Remote patient monitoring has witnessed major transformations since its emergence in the global healthcare landscape. With cutting-edge technologies taking over, it is progressing toward the development of more contactless medical systems.
As per Wissen Research, a worldwide IP, technology, and market research consulting firm, the global remote patient monitoring market is about to collect a whopping $80 billion by 2030. It is poised to grow at a CAGR of 12 percent during the forecast period 2024-2030.
To successfully contribute to this growing market, Healthcare organizations in the US and across the globe need to invest in the right set of tools and technologies. Utilizing the capabilities of Python and AI for remote patient monitoring is a plausible solution to improve patient outcomes, reduce overheads, and maximize financial viability.
According to MarketsandMarkets Research, a revenue impact and advisory company, the surge in geriatric population is one of the key reasons behind the rising demand for remote patient monitoring. Some other factors are as follows.
With the help of remote patient monitoring systems, Healthcare organizations can enhance patient engagement like never before. Physicians can assist patients with self-management and reporting of their key health vitals.
Mayo Clinic, a non-profit medical organization, conducted a remote patient monitoring program. They found out that this program improved engagement rates by 80 percent. Patients were able to save more than $1 million collectively.
Not just patients, healthcare providers can also reduce overheads with remote patient monitoring. Thanks to mobile apps, wearable devices, digital healthcare solutions, and technology advancements like AI agents and ML-based Python libraries and frameworks.
In a research on remote patient monitoring devices, University of Pittsburgh Medical Center, an American non-profit healthcare organization, found out that these devices can decrease repeat admissions by 76 percent. This was a great move toward reducing additional costs related to human-driven administrative tasks.
On the other hand, American Journal of Managed Care, a peer-reviewed medical publication, states that remote patient monitoring can help healthcare providers save $1,000 per patient in expenses annually.
Apart from this, increasing chronic conditions is one of the significant aspects that make remote patient monitoring a dire need for patients globally. According to Accenture, a global multinational professional services company, 60 percent of patients dealing with chronic conditions are keen on using remote patient monitoring technologies.
Small healthcare providers, emerging diagnostics centers, and bootstrapped clinics often find it difficult to explore the true potential of remote patient monitoring systems. Why? Due to the lack of sophisticated IT infrastructure, technology expertise, and data management capabilities. Besides, adoption of these systems demands strategic investments in maintaining EHR platforms and evaluating data processing procedures.
However, healthcare organizations and caregivers can easily overcome such challenges by combining the capabilities of AI and Python. Implementing AI in remote patient monitoring can provide physicians with multiple advantages. They can focus on integrating wearable devices with Python for patient monitoring at scale. Let’s understand this in detail.
As per Philips’ Future Health Index report, remote patient monitoring is the topmost domain where AI will be implemented in the upcoming three-year time span. About 41 percent of healthcare professionals plan to make strategic investments for gaining traction. Here are some benefits of implementing AI for remote patient monitoring in healthcare.
Healthcare organizations in the US can embed wearable devices and its sensors with the latest AI technologies like GenAI, LLMs, and SLMs. These technologies streamline human-device connectivity with powerful gesture control backed by neural data. They enable consistent data streams to improve interoperability and enhance clinical decision-making. With real-time data gathering, AI-based wearable devices can notify minimalistic changes in health vitals from a pre-defined reference.
Private clinics in North America and worldwide can use trained AI models to identify changes in behavioral patterns. They can fine-tune these models to collect critical data, such as irregularities in SpO2 levels, surge/decrease in blood pressure, and other disruptions in vital activities. The AI models can discover high-risk scenarios that seem negligible to a human eye.
With AI and ML algorithms, healthcare provides can change the way they approach remote patient monitoring. These algorithms can easily detect potential variations and disturbances other than normal pre-defined activities. They can trigger the monitoring system to inform healthcare experts and take necessary curative measures.
AI health monitoring devices can go beyond identifying existing discrepancies. With successful integration of AI agents, they can analyze patient history and related trends to anticipate concerns that may arise in the future. For instance, AI agents can evaluate physical activity data in elderly people to avoid the risks of falling.
Telemedicine and telehealth apps are the most important building blocks of a reliable remote patient monitoring system. Technology advancements like Python are making it more stronger and future-proof with its ML-based libraries and transformative frameworks.
Python frameworks like Tornado, Flask, Django, and Pyramid can help healthcare organizations in USA and across the globe create scalable telehealth apps quickly and effectively. They enable precise integration of features like EHR integration, online video-based consultations, virtual therapy sessions, and early discovery of health issues for secure patient monitoring.
Physicians in Canada and worldwide can leverage the ML-based capabilities of Python libraries such as PyTorch, TensorFlow, NumPy, Seaborn, Matplotlib, and Scikit-Learn. These ML libraries can analyze current conditions, notify future issues, and provide tailored medical treatment plans. They can evaluate patient information in real-time and optimize remote diagnostics methods to help healthcare experts make informed decisions.
Medical facilities in the US can improve patient engagement, experience, and satisfaction remotely via Python’s NLP tools. They can use NLTK and SpaCy to provide 360-degree patient support via AI-powered virtual assistants. They can even leverage Python packages like OpenCV to support image-enabled diagnosis via medical scans.
Adopting AI and Python can assist medical stakeholders embrace digital transformation that can take remote patient monitoring to the next level. It can enable smart data management, decrease repeat hospital visits, improve diagnostics approach for chronic diseases, and facilitate on-demand interventions from doctors.
Experts from Vector Institute of Artificial Intelligence in association with the Department of Computer Science developed an AI-enabled remote patient monitoring system. This system has gone through a thorough testing across the medical facilities affiliated with the University of Toronto.
AI-backed remote patient monitoring system can help doctors communicate with patients remotely. It continuously tracks wearable sensors to offer health data in real-time for informed decision-making. It facilitates comprehensive data analysis via ML algorithms to separate pertinent health metrics from medically irrelevant information provided by sensors. Here are the key impact pointers of this AI-based platform.
Healthcare and technology experts in the US developed a smart patient monitoring app using Python and its advanced technology capabilities. This app utilizes ML algorithms to analyze patient input data to offer valuable health insights. It evaluates patterns, anomalies, and trends in collected data to alert patients about any significant changes in their health vitals.
With this Python-based health monitoring app, healthcare providers can offer personalized care to their patients by analyzing progress over time. Here are some key advantages of using this app for remote patient monitoring.
Healthcare organizations across USA, North America, Canada, and the globe need to start leveraging Python and AI for remote patient monitoring in healthcare. This will not only boost areas like early disease detection and primary caregiving but also positively impact aspects such as preventive medicine, post-operative care, and chronic disease management.
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