How AI-Powered Remote Patient Monitoring Can Improve Healthcare

By now, everyone is familiar with the Triple Aim of healthcare: improve the care experience, enhance health outcomes, and reduce costs. It’s a frequent topic of conversation across the industry, but many leaders struggle to find consensus on how to achieve these admirable goals.

Fortunately, artificial intelligence (AI) offers healthcare organizations an ideal opportunity to utilize their existing data infrastructure to modify the traditional care delivery model in a meaningful way for their patients. This moves organizations closer to achieving the vaunted Triple Aim.

AI represents a superpower for clinicians to scale their impact, acts as a welcome tool for the physicians and nurses who have heroically served our country during the COVID-19 pandemic, and supports the consequential rising levels of burnout from it.

One clinical tool powered by AI is remote patient monitoring (RPM), a technology that “refers to interventions that allow patients to share data using information, connected devices, and communication technologies.” Additionally, providers are able to utilize RPM to respond to patient needs outside of a traditional healthcare setting.

Throughout the COVID-19 pandemic, there have been increased opportunities for the use of RPM to assist vulnerable patients with critical treatment needs as well as support the clinical staff they rely on.

The coronavirus outbreak has amplified the adoption of RPM and challenged the traditional boundaries of institution-centric care. In parallel, RPM care has rapidly evolved, driving positive changes in care delivery models.

There are three major areas at the intersection of AI and RPM: clinical efficiency, care outcomes, and cost of care. Unsurprisingly, they mirror the Triple Aim. When properly utilized, AI can reduce the administrative burden and improve patient outcomes, resulting in a targeted effort to drive cost reduction across the healthcare system in a meaningful way.

This could not come at a better time, as a McKinsey & Co. study released in October 2021 found that the total administrative spending in healthcare in 2019 was $950 billion, accounting for nearly one-quarter of the total US healthcare spending.

As the industry looks ahead to a post-COVID-19 future, it must consider the technology and resources needed to achieve the full promise of the moment. AI-powered RPM represents one of healthcare’s best options to meet the challenges and expectations that lay ahead.

Promoting Clinical Efficiency

A study published in NPJ Digital Medicine in 2018 found that “passive gathering of data may also permit clinicians to focus their efforts on diagnosing, educating, and treating patients, theoretically improving productivity and efficiency of the care provided.”

Industry leaders strive toward a system like this—one in which clinicians don’t have to spend nearly as much time requesting health status information from patients due to RPM sensors and AI tools minimizing the effort to submit accurate information. As more responsibility shifts from manual processes over to automation and AI-powered technology, this can be viewed as a positive for both patients and providers alike.

All types of healthcare organizations are exploring how to leverage automation and AI to streamline workflows. A realistic aspiration for AI-powered RPM is to encourage patients to adhere to their care plans while also returning associated information to providers. This allows clinical staff to calibrate a patients’ care plan in response to their needs.

Delivering Better Outcomes

When it comes to improving patient outcomes and personalizing protocols, look no further than AI’s ability to use algorithms that recognize patterns in large clinical datasets and classify patients based on these patterns.

By flagging patients at high risk, AI allows the clinical team to predict patient outcome trajectories and organize data with great speed and accuracy. To that end, advanced algorithms can also analyze large data sets like to identify individuals at risk and monitor the impact of interventions on outcomes for proactive and data-driven changes in care protocols.

Notably, the patient, the most important stakeholder, is engaged in all stages of the RPM process. This proactive engagement method drives better outcomes.

On top of these classifications, healthcare technology providers and clinicians can build process automation and workflow standardization.

Patients with chronic diseases like hypertension, heart failure, diabetes, and obesity require daily adherence to medication, exercise, and nutrition care plans. With RPM tools, these daily actions can produce data that flows into AI systems as a feedback loop on the effectiveness of said care plans.

AI can also offer precise recommendations, which improves patient engagement and adherence to care protocols. This is the clearest opportunity for AI to shift healthcare from reactive treatment toward evidence-based therapeutic interventions and preventative intervention.

Natural language processing (NLP) can also be utilized to draft and sometimes fully automate personalized patient-facing communications. This materially scales clinicians’ bandwidth for empathetic and informative patient communications.

Researchers from Penn State University and Geisinger Health aimed to use AI to improve patient satisfaction. The results showed that “variables related to the courtesy and respect of nurses and doctors and the communication between health professionals and patients significantly impacted patients’ overall hospital experiences.”

Additionally, access to data and information in-between visits lays a foundation for a better care delivery model by eliminating the gaps and barriers that exist without RPM. The 2018 NPJ Digital Medicine study also noted that RPM showed “early promise in improving outcomes for patients with select conditions, including obstructive pulmonary disease, Parkinson’s disease, hypertension, and low back pain.”

By arming clinicians with AI-powered RPM, they can see trends over time, in-between visits, enabling changes in the plan of care toward better outcomes. They also have access to dashboards facilitating decision-making. By increasing access to care when it’s needed, clinicians can eliminate the barriers associated with patients in rural areas.

Reducing Costs

An added benefit of AI-powered RPM solutions is the downstream effects of more proactive treatment for patients. This technology improves the timeliness of care and supports earlier detection of clinical deterioration, which enables earlier intervention to avoid unnecessary emergency room (ER) visits and inpatient utilization.

If providers can intervene on behalf of their patients because they have accessible data forecasting, it can reduce the chances of visits to the ER or costly overnight stays in the hospital.

A 2018 study conducted by Canadian researchers examined the effects of RPM on patients with chronic obstructive pulmonary disease (COPD) and chronic heart failure (CHF) to see if there was any impact on high costs related to ER visits. The researchers acknowledged that while RPM requires an upfront investment, it has “the potential to reduce health care costs to the system over time.” This finding is a promising reminder that when patients can be handled in a more proactive manner, not only are their outcomes affected, but their costs are also as well.

Additionally, by reclaiming clinicians’ time and delivering improved care outcomes, AI can also improve healthcare organizations’ bottom lines.

Elsevier researchers cited estimates that AI could reduce US healthcare costs by $150 billion in 2026, due in large part to “changing the healthcare model from a reactive to a proactive approach” and “focusing on health management rather than disease treatment.”

The latter part of Elsevier’s analysis is key to the core nature of AI: a forward-facing technology that seeks to address health conditions before they become problematic. These dynamics stem negative, costly downstream effects and benefit both patients and their providers.

A Time for Change

As data continues to make its impact in the industry, it will be helpful for leaders to look around at functionalities where AI is not currently applied and see if the industry can move in that direction.

While most of AI’s focus on healthcare has been in the digital therapeutics space, there is ample opportunity for this technology to apply toward actual patients, improving operational workflows, and other efficiencies.

AI can upend the industry’s reliance on manual data entry and multiple inputs, making it no longer incumbent upon the patient to provide accurate information, or on the front-line worker to procure this data before analyzing it. This potential reality is something worth striving for.

The healthcare industry stands at the precipice of a grand, data-driven future. There is still room for growth and exploration, but the foundation has been set for AI to make healthcare more efficient and strategic.

AI is here to supplement healthcare workers, not replace them. This technology—these concepts—can be a boost to clinicians, support the bottom line, and measure up with the most complicated healthcare challenges.

Rosemary Kennedy, PhD, RN, is the chief health informatics officer for Connect America.