Lucienne Marie Ide

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Not long ago, wearables – from activity trackers, like Fitbits to health devices, like heart-rate monitors – were only a small part of the annual Consumer Electronics Show. But there's been dramatic change in just the past few years. In fact, in January, the Consumer Electronics Show (CES) 2022 prominently featured more than 100 companies in a new digital health track, all of which are seeking to address the growing demand for remote monitoring in the healthcare sector.

What took center stage at this year’s CES goes far beyond smart watches. Solutions included devices and applications for managing blood pressure and nutrition, 

measuring ketones and blood glucose levels

, monitoring body composition and babies’ sleep patterns, tracking nerve activity and cardiovascular health, and more. Data generated from these more sophisticated solutions is not only to satisfy individuals' curiosity about their fitness, however. Many of these solutions facilitate sharing of data with physicians to help deliver more personal and precise care for serious health conditions and to continue to enable the transition to value-based care models.

The Rise in Digital Connectivity in Healthcare

Today, healthcare consumers of all ages are open to -- and increasingly comfortable with -- their physicians suggesting using connected health devices or prescribing remote patient monitoring (RPM) technology. This continuous connection between consumers and their providers creates a shared language and the opportunity for shared decision-making. In fact, a recent study found that 

70% of patients believe remote patient monitoring

The Consumer Technology Association (CTA) 23rd annual Consumer Technology Ownership & Market Potential Study also noted that more people see technology as essential because they discovered during the pandemic how such innovations can improve their lives. Currently, about 

one-quarter of consumers surveyed have smart or connected health monitoring devices

, and these numbers are expected to continue to grow over the next 12 months.

As consumer interest grows and new applications are developed, so will use in healthcare settings. Healthcare providers understand the potential of RPM as a key component in the evolution towards more proactive, continuous models of care, and are adopting it in their practices as solutions are authorized by the Food and Drug Administration for clinical use and are eligible for reimbursement. By 2025, one report estimates that the number of U.S. 

remote patient monitoring users will almost double to 70.6 million

 – more than one-quarter of the population. This growth will be facilitated by the continued push to move patient care into patients' daily lives, rather than being relegated to the four walls of the doctor’s office or healthcare facility.

With even more mobile and remote devices gathering personal health information, individuals and their healthcare providers could easily find themselves drowning in a sea of data. Today, about 

30% of the world's data volume is being generated by the healthcare industry

, with estimates that it will continue at a 36% compound annual growth rate through 2025. Not surprising, reports also show that each person could have nearly 5,000 digital device interactions a day by that same year, with the majority being healthcare related. The sheer quantity of data will be overwhelming for clinicians–let alone patients– to decipher.

One of the major weaknesses of the connected health market is the disconnect between RPM and electronic health records (EHRs) and physician workflows. Generally, doctors can prescribe devices like continuous glucose monitors (CGMs) or blood pressure monitors from their EHR system, but bringing the resulting data full circle back into the clinical workflow to drive improvements in patient outcomes is not happening. Often, gathering the data remains the responsibility of the patient or requires the clinician to log into a separate software system to access the data. Because this RPM-generated data is not automatically integrated into EHR workflows, the full context of the patient’s health is missing. That complete, whole-person assessment is key to driving a continuous, iterative, collaborative model of care.

Achieving the Promise of Connected Health

Studies have shown that data from remote patient monitoring devices has helped improve patient outcomes. For example, Leon Medical Centers, an outpatient medical care institution in Miami-Dade County, recently found that a remote patient monitoring program for high-risk diabetes patients 

helped lower A1c levels below 9% for 84% of patients

 – a key indicator of better diabetes control.

Successful implementation of RPM will require an ecosystem of devices, applications and platforms that integrate with EHR workflows to tell the story of each patient’s health. The ability to gain a better understanding of a patient's condition on a day-to-day basis through connected devices, combined with details from provider-patient interactions, diagnostic results and other key data points will give providers the tools they need to help patients realize better outcomes. 

 will also be an important component to make patient data actionable.

If clinicians are unable to see the data curated and streamlined within their workflow, and act on the insights in a timely manner, it will be impossible to achieve the benefits to individual patients and to population health promised by RPM and connected health innovations.

Lucienne Marie Ide, M.D., PH.D., is the Founder and Chief Executive Officer of Rimidi, a leading clinical management platform designed to optimize clinical workflows, enhance patient experiences and achieve quality objectives. She brings her diverse experiences in medicine, science, venture capital and technology to bear in leading Rimidi’s strategy and vision.

Articles

A recent study found 70% of patients believe remote patient monitoring enables better care management.

Meeting the Goal of Remote Patient Monitoring: Leveraging Data to Improve Clinical Workflows and Patient Outcomes

Many have claimed that technology – from virtual care solutions to clinical decision support (CDS) systems – will be the great equalizer in healthcare. 

