Chronic Diseases Overview
Diabetes: a subtype
Diabetes is one of the top three most prominent diseases in the global population. Major causes are unhealthy lifestyle. Developed countries report working in the pharmaceutical and drug manufacturing industry to support this. Currently, there is no known cure, and diabetes must be managed and controlled on a regular basis. There are more than 420 million cases of diabetes in the world. This number is expected to grow in the future.
Symptoms, Examinations, and Treatment
Firstly, it is characterized by high blood sugar levels in the body. After testing, it can be diagnosed. There are two types, type I and type II. Type I is the most common in the world. The condition is characterized by the body’s inability to produce insulin that can regulate blood sugar. Type II is slightly different because the body produces the insulin, but it cannot function well. Examinations by a medic are accompanied by blood sugar tests. It cannot be subtracted, but it can be controlled. People need to maintain healthy eating habits and engage in workouts on a daily basis.
Following the World Health Organization, liver diseases have increased by 47% in the past two decades. The largest of these is type II diabetes. It increases the chances of getting diseases such as cancer. It also increases the chances of getting heart diseases and heart attacks by up to 50%. In addition, the growth possibility is high.
Healthcare technology has gone a long way over the past decades and really helped improve how different conditions are diagnosed, treated, or managed. From the development of a range of sophisticated imaging techniques to the adoption of universal electronic health records (EHRs), the advancement of technology allows for more precise and efficient ways to provide patient care than ever before. With telemedicine, wearables health devices, and mobile applications patients can be cared for at a distance with the option of remote monitoring as well as the delivery of personalized health advice to them. Healthcare Technology throughout History
AI (Artificial Intelligence) Basics – a game changer
AI has been revolutionizing every aspect of the healthcare industry, creating opportunities never seen before to adopt the best available treatment plans and improve patient care. AI is a set of technologies, such as machine learning, natural language processing, and robotics used for collecting and analyzing large amounts of health data rapidly and finding patterns that can inform quality decision-making. AI in Understanding Individuals Health: AI is able to recognize early symptoms, diagnose better and provide more than personalized treatment plan; adds guarding the patient’s condition for better outcomes. Learn more about AI in healthcare.
Chronic Disease Management with AI
Early Detection and Diagnosis
AI-powered Diagnostic Tools
The use of AI-powered diagnostic tools in early detection and diagnosis of chronic diseases is revolutionizing the way medicine is practiced. Machine learning algorithms are powerful tools that can scan medical images and genetic information as well as the pool of patients’ health records already uploaded to produce accurate results. For example, AI can detect diabetic retinopathy in the retina images of the eyes that was previously undetectable by any machine or find lung nodules indicating the possibility of lung cancer using CT scans more accurately then a human radiologist. They help with the accuracy of the diagnoses, which is key to address any condition and manage chronic conditions effectively as time is of utmost importance in this area. AI diagnostic tools Case Read More
How can AI-enabled technologies improve the future of healthcare?
Predictive Modelling and Risk Scoring
The Future of healthcare reimagined
AI predictive analytics can measure the risk of chronic diseases. By aggregating large datasets of both numeric and unstructured data from electronic health records, claims data, consumer and other data, in combination with biometric data from wearable devices, AI can predict who, whether the patients will get the disease like diabetes, heart disease or hypertension or not even before there are any physical signs. Thus a proactive, patient–centric model is followed, ensuring chronic disease does not occur or progress, hence reduced morbidity, mortality, costs. Explore how predictive modelling is changing healthcare?
Personalized Treatment Plans
ML Algorithm for Individualized care
Personalized medicine in Chronic Disease Management is useful through creation of a treatment plan using machine learning algorithms. The first two algorithms work operating on information from patients’ medical record and lifestyle, as well as genetic data and, if available, disease outcomes can be used to predict the most appropriate care plan for patients, thereby improving outcomes. Machine learning can be used for diabetes, where the algorithms can be used to calculate the optimum dose of insulin for each patient, thereby improving glycaemic control and reducing complications. Get to know more about machine learning and personalised medicine?
