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akshay-s · 6 years ago

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Most Important Artificial Intelligence and Machine Learning Interview Questions

Youngsters are now enthusiastic about pursuing a career in Artificial Intelligence and Machine Learning, which is the latest technological revolution. If you are one of the many that are pursuing an Artificial Intelligence and Machine Learning Course, it is crucial to prepare yourself to face interviews.

Here we share the top and frequently asked interview questions regarding Artificial Intelligence and Machine Learning :

1. What is Artificial Intelligence? Give an example of where AI is used on a daily basis.

Artificial Intelligence is the science that deals with creating intelligent machines that work and act like humans. Artificial Intelligence is rapidly expanding its applications to several fields benefitting human lives by making our lives easier. Google maps are using a rudimentary form of AI in providing the best route to the user by using a built-in algorithm along with data like current traffic flow. Apple's Siri also employs Artificial Intelligence in a naive form. Google Home and Amazon's Alexa are the most used devices that employ AI to help the customer in various activities like search, control smart devices, customizing the output based on voice recognition.

2. What is Machine learning? What are the different types of Machine Learning?

Machine learning is a science of training computer systems to learn and act as humans do. Machine learning is achieved by feeding appropriate data to the machines without providing any explicit instructions.

There are three types of Machine Learning :

Supervised learning - The word Supervised learning itself describes that a machine will work under supervision. The machine works with the help of data sets. A labeled set is data for which you know the target answer. The supervised learning algorithm teaches the machine from the past data and provides the response from the past labeled dataset.

Unsupervised learning - There exists no supervision in unsupervised learning. No labeled datasets will be given to the machine rather allowing them to act on the given unlabelled data. The machine tries to identify and act on the given data set by observing patterns.

Reinforcement learning - Reinforcement learning concentrates on performing the most suitable action to optimize the output in a specific scenario. It occurs in a situation where an agent interacts with an environment. The most basic example of reinforcement learning is game theory.

3. What are the differences between the classification and regression techniques of machine learning technology?

Classification and regression are the two techniques applied to achieve supervised learning. Supervised learning is defined as defining an algorithm that maps an input variable to output. The algorithm will be defined in such a way that the input variable is mapped as accurately as possible to the output variable. The major difference between classification and regression is a type of outcome. The output of classification is categorical while the output of regression will be mapped to a numerical value or continuous output variables. The output of regression is quantitative. A vivid example of classification is noticing a spam email. Google uses an algorithm that classifies emails based on the word pattern. Depending on the word pattern, the algorithm will be able to classify an email as spam. Weather prediction, fraud recognition, risk analysis, etc are examples of regression.

4. What is the difference between inductive and deductive learning?

Inductive and Deductive methods are two methods for teaching in Machine learning. The inductive method involves teaching a machine by providing information concerning a particular scenario. The flow of the output will be from an Observation to a Conclusion. While in the case of the Deductive method, the process will be an inversion inductive method. The deductive methodology starts with a deduction concerning a scenario. The deductive method allows you to define a true statement in a particular situation. A more generalized example of the Deductive method is Euclidean Geometry. Every true statement in the Euclidean Geometry system can be deductively concluded from a basic set of axioms.

Inductive Reasoning, on the other hand, allows you to make statements based on the evidence collected so far. However, the evidence is not the same as fact. The evidence can be used to make an accurate prediction.

5. How to handle missing data in a dataset?

There are various techniques to handle missing data in a specific data set. Below are a few of them.

Deleting the records that possess missing values is one of the techniques to handle missing data. This technique is highly preferred when you have got a huge dataset.

Creating a separate model to handle missing values. This technique consumes a lot of time.

Employing various statistical methods like mean, median, mode. This technique is preferred when there exist numerical values

Applying regression imputation to the given data. This method follows replacing missing values with predicted values by considering the given data set.

You can choose the best method that fits your specifications.

6. What’s the difference between Type I and Type II error?

Type 1 and Type II are two errors that can occur in a hypothesis.

Type I error can also be defined as a False positive. Type II error can also be defined as a False-negative. Type I error refers to non-acceptance of hypothesis which must have been accepted. Type II error is the acceptance of the hypothesis which should be rejected. To demonstrate the difference we can consider an example of Biometrics. When someone scans their fingers for a biometric scan, a Type I error is the occurrence of rejection even with an authorized match. A Type II error is the occurrence of acceptance even with a wrong/unauthorized match.

