Learning objectives for this course are to identify and explain key advancements in artificial intelligence technology and their potential impact on durable medical equipment. Describe the role and benefits of range of motion tracking systems in monitoring patient progress and outcomes. Differentiate between remote patient or physiologic monitoring, RPM, and remote therapy monitoring, RTM, technologies and demonstrate how to implement these systems effectively in an orthopedic clinic setting. Evaluate and apply best practices for integrating new technologies into clinic workflows to enhance efficiency and patient care. AI, or artificial intelligence, refers to the field of computer science focused on creating machines or software that can perform tasks typically requiring human intelligence. These tasks include things like understanding language, recognizing patterns, making decisions, and learning from experience. AI aims to simulate aspects of human cognition, enabling machines to solve problems, reason, and adapt to new situations. It's about creating systems that can think, learn, and adapt like humans to perform tasks. AI is already integrated into many aspects of modern life, from virtual assistants to driving cars to medical diagnostics. There's a lot of potential to impact DME with artificial intelligence, including predictive analytics and automated supply chain management, in which we can analyze patient and clinic data to predict supply and demand to optimize inventory and reduce waste. Remote monitoring, in which devices can monitor patient health metrics in real time, allowing for proactive adjustments to treatment plans. Decision support systems allows healthcare professionals to assist in making informed decisions regarding DME prescription, considering factors like patient history and current health status. There's also voluntary compliance and safety monitoring, which would help ensure that DME meets safety regulations by continuously monitoring performance and identifying potential issues. Insurance coverage and billing assistance can help analyze individuals' insurance policies to determine coverage criteria and billing requirements for specific products being dispensed to patients. It is incredibly important to understand the limitations and precautions to better harness the power of AI. Data and quality bias. AI models are highly dependent on the data they are trained on. If the data is incomplete, noisy, or inaccurate, the model will likely perform poorly. AI models can unintentionally learn and amplify biases present in the data. For example, if a model is trained on historical data that reflects disparities in healthcare, for example, racial, gender, or socioeconomic biases, the model may perpetuate these biases, resulting in unfair or unequal treatment. Bias in AI can harm vulnerable or underrepresented groups. Number two, interpretability. Many advanced AI algorithms, particularly deep learning models, are often difficult to interpret. In healthcare, this lack of transparency is a significant concern, as clinicians need to understand how AI systems arrive at their conclusions in order to trust and act on their recommendations. Number three, clinical validation. Before AI models can be used in clinical settings, they need to be rigorously validated in real-world healthcare environments. Validation must ensure that AI performs consistently across different demographics, healthcare systems, and conditions. Four, integration into workflows. Implementing AI tools in healthcare settings often requires significant changes to workflows. If the AI tool is not seamlessly integrated into existing practices, it can create inefficiencies, frustration, or even resistance from clinicians. Five, ethical considerations. Biases in fairness, patient autonomy, and privacy and data security are all important considerations when teaching AI models. Regulatory compliance. Healthcare AI tools must comply with a range of regulations, such as FDA approvals and data privacy regulations like HIPAA. Regulatory bodies are still catching up with the rapid development of AI technologies, which can delay the deployment of these AI solutions. Number seven, over-reliance on technology. One significant risk is the over-reliance on AI, which could lead to clinicians deferring too much decision-making to the technology, potentially ignoring their own clinical judgment or the nuanced aspects of patient care that AI systems may not capture. If clinicians become overly reliant on AI tools, they may lose their critical skills or become less adept at recognizing patterns or making decisions independently. This could reduce the quality of care in cases where the AI system fails or is unavailable. Number eight, continuous monitoring and improvement. AI systems need to be continuously monitored to ensure that their performance remains high. AI models, unlike humans, need regular updates and retraining to stay relevant and accurate. This requires ongoing data collection, model evaluation, and maintenance to ensure that the system is always operating optimally. Nine, patient engagement. Patients may be skeptical about AI's role in their care, especially if they don't fully understand how it works or if it makes decisions that seem to override their doctor's expertise. Building patient trust is essential, and this may require transparent communication about how AI is used in diagnosis, treatment, and decision-making. Patients must be adequately informed about AI's role in their care and provide consent for its use. This requires clear communication about how AI tools work, their potential risks and benefits, and the limits of the technology. And finally, interdisciplinary collaboration. Successful AI adoption in healthcare requires close collaboration between clinicians, data scientists, AI developers, and regulatory bodies. Each group brings a unique perspective and expertise to ensure that the AI system is clinically valid, safe, and effective. I'm sure many of us are aware that range of motion tracking plays a critical role in both acute and long-term patient care by regularly measuring and monitoring joint movement. The ability to track range of motion over time allows healthcare providers to intervene proactively, optimize rehab, and ensure that patients regain full functional use of their joints and limbs. Range of motion tracking can provide objective measurements, precise and objective measurements, of joint movement. This can help healthcare providers assess the joint range of motion post-operatively. It can also provide real-time monitoring and data collection, which will allow for immediate feedback on patients' mobility, inform adjustments to follow-up appointments and rehab plans, as well as track progress over time and identify any trends and improvements and setbacks. Range of motion tracking can also improve patient engagement. This may include features that engage patients in recovery, provide visual feedback on progress, or motivate patients to adhere to rehab exercises. There are many benefits of range of motion tracking. It can allow for providers to enhance recovery protocols for the patient, allow early detection of potential complications, improve communication between the provider and the patient, allow for more realistic goal setting for both the patient and provider, improve quality of care for the patient, and allow for additional research and benchmarking. I'm going to discuss remote therapy monitoring, RTM, and remote patient