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5 Ways Digital Transformation benefits businesses

Posted on April 7, 2022 - 6:41 pm by Bhaskar Sharma

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5 Ways Digital Transformation benefits businesses

Digital Transformation is no longer a thing of the future or a buzzword, it is essential for every organization that plans to grow and stay relevant in near future. Business leaders agree with the fact that Digital Transformation is vital to their business growth and to beat the competition.

In simple terms Digital Transformation is about Improving current processes by removing repetitive manual steps, defining new automated operational processes, and leveraging technology advances using AI/ML. It represents a cultural shift to a more agile and intelligent way of doing business, powered by technology.[/text]

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Here we are highlighting 5 Ways Digital Transformation benefits businesses:

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1. Revenue Growth

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As per a recent SAP survey, leaders expect 23% higher revenue growth in the next two years for organizations that have adopted the culture of Digital Transformation.
Digital Transformation has enabled organizations to deliver more value to their existing and future customers without increasing the workforce and increased revenue. Efficient automated processes, better decision making.
Increased agility results of Digital Transformation are recipes for growth.

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2. Cost Reduction

[/custom_heading][text]One aspect of Digital Transformation is to automate repetitive manual business processes. It reduces the cost being spent on technology debt, legacy software, and systems. With the help of automation tools, AI/ ML/NLP, integrated systems, and processes organizations can save more than 20% in cost. According to SAP Digital Transformation and Executives study, 80% of leaders say the transformation has reduced cost, enabled them to run efficiently, and given them extra cash to invest for the future.[/text][image float=”center” lightbox=”” width=”” is_gallert_item=”” src=”14832″ alt=”” href=”” title=”” popup_content=”” id=”” class=”” style=””][text]Cost decreases from adopting Digital Transformation in global companies 2019, by function.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

3. Customer Retention

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Better technology stack enables an organization with capabilities that can help in acquiring, retaining, and making customers’ buying decisions easier. Same time reducing marketing and advertising spend.
Digital tools enable your customers’ lives easier and make you look better in front of your competitors.
Businesses that offer an outdated and clunky experience have trouble competing with those who innovate and stay updated with modern technology.
Digital Transformation also helps in improving Customer Experience that is the most important need of today’s time.

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4. Enhanced Customer Experience

[/custom_heading][image float=”center” lightbox=”” width=”” is_gallert_item=”” src=”14833″ alt=”” href=”” title=”” popup_content=”” id=”” class=”” style=””][text]Image source: https://www.sap.com/dmc/exp/4-ways-leaders-set-themselves-apart/index.html

The commitment to DT pays off. In a recent survey, 70% of leaders say that they are already seeing increased customer satisfaction.
One of the major objectives of DT is enhancing the customer experience using technology.
Increased focus on customers’ wants and needs, customer service is improved consistently throughout all touchpoints and channels.
As per a Gartner report, more than two-thirds of companies admit they are competing mostly on customer experience.
Customer Experience has become the new battlefront for organization growth.

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5. Innovation in business/technology

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Digitalization creates opportunities for industries for new product development and service offerings that would have not been possible to create in past. DT allowed to innovate better and find new niches to expand the business.
It is not limited to big players like Amazon and Google, even legacy firms have begun to create new businesses that were beyond their core competencies in past. AI/ML adoption can help in creating new products and offerings by understanding market needs and customer behavior.

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H2O – An Opensource Artificial Intelligence

Posted on April 7, 2022 - 4:45 pm by Bhaskar Sharma

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Artificial Intelligence

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H2O – An Opensource Platform for AI Model development

H2O – An open-source artificial intelligence (AI) platform available through H2O.ai. It is trusted by many data scientists and has become the go-to platform for AI-based model development. Many widely used statistical models and algorithms can be developed using H2O, adding to its popularity amongst data analysts and practitioners. Deep learning, generalized linear models, and boosted machines are some of the models supported by H2O.

One of the main features of H2O is that it automatically runs through all the algorithms (using its AutoML functionality) to produce a leaderboard of all the best applications. H2O provides its API support for Python, R, Scala, and as a GUI framework Flow making it quite accessible to programmers with different skillsets.[/text]

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[text]There are some alternatives to H2O such as Splunk, KNIME, Amazon SageMaker, Microsoft Azure Machine Learning Studio, RapidMiner, and IBM Watson Studio that are also available.[/text][clear by=”40px” id=”” class=””]

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However, H2O remains the framework of choice due to the following reasons:

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It has AutoML functionality

Also has, Bigdata support with H2O’s Sparkling Water

Readily available algorithms, easy to use in the analytical projects

Faster than Python scikit learn (in machine learning supervised learning area)

It is available as an opensource

Its development is supported on local machines, AWS, Azure, IBM, GCP

Its deployment is supported across a broad range of production environments (as a REST service (Local and Cloud) as well as an AWS lambda function)

Access to the core development team, speed of problem resolution, and feature additions are excellent

In addition to H2O, H2O.ai offers the following Enterprise platforms:

H2O wave – it is used to build visually attractive images and interactive Artificial Intelligence applications.

H2O Driverless AI – it is like a web application with its own UI and UX; it is used to tune parameters while configuring models saving some time for the data scientists.

There are no major challenges with H2O. Of course, more detailed documentation could be made available for H2O. Overall, H2O – An open-source artificial intelligence (AI) is one of the most popular frameworks available for AI and ML-based model building. At NextGen Invent, our data scientists are experienced in building many such models using various frameworks including H2O.

H2O – An open-source artificial intelligence (AI)

We would love to hear from you about your framework of choice!

To find out more about H2O and how we can help you, please visit our website at www.nextgeninvent.com[/text][clear by=”40px” id=”” class=””]

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Automatic Labelling to the rescue

Posted on April 7, 2022 - 3:22 pm by Bhaskar Sharma

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Artificial Intelligence, Data + AI+ Analytics

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Automatic Labelling: A big leap in data prepration for AI/ML Models

Tagging or labeling of data is an essential step in training computer vision models. With more and more data being needed for training, it is imperative to label the data in a hassle-free and less time-consuming fashion. This is where automatic labeling comes into the picture.

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Challenge

[/custom_heading][text]When we started the computer vision model that can identify objects in an image and video, we never realized that the objects, we need to identify may take us over a year to label. We needed to label thousands of objects in millions of images to train our model. Innovation is the mother of necessity and we were forced to come up with options to automate our instrument labeling task.

We could not use solutions from companies such as https://thehive.ai/ as those label objects in rectangle boxes and we needed to label exact boundaries of instruments.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Solution

[/custom_heading][text]We analyzed multiple tools which could help us in reducing the time and cost of labeling. The following are the tools for evaluation:

Tool	Pros	Cons
Amazon Sagemaker Ground Truth	Accurately labeled data can manage big data, competitive pricing ($8/100 objects)	Need machine learning experience to carry out labeling jobs
Lionbridge AI	Highly accurate labeled data, better project management features	Higher pricing
V7 Darwin	Speeds up labeling time dramatically	Bugs are not managed
Label	Opensource tool, user friendly	No project management features

Based on our selection criteria,3-4 seconds for labeling, we selected Label.

We needed to select a model which can help in automating the labeling of objects. Among the various options below, we selected Detectron2.

This approach allowed us to finish the labeling task in a week.[/text][image float=”center” lightbox=”” width=”” is_gallert_item=”” src=”14840″ alt=”” href=”” title=”” popup_content=”” id=”” class=”” style=””][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Results and Learning:

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With the advent of Big Data, the future of  data labeling is active learning.
Data labeling requires quality control, manual intervention , and collaboration to produce high-quality training data.
The cost of data annotation was scaled down by 5 times.
Too many data points were created by automation, hence, another algorithm was created to reduce the number of data points.

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[text]Our Automatic Labelling to the rescue solution is available now to all our customers at no charge for the models which we are developing. In case you have any queries on how to auto-label the images, please contact us for more information at [email protected].

Visit our website at www.nextgeninvent.com[/text]

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Demystifying AI for Future Leaders in Healthcare

Posted on April 7, 2022 - 2:55 pm by Bhaskar Sharma

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Artificial Intelligence, Digital Health

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Demystifying Artificial Intelligence for Healthcare leaders

Artificial Intelligence (AI), as defined by the English Oxford Living Dictionary “The theory and development of computer systems capable of doing activities that require human intellect, such as visual perception, speech recognition, decision-making, and language translation.”

AI works on a set of rules underlined by algorithms without any supervision. When used for the right purpose and goals with ethics AI can deliver tremendous value. Many people fear that higher dependency on AI could result in unethical practices in the future.[/text]

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[text]Many questions and misconceptions concerning Artificial Intelligence (AI) in healthcare are still prevalent. These challenges and misconceptions are especially relevant because healthcare is the next frontier for using AI technology.

Today AI is being used to:

Minimize human errors
Automate large and complex computations to improve diagnostics and treatments
Automate time-consuming contract negotiations with insurance companies
Provide better overall outcomes and more cost-effective patient experiences.
Automate time-consuming processes, and elimination of menial tasks

Thus, Healthcare startups or established enterprises in healthcare are looking to get ahead of the curve and starting to incorporate AI in their business model. Artificial Intelligence (AI) will be at the forefront of healthcare and future leaders in healthcare will bring new business models. The adoption of AI in healthcare solutions will eliminate human error, increase patient safety, and mitigate risks while reducing administrative costs. It is therefore essential to demystify the application of AI for future leaders in the healthcare domain.[/text][clear by=”40px” id=”” class=””]

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Artificial Intelligence has become a growth engine for Healthcare Startups

[/custom_heading][text]In the quest to provide solutions to healthcare problems (prevent, cure, and treat any disease), entrepreneurs are now taking the help of Artificial Intelligence. Healthcare startups that bring Data, Analytics, and AI-powered solutions to the market are insight-driven organizations. Being an insight-driven organization helps healthcare startups develop, strengthen, increase productivity, and scale their organization by reaching the target audience.

According to the report published by Research Expert Shanhong Liu, Statista,2020, marketing and sales get more benefit from adopting artificial intelligence (AI) technologies. The McKinsey Global Survey on Artificial Intelligence also showed that organizations are using AI to generate value in terms of revenue.

Adopting Artificial Intelligence by healthcare startups in their business model leads to:

Increase in annual revenue
Reduction in operational cost
Gaining an advantage over competitive organizations
Increases the capability to bring new products through innovation
Monetization of Data Assets

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How can AI-enabled healthcare enterprises be the future?

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1. Understanding the development and deployment of AI-enabled solutions

[/custom_heading][text]AI can execute healthcare duties similarly or better than humans in many cases. However, implementation issues may delay the large-scale automation of AI-based services in healthcare. To become future leaders in AI-based solutions for healthcare, organizations should know about identifying the right AI tool for different challenges and opportunities. Apart from this, organizations need exemplary implementation and project management skills to manage AI projects in healthcare. In addition, the decision-making body of such organizations should be aware of the significant trends in cybersecurity, ethics, and bias in algorithms, telemedicine, and clinical decision support.

AI applications are moving into domains that were previously regarded as only the domain of human ability. This is only possible because of recent advances in digitized data collecting, machine learning, and computing infrastructure. Therefore, the application of AI is common for diagnoses and treatment recommendations, patient engagement and adherence, and repeated administrative duties.