7. Explain Cluster Sampling :

A cluster sampling technique is a simple random sampling technique. Cluster sampling is widely used in marketing research as it maintains both homogeneity and heterogeneity. Sample clustering involves dividing the given data into simple groups called clusters. One sample will be chosen among multiple samples available. The ultimate goal of cluster sampling is to bifurcate the data to build effective modules. Cluster sampling is cost-effective and consumes less time as it is an easy task to perform sampling geographically. Cluster sampling is simple to implement yet derives the most accurate outcomes as it enhances competence.

8. What is Deep Learning and How does it work?

Deep learning is a subset of Machine learning which is often referred to as an end to end learning. It can also be defined as a type of Machine learning. It is designed to work along the same lines with a human brain. The structure employed in building deep learning technology is called Artificial Neural network. Deep learning involves the consumption of a vast amount of data to study and analyze the patterns, systems, methods involved to produce meaningful outputs. In other words, deep learning can be simply be defined as acquiring knowledge by studying huge datasets. The data can be images, sound, text, videos. The deep learning techniques are widely applied in various applications such as natural language processing.

9. How Do You Choose an Algorithm for a Classification Problem?

Classification is nothing but predicting the class, category or a label to which a new observation belongs. It is not simple to figure out the best algorithm for a classification problem. Every scenario in Machine learning requires being tried with multiple datasets with a wide variety of algorithms and choose the best among them. A more generalized process starts with Studying the data and proceeds with creating dependent and independent datasets based on our dependent and dependent features. The process follows splitting the data into training and testing sets. Then, it will proceed with training the given model for different classification algorithms like XGB Classifier, Decision Tree, SVM classifier, Random Forest Classifier. The best algorithm will be selected after performing all the prescribed actions.

10.Difference between Machine Learning and Deep Learning technologies?

Both Machine Learning and Deep learning techniques are applied to achieve Artificial Intelligence. One of the major differences between these two technologies is that Machine learning is a subset of artificial intelligence while deep learning is the subset of machine learning.

Machine Learning - It is an application of artificial intelligence (AI) that enables systems to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Deep Learning - It employs multi-layered neural networks to work on the given data in many complex methods, targeting the systems to attain perfection in performing several untold tasks.

Learn More: Differences Between Artificial Intelligence, Machine Learning, and Deep Learning

#artificial intelligence #machine learning #deeplearning #aiml #ai #career advaice #career course #employment #technews #career opportunities #placements #jobsearch #job opportunities #interview

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scalacode · 3 years ago

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If you are an IT graduate or willing to switch your career in this industry because rapid technology advancements are exciting, then you must know about the 20 latest trending technologies.

rtificial Intelligence & Machine Learning (AI-ML)

AI-ML is not new to the IT industry, and it’s getting valuable hype for the past few years. The comprehensive response will also bring it to the next level in 2023. As a result, AI-ML is a hot skill to learn and make a career in the IT industry.

AI-ML, usually considered synonyms for each other, are two different technologies, though these both need to be used together in an application.

Artificial intelligence is a computer science stream where digital solutions are developed with the human ability to think and respond. Business chatbots, Amazon Alexa, and Google Siri are widespread instances of Artificial intelligence.

Machine Learning, usually known as ML, is a sub-part of AI technology. ML technology extracts the requested information from backend databases and serves through AI-enabled applications. For instance, Alexa receives the request from end-users, and through its machine learning, it extracts the relevant data from the backend and offers it according to the received request.

So in order to make a career in AI-ML, you need to learn both Artificial intelligence and machine learning.

Data Science

If you like data and have the analytical ability to extract information, then you can become a data analyst. Data science allows non-technical individuals to play a key role in a market research team and, based on the survey and data evaluation, facilitates top management to make decisions for the company's benefit.

Information of Things (IoT)

IoT stands for Internet of Things is the latest IT trend you can learn. IoT is a technique to learn human behaviour, habits, and expectations. Business sectors like healthcare, Food & beverages, eCommerce, and many others require such helpful information to satisfy their need. IoT technology is vital in knowing customers’ natures and serving them the best.

