How AI Can Help Improve Orthopedic Practice Health

Using AI to help boost orthopedic practice efficiency and scalable growth

The steady rise of AI in healthcare is transforming how medicine is practiced across the board, and orthopedics is no exception.

Whether you’re a single-surgeon specialist repairing ligaments for elite athletes or a bone and joint expert helping seniors manage chronic pain — the challenge is the same: ensuring truly coordinated care from diagnosis to rehabilitation. But no matter the orthopedic specialty or care setting, artificial intelligence is helping orthopedic providers manage fast-paced workflows and navigate the administrative and procedural complexities.

Intelligent interoperability and AI-native integrations

Orthopedic providers practice across the full spectrum of care settings: hospitals, ambulatory surgery centers (ASCs), rehabilitation centers, and private practices ranging from large university-affiliated groups to single-surgeon small clinics. With specialties spanning spine, joint, sports medicine, foot and ankle care and more. Regardless of their focus, they all depend on seamless access to lab results, imaging, and other critical data to get a holistic view of the patient and ensure that every stage of care from surgical through rehabilitative and pain management is connected and coordinated.

Electronic health records (EHR) initially addressed this problem by centralizing patient data and documentation, but that often led to information overload. Now, AI-powered orthopedic EHR systems can reduce complexities by structuring incoming healthcare data, deduplicating, summarizing, finding relevant information, and inserting it in the patient’s health record for provider review.

In clinics and ASCs or hospital settings, predictive models aid in identifying and coordinating equipment needs for procedures, with some AI-powered equipment even aiding in procedures themselves. By learning from prior cases and clinical notes, AI can also help create personalized treatment plans — whether for the teenage soccer player rehabbing a torn meniscus or an 80-year-old patient getting cortisone shots to help maintain their mobility.

Artificial intelligence can also reduce administrative burden. The ability to flag possible claims errors to reduce the chance of denials and pre-populating prior authorization documents, freeing orthopedic providers to focus on providing personalized treatment plans for better patient care.

With the power of advanced AI algorithms and machine learning, orthopedic practices can deliver more personalized care, drive better outcomes, and create new opportunities for growth.

The top ways AI can help improve orthopedic practice health

Orthopedic practices can use AI tools in EHRs and tailor them to specific business needs. Here are a few ways:

Improved claim accuracy and revenue cycle management (RCM)

Orthopedics is high-volume and complex: billing errors and claim denials can drastically increase financial burden on practices. To flag potential errors before claims are submitted, AI algorithms embedded in the athenaOne® revenue cycle management tools analyze coding patterns and payer requirements. Automated scrubbing and error flagging allows clinicians to make quick adjustments before submitting. A more accurate claim means a healthier revenue cycle and maximizing reimbursements. Faster decision-making and automated processes allow practice administrators the chance to reallocate their time elsewhere.

Native AI tools assist with patient registration and intake. For example, the Automated Insurance Selection workflow auto-matches patients with insurance packages from an uploaded insurance card image. athenahealth customers who created claims using the Automated Insurance Selection Workflow saw a 7.4% decrease in patient insurance related denials on those claims¹.

Read about the advantages of athenaOne EHR for orthopedics

Meanwhile, AI-powered Interface Insurance Mapping can help integrate third-party information into athenahealth’s library of insurance packages by mapping patient demographic and charge data during new client onboarding. That automation helped athenahealth customers saved 3,600+ staff hours on manual mapping².

Some AI tools now support orthopedic providers by recommending the best timing to follow up with payers on specific claims. The Intelligent Claim Follow-Up capability analyzes historical patterns with each payer and automatically creates tasks for athenahealth agents to follow up with providers regarding claim statuses. This can help orthopedic practices strike the balance between allowing time for claims to be adjudicated while also ensuring timely and proactive follow-up with payers. Soon, AI agents can fully automate this process with physician approval.

Automation can help providers enter claims more accurately—helping reduce the risk of denials—and accelerate reimbursement cycles and improve cash flow and overall practice management. Enhanced claim accuracy also reduces administrative overhead, allowing staff to concentrate on strategic practice growth initiatives rather than rework.

