Your Revenue Cycle Management Data Template

This attribute describes a single step or milestone within the revenue cycle management process for a given billing event. Activities represent the work being done, such as 'Charges Captured', 'Claim Submitted', or 'Payment Posted'.

Analyzing the sequence of activities is the core of process mining. It allows for the discovery of the actual process flow, identification of bottlenecks where activities take too long to start, and detection of rework loops where activities are repeated unnecessarily, like 'Claim Denied' followed by 'Denial Rework Started'.

It defines the steps in the process, allowing for the visualization of the process map, calculation of transition times, and identification of process deviations and rework.

This information is typically derived from event logs, status change records, or transaction codes within various R1 RCM modules. It may require mapping from technical codes to business-friendly names.

The Billing Event ID represents a distinct instance of a service or product delivery that results in a charge. It acts as the central thread connecting all related activities, from the initial service rendering and charge capture through claim submission, payment posting, and eventual account closure.

In process mining, analyzing the lifecycle of each Billing Event allows for a comprehensive view of the end-to-end revenue cycle. It is used to trace the complete journey of a single charge, identify common pathways, measure cycle times between key milestones, and understand variations that lead to delays or revenue leakage.

This identifier is essential for grouping all related activities into a single case, enabling a complete and accurate process analysis of the revenue cycle for each billable event.

This is the primary key linking various tables related to patient encounters, charges, claims, and payments. Consult R1 RCM documentation for the specific field, often related to an encounter or claim identifier.

Event Time provides the precise date and time that an activity was recorded in the system. This temporal information is fundamental for understanding the process from a time-based analysis.

In process mining, this timestamp is used to order events chronologically and calculate durations between activities, which is critical for performance analysis. It enables the calculation of key metrics like cycle time, processing time, and wait time, which are essential for identifying bottlenecks and measuring efficiency.

This timestamp is the foundation for all time-related analysis, including calculating cycle times, identifying bottlenecks, and monitoring process performance against SLAs.

Typically found as a 'Creation Date', 'Timestamp', or 'Last Update Date' field associated with each transaction or status change record in R1 RCM.

This attribute indicates the last time the data for the process mining analysis was updated. It provides context on the freshness of the data being analyzed.

This information is important for reporting and dashboarding, as it tells the user how current the process insights are. It helps manage expectations about the data's timeliness and ensures that decisions are made based on an understood timeframe.

Provides crucial context on data freshness, ensuring that analysts and stakeholders are aware of how up-to-date the process insights are.

This timestamp is generated during the data extraction, transformation, and loading (ETL) process and is usually applied to the entire dataset.

This attribute identifies the source application or module that generated the data for a particular event. In a complex environment like healthcare, data may come from an EMR, a billing module, a claims clearinghouse, or a collections platform.

Understanding the source system is crucial for data validation and for analyzing process variations that may be specific to certain systems. It helps in troubleshooting data inconsistencies and understanding the technological landscape of the process.

Identifies the origin of the data, which is crucial for data governance, validation, and understanding how different systems interact within the end-to-end process.

This is often a static value added during the data extraction process, identifying the system (e.g., 'R1 RCM') from which the data originates.

This attribute identifies the individual responsible for executing a specific task in the process. This could be the biller who created the claim, the analyst who reworked a denial, or the specialist who posted a payment.

Analyzing by user helps to understand workload distribution, individual performance, and training needs. It can highlight which users are most efficient or which may be associated with higher error rates, enabling targeted management and process improvement efforts.

Enables analysis of team and individual performance, workload distribution, and helps identify training opportunities or user-specific process deviations.

Typically found as a 'UserID', 'Processor', or 'UpdatedBy' field in transaction logs within R1 RCM.

This attribute specifies the organizational unit, such as 'Charge Entry', 'Claims Submission', or 'Denial Management', that executed a particular process step. It helps in understanding how work is handed off between different teams.

