How can AI optimize the ROI in Healthcare RCM?

The pressure to deliver high quality care under the weight of administrative cost is significant among healthcare organizations today. Key to the survival of this dilemma is revenue cycle management (RCM) or the series of activities providers utilize to capture services rendered, bill payers and patients and ultimately collect payment. Most importantly, return on investment (ROI) remains an invaluable metric to assess both a technology, or process, shift. This blog will explore the role of artificial intelligence (AI) in healthcare RCM in order to help providers maximize ROI through efficient work flows or processes, fewer denials, improved coding and documentation efficiencies, faster reimbursement and better patient access (and satisfaction). We will also address how the Carevyn platform enhances these efficiencies, outline best practices and challenges as well as share a general roadmap to integrate RCM with AI support.

Understanding the Landscape: RCM, ROI and Healthcare

Health care revenue cycle management (RCM) illustrates the end-to-end process of managing a patient's care from the time a patient makes an appointment (scheduling and registration), documentation, coding, claims submission, payment posting, denial management, collections, as well as reporting. Therefore, an accurate and effective RCM process is essential, because a mistake or delay in any of these parts of the RCM process would erode revenue, increase costs and reduce cash flow.

Why is ROI important in healthcare RCM?

When healthcare organizations allocate funding for technology improvements or operational enhancements for their RCM, they seek tangible ROI. This ROI could show up as:

Reduced administrative costs (less manual time, fewer errors).
Lower denial rates and lower re-work.
Faster claim submission, faster payment, better cash flow, and as a result, fewer days in A/R.
Better coding accuracy and compliance, lower risk of audit.
A better patient financial experience and collections.
Better provider productivity (more time for clinical work instead of administrative burden).

A recent survey indicated that RCM will provide a hard-dollar ROI for accurate documentation and coding — cleaner claims, fewer denials, and recorded gains on both the revenue and expense lines. In an additional study, 96% of providers indicated that “strong ROI” was a top consideration when purchasing new RCM software.

Why is healthcare RCM under pressure?

Three forces are working against traditional RCM:

Increasing regulatory complexity (multiple payers, prior authorizations, bundling, value-based models).
Increasing volume of transactions, increasing patient financial responsibility, more complex reimbursement.
Legacy systems, data silos, manual workflows, not a lot of focus on managing administrative burden.
The need to improve patient access, patient experience, transparency, and revenue performance.

1 (1).png — The core challenges putting traditional RCM under stress

Within this framework, artificial intelligence has surfaced as an appealing lever to enhance healthcare revenue cycle management (RCM) and achieve valuable return on investment (ROI).

How AI Delivers Improved Healthcare RCM and ROI

We explain below the major ways AI influences RCM in healthcare and thus increases ROI.

1. Pre-service / Patient Access: Eligibility, Prior Authorization, Scheduling

By utilizing AI, the revenue cycle interventions can be moved even more upstream-where no claim is available-yield functions such as eligibility check, real-time benefits verification, prior authorization management and schedules as well as capacity optimization.

• Eligibility & benefits verification: AI-based tools can look through historical data and the payer rule engine to very fast figure out patient-insured coverage thus cutting down on mistakes in eligibility and denials.

• Prior authorizations: AI-run workflow is able to generate alert for staff concerning how many services require authorization, automate the gathering and submission of the proper documentation, thus cutting off the waiting time and facilitating patient access.

• Scheduling optimization: Through AI, volume can be increased by directing the patients to the right doctor, verifying the doctor’s credentials, and matching with the payer networks, thus reducing the cases of services that have been scheduled wrongly resulting in loss of revenue.

AI can help by eliminating errors and inefficiencies at the front to lessen the instances of re-work downstream, increase the correctness of submissions and reduce denials, thus leading to better ROI.

This is the point where AI, by handling problems and inefficiencies at the very source of care, cuts down on the re-work that has to be done downstream, leads to improved submission accuracy and decreases the number of denials, all of which contribute to ROI.

2. Documentation, Coding & Charge Capture

Proper clinical documentation and charge capture are essential to the submission of correct claims, reimbursement, compliance, and audit ‍‌‍‍‌readiness.

AI technologies (NLP, speech-to-text, real-time scribe) can transcribe patient-provider interactions, auto-populate documentation and reduce provider burden to get higher quality documentation to allow for faster and easier coding. For example, Carevyn touts "98% + accuracy in coding & documentation."

Carevyn could also automatically suggest or assign ICD-10, CPT codes from documentation the AI documented which enhances speed, reduces human error, and optimizes reimbursement. This helps ensure providers are capturing all relevant charges.
Risk adjustment & value-based care: AI can assist with risk stratification, patient-population analytics, and value-based care metrics allowing providers and payers to optimize reimbursement in alternative payment models.

