Practical pointers for using AI in remote patient monitoring

AI software embedded in video devices, wearables and sensors—not to mention actual patient monitors—can continuously track post-surgery patients in real time, sending predictive insights to care teams regardless of where they’re stationed. 

“These technologies can be used to detect post-operative complications, provide personalized recovery protocols, and assist in shared decision-making for tailored treatment options,” explain two orthopedic surgeons at Mayo Clinic Arizona in a scientific paper published Sept. 10.

AI-enabled remote patient monitoring, they add, “analyzes massive quantities of data and utilizes technologies to allow [surgery and rehabilitation teams] to track patient progress, post-operative symptoms and recovery in real time—with the capacity to augment or replace in-person visits.” 

Eugenia Lin, MD, and Kevin Renfree, MD, aim their overview of AI’s growing role in remote patient monitoring at peers in their field who subspecialize in musculoskeletal care of the hand. But their guidance is broad enough to generalize across many if not most clinical specialties that use remote patient monitoring. 

In a section offering practical implementation tips, the authors break down AI adoption into six steps. 

1. Workflow assessment.

Identifying tasks that can be automated informs further decision-making, Lin and Renfree state. 

‘Through mapping of systems processes, interviews of various stakeholders including clinicians and administrators, and analysis of existing electronic health record data, an intervention that can be automated is targeted.’

2. Technology and vendor selection. 

As platforms for technologies and vendors are quite formative, selecting platforms that offer monitoring capabilities is important, the authors point out. “Iterative platforms with transparent modeling offer the capacity to consider growth in the future,” they add. “Furthermore, technology and vendors that work with existing systems, such as the electronic medical record, provide seamless data flow and have likely met validated safety requirements.”

‘Considerations for types of technologies, such as wearables, patient communication, or clinician facing tools should be made.’

3. Clinical integration. 

Clinical integration of AI-enabled remote patient monitoring includes the interface with not only existing EMR systems but also with the clinical workforce, the researchers note. “Training and educating staff about the use, maintenance and continued evolution is imperative for a successful implementation,” they write.

‘Creating dedicated roles and teams ensures operational improvements, from patient communication protocols to data analysis and interpretation.’

4. Patient onboarding.

Patients are key stakeholders in the success of AI-enabled remote patient monitoring, Lin and Renfree emphasize. It’s essential, they maintain, to identify suitable candidates in appropriate settings from consultation to post-operative visits. Equally key is “ensuring the appropriate consent, privacy compliance and instructions for use of these technologies.”  

‘Consent for both the use of these technologies and future device usage and data sharing should be clearly outlined.’

5. Sustainable data collection and processing. 

“As integration advances, considerations for sustainable data collection and processing should be made, including automation of data analysis for current trends or outcomes to allow for regular review of collected data,” the authors advise. 

‘Continuing to qualitatively and quantitatively monitor key performance indicators—including patient adherence, outcomes and satisfaction—is important.’

6. Reimbursement and compliance. 

“Finally, implement processes to document AI-enabled remote patient monitoring activities for reimbursement purposes,” Lin and Renfree urge. 

‘Be familiar with the most up-to-date procedural terminology codes for therapeutic monitoring and intervention—and stay informed about evolving regulations.’

The paper posted Sept. 10 in Hand Clinics and can be found here.

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