Thus, healthcare organizations should implement AI-based solutions and involve personnel with a technical background for a smooth deployment. This will help meet the patients’ high expectations from smart hospitals regarding services and outcomes.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Understanding the current scenario and predicting the future trends in the following-

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• Artificial Intelligence in Healthcare Financial Management

[/custom_heading][text]In the e-commerce and financial sector, AI has improved customer experience, supply chain management, operational efficiency, and mate size. Their main goal is to create an inexpensive model high in quality, reliability, and which has a more extensive reach. Healthcare organizations, biopharmaceutical firms, etc., also use these models to predict and learn from the data in a similar approach.

Most existing and useful AI applications in healthcare finance focus on robotic process automation (RPA). The goal is to automate time-consuming and labor-intensive tasks like combining billing data from many sources or performing monthly account reconciliations. Machine learning is another AI technology relevant to claims and payment administration. Healthcare Insurance enterprises can use AI for probabilistic data matching across different databases. AI-based solutions can reliably find, analyze, and correct coding issues and incorrect claims. This will save all the stakeholders involved, a lot of time and money.

Hospitals and other Healthcare enterprises can use Artificial intelligence in many ways. Some of them are mentioned below:

Data and medical records management- Data management is the most visible application of artificial intelligence in healthcare. Gathering, storing, standardizing, and tracking patient information records is the blood for any solution. It is the first stage for developing any healthcare solution. Digital healthcare can be brought to market only when data is integrated, automated, powered by AI.
Tasks involving repetitive work- AI can analyze tests, X-rays, CT (Computed Tomography) scans, data input, and other laboratory and diagnostic tasks faster and more accurately than humans. Especially in cardiology and radiology, the amount of data to analyze can be overwhelming. Thus, incorporating AI-based solutions will save time and improve diagnosis and treatment.
Customized designing of treatments- Data analysis from medical images obtained through MRIs, CT scans, ultrasounds, and x-rays, can be carried out quickly with the help of AI. This helps in rapid diagnosis and choosing suitable treatment options for every patient specifically.
Consultation with the help of AI– In healthcare, the primary purpose of AI is to improve patient participation. Telemedicine through a smartphone can provide real-time assistance to patients, handle prescriptions, provide information on a wide range of medications, and suggest the dose range.
Drug designing– With several breakthroughs, machine learning algorithms are now being used to decrease drug discovery times. Artificial intelligence (AI) can make parts of the drug development process faster, cheaper, and safer.
Diagnosing diseases- Disease diagnosis and treatment have been a focus of AI since its introduction in healthcare applications. Artificial intelligence has been proven effective in the healthcare sector in several studies, and it can effectively detect and cure disease. Cancer diagnosis, genetic disease monitoring, mental illness treatments, diabetic management, and other applications are only a few examples, from the studies.
Robotically assisted surgery (RAS)– RAS using AI addresses the limits of prior minimally invasive surgical treatments and boosts ‘open surgery’ surgeons’ capability.
Patient engagement- To overcome the difficulties of patient engagement, big data and artificial intelligence are increasingly being deployed. Across the healthcare sector, machine learning and business rules engines are increasingly being employed to develop complicated interventions.

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• Artificial Intelligence and Machine Learning in Health Insurance

[/custom_heading][text]Artificial Intelligence in healthcare is not limited to hospitals and pharmaceuticals. Healthcare Insurance providing enterprises also need AI. The use of Artificial Intelligence models in Health Insurance can be of many ways, including:

Use of Chatbots
Faster Claim Settlements
Personalized Health Insurance Policies
Cost Efficiency
Fraud Detection
Choosing the right health insurance plan for customers

AI-based enterprises can develop sophisticated models for Healthcare Insurance companies to offer services for patients suffering from any chronic condition. Models like home delivery pharmacies through healthcare insurance plans are also possible. Case managers can use cognitive systems to effectively screen situations, assess the data produced by AI more precisely, and make informed judgments. For example, usage-based insurance (UBI) plans that are highly dynamic and customized to the behavior of individual clients are becoming increasingly popular.[/text][clear by=”40px” id=”” class=””]

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Conclusion

[/custom_heading][text]Studies have shown that AI can perform better and faster than humans in many crucial healthcare activities. A few ongoing studies on AI applications in healthcare paint a picture of a future where healthcare systems use is more cohesive and human-like. Algorithms are already surpassing radiologists in detecting dangerous tumors and advising researchers on building cohorts for expensive clinical trials.

Today, in this fast-changing world, AI is being used to address many of our problems. However, it is essential to know that as we study more and demystify AI, we will be able to explore several different uses of it. With such advancement in science, humans can explore uncharted territories in engineering and medical sciences, which once seemed impossible. AI for future leaders will involve discovering the phenomenal secrets of successful technology implementation in global health care. Thus, AI for future healthcare leaders will bring a new source of revenue, enhance innovations, generate a better lifestyle, and limitless possibilities for the greater benefit of society. Thus, enterprises involved in healthcare and pharmaceuticals should include AI-based business and service models and be the next leaders in the world of MedTech.[/text][clear by=”40px” id=”” class=””]

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What AI and its adoption means to business?

Posted on April 5, 2022 - 8:40 pm by Bhaskar Sharma

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Digital Transformation, Data + AI + Analytics

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What Artificial Intelligence and its adoption means to business?

AI is no longer a dream for the future: it has become a reality across all industries and spheres of life, especially the business world. As with anything new, it can be a challenge to adopt AI within existing systems. So, how can you harness the power of AI?

It all starts at the top, business leaders are the change agents, and game-changers, it falls upon them to enlighten their colleagues, employees, and others. They articulate the pros and cons of AI as well as how it can help accelerate business growth.[/text]

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The Benefits of AI for Business

[/custom_heading][text]Regardless of your niche, there’s a way that AI can help you. A few broad advantages you can expect with AI integration include:

Improved employee efficiency – Automate processes can save time and effort that employees can direct towards other goals instead of being bogged down with repetitive manual tasks.
Improve Quality – Using Machine learning for analysis and processes can bring accuracy. That delivers consistently better results and avoid room for error with manual work
Proactive than Reactive – AI and its adoption can provide you with predictive analytics allowing you to tackle unforeseen problems, meet customer demands with ease, and find better ways to grow your business.

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How Do You Increase AI Adoption?

[/custom_heading][text]Most businesses are well aware that AI could help them innovate but not many understand how exactly that happens. Change agents struggle on how to reap the benefits of AI via driving its adoption. To maximize benefits and increase adoption, we recommend a 2-pronged approach:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

1. Enabling Workforce with data and Insights

[/custom_heading][text]Our first recommendation is to initially focus on building the main KPI-based dashboards so that power is in the hands of employees and managers. The process of defining these KPIs and dashboards will help you refine your business goals and truly figure out what you need your AI system to accomplish.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Build on the success of the prior step using a four-step approach: discover, plan, act, and optimize.

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Discovery involves accessing the state of your data, the capabilities of your people, the feasibility of your system, etc. Assessing your data value is the single most important thing you can do to ensure the success of your AI system. Data is the lifeblood of any AI system and having structured, clean data can make or break AI initiatives.
Plan out your mission and vision for your AI solution by defining the obstacles in your business and converting insights into potential solutions.
Then we move on to the act – Implementation of the planned activities. We suggest choosing the right problem to solve based on its impact and adoption rate. AI use cases impact is calculated based on which business workflow it can be part of.
Lastly, you need to optimize systems, and processes. AI systems thrive on learning and constantly optimizing will ensure that insight is real and valid with changing business, and your system stay trustworthy.

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Decision Making Framework for AI based Product businesses

Posted on April 5, 2022 - 6:50 pm by Bhaskar Sharma

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Artificial Intelligence, Digital Transformation

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Decision Making Framework for AI based Product businesses

NextGen Invent Corporation has built a decision-making framework for entrepreneurs and business innovators. To address the following questions that arise when you have a business idea using AI and workflow enhancement.

I have an AI business idea, but what’s next?
What will increase the adoption of my AI solutions?
How can I make sure that my data science team is going to build an accurate model?
What are the success factors and forces that go into making my AI model effective?
What are the risks associated with my AI product/service adoption and sales cycle?

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[text]A business framework (decision-making framework) for AI-based products.

services include evaluating AI models and streamlining workflow automation that can contribute to business growth.

An agile business framework is key to avoiding expensive mistakes and ensuring a positive business outcome. Whether the idea is to start a new AI-based product/service business or to transform an existing product, a business framework plays a crucial role.

Most entrepreneurs would agree that they had a million-dollar AI-based product idea but were unable to bring it to reality because they were unaware of the direction they were supposed to take.

Therefore, having a solid business framework to guide decision-making processes is an important tool.

NextGen Invent’s decision-making framework is based on the experience gathered from over three hundred AI model developments and from interviews with 50+ executives and 20+ successful entrepreneurs. The proposed business framework highlights five key principles that play a vital role in determining the course of your AI business:[/text][image lightbox=”” width=”” is_gallert_item=”” src=”14786″ alt=”” href=”” title=”” popup_content=”” id=”” class=”” style=””][text]An entrepreneur should consider these five key principles to be successful in defining an AI/ML/Deep Learning model for bringing innovative products and services to the market. Let’s understand each key principle in detail.[/text][clear by=”40px” id=”” class=””]

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Be the Model User

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If you are an entrepreneur with a goal to launch an AI model-based product or service, you should imagine yourself to be the model user. Define your expectations from the model. This will help you determine the input format, the manner in which it is processed.
The output is given to your users.
After receiving the input, you must understand, as a model, the success rate of the output provided. In other words, ask yourself how knowledgeable you should be as a model used to provide the input to utilize model output.
You should make sure that your model output is relevant to users’ needs based on vast data knowledge.
If you are an entrepreneur and not a data scientist then this step brings you closer to understanding your AI product and services at a deeper level.
This step will help you in asking the right questions as well.

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Entry barrier

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In Michael Porter’s five forces, the most critical force for the success of a product or service is Entry Barrier. In most cases, the entry barrier for an AI model comes from its input data.
Most companies are moving towards gathering Real World Data, gaining exclusive data rights, and improving data quality.
360-degree data approach along with strong data quality standards are crucial ingredients in making a unique model.
value proposition and an entry barrier to your business.
Once your model has an entry barrier and value to the customer, it is important yet difficult to maintain that position.
Continuous learning, additional data to increase model intelligence, and expanding strategic partnerships/distribution channels may help you in staying ahead of curve.

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Ethics

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“Ethics is knowing the difference between what is right and what is wrong”.
Unfortunately, there will always be a gray area as law and social awareness will change over time.
As an entrepreneur, the boundaries you set and the decisions you make in regard to ethics.
Having an AI model with high standards for data privacy will tend to provide sustainable products and services.

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Model Risk

[/custom_heading][text]Like all other businesses, AI-based products and services have their own set of unique risks. We can categorize these risks as follows:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

1. Trust

[/custom_heading][text]An AI model is seen as a black box, i.e. input is provided to receive an output, but is it very hard to gain insight into what happened inside the box.

