Tobias Foster

1626698277

Impact of AI on predictive analytics in healthcare

For a long time, healthcare professionals have tried every necessary means to help their patients get better with the purest of intentions. However, many times, they are limited by the bare fact that they are humans. Being human means that there’s only so much that they can do with the information, energy, time, and resources available to them. Yet they strive to search for, process and remember all necessary information related to the different medical conditions they are managing while considering the personal medical histories of all the patients they are managing. This is a lot to take in at a time, and there is no denying that. That is why predictive analytics and artificial intelligence come in to play crucial roles in healthcare delivery.

As a field of study, artificial intelligence seeks to replicate humans’ abilities without the limitations of power, energy, and time. With the use of advanced algorithms, IT systems, and data processing capabilities, it is possible to produce prediction driven by data within a few seconds without human intervention. Predictive analytics uses statistical methods and technology to run through a huge volume of information and analyze it to predict individual outcomes. These predictions, in medicine, can vary from hospital readmission rates to responses to medications, etc. Some possible examples are determining a disease’ likelihood, predicting infections, calculating future wellness, etc. When historical data and real-time back it, predictive analytics in healthcare can identify risky medical conditions ahead of time.

Predictive analytics has many positives and benefits in healthcare. According to a best cv writing service uk, it has played a massive role in improving the healthcare industry in the following ways.

Predicting epidemic conditions

Many years ago, it would have been impossible to even think of predicting an epidemic before it already starts, but with predictive analytics in healthcare, this is a reality now. It is now possible for health organizations to predict infectious diseases using their access to data such as population density, economic profile, reported cases, weather reports, etc.

The primary source of big data analytics is now machine learning models, and they play a significant role in the improvement of healthcare service delivery, especially in highly prone areas. We can now predict chronic diseases such as heart attacks and more accurately and efficiently. These leads can massively bring about an upgrade in the quality of treatment a patient gets while also significantly reducing the cost.

Predicting the growth of chronic diseases

With the ever-rising world population, there is an increasing importance for medical authorities to track the general well-being and health of the people to take timely steps to prevent the rise of chronic diseases when necessary. As it was not possible to predict disease risks, this caused many people to develop long-term chronic conditions that always become harder to treat and affect the patient’s health massively.

Healthcare organizations can now use AI-powered predictive analytics to manage the population’s health, especially with the kind of capabilities that machine learning has and the continuous advancement of predictive analytics. Different factors are combined to get insights into big data analytics. An example is risk score prediction.

Risk score prediction is based on reports from lab tests, electronic health records, biometric data, and a few other social determinants combined to provide insight into the population’s health. The machine uses this data to identify the population sections with plenty of high-risk patients. The doctors become alert on areas that need interventions and start to take adequate steps.

**Optimum allocation of resources and staff **

In many regions, the major problem healthcare organizations have, and one of the reasons they suffer poor healthcare delivery in that region is an imbalance in the distribution and allocation of healthcare facilities and resources. This is what differentiates and is the problem of hospitals in villages and suburban areas. Medical practitioners often fail to judge an excessive demand for resources for healthcare and unprecedented critical conditions. What this causes is an overflow of emergency wards and mismanagement of resources.

With the help of artificial intelligence-driven predictive analytics in healthcare, it is now possible for healthcare institutions to streamline medical resources allocation, and there are different ways to do it:

● They predict the patient flow and the fluctuations to ensure there are enough resources allocated.
● Staff is rescheduled based on the flow of patients to ensure more efficient and effective patient care.
● Utilization patterns are detected from patients’ data, making it possible to manage their service and rate of appointment properly.

**Conclusion **

Predictive analytics in the healthcare industry can only be positive for all involved parties. The healthcare practitioners and providers are more effective and efficient with their work as they’re equipped with the knowledge of the places to focus on at a time and the disease they’re battling. Invariably, this means that the patient gets improved healthcare as there will be enough resources allocated towards their health. Health givers find it easier to do their jobs, and the patient enjoys an improved and more affordable service.

#ai #healthcare

Ken  Mueller

Ken Mueller

1591112700

Importance of AI in Healthcare Sector

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.

#artificial intelligence tutorials #ai applications in healthcare #ai in healthcare #applications of ai in healthcare #artificial intelligence and healthcare

Importance of AI in Healthcare Sector
Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

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.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Making Sales More Efficient: Lead Qualification Using AI

Tobias Foster

1626698277

Impact of AI on predictive analytics in healthcare

For a long time, healthcare professionals have tried every necessary means to help their patients get better with the purest of intentions. However, many times, they are limited by the bare fact that they are humans. Being human means that there’s only so much that they can do with the information, energy, time, and resources available to them. Yet they strive to search for, process and remember all necessary information related to the different medical conditions they are managing while considering the personal medical histories of all the patients they are managing. This is a lot to take in at a time, and there is no denying that. That is why predictive analytics and artificial intelligence come in to play crucial roles in healthcare delivery.