Predictive analytics across orthopedic workflows

Another way practices can look to harness AI is through predictive analytics, which offers ample value when it comes to determining prior authorization. Machine learning within the athenaOne authorization rules engine helps determine when a prior authorization is required.

AI to assist with prior authorizations

This form of predictive analysis can be especially valuable to orthopedic providers. Whether an orthopedic provider prescribes medication for bone density loss or an orthopedic oncologist orders a CT scan, AI analyzes millions of claims submitted on the athenaOne network annually to identify proper prior authorization requirements and surface supporting documentation. Soon, emerging agentic AI capabilities will extend that assistance even further.

AI to mitigate risk and improve value-based care success

Other AI-driven predictive models can help analyze patient data so clinicians can better identify those at risk for complications or poor outcomes. By identifying risks and engaging patients at important points of care, predictive models may help reduce hospitalizations and build pathways to better patient outcomes – all integral parts of value-based care success. These analytics can also help support personalized care plans and timely outreach, fostering stronger patient engagement and physician-patient relationships.

Enhance patient recovery and reduce no-shows using AI

Predictive insights can guide orthopedic practices in scheduling follow-ups with the hopes of reducing no-shows. Predictive analysis can also help practitioners devise rehabilitation programs tailored to individual recovery trajectories. By synthesizing data across a wider range of patients than the individual practice or health system, predictive models can give orthopedic clinicians clearer insights and support to make more actionable recommendations for each patient.

Engaged patients are more likely to adhere to treatment plans and return for ongoing care, which fuels patient retention, improved VBC metrics, sustainable practice growth and better health outcomes.

With the power of advanced AI algorithms and machine learning, orthopedic practices can deliver more personalized care, drive better outcomes, and create new opportunities for growth.

Faster documentation with AI-driven automation

Orthopedic encounters are complex. Providers spend valuable time with patients not only discussing symptoms but also conducting hands-on evaluations. From pinpointing areas of pain to testing flexibility and range of motion, these comprehensive assessments are essential for uncovering the root cause of a condition and guiding effective treatment. Before voice-activated, AI-powered tools, providers either switched frequently between the patient and their keyboard or relied on scribes to note-take during the encounter.

AI-powered natural language processing (NLP) tools are dramatically reducing the time orthopedic providers spend on documentation so they can be more engaged during each visit. That’s a major potential benefit since orthopedists often need to be on their feet.

The Ambient Notes Medical Scribe embedded in athenaOne seamlessly inserts note summaries into patient records, allowing providers to spend more time on the patients. Additionally, speech recognition tools allow clinicians to retrieve data hands-free, promoting more face-to-face with the patient, rather than facing the screen to enter their notes.

Tedious paperwork can also divert orthopedic providers away from patients. But AI-predicted document labeling for administrative documents can help reduce the time spent on labeling external faxes from imaging centers or labs.

These efficiencies can help boost orthopedic provider productivity and add much needed flexibility. However, human oversight matters. These tools can help streamline clinical documentation, but it’s up to orthopedic providers to verify the accuracy of AI outputs and ensure they appropriately reflect each patient encounter.

The growing AI use cases in orthopedics

The orthopedic field has already experienced some of the transformational capabilities of AI in image processing. For example, AI-powered imaging tools can analyze X-rays, MRIs, and CT scans to help orthopedic providers more accurately diagnose fractures, joint degeneration, and more. Robotic technologies, such as the MAKO robotic arm technology, can even aid surgeons during procedures.

Now, with more AI capabilities native to EHRs, orthopedic practices can improve practice health and clinical workflow efficiency. By integrating AI capabilities through comprehensive platforms like athenaOne, orthopedic practices can optimize financial performance and deepen patient engagement — including leveraging agentic AI and chatbots for support.

Find out how athenahealth’s AI integrations can service orthopedic practices with an EHR that can provide a stable foundation for achieving scalable growth.