PODCAST #14. How to Excel in Strategic Planning for Effective Product Management: Tips from an Industry Expert

During this episode of our Careminds podcast, we discuss the complexities of product management and go-to-market strategies with our guest, Donna Cichani. Donna has a background in product management, A/B testing, and data analysis, and has worked with notable organizations such as Johns Hopkins Medicine, KPMG US, and JP Morgan. Currently, she is the lead product manager at Heal.

Our conversation with Donna covers topics like data analysis and strategic product planning, the differing mindsets between 0 to 1 and one to end product development, and methods to increase user engagement and product optimization. Drawing from her diverse experience in industries like healthcare, technology, banking, and finance, Donna shares her thoughts on the importance of strategic planning in product management.

Defining Success Criteria for Product Stages

When determining the success of a product, you consider both the user perspective and the business perspective. Using the example of an RPM solution called Pulse, designed for chronic disease management at Heal, we can explore the key performance indicators (KPIs) and metrics that matter most.

Firstly, there are patient-centric KPIs that focus on adoption and usage. Monitoring how often users engage with the solution to record their vitals and biometrics is crucial. The main goal is to encourage patients to stay proactive in managing their chronic conditions by using the solution more frequently.

User centricity is key, focusing on how you are improving life and the experience for the end user.

Secondly, clinical outcomes are also important. By tracking improvements in specific health measures, such as A1C levels for diabetic patients or maintaining healthy blood pressure ranges for hypertensive patients, we can gauge the effectiveness of the solution in promoting better health.

Also, business KPIs, such as attribution, play a significant role. For the RPM solution, it is important to know what percentage of patients using the solution are attributed to Heal for their primary care doctors.

Defining the best approach for optimizing a product depends on the specific product and its maturity curve. Take, for example, the RPM solution mentioned earlier. The primary goal of any RPM solution is to encourage users to engage with it consistently and measure their biometrics routinely.

At one point, the team behind the RPM solution considered expanding its features to include medication refill reminders, envisioning a more comprehensive ecosystem for patient monitoring. However, they quickly recognized the importance of perfecting their core RPM capabilities before adding secondary features. By maintaining focus on their core competency, they ensured they wouldn’t dilute the solution’s main purpose.

Optimization often involves considering the user experience, especially when it comes to healthcare solutions. In the case of the RPM solution, refining its core features contributed significantly to increased patient engagement. This example highlights the importance of prioritizing the optimization of a product’s primary functions before expanding its scope.

When to Focus on New Features or Enhancements in Product Development

You should invest heavily in user research as it’s crucial for driving customer adoption and engagement. During the discovery phase, our team spent considerable time observing patients in their natural environments, using existing products like glucometers, and capturing their day-to-day experiences. This research also included understanding how nurses, doctors, and other providers utilized data points during home visits.

By conducting ethnography studies, user research, and interviews, we were able to identify key pain points, which we then translated into enhancements and feature opportunities to drive engagement. To ensure customer adoption, it’s essential to focus on understanding users’ pain points, observe their interactions with your product or similar products, and avoid relying solely on secondary sources or high-level questions.

I don’t think that user research for usability testing ends during the discovery phase.

It’s important to note that user research and usability testing don’t end during the discovery phase. After creating our first prototype, we went through two additional rounds of usability testing to validate our assumptions, identify any flaws in our user flow, and refine the solution iteratively. This process continued up until the launch of the minimum viable product (MVP).

The ability of product managers to remain detached from their original plans, even after investing significant time and effort, is fascinating. When real data no longer supports the initial plan, it’s crucial to let it go, find a new direction, and create a better product that serves users more effectively. This adaptability is an essential aspect of successful product management.

Effective Optimization Techniques & The Best Ways to Apply Them

Optimization techniques focus on understanding existing processes, examining them through the lens of various stakeholders involved in the end-to-end flow, and identifying opportunities for efficiencies. For instance, by analyzing a process that takes 10 days and involves five stakeholders, you can uncover ways to reduce the number of stakeholders or the time each takes to complete their part.

Process mapping, a technique that visually represents the steps involved in a process, helps identify bottlenecks, redundancies, and areas for improvement. A/B testing is another valuable technique, where two different versions of a feature or product are tested with the target audience to determine which performs better.

In my experience, one of the keys to successful optimization is to involve the entire team in the process.

Involving the entire team, including product, engineering, design, sales, and marketing, leads to a more holistic view of challenges and opportunities, ultimately driving better optimization decisions. Keeping the end user in mind is crucial, as the goal is to enhance their experience.

It’s important to acknowledge that the rapid growth of product management as a career has led to a mix of undisputed go-to practices and those still being defined through trial and error. Sharing experiences and learning from others in the community can help navigate this evolving field and contribute to its development.