This is crucial for analyzing departmental throughput and identifying cross-functional bottlenecks. By filtering the process map by department, organizations can see where handoffs are smooth and where delays occur, supporting resource allocation and organizational process optimization.

Allows for analysis of process performance by organizational unit, helping to identify team-specific bottlenecks, resource constraints, or best practices.

This may be derived from the user's profile in R1 RCM or stored as a 'Department Code' in the transaction data.

When a payer denies a claim, they provide a reason code, such as a CARC (Claim Adjustment Reason Code), to explain the decision. These codes are standardized and indicate issues like 'Service Not Covered' or 'Duplicate Claim'.

This attribute is extremely important for denial management. By analyzing the frequency of different denial reason codes, organizations can identify and address the root causes of denials, whether they are related to patient eligibility, coding errors, or lack of medical necessity. This directly supports efforts to reduce rework and accelerate cash flow.

Provides the specific reason for claim denials, enabling root cause analysis to reduce future denials, decrease rework, and improve first-pass payment rates.

This information is received in the electronic remittance advice (ERA or 835 file) from the payer and is stored in the claims management module of R1 RCM.

This attribute represents the total billed amount for the services rendered in a given billing event. It reflects the revenue expected from the claim.

Analyzing the invoice amount is critical for financial process mining. It allows for prioritizing high-value claims, understanding the financial impact of process delays or denials, and segmenting the process based on value. For instance, an analysis might reveal that claims over a certain amount follow a different, more manual process path.

Provides financial context to the process, enabling analysis of how process variations impact revenue, and helps prioritize high-value cases for improvement.

Located in the main claim or invoice header table in R1 RCM, often named 'TotalBilledAmount' or similar.

This attribute indicates the last known state of a billing event, such as 'Submitted', 'Paid', 'Denied', or 'In Collections'. It provides a snapshot of where an invoice is in the process at a given time.

Invoice status is vital for creating aging reports and monitoring the health of accounts receivable. In process mining, it can be used to filter for cases that are stuck in a particular state or to analyze the outcomes of different process variants, for example, comparing the paths of 'Paid' vs. 'Denied' claims.

Provides a current-state view of each case, essential for building aging reports and analyzing the final outcomes of different process paths.

This is typically a status field on the main claim or account record in R1 RCM.

This attribute identifies the primary payer for the claim. This could be a commercial insurer like Aetna, a government payer like Medicare, or the patient themselves for self-pay portions.

Analyzing the process by payer is fundamental to revenue cycle management. It can reveal that certain payers have higher denial rates, longer payment cycles, or more complex submission requirements. These insights allow the organization to tailor its processes and resources to manage payer-specific behaviors effectively.

Enables segmentation of the process by payer to identify payer-specific delays, denial patterns, or payment behaviors, which is crucial for optimizing revenue.

Found in the patient's insurance or demographic information linked to the claim in R1 RCM.

This attribute captures the value of any financial adjustments made to a patient's account after the initial billing. Adjustments can be positive or negative and include contractual allowances, write-offs, or corrections.

Tracking adjustment amounts is critical for understanding revenue integrity. High levels of negative adjustments can indicate revenue leakage due to issues like incorrect charge capture or uncollectible debt. Analyzing this data helps identify the financial impact of billing errors and collection inefficiencies.

Quantifies revenue leakage and financial corrections, helping to pinpoint the monetary impact of billing inaccuracies, contractual obligations, or bad debt.

Found in the transaction logs related to account adjustments or payment posting in R1 RCM.

This attribute provides context for why a financial adjustment was made to an account. Reasons are often standardized codes or descriptions that categorize the type of adjustment.

Analyzing adjustment reasons helps to diagnose the root causes of revenue leakage. For example, a high frequency of 'Small Balance Write-off' might suggest an inefficient collections process for small amounts, while frequent 'Contractual Allowances' are an expected part of payer negotiations. This analysis supports the Billing Adjustments & Compliance Audit dashboard.

Explains the 'why' behind revenue adjustments, helping to identify root causes of revenue loss, such as contractual issues, billing errors, or collection failures.