Smart device

There are various types of smart devices, including smartphones, smart TV, smart watch, smart outfit, and many more. These smart wearables ease the life of individuals. Smart watches facilitate their wearer to receive and disconnect phone calls, connect with friends through social media, and report daily diet and calorie burning by walking. These devices are IoT-enabled devices. As a result, you can utilise your IoT knowledge in smart device manufacturing.

Augmented Reality and Virtual Reality

The following exceptional technological trend - Virtual Reality (VR), Augmented Reality (AR), and Extended Reality (ER). VR immerses the user in an environment, whereas AR enhances their surroundings. Although this technology trend has primarily been used for gaming, it has also been used for training. For example, with VirtualShip, a simulation software AR and VR have enormous potential in training, entertainment, education, marketing, and even injury rehabilitation. They typically work in tandem with some of the other emerging technologies mentioned in this list. Read more...

#Mobile app development company #Mobile app design services #Hire Mobile app developers

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oladesanmi-blog · 6 years ago

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HEALTHCARE INFORMATICS’ EVOLUTION AND REVOLUTION

Change is everywhere. And whether you’re talking about your hometown or your day-to-day activities, adjustment and fine-tuning is inevitable. Within the context of the organizations in the Healthcare Industry, the challenge is learning to handle change effectively and to manage change with evolution and revolution as an end goal. From this perspective, there are basically two ways to understand change: Evolutionary or incremental change and Revolutionary or transformational change. Understanding the differences and learning how to make the most of these opportunities can be a challenge, but one that ensures the industry not only survives but also thrives.

Evolutionary and Revolutionary Change

Evolutionary change is incremental and takes place gradually, over time. Slow, gradual change often takes place to ensure the survival of the organization. It’s incremental in that it happens step by step, little by little. Organizations undergoing evolutionary change may have been prompted by outside pressure, in order to keep up with technology or addressing the needs of stakeholders more effectively.

By contrast, revolutionary change is profound. When we think revolutionary change, we envision complete overhaul, renovation, and reconstruction. Change is fundamental, dramatic and often irreversible.

From an organizational perspective, revolutionary change reshapes and realigns strategic goals and often leads to radical breakthroughs in beliefs or behaviours. When an organization decides to engage in revolutionary change, radical transformations to products or services often follow. In efforts to stay ahead of the curve and reach evolution, outstanding organizations often pursue revolutionary change.

The challenge in today’s healthcare industry is not in learning how to accept change, but in how to orchestrate the most efficient change leading to organizational evolution. Staying in touch with core values, maintaining a culture of innovation and learning to make the most of resources

What is Healthcare informatics?

Healthcare Informatics is a discipline that involves the use of information technology to organize and analyze health records to improve healthcare outcomes. Health Informatics deals with the resources, devices, and methods to utilize acquisition, storage, retrieval, and use of information in health and medicine. Other related areas include clinical research informatics, consumer health informatics, and public health informatics, biomedical informatics, imaging informatics, and nursing informatics.

In a nutshell, Health informatics is a specialization that links IT, communications and healthcare to improve patient care.

Why Health Informatics?

A few years ago, clinical care and documentation were all paper-based. Now, with the advent of clinical documentation that enables secure electronic sharing of patient data, healthcare providers can reduce wait times, improve inter-disciplinary collaboration, and minimize errors. Additionally, because we now have a database on every patient, we can analyze aggregated clinical data to help us to understand what is going on with larger groups of patients and identify trends in population health.

The fact that technology is rapidly transforming health care should come as no surprise to anyone. From robotic arms that perform surgery to nanorobots that deliver drugs through the bloodstream, the days of being tended to by the human country doctor seem to have fully given way to machines and software more in keeping with the tools of Dr Leonard H. "Bones" McCoy from “Star Trek.”

In a nutshell, It has come to stay!

Healthcare Industry vs. Technology Evolution and Revolution

First of all, health care isn’t just expensive; it’s wasteful. It’s estimated that half of all medical expenditures are squandered on account of repeat procedures, the expenses associated with more traditional methods of sharing information, delays in care, errors in care or delivery, and the like. With an electronic and connected system in place, much of that waste can be curbed. From lab results that reach their destination sooner improving better and more timely care delivery to reduced malpractice claims, health informatics reduces errors, increases communication, and drives efficiency where before there was costly incompetence and obstruction.