Even though a model is using several black-box components one should spend extensive time in quality test data and scenarios.

Trust in a model normally comes from well-documented benefits achieved from the AI model in real scenarios.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Future Redundancy

[/custom_heading][text]Due to ever-changing and fast-paced technology, the model that is considered worthy today might end up being redundant in the future. Therefore, plan to periodically audit and analyze your model’s performance in different scenarios.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

3. Data Compliance

[/custom_heading][text]In most cases, the data used for your AI model has to follow a set of rules and regulations like HIPAA, GDPR, etc. As these laws are changing, a model that is compliant today may not be in the future.

Staying ahead of the curve and using technologies such as federated learning are commonly implemented approaches in these cases.[/text][clear by=”40px” id=”” class=””]

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Model Adoption

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This is where an entrepreneur or other business executives spend most of their time.
Executives should keep working on how to increase model value and make it easy to use.
The adoption of AI is one of the hardest dimensions in any organization.
It must adopt at all levels of the organization for it to be successful.
Entrepreneurs and executives should be aware of the above business framework and related key principles.
We would love to know how this framework has helped your business.
To get more information or discuss your AI initiative, please click here.

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Rise of Digital Healthcare/Telemedicine during Covid

Posted on April 5, 2022 - 4:09 pm by Bhaskar Sharma

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Digital Health, Digital Transformation, Telemedicine

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Rise of Digital Healthcare/Telemedicine during Covid

Prior to the Coronavirus pandemic, digital healthcare or telemedicine was a relatively new form of healthcare delivery that was still far from mass adoption in the industry. However, Covid-19 completely changed existing healthcare delivery models in the blink of an eye. Now, telemedicine has become a mainstay of healthcare delivery and is definitely here for the foreseeable future.

As the pandemic kept people restricted to their homes, no one could go to a hospital or clinic unless it was an emergency. It became necessary to deliver healthcare remotely as much as possible to prevent the further spread of the pandemic. This was a huge boost in the adoption of digital health measures like telemedicine, remote monitoring, and digital health tracking.[/text]

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The Use Of Technology And Analytics In Healthcare:

[/custom_heading][text]The healthcare sector is slow in adoption of technology. There is hesitancy in healthcare providers to depend too much on technology. After all, medicine does require a human touch. However, technology can provide the necessary data to drive these human decisions leading to better patient outcomes and this can be done without sacrificing empathy and compassion.

AI and machine learning can be vital tools for doctors, helping them make smarter and quicker decisions. The Coronavirus pandemic has hastened the use of telemedicine by both healthcare providers and patients, that has helped save many lives in these times.[/text][clear by=”40px” id=”” class=””]

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Digital Healthcare During The Pandemic

[/custom_heading][text]The Coronavirus pandemic has familiarized the industry with several digital transformations including:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Telemedicine

[/custom_heading][text]Telemedicine involves doctor-patient consultations happening online, usually through video conferencing. As video conferencing became a part of daily life, patients and doctors alike are now more comfortable with online consultations. Telemedicine is perfect for remote care of chronic conditions, minor emergencies, counseling, follow-up care, and more.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Remote Monitoring

[/custom_heading][text]Remote monitoring and digital tracking of health parameters have made it possible for doctors to safely monitor their patients at home. This allows the patients to remain comfortable at home while also allowing the doctor better monitoring through tracking devices, sensors, etc.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Mobile applications

[/custom_heading][text]The pandemic has driven the use of mobile apps by patients and caregivers to track symptoms, treatment, and outcomes. This is also an easy way for doctors to track multiple patients and use the data to drive better treatment over time. Mobile apps also played an important role in contact tracing.[/text][clear by=”40px” id=”” class=””]

[text]Other digital transformations during the pandemic include the use of AI bots for tele-consulting, predictive analytics, AR training, and bringing sustainability to the healthcare supply chain.

The future of digital health is bright with the entire industry rapidly adopting cloud computing, big data analytics, and digital communication. As this field expands, the question of data security around patient records is at the forefront of every discussion. Only time will tell how far digital health can take us.[/text][clear by=”40px” id=”” class=””]

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Healthcare Data Analytics and why it matters?

Posted on April 5, 2022 - 4:08 pm by Bhaskar Sharma

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Data + AI + Analytics, Digital Health

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What is Healthcare Data Analytics and why it matters?

What is Healthcare Data Analytics and why it matters? The healthcare sector is a unique field where each decision can change the course of many lives. In this highly stressful scenario, making the right decision is of utmost importance. Data-driven decision-making in healthcare is still in its nascent stages but shows tremendous promise for the future.

Data analytics in healthcare is a game-changer— now healthcare providers, researchers, and others can make more accurate decisions that can improve patient outcomes and save lives. Healthcare analytics can also help reduce healthcare costs, stop the outbreak of epidemics and pandemics, improve compliance to medication, and hasten drug research. Each patient in a healthcare system generates massive amounts of data that can be collected and analyzed to optimize patient care.[/text]

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What Is Data Analytics In Healthcare?

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Data analytics in healthcare involves the collection and processing of data to drive healthcare insights and decisions. Major areas of interest in healthcare data analytics include medical expenses, patient behavior, pharmacology, and clinical data processing. Doctors and other healthcare providers can now gain valuable insights into their patients which improves the delivery of care. Doctors can identify problems sooner leading to faster diagnosing. Data can also drive the selection of medications and treatments to give the patient their best chance at success. Overall, data analytics in healthcare can improve operational efficiency, patient prognosis, and reduce unnecessary medical expenses.

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What Are The Advantages Of Healthcare Data Analytics?

[/custom_heading][text]The analytical approach to decision-making can make a positive change in the following ways:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

1. Predictive analytics

[/custom_heading][text]Predictive analytics utilizes large pools of data to predict outcomes. This is used to help doctors make quick decisions to enhance patient care. This is particularly useful in cases with complicated medical histories or patients with multiple health conditions.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Delivering care in high-risk situations

[/custom_heading][text]Emergency care is fraught with difficulties and can get costly for patients, with no guarantee of a good outcome. Data analytics can help even the odds for patients by providing doctors with the necessary analysis to treat high-risk patients successfully.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

3. Cost control

[/custom_heading][text]Many hospitals, clinics, etc. lose revenue due to staffing errors. Data analytics can help reduce understaffing or overstaffing by using data to predict patient flow. This makes the whole process of patient care more efficient and reduces the cost for both patients and providers.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

4. Global advancement

[/custom_heading][text]Data analytics can help predict and control the outbreak of epidemics and pandemics. Big data can also help predict treatment outcomes, improve quality of life, and provide early risk detection and assessment. Healthcare data analytics can change the way medical care is delivered. As we live longer, the demand for good medical care is never-ending. Data analytics can ensure that medical care is less risky, more efficient, and makes a positive change for patients across the globe.

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Will Artificial Intelligence replace physicians?

Posted on April 5, 2022 - 4:00 pm by Bhaskar Sharma

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Artificial Intelligence, Digital Health

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Will Artifical Intelligence replace physicians and face of healtcare?

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Imagine yourself walking into the hospital with a humanoid robot greeting you with a calming voice, asks you about your symptoms, and reassures you while giving you a prescription with a smile. While this may sound like complete science fiction, the question remains, “Will Artificial Intelligence replace your doctor in the future?” While we realize the advancement in technology especially in the field of neural networks has been remarkable, AI is enabling physicians with tools and decision-making power but not replacing them, at least not anytime soon.[/text]

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[text]Here is our CEO, Deepak Mittal’s opinion about AI replacing physicians: Click Here[/text]

[text]The same opinion other leaders have expressed in a poll conducted by NGI. Results were quite clear, 63% of the respondents believe that AI won’t replace physicians:[/text][text]Artificial Intelligence (AI) acts as an enabler to medical care. AI/ML shines the most when it is assisting physicians in making better medical decisions. More than accuracy, we as humans need human empathy from a physician along with effective treatment.[/text][text]Al/ML is bringing the power of object identification, classification along with question/answering, but a physician’s power lies in linking various pieces of information to make decision. Diagnosing a condition is an np-complete problem (specifically set cover: http://en.wikipedia.org/wiki/Set_cover_problem) and even with quantum computers, np-complete problems cannot be solved in polynomial time. The point can be well proven by the experiment of Waldo.[/text]

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Waldo experiment:

[/custom_heading][text]Waldo wears a stocking cap, even in the summer, is skinny, usually wears a striped shirt, needs a haircut, and always hangs out with lots of other characters. Can you find him in the image? And if you did, how long did it take?

The viral video where a robot built with Google AI finds Waldo from a cluster of images within seconds which a human eye would normally take minutes. But if we change the question to who all needs a haircut in the picture then will the computer do that. That’s where in our opinion, a physician’s power lies. Or better question will be which haircut will look best on which person based on his liking/ethnicity etc. Please note that we are only highlighting two of the thousand parameters that might be going in a physician’s mind. Unfortunately, not all decisions are black and white in our human world.

Even if we consider fully automated surgeries, we have history books to offer wisdom. The advent and progress in AI has been remarkable, and we have had our fair share of lessons from shortcomings and mistakes like Therac-25 in the past, where admittedly so we’ve realized there is a long, long way to go before AI can even dream of replacing surgeon.

I would also like to point to the problem with malpractice insurance and FDA approval. Think of a hospital having 1000 physicians and malpractice points to one physician vs malpractice. Pointing to AI that does the work of 1000 physicians. Imagine a situation where If we may run out of physicians overnight because of one blunder or mistake.

In nutshell, we believe that it is unlikely we’ll get humanoid “robot physicians” for a long time to come. Though technology will replace some of the more routine aspects of medical care, or improve it but for now. The physicians are here to stay.[/text][clear by=”40px” id=”” class=””]

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AI Adoption: Why Business leaders should excel it?

Posted on April 1, 2022 - 9:41 pm by Bhaskar Sharma

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Artificial Intelligence, Data + AI + Analytics

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AI Adoption: Why Business leaders should accelerate it?

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Business Leader’ decisions and actions have widespread consequences on company success and as such, great leaders excel at preparing their company to beat the competition. They lead the company and employees, spearhead strategy, and inspire confidence in the set targets and goals. Equipping business leaders with more sophisticated and powerful tools enables better decision-making. In this aspect, the importance of Artificial Intelligence (AI) cannot be overlooked.

The 2019 study from Microsoft and IDC Asia/Pacific, “Future Ready Business: Assessing Asia Pacific’s Growth Potential Through AI”.[/text]

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[text]Which surveyed 200 business leaders and 202 workers, concluded that the rate of innovation improvements and employee productivity gains were estimated to rise by 2.2 times and 2.3 times, respectively. For the organizations that have implemented AI initiatives.[/text][clear by=”35px” id=”” class=””]

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The top five business drivers to adopt the technology were:

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Higher competitiveness (24% of respondents chose it as the number one driver)
Accelerated innovation (21%)
Better customer engagement (15%)
Higher margins (14%)
Productive employees (9%)

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[text]Therefore,the biggest advantages of Artificial Intelligence (AI) come in the form of reducing the load of repetitive tasks for employees and providing deeper insights over extended periods of time. AI is a great tool to bring in agility, filtering signals from noise. Also, it has the ability to handle big data easily, extract meaningful patterns and models.