monitoring, RPM, what they are and what some of the similarities and differences are. Remote therapy monitoring monitors the progress and adherence to therapeutic protocols often associated with rehab exercises to ensure patients are following prescribed physical therapy regimens. Remote therapy monitoring utilizes a device that must meet FDA definition of a medical device. Data collection must be over at least 16 days on at least one medical device within a 30-day period. This data must be collected electronically and automatically uploaded to a secure location. There must be at least 20 minutes of provider interaction of RTM services, such as reading and interpreting data, changing care plans, and communicating with the patient within that 30-day period. There must be a medically reasonable and necessary reason to utilize RTM. Eligible practitioners include physicians, nurse practitioners, occupational therapists, and physical therapists. Only one practitioner can bill for RTM services per patient per 30-day period. Some examples of RTM include video platforms for exercise demonstrations, apps that guide patients through rehab routines, or motion sensors that provide feedback on exercise performance. Remote patient or physiologic monitoring, RPM, is the process of collecting and analyzing health data from patients outside of traditional clinical settings. This monitors vital signs, symptoms, and other health metrics to manage chronic conditions or track recovery. Similar to RTM, the device that's collecting this data must meet FDA definition of a medical device. This data collection must be done within a 30-day period with at least 16 days of data on at least one medical device. This must also be wirelessly synced for evaluation. A physician or other qualified health care professional must order the RPM service. And the medical necessity must be reasonable and necessary. In order to bill for these services, the patient must be an established patient. Only physicians or other qualified health care professionals can bill for these services. Some examples of RPM include wearable devices measuring heart rate, blood pressure, or oxygen levels, applications that track pain levels, or mobility. There are some similarities and vital differences between RTM and RPM. RTM is monitoring the progress and adherence to therapeutic protocols, whereas RPM is monitoring vital signs, symptoms, and other health metrics, typically to manage chronic conditions or track recovery. RTM can be billed by physical therapists or occupational therapists for services, whereas RPM is typically billed by a physician or other provider and must be an established patient. RTM also requires at least 20 minutes of services to be provided in order to bill for those services. Similarities include that patient must have 16 days of data within a 30-day period. Only one practitioner can bill for those services within that 30-day period. The service must be deemed as medically reasonable and necessary, and the device that's tracking these services must meet FDA criteria for medical device. And as always, these services must be HIPAA compliant. Here are some implementation considerations for RTM and RPM if you're interested in implementing this into your practice. First, we must assess the needs and select appropriate technologies. Is there a need for this technology for your patients for your practice? And what is the appropriate technology to collect data? How will you identify how data will be monitored or who will be monitoring data? We want to make sure that data is coming in and being sent somewhere and analyzed for the patient. We want to create patient education and engagement protocols to ensure the patient understand and are comfortable with the tasks they're being asked to complete. It's important to establish regular communication and follow up with patients to ensure that they are meeting their goals and working towards their goals, as well as to ensure safety and compliance. Collaboration and established shared goals are important for both the patient and the provider to keep everyone on track and moving forward. How will data be analyzed and reported out? What data will be analyzed? What data is important? And what are the reporting mechanisms for that data? And as always in health care, we want to ensure privacy and security compliance to ensure PHI is kept safe and the patient's privacy is kept up. New technology can often be flashy and exciting. However, we want to establish some best practices and considerations for integrating that new technology to ensure success. Assessing the needs and defining the goals of this new technology is important to start off with. What is the purpose of that technology? What is it going to do for your clinic? What is success going to look like? Selecting those the appropriate technologies for your individual clinic is also important, as everyone may have a little bit of a different workflow. Ensuring staff training and engagement is important to maintain that that technology is successful. Streamlining workflows helps minimize any additional work within the clinic. Enhancing patient engagement is important overall in your clinic. We want to utilize data analytics to determine how success is being measured. What is success and how is that technology improving the lives of your patients or the workflows of your clinic or whatever else, those goals that you had defined above. As always, we want to prioritize security and compliance. That is of utmost importance in any health care setting. And you want to make sure any new technology that you're implementing has top notch security. We want to encourage multidisciplinary collaboration. Technology is going to enable that collaboration and make it a bit easier. So encouraging that across maybe multiple disciplines or multiple departments is incredibly important in streamlining those workflows. We want to collect feedback and iterate that feedback is going to be really important in determining whether or not that technology is helping. And promoting a culture of innovation, technology is here to help you not make things more difficult. So determining what's going to help you and how that new technology can potentially help and improve your clinic workflow is really important. So promoting that culture is going to help identify promising new technologies as well as take your clinic forward and make it more successful. In summary, AI is forever involving and will have a huge impact on our future. The key to optimizing AI is to understanding its limitations. Range of motion tracking systems can enhance the rehab process post-operatively for our patients. It can provide objective data, improve patient engagement, and allow for personalized care, ultimately leading to better recovery outcomes. By clearly differentiating between RPM and RTM and implementing tailored strategies in an orthopedic clinic, healthcare providers can enhance patient engagement, improve recovery outcomes, and streamline the overall treatment process. Clinics can effectively integrate new technologies into their workflows, enhancing both operational efficiency and patient care. The key is to maintain a patient-centered approach while ensuring that staff are well equipped and supported throughout the process. This will lead to improved outcomes, higher patient satisfaction, and more streamlined clinic environment.

artificial intelligence

durable medical equipment

remote monitoring

patient engagement

data security