Table of Contents
Remote Patient Monitoring Basics
Benefits of Remote Monitoring of Patients
Cost Savings
Improving patient safety
Quality of Care
Better Patient Outcomes
Improving Healthcare Accessibility
How RPM Systems Work
RPM solutions provide home care telemedicine capabilities that can be embedded into:
Here is the step-by-step process for transferring a patient's vital signs from the RPM software to the healthcare provider:
How Artificial Intelligence Can Help Telemedicine
DIAGNOSIS
Treatment Options
Patient Engagement
Chronic Disease Management
Artificial intelligence and remote patient monitoring are a match made in heaven
Home Technology peripherals AI How artificial intelligence can improve the quality of healthcare by monitoring patients remotely

How artificial intelligence can improve the quality of healthcare by monitoring patients remotely

Apr 12, 2023 pm 11:04 PM
AI Telemedicine

How artificial intelligence can improve the quality of healthcare by monitoring patients remotely

The 2019 coronavirus has wreaked havoc on our world, but it has also accelerated the adoption of telemedicine as a safe alternative to in-person appointments. One area of ​​telemedicine that has gained a foothold over the past two years is remote monitoring.

Let’s take a look at what remote patient monitoring is and how artificial intelligence can save the world again.

Remote Patient Monitoring Basics

Remote patient monitoring is a growing field in the healthcare industry that uses technology to collect patient data outside of the traditional doctor's office or hospital setting. Collect a variety of patient data including vital signs, activity levels, and more.

According to reports, the global remote monitoring patient equipment market is expected to exceed US$101 billion by 2028. The growing prevalence of chronic diseases such as diabetes, cardiovascular diseases, etc. is driving the market.

Benefits of Remote Monitoring of Patients

Remote monitoring of patients is an effective method of monitoring individuals or groups who cannot be monitored in person. In some cases, remote monitoring can be used to track a person's vital signs, such as blood pressure or pulse rate. Remote patient monitoring can also be used to monitor patients who are at risk for hypothermia or other medical conditions that require ongoing attention.

Cost Savings

The cost saving potential of RPM solutions is huge. As a result, 69% of healthcare professionals rank RPM as the number one reducing factor in overall cost.

Remote monitoring allows patients to obtain professional diagnosis without spending time and money traveling to the hospital or clinic where they receive treatment. Additionally, teletherapy translates to:

● Optimize time with patients: No need to take routine vitals and questions since the data is already available.

● Improved communication due to increased accessibility of RPM solutions.

Improving patient safety

During the epidemic, hospitals have become centers for the spread of infectious diseases. Therefore, booking an appointment online becomes one of the safest options for getting professional consultation. Through remote monitoring of patients, doctors and nurses can monitor their patients at home, thus preventing individuals from contracting anything in the hospital.

Quality of Care

Remote monitoring also helps improve the quality of care because it allows nurses and doctors to monitor patients' vital signs without visiting them in person. Having this information also allows patients with chronic diseases to be better treated because they can be monitored more frequently.

Better Patient Outcomes

Since doctors and nurses can monitor data around the clock, this increases the likelihood of better adherence to treatment. Patients can also live more autonomously and be more involved in their treatment.

Improving Healthcare Accessibility

Finally, remote patient monitoring reduces the inequalities associated with traditional healthcare. Online monitoring solutions can also provide remote consultation and follow-up services to people living in rural areas.

How RPM Systems Work

There are many RPM systems on the market, and they come in all shapes and sizes. Some RPM systems are stand-alone devices, while others are integrated into existing electronic health records. But what all RPM systems have in common is the ability to collect patient-generated health data and then send the data to healthcare providers for monitoring.

RPM solutions provide home care telemedicine capabilities that can be embedded into:

● Standalone medical measurement devices such as patches, blood glucose concentrations, pulse oximeters, etc.

● Implantable devices, for example, cardiac implantable electronic devices.

● Digital platforms enable continuous patient monitoring and support around the clock, including telemedicine.

