Many industries have been disrupted by the influx of new technologies in the Information Age. Healthcare is no different.Particularly in the case of automation, machine learning, and artificial intelligence (AI), doctors, hospitals, insurance companies, and industries with ties to healthcare have all been impacted – in many cases in more positive, substantial ways than other industries.

According to a 2016 report from CB Insights, about 86% of healthcare provider organizations, life science companies, and technology vendors to healthcare are using artificial intelligence technology. By 2020, these organizations will spend an average of $54 million on artificial intelligence projects.

So what solutions are they most commonly implementing? Here are 10 common ways AI is changing healthcare now and will in the future.

1. Managing Medical Records and Other Data

Since the first step in health care is compiling and analyzing information (like medical records and other past history), data management is the most widely used application of artificial intelligence and digital automation. Robots collect, store, re-format, and trace data to provide faster, more consistent access.

2. Doing Repetitive Jobs

Analyzing tests, X-Rays, CT scans, data entry, and other mundane tasks can all be done faster and more accurately by robots. Cardiology and radiology are two disciplines where the amount of data to analyze can be overwhelming and time consuming. Cardiologists and radiologists in the future should only look at the most complicated cases where human supervision is useful.

3. Treatment Design

Artificial intelligence systems have been created to analyze data – notes and reports from a patient’s file, external research, and clinical expertise – to help select the correct, individually customized treatment path.

4. Digital Consultation

Apps like Babylon in the UK use AI to give medical consultation based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to compare against a database of illnesses. Babylon then offers a recommended action, taking into account the user’s medical history.

5. Virtual Nurses

The startup has developed Molly, a digital nurse to help people monitor patient’s condition and follow up with treatments, between doctor visits. The program uses machine learning to support patients, specializing in chronic illnesses.

In 2016, Boston Children’s Hospital developed an app for Amazon Alexa that gives basic health information and advice for parents of ill children. The app answers asked questions about medications and whether symptoms require a doctor visit.

6. Medication Management

The National Institutes of Health have created the AiCure app to monitor the use of medication by a patient. A smartphone’s webcam is partnered with AI to autonomously confirm that patients are taking their prescriptions and helps them manage their condition. Most common users could be people with serious medical conditions, patients who tend to go against doctor advice, and participants in clinical trials.

7. Drug Creation

Developing pharmaceuticals through clinical trials can take more than a decade and cost billions of dollars. Making this process faster and cheaper could change the world. Amidst the recent Ebola virus scare, a program powered by AI was used to scan existing medicines that could be redesigned to fight the disease.

The program found two medications that may reduce Ebola infectivity in one day, when analysis of this type generally takes months or years – a difference that could mean saving thousands of lives.

8. Precision Medicine

Genetics and genomics look for mutations and links to disease from the information in DNA. With the help of AI, body scans can spot cancer and vascular diseases early and predict the health issues people might face based on their genetics.

9. Health Monitoring

Wearable health trackers – like those from FitBit, Apple, Garmin and others – monitors heart rate and activity levels. They can send alerts to the user to get more exercise and can share this information to doctors (and AI systems) for additional data points on the needs and habits of patients.

10. Healthcare System Analysis

In the Netherlands, 97% of healthcare invoices are digital. A Dutch company uses AI to sift through the data to highlight mistakes in treatments, workflow inefficiencies, and helps area healthcare systems avoid unnecessary patient hospitalizations.

These are just a sample of the solutions AI is offering the healthcare industry. As innovation pushes the capabilities of automation and digital workforces, from providers like Novatio, more solutions to save time, lower costs, and increase accuracy will be possible.

Thanks to the digital revolution, medical professionals don’t have to memorize nearly as much information as they did 50 years ago. Digital technology has liberated physicians, nurses and researchers to focus more mental energy on higher-level cognitive tasks and patient care. Artificial intelligence is poised to take this to the next level.