These tools are viewed as an ideal solution for bridging the chasm that has prevented medically underserved communities - racial and ethnic minorities, those who live in rural and socioeconomically disadvantaged areas - from receiving the same quality of care as others. Yet, while technology is purportedly objective and purely data-driven and without prejudice, there can still be inherent biases that hamper the ultimate goal of health equity.

healthcare data accounting for 30% of the world's data volume

, it's no surprise providers can feel overwhelmed at the point of decision making, especially given time constraints. When challenged with assessing too much data in a limited period of time, our brains 

look for patterns and fall back on gut instinct

, which can bring with it unintended biases. 

CDS systems have been designed to help providers assess the vast amounts of data that can play a role in care decisions. But CDS should not be all about guideline-driven care; it should also facilitate health equity. As new technology is developed and current capabilities are advanced, it’s critical we seek to combat bias through the guidelines themselves and the underlying clinical algorithms that drive CDS.

Health equity implies, “a system designed and built for inclusion and equal access to the best health outcomes for all," 

including socioeconomic status, such as education, income and occupation

, according to the Agency for Healthcare Research and Quality (AHRQ). 

However, the agency believes, in reality, the definition is more expansive, referring to "inclusion and best health outcomes for all no matter their physical environment, personal behaviors and abilities, or social circumstances (gender identity, military services, sexual orientation, citizenship status, social status, history of incarceration, culture and tradition, social connectedness, work conditions, early childhood education and development)."

Overcoming all of these factors can be difficult for the average provider. One study utilizing the Implicit Association Test (IAT) revealed that 

80% of sample clinician groups exhibited evidence of implicit bias

. These biases are most pronounced among racial and ethnic groups. 

For example, a groundbreaking Institute of Medicine report found that Black Americans, who are 

only 3% to 6% more likely to deny treatment than other populations

, received significantly less needed care than white Americans, even when controlling for factors such as insurance coverage and patient income.

 from breast cancer, 20% less likely to receive treatment for depression, and two times more likely to receive a less desirable treatment for diabetes, such as limb amputation. 

While much of this problem can stem from providers' attitudes, implicit biases and discrimination, it's likely that clinical algorithms, tools and guidelines are factoring into the biases as well. The 

 noted that algorithms for CDS could introduce bias if they're built on data that is not reflective of a diverse population.

Refining Clinical Decision Support for Greater Health Equity

CDS has been defined as a “process for enhancing health-related decisions” that provides “clinicians, staff, patients or other individuals with 

knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and healthcare

However, to truly support both guideline-driven and equity-driven care, the developers of the algorithms and the CDS systems themselves must strive to eliminate bias.

A study completed in 2019 explained how a 

widely used commercial healthcare algorithm reflected serious racial bias

. Specifically, the risk score used healthcare expenditures as a proxy for health status. Researchers found that Black patients were consistently and significantly sicker than white patients with the same score. Since the algorithm was designed to predict healthcare costs, rather than illness, it was flawed because unequal access to care means that less money is typically spent caring for Black patients than white.

Additionally, the National Kidney Foundation (NKF) recently announced 

 the most widely used kidney test, after many years of pushback from physicians and medical students about the flaws of its race data, which removes the race variable from the equation that estimates kidney function.

In spring of 2021, the AHRQ, at the request of Congress, 

 on clinical algorithms used in medical practice that may introduce bias into decision making or negatively affect access, quality or health outcomes among racial and ethnic minority groups. From the responses, AHRQ plans to commission an evidence review to critically appraise whether race/ethnicity and prior utilization are included as explicit variables in commonly used algorithms, and how algorithms have been developed and validated. They also plan to identify and review approaches to clinical algorithm development that avoid the introduction of racial and ethnic bias into clinical decision making and resulting outcomes.

Done right – through careful design, greater awareness and a thoughtful approach to eliminating potential biases – algorithms and CDS systems can mitigate such problems. And in the process, they can decrease disparities in care to achieve greater health equity. 

Additionally, when algorithms are developed based on data that accurately represents the diversity of the patient population to which it is being applied, they can also achieve greater patient outcomes. Therefore, demographic, clinical, socioeconomic and even patient preference data should be included when leveraging CDS systems. This data may not be available in the EHR directly, and can require retrieving SDOH and PRO data from other sources.

Looking ahead, it is vital that designers of CDS solutions think about and plan for health equity from the start of development. By taking this approach, CDS can truly deliver on the promise of fostering both guideline-driven and equity-driven care that improves outcomes and the overall health of everyone.

, is the Founder and Chief Executive Officer of 

, a leading clinical management platform designed to optimize clinical workflows, enhance patient experiences and achieve quality objectives.

Many have claimed that technology will be the great equalizer in healthcare. While technology is objective and purely data-driven and without prejudice, there can still be inherent biases that hamper the ultimate goal of health equity.

