As provider buy-in lags, analyst touts 6 tenets of remote patient monitoring
Though many think remote patient monitoring is the future of healthcare, a new report by Chilmark Research claims issues with implementation are keeping providers from jumping in despite its many promises.
"Though connected health models make sense for patient care, their adoption among healthcare providers has historically lagged due to poor reimbursement by both public and private payers," the analyst firm wrote in a report published Thursday that outlines key tasks providers should focus on to embrace the technology.
[See also: Remote patient monitoring steps toward new era.]
Chilmark said the upsides are far reaching: reducing readmissions, slashing the cost of chronic conditions, fixing the physician shortage issue and fueling the shift from fee-for-service to value-based reimbursement models, just to name a few.
In "Migration to connected health: Opportunities and challenges in remote patient monitoring," Chilmark outlined half-a-dozen critical aspects of such a program:
1. Understanding the model: This one seems straightforward enough but as Chilmark explained "what was once purely a reactive approach to keeping track of vitals is becoming proactive through the use of an array of biometric sensors and connected devices as well as algorithms to track biomedical events as they happen and, to some extent, predict them before they unfold."
2. Identifying and recruiting patients: This is obviously a critical first step for any remote patient monitoring initiative. Chilmark found that healthcare organizations identify eligible patients either in-house with clinical data or by working with EHR makers or payers to analyze claims information.
3. Onboarding: This step involves training patients to use self-management resources and education materials, Chilmark noted, as well as end-user devices, a transmission hub and an engagement platform.
4. Managing patient and population data: Once patients are recruited and become part of the program, the data starts to accumulate, likely fast and furiously. "Data must be processed, normalized, and stored," Chilmark wrote. "Then it is analyzed against a series of standardized clinical guidelines and proprietary benchmarks to determine whether a particular reading is out of range."
5. Analyzing information: Healthcare organizations typically run analytics against a somewhat limited number of sources, including published clinical guidelines, their own internal benchmarks, and patients' clinical data but tech vendors, Chilmark added, "are already beginning to expand beyond these approaches, planning for a future driven by diverse data capture as well as machine learning."
6. Making data actionable: Armed with the ability to collect and analyze patient data, the next challenge is to transform it into information that clinicians can harness to benefit patients – and the way that Chilmark found this is starting to happen might surprise some hospital executives: Call centers made up of clinically trained staff and data analysts that evaluate alerts and send notifications when necessary.
"The promise of connected health models – their potential to improve cost-savings, reduce hospital admissions, and even save lives – is well understood at this point," Chilmark said. "The conversation has shifted recently from debating the merits of such approaches to discussing the specific conditions for their success – reimbursement models, pricing issues, risk stratification, workflow optimization, and so on."