AI in Medication Management
Long Term care patients with chronic disease will have AI for better medication management
AI can now predict which medications and dozes are likely to work best for each patient, it can track how well patients take their medicines over times; AI can also track the interactions between different medicines and how they can cause harm. With AI, these systems can now contact the patients and remind them of the time to take the medication and the time to stop and at the same time, they track the intake and send the data to the healthcare professionals like doctors, in case they stop the medication, more stringent interventions will be necessary from the healthcare personnel. Learn more on AI in medication management.
AI-enabled health apps and wearable devices are redefining remote monitoring in chronic disease management Continuous monitoring of vital signals like heart rate, blood pressure, and glucose levels can be achieved if a smartwatches/ wearables / Fitbits are worn. On-the-fly artificial intelligence algorithms process this data and deliver alerts and outcomes to the patient and the care provider. The alerts generated through continuous monitoring ensures early warning of bad news and right action at the right time which enhances disease betterment. Explore about AI in Wearable Health Tech
Telemedicine Platforms by AI
AI-driven telemedicine platforms allow better access and delivery of healthcare, especially to the people with chronic diseases. These platforms offer virtual visits, diagnostics, and care plan follow-ups in between the physical visits. AI in telemedicine will make the remote health support clinicians to better decide and analyze patient data in virtual visits and ensure that patients are having proper care treatment even at distance. For More Information Visit How to use AI in telemedicine.
Patient Engagement and Education
AI Chatbots and Virtual Care AIs
What are the Ways AI Chatbots are making the Patient Experience and Knowledge of how to better manage chronic Disease? These tools provide personalized patient information about chronic diseases, answer the questions of the patients, and guides them to solve the underlying issues too. In fact, these systems use Natural language processing to talk/understand with the patients in a more human way, and create health information in a way which is at the level patients can understand. Find more: AI Chatbots in healthcare
Interactive patient education
AI-driven, interactive patient education tools are changing the way patients educate themselves about their chronic conditions. Multimedia, customizable learning pathway, gamified, interactive simulation tools educate the patients about their chronic disease condition as well as make them know how the treatment options/life style changes. AI adapts the educational to the content to the needs of individual patients. As a result, AI patient education results in a better education, and the more engagement patients have about chronic disease condition. AI in Patient Education
Pros of Using AI in Chronic Disease Management
AI technologies significantly improve the accuracy of diagnoses and help to perform a timely intervention. When it comes to managing chronic diseases, these are especially important. By analyzing mega-datasets and identifying patterns that might not be visible to human clinicians, AI tools can detect diseases at the earliest stage possible. For example, as its authors describe in their insights on computer models, AI algorithms can detect the early symptoms of a disease such as diabetic retinopathy from a series of images that can be taken with the help of sensitive cameras that scan the fundus [65]. It can also predict the possibility of a heart attack or some cardiovascular event on the basis of ECG data and timely get the treatment. For the patients, it can mean that it might prevent the disease from further development and hence improve the outcomes. For instance, with regard to the diabetes patient, read the application of AI to early-intervention efforts to prevent the readmission of the patient to the hospital for multiple times for an attack, stroke, or something else. In general, since healthcare organizations will be able monitor the state of their chronic patient with predictive analytics, they will be able to prevent the possible complications well in advance using artificial intelligence-driven monitoring and detect it on time to change the care path and prevent the severe health crises which usually ends in hospitalizations. For example, there is a group of AI-powered wearable continuous glucose monitoring systems, which can accurately predict hypoglycemia in a diabetic and alert him before he gets dizzy and rushes to the hospital bed to arrest the problem. This will help to improve the patient outcomes and avert the readmissions from the diseases etc. See how AI can help in such areas as “ hospital readmission for multiple chronic patients ”. In general, healthcare will be able to save the cost through this technique of preventing the diseases. For example, a 182-bed hospital in Louisiana is seeing a 30% decrease among the collective patient population groups monitor in 2006 in the number of complications factored by the use of artificial intelligence-driven monitoring system in which the systems will be able to minimize the complications based on such trends over the years from 2013 as indicated in the case.