Finally, make responsive dashboards that cannot be overstated.[/text][clear by=”35px” id=”” class=””]

[text]AI adoption can also serve to make the organization agile, initiating the development of new products through careful listening and appropriate action.

Trailblazing leaders can build a knowledge base and disseminate that information consistently, motivating continuous learning in an environment that seeks to progress rapidly and radically.[/text][clear by=”40px” id=”” class=””]

[text]In addition, the benefits of AI come with some considerations. To implement AI effectively in your business, you need big data in diverse forms. To utilize this data, you will need more computing power and storage than you’ve used before, and additional skills to integrate this software and hardware into your current technology and business teams.

As you start to use AI technology, the more data you get, the more ways you’re likely to discover how to use it. Successful use of AI technology to improve business is an ongoing and continuous effort.[/text][clear by=”40px” id=”” class=””]

[text]In conclusion, as a business leader, you are at the helm of this effort, helping others to adjust and thrive.

You can also, steer how an organization prepares for and responds to the opportunities and risks around AI in business.[/text][clear by=”40px” id=”” class=””]

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Machine Learning – Supervised, Unsupervised, & Reinforced Learning

Posted on April 1, 2022 - 9:32 pm by Bhaskar Sharma

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Artificial Intelligence, Machine Learning

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Machine Learning – Supervised, Unsupervised, & Reinforced Learning

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Machine learning is a vast topic with so many intricacies that it can be confusing where to start. Machine learning is the force behind many of the algorithms that govern our lives like Amazon’s recommendation engine, fraud detection software, financial market tracking, and supply chain logistics management across the industry.

The fundamental function of all these algorithms is their ability to learn. Artificial intelligence and machine learning models can learn in many ways. Each of these learning methods can sound complicated if you don’t have in-depth technical knowledge, so let’s dive in for a simple explainer on different learning models in machine learning.[/text]

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Supervised Learning

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Supervised learning is the simplest to understand. You need to provide an input to which you already know what the output should be. What you don’t know is how you can reach this output, and this is what the model will study and base a prediction on.

Supervised learning involves the use of existing data to train models. The input data is training data. The algorithm works on this to produce a prediction. It can compare the output produced to the intended output and find errors that is modified as needed.

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Unsupervised learning

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Unsupervised learning involves the use of unlabelled data to train the machine to identify patterns and cluster the data. It is best for situations where you have complex data and are not sure what your desired outcome is. This method of learning gives you a better understanding of the inner relationships within your data that you may process further.

The two most important types of unsupervised learning are clustering and association. Clustering involves the grouping of items based on similarities. Association learning is used when you want to find the link between data columns.

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Reinforced learning

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In reinforced learning, models are created to incorporate rewards and punishments. This encourages the model to chase rewards and minimize punishments, teaching it how to make decisions. The model learns to recognize relevant signals and decide the best action to maximize the reward. When a loop is completed, a reinforcement signal is needed to give feedback to the model on how to proceed further.

Reinforced learning is a sort of middle ground between supervised and unsupervised learning. The model is provided labeled data, like in supervised learning but the model is able to make judgments by itself, like unsupervised learning. Recommendation algorithms often function on reinforced learning.

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Machine learning as a field is expanding by the day, with more accurate and complex algorithms. A solid understanding of the basic way these algorithms learn will help you get a better understanding of what you can achieve with AI and machine learning systems. Much like how we learn, an abundance of patience and effort we need to ensure your AI system learns well. Remember, it only gets better with every round of learning!

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AI Driving 2022 Future Business Trends

Posted on April 1, 2022 - 9:25 pm by Bhaskar Sharma

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Data + AI + Analytics, Machine Learning

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Artificial Intelligence, driving 2022 future business trends

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]The role of AI in work life and business is undisputed. As we move forward, this role will only expand to include more functionalities and use cases like artificial intelligence, machine learning, and data processing becomes more advanced and efficient. In the past few years alone, we’ve seen AI grow by leaps and bounds and this growth shows no signs of slowing down just yet.

Looking to the future, how will AI impact work and business? It can be hard to predict but as an overview, we can expect the following changes globally:[/text]

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1. Productivity will increase

[/custom_heading][text]One of the biggest advantages of integrating AI into your business is the major boost in productivity that you receive. An efficient and effective AI system can reduce human error, find new solutions to old problems, and help you avoid pitfalls in the future. As the technology behind artificial improves, we can expect this productivity boost to keep getting better. The goal is to reach a stage where less training is needed for your AI model in order to get more things done. This will directly result in enhanced productivity across industries.[/text][clear by=”35px” id=”” class=””]

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2. Consolidation

[/custom_heading][text]Services via companies using the power of Artificial intelligence will have lower operations costs and will be able to scale operations faster and cheaper. That will accelerate consolidation in different industries. Think of the Chrome browser, it’s so ubiquitous in its market that many users forget that other browsers exist.[/text][clear by=”35px” id=”” class=””]

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3. Personalization

[/custom_heading][text]Consolidation can spell doom for smaller companies unless they pivot and adapt. Personalization of products and services should be the focus of small companies to stay competitive and profitable. This means personalization not only at a customer segment level but delving deeper so that each customer becomes a segment unto themselves. For example, personalized medicine is one such opportunity for hyper-personalization.

Integrating AI systems into their businesses models is the only way for small companies to make this shift towards personalization. The strength of a company’s AI capabilities will likely be one of the biggest factors for business success in the future.[/text][clear by=”35px” id=”” class=””]

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4. Less time from ideation to launch

[/custom_heading][text]The current turnaround time for various product development clocks in at about 6 months, from ideation to launch. This production window varies based on the industry or product. Overall, we can expect a shortening of the time taken to ideate, develop, test, and launch new products and services. Artificial intelligence and machine learning can play vital roles in reducing the development process. Zara is known today for taking less time from idea to launch. But in the future, this duration will be in hours and days and not in weeks or months. Further along, AI can speed up the testing process and help products launch quicker and with fewer setbacks.[/text][clear by=”40px” id=”” class=””]

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5. Convergence of physical and digital world

[/custom_heading][text]By 2030, about $5.5 trillion to $12.6 trillion of value will be unlocked globally using IoT products and services. Virtual Reality, Mixed Reality, and extended reality are continually challenging customer experience and re-writing the rules. The convergence of the digital and physical worlds is going to be a reality in the future.

AI and machine learning are now a mainstay of the business. As the field grows, it’s still not too late to get in on the action. Adopting AI in your business can help you grow and explore exciting new opportunities. AI can give your company a competitive edge while ensuring that your technological strategy stays updated. Now is a great time to invest in AI and see the wonders it can do for your business.[/text]

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Four factors that hinder Digitization

Posted on April 1, 2022 - 8:38 pm by Bhaskar Sharma

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Digital Transformation

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Four primary factors that can hinder digital transformation

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Digitization isn’t something that happens overnight. It requires strategic planning and change in company processes and mindset. Your company will observe major changes on the way to digitization including a shift from siloed work to extensive cross-departmental collaboration and a shift towards data-driven decision making.

These are fundamental shifts in how a company operates and so you may face many challenges along the way. How well you anticipate these bottlenecks and plan around them will go a long way in determining the success of your digitization efforts. Most companies face the following four major bottlenecks when attempting a digital transformation:[/text]

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1. Acceptance of the status quo

[/custom_heading][text]It’s easy to stay in your comfort zone. This acceptance of the status quo and a mindset of “this is how it’s always done” can be the first major bottleneck that managers and leaders may face when trying to push towards digitization. The best way to overcome this is to spend time educating your peers and employees on the benefits of digitization, why it needs to be implemented in your company, and how you can make it happen.[/text][clear by=”35px” id=”” class=””]

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2. Lack of defined incentives (Incentives are a key tool to promote cultural change)

[/custom_heading][text]The easiest way to motivate a person to pursue a goal is to define what incentives they can obtain by achieving this goal. If this is not done when making a shift towards digitization, it can leave employees feeling like they don’t know what they’re working towards and what will happen once that goal is completed. Clear communication with your team and setting regular milestones and incentives can help create a motivated and open mindset towards digitization.[/text][clear by=”35px” id=”” class=””]

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3. Fear of change

[/custom_heading][text]The fear of change is a constant, in most aspects of life, so it’s no surprise that this is one of the major roadblocks in the journey towards digitization. Over the last few decades, we’ve seen companies fail to confront this fear of change when faced with disruption (Blockbuster comes to mind, and traditional cab companies). Many people also fear job insecurity and job losses triggered by increasing automation along with the effort needed to upskill to stay competitive. This can be alleviated by reassuring your employees regarding their role in the company. what support you can provide them to help them make the transition?[/text][clear by=”35px” id=”” class=””]

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4. Risk of the unknown

[/custom_heading][text]Stepping into the digitization mindset can feel like stepping into a whole new world. It can be daunting to face new technologies if you don’t have an understanding of them and how they work. Start tackling this bottleneck by setting up a clear strategy of what you want to achieve.

How you will implement this strategy across different departments. Consult with industry experts who can help you chart your course towards digitization. Taking on experts is the best way to dispel the risk of the unknown.[/text][clear by=”40px” id=”” class=””]

[text]All the above four factors are part of your organization’s culture and DNA. And, Digitization requires a cultural change and careful planning to make that happen.

Digitization is no longer optional in many industries- this is the future of work. Don’t lose out on the power of digitization due to a fear of any of the bottlenecks we’ve outlined. With the right mindset, agile strategies, and the support of experts, you too can make the shift towards digital future.[/text]

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The Future of Digital Health Technology: Personalization is the Key

Posted on April 1, 2022 - 8:24 pm by Bhaskar Sharma

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Digital Health, Digital Transformation

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The Future of Digital Health Technology: Personalization is the Key

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Over the past decade, the healthcare industry has seen tremendous developments and innovations, and without a doubt, we have only scratched the surface. The inner workings of the healthcare industry are being improved and, by extension, transformed with the implementation of new innovations such as artificial intelligence and other digital health tools.

For long, health care systems have been provider-centric, meaning that patients visited healthcare providers based on the provider’s schedule as opposed to the patient’s convenience. The new transformations, however, are leading the way into creating a healthcare domain that is more patient-centric and personalized.[/text]

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What is Digital Healthcare and How it is Helping?

[/custom_heading][text]Digital health has become an increasingly adapted technology and is functioning as a core component of personalization. It incorporates digital transformation into the healthcare sector by integrating software, hardware, and services. Components such as telehealth & telemedicine, AI-empowered medical devices, electronic health records, mobile health apps, and wearables are a few of the examples of digital transformation in healthcare. Such components are entirely reforming the way one interacts with health professionals. The abundance of data that is collected is shared among the providers to help make accurate decisions about treatment plans.

The main characteristic of personalized health care is providing the patient with choices and options that address a person’s unique needs and life situations. This ensures that care is offered anytime, anywhere. In such a system, care providers communicate with patients to acquire their definition of health goals and preferences. This helps the care providers make informed health decisions, while also keeping in mind the patient’s wishes and goals. With that goal in mind, healthcare tools primarily manage health conditions and track progress digitally. They also accommodate virtual visits with a doctor or any such caregivers to further support the patients to actively manage their health.