Apart from that, there’s a reason medicine is referred to as a “practice,” and it’s because health care providers are always learning more and honing their skills. Health informatics provides a way for knowledge about patients, diseases, therapies, medicines, and the like to be more easily distributed.

Also, when patients have electronic access to their own health history and recommendations, it empowers them to take their role in their own health care more seriously. Patients who have access to care portals are able to educate themselves more effectively about their diagnoses and prognoses, while also keeping better track of medications and symptoms. They are also able to interact with doctors and nurses more easily, which yields better outcomes, as well. Health informatics allows individuals to feel like they are a valuable part of their own health care team because they are.

More so, one criticism of approaching patient care through information and technology is that care is becoming less and less personal. Instead of a doctor getting to know a patient in real time and space in order to best offer care, the job of “knowing” is placed on data and algorithms.

Nevertheless, as data is gathered regarding a patient, algorithms can be used to sort it in order to determine what is wrong and what care should be offered. It remains to be seen what effects this data-driven approach will have over time, but regardless, since care is getting less personal, having a valid and accurate record that the patient and his care providers can access remains vital.

Moreover, Health care is getting more and more specialized, which means most patients receive care from as many as a dozen different people in one hospital stay. This increase in specialists requires an increase in coordination and it's health informatics that provides the way forward. Pharmaceutical concerns, blood levels, nutrition, physical therapy, X-rays, discharge instructions. It’s astonishing how many different conversations a single patient may have with a team of people regarding care, and unless those conversations and efforts are made in tandem with one another, problems will arise and care will suffer. Health informatics makes the necessary coordination possible.

Furthermore, the most important way in which informatics is changing health care is in improved outcomes. Electronic medical records result in higher quality care and safer care as coordinated teams provide better diagnoses and decrease the chance for errors. Doctors and nurses are able to increase efficiency, which frees up time to spend with patients, and previously manual jobs and tasks are automated, which saves time and money. Not just for hospitals, clinics, and providers, but for patients, insurance companies, local government, state and federal governments too.

Health care is undergoing a massive renovation thanks to technology, and health informatics is helping to ensure that part of the change results in greater efficiency, coordination, and improved care.

Artificial Intelligence (AI) and Healthcare Informatics

Artificial Intelligence (AI) is devoted to creating computer software and hardware that imitates the human mind. The primary goal of AI technology is to make computers smarter by creating software that will allow a computer to mimic some of the functions of the human brain in selected applications. Applications of AI technology include; general problem solving, expert systems, natural language processing, computer vision, robotics, and education. All of these applications employ knowledge base and inference techniques to solve problems or help make decisions in specific domains.

The global artificial intelligence market is expected to reach $19.47 billion by 2022, according to the research firm Allied Market Research. As AI is marking its presence, tech giants are working to capitalize on new opportunities. The healthcare sector is a natural fit, according to Sanjay Gupta, managing director, South Asia, and the Middle East for NICE.

Time and Life Saver

Among Google’s many AI ventures is an effort to develop new products targeting the health sector. The company is focusing on applications for life preservation, preventive care and improving health care services.

The company plans to launch a trial in India to test an AI system that scans a person’s eyes to look for signs of diabetic retinopathy. The company aims to license the technology to clinics. The system already has proven itself adept at detecting high blood pressure, or risk of heart disease or stroke, according to a study published in early 2018.

Accuracy and Scalability

AI advancements could be of great help to patients with an age of 65 years or older. According to the recent study published in the journal NPJ Digital Medicine, Researchers implemented AI to screen electronic health records along with notes taken by doctors for finding potential health risks. This included nearly 48 billion data points used in a deep learning model.

The AI analyzed the data and determined medical issues such as mortality rates, unplanned readmission, and long hospital stays with an accuracy of 90 per cent. In comparison to traditional predictive analysis models, the deep learning model provided 10 per cent more accuracy and scalability. The system did not only analyze electronic records but also took into account doctors’ notes and information on old charts stores as PDF files.

Blockchain and Healthcare Industry

A Blockchain approach offers several benefits over traditional location tracking products. The most obvious of which is the immutability and tamper-proof qualities of the Blockchain. This prevents a malicious user from changing the location history of a device or deleting it from the record. This is a particularly important factor considering that medical device theft and shrinkage has

There are several areas of healthcare and well-being that could be enhanced using Blockchain technologies.