What Drives a Product Manager: The Exciting Facets of a PM’s Career

Effective management in product management involves three key aspects. First, tailor your approach to the needs of each individual on your team, recognizing that there is no one-size-fits-all solution. Second, invest in the long-term career growth of your team members, extending beyond the scope of your organization, by providing mentorship and opportunities for personal and professional development.

The third aspect involves being able to oversee the work of your team without micromanaging, while still being prepared to jump in and help when necessary. Balancing trust and autonomy with support is essential for successful management.

It’s an exciting time for all the PMs because we are focusing on doing good and building impactful products and services that can make people’s lives better.

In terms of current excitement in the field, AI and machine learning are opening many doors in product management. There’s a rewarding shift in focus in both healthcare and fintech industries. In fintech, increased emphasis on financial literacy and access to banking products for the unbanked population is driving positive change. Meanwhile, healthcare is moving towards value-based care, focusing on preventative measures and overall population health, which reduces costs and the burden on the healthcare system. This is an exciting time for product managers as they work on building impactful products and services that improve people’s lives.

Wrapping Up

As product managers continue to navigate this rapidly evolving field, learning from industry experts like Donna and sharing experiences within the community will be invaluable in driving growth and creating impactful products that make a difference in people’s lives. Key takeaways from our conversation include:

  • Defining success criteria for product stages: It’s crucial to consider both user and business perspectives when determining the success of a product.
  • Focusing on core competencies in optimization: Prioritize optimizing a product’s primary functions before expanding its scope or adding new features.
  • Conducting user research and embracing adaptability: Engage in user research, usability testing, and iterate on your product based on data and feedback, and remain open to change when necessary.
  • Effective management and exciting developments in the field: Tailor your approach to individual team members, invest in their long-term career growth, and maintain a balance between autonomy and support. Embrace the exciting opportunities in AI, machine learning, and the shifting focus of various industries.


PODCAST #13. The Psychology of Product Management: Unlocking Human Insights & OKRS






The APP Solutions launched a podcast, CareMinds, where you can hear from respected experts in healthcare and Health Tech.

Who is a successful product manager in the healthcare domain? Which skills and qualities are crucial? How important is this role in moving a successful business to new achievements? Responsibilities and KPIs?

Please find out about all this and more in our podcast. Stay tuned for updates and subscribe to channels.

Listen to our podcast to get some useful tips on your next startup.

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PODCAST #22. EMR Interoperability and Data Standardization Issues Amid AI Adoption in Healthcare

Welcome to another CareMinds podcast episode featuring Sameer Desai, Senior Director of Engineering and Product Management at Verona Health. In this two-part episode, Sameer Desai shares his invaluable insights into the limitations of Electronic Medical Records (EMR) in addressing interoperability challenges comprehensively.

Sameer Desai’s expertise allows us to delve into the specific hurdles smaller and niche healthcare practices face in achieving interoperability. With over 12 years of experience in software development and HL7 C certification, Sameer Desai has extensive knowledge of EHR systems and their intricacies. 

Throughout the episode, he sheds light on slower adoption of the FHIR standard and the cumbersome process of custom integrations they must endure to overcome interoperability challenges.

Let’s dive right in!

The Role of AI in Healthcare and Addressing Data Standardization Challenges

“I think we have heard about everybody transitioning to FHIR. Now, especially in the space I work in, we are going across 50 different EHRs. When you look at the FHIR standard, the maturity of FHIR APIs across EHRs varies a lot.”

Sameer Desai – Senior Director of Engineering & Product Management at Verana Health

According to Mr. Sameer Desai, the problem of lack of standardization has persisted over time. While there are standards in place, most healthcare providers consider them guidelines rather than strict requirements, leading to issues.

He mentions the transition to the FHIR (Fast Healthcare Interoperability Resources) standard, which many in the industry adopt. However, the maturity of FHIR varies significantly across different EHR systems. For example, one EHR may populate all the required fields correctly, while another may not adhere to the same structure or location for data population.

Mr. Sameer Desai also highlights the challenges faced in specialty areas like ophthalmology, where specific EHR systems may lack the resources or capabilities to implement the FHIR standard. Thus, some EHR systems can communicate effectively using standard formats, while others lack the capabilities or resources to do so. This presents a dilemma for building an inclusive AI program that accommodates all EHR systems, regardless of their size or resources.

He emphasizes the importance of enabling participation in AI advancements for all healthcare providers, not just those who can afford or have implemented systems like Epic. However, the customization of workflows within EHR implementations adds another layer of complexity to the FHIR framework. This is because even two Epic implementations may differ in appearance and data organization. Critical information may be stored in notes rather than standardized fields in certain fields like neurology, further complicating data extraction for algorithm development.