Erasing Bias in Clinical Decision Support to Achieve True Health Equity

 that impact patient care in an average day – everything from ordering tests and prescribing medication, to recommending procedures and admitting patients to the hospital. 

Even with extensive education and vast experience, medical decision-making is challenging given the quantity of data and volume of evidence that exists. An Institute of Medicine study in 2000 titled 

To Err is Human: Building a Safer Health System

put a spotlight on the issue of errors in clinical decision-making, revealing the morbidity and mortality associated with such errors.

Much has changed in the two decades since that study was published. In 2010, the Affordable Care Act (ACA) focused the healthcare market on moving to a model that values outcomes instead of focusing on transactional encounters that have historically driven payments. The ACA also dramatically accelerated the digitization of medical records, with a recent survey showing that more than 

89% of physicians currently use an electronic health record (EHR) system

Designed to take into account all of the data available in an EHR, clinical decision support systems (CDSS) – which have also been around for several decades – have now matured to a point where the data and insights they provide are aiding providers in delivering the right care to the right patient at the right time. Driven in part by the 

 Century Cures Act enabling interoperability in health IT

, CDSS can reach its full potential of helping practitioners adhere to guidelines, while further improving patient safety, as well as the quality, efficiency and effectiveness of healthcare.

The Evolution of Clinical Decision Support Systems

Today’s providers are faced with an onslaught of data about each patient – from traditional diagnostic labs and imaging studies to expert interpretations in clinical notes and biometric data captures from patients in the home. Advanced CDSS are designed to take in all of these data sources, analyze the inputs, and present insights to providers to inform decision-making. The insights derived are intelligently curated by the CDSS and presented at the right time in the clinical workflow – often at the point of care – to enhance patient care and outcomes.

For example, CDSS can identify divergence from standard of care treatment, such as prescribing a medication class shown to have superior benefit for a specific patient cohort, and provide recommendations for clinicians to consider. The systems can also offer computerized alerts and reminders to both patients and providers, with condition and context-specific diagnostic recommendations or expert assessment needs, while highlighting both the patient characteristics driving the notification and the reference data behind the recommendation.

To date, CDSS have typically been deployed through EHR solutions, but challenges arise when software systems primarily designed as databases and billing systems are stretched to become clinical workflow optimization engines. One problem in particular is alert fatigue. Providers can receive so many alerts from their various systems, including low-value ones that do not enhance care, which wastes valuable time and contributes to the burnout they’re already experiencing.

Without regular maintenance, review, iteration and optimization programs, clinical decision support also has the potential to become outdated or malfunction. Such challenges often fall on the in-house EHR IT team to build and maintain these systems so that these decision support tools better live up to user experience expectations.

Improving the Experience with Clinical Decision Support Systems

providers do see the promise in CDSS. Done right, CDSS can

Improve the quality of care and deliver on the goal of enhancing health outcomes

Drive greater efficiency in care delivery

Help providers avoid errors and adverse effects

Improve patient and provider satisfaction

CDSS play an important role in addressing the information overload that providers face, offering a platform to integrate evidence-based knowledge into the care delivery process, and automating many of the mundane tasks that take away from the precious time they can spend with patients. For example, they can improve on existing workflows with better retrieval and presentation of data than EHRs. In addition, CDSS can improve the quality of clinical documentation by providing support for coding, ordering of procedures and tests, and patient triage.

It’s important to remember, however, that one size does not fit all when it comes to CDSS. Practices can be limited if clinical decision support is simply a function of an EHR. Instead, providers must consider how solutions fit into their entire existing workflow to prevent friction and ensure that the CDSS can accommodate the unique needs of each client environment, patient population and practice patterns.

To overcome traditional barriers and deliver on the full promise of clinical decision support, practices should consider cloud-based composable platforms that bring leverage advancements in interoperability – such as SMART on FHIR and CDS Hooks – to enable a more flexible, configurable approach to CDSS. By layering the CDSS engine and experience on top of the EHR, it affords the opportunity to include additional data sources such as remote patient monitoring and patient reported outcomes into the model. 

Integrating this flexible platform with existing EHRs will enable providers to close gaps in care according to evidence-based guidelines, identify the right level of intervention and make practices more efficient as their clinical workflows evolve.

Medical decision-making is challenging given the quantity of data and volume of evidence that exists. To overcome traditional barriers and deliver on the full promise of clinical decision support, practices should consider cloud-based composable platforms that bring leverage advancements in interoperability.

Lucienne Marie Ide

Articles

Meeting the Goal of Remote Patient Monitoring: Leveraging Data to Improve Clinical Workflows and Patient Outcomes

Erasing Bias in Clinical Decision Support to Achieve True Health Equity

Clinical Decision Support: Why the Right Data is Needed to Drive the Right Outcomes