Mostly, the healthcare sector is divided into two parts, one is the emergency services and the other one is the chronic disease segment. Between the two sectors, chronic disease is responsible for a huge part of the total healthcare costs of individuals. Diseases such as diabetes, hypertension, or heart disease, require somewhat tailored care delivery to one’s personal health goals. To start with, chronic disease symptoms mostly are unique to every person and usually become a part of the patient’s life that requires regular monitoring and treatment.[/text][image lightbox=”” width=”2″ is_gallert_item=”” src=”14605″ alt=”Digital Healthcare” href=”” title=”” popup_content=”” id=”” class=”” style=”padding: 0px 80px 0px 80px;”][text]The entirety of 2020 and 2021 made the world stop and think about the ways of conventional living. When the whole world was restricted from leaving their homes, everyone decided to bring everything home. In 2020, people became accustomed to smart homes, online groceries, and work-from-home set-ups. But unfortunately, the healthcare industries were still lagging when it came to implementing digital strategies. During Covid-times, digital health became a sensation very fast. With the click of a button, one can make informed decisions about their health, track their health progress and connect to provider teams.[/text][clear by=”35px” id=”” class=””]

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Let’s enlist the factors that make healthcare personalization a hit in the masses.

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Healthcare does not fall under one-size-fits-all. As discussed, the same disease can manifest itself very differently between two different people. Therefore, different patients might need different approaches to treatment for the same condition, in order to create their healthiest outcome. When a patient visits a doctor in the office, storing patient history becomes an elaborate exercise. A telehealth tool not only makes the visit easier but also helps collect and store much more data thereby making the data accessible in an instant for further decision making.
Have you wondered how Netflix comes up with suggested content that is tailored to your interests? Or Amazon exactly knows your preference in books. Well, by now, most of us are very familiar with the concept of Machine Learning that decodes the “behavioral phenotype”. Implementing this concept in healthcare can help determine how to tailor the data or information for a better-personalized experience. This is just one example of using data for making digital health solutions more personalized and effective. As more devices like wearables are becoming mainstream, the data will become more and more available and accessible. This data can then be used by providers through AI and Machine Learning to analyze and draw a better picture of each patient.

Now that we know personalized technology is all about a vast amount of data and its analysis, we can say that it has the power to revolutionize healthcare both for common as well as rare conditions. Similar to Big Data, where there is a data dump in an open platform for everyone to access, in the healthcare domain, the technology collects patient data from around the world. From the abundance of data, healthcare providers can have all the resources available. Consider the situation where a person with an incredibly rare condition is located in the US, and the care provider is clueless about the symptoms while a person with the same rare condition is located in Norway but receiving treatment. With a vast number of resources, the care provider can access their treatment data to decide the best course of action.

The future of data analysis is mostly about risk or threat analysis. To look for ways to prevent loss rather than recover from it. The Healthcare domain is no exception, it is more about preventing disease than treating it. Healthcare providers are focussing more on preventative measures instead of preparing to perform risky or emergency procedures. Personalized healthcare is understanding each patient and their risk factors.[/text][clear by=”35px” id=”” class=””]

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Conclusion

[/custom_heading][text]For an industry like healthcare which is predominantly based on a myriad of data, the best and the most problematic part is humans. On one hand, it is often too difficult for humans to stay updated on data and treatment options. On the other hand, caregiving always needs the human touch to comfort an ailing patient. The future of healthcare personalization will eventually automate everything from data entry to medicine distribution to diagnosis and scientific analysis. This will provide the caregivers with everything they need to curate the best-personalized care. Not only that but, while machines take up all the redundant tasks, providers can also focus more on delivering quality care to each patient. Additionally, the machine is precise and beyond human errors, therefore reducing diagnostic errors and providing accurate outcomes.

In summary, the future of digital healthcare is “personalization and efficiency” and it has already started. With strong and deep-rooted data at their disposal, health care providers will be able to treat patients for the best of outcomes and share their findings more readily and with greater ease. With the combination of digital transformations and the concept of personalization, healthcare will provide the best of both worlds, human touch, and technology.[/text]

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Changes needed to promote AI Adoption

Posted on April 1, 2022 - 8:02 pm by Bhaskar Sharma

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Artificial Intelligence, Data + AI + Analytics

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Deeper look at change needed to promote AI adoption

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]The nature of work and the status quo of business are rapidly changing. To beat the competition, leaders are challenged today to embrace the change required within the company to transform it into an AI-enabled company first with AI Adoption. The rise of Artificial Intelligence, Machine Learning, and automation has changed the way we train labor, how we leverage machines, and how we improve productivity. In these exciting times, no one can afford to be left behind.

AI is changing the face of business every day. From fully automated customer service to guiding banking decisions, the scope of work undertaken by AI is expanding. Firstly, not every business has been able to harness the power of AI in its processes and goals.[/text]

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Tapping Into the Power Of AI

[/custom_heading][text]Truly mobilizing the power of AI requires a huge shift in mindset, company culture, and company processes. Acceptance and understanding of AI among managers and executives is the biggest change and is absolutely necessary for a shift towards an AI-powered future.

At the leadership level, one of the major roadblocks is taking a plug-and-play approach to AI Adoption. This happens when company leaders rush into their AI implementation and build their systems with a narrower view of their goals. But this isolated, small-scale approach can backfire leading to a failure of company-wide adoption.

The second roadblock leaders might face is taking a narrow perspective on their AI goals and a lack of understanding of the capabilities that AI has. Leaders first need to analyze their goals, align their company culture and mission to their goals, and work with experts to build AI systems that will stand the test of time.[/text][clear by=”35px” id=”” class=””]

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What Changes Are Needed For AI Adoption?

[/custom_heading][text]The first change is always a change in your strategy. Think about what your goals are, what processes you need to change or initiate, and how you can maintain this long-term. Once company leaders lock in this strategy, they can then move towards educating their peers and employees on the pros and cons of AI and machine learning.

Broadly, these are three changes you will need to consider:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

1. Creating a collaborative work culture

[/custom_heading][text]AI thrives on data and the best way to create valuable data is to encourage cross-departmental collaboration. This will help you take a broad view of your challenges and goals and avoid the pitfalls of a limited or one-sided perspective. By involving people across different disciplines, you can enrich your insights and help create a more robust AI system. Additionally, it is important that leaders address the fear of failure for a company to embrace innovation.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

2. Shifting to data-driven decision-making

[/custom_heading][text]This onus falls squarely on company leaders. Gone are the days when we relied on purely leader-driven decision-making. Company leaders must now lead the charge towards data-driven decision-making. Trusting your AI system and augmenting company decisions with data and analytics is an important step in seamless AI adoption.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

3. Accepting a more dynamic, agile workflow

[/custom_heading][text]AI works best with practice. This means it may not be perfect at first. It takes time to learn and gets better with every iteration. This means companies will have to adjust to a trial-and-error mentality with the resilience to learn from failures.

Technologically sound, data-driven processes involving skilled people are the future of business. however, don’t rush to adopt AI systems without first analyzing your company, your goals, and your processes. Evaluate each step and analyze generated data, and always consult with experts for guidance.[/text]

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Insight-driven Organization – Four steps to start your journey

Posted on April 1, 2022 - 7:52 pm by Bhaskar Sharma

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Data + AI + Analytics, Insight driven

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Insight-driven Organization – Four steps to start your journey

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Data is easily the most valuable commodity on Earth in our time. Every business, big or small, generates massive amounts of data every day. But what use is all this data if we do not put it to work?

Harnessing your data to provide insights and predictions is the cornerstone of being an insight driven organization. An insight driven organization is one that relies on data analytics to make decisions. Being an insight driven organization means valuing the power of analytics and building teams and processes that nurture data to generate accurate and useful insights.[/text]

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Why Should You Be an Insight Driven Organisation?

[/custom_heading][text]Relying solely on human insight and decision-making leaves a lot of room for error. While this approach has served us thus far, it is time to revolutionize how companies makes decisions. Data analytics has grown rapidly in the last decade. By creating an insight driven organization, you can innovate quicker, be more efficient, and differentiate yourself in your chosen market.

Once successfully implement, an insight driven approach only gets better and better as each cycle of insight or prediction generates data that can be used to further refine your results and chart your course towards a data-first future.[/text][clear by=”35px” id=”” class=””]

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How Can You Become an Insight Driven Organisation?

[/custom_heading][text]The shift towards an insight driven organisation does not happen overnight. It requires careful planning and a determination to create an organizational shift in how your company operates. Broadly, consider the following 4 steps to start your journey: [/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Step 1: Build a strategy

[/custom_heading][text]As with most things, your success depends on how you plan and strategize a shift towards being insight driven. Analyse your business, your goals, and your capabilities before embarking on this organizational change. [/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Step 2: Acquire data

[/custom_heading][text]Nobody wants incorrect or inaccurate insights. To avoid this common pitfall, you need to ensure that you collect and organize high-quality, relevant data to feed into your AI system. Data is the life of any successful AI system, so invest time in building data sets that provide a comprehensive view of all data. [/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Step 3: Be change agent

[/custom_heading][text]A change in company culture and standard operating procedures is crucial for a shift towards being an insight driven organization. Let go of a simplistic departmental view of your organization and work towards building a dynamic and integrated workflow that meshes well with the goals of your AI system. [/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

Step 4: Technology

[/custom_heading][text]Acquiring data, organizing it, and analysing insights necessitates the use of complex technology. This includes software to collate your data, technology to provide an all-round view of your data, and of course, an AI system to process data and provide you with the insights you need. This is best done by partnering with experts in data science to ensure your systems are secure, trustworthy, and stand the test of time.

Creating an insight driven organization is a marathon, not a sprint. Remember to pace yourself, take your time, and start small. When in doubt, consult with experts rather than going blindly in the wrong direction. With the right mindset and support, you too can make the leap towards being an insight driven organization. [/text]

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Ready for HIPAA Compliance?

Posted on April 1, 2022 - 7:36 pm by Bhaskar Sharma

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Data + AI + Analytics, Data Privacy

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HIPPA compliance, A must have for Healthtech organization?

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Don’t go online until you’ve fully considered every aspect of what Health Insurance Portability and Accountability Act (HIPAA) really means to your business and your business associates.[/text]

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What is HIPAA, HITECH, and its Objectives?

[/custom_heading][text]Health Insurance Portability and Accountability Act (HIPAA), a US federal law, issued in 1996, upholds the data privacy and security of protected health information (PHI) and provides guarantees to patients that their data is handled in a safe and secure way.

HIPAA is created to:

Improve the portability and accountability of health insurance coverage for employees between jobs
Combat fraud and abuse in health insurance and healthcare delivery
Promote the use of medical savings accounts by introducing tax breaks, provides coverage for employees with pre-existing medical conditions
Simplify the administration of health insurance

Health Information Technology for Economic and Clinical Health Act (HITECH Act), issued in 2009, promotes and expands the adoption of health information technology, specifically, the use of electronically protected health information (ePHI) by healthcare providers and tightens HIPAA compliance.