Mr. Sameer Desai acknowledges that such diverse data formats pose a challenge, despite recognizing that healthcare data is valuable, akin to oil. Still, it is not uniformly accessible or structured across all EHR systems. He underscores the need to address these issues and achieve standardized data formats to facilitate the development of accurate algorithms, predictions, and improvements in care quality and drug development.

Exploring the Relationship Between the Adoption of FHIR Standard and EMR/EHR Efficiency”

Mr. Sameer Desai expresses his perspective on adopting the FHIR standard and its limitations. He mentions that FHIR is still in its early stages of development and does not address all types of problems in healthcare data interoperability.

He provides an example of their current focus on helping providers submit MIPS reports, which involves administrative aspects of data. Specifically, he mentions the challenge of reconciling medications when patients visit healthcare providers. This type of specific information may not have an exact place within the FHIR standards, as FHIR is primarily evaluated as a clinical data standard. However, he notes that FHIR is also evolving to encompass financial and initiative spaces.

“So I think in the newer world, we expect, like now, we’re going to do something with images; we’re also going to do something with genomic data, which will always result in different formats.” 

Sameer Desai – Senior Director of Engineering & Product Management at Verana Health

Mr. Sameer Desai emphasizes that healthcare data goes beyond just clinical information. The data requirements become more extensive as the industry shifts from transactional to value-based healthcare. They must consider factors beyond diagnosis and disease treatment, such as socioeconomic factors. The scope of data expands to include non-healthcare-related information. Progress must be made toward achieving standard formats.

Looking ahead, Mr. Sameer Desai mentions integrating images and genomic data, which will introduce further variations in data formats. However, he highlights that the challenges extend to the core clinical data, which is not yet standardized. He believes that the pace of FHIR standard adoption will help address these issues, noting that larger DH organizations have already taken the leap, and he expects others to follow suit.

Challenges in Data Plumbing: Addressing Development Obstacles for Integrating Diverse EHR Systems

“So at some point, you have to take a hit to convert that to a common model where you can apply these algorithms at scale and move forward.”

Sameer Desai – Senior Director of Engineering & Product Management at Verana Health

Mr. Sameer Desai expresses his opinion on the challenges and significance of working on healthcare data interoperability. He believes that although this job may not appear shiny or exciting to most engineers, it is crucial for the healthcare industry. Waiting for everyone to adopt the same standards is not feasible; therefore, immediate action is necessary to solve the problems at hand and make progress. He emphasized the need to address the challenges faced in the healthcare space today.

According to Mr. Sameer Desai, the challenges in this field start with technical problems such as establishing connections and sharing data, which can be solved through APIs or direct database connections. However, the real challenge arises once the data is in the environment and needs to be understood. This requires collaboration with EHR vendor partners, who may have different priorities and may be hesitant to cooperate, especially when dealing with startups that lack the leverage of larger organizations. Convincing EHR vendors to work together and establish a common data model becomes crucial, particularly when working across multiple entities.

Another obstacle is the operational aspect, where people become more dependent due to the complexity involved. Working with multiple EHR systems (30 to 50 in this case) requires finding a common data model to apply machine learning and analytical algorithms at scale. Operational challenges also arise from capturing data within EHRs, as different systems may have varied data entry and organization approaches.

He provided an example of the complexity involved in medication reconciliation, where different EHRs use diverse methods such as procedure codes, flags, reverse flags, or note templates. Human involvement becomes essential in resolving such discrepancies, leading to a greater need for larger teams to handle multiple EHR systems effectively.

“It’s also about figuring out these operational things – where does it make sense to invest in automating, and where does it make sense to actually just have people do it?”

Sameer Desai – Senior Director of Engineering & Product Management at Verana Health

Additionally, Mr. Sameer Desai mentioned the complexity at the practice level, where non-standard EHRs allow unstructured notes, and each provider or nurse practitioner may have a way of documenting information. These technological and operational challenges require balancing automation and human intervention, depending on the specific situation and the value derived from solving the problem.

He concludes by emphasizing that all startups encounter these challenges, and the key lies in finding a happy balance or a happy medium. This balance involves determining the value of solving problems and deciding whether automation or human effort is the most suitable approach. Mr. Sameer Desai considers achieving this balance to be an art or science in itself.

Unveiling Verana Health’s Strategies for Tackling Standardization Challenges in Healthcare”

Mr. Sameer Desai shares his perspective on Verana Health’s unique position and approach to solving healthcare data challenges. He believes that Verana Health has a distinct advantage in working with societies and specialties, enabling them to leverage their influence with HR vendors. By collaborating with these societies, Verana Health can request additional support in terms of data mapping and establishing connections with HR vendors.

