The COVID-19 pandemic has accelerated the adoption of remote patient monitoring technology, which provides exciting opportunities for expanded remote connected care. Even though the mode of clinicians’ interactions with patients and their health data has changed, the larger framework of how we deliver care is still driven by an episodic care model that does not allow for this new frontier. To fully realize a transformation to a system of continuous connected care augmented by remote monitoring technology, clinicians and health systems must change their approach to care delivery technology and the associated data volume and complexity. In this article, we present a solution that organizes and optimizes the interaction of automated technologies with human oversight, allowing for the most effective use of data-rich tools while preserving the uniquely human components of medical care. We discuss the clinical implications of this “augmented continuous connected care” model of remote patient monitoring and offer human-centered design-informed next steps to encourage innovation around these critical issues.
Health information technology, telehealth, remote patient monitoring, mobile health, eHealth, eHealth, digital health, innovation, process model, information technology, digital medicine, and automation are some terms used in this article.
Remote Patient Monitoring’s Expansion: Accelerating the Transition from Episodic to Continuous Care
The COVID-19 pandemic has accelerated the adoption of remote patient monitoring (RPM), which uses mobile, noninvasive digital technology to capture and transmit real-time patient data for care delivery and disease management. During the pandemic, RPM technology-enabled remote care in unprecedented health uncertainty and disruption . However, while clinicians’ interactions with patients and their health data have changed, the larger framework of how we deliver care has only shifted incrementally. Most health care is still delivered in episodes—synchronized moments of connection between clinicians and patients facilitated by discrete hospitalizations, office visits, or video and audio calls. However, health is not an episodic experience; it is a fundamental part of the human condition that is felt on a regular and continuous basis, more akin to a utility (such as energy, water, or even education) than a traditional professional service (e.g., tax preparation). The conflict between these competing visions of care has contributed to a fragmented, inconsistent healthcare delivery experience; it has also limited the health-information-technology resources, innovations, and capital required to create a world of data-driven continuous care. RPM technologies have the potential to facilitate and accelerate the shift from episodic to continuous care. We outline the current state of RPM, its clinical practice challenges, and how a continuously connected care model can be organized based on technology-driven transitions that include not only RPM but also the larger world of digital health technologies (e.g., telehealth, machine learning, and artificial intelligence [AI]).
Go to: The RPM Data Tidal Wave
The rapid adoption of new digital healthcare technologies for remote delivery has begun to break down the barrier between the clinic and home. The influx of health data from RPM devices has the potential to realize a new medical framework—not just of higher quality episodic medicine, but of a continuously connected care model that reflects the true interaction of health care and the human experience. While more continuous and complete patient data can help this new world of care, its introduction into the healthcare landscape has raised some concerns . RPM devices generate more data than a clinician can manage. Current health information technology systems are inadequate for data curation and visualization, which are required to use these data to help patients  effectively. At the same time, some concerns simply collecting more data about a patient will not result in improved health outcomes and that a better understanding of RPM’s quality requirements is required . Finally, the increased burden on patients and clinicians to be constantly connected—whether to their health or their jobs—has raised concerns about medical overuse, burnout, and excessive consumerism; even the term “remote patient monitoring” is problematic, conjuring images of invasive surveillance and control rather than supportive care [5,6].
Visit RPM and the Augmented Continuous Connected Care Pyramid for more information.
To address these challenges, health systems have taken various approaches to manage RPM in clinical practice, including limiting digital access and services for patients, hiring new staff, and implementing automated tools such as chatbots and AI models to manage data and its clinical implications [7,8]. Digital health vendors are increasingly offering (and payors are reimbursing) digital and virtual health services that include RPM devices, with vendors and electronic health records improving interoperability (via standard interfaces) [9-11]. These new platforms allow more effective acute, chronic, and home hospital management [12-15]. Other digital health tools that use AI to evaluate RPM data inputs and help boost the important signals buried within the noise have been implemented. DreaMed, for example, uses AI to sift through mountains of RPM-generated continuous glucose monitoring and insulin pump data to make specific insulin titration recommendations . The clinical staff is increasingly being tasked with managing incoming RPM data streams and assisting providers in providing a more seamless connected care experience. Ochsner’s RPM-driven hypertension program successfully applies this new paradigm [17,18]. Finally, a solution that optimizes the interaction of automated technologies with human oversight, both routine and specialized, will most likely prevail, allowing for the most effective use of data-rich technologies while preserving the aspects of medical care that are “uniquely” human.