As a field of study, artificial intelligence seeks to replicate humans’ abilities without the limitations of power, energy, and time. With the use of advanced algorithms, IT systems, and data processing capabilities, it is possible to produce prediction driven by data within a few seconds without human intervention. Predictive analytics uses statistical methods and technology to run through a huge volume of information and analyze it to predict individual outcomes. These predictions, in medicine, can vary from hospital readmission rates to responses to medications, etc. Some possible examples are determining a disease’ likelihood, predicting infections, calculating future wellness, etc. When historical data and real-time back it, predictive analytics in healthcare can identify risky medical conditions ahead of time.

Predictive analytics has many positives and benefits in healthcare. According to a best cv writing service uk, it has played a massive role in improving the healthcare industry in the following ways.

Predicting epidemic conditions

Many years ago, it would have been impossible to even think of predicting an epidemic before it already starts, but with predictive analytics in healthcare, this is a reality now. It is now possible for health organizations to predict infectious diseases using their access to data such as population density, economic profile, reported cases, weather reports, etc.

The primary source of big data analytics is now machine learning models, and they play a significant role in the improvement of healthcare service delivery, especially in highly prone areas. We can now predict chronic diseases such as heart attacks and more accurately and efficiently. These leads can massively bring about an upgrade in the quality of treatment a patient gets while also significantly reducing the cost.

Predicting the growth of chronic diseases

With the ever-rising world population, there is an increasing importance for medical authorities to track the general well-being and health of the people to take timely steps to prevent the rise of chronic diseases when necessary. As it was not possible to predict disease risks, this caused many people to develop long-term chronic conditions that always become harder to treat and affect the patient’s health massively.

Healthcare organizations can now use AI-powered predictive analytics to manage the population’s health, especially with the kind of capabilities that machine learning has and the continuous advancement of predictive analytics. Different factors are combined to get insights into big data analytics. An example is risk score prediction.

Risk score prediction is based on reports from lab tests, electronic health records, biometric data, and a few other social determinants combined to provide insight into the population’s health. The machine uses this data to identify the population sections with plenty of high-risk patients. The doctors become alert on areas that need interventions and start to take adequate steps.

**Optimum allocation of resources and staff **

In many regions, the major problem healthcare organizations have, and one of the reasons they suffer poor healthcare delivery in that region is an imbalance in the distribution and allocation of healthcare facilities and resources. This is what differentiates and is the problem of hospitals in villages and suburban areas. Medical practitioners often fail to judge an excessive demand for resources for healthcare and unprecedented critical conditions. What this causes is an overflow of emergency wards and mismanagement of resources.

With the help of artificial intelligence-driven predictive analytics in healthcare, it is now possible for healthcare institutions to streamline medical resources allocation, and there are different ways to do it:

● They predict the patient flow and the fluctuations to ensure there are enough resources allocated.
● Staff is rescheduled based on the flow of patients to ensure more efficient and effective patient care.
● Utilization patterns are detected from patients’ data, making it possible to manage their service and rate of appointment properly.

**Conclusion **

Predictive analytics in the healthcare industry can only be positive for all involved parties. The healthcare practitioners and providers are more effective and efficient with their work as they’re equipped with the knowledge of the places to focus on at a time and the disease they’re battling. Invariably, this means that the patient gets improved healthcare as there will be enough resources allocated towards their health. Health givers find it easier to do their jobs, and the patient enjoys an improved and more affordable service.

#ai #healthcare

Alayna  Rippin

Alayna Rippin

1597622400

The Trending Healthcare App Features for 2020

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.

Use-Case specific app features

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.

Digital Prescription and Reports

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.

Wearable Connectivity

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.

On-Demand Medicine

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.

#healthcare #health-tech #health-tech-and-cyber-security #healthcare-apps #healthcare-application #healthcare-mobile-apps #healthcare-trends-in-2020 #top-healthcare-trends

The Trending Healthcare App Features for 2020
Abigail  Cassin

Abigail Cassin

1597711196

The Future of Jobs and AI

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.

#ai #analytics #artifical intelligence #predictive analytics #skills #advanced analytics #data scientists #job creation

The Future of Jobs and AI