Mr. Sameer Desai emphasizes that Verana Health’s primary focus is to provide the best customer satisfaction for its registry members. To achieve this, they meet their customers where they are. For practices using Epic, Verana Health has an FHIR injection API that allows them to easily ingest the data. This minimizes the burden on hospitals or practices. However, for practices using smaller, specialized HR systems that may not have similar integration capabilities, Verana Health is responsible for directly obtaining data from their databases.

They then work closely with the HR vendors to understand data mappings and ensure compatibility. Alternatively, if the HR systems have standardized data extracts, Verana Health works with those extracts and maps them to their common data model. This approach provides multiple options to customers, allowing them to participate in the registry and benefit from insights into the quality of care while receiving suggestions for improvement.

Additionally, Mr. Sameer Desai highlights that Verana Health considers patients’ well-being. They offer practice opportunities to participate in clinical trials, ultimately benefiting patients. While certain regions may have limited access to breakthrough treatments and trial participation, Verana Health strives to solve data-related problems for them. They facilitate connectivity to platforms and ensure that these regions are included, enabling them to be part of the larger healthcare ecosystem.

Furthermore, Verana Health leverages artificial intelligence (AI) to go beyond structured data. They analyze unstructured data such as notes and employ AI models to identify additional information. Verana Health excels not only in identification but also in converting this unstructured data into a structured format. By doing so, they can provide valuable structured data to research organizations and clinical trials, aiding in research advancements.

Achieving Effective Problem Solving and Execution in Product Development: Verana Health’s Collaborative Model and Success Stories

“I build the platform, I get the data, and then my outbound product managers are building experiences based on which customer they are serving.” 

Sameer Desai – Senior Director of Engineering & Product Management at Verana Health

Mr. Sameer Desai discusses the collaborative structure and roles within Verana Health’s product management team. He explains that the structure resembles a common model seen in Silicon Valley, known as inbound or outbound product managers or technical product managers versus traditional product managers. Regardless of the terminology, Mr. Sameer Desai’s focus at Verana Health is on building the platform.

“So I am more technically oriented in terms of setting up the platform and looking at how we can scale this.” 

Sameer Desai – Senior Director of Engineering & Product Management at Verana Health

As a technical product manager, Mr. Sameer Desai is primarily responsible for platform development and scalability. He considers the developers and individuals who will create additional applications on top of the platform as his customers. He focuses on the technical aspects of platform setup and operational scalability rather than direct customer interaction.

On the other hand, the outbound product managers work with the data and insights generated by the platform. They use this information to create tailored experiences for different customer segments. Verana Health serves various customer bases, including societies, doctors/providers, and clinical trial sponsors. Each customer base has specific needs, and the outbound product managers build experiences and applications to address those needs.

Mr. Sameer Desai emphasizes that the platform he develops remains agnostic to the specific customer bases. He acts as a layer between the data insights and the engineers, ensuring they clearly understand how the data is used without burdening them with customer-specific details. This structure allows for effective collaboration and streamlines the product development process.

Verana Health’s Resourceful Approach to Ensuring Smooth and Efficient Scaling

According to Mr. Sameer Desai, operational scaling at Verana Health involves several key aspects. Firstly, connecting with different electronic health record (EHR) systems is challenging, some of which are cloud-hosted while others are on-premises. With over 1,500 connections to individual practices, the goal is to make the setup process as easy as possible, particularly for small practices with limited IT resources. Verana Health focuses on building user-friendly and remotely manageable solutions to alleviate the burden on these practices. 

In addition to the operational challenges, there is a focus on reducing data latency. In contrast to the traditional approach of working with claims data that may have a lag of 90 days, their goal is to shorten the lag to weeks. Maintaining connections and ensuring stability is crucial in achieving this objective. The company takes responsibility for ensuring the smooth running and uptime of these connections, focusing on maintaining low latency for data refreshes.

Another aspect of scaling involves the staggered implementation of different EHRs. Each EHR system may be adopted by practices at different times, which requires careful planning and program management. Resources on their side and the EHR partners’ side are limited, so efficient planning is necessary to make the implementation process feasible. Verana Health has dedicated mapping and clinical data transformation resources available for this purpose.

Once the data is received, another scaling layer comes into play, addressing data curation and organization for specific disease areas. Verana Health focuses on understanding market needs and the requirements of research organizations to effectively curate and transform the data for analysis and research purposes.

While these aspects are important, Mr. Sameer Desai emphasizes that the first two aspects, which are external-facing and involve operational scaling, hold greater significance. Meeting their partners’ needs is a priority, and achieving it requires a combination of art and induction in the planning process. It is not solely a scientific endeavor but also involves carefully considering various factors to ensure successful scaling and operational efficiency.

