Self-charging Wearable Technology!

 Smart remote patient monitoring (RPM) insoles with non-invasive optical sensors are an innovative technology designed to provide comprehensive health monitoring capabilities through the foot. These insoles incorporate advanced optical sensors that can measure various physiological parameters with medical-grade accuracy, all while being able to accurately "see through" the user's socks.  Plus, the insoles convert the patient's steps into electricity and it is stored on its rechargeable battery (see "Self-Charging? What is Energy Harvesting?" below).

The key physiological parameters that can be measured include heart rate (HR), resting heart rate (RHR), heart rate variability (HRV), respiratory rate (RR), percentage of blood oxygen saturation (SpO2), and body temperature. By leveraging optical sensing technology, these insoles enable continuous monitoring of these vital signs without the need for invasive methods or additional wearable devices.

In addition to the optical sensors, the smart RPM insoles are equipped with force sensors that assist in maintaining balance and detecting any potential issues related to gait or stability. These sensors help capture data related to movements, acceleration, gyration, and compass/magnetometer measurements through a 9-axis inertial measurement unit (IMU). This information allows for the determination of burned calories, fall detection, and provides valuable insights into the user's activity levels and overall mobility.

To enhance the functionality and usability, GPS technology is integrated into the insoles, enabling location determination. This feature can be particularly useful for tracking the whereabouts of loved ones or individuals with specific medical conditions that require constant monitoring or supervision.

The calculated data, such as accurate SmartSteps™ (including step type classification), accurate SmartCalories™, and other relevant health metrics, can be processed either through edge computing within the insoles themselves or at the mobile app level. This flexibility allows for real-time analysis and immediate feedback to the user, promoting proactive health management.

Data transmission is facilitated through Bluetooth® Low Energy (BLE) when LTE cellular connectivity is unavailable. When LTE cellular communication is accessible, the collected data is securely transmitted to a cloud platform for more sophisticated data analytics. This includes the utilization of artificial intelligence algorithms to derive meaningful insights and identify potential health issues or trends.

The cloud platform securely stores all collected data, allowing licensed medical professionals to remotely monitor their patients. They can access the data via smart apps or through phone calls, enabling timely communication and intervention to address any health concerns.

To ensure the privacy and security of sensitive health information, robust cybersecurity measures are implemented throughout the entire data transmission and storage process. This helps safeguard patient data, adhering to the stringent regulations and standards governing healthcare data protection.

In summary, smart RPM insoles with non-invasive optical sensors offer a holistic approach to remote patient monitoring in a HaH environment. By providing real-time, medical-grade accuracy measurements of vital signs, activity levels, and location determination, these insoles empower both patients and healthcare professionals to proactively manage health, enhance patient care, and potentially prevent adverse health events.

Hospital-at-Home (HaH) is a model of care that allows patients to receive acute care services in their homes instead of in a traditional hospital setting. HaH programs provide patients with access to a range of services, including skilled nursing care, intravenous medications, laboratory testing, and imaging services. The program is designed to provide patients with the same level of care as they would receive in a hospital but in the comfort of their own homes.

The HaH model is based on the premise that many patients can receive high-quality care in their homes and avoid the risks and stress associated with hospitalization. This model is particularly beneficial for patients with acute conditions who do not require complex interventions or surgery, such as patients with pneumonia, cellulitis, or heart failure.

HaH programs typically include a team of healthcare professionals, including a physician, nurse, and therapist, who work together to provide coordinated care to patients. The team communicates with the patient's primary care physician and other specialists as needed to ensure continuity of care.

HaH programs have been shown to reduce hospital readmissions, lower costs, and improve patient satisfaction. They also help to alleviate pressure on hospital resources by freeing up hospital beds for patients who require more complex care.

Overall, Hospital-at-Home is a promising model of care that has the potential to transform healthcare delivery by providing patients with high-quality care in the comfort of their own homes while reducing healthcare costs and improving outcomes.

Digital therapeutics (DTx) refers to a subset of digital health technologies that use evidence-based interventions delivered via software programs, mobile apps, or other digital platforms to prevent, manage, or treat medical conditions. Unlike traditional medical treatments, which may rely on drugs or surgical interventions, DTx focuses on using digital tech to deliver therapeutic interventions.

Digital therapeutics may incorporate a range of techniques, such as cognitive behavioral therapy, mindfulness-based interventions, and biofeedback. These techniques can be delivered through a variety of digital channels, including wearable tech, mobile apps, virtual reality platforms, and other digital tools.