HITECH is created as an extension to HIPAA to cover:

Improvement of healthcare quality, safety, and efficiency
Application and use of health information technology standards and reports
Testing of health information technology
Grants and loans funding
Privacy and security of electronic health information
Revisions to permitted uses and disclosures of PHI
Business associates are prevented from using ePHI for marketing purposes without authorization
Patients are given the right to change/revoke any authorizations they had previously given
Requirements for accounting for disclosures of PHI
Maintaining records of disclosures including to whom PHI had been disclosed and for what purpose.

The HIPAA Final Omnibus Rule of 2013 expands regulations for privacy, requirements for breach notifications, business associate liabilities, and business associate agreements. This rule mandates business associates of covered entities also subject to HIPAA compliance and audits.[/text][clear by=”35px” id=”” class=””]

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For which businesses HIPAA and HITECH Acts are applicable?

[/custom_heading][text]It is applicable to practically all health plans, health care clearing houses, health care providers and endorsed sponsors of the medical care prescription drug discount card. These entities, directly create., maintain, and use PHI on a regular basis, are referred to as “HIPAA Covered Entities” under the Act.

“Business Associates” of “Covered Entities” are also covered by HIPAA. Business Associates entities provide third party services during which they will encounter PHI. Prior to undertaking a service on behalf of a Covered Entity, a Business Associate must sign a Business Associate Agreement guaranteeing security and privacy of any PHI to which it has access.

As health care providers are now required by law to give patients a Notice of their Privacy Policy, it will be necessary to explain HIPAA and Privacy Policy to patients as they must sign a copy of the policy to say they have received it.

It is not applicable to entities dealing neither with patients’ personal data nor patients’ health information.[/text][clear by=”35px” id=”” class=””]

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What happens in case of HIPAA violations?

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The HIPAA regulations are enforced by the U.S. Department of Health & Human Services´ Office for Civil Rights, while state Attorney Generals can also act against parties discovered not to be compliant with HIPAA.
The Office for Civil Rights has the authority to impose fines on Covered Entities and Business Associates for violations of HIPAA and data breaches unless the offending party can demonstrate a low probability that health information has been compromised.
Civil and criminal penalties could be issued directly to business associates for the failure to comply with HIPAA Rules regardless of whether a data breach had occurred or not.
Penalties of HIPAA violations, in 4 levels of negligence, range from USD 100 to USD 50,000 per violation and maximum of USD 1.5 million per year of violation of identical type.
Restitution may also need to be paid to the victims.
In addition to the financial penalty, imprisonment is likely for a criminal violation of HIPAA Rules.

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Benefits of HIPAA and HITECH

[/custom_heading][text]While the initial cost of investment in the necessary technical, physical, and administrative safeguards to secure patient data may be high, the improvements can result in cost savings and higher revenue over time because of improved efficiency.

Since healthcare organization employees’ workflows are streamlined, and the workforce has become more productive, healthcare organizations can reinvest their savings and deliver a higher standard of healthcare to patients. [/text][clear by=”35px” id=”” class=””]

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Challenges to Implement HIPAA + HITECH

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The technologists are often unaware of the expectations of these Acts of Law
Initial high investment of securing data/information
Shorter time-to-market may overlook requirements of the Act

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B2B perspective for sharing data

[/custom_heading][text]Data Protection Agreements for guarantees of security and privacy of PHI need to be established prior to sharing of PHI among covered entities and their business associates

Technology controls need to be established to managed to prevent and detect any loss of data privacy and security by covered entities and their business associates[/text][clear by=”35px” id=”” class=””]

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Digital Health to tackle diseases

Posted on April 1, 2022 - 6:59 pm by Bhaskar Sharma

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Digital Health, Telemedicine

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Digital Health is here to tackle wellness and diseases

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]Digital Health is defined by The Healthcare Information and Management Systems Society (HIMSS) as the bridge that transforms healthcare by connecting and empowering people to manage health and wellness, augmented by accessible and supportive provider teams working within flexible, integrated, interoperable, and digitally enabled care environments that strategically leverage digital tools, technologies, and services.

Digital Health comes with the objective of delivering quality healthcare better and faster by using computing platforms, connectivity, application software, and sensors. The unique selling proposition of Digital Healthcare is that it optimizes the system, streamlines the work of HCPs, improves patient outcomes, reduces human error, and reduces the cost of healthcare. [/text]

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[text]Digital Health broadly includes mobile health (mHealth), health information technology (IT), wearable healthcare tech devices, telehealth & telemedicine, electronic communications, blockchain electronic health records, and personalized medicine.

Nowadays, patients want to be actively involved as the “co-managers” of their health. Among several aspects related to personal health, here are the most common topics many want to know:

The number of calories they burn in a day
The food they should eat to get a healthy living
Their blood sugar level within minutes
The number of footsteps they have walked in a particular day
The recommended exercises to do exercise at home
The amount they spent on health and medicines, compare healthcare insurance plans, and get medication delivered at their doorstep
Receive reminders SMS about their medicine schedule or appointment with the doctor
Laboratory tests at home and reports of the test within 12 hours on their phone
To meet doctors from the comfort of their home

As a result, Digital Health satisfies these patient requests by utilizing technology and software applications designed for use as a medical product, as companion diagnostics, or as a supplement to other medical products (devices, drugs, and biologics). [/text]

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Advantages of Digital Health

[/custom_heading][text]Digital Health tools have a booming potential to improve the ability to accurately diagnose and treat disease and enhance healthcare delivery for the individual. Digital Health can provide patients with the knowledge and support for almost any age or health condition. The tools used in Digital Health give a holistic view of patient health through data access thus giving patients more control over their health. For example, healthcare providers may use Digital Health to minimize inefficiencies, improve access, lower costs, improve quality, and tailor healthcare programs towards better patient outcomes. Innovations in Digital Health support healthcare professionals to become guides and advisors to patients, thereby helping patients indirectly gain more knowledge and control over their conditions safely and inclusively. Digital Health helps HCPs view and analyze real-time information of the patient’s condition, thereby providing quick, high-quality, tailored service.

Digital Health helps patients get medication at their doorstep from the pharmacies. With a mere scan of the prescription through a unique customer ID, patients can request medicines from the nearest pharmacy. Thus, Digital Health provides an opportunity to improve safety, quality, and efficiency in the delivery of patient care and assist hospital pharmacists prevent medication errors and improve interface management of patient care.

To summarize, the advantage of Digital Health includes:

Digital Health integrates clinical information and knowledge management tools to deliver systematic and high-quality care.
Digital Health helps deliver more proactive, coordinated & targeted care, reduce healthcare costs, and improve patient outcomes.
Digital Health tools enhance patient engagement, strengthen the relationship between patient and care provider, and improve resource management.

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Digital Health in the Management of Chronic illnesses

[/custom_heading][text]Diabetes is a chronic disorder and is one of the most common diseases found globally. However, delay in diagnosis and lack of proper management can lead to adverse health outcomes like cardiovascular disease, renal dysfunction, neuropathy, diabetic retinopathy, foot ulcers or infections, and even tinnitus. Innovation in Digital Health solutions have been involved in designing support tools for diabetes patients. These Digital Health or eHealth tools help diabetes patients manage their diabetes on their own and reduce the workload on Health professionals. Patients can use various digital systems, like wearables and mobile apps, to record and track their diets, medications, carbohydrate intake, physical activity, simple blood glucose, blood pressure, and weight measurements.

Every year, cardiovascular diseases account for the death of around 17.5 million people (about the population of New York) around the world. Innovation and effective implementation of Digital Health solutions can create possibilities towards improved management, prevention, and treatment of CVD. Numerous Mobile Apps and Wearables are available for the management of CVD as well.

The table below shows the healthcare mobile applications and wearable technologies as a part of Digital Health that is being used for chronic diseases.

Healthcare Condition	Examples of Healthcare apps available over smartphones towards the management of the disease/health condition	Examples of Wearable Technologies
Cancer	Oleena by Voluntis Breast Cancer Diagnosis Guide by Braestcancer.org Cancer Coach by Breastcancer.org, Fight Colorectal Cancer, and Genomic Health, Inc. CaringBridge chemoWave Pocket Cancer Care App	Wearable Health Trackers Wearable devices used in Oncology clinical trials.
Mental Health	Moodkit Talkspace Headspace Better Stop Suicide Happify	Abilify MyCite by Otsuka America Pharmaceutical and Proteus Digital Health TouchPoints by Amy Serin.PhD
Excessive Alcoholism	Sober Grid Twenty-Four Hours a Day Sobriety Counter Nomo AlcoDroid Alcohol Tracker I Am Sober Happify Coach.me	Wearable breathalyzer Mobile alcohol specific and physical activity biosensors Emotional Focused Biosensors
Smoking	Smoke Free Quit Tracker Easy Quit Quit Genius My Quit Buddy Flamy Stop Smoking Smoking Log-StopSmoking	Wearable technologies for quitting smoking are still under research. Some of the examples of the devices that are available as research prototype are: CigFree (App + Smartband) SmokeBeat (App + smartwatch) Smokesense Smart watch

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Conclusion

[/custom_heading][text]The world has seen a tremendous rise in the demand for Digital Healthcare after COVID-19. Although people have been using smartphones for a long time, the lockdown implemented in many countries to stop COVID transmission escalated the need to access healthcare through smartphones. Starting from fixing an appointment with a doctor, followed by laboratory tests, analyzing reports & diagnoses, getting medicine at the doorstep, and follow-up visits, patients are demanding everything to be online. People want to be in touch with their healthcare providers 24/7. They want to use medical devices to guide them, to take care of their health and manage their preexisting health conditions. Digital Healthcare is a revolutionary idea, and it will change the medical science landscape to a substantial extent in a matter of a couple of years. Large-scale Digital Healthcare project plans can be created with the help of computer algorithms and data science to make the entire globe technologically advanced and healthy.[/text]

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Unfolding Health Tech Revolution: The Next Big Thing in Indian Healthcare

Posted on April 1, 2022 - 6:46 pm by Bhaskar Sharma

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Data + AI + Analytics, Digital Health, Telemedicine

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Unfolding Health Tech Revolution: The Next Big Thing in Indian Healthcare

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]In the last few decades, the advent of technology has touched all aspects of our lives. One field that has seen major growth and transformation due to technology is the healthcare field.[/text][text]Healthcare has been the Achilles heel of India for far too long now. Despite massive planning by successive governments over the years, the healthcare field is still in a precarious situation. The COVID pandemic has fully exposed the vulnerabilities and shortcomings of the healthcare sector in India. One of the biggest challenges that remains is the extremely disproportionate doctor-patient ratio in India. So, in response, various governmental and private stakeholders have been consistently trying over the last few years to leverage the recent technological advances to ensure affordable, quality, and timely healthcare for everyone in the country.[/text]

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How is technology changing the narrative?

[/custom_heading][text]India is a huge and diverse country in many aspects; the most prominent aspect being the socioeconomic situation of individuals. Therefore, one can infer the difficulty faced in trying to ensure quality healthcare for everyone in a society with traditional thinking and planning. Now, advancements in Health Tech have been able to change the narrative up to a substantial extent.