The Future of Interoperability: Navigating Integrations and Data Streams for Smaller Startups and Niche Practices

“We are moving towards data set marketplaces, where startups can leverage pre-cleaned data sets and build experiences that other competitors are not focused on.” 

Sameer Desai – Senior Director of Engineering & Product Management at Verana Health

According to Mr. Sameer Desai, the healthcare industry lags behind other sectors in effectively leveraging data. He acknowledges that there are reasons for this discrepancy, noting that healthcare cannot acquire data in the same way as consumer industries.

However, Mr. Sameer Desai points out an emerging trend in the overall data landscape: the rise of data set marketplaces. He cites AWS as an example of a company that has recently introduced its marketplace, and he believes that other vendors are pursuing similar initiatives. This development will make the data space more interesting as organizations undertake the initial groundwork. They’ll be responsible for the data cleaning and preparation processes, making curated data sets available in these marketplaces.

Mr. Sameer Desai highlights the potential benefits for startups in this evolving landscape. By leveraging these curated data sets, startups can explore developing new AI models to address challenges that other industries and competitors may not be focusing on. Alternatively, they can utilize the data to build unique experiences that competitors have not yet explored or may not be interested in pursuing.

He emphasizes exhaustively exploring these options before resorting to expensive data acquisition methods. Mr. Sameer Desai acknowledges that establishing numerous connections and acquiring data through traditional means can be a capital-intensive process.

Let’s Sum it Up

Here are five key takeaways from our discussion with Mr. Sameer Desai:

  • Data standardization challenges persist in healthcare, hindering interoperability and AI’s full potential.
  • Although still in its early stages, adopting the FHIR standard is essential for achieving data interoperability in healthcare. 
  • Technical and operational obstacles must be addressed, including reconciling different data entry methods and addressing variations in data organization across different systems.
  • Verana Health employs unique strategies to tackle data standardization challenges. They also offer multiple options for practices of different sizes and capabilities to participate and benefit from insights into care quality.
  • Operational scaling, reducing data latency, and effective data curation are crucial for successful healthcare data management.








The APP Solutions launched a podcast, CareMinds, where you can hear from respected experts in healthcare and Health Tech.

Who is a successful product manager in the healthcare domain? Which skills and qualities are crucial? How important is this role in moving a successful business to new achievements? Responsibilities and KPIs?

Please find out about all this and more in our podcast. Stay tuned for updates and subscribe to channels.

Listen to our podcast to get some useful tips on your next startup.

Article podcast YouTube

Does Your Business Really Need an Enterprise Artificial Intelligence

Any technology, taking progress several steps forward, always raises concerns that border on excitement and disappointment at the same time. This trend has not spared artificial intelligence. Even though the technology is not new (the first solutions appeared in the 1960s), the real breakthrough and active use of AI in business appeared only in the 21st century. Computing power, larger datasets, and the rise of open-source software allowed developers to create advanced algorithms.

Nowadays, almost all businesses want AI, regardless of size and tasks. So let’s see if artificial intelligence is really so beneficial. For whom it’s too early to implement it, and who needs it as of yesterday.


AI Application for Business

Artificial intelligence is an imitation of the mental properties of the human brain by computer systems. The algorithm learns itself, becoming more and more perfect. To reach the level of a full-fledged thought process, still enough time must pass (although some experts argue that a machine and a person will equal in intellectual abilities in the next decade).

Project Management For Software Development: The App Solutions Tips

Nevertheless, AI is designed to solve relatively voluminous and straightforward tasks from document flow to primitive communication as support. These AI capabilities alone save businesses around the world thousands and thousands of labor hours. Already, 72% of companies using AI in their work say that it makes doing business easier.


In this regard, there are fears that many people will be left without work. Indeed, according to forecasts, secretaries, accountants, administrators, auditors, repairers in factories, and even general and operational managers can lose their jobs. In contrast, new jobs will receive big data and data analysts specialists, AI and machine learning engineers, software and app developers… 

The World Economic Forum says 85 million jobs will be eliminated by 2025, while 97 million new jobs will appear. So the reformatting of the labor market towards technical specialties is inevitable one way or another.


According to Fortune Business Insights, the global AI market was estimated at $27.23 billion in 2019 and is projected to reach $266.92 billion by 2027, with a 33.2% CAGR over the forecast period.

At the same time, IDG claims that in 2021 the cost of AI and similar systems reaches $57.6 billion. For instance, Netflix spends $1 million annually to develop its recommendation engine. According to company representatives: “The typical Netflix contributor loses interest after about 60-90 seconds of selection, having watched 10 to 20 titles (possibly 3 in detail) on one or two screens.” It’s cheaper to spend money on a good advisor than to lose views.