These include device tracking, clinical trials, pharmaceutical tracking, and health insurance. Within device tracking, hospitals can trace their asset within a Blockchain infrastructure, including through the complete lifecycle of a device.

The information gathered can then be used to improve patient safety and provide after-market analysis to improve efficiency savings. This paper outlines recent work within the areas of pharmaceutical traceability, data sharing, clinical trials, and device tracking.

Social Media within Healthcare Industry

Social media including Web sites like Facebook Twitter Instagram and Linked-In have become part of the fabric of modern life online communities can hardly be avoided by anyone who lives even a modestly engaged life.

There are many advantages to social media both personal and professional. Businesses have become quite sophisticated in using social media to extend their message and to present their products and services to the public. But what about professionals or people like you who are involved in the healthcare industry.

What role does social media play in your work and what restrictions our health professionals are under when it comes to using social media?

First, it's important to keep in mind that most health care providers have policies and procedures for making public announcements. If you are not an official spokesperson vested with the authority to speak on behalf of your organization, please refrain from sharing news and occurrences on social media.

Unless you've been given specific permission to do so in your organization's official branded social media accounts so you can read tweets, like, share and comment on items posted there if you choose.

Secondly, you must not underestimate the valuable role that social media can play in the medical profession. For example, trauma teams in Maiduguri were able to prepare their ears quickly after learning of the Banki town bombing over social media networks.

In conclusion, change is the key to success.

About

Oladesanmi Arigbede is a Health IT expert, an entrepreneur, a technology enthusiast who likes cutting-edge technologies. In a career spanning one decade, he has been a business owner, technical architect, startup consultant, and CTO.

References

https://geneticliteracyproject.org/writer/pratik-kirve/

https://www.alliedmarketresearch.com/artificial-intelligence-market

https://ai.google/

https://hitconsultant.net/2016/03/02/health-informatics-transforming-health-care/

#HealthInformatics #Techonology #HealthcareIT #ArtificialIntelligence #Blockchain #SocialMedia #MedicalInformatics

#HealthInformatics Techonology HealthcareIT ArtificialIntelligence Blockchain SocialMedia MedicalInformatics

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aianalytics · 4 years ago

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TOP HUMAN BRAIN INSPIRED AI PROJECTS TO KNOW IN 2021

Brain-inspired AI projects are helping in the advancements of artificial intelligence

Scientists and researchers are continuously working on artificial intelligence and multiple AI technologies such as neural networks to create advanced products and services for the welfare of society. AI projects are thriving in the tech-driven market from multiple reputed tech companies and research centers to improve and discover new ventures in this domain. Thus, it has led to hundreds of human-inspired AI projects available on the internet to gain sufficient knowledge of smart functionalities of artificial intelligence. Let’s explore some of the top human brain-inspired AI projects to approach in 2021.

Top Human Brain-Inspired AI Projects

OpenNN

OpenNN is one of the top human brain-inspired AI projects that helps to build the most powerful AI models with C++. It is known as an open-source neural network library for machine learning and artificial intelligence available for multiple industries. It consists of sophisticated algorithms to develop artificial intelligence solutions for AI projects.

A(rtificial)Human

a(rtificial)Human is a human-inspired AI project that was started in 2008 to implement human personality with the integration of a computer program. This AI project utilized strong computer science background with artificial intelligence knowledge. AI models help to learn about neurobiology and approaches assisting in building software programs.

Numenta Platform for Intelligent Computing

Numenta Platform for Intelligent Computing is of the top brain-inspired AI projects consisting of a set of learning algorithms. Learning algorithms are known for capturing different layers of neurons for neural networks in artificial intelligence. Visual pattern recognition, NLP, object recognition, and many more can be done by human brains with the help of the neocortex. This AI project helps the machines to approach and take over human-level activities efficiently and effectively.

Neu

Neu is known as a C++ framework with a collection of multiple programming languages as well as multi-purpose software systems. These help in creating artificial intelligence applications, AI models with simulations, and other technical computing for further advancements in AI technologies.