Accessibility, affordability, and availability are the three major elements to be considered regarding the healthcare sector in India. Technology is helping to address all these parameters and plenty of innovations are underway for people-friendly solutions.

Artificial intelligence, data analytics and big data have been utilized and leveraged by various start-ups and government bodies to create services and products that allow healthcare to be more readily available to the masses. The COVID-19 pandemic has catalysed the adoption of these technologies much faster than we imagined and transformed the face of healthcare in India. [/text][clear by=”35px” id=”” class=””]

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Some real-life examples of the Health Tech revolution

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Telemedicine or telehealth

[/custom_heading][text]The overall doctor-patient ratio is poor in India. This is the reason most of the people living in rural India never get to consult a doctor in their lifetime. But with the massive penetration of mobile internet, now various private healthcare institutions have entered the villages and are helping them connect to specialists in the cities. Some NGOs and government authorities have also taken advantage of the telemedicine facilities to reach out to India’s poor and remote population.[/text][custom_heading id=”” class=”” style=””]

Self-monitoring devices and services

[/custom_heading][text]The self-monitoring device sector has seen a major upsurge in recent times. People are becoming more alert nowadays with regards to their health conditions. Moreover, people without secondary help or recuperating from various surgical and other major medical procedures can easily rely on self-monitoring devices. Such devices help them save significant money and time as they do not need to go out for basic tests. Private healthcare innovators have been able to utilize technology to fine-tune and take forward such devices, breaking away any boundary. The devices range from a simple bodyweight scale to a BP checker to an advanced massage gun to a whole-body multi-parameter fitness checker. [/text][custom_heading id=”” class=”” style=””]

Big data and data trends

[/custom_heading][text]COVID management and vaccination drives received a massive boost with the help of predictive data analysis techniques, data trends and big data analysis. The COVID situation would have been much worse without the help and use of these modern-day technological phenomena. [/text][custom_heading id=”” class=”” style=””]

Virtual integration of ICU units with specialist doctors

[/custom_heading][text]In India not only is it a bit difficult to set up a fully functioning ICU unit but it is far more difficult to get ICU specialist doctors. So, when recently some healthcare innovators produced the idea of virtually integrating ICU units of faraway places with specialist doctors sitting in a different city, it was well received by everyone involved in the care delivery and receiving process. Such innovations will surely address the abysmal doctor-patient ratio in India and help people receive quality ICU treatment even in the far corners of the country. [/text][custom_heading id=”” class=”” style=””]

Health kiosks

[/custom_heading][text]The concept of a health kiosk is another innovative idea to bring quality healthcare to the masses. However, the penetration and implementation of such kiosks are quite low in comparison to its prospects. Such kiosks can help people test for various biochemical as well as other body parameters in the comfort of their own backyard. This significantly reduces travel time and in turn, saves their working days. Furthermore, such health kiosks help in the early diagnosis of various critical diseases.[/text][custom_heading id=”” class=”” style=””]

The hybrid model

[/custom_heading][text]This model is a combination of traditional practices and modern-day technological advancements. Governments, as well as various private entities, have employed village level health workers who work as foot soldiers amongst the rural population. They visit every household in their demarcated areas to educate them about various preventive health tips, check their vitals with portable devices and upload the data to the cloud storage which can later be accessed by their employers. Such practices help organizations formulate accurate healthcare solutions for a target audience as they have plenty of patient data at their disposal for a detailed pattern study. Additionally, they can easily help people in need of immediate medical attention. [/text][clear by=”35px” id=”” class=””]

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Conclusion

[/custom_heading][text]The Health Tech revolution is still in its nascent phase in India. We can expect some major disruptive innovations in the coming years, around Health Tech. Proper and just use of technology can surely reduce the burden on the doctors. This great unfolding Health Tech revolution will make healthcare more affordable, accessible, and available to everyone in our society. [/text]

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Future of Digital Healthcare: Where are we heading?

Posted on April 1, 2022 - 4:54 pm by Bhaskar Sharma

[blog_5_secrets_of_successful_data heading=”Future of Digital Healthcare: Where are we heading?” description=”The most outstanding entrepreneur Steve Jobs once said, “We cannot connect the dots by looking forward but can connect them by looking backward.” Thus, to understand where we are heading with digital healthcare, it is essential to know what developmental changes digital health has been through to date.” image=”17634″][/blog_5_secrets_of_successful_data]

Below is the timeline of a few important milestones in Digital Healthcare to date:

1897-1980

Telemedicine, which is now mainstream treatment in healthcare, dates to 1897. Adam Darkins and Margaret Cary’s book “Telemedicine and Telehealth: Principles, Policies, Performances and Pitfalls” mentions the first reported use of telemedicine on a young child with croup illness. However, the subsequent use of telemedicine for diagnosis was reported after nine decades post-1987, during the Antarctica expeditions and space missions.

Similarly, during the mid-1960s, Lockheed developed an electronic clinical information system that laid the foundation for Electronic Health Records (EHR). By the 1980s, hospital administrative efforts were made to use EHR among medical practices.

1990-1999

As the world entered from the 80s to 90s, digital health met its golden period. Polygraph lie-detector test was invented in 1921. It was the first machine to include sensors that measured Galvanic Skin Response (GSR), pulse rate and blood pressure. The technology used back then is now commonly found in fitness trackers. In 1938, the first wearable hearing aid was developed. Healthcare delivery through digital communication showed potential to upgrade the relationship between patients and healthcare providers. Many professional associations appeared in the USA and across the globe. A few examples include the International Medical Informatics Association, the American Telemedicine Association, and the European Health Telematics Association.

Significant technological advances during 1950-1999 lead to the invention of ultrasound imaging techniques, artificial organs, and DNA sequencing. These techniques laid the following founding base on using technology in medicine for patient benefit.

2000-2015

2003 witnessed the world’s first fully digital pacemaker where a physician can download patient information in just 18 seconds. To enhance the experience during physical exercise, Nike and iPod launched a fitness tracking wireless system in 2006. Physician Tom Ferguson invented the word “e-patient” and wrote the first white paper on the concept of e-patient in 2007. The physician created a website epatient.net and wrote blogs. The primary intention behind e-patient is to make patients aware of using the internet to socialize, stay well-informed, and take healthcare into their own hands.

Similarly, in 2010, Health keynote speaker Engelen started the #PatientsIncludedmovement. The movement’s goal is to empower the patient to be the caretaker of their health and increase patient literacy. Delocalization of healthcare (Telemedicine) using technological developments is also the goal of #PatientsIncludedmovement.

In 2014, the British Medical Journal created the Patient Panel to take patient and public partnership to the next level of scientific research publication. The journal realized that affordable, safe, quality, and effective healthcare could be possible if patient perspectives were also given importance. Thus, BMJ brought the following changes:

Calling on authors to involve them in the production of their papers
Requesting authors of research papers to highlight how they involved patients in designing the research question.
Also included papers reviewed by patients in their standard peer-review process.

2017-2019

In 2017, USFDA launched the Digital Health Unit to expand the opportunities for digital health tools to become part of general healthcare. The American Medical Association in 2018 published its Artificial Intelligence Policy. The goal was to get doctors involved in the development of healthcare A.I. It also stressed patient and physician education on the potentials and limitations of A.I.

2020

“Innovations in Digital Healthcare during COVID-19 Pandemic”

During the COVID-19 pandemic, Telehealth saw massive growth. Online Services like COVID test from the comfort of your home, booking an appointment with a consultant physician, with pathology labs to collect blood, urine, or other samples as directed by the physician, and ordering medicine from a pharmacy shop et al. have increased dramatically. Virtual healthcare became the new norm. Artificial Intelligence-based diagnostic testing and over-the-counter tests for accurate and fast diagnosis of COVID-19 also came into the picture.

Apart from this, here are a few innovations that we witnessed during the COVID-19 pandemic in terms of digital health:

Artificial Intelligence (AI) designed 3D-printed swabs
Ultra-wideband (UWB) technology to monitor social distancing
Light signal processing technology to detect COVID-19 via smartphone

Predictions 2022: 5 Potential Trends in Health Care 4.0

Posted on March 17, 2022 - 8:22 pm by Bhaskar Sharma

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Digital Health, Telemedicine

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Predictions 2022: 5 Potential Trends in Health Care 4.0

Health Care 4.0 involves smart Healthcare delivery with the use of new-age imaging technology, intelligent sensors, medical robots, wearables, medical devices, and mobile applications combined with big data, cloud computing, Artificial Intelligence (AI), and decision support techniques. The rise in the application of Digital Healthcare during and Post COVID-19 has surged the revolution of Health Care 4.0. Here are the predictions on the five trends that we think will see innovations in 2022 and strengthen Health Care 4.0.

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1. Artificial Intelligence in Healthcare

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In 2022, the growth in the application of Artificial Intelligence in Healthcare will be more aggressive than in the years 2020 and 2021. For instance, we may see innovations in imaging-based diagnostics, robotic systems in surgery, robotic systems to automate hospital workflows, and symptom checker chatbots. Innovative thinking in the application of pre-existing Artificial Intelligence will lead Health Care 4.0 in the following domains:

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[custom_heading id=”” class=”” style=”margin-bottom: 0px;”]Smart Hospitals[/custom_heading][text]Smart hospitals focus on making extensive use of cutting-edge technologies to improve patient experience & care and reduce the cost of Health Care. Hospitals will be in demand in the upcoming years for the following reasons:

The government and people are paying more attention to Health Management rather than the treatment of diseases. People are more involved in using AI to check their calories, monitor stress levels, reduce hospitalization, prevent diseases, and follow other criteria of wellness & healthy living.
Accurate diagnosis of the disease and identifying the accurate surgery site to provide high-quality care. This reduces undesired health-related infections experienced by hospitalized patients and reduces unwanted services from the hospital end.

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[text]The prominent market leaders in smart hospitals are Medtronic (Ireland), Philips (Netherlands), Microsoft (US), GE Healthcare (US), Qualcomm Life (US), Honeywell Life Care Solutions (US), SAP (Germany), Siemens AG (Germany), STANLEY Healthcare (US), Cerner Corporation (US) and McKesson (US).[/text]

[custom_heading id=”” class=”” style=”margin-bottom: 0px;”]AI in COVID-19 Diagnosis[/custom_heading][text]The entire globe has witnessed the strain on doctors and pathology laboratories in carrying out multiple diagnostic tests every day to detect COVID-19. Eventually, as new variants of COVID-19 unfolded, the workload on radiologists has also increased dramatically. Identifying patterns of pneumonia caused by COVID-19 from CT (Computed Tomography) scans of thousands of patients’ data requires skilled personnel and in large numbers. Artificial Intelligence-based diagnostic testing for accurate and fast diagnosis of COVID-19 in many patients could be a solution to that problem. An example of such an innovative project in Artificial Intelligence-based diagnostic testing for COVID-19 is Imaging COVID-19. It is a multicentric European project that enhances computed tomography (CT) in diagnosing COVID-19 by using Artificial Intelligence. Continuing the case of COVID-19, innovations in AI have led to advancements in machine learning to develop vaccines. Thus, we can predict that in 2022, Health Care 4.0 will involve many more innovative AI-based diagnostic technologies and treatment options for COVID-19 and other dreadful diseases.[/text]

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2. Telehealth 3.0

[/custom_heading][text]Before COVID-19, Telehealth was a lesser-known word in our day-to-day Healthcare activities. During COVID-19, when many countries imposed a lockdown to control the spread of COVID-19, Telehealth entered its 2nd phase (Telehealth 2.0). Online Healthcare Services then became more common in practice.[/text]

[text]People started to use the following virtual Healthcare services from the comfort of their homes:

Booking COVID diagnostic tests
Consulting physician by booking an appointment
Booking an appointment with pathology labs to collect blood, urine, or other samples as directed by the physician, and
Ordering medicine from the pharmacy shop without stepping outside their homes.