How AI might deliver major healthcare savings through the prediction and prevention of complications from chronic conditions:
Data hygiene accompanied with AI analytics leads to predictive and preventive care. Preventive and predictive care in the context of chronic disease moves the equation from expensive emergency interventions in facilities like hospitals, where conditions have advanced to a point of chronicity needing exacerbation that increases the cost exponentially all the way down the line, to concentrating on early detection and management, which in turn reduces the burden of a healthcare system applied across it – facilitating the most for savings. Ultimately, AI tools that serve to alert providers to patients that are at a high risk of getting chronic disease might move a scenario where costly management strategies must be implemented to ameliorative care to the onset with early lifestyle interventions keeping these individuals from advancing their pathology, or condition. Read More about How AI reduces costs
Resource and workforce optimization:
With AI programmed to ensure that workflows and administrative tasks are handled more cleanly and efficiently, providers will be better positioned with better decision making capabilities. This means that healthcare providers might be managing AI systems that are capable of Markov decision-making to automate routine tasks and updates i.e. appointment scheduling, patient triage, and intake data tasks. Moreover, other forms of AI software might run facility operations that accurately predict patient demand and manage staff and supplies in such a way that the resources are not over utilized. This results in dramatic and effective use of these resources to maximize savings and better patient centrism. Find out how AI drives resource use efficiency
Accessibility and convenience:
Engaged Service for Remote Areas
AI tech, and specifically telemedicine and remote monitoring tools, have greatly changed how those in relatively more out-of-the-way areas or over-looked communities get care. With AI-driven telemedicine platforms, patients can get quality advice from specialist doctors without the hassle of long-distance travel and other logistical issues. By collecting health data through the wearable devices or mobile applications that the patient constantly uses and sending them in real-time to your healthcare providers, patients can ensure that they get no less of the care they need no matter where you are. This is especially crucial for the remote health management of chronic diseases. Find out more about remote health through AI
24/7 Monitoring and Support
CONS – The ability for remote monitoring and support are real pluses of AI in chronic diseases. Artificial intelligence-powered devices monitor and keep track of the patient’s vital signs, medication intake, and overall health behaviors 24/7. If there are any bizarre and unusual measurements of health in the patient, the AI system quickly responds and alerts those in power to set the intervention in motion. Furthermore, AI chatbots and other digital assistants are available to answer patient’s questions and provide them with personalized medical advice whenever users would need them, 24/7. This raises patient engagement in their health and medication consumption. Investigate 24/7AI health monitoring
There is a pressing concern about doctor-patient confidentiality in the age of large health data systems. When large volumes of patient data are being collected and stored, there are legitimate doubts about privacy and how well it is being protected. Issues can arise where glitches in the system cause leaks that can result in identity theft. Moreover, patients who fear this barrier to unauthorized access are less likely to consult doctors and compromise the credibility of large-scale healthcare data. To avoid such issues, strong cybersecurity measures and safeguards must be put in place to restrict access to patient records and access only authorized applications. There are also regulations and rules of compliance with the governmental ruling that change the way we can even use AI to process data, the most prevalent of those could be the General Data Protection Regulation and the Health Insurance Portability and Accountability Act that provide health data protection guidelines that should be followed under any circumstance. Furthermore, it’s also important to ensure that AI within the healthcare organization is working and ensure that these organizations comply with the rules surrounding AI. There is also a problem of leveraging and fixing the way of performing these. For instance, when it comes to Implementing AI’s Base, which is not an easy task in and of itself. It has historically centered on digital health’s low hanging fruit – using various combinations of machine learning in the chronic disease and case management arenas could take advantage of the current way and its existing strengths. Many healthcare organizations are unable to make use of the new AI technologies in the field due to the fact that technology is rapidly changing and upgrades or architectural changes may be necessary for the new AI to match in a legacy healthcare system.
