Laboratory billing processes are highly inefficient, relying on manual data entry, disjointed systems, and reactive denial management. This leads to revenue loss, high costs, and frustrated staff.
Healthcare data analytics presents a solution to optimize laboratory billing:
- Developing structured data models
- Centralizing data sources
- Implementing AI for validation
- Leveraging analytics
However, there are hurdles to overcome:
- Integration across systems
- Digitizing unstructured data
- Managing large volumes
- Ensuring security compliance
- Finding qualified staff
Labs must invest in the following:
When executed effectively, data management delivers measurable improvements in revenue, costs, efficiency and staff productivity. In summary, healthcare organizations should view data as an asset and manage it strategically to transform laboratory billing.
Inefficient Laboratory Billing Processes
Laboratory billing is rife with inefficiencies that make the process more difficult, time-consuming, and error-prone. Here are some of the most common issues:
- Manual data collection
Laboratory staff needs to manually gather relevant test data from various sources before they can assign billing codes.
90% of providers still utilize paper and manual processes for collections, and 77% say that it takes over a month to collect payments (InstaMed 2018 Report). This is tremendously labor-intensive and susceptible to human error.
- Claim denials
Staff must review each claim when Payers deny or reject them. They need to find the reason for the denial or rejection. Then, they need to strategize and take steps to fix the claim. Finally, they are able to resubmit the claim in the hopes that they remedied the error.
According to a recent MGMA poll, 48% of healthcare leaders responded that claims payment was their biggest revenue cycle challenge, followed up by denials, prior authorization and staffing issues. This manual, reactive work to manage denials is extremely inefficient.
Learn more about preventing denials with revenue cycle analytics.
- Disjointed systems
Most labs use a LIMS to store test data, and a separate Practice Management/Billing system to handle claims. Without tight integration between these two systems, billing becomes complex and opaque.
A whitepaper by Dark Daily noted that only 23% of labs have a "seamless LIMS-billing system integration.”
- Poor data quality
Insufficient or inaccurate data often leads to claim denials and revenue loss. Yet cleaning and enhancing data is difficult without automation. According to research firm Gartner, poor data quality is responsible for an average of $15 million per year in losses.
- Multiple/outdated systems
Some labs operate on multiple LIMS or EMR systems due to mergers, acquisitions, or reluctance to replace legacy systems. This further complicates billing as data comes from disparate sources.
Labs have tried outsourcing billing, keeping it in-house, and a hybrid approach. But these efforts have had mixed success due to lack of transparency, control, or insufficient resources. It's clear laboratories need a better approach to manage their billing processes. Data management offers a strategic solution.
Data Management in Healthcare
With the right data management strategy, laboratories can eliminate many billing inefficiencies, reduce costs, and maximize revenue. Here are some of the most effective techniques:
- Building structured data models that allow for quick analysis and simplified billing code selection. This does away with the current method of 1:1 mapping between tests and codes.
- Creating a single operational data layer that unites data from across systems into one centralized, accessible source.
- Implementing AI tools to validate and clean data during order entry and testing, preventing issues before billing.
- Developing analytics platforms that spot claim errors, model outcomes, and score claims by denial probability. Learn more about why your data analytics strategy is failing.
- Enhancing data security through restricted access, encryption, and compliance with healthcare regulations.
- Hiring knowledgeable staff to oversee data initiatives and ensure adherence to best practices.
With these methods, forward-thinking labs are cutting denial rates, slashing billing costs, and improving cash flow. But implementing data management does come with challenges...
Challenges and Solutions in Data Management
Implementing effective data management comes with significant hurdles that laboratories must overcome. According to a 2017 whitepaper on machine learning, 25% of healthcare businesses say that data collection and processing are their greatest challenges to embracing a modern revenue cycle analytics model.
- Accessing all relevant data across disparate systems
As mentioned, labs often have multiple disconnected systems like LIMS, EMRs, and billing software. Integrating data across these systems is incredibly challenging according to a 2021 Journal of AHIMA article. APIs offer one solution for bidirectional data flow.
- Digitize unstructured data
You must digitize important billing and patient data currently trapped in faxes, PDFs, and paper. Though OCR (Optical Character Recognition) can aid in this task by digitizing various documents, poorly handwritten texts and unusual patterns can bring the error rate to 20%. Advanced machine learning OCR (Optical Character Recognition) delivers higher accuracy. Learn more about optimizing your laboratory automated order entry workflows using OCR.
- Large data volumes
Every day, labs generate vast amounts of data that require storage, processing, and analysis. This demands robust IT infrastructure, enterprise data warehouses, and leveraging cloud scale.
- Security and compliance
Keep billing data secure and compliant with HIPAA, HITECH, and other regulations. Failure leads to steep penalties. Having dedicated security and compliance experts is key.
- Qualified staff
There continues to be a talent shortage when it comes to qualified healthcare data analysts, scientists and engineers (2021 AHA). Investing in talent development, training and retention is crucial.
To unlock the full value of their data and improve billing, laboratories can enhance integration, digitization, infrastructure, compliance, and staff skills. The effort required is substantial but worthwhile, as effective data management delivers major rewards.
Measuring the Success of Billing Process Improvements
How can labs know if their data management initiatives and billing process improvements are working? Metrics to monitor include:
- Increased cash collection rates
- Reduced claim denial rates
- Lower costs associated with denial management
- Faster accounts receivable turnover
- Shortened revenue cycle times
By tracking these key performance indicators over time, laboratories can quantify the impact of their efforts and identify areas needing refinement.
Streamlining laboratory billing through data management provides a multitude of benefits: optimized revenue, reduced costs, improved efficiency, and higher staff productivity. For IT and Operations leaders seeking to transform their billing function, leveraging analytics, AI, and centralized data delivers measurable results. With the right expertise and solutions, laboratories can overcome billing bottlenecks. The future looks bright for organizations that harness the power of their data.