The PwC’s forecast claims that in 2030 AI can contribute up to 15.7 trillion dollars to the global economy. For comparison, the combined output of China and India in the world economy is currently less. However, the PwC predicts that an attractive, innovative business that has yet to emerge could become a market leader based on AI technology in ten years.

AI is already used by 38% of healthcare providers as computer diagnostics and 52% of telecommunications companies as chatbots. It is not surprising. Consumers are increasingly demanding round-the-clock support and are ready to receive more primitive but instant answers to their questions; that is, they are ready to sacrifice quality to save their time.


Benefits of AI for Business 

Regardless of what field you work in – from law to marketing, from medicine to restaurant business – AI will find an appliance everywhere. Several undeniable benefits of AI will be effective for any business.

  1. Improving customer engagement. Chatbot has already become the most popular way to communicate with consumers. Enterprise artificial intelligence contributes to increased customer satisfaction, leading to lower costs, in particular, on the payroll. Moreover, chatbots have become a real salvation for small businesses, which do not have the opportunity to hire a large staff for support.
  2. Increased brand loyalty. Personalization is the key to the consumer’s heart, as evidenced by the investment of Netflix mentioned above in personalized search. With an individual approach, you will inevitably win the preferences of your customers, making them permanent. But to solve this problem, you need to collect a considerable array of analytics of behavioral factors. AI can solve it. Various studies say that this approach increases conversions from 14 to 30%.
  3. Data security and fraud prevention. Primarily relevant for financial enterprises. AI not only finds weaknesses in security systems but can also determine the characteristic behavior during transactions.
  4. Improving the accuracy of forecasts. Artificial intelligence allows you to avoid the human factor when making decisions, reducing the risk of mistakes. For example, lead scoring analyzes and predicts which leads will be the most promising. Other algorithms help control financial flows and trade. Also, you can be sure of compliance with all requirements, standards, and regulations that your company sets.
  5. Recruiting optimization. By automating the analysis of candidates’ CVs, human bias in preliminary checks is eliminated. In due course, PepsiCo needed to hire 250 people out of 1,500 applicants in two months. AI was drawn into the first round of interviews. Thus, all candidates were interviewed in nine hours. It would take human personnel nine weeks, by contrast. During this time, “live” recruiters could deal with more complex creative tasks. The latter concerns other employees of your company – let them develop while AI does the whole routine for them.

How to Get the most out of AI Benefits 

There will be no benefits at all if enterprise AI software is not implemented efficiently. To prevent this in business processes, it is better to follow a few tips that will allow you to comprehensively approach the implementation of artificial intelligence.

  1. New technologies need new people. Without hiring the appropriate specialists, only with the forces of the old state, you most likely will not succeed. Probably, you will need a whole department, but do not be afraid of such expenses – they pay off significantly. Of course, you can use the already developed AI technologies that other companies offer. Still, sooner or later, almost any business comes to the point when it becomes unprofitable and even unsafe to use third-party services.
  2. Don’t be afraid to expand. The introduction of new technologies should bring benefits and profits to the business. However, to reduce costs, in the end, they will need to be increased first. And it concerns not only the increase in staff but also the expansion of new markets because with AI, it is possible to work with large amounts of information. Accordingly, new expenses cannot be avoided; however, the competent use of AI will very soon turn your expenses into income.
  3. Don’t be afraid to change your motion vectors. Artificial intelligence often helps business owners understand that changing the business model will help them move on with greater efficiency. There is no need to be afraid to change anything because it is to change for the better that you started working with AI, right?


Signs Your Enterprise Needs AI Solutions 

Artificial intelligence is complex, and many businesses still don’t know how to implement and benefit from this technology. Companies around the world are at different stages of AI adoption:

  • Awareness (there is the only talk of introducing AI when planning business processes and strategies)
  • Activation (technology is not yet widely used, only as a test for some pilot projects)
  • Operation (at least one project from start to finish uses AI in its work, a separate budget and team is allocated for this)
  • Consistency (all new projects, products, and services are created using AI, all technical employees of the company are aware of the nuances of work, actively apply technology in their daily routine)

However, not all companies decide to implement AI, even if they see an obvious benefit. To understand if you really need AI, think about the following things.