The medical field must learn to better delegate repetitive, lower-level cognitive functions in order to allow medical professionals to focus more of their mental energy on higher-level thinking. To understand this need, let’s start by looking at a quote from J.C.R. Licklider’s 1960 paper Man-Computer Symbiosis:

“About 85% of my ‘thinking’ time was spent getting into a position to think, to make a decision, to learn something I needed to know. Much more time went into finding or obtaining information than into digesting it … Several hours of calculating were required to get the data into comparable form. When they were in comparable form, it took only a few seconds to determine what I needed to know.”

Herbert A. Simon captured a similar idea when he coined the phrase bounded rationality. The idea is that human decision making is at its best when people are given limited, relevant information and enough time to process the information.

Computers allow us to optimize our decision-making faculties by granting us easier access to information that is critically relevant to a decision while sorting out non-relevant facts or data. Humans now spend less time trying to decide what information to look at and can spend more time applying our minds’ higher-level computational abilities to the information before us.

As AI continues to advance, it has the potential to extend the power of human thinking in three critical areas: advanced computation, statistical analysis and hypothesis generation. These three areas correspond to three distinct waves (paywall) within AI development.

Artificial intelligence (AI) – the ability of computers to learn human-like functions or tasks – has shown great promise. What was previously considered the sole domain of human cognition is already being leveraged successfully across many industries. Now, the technology sector is witnessing what appears to be important new advances in AI that are bringing a new wave of interest for how it might shape the future of health and healthcare.

The rapid digitization of health data through the use of heath information technology (health IT) in the United States has created major opportunities in the use of AI. Innovators and experts see potential in using digital health data to improve healthcare and health outcomes from the home to the clinic to the community. Yet, current AI is powered and limited by its access to digital data. With a range of health-related data sets, AI could potentially help improve the health of Americans.

AI in healthcare and medicine could organize patient routes or treatment plans better, and also provide physicians with literally all the information they need to make a good decision.

And do not think it is the tale of the distant future. “I have no doubt that sophisticated learning and AI algorithms will find a place in healthcare over the coming years,” Andy Schuetz, a senior data scientist at Sutter Health said. “I don’t know if it’s two years or ten — but it’s coming.”

While the increased availability of high quality digital health data can facilitate the use of AI in clinical practice, the accessibility, privacy and security of that data must also be addressed.

The great benefit of AI for healthcare is the democratization of knowledge. AI can provide more wisdom of experience to physicians and patients about complex diseases, about treatments, and the outcomes that are achieved. What if you could open up all the knowledge of the world, all the experience of physicians around the world, and make that available to any physician anywhere in the world?

Next Steps

It is important to understand the limitations of AI in health and healthcare. The maxim “do no harm” can perhaps best be upheld by the development of processes and policies to ensure the transparency and reproducibility of AI methods and results.

Consistent accuracy is important to preserve trust in the technology, but AI is still in its infancy. Whilst AI systems may have been trained on comprehensive datasets, in the clinical setting they may encounter data and scenarios that they have not been trained on, potentially making them less accurate and reliable and therefore putting at risk patient safety. As aforementioned, medical AI systems may work with consumer-facing smart wearables, and use the data they generate. A recent study showed that the heart rate readings provided by one of the most popular smart wearables, the Fitbit PurePulse Trackers, “do not provide a valid measure of the users’ heart rate and cannot be used to provide a meaningful estimate of a user’s heart rate”, and in fact differed from ECG readings by an average of 20

Demographic shifts and societal changes are intensifying pressures on health systems and demanding new directions in the delivery of healthcare. We are getting older. Ageing populations in both emerging and developed nations are driving up the demand for healthcare.

According to the United Nations, the world’s population is expected to increase by one billion people by 2025. Of that billion, 300 million will be people aged 65 or older, as life expectancy around the globe continues to rise. Additional healthcare resources and service innovation is needed globally to deliver the long-term care and chronic disease management services required by a rapidly increasing senior population.

A rising middle class will fuel increasing demand for more health options. Looking forward, more effective partnerships are needed between the public and private sectors to meet these expectations. Collaborations that in the past may have seemed unlikely will become commonplace. Changing technology and consumer needs will inspire partnership innovations that cut through conventional thinking.

Different parts of the world will be impacted differently by these demographic shifts. Successful and sustainable change across the globe will require flexible and adaptive models to fit the new health economies.

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