By improving documentation and coding accuracy, AI reduces coding queries, claims rejections/denials and audit risk which deliver stronger ROI through increasing net realized revenue and reducing administrative costs associated with claims processing.

3. Claim submission, denial prevention/management

Claims denial, correction, and re-submissions is one of the biggest areas RCM suffers from revenue leakage. AI can significantly reduce that leakage.

Using predictive analytics to find claims at-risk of denial: AI systems can learn from historical data and predict, prior to submission, which claims are likely to need correction if submitted.
Automated workflows and appeals: AI can initiate automated workflows for denial appeals, gathering and submission of documentation and processes which speed recovery and reduce manual processes.
Analytics & dashboards: Real time dashboards provide revenue cycle managers the capability to identify patterns, bottlenecks and payer trends that can be addressed proactively to avoid denied claims in the future.

Reducing claim denials will lead to increased first-pass approval of claims, quicker processing of first-pass appeals, improved cash-flow, reduced AR days, less operational expense and therefore a higher RCM ROI.

4. Payment posting, reconciliation, collections & patient financial experience

After claims have been submitted and paid there still remains several areas in the revenue cycle that are inefficient and drive cost or revenue leakage. AI can optimize these areas.

Automated payment posting & reconciliation: AI/RPA can parse remittance advice, post payments, identify exceptions and short payments optimizing process and automatically route tasks.
Engaging patients in financial responsibility: AI-based portals, chatbots and self-service solutions support patients in understanding their financial responsibility, making payments, decreasing bad debt and improving collections.
Analysing receivables, ageing and collections: AI can identify and prioritise the accounts with the highest likelihood of payment, initiate escalation when warranted, and streamline existing workflows.

Improved collections and accurate payment posting prevents leakage, speeds the realisation of revenue, and decreases overhead - all of which clearly contribute to improved ROI.

5. Analytics, reporting and continuous improvement

AI's value is beyond automation, it provides insights, thus allowing for strategic decision-making and process improvement.

Real-time analytics: AI platforms can provide dashboards that display KPIs related to the revenue cycles such as denial rates, days in accounts receivable, clean claim rates, etc. This allows leaders to act upon the information early and correct the course.
Predictive: AI can provide predictive insights of future revenues, detect staffing/resource gaps, or model "what-if" scenarios that could potentially improve the process.
Benchmarking and population health: AI can help providers include a broader data set (clinical, operational, etc.) in order to line up revenue cycle performance with care delivery and value-based initiatives, in this way bridging the clinical and financial outcomes.

This cognitive intelligence helps organisations continuously improve workflows, lower costs, enhance margins, and maintain ROI over time.

6. Scaling value-based care and alternative payment models

With the shift from fee-for-service to value-based care, risk-based contracting and alternative payment models (APMs) are now being adopted widely. RCM must evolve to address value-based care models — and AI has a critical role to play.

Risk-adjusted coding and stratification: AI can determine who has a higher risk, ensure proper documentation to identify care bundles and risk contracts to capture full allowable revenue.
Population health analytics: AI can allow providers to integrate clinical and financial data to manage cohorts, outcomes, costs, and reimbursement within value-based contracts.
Strategic resource allocation: AI can help organisations move from a reactive mode of claims processing to proactively managing population health and risk contracts — allowing for new revenue opportunities not traditionally available through RCM.

As organisations align RCM with value-based care programs and initiatives, organisations better identify new opportunities for revenue and margin improvement — both critical components of ROI in an evolving healthcare ecosystem.

How Carevyn Supports AI-Focused RCM Optimization?

As noted on the Carevyn website, the platform offers an all-in-one AI-enabled healthcare automation platform that helps alleviate RCM challenges illustrated above. Lastly, this analytic intelligence provides the organisation to continuously optimise workflow processes and reduce costs.

Significant features of how Carevyn helps to enable an improved ROI and healthcare RCM are:

AI-enabled documentation & coding: Carevyn asserts "98 %+ accuracy in coding & documentation" — allowing providers revenue to more efficiently and accurately capture revenue, and markedly lessen downstream errors.
Prior authorisation & eligibility automation: Carevyn automates prior authorisation workflows, and real-time eligibility checking, eliminating unnecessary delays in admin and maximising patient access.
Denial prevention and revenue optimisation: In combination with predictive analytics and workflow automation capabilities allow Carevyn to assist in decreasing claim denials (noted at over a 30 % decrease) and revenue leakage.
Scalable integration: Carevyn platform is integrated with leading EHR systems (Epic, Cerner, Athena) and scalable to enable providers to support population health and value-based care models.
Measurable ROI: As noted, the “Proven ROI for Providers & Payers” reflects costs reductions, gain efficiencies and revenue generated.