Medical Professional Training/Adoption
The acceptance by the healthcare professionals is also a key factor that is related to the success of the adoption of AI. On the other hand, AI technologies are relatively new, and people that are involved in the medical sphere may not know how they work since technologies like AI/ML will need more training to be sure of how they work, what they do as well as where not to use them. Chronic disease management will be one of a variety of solutions developed by AI/ML, while removing the resistance to change can make it beneficial: that is, the AI Product should be developed in a way that doctors and healthcare personnel not just feel comfortable to deal with the systems, but engage more productively with them. I want you to edit my essay or write my paper from scratch. Ethical and Legal Issues
Transparency in AI Decision-making
Another point of concern in the practice of healthcare services is the ethics of how transparent the AI decision-making is. Many AI algorithms, particularly those based on deep learning, work as black boxes: it can be very difficult to make out why a decision was made at all 300. Such a lack of transparency can limit the usability and trustworthiness of these AIs for both patients and healthcare providers if the decisions made are not “understandable or interpretable”; results are not reliable. At the same time, the environmental aspect of the technology is never considered in the ethics of AI or robots in waiting. What Does AI in Healthcare Mean for the Law?
AI in healthcare poses legal issues to the law in that it further creates the problem of who should be responsible for the decisions made by AI. Hence, it is still a matter up for discussion and debate, once the AI system delivers the wrong or harmful decisions about a patient should the healthcare provider be responsible to cover the damage? Or should the AI developer take that responsibility, or their institution? Thus and in order to provide and ensure that the legality of AI applications and solutions is regulated in compliance with the rules of law and the ethical standards, predefined regulatory frameworks should exist to establish how these technologies should be implemented in the healthcare system. If you need further help formatting your paper, there are experts who can help you. Find more information at the edition The Legal Implications of AI in Healthcare.
The Future of SSIS
There are more and more tools consistently being created based on AI to better facilitate chronic disease management in their field.. Deep Learning, Natural Language Processing and Reinforcement Learning have become much better at complex medical data analytics. The deep learning algorithms and tools are now getting better to be cognitive health platforms which are AI based platforms that are getting better at being able to leverage the variety of information such as genetic data, lifestyle details and clinical records in providing seamless integrated care, which is also personalized. AI based robotic surgery that is already commercially tested as well as complex diagnostic imaging is yet another advancement that is just around the corner that will revolutionize the management of chronic illness. Ways to learn more about new AI innovations
How Big Data and IoT are helping in Chronic Disease Management
The future where AI can be more effectively used in being one with big data and the internet of things opens up many possibilities. Big data analytics is at its best and is the only science to better analyze health data to find patterns and insights to be able to create better treatment planning. IoT devices such as wearable sensors and connected medical appliances need to provide the best quality continuous data about one’s health that is real-time. Using AI algorithms properly applied to this data can provide real-time feedback and intervention long before any chronic conditions comes across for patients. The combination of Big Data and IOT is an extraordinary source. Big data access with IOT will help in better identifying the patients or driving personalized medicines. Items Source: Massive big data and iot opportunity
Partnerships and Collaborations
AI in Healthcare and Community Engagement Through Public-Private Partnerships.
Public-private partnerships can be used especially in the provision of chronic diseases and to support. These partnerships combine the knowledge, resources and potential for provision of in public authorities with the knowledge, resources and potential for innovation in relation to AI solutions developed or being developed by the private companies. That is to say, a collaboration between healthcare providers, technology companies and research could mean that the creation of powerful AI-based platforms capable of predicting diseases whilst providing precision diagnosis and management. These types of partnerships usually accelerate the process of research and development, therefore take the cutting-edge AI technologies to the patients earlier. You can learn more about the public private partnership based on AI in healthcare study.
Case Studies and Global Initiatives
Many of the global initiatives and case studies have been investigating how AI can positively impact the provision of services in relation to chronic diseases. Numerous case studies examine why the AI is a area of interest in the fight against ever-growing level of chronic diseases. To combat the escalating epidemic of chronic disease, countries and multi-national entities pour money into AI. The AI is used by various governments and the WHO in such instances as delivering healthcare in the most remote and under-developed regions.
So can the world show us case studies of how its being done…from predictive analytics to it helping a healthcare provider deliver better diabetes care in India. AI, and particularly AI-powered telehealth services in rural Africa. These are great examples of how the AI is able to put health systems in better shape having individuals in the context of healthy life across the globe. Details of international healthcare-based AI projects are here.
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