcomes depend on a constant flow of high-quality data.

Where healthcare organizations stand with data mining today

With more budget and talent, large health systems, academic medical centers, for-profit healthcare organizations, and specialized clinics lead healthcare digital adoption. They have the resources, research capabilities, and competitive incentives to drive innovation. 

In contrast, small and rural hospitals, public health departments, long-term care facilities, and behavioral health providers typically lag in data-driven digital transformation efforts, often due to resource constraints and outdated infrastructure. These rural outposts struggle to get the best care they can to their communities. 

The healthcare industry as a whole is still in the midst of this transformation. Only 7% of healthcare and pharmaceutical companies report full digitalization, compared to 15% in other industries. While investments in advanced technologies like robotics and analytics show high satisfaction rates among adopters, the smaller organizations are still working out foundational elements such as electronic health records and basic telehealth capabilities. 

Healthcare organizations that sense that they have catching up to do in their digital transformation should focus their efforts on cloud-based solutions, data analytics, and AI integration. Slow and steady progression beats putting our heads in the sand. 

Where is your RCM data? 

Most likely, you’re already using your big patient database – your EHR. The following sources, too, contain a treasure trove of data that can inform revenue cycle processes and decisions.

1. Electronic Health Record (EHR) Systems

Description: Comprehensive platforms for managing patient health information and clinical workflows. They serve as the primary repository for patient data in healthcare organizations.

Data collected: Patient demographics, medical history, diagnoses, medications, lab results, imaging studies, clinical notes, and treatment plans.

2. Practice Management Systems (PMS)

Description: Software designed to manage the day-to-day operations of healthcare practices, including scheduling, billing, and administrative tasks.

Data collected: Appointment schedules, patient registration information, insurance details, billing codes, claims data, and payment information.

3. Revenue Cycle Management (RCM) Systems

Description: Specialized software for managing the financial aspects of healthcare, from patient registration to final payment of a balance.

Data collected: Insurance eligibility data, claims data, remittance information, denial rates, accounts receivable metrics, and payment trends.

4. Contract Management Systems

Description: Tools designed specifically for managing payer contracts, including storage, analysis, and optimization of contract terms.

Data collected: Contract terms, fee schedules, performance metrics, compliance requirements, and renewal dates.

5. Financial Modeling and Analytics Platforms

Description: Advanced software for financial analysis, forecasting, and scenario modeling in healthcare settings.

Data collected: Historical financial data, cost data, revenue projections, market trends, and operational metrics.

6. Health Information Exchange (HIE) Systems

Description: Platforms that enable the secure sharing of patient health information across different healthcare organizations.

Data collected: Consolidated patient records, care summaries, lab results, and medication lists from multiple providers.

7. Quality Reporting Systems

Description: Software designed to track, analyze, and report on quality measures for regulatory compliance and performance improvement.

Data collected: Clinical quality measures, patient satisfaction scores, outcomes data, compliance metrics.

These systems often integrate to provide a comprehensive data ecosystem for healthcare organizations, enabling holistic analysis and decision-making across financial, and operational domains.

Who chases down and analyzes this RCM data? 

Yes, RCM data pools are VAST.

But the math people love them, and are even excited by them. Just arm them with some cool algorithms (or get them to create the algorithms) and unleash them on your numbers. This section explores three primary options for handling this critical task: 

• employing in-house data specialists
• engaging third-party contractors, or 
• implementing advanced software solutions designed specifically for RCM data analysis.

Data specialists

Data specialists in healthcare play a pivotal role in leveraging information to improve patient care, operational efficiency, and strategic decision-making. Their primary responsibilities are:

• collection and management of diverse datasets from electronic health records, claims information, and medical databases. 
• ensure data quality, accuracy, and consistency while adhering to healthcare regulations and privacy laws like HIPAA. 
• apply advanced analytical techniques, including statistical methods and data mining, to uncover patterns, trends, and correlations within the data. 
• develop algorithms and predictive models to anticipate population demands. 

Data specialists collaborate closely with clinical, financial, and operational teams to understand their specific needs and provide relevant, data-driven recommendations. They monitor key performance indicators, identify areas for improvement, and support decision-making processes across the organization. Additionally, these professionals stay abreast of the latest trends in healthcare analytics, adapting to new technologies and methodologies as they emerge. 

If you already have these data specialists, you’re ahead of most healthcare organizations. 

In-house v. outsourced data specialist

If you don’t have your own data team, the first fork in the road is to consider whether you need a full-time, in-house specialist or a part-timer or outsourced, even a third-party solution. 

The great trade-off for any healthcare specialist will always be: hiring  in-house staff provides greater operational control but higher costs. 

The next variable is the size of the organization. For small practices, outsourcing may be more cost-effective and practical than hiring full-time data mining and analytics staff. A contractor can come in during one quarter and deliver a year’s worth of action steps and insights. 

Part-time data specialists are available. 

Where to find a part-time healthcare data specialist

Healthcare systems, physician groups, and practices can get started with robust data mining and analytics by bringing on temporary healthcare data contractors to augment their existing teams.  

Start with specialized recruitment firms like Dataspace which specializes in recruiting for healthcare analytics talent. Platforms like Toptal and even Fiverr also list data analysts ready to work part-time. 

Take care in vetting these remote workers. Those who seek these data specialists should look for: 

•  healthcare-specific knowledge and experience.
• a verifiable list of past clients.
• proficiency in various data analysis software, programming languages, and visualization tools.
• the ability to leverage advanced techniques like machine learning and artificial intelligence when appropriate.

Data analytics via a third-party partner

 Engaging a third-party data analytics partner in healthcare offers advantages and disadvantages that organizations must carefully consider. 

On the positive side, specialized analytics partners can bring extensive expertise and experience to the table, potentially driving improvements in customer satisfaction, user experience, marketing optimization, and revenue maximization. 

They often have access to advanced tools and methodologies that might be cost-prohibitive for individual healthcare organizations to develop in-house. Additionally, third-party partners can provide fresh perspectives and insights, drawing from their work across multiple clients and industries. 

However, there are also potential drawbacks to consider. First, engaging with these companies can mean a yearly fee. Your own freelance data specialist won’t charge a year-round fee. 

Then, too, data security and privacy concerns are paramount in healthcare, and sharing sensitive information with external parties can increase the risk of breaches or non-compliance with regulations like HIPAA. For instance, Newsweek listed Change Healthcare as one of the top data analytics firms, but its security breach had providers and physician groups heading for the exits. 

There may also be challenges in integrating external analytics processes with existing internal systems and workflows. Furthermore, relying too heavily on external partners might limit the development of in-house analytics capabilities over time.

Payer contract data is the foundation of your data endeavors

Many provider groups, practices, and healthcare management services organizations are just beginning to find and analyze their data. Its power is remarkable.  

Our automated contract management and modeling solution, RevFind, uncovers the contract data that identifies top and underperforming payers, recognizes payment patterns, and flags potential underpayments. This data helps you understand which payers are underpaying you, which of your CPT codes are getting the most denials, and which locations don’t measure up in revenue. Its healthcare contract modeling software lets you model proposed payer changes and new contracts to determine how they will impact your revenue. 

RevFind not only centralizes all contracts in a digital format within a single platform, but it also features automated alerts for crucial contract dates, including expiration, renewal, and termination deadlines, ensuring compliance with deadlines. Users embrace the options to customize advance warnings, such as 90 days before key dates. 

Schedule a demo to see how RevFind can deliver the contract performance data that gives you the confidence and backup to justify your demands. 

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