The collaboration of artificial intelligence (AI) with massive healthcare data is transforming the way care is delivered. At Oodles, we constantly innovate new therapeutics and strive to improve the existing ones by channelizing medical data using machine learning technologies. Recent years have witnessed a significant rise in the applications of predictive analytics in the healthcare sector for strengthening care decisions and facilities.
The underlying machine learning techniques of predictive analytics enable organizations to combat medical emergencies and take timely preventive measures. This blog post cumulates effective applications of AI development services for predictive analytics to improve health and healthcare.
Predicting Widespread of Chronic Diseases
In the face of rising population growth, it is cumbersome for medical authorities to track population health and take timely preventive measures. Inefficient risk prediction leads to the development of long-term chronic conditions that are difficult to treat and affect patient care drastically.
With advancements in machine learning capabilities, healthcare organizations are now beginning to infuse AI-powered predictive analytics in population health management. Here’s how big data analytics derive valuable insights for patient care-
A) Risk Score Prediction based on electronic health records (EHR), biometric data, lab test reports and social determinants of health provides in-depth health insights. Machines trained using this data can identify population sections with high-risk patients and signal doctors to plan relative interventions.
B) Prediction of extreme epidemic conditions is now possible with big data analytics, machine learning, and high computational power. Prediction of infectious diseases is done with various data sources such as weather reports, reported cases, population density, economic profile, etc.
Predictive analytics for epidemic conditions
With big data analytics, machine learning models become a key source to improve healthcare services in highly prone regions. Accurate and efficient prediction of chronic diseases such as heart attacks and cancer can improve healthcare quality and cost significantly.
How does Oodles practice predictive analytics in healthcare to prevent chronic disease?
At Oodles, our AI team is skilled at deploying machine learning algorithms for data-driven predictive analytics. We have hands-on experience in training ML models with EHR, 2D, and 3D medical imagery to generate accurate health insights.
Our most recent AI solution for healthcare institutes combine machine learning libraries like Scikit-learn to develop a Diabetic Prediction System. The model works on structured data inputs such as Plasma Glucose, Tricep Thickness, and blood pressure to predict diabetes in patients.
The model’s USP is that it does not require any intervention of a physician to measure diabetes. Supported by a simplified interface, the system enables common individuals to test diabetes in an easy, quick, and accurate manner.
Ensuring Optimal Staff and Resource Allocation
Improper resource allocation and unbalanced distribution of healthcare facilities has been a major concern for hospitals in villages and suburban areas. Medical authorities often fail to judge unprecedented critical conditions and excessive demand for medical resources leading to overflowing emergency wards and mismanagement.
With the advent of AI-driven predictive analytics, healthcare institutes can streamline medical resource allocation by-
A) Predicting the fluctuations in patient flow to ensure proper bed allocation.
B) Rescheduling staff according to patient flow to enhance patient care effectively.
C) Detecting patterns of utilization from patient data to manage appointment rate and service.
Deploying AI-powered Predictive Analytics in Healthcare with Oodles AI
We, at Oodles, build industry-specific predictive engines for eCommerce, marketing, healthcare, and financial businesses. Our AI team enables healthcare organizations to channelize big data analytics to extract meaningful insights from complex medical records.
In addition, we have expertise in improving healthcare services with AI-powered conversational chatbot development. We enable doctors to automate remote assessment with natural language-based chatbots and other AI solutions.
Talk to our AI team to know more about our artificial intelligence services.
#AI development services #artificial-intelligence
AI and related advancements are progressively playing the role of a disruptor in business and society. The application of AI is also increasing in the healthcare domain. These advances can possibly change numerous parts of patient care, just as regulatory procedures inside supplier, patient experience, and pathology labs.
There are as of now various researches recommending that AI can proceed just as or better than people at key human services, for example, diagnosing the ailment. Today, algorithms are beating radiologists at spotting harmful tumors. They are directing specialists on how to build companions for expensive clinical preliminaries.
Nonetheless, for an assortment of reasons, we accept that it will be numerous prior years AI replaces people for wide clinical procedure areas. In this article, we portray both the potential that AI offers to mechanize parts of care and a portion of the hindrances to the fast execution of AI in social insurance.
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If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.
AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.
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The recent technological trend in the healthcare industry has brought a virtual doctor into many of our pockets. Be it a serious health condition or a need to track our fitness level, there are thousands of mHealth apps for most of the healthcare use cases.
In 2020, the health-tech industry offers many mHealth apps ranging from heart rate monitoring to nutrition and fitness apps. Undoubtedly, there will be variation in the app functionality according to the target market, customer base and the purpose.
There are thousands of healthcare mobile apps available in the market and depending on the use-cases, they will incorporate specific features that help them serve the purpose. Following are some of the most trending app features.
Almost every hospital or pharmacy-related healthcare app should have the functionality to handle digital prescriptions and reporting. Misplacing or losing medical prescriptions and reports is very common for the patients. It even becomes a hassle for the healthcare institutions or doctors to manage and access the patient files. This is where the digital prescription feature comes handy for both the patients and doctors.
This feature should also incorporate the functionality to download the reports and prescription information in a documented format. It allows the patients to manage their lifetime medical history in one place. This feature is very crucial to speed up the medical treatment process.
Wearables are the most trending discussion in the health tech space. Up until now, this feature is most commonly used by fitness tracking apps. But as the healthcare industry has now paced up the technology adoption, healthcare providers and medical practitioners have started to trust the wearable technologies to monitor their patient health continuously.
In 2020, wearables do not just mean some gadgets like Fitbits. There are many clinical-grade IoMT (Internet of Medical Things) devices used by the healthcare industry that are used in a form of belts, chest straps etc.
The wearable connectivity feature allows the healthcare apps to record the user’s data, which can be shared with the doctors. The doctors can provide a better consultancy if they have access to their patient’s all-time health status.
Similar to amazon for x apps, this on-demand app feature is focused on making the medicines accessible anytime, anywhere. Using this feature, the patients will be able to refill their stock of medicine without having to visit the pharmacy.
The feature would require the functionality to allow admin to list out the pharmacies in the locality, so that the users can order from the nearest one. To make this feature more effective, you also would need to add an online payment feature within the app.
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The first few times I was asked this question, I brushed it off with a brief answer, assuming that it was a passing curiosity by the person asking the question, and that there was no real concern or emotion behind the inquiry. I was wrong in that regard.
This question is on the minds of many people and it is weighing on them as a real concern. In the past year, I have been asked the same or similar questions in presentations and discussions in Australia, the United Kingdom, Germany, Switzerland, and the United States.
Rather than continuing to brush aside the question, I have started answering it with one of the many studies that have proven, again and again, that AI and related technologies and systems are net job creators in the short and long term. Let’s examine a few of those recent studies.
This article is an excerpt from the book, Building Analytics Teams authored by John K Thompson. In this book, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team.
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Agrochemical companies manufacture a range of offerings for yield maximisation, pest resistance, hardiness, water quality and availability and other challenges facing farmers. These companies need to measure the efficacy of their products in real-world conditions, not just controlled experimental environments. Single-crop farms are divided into plots and a specific intervention performed in each. For example, hybrid seeds are sown in one plot while another is treated with fertilisers, and so on. The relative performance of each treatment is assessed by tracking the plants’ health in the plot where that treatment was administered.
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