DTx is designed to be used in conjunction with traditional medical care and is often prescribed or recommended by healthcare providers. DTx interventions may be used to treat a variety of conditions, including chronic diseases (e.g., multiple sclerosis), mental health conditions, and substance abuse disorders.

DTx has the potential to improve patient outcomes by providing more personalized and accessible treatment options that can be delivered remotely, reducing the need for in-person visits to healthcare providers. It can also help to reduce healthcare costs by providing more cost-effective treatment options and improving patient adherence to treatment regimens.

DTx is an area of healthcare innovation that has the potential to transform the way we prevent, manage, and treat medical conditions.

Edge computing for wearables refers to the concept of processing and analyzing data on the device itself, rather than relying solely on a cloud platform. It involves performing computational tasks and running algorithms directly on the wearable device, closer to the point of data generation, instead of sending all the data to the cloud for processing. 

Edge computing offers several advantages for wearables:

1. Reduced latency: By processing data locally on the wearable device, edge computing minimizes the time it takes to transmit data to a remote server and receive a response. This is crucial for real-time applications where immediate processing and decision-making are required.
2. Enhanced privacy and security: Edge computing allows sensitive data to be processed and analyzed locally, reducing the need for transmitting data over networks where it could potentially be intercepted or compromised. This helps protect user privacy and strengthens data security.
3. Bandwidth optimization: Transmitting large amounts of raw sensor data from wearables to the cloud can consume significant bandwidth. Edge computing enables data filtering and preprocessing on the device itself, reducing the volume of data that needs to be transmitted. Only relevant or summarized information is sent to the cloud, optimizing network bandwidth.
4. Energy harvesting to enhance edge computing capabilities: By eliminating or reducing the reliance on external power sources self-charging wearables can operate autonomously for extended periods. This allows them to continue performing edge computing tasks without interruptions due to power constraints. This harvested energy can be used to power the wearable device and its computational processes, enabling continuous edge computing functionality. The combination of edge computing and self-charging capabilities in wearables can lead to increased efficiency, prolonged operation, and greater autonomy, making them suitable for continuous monitoring, real-time decision-making, and resource-constrained environments. 
5. And vice versa: To understand more read the next section related to machine learning at the wearable device's edge, including minimizing wearable device power consumption.

PredictiveWellness™ monitoring, analytics and modeling for healthcare involve using data from various sources such as electronic health records and databases, patient monitoring devices, and medical imaging to predict patient outcomes and improve healthcare quality.

Our predictive AI models can be used to identify patients at risk of developing a particular disease or condition, predict the progression of a disease, determine the most effective treatment options for patients, and even be used to predict falls before they happen.  These AI models can help healthcare providers make more informed decisions, optimize resources, and improve patient outcomes and safety.

For example, predictive AI models can be used to identify patients at risk of readmission to the hospital within 30 days of discharge. This can help healthcare providers take proactive measures to prevent readmissions, such as providing additional care or resources to at-risk patients.  Reducing hospital readmission rates lowers healthcare costs, because 40-49% of U.S. hospitals are fined between $350M to $535M annually by the Centers of Medicare and Medicaid Services (CMS).  This includes readmission of post-acute cardiac (e.g., heart attack and heart failure), respiratory (e.g., COPD and pneumonia), pulmonary (e.g., Coronary Artery Bypass Graft "CABG" surgery), and orthopedic (e.g., hip and knee replacement) patients being readmitted to the hospital within 30 days from discharge.

Another example is using predictive models to personalize treatment plans for patients based on their specific characteristics and medical history. This can improve treatment outcomes and reduce the risk of adverse events.

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law that required the creation of national standards to protect sensitive patient health information from being disclosed without the patient’s consent or knowledge. The US Department of Health and Human Services (HHS) issued the HIPAA Privacy Rule to implement the requirements of HIPAA. The HIPAA Security Rule protects a subset of information covered by the Privacy.

HIPAA Privacy Rule

The Privacy Rule standards address the use and disclosure of individuals’ health information (known as Protected Health Information or PHI) by entities subject to the Privacy Rule. These individuals and organizations are called “covered entities.”  The Privacy Rule also contains standards for individuals’ rights to understand and control how their health information is used. A major goal of the Privacy Rule is to make sure that individuals’ health information is properly protected while allowing the flow of health information needed to provide and promote high-quality healthcare, and to protect the public’s health and well-being. The Privacy Rule permits important uses of information while protecting the privacy of people who seek care and healing.