AILEENN

AILEENN is known as Artificial Intelligence Logic Electronic Emulation Neural Network. This is one of the top human brain-inspired AI projects that act as a cloud-based PaaS platform and IaaS infrastructure based on neural networks and fuzzy logic. This AI project helps in the decision-making process in this tech-driven world.

Visual Hierarchical Modular Neural Network

Visual Hierarchical Modular Neural Network is a brain-inspired AI project that visually constructs a human thought process and logic with a flow to generate artificial intelligence automation. It provides a wide range of innovative and user-friendly tools frameworks that can integrate professional human-like decision-making into commercial systems. It also protects users from mathematical associated with neural networks as well as artificial intelligence algorithms.

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itsrahulpradeepposts · 5 years ago

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Flipkart Collaborates with IIT Patna over AI Research and ZIM deploys AI to discard misdeclared cargoes.

Flipkart collaborates with IIT-Patna over research in artificial intelligence, machine learning and more

Flipkart has announced its collaboration with IIT Patna for research in artificial intelligence, machine learning, and natural language processing. The two signed an MoU that would help to create industry-focused applied research in the field of AI, ML, and NLP. As a part of the MoU, IIT Patna would undertake various programs including research activities, organising seminars, and providing internship and mentorship opportunities. The academic collaboration would bring real-world industry exposure to students and scholars at IIT Patna. The faculty members of the institute will have the opportunity to work with Flipkart on research projects and help them create capabilities in automation, AI, ML, and NLP.

ZIM deploys artificial intelligence to root out misdeclared cargoes

ZIM, the Israeli carrier, has developed and implemented a screening software that is backed by AI and detects the incidents of misdeclared hazardous cargo before it is loaded to the vessel. Misdeclared cargoes have been the reason behind many boxship blazes in recent years and it is crucial to address this issue.

The ZIMGuard system flags potential cases of wrongly declared cargoes by scanning shippers’ cargo declarations at an early stage. The system uses artificial intelligence and natural language processing capabilities to analyse the documentation and alert operations teams of omissions, concealments, or erroneous declarations of hazardous cargoes in real-time.

Among the several Artificial intelligence programs that are offered by various institutions, the artificial intelligence courses in Great Learning are really productive because these AI courses not only help you grow individually but their placement cells make sure that you get into one of these top companies. So, if your aim is to learn Artificial Intelligence then Great Learning should be the destination.

#artificalintelligence #machine learning #flipkart #online learning #career options

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sparkinvention-blog · 6 years ago

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People are getting digitalizes with the passing time. Technology here plays a vital role in making the people modern and digitalizes with the help of it. With the changing, the technology will develop further and the next generation of ours will fully depend on the technology. The better version of society is possible with the help of technology. The youth generation is adapting with this technology by taking the technical courses which will help them in forming a better career. Students are nowadays inclining towards Artificial Intelligence or AI because of the increasing need for intelligent and accurate decision making, there is exponential growth. If you are also thinking of taking artificial intelligence training in Erode then our institute will be the best one to take admission.

#Artificial Intelligence Training #Artificial Intelligence Training In Erode #Artificial Intelligence Training In Karur #Artificial Intelligence Training In Chennai #Artificial Intelligence Training In Tamilnadu

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maxihealth · 6 years ago

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Healthcare Just Got SMAC’ed – Accenture’s Post-Digital Era for Health

Social, mobile, analytics and the cloud now underpin the health care industry. We’ve been SMAC’ed, and Accenture’s Digital Health Tech Vision 2019 believes we’re in a post-digital era ripe with opportunity.

Five trends comprise the Vision:

DARQ Power, the acronym for Distributed ledger technology, Artificial intelligence (AI), extended Reality, and Quantum computing. Adopting these applications can help health care reduce costs, drive labor efficiency and support people-centered design and experience.

Get to Know Me is the use of technology to develop and deepen relationships with people. As an example of this trend, Accenture points to Mindstrong which leverages AI and machine learning to divine digital phenotypes of consumers-patients that inform mental health support. (For good background on the promise of digital phenotyping for mental health, see this article by Mindstrong’s co-founder Dr. Thomas Insel in World Psychiatry).

Human +Worker recognizes that workers in organizations are more digitally advanced than the organizations themselves, playing “catch-up” with people. In health care, two-thirds of management told Accenture that in the next three years, staff will have access to a “team of bots” to accomplish their work.