Data Mining Vs. Predictive Analytics: Know The Difference

Well-established Data Collection 

Determine how much information your employees will have to work with. You won’t be able to endlessly hire new specialists to cover all your database needs. If the costs of implementing AI outweigh the other concerns, then prepare your data to ensure that AI adoption runs as smoothly as possible. This requires:

  1. Keep data up to date. The algorithm will not be able to make accurate predictions and provide relevant analytics if your data, for example, on customer behavioral factors, is not updated. To put it bluntly, you don’t need a smartphone if you still use pigeon posts. Spending on AI should pay off. It can process huge amounts of information and produce specific results, but who will need them if the initial data is outdated.
  2. Check your details for errors. A machine can process a large amount of data faster than you, but at the same time, it can get confused about some elementary thing that a first-grader would easily understand. Where the human brain sees a typo in the same word, the machine sees two different words. Of course, the AI ​​has reached the level where it realizes that you made a mistake (for example, when the search engine suggests that “you must have meant” something completely different). However, the search engine has enough experience to conclude the error, but will your algorithm have enough experience from scratch?
  3. Use a consistent format for storing data. For AI to collect all the information stored in your company and process it correctly, you should contain it in one setup.

How to make your IT project secured?

Particular Business Problem to Solve 

So, you have prepared the technical basis for the AI implementation, and now you need to decide what algorithm can help you in the first place? Perhaps, to solve critical problems, or are you already doing well, but you want it to be even better?

  1. Increase the price of the existing product. As we mentioned earlier, the attractiveness of a product or service increases not only due to the quality of the product in front of competitors but also due to a personalized approach to the client. Are you selling cosmetics? Let your AI match the eyeshadow palette, mascara, or 50 shades of lipstick from the same producer to the one chosen by the client.
  2. Analysis of the current status of the business. The algorithm can help you find weaknesses that you didn’t even know about: logistics, marketing, sales, manufacturing – all these can be bottlenecks. Plan resources and forecast demand correctly with AI technology.
  3. Business process automation. When you have identified and eliminated the problems and perhaps even radically changed the business model, it’s time to think about automating processes and, accordingly, optimizing the staff and retraining for more intelligent work.

Culture of Innovations 

Before implementing AI, make sure that your employees share a philosophy of innovation and progress with you, that they have no fear of not coping and fear for their workplace. New technologies can be quickly, organically, and painlessly introduced only if your company is constantly engaged in them.

  1. Corporate strategy. Don’t innovate for the sake of innovation. You will never make a profit this way. You should not put all products under the auspices of AI at once. Try with small, not very resourceful projects. Then there is no risk that your company will collapse like a house of cards in case of failure.
  2. Metrics. Be sure to define the criteria by which you will measure the success of the implementation of AI to understand when the payback comes.
  3. The right to make mistakes. Yes, this also needs to be incorporated into the business strategy. One of the indisputable advantages of AI is considered to be that it excludes the human factor. However, the machine can malfunction; this is a well-known fact. Do not assume that this risk negates all the other advantages of a smart algorithm. Just take into account that you need to spend money not only on the development but also on the support of the algorithm at first.


For all the attractiveness of AI technology, consider whether you really need it. Do your capacities give reason to implement it? If the amount of information is large and the corporate business strategy and tasks are flexible enough, there is no point in delaying.

The APP Solutions is a web and mobile app development team aware of AI algorithms development and implementation for Enterprises. Suppose you are already ready to introduce AI technologies into your business but cannot decide on a development team. In that case, we are ready for fruitful cooperation and are waiting for you!

What is Artificial Intelligence in Healthcare?

As life expectancy increases, healthcare organizations face an increasing demand for their services, rising costs, and a labor force struggling to meet the needs of their patients. By 2050, one in four people in Europe and North America will be over 65, which means the healthcare system will have to deal with many patients with complex needs. Managing these patients is costly and requires systems to move from an episodic-based service to a more management-oriented long-term care.


AI Technology of Healthcare Providers

Artificial intelligence based on automation can revolutionize healthcare and help to solve vital problems. Few technologies are advancing as rapidly as AI in the healthcare industry. AI is now used in many life spheres, but the health-care industry was one of the first to use it widely. According to Statista, from 2016 to 2017, the AI ​​market in healthcare grew by $ 500 million (from 1 to 1.5 billion), and by 2025 is predicted to grow to 28 billion.


An even more optimistic forecast is given by Tractica – by 2025, growth is projected to be 34 billion, and by 2030 to 194.4 billion.

All of these investments include case studies on patient data processing and management, and transformation from paper to digital format, digital image interpretation (for example, in radiology, ophthalmology, or pathology), diagnosis and treatment, biomarker discovery, and drug efficacy calculations.


Forbes says AI tools are already being implemented in 46% of service operations, 28% in product and service development, 19% in risk management, 21% in supply chain management, and 17% in marketing and sales in the healthcare industry.

