Remote Patient Monitoring is the process for how hospital’s track patients after release. This is accomplished by monitoring a patient from a remote location outside of the hospital system’s care through non-invasive means. Current remote patient monitoring includes blood pressure monitors, glucometers, and pulse oximeters, amongst many others. These devices collect and report the data back to hospitals and healthcare teams.These devices are becoming even more accurate as they are integrated with machine learning. With emerging health-tech innovations, hospital systems are looking into custom solutions to improve data accuracy, collection, and visualization.
With the current transformative era of healthcare bringing innovation inside the hospital systems, many are looking into custom RPM solutions. AI integration in monitoring can reduce the burden on staff while also increasing accuracy and data reporting. With custom solutions, hospitals can tailor unique options and objectives to suit their size, type, specific patient requirements, and existing processes.
The current and potential uses of RPM seem to be limitless with the continuous advancements in health-tech. Some of the common applications current healthcare clients are investing in include…
RPM gives patients ownership and involvement in their care, which is typically outside of their control. They are able to take part in their treatments and regain a sense of individuality over their health. For hospital systems, they are able to increase efficiency and decrease a patient’s time spent in the hospital through digital products such as pre-appointment checklists and sign-in procedures. By streamlining the process for patients, time required for check-in monitoring and hospital visits can be reduced. This ultimately lightens the burden on hospital staff and allows nurses and doctors to have increased patient access.
The goal of both patients and caretakers is to reduce the amount of time spent in the hospital without sacrificing the quality of care. With investments in RPM, care is continuable within the comfort of a patient’s home. Post care monitoring can be utilized in post surgery recovery, gariatric/aging monitoring, pregnancy monitoring and recovery, and follow-up appointments that can be done virtually. The stress of recovery can be alleviated with accurate round-the-clock monitoring and allows the nurses or doctors to be proactive in decision making.
Cardiology and diabetes are two of the most common chronic diseases in America. RPM has the potential to improve disease management and prevention through advanced usability of tracking mechanisms, increased data accuracy, and enhanced speed of data collection and analysis. The integration of machine learning in heart monitor data has proven to be more accurate than previous systems. Improving the management of chronic diseases through improved RPM allows patients to strengthen their lifestyles and live a greater quality of life.
One of the most impactful potential advancements in RPM is around patient data. Automation of data collection may improve accuracy and speed; two vital aspects of patient monitoring. Collection of data can be tricky for employees who are new or less technical. Custom solutions standardize collection processes and ensure the data-stream flows correctly. To improve hospital efficiency, new custom RPM innovations can integrate and expand with existing EHR systems, directly impacting productivity of hospital workers and allowing staff to spend their time caring for patients rather than with data.
At the center of all advancements today is AI, so how does AI come into play with RPM? The excitement around AI integrations with RPM is largely centered around its increased speed and accuracy in care and data management. One area of AI advancements with RPM is image analysis. Hospital systems are able to use AI to analyze images taken at the hospital or at home to detect and identify conditions with increased speed and accuracy. Additionally, AI can also be integrated within the hospital’s ERP system to create better schedules and allocate resources more efficiently. The limitations of AI in healthcare seem endless but knowing where and when to start can be a difficult task.
At this point in the digital transformation of healthcare, many are aware of the emerging technology; however, a question many have is, the technology is exciting, but how does this apply to us?
Case Story: Philips Catholic Home Care, a division of Catholic Health Services of Long Island, identified an opportunity to improve their readmission rates: reduce readmissions, expand telehealth, and improve consistency of care. In 2015, they partnered with Philips to launch a RPM program. The partnership program resulted in a reduction of 30-day readmission rate by 50%. Moving forward, the partnership looked to scale the RPM process to care for other conditions outside of the congestive heart failure it was initially designed for; such as, diabetes, pre-eclampsia, and other heart related conditions.
What do the advancements in AI and machine learning mean for the future of RPM innovations?
(1) AI and machine learning will support scalability and adaptability of current and emerging RPM innovations with its continuous learning model.
(2) Data management and visualization will be two key areas of investment to improve speed and quality of care and detection.
(3) After initial investments and development, RPM innovations can be expanded to impact other areas of care.
(4) Future innovations in RPM will focus on giving caretakers time back to spend caring for the patient and being bedside, rather than manual tasks and being ‘computerside’.
(5) Allowing patients to regain ownership over their health and treatment will become a major focus in RPM tech to improve patient experience.
Remote Patient Monitoring stands at the forefront of a transformative era in healthcare. This technology, enabled by custom solutions and AI integrations, has the potential to revolutionize patient care. RPM not only empowers patients to take an active role in their health but also streamlines processes for healthcare providers, ultimately leading to more efficient and effective care. Embracing these innovations now positions healthcare teams to lead the way in shaping the future of healthcare delivery. The true future of health-tech is unknown with the undetermined future of AI and automation technologies. Given the speed of digital innovation today, it’s something worth exploring with your teams now so that you can be a pioneer in improving healthcare.