When considered together, RPM tools and their implementation in health care systems can be an example of a novel pyramid of health care delivery, “augmented continuous connected care” (Figure 1). Continuous connected care is based on a layer of holistic health data capture that is always “on,” automated, and often passive. An algorithmic (or machine learning) layer integrates and standardizes these disparate data points, allowing it to “listen” to and interpret data and either respond autonomously or reduce noise and boost signals to generate more actionable insights for human interpretation. Decisions that fall outside the scope of this “digital clinician” are routed to the clinical team for management, allowing them to work to the top of their licenses and provide the parts of medical care that benefit the most from human touch—patient education, shared decision-making, and complex medical decision-making.
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Figure 1: Pyramid of augmented continuous connected care. EHR: electronic health record; PRO: patient-reported outcomes.
As the pandemic progresses and healthcare systems respond and evolve, the benefits of RPM-enabled connected continuous care become clearer. Technologies once thought to be niche products for rural health or areas with resource or accessibility barriers have proven adaptable to a wide range of healthcare settings and contexts, allowing diverse populations to manage both acute and chronic conditions with improved information, safety, and convenience [19,20]. Shared RPM data management, particularly after AI processing, is a more sustainable model for supporting continuous care and allows patients to feel more connected to their healthcare team.
Go to: Using Design Thinking to Identify and Overcome RPM-Enabled Continuous Care Challenges
Patients, providers, and care delivery systems will all feel the effects of health care’s shift to an augmented continuous connected care paradigm. We frame key implications to encourage new thinking about these issues as a series of Design Thinking-informed “how might we” questions . These questions are by no means exhaustive of the issues confronting the future of continuous connected care. Still, they are intended to inspire healthcare leaders, designers, and clinicians to reimagine these and other key concerns related to RPM and the larger world of digital health technology.
How can RPM technologies enable patients to easily share their “daily life data” without overwhelming their healthcare teams?
How can we change how patients and healthcare teams communicate to take advantage of continuous connected care?
How might we reshape the healthcare workforce to deal with this new type of high-volume, real-time data?
These questions recognize both the potential and the challenges of using RPM and other digital health tools to help shape the future of health care. To reduce the burden of patient work and enable equitable access to and use of this transformative technology, the answers to these questions necessitate improvements in the design, usability, and interoperability of RPM tools. It will also necessitate carefully balancing the benefits of continuous care versus the risks of being “overconnected.” New approaches will be required to assist patients and clinicians in staying informed and making actionable decisions about key events in their health while also protecting themselves and their data.
Visit the page Implications of Augmented Continuous Connected Care for Practitioners, Researchers, and Policymakers.
The rapid growth of RPM and the continuously connected care paradigm it enables necessitates new research and development to improve RPM tools’ devices, platforms, integrated AI, interoperability, and usability. To help healthcare systems and patients fully benefit from this enabling technology, new team structures, personnel, and workflows will need to be identified, tested, and disseminated. Clinicians will require improved EHR-integrated tools, training, and team-based support to manage patients using a combination of clinic and home-based data collection and AI-based tools to highlight critical insights and actions. To support these new workflows, clinical delivery system leaders will need organizational and regulatory flexibility to change the composition and scopes of work of health care workers; such support may necessitate investment in new roles such as RPM “navigators,” community health workers, and others who manage data and devices in collaboration with patients and clinicians. Policymakers will need to create a regulatory environment and financial incentives that encourage RPM innovation while guiding the industry to adhere to common standards and allowing data to flow freely between systems.
RPM and other digital health technologies are ushering in a new and exciting care model that is continuous and connected rather than episodic. However, there are numerous challenges to this transition, and how we respond to them will determine the ultimate impact of these tools and our ability to use these technologies to improve healthcare experiences and outcomes.
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Prepare a brief (8-10 slides) PowerPoint presentation in which you do the following:
Identify at least two technology innovations to connect patients, providers, and insurers across the care continuum.
Describe how the technologies work to provide patients and providers with data necessary for health care decision making.
Discuss how the real-time data encourages outcome-focused planning.
Predict what impact the technology will have on future health care delivery. Provide rationale and examples.
Presentations must include speakers’ notes on each slide, as well as references for the presentation.
SLIDES SHOULD BE CLEAN AND NEAT AND CLUTTER FREE AND STRIAGHT TO THE POINT. ALL ADDITION INFO SHOULD BE PLACED IN SPEAKER NOTES.
BE SURE TO USE IN-TEXT CITATIONS!
A minimum of three academic references from credible sources are required for this assignment.
The slide count (8-10 slides) does not include the introduction and References slide(s).
Prepare this assignment according to the APA guidelines.
You are required to submit this assignment to Turnitin, SIMILARITY INDEX SHOULD NOT EXCEED 15%