RCM Health Information Management: 3 Ways Switching from Manual to Automated Improves Revenue

1. Better operations

The tasks listed above are often delayed when executed with manual processes. 

Manual processes involve spreadsheets and staff that may or may not have sufficient RCM and contract performance training. 

The AI, machine learning, and robotic process automation involved in automated RCM solutions leverages aggregated, centralized data, unleashing millions of calculations per minute. AI processors conduct trillions of operations per second. Algorithms and automation not only never tire, but they also work 24/7. 

Automated RCM improves revenue for healthcare organizations by streamlining processes, reducing errors, and accelerating cash flow. Here’s how: 

• Faster claims processing:  Automated RCM systems expedite claims submission and processing, reducing delays and ensuring timely reimbursements. Organizations implementing automation have reported up to  30% reductions in claim denials and faster payment cycles, directly improving financial health. Most automated RCM health information management systems leverage real-time visibility into contract performance and RCM tasks.  When identifying discrepancies, trends, and opportunities in real time, the time to start denial appeals or underpayment recovery processes shortens. While payers may not respond more quickly to proactive corrections, at least they can’t use the excuse that you notified them too late. Missed deadlines sap provider revenue. 

•  Reduced errors and denials: Automation minimizes human errors in billing, coding, and claims submission by standardizing processes and ensuring compliance with payer requirements. This leads to cleaner claims and fewer denials, saving time and resources spent on corrections.

•  Lower cost-to-collect: By automating repetitive tasks like eligibility verification, claims tracking, and payment posting, healthcare providers reduce administrative costs. A Black Book survey of 1,302 healthcare financial professionals found that revenue cycle automation software lowers the cost-to-collect by up to 27%, freeing up resources for reinvestment in patient care and operations. 

• Predictive analytics for revenue optimization: Advanced technologies like AI and machine learning enable predictive analytics that identify trends in denials, payer behavior, and cash flow patterns. These insights help healthcare leaders optimize revenue cycle strategies and improve financial outcomes.

By integrating automation into RCM processes, healthcare organizations achieve significant improvements in efficiency, cost reduction, error minimization, and revenue growth—essential for maintaining financial stability in an increasingly complex healthcare environment.

2. RCM staff time savings

A hefty 63% of healthcare providers are grappling with revenue cycle management RCM staffing shortages, according to CWH Advisors. The shortage has spiked labor costs, further pushing healthcare organizations toward less-costly, AI-driven revenue cycle management automation. 

Checking payer contracts and government regulations against treatment plans and rates gets tedious and confusing pretty fast. Consulting firms like Huron and Bain repeatedly warn healthcare organizations that piling an abundance of menial, repetitive work on RCM staff causes burnout. Automation and process improvement keep staff on higher-value, more interactive work that energizes rather than depletes them. 

When providers use automated RCM healthcare information management to support staff, rather than replace it, they not only improve staff experience, they cut the time needed for each RCM task. For instance, 

• MGMA reports that manually posting denials takes an average of 2.10 minutes per claim, whereas robotic process automation (RPA) reduces this time to just 2 seconds per claim. 
• Deloitte Healthcare finds that automated health information management and coding is completed 61% - 70% faster than manual work. It analyzed medical records management, such as collecting and digitizing documents related to patient care; facilitating access, retrieval, and sharing of patient information; as well as dictation and transcription.  

The speed of staff juggling a flurry of spreadsheets can’t compete with the nearly instant results automated RCM solutions deliver. Read how a women’s reproductive health group avoided adding 4 to 8 new staff members that would be necessary to compile upfront good faith estimates after the rollout of the No Surprises Act. By using eligibility and estimation generation automation, the group avoided a possible $344,000 annual labor expense. 

3. Modernization via data standardization and centralization 

Anymore, it takes data-driven insights to impress boards, lenders, investors, and buyers.They know that, as Deloitte Healthcare explains, in its recent Achieving a Revenue Cycle of the Future, 

“The revenue cycle can serve as the main source of insights for operational and financial performance, as well as clinical insights. Cognitive machine learning and predictive models, in particular, are important for ingesting data across a multitude of systems and sources and generating actionable insights. They will allow organizations to craft intelligent, streamlined workflows with advanced prioritization and to rapidly identify and understand trends.”

Given the explosion of data, both for individual patients, patient populations, and revenue cycle workflows, most providers recognize it’s time to modernize by standardizing and centralizing their data. Aggregated, this data is a treasure trove revealing insights into patient care trends, payer behaviors, denial patterns, operational inefficiencies, and financial performance, enabling providers to make data-driven decisions that optimize revenue cycle processes and improve overall organizational outcomes.

Data standardization, centralization, and analysis are inescapable. This year, the compound annual growth rate of data for healthcare reached 36%, according to the global investment banking and capital markets division of the Royal Bank of Canada. In other words, in less than three years, healthcare’s data will have doubled. That means the data detailing each patient, each population, each treatment, and each RCM process will double.  

Every healthcare organization should establish a uniform data integration strategy not only to modernize administrative and financial systems but to improve patient outcomes, reputation, and revenue right away. 

With systemic data integration, providers:  

• Consolidate financial and administrative records for KPI analysis.  
• Improve claims accuracy to minimize denials.  
• Automate data transfer, provide real-time updates, and eliminate duplicate entries to reduce manual data entry and reconciliation.  
• Boost staff productivity.  
• Empower leadership with a comprehensive view of operations, patient care, and outcomes for better decision-making.  
• Simplify the usability of patient information.  
• Ensure the accuracy of patient data.  
• Identify patterns in down-coding and take corrective action proactively
• Enhance patient health outcomes by leveraging complete medical histories and patient characteristics.  
• Strengthen care coordination across multiple facilities.  

Ultimately, centralized, standardized data management and analysis provide detailed insights into individual patients, patient populations, facility operations, and revenue cycle performance, enabling actionable improvements across the organization. Standardized financial data enables more accurate billing, claims processing, and revenue cycle management, leading to improved cash flow and reduced denials.

Manual data compilation and analysis require staff members or teams to cross-check multiple sources and derive insights. The most effective revenue cycle analytics software solutions standardize data sets and integrate these sources to deliver a unified view of an organization's financial health. With government initiatives pushing for improved data integration, healthcare organizations are increasingly adopting these practices.  

Data analytics software typically employs ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes to extract data from diverse sources, integrate it into a centralized repository, and prepare it for analysis. These tools then apply advanced analytical techniques to generate insights and reports, enabling organizations to make informed, data-driven decisions.

Data standardization and centralization boost healthcare organization revenue in several ways:   

• Improved decision-making: Centralized data provides a unified view of operations, enabling healthcare leaders to make more accurate data-driven decisions across clinical, financial, and administrative domains.
•  Enhanced patient care / organization reputation: Standardized data ensures accurate and complete patient records, allowing clinicians to deliver better care through improved diagnosis, treatment planning, and care coordination. In other words, it’s a reputation builder.
• Operational efficiency: By centralizing and standardizing data, organizations can reduce redundancies, streamline workflows, and improve overall operational performance. All of these improvements lower operational costs, thereby improving revenue. 
• Regulatory compliance: Centralized data management simplifies compliance with healthcare regulations by ensuring consistent reporting and documentation practices.
• Cost reduction: Data integration reduces administrative burdens, minimizes errors, and lowers costs associated with manual data reconciliation and fragmented systems.
• Scalability: A centralized data framework allows healthcare organizations to scale operations efficiently while maintaining consistency in processes and outcomes.