This is typically a code or text field in the same transaction record as the AdjustmentAmount in R1 RCM.

This attribute describes the outcome of efforts to collect payment on an overdue account. Possible outcomes include 'Paid in Full', 'Settled', 'Placed in Bad Debt', or 'Unresolved'.

Tracking collection outcomes is essential for evaluating the effectiveness of the collections process. By analyzing which activities lead to which outcomes, organizations can optimize their collection strategies, improve recovery rates, and make informed decisions about when to cease collection efforts and write off balances. This supports the Collection Activity Performance dashboard.

Measures the effectiveness of the collections process by tracking the final resolution of overdue accounts, helping to optimize collection strategies.

This is likely a status field on the patient account or in a dedicated collections module within R1 RCM.

This boolean attribute distinguishes between tasks executed by software automation, such as an RPA bot for claim submission, and tasks performed manually by an employee.

Analyzing this attribute is key to understanding the impact and effectiveness of automation initiatives. It allows for comparing the speed, cost, and error rates of automated versus manual processes, helping to identify new opportunities for automation and measure the ROI of existing bots.

Distinguishes between human and system-driven activities, which is critical for measuring the impact of automation on process efficiency, cost, and quality.

This can be derived from the 'AssignedUser' field, where specific user IDs are reserved for bots (e.g., 'BOT_RPA01'). Alternatively, some systems have a dedicated field to flag automated transactions.

This attribute is a boolean flag that is typically calculated during process mining analysis. It becomes 'true' if an activity is a repetition of a previous step or is part of a sequence that indicates correction of an error, for example, any activity following 'Claim Denied'.

Identifying rework is one of the most powerful capabilities of process mining. It quantifies the amount of wasted effort, time, and resources in a process. By flagging rework, organizations can focus their improvement efforts on preventing the errors that cause it in the first place, leading to significant efficiency gains.

Helps quantify the frequency and impact of rework, such as claim denials, allowing for targeted analysis to reduce inefficiencies and wasted effort.

This is not a field in the source system. It is calculated by the process mining tool based on the sequence of activities, such as detecting when a 'Claim Submitted' activity occurs more than once for the same case.

This attribute is the unique ID assigned to a patient within the healthcare system, often referred to as the Medical Record Number (MRN).

While individual patient care is not the focus, the Patient ID can be used to analyze recurring billing issues for the same patient over time. It can also help segment the process by patient demographics or history if linked to other patient data, potentially uncovering systemic issues affecting certain patient groups.

Allows for analysis of billing events at the patient level, helping to identify recurring issues or patterns for specific patients over multiple encounters.

This identifier is a core part of the patient demographic data linked to every encounter and claim in R1 RCM.

Service codes, like CPT (Current Procedural Terminology) codes, are standardized medical codes used to report medical, surgical, and diagnostic procedures and services to payers for reimbursement.

Analyzing the process by service code is essential for identifying billing issues related to specific types of care. It can highlight which procedures are most often denied, have the longest payment cycles, or require the most rework, allowing for targeted improvements in coding and billing practices.

Enables process analysis based on the type of service rendered, which is key to identifying denial patterns or payment delays associated with specific procedures.

This information is found at the line-item level for each charge or claim within R1 RCM.

This metric measures the end-to-end duration of the revenue cycle for a single billing event. It represents the total time it takes for an organization to convert a provided service into cash.

This is a critical Key Performance Indicator (KPI) for financial health. Analyzing this duration helps identify major areas for process acceleration. By breaking down the cycle time into its component parts, such as 'time to bill' and 'time to pay', organizations can pinpoint the biggest opportunities to improve cash flow.

This is a critical, high-level KPI that measures the overall efficiency of the cash conversion cycle, directly impacting the organization's cash flow.

This is a calculated metric. It is the time difference between the timestamp of the 'Service Rendered' activity and the 'Payment Posted' activity for a given Billing Event.