Secure US to Secure ME calls out the risk of cybersecurity across healthcare organizations’ entire ecosystem of inter-connected payers, providers, technology vendors and, to be sure, patients whose data are a personal precious asset at increasing risk of hacking.

My Markets is about “meeting consumers’ needs at the speed of now,” in Accenture’s words, because healthcare is more connected in the post-digital age. We live on-demand lives in daily life-flows and tasks, so people expect that experience and service level in health care. The AI part of “SMAQ” is one of the tools the health care industry will increasingly use to personalize, customize, and “immediate-ize” health care across the continuum, from wellness and fitness to acute care and rehabilitation. Underlying technology like the emerging 5G networks, drones, and autonomous vehicles will enable some of the My Markets’ scenario.

The report points out that the health care industry hasn’t felt the level of disruption that other sectors, shown at the bottom right corner of the company’s “Where Are You Now?” bubble chart of industry positions. The diagram illustrates Accenture’s “Disruptability Index” study published last year. Industries that have already experienced a high degree of disruptions that have been volatile (versus viable) are energy, infrastructure and transportation services, and natural resources. Industries with low current disruption levels that have been durable include consumer goods, industrial equipment, and chemicals.

Health care sits in the higher vulnerability with low current level of disruption — highly susceptible to future disruption.

Accenture believes that, “Those most vulnerable to disruption are under pressure to scale new technologies….[some] experimenting to learn how these technologies can deliver new sources of value.”

Now return to the first chart to re-visit the five trends, the first four all about the human in health care.

Health Populi’s Hot Points: “Me,” “US,” My,” pronouns repeat throughout the Accenture 2019 Health Tech Vision report. As I re-read the findings, I kept hearkening back to the George Harrison song, “I Me Mine,” recorded on the Let It Be album — and also the title of Harrison’s (semi-auto) biography. The first stanza of lyrics of “I Me Mine” go:

“All thru the day, I Me Mine I Me Mine I Me Mine

All thru the night, I Me Mine I Me Mine I Me Mine

Now they’re frightened of leaving it

Everyone’s weaving it

Coming on strong all the time

All through the day I Me Mine I Me Mine I Me Mine….”

George could have been writing about the vulnerability of health care organizations late to the digital health era, let alone the post–digital health era. It’s a time for the legacy healthcare system to leap-frog, to turbocharge that scaling of new technologies and experiment, as Accenture recommends at the start of the report. We see such leap-frogging in parts of the world that were indeed late to the Health Care v1.0 era, and now have the opportunity through SMAQ to scale and not be too concerned about sunk investments into slow tech.

“Now they’re frightened of leaving it,” George observed on the third line of the song. Indeed, many healthcare players have been slow to change and pilot, but Accenture’s survey research of health execs demonstrates that most of these folks believe it’s time to get off the dime and embrace that post-digital era.

I’m particularly keen on the I-Me-Mine analogy as it speaks to the central player in health care I’ve been focusing on for many years: the patient, morphing into the consumer and now, the payor. As I wax on about in my book, HealthConsuming: From Health Consumer to Health Citizen, the new retail health landscape, coupled with digital health platforms (baked into the SMAQ acronym), enables people to take on more self-care, and also connects providers (doctors in traditional settings and new entrants in retail clinics, telehealth channels, and grocery stores, among them) to consumers to inform and support that care.

I’m an omnivore of Accenture’s research, and this year’s Digital Health Tech Vision 2019 tells the big top-line truth that, “digital is no longer a differentiator.” There’s no more eHealth or mHealth — it’s all health, it’s all digital.

The great irony in all this — which I, not Accenture, term as ironic — is that “together, humans + machines produce better results.” This is the subtext and subtitle of Dr. Eric Topol’s latest book, Deep Medicine — that AI can and should be artfully deployed to “humanize healthcare.”

Some folks in healthcare may well need that explicit, big “smack” in the head (speaking metaphorically, not literally).

The post Healthcare Just Got SMAC’ed – Accenture’s Post-Digital Era for Health appeared first on HealthPopuli.com.

Healthcare Just Got SMAC’ed – Accenture’s Post-Digital Era for Health posted first on https://carilloncitydental.blogspot.com

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