2 (1).png — AI-powered RCM made smarter, faster, and more efficient with Carevyn

In conclusion, Carevyn is uniquely positioned to help health systems (Providers) systematically introduce AI across major RCM workflows, allowing them to gain more revenue, diminish costs, improve productivity and thus develop positive ROI.

Quantifying ROI: Metrics & Outcomes

Assessing ROI with AI-enhanced healthcare RCM involves tracking important metrics pre and post implementation. Some of the key metrics and performance indicators include:

Clean claim / first‐pass resolution rate.
Denial rate (claim denials expressed as a percentage of claims submitted).
Days in accounts receivable (AR).
Net reimbursement (actual payments vs billed).
Re-work or re-submission cost (time and labour).
Administrative cost per claim (staff time, overhead).
Patient collections and bad debt rate.
Provider productivity (care vs documentation).

With increasing administrative complexity, regulatory changes, rising patient responsibility and reimbursement model changes, optimising revenue cycle management (RCM) is imperative! AI provides a means of redefining accuracy, and improving speed, efficiency and insight throughout the revenue cycle - leading to proper ROI, revenue protection, and cost reduction, while enhancing the patient experience and provider satisfaction.

When considering a strategic AI plan to RCM, healthcare organisations can apply AI at key workflow points; eligibility verification, documentation and coding, claim submission, denial management, payment posting, patient collections and analytics for ongoing improvement. With the appropriate and affordable AI technology, care organisations can structure a sustainable competitive advantage with healthcare financial performance improvements.

In conclusion, there is an obvious strategic path for health care providers and payers; establishment of measurable goals, match those goals to high-impact use case(s), iterate, scale, evaluate results and embed technology into human clinical workflows. Carevyn's platform was designed to execute these expectations seamlessly with AI-facilitated automation, all-in-one revenue cycle optimization, compliance, and value-based care enablement.

When you are ready to optimise your revenue cycle and achieve ROI in healthcare, now is your time!

Swiftly go and check out Carevyn's official website, or to get more information about Carevyn schedule a demonstration

Frequently Asked Questions:

1. What does RCM mean in healthcare?

RCM stands for Revenue Cycle Management. In healthcare, it describes the steps a provider follows to track patient care episodes, beginning with registration and appointment scheduling and concluding with the final payment of a balance. The cycle incorporates billing, coding, claims management, payment posting, and collections.

2.‍‌‍‍‌ Why is ROI important in healthcare RCM?

ROI or return on investment, is a metric that greatly supports healthcare organisations in their decision-making processes. For instance, it can be used to measure the impact of AI-powered RCM innovations on the financial outcome of the healthcare sector. In this way, providers through the lens of ROI analysis can figure out if costs are saved, cash flow is enhanced, more claims are approved, and overall operational effectiveness is elevated.

3. How can AI improve ROI in healthcare RCM?

Firstly, AI boosts ROI in the following ways:

· One of the main reasons for the use of AI is to lower claim denials by utilising predictive analytics.

· Through automation, the process of coding and documentation becomes quicker, and thus, the claim submission can also be done in less time.

· To help increase the correctness with charge capture and billing, AI can be leveraged as a tool.

· Prior authorisation and eligibility verification is made efficient through AI-powered solutions.

· Helping payment posting and patient collections through AI capabilities is another method of optimizing the healthcare revenue cycle.

· The loading of administration on healthcare professionals can also be put to an end with AI.

To sum up, AI technologies are capable of not only revitalizing the revenue that has gone missing but also of lessening the process inefficiencies that directly affect ROI.

4. Why Carevyn over other AI-based RCM solution?

Carevyn is considered as a top-notch product because of its:

· Ability to provide coding and documentation results that are accurate and can be verified.

· Automation that is complete from the beginning to the end across RCM workflows.

· The integration of AI with top-of-the-line EHR allows for the scaling up the functionality of the system.

· The main business objective behind the deployment of AI is to come up with ROI measurement tools.

· Additionally, the AI components integrated in the RCM are both mutually supportive.

5. Is Carevyn appropriate for small clinics and big hospitals?

Absolutely. Carevyn is an AI platform that is not only scalable but also flexible; thus, healthcare organizations of any size, ranging from single clinics and specialty practices to large hospital networks, can benefit from ‍‌‍‍‌it.