Typically, RPM solutions connect to the cloud, enabling compliant data sharing and seamless access to patient data.

Here is the step-by-step process for transferring a patient's vital signs from the RPM software to the healthcare provider:

● The patient is registered with the system so that the system can authenticate the specific device.

● The system initializes monitoring and data collection through medical equipment.

● The device collects and transmits data to the RPM server or cloud.

● Algorithms analyze patient data and the system generates reports and visualizations.

The doctor accesses the visualization and follows the corresponding actions, whether adjusting the treatment course, changing the treatment plan, or any other subsequent actions.

How Artificial Intelligence Can Help Telemedicine

The significant impact of artificial intelligence on healthcare has led to the growth of the artificial intelligence market. By 2030, the value of artificial intelligence in the healthcare market is expected to exceed $187 billion.

The potential of artificial intelligence is also reflected in telemedicine and remote monitoring. Therefore, AI-driven technology has transformed RPM solutions from simple data aggregators into advanced data analysis platforms. Combined with analytics, the RPM platform allows physicians to integrate patient data into clinical workflows, generate accurate predictions, and flag individual patients at risk.

As a result, AI can enable proactive care and more personalized data-driven treatments. So, where does machine learning fit in?

DIAGNOSIS

According to data, remote health monitoring of diabetic retinopathy reduced patient visits by approximately 14,000 times. If we add artificial intelligence to the screening phase, we expect the number of visits and patient wait times to fall even further.

Thus, machine learning classification algorithms can analyze patient data in RPM solutions and flag patients who are at risk for certain diseases. Patients can also upload medical images to a secure server, where AI-based image recognition can spot abnormalities without professional help.

Treatment Options

Artificial intelligence is also proving helpful in precision medicine. The AI-powered system compares the patient's medical images to a database of high-quality treatment options created by certified experts. It then combines these insights with personal health data to generate a personalized treatment plan.

According to IBM, the expert system can also group patients based on similar responses to treatments to produce optimal treatment options.

Patient Engagement

Keeping patients adhering to their medications or making timely appointments is another responsibility of artificial intelligence in monitoring patients remotely. By analyzing software data, AI can be used to generate action items, including appointment reminders, follow-up actions, and more. Powered by artificial intelligence and natural language processing technologies, chatbots are integral in automating communications and improving access to healthcare.

Chronic Disease Management

The complexity of chronic disease management has always been uncharted territory for the healthcare industry. However, AI can prevent chronic diseases such as diabetes, cancer and kidney disease by identifying early signs of these diseases in patient data. Therefore, the algorithm can identify patients with chronic kidney disease by stage and the presence of acute kidney injury.

Artificial intelligence and remote patient monitoring are a match made in heaven

Remote patient monitoring is a much-needed iteration of the traditional healthcare system, making professional diagnosis and treatment accessible to all. Artificial intelligence is gradually entering RPM software to enhance its data processing capabilities and transform it into a viable tool to complement offline treatment. Artificial intelligence supports the efficiency of disease diagnosis, personalized treatment and disease prevention to improve patient outcomes and proactive treatment.

The above is the detailed content of How artificial intelligence can improve the quality of healthcare by monitoring patients remotely. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions Jun 28, 2024 am 03:51 AM

This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Context-augmented AI coding assistant using Rag and Sem-Rag Context-augmented AI coding assistant using Rag and Sem-Rag Jun 10, 2024 am 11:08 AM

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations Jun 11, 2024 pm 03:57 PM

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

Seven Cool GenAI & LLM Technical Interview Questions Seven Cool GenAI & LLM Technical Interview Questions Jun 07, 2024 am 10:06 AM

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Five schools of machine learning you don't know about Five schools of machine learning you don't know about Jun 05, 2024 pm 08:51 PM

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework Jul 25, 2024 am 06:42 AM

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time Jul 17, 2024 pm 06:37 PM

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S

SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc. SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc. Aug 01, 2024 pm 09:40 PM

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year

See all articles