Telehealth thus became mainstream, and eventually, Healthcare authorities issued multiple guidelines for good telehealth practices.

Hospitals were also able to cater more extensive patient base and provide virtual patient management, which was a lesser-known concept before COVID-19. One can analyze the rise of telehealth from the fact that the second quarter of 2020 saw 33 million telehealth visits which were 37% of all healthcare visits. A positive experience by patients (increased satisfaction and outcomes), improved patient access by clinicians, increased efficiency, low costs and high return on investment, and flexible work-life balance during the pandemic were some of the critical drivers for the transition of Telehealth towards Telehealth 2.0.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]How is Telehealth 2.0 transforming to Telehealth 3.0?[/custom_heading][text]Telehealth is witnessing its entry into the 3rd phase of Telehealth 3.0. Becker’s hospital review defines Telehealth 3.0 as “virtual Healthcare that is now shifting from pandemic-fueled expediency and sprawl to consolidation, integration and purposeful central planning.” Telehealth 3.0 is all about strategic investment and planning in most hospitals and health systems, to raise the possibility of integrating virtual Healthcare in the day-to-day life of human beings. Since people are now aware of and enjoying the services provided by Telehealth, the innovations in this sphere will be to ensure a seamless and easy-to-use experience for patients and providers. The target is to achieve greater integration and interoperability in terms of workflow and data-sharing and equipping the organizations with a scalable, sustainable enterprise-level digital infrastructure.[/text]

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3. Smart Wearables in Healthcare

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Wearable fitness technology is not an unknown term to all of us. Fitbits and Smart watches equipped with sensors and synced to various smartphone apps have been in the mainstream of wearable technology for a long time now. Investments in smart wearables in Healthcare have led to the innovation of wearable ECG monitors, blood pressure monitors, and heart rate monitoring devices.

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[clear by=”20px” id=”” class=””][text]Smart wearables available in the market have many capabilities, including:

Monitoring ECG
Monitoring Heart rate
Monitoring Blood Oxygen Level
Monitoring the Quality and Quantity of Sleep
Tracking Hydration
Monitoring Pulse
Tracking Calorie Intake
Monitoring Respiratory rate

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[text]The section of smartwatches and fitness trackers in Healthcare is undergoing continuous innovation by a few tech giants like Apple, Samsung, Fitbit, and Philips. In September 2018, Apple launched the ECG and irregular rhythm notification features in the Apple Watch Series. In the same year, Apple released the “CMMovementDisorderManager” to help researchers gather new insights measuring and recording tremors and dyskinetic symptoms for Parkinson’s disease.

The smart wearables market in Healthcare is booming rapidly since people now want to take control of their health and prevent many lifestyle diseases. Thus, the future of smart wearables is not likely to slow down anytime soon.

In October 2021, Apple launched the Series 7 of the Apple watch with Fall Detection Technology. So, what can we predict for 2022 in the niche of smart wearables that will enhance the quality of life and lead Health Care 4.0?

We can expect innovations in smart wearables in 2022 that will

Develop smart hearing aids
Monitor blood sugar and alcohol levels
Supply emergency relief during seizure attacks
Provide support for any musculoskeletal disorder
Provide warning messages while entering COVID-infected areas, and many more.

The Consumer Electronics Show held on January 05, 2022, witnessed Abbott Laboratories, as exhibitors, unveiling their new line of biometric wearables towards strengthening Virtual Health. Abbott’s new line of consumer-facing wearables has been termed “Lingo.” These wearables are equipped with sensors to track biometrics such as blood glucose levels, ketones, lactate, and even alcohol. However, Abbott has mentioned that these wearables are not for medical use, and it depends on the user on how to use the data. One can read more about Abbott’s new biometric wearables by visiting the following link.

The Consumer Electronics Show, 2022 also witnessed significant health and Wellness Exhibitors like AT&T Business, Revival Health, Sleep Number, and Dassault Systems Americas.[/text]

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4. Healthcare Privacy and Security

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With the fusion of the digital era into healthcare, it’s no surprise that the challenges owing to the survival of the digital Healthcare ecosystem would be much more coherent. Every second, with loads of data getting recorded and transmitted onto the mainframe servers, it has its own set of challenges to cope with.

The first and the most intrinsic part is handling the privacy of the user’s information, being tracked by the smart devices. The primary objective in developing an AI-based Healthcare application is to provide the medical professionals with all the patient’s relevant clinical information. This offers better treatment and management of any patient’s medical condition without hassle. However, one should not ignore that the same technology is constantly under the radar of exploitation by hackers befitting personal interest.

To be more precise, the term “Cyber Security” is no longer restricted to malpractices in the banking or corporate sectors but has also doomed the present era of the digital Healthcare ecosystem. One such instance to validate the fear that the user’s information would get compromised at some point in time is the “WannaCry” Ransomware attack in the year 2017. The breach collapsed a significant section of the digital Healthcare community, thereby leaving no option other than to meet the demands of the hackers to rescue the users’ information enrolled onto their systems.

As more devices are getting integrated into the digital Healthcare ecosystem, 2022 may see a breakthrough in neutralizing the constant threat of information breaches, jeopardizing the personal interest of hackers. Improvising the AI algorithms and introducing more sophisticated, reliant Healthcare applications would be prudent to tackle the unforeseen circumstances. Safeguarding patient information will ultimately strengthen Health Care 4.0.[/text]

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5. Data Connectivity in Healthcare

[/custom_heading][text]The primary objective of Digital Health Care is to increase Healthcare facilities using the latest technologies for better clinical outcomes and decrease medical costs to patients without jeopardizing their social life. One aspect of Digital Health Care is breaking down traditional data integration and Healthcare management practices into electronic formats. Moving on from the conventional pen-paper records to electronic means of handling patient medical records, biometrics details, physician’s notes, clinical and laboratory reports, medical billing details, regular health update reports, and Healthcare insurance details, big data has revolutionized the Healthcare domain in favor of a massive digital infrastructural change. Thus, Big data is a pioneering revolution driving Health Care 4.0 to achieve its objectives.

A systemic review article published in the Scientific Programming Journal, 2020, estimated the size of electronic Healthcare data across the globe to have reached 25 petabytes in the year 2020. With terabytes of health care data accumulating onto the servers, the primary focus is to successfully integrate this enormous amount of data, interconnect the dots and squeeze the impactful information out of the integrated digital Healthcare ecosystem.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]So, what can we predict in 2022?[/custom_heading][text]With the rapid growth in cyber security, science, and technology, the year 2022 may become revolutionary for applying data in the early-stage detection of life-threatening diseases. Healthcare Professionals and Industry leaders will move towards Data Driven Decision making to address the key quality parameters in provide patients with the best possible healthcare outcomes and reduce cost for hospitals and insurances.[/text]

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Client Testimony From DiscernDx – NextGen Invent Corp

Posted on October 22, 2020 - 7:20 pm by Bhaskar Sharma

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We are delighted to share the testimony of our client, A leading Healthcare Company. Watch what Iman Mohtashemi, Sr. Informatics Scientist at DiscernDx has to say about NGI helping his company by developing algorithm, machine learning and building knowledge graphs to predict the disease better. Our team of professionals at NGI ensures best customer experience throughout the journey with us.

Transforming businesses and enabling Entrepreneurs using disruptive technology landscape

Posted on September 12, 2020 - 11:47 am by Bhaskar Sharma

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NextGen Invent (“NGI”) is a leading global professional services company that specializes in the area of data science, Product development and Off the shelf product implementation. NGI helps its clients achieve desired business outcomes by executing industry-specific strategies using state-of-the-art technology.

NGI has always been at the forefront in providing cutting edge technology solution for AI/ ML Model creation, Advanced Analytics and data management services. Our in-depth industry knowledge, specialized technical expertise, unmatched data science experience, and global delivery network along with our “Customer First” policy has allowed us to grow via word of mouth.

Forbes News

Posted on August 13, 2020 - 2:52 pm by Bhaskar Sharma

"Fit the position to the person."

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The best hires come from referrals, but you don’t always have to try to find the “right” person to fill a specific position. In some cases, it is wise to create a job for the right person to ensure your business doesn’t miss out on talented individuals.

[/text][text]Deepak Mittal
Founder and CEO[/text][text]

Image Source- Forbes

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Email us with any question or inquiry. We would be happy to answer your questions and setup a meeting with you. NGI can help set you apart from the flock

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7 reasons why analytics project fail?

Posted on May 15, 2019 - 5:19 pm by Bhaskar Sharma

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7 Reasons Why analytics project fail

Advanced Analytics Video

Posted on May 15, 2019 - 5:17 pm by Bhaskar Sharma

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Power your decision using Transformational Insights. NGI skilled analytics team brings in-depth knowledge and expertise across a broad spectrum of industries including, Finance, Insurance, Healthcare, Retail, Logistics, and many more.

What keeps you up at night?

Posted on May 15, 2019 - 2:26 pm by Bhaskar Sharma

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How NGI team can be ahead of the curve and be the change agent for our customers to enable AI solutions in their organisation. Needless to say adoption is the key in getting ROI from Artificial Intelligence initiatives.

How to evaluate strength of AI in a company?

Posted on May 15, 2019 - 2:23 pm by Bhaskar Sharma

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Ignite the engine and run it to showcase the results. There is no better way to know the strength of AI enabled products, is to run it in different business scenarios and multiple historical data sets.

ROI of Artificial Intelligence

Posted on May 15, 2019 - 10:53 am by Bhaskar Sharma

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Adoption is the key in getting ROI from Artificial Intelligence initiatives. Leaders recognize that change is coming and they need an AI strategy to ensure adoption and competitive edge.

Adobe Solution Partner

Posted on May 14, 2019 - 5:00 pm by Bhaskar Sharma

NextGen Invent (NGI) is recognized as an Adobe Solution Partner focused on Adobe Experience Manager.

Adobe Experience Manager is a comprehensive content management platform solution for building websites, mobile apps and forms – making it easy to manage your marketing content and assets.

Adobe Experience Manager brings your content management system (CMS) and digital asset management (DAM) together. Product and services give digital advertisers and publishers the tools they need to control and leverage their data assets to help drive sales success. Data management platform helps to build unique audience profiles so you can identify your most valuable segments and use them across any digital channel. It will provide you a complete view of your audience by combining all your data sources in one place.

Content Management
AEM Migration
Managed Services

For more details, click Adobe Solution Partner