North America dominated the healthcare AI market with the largest share of revenues at 58.9% in 2020. Factors which determine the market in the region are a broader adoption of AI technologies, growing licensing and partnerships, and favorable government initiatives.

AI has proven to be an important resource for evaluating patient scan data and identifying treatment options throughout the pandemic. It has also been also used to improve the administrative operations of hospitals and health centers. As a result, we may see more business applications from healthcare providers for more widespread use in medical procedures. 

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EIT Health and McKinsey, in their report 2020, drew attention to which areas of medicine artificial intelligence is most often used.


As you can see, first of all, these are diagnostic tests and clinical research. However, a large amount of investment is also spent on technologies related to managing the way hospital’s function. Education and prescription automation are also included.

For example, AI is already being used to more accurately detect diseases such as cancer in their early stages. According to the American Cancer Society, most mammograms give false results. One in two healthy women are being told they have cancer. Using AI, mammograms can be viewed and translated 30 times faster with 99% accuracy, reducing the need for unnecessary biopsies.


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Three Phases of Scaling

AI in healthcare is a pervasive technology that can be successfully applied at different levels, depending on the complexity of the development.


First Phase

AI is solving routine paper, managerial, administrative processes that take time for doctors and nurses.

Second Phase

Remote monitoring. According to Accenture, artificial intelligence and machine learning can help meet 20% of all clinical requirements by reducing unnecessary clinic visits. At the same time, it is possible to reduce the number of readmissions to hospitals by 38%. 

As AI in healthcare improves, patients will take more and more responsibility for their treatment. Already, successful developments are being applied in such complex fields of precision medicine as oncology, cardiology, or neurology. For example, clinicians can be virtually close to their patients and observe certain conditions without personal visits.


This technology has proven to be especially useful during the pandemic when personal care was limited, but patients still needed support from their medical providers. 

Third Phase

AI in healthcare will become an integral part of the healthcare value chain, from learning, researching, and providing care, to improving public health. The integration of broader datasets across organizations, and robust governance for continuous quality improvement, are essential prerequisites for greater confidence among organizations, clinicians, and patients for managing risk when using artificial intelligence solutions.


AI Tools

Artificial intelligence is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP), deep learning (DL), and others enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster and with more accuracy.

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AI vs. COVID-19: Patient Outcomes

Artificial intelligence technologies have played a critical role in the ongoing pandemic and positively impacted connected markets. It is used to quickly detect and diagnose virus strains and combat outbreaks with personalized information. For example, AI algorithms can be trained using chest CT images, infection history, symptoms, clinical documentation, and laboratory data to quickly diagnose COVID-19 positive patients.

In 2020, an NCBI study found that an artificial intelligence system identified 17 out of 25 COVID-positive patients based on typical computed tomography images, while experts diagnosed all patients as COVID-negative.

Thus, AI-based diagnostics can be used to accurately detect the disease even before the onset of apparent symptoms. In addition, these systems can be trained to analyze images and patterns to create algorithms to help healthcare professionals diagnose the disease accurately and quickly, thereby increasing the spread of AI technologies in healthcare. This will significantly reduce the load on the system and improve patient outcomes.

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Benefits of AI/Machine Learning in Healthcare 

There are several areas in which AI has excelled, especially significantly helping doctors and medical institutions with the challenges that are becoming more and more in the modern world.


Predictive Analytics

With the rapid growth of medical knowledge, it is becoming increasingly difficult for doctors to keep up with the times. AI solutions that extract relevant medical expertise for each patient, and present it in a structured way can help clinicians choose the best treatment option, saving time and leading to more complex fact-based decision-making.

In a routine clinical setting, AI models can also detect patients at high risk of complications, or early deterioration, and provide recommendations to further support clinical decision-making with the prevention or early intervention. Reducing complications through early intervention can lead to improved health outcomes and reduced length of hospital stay and associated health care costs.

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AI can help identify a patient’s condition and recommend possible care and treatment options. This can save physicians doing research and, in turn, spending more time evaluating the possibilities presented by the AI ​​and discussing them with the patient.

One successful, and most importantly, relevant examples (in the midst of COVID) is a technology that predicts the oxygen levels of each patient. The engine indicates oxygen requirements within 24 hours of arrival in the emergency department with a sensitivity of 95% and specificity of over 88% based on previously examined X-rays. Software is being created that makes the work of radiologists unrealistically easier. 

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In the end, AI in healthcare could create a complete “home” version. For example, technologies already make it possible to produce “smart” toilets that could analyze urine and feces “on the spot.” Another question is that it is unlikely that the invention will have many fans at this stage of human development.

However, this extravagant decision could free up many laboratory specialists involved in this type of analysis for more complex work. And if you look more into the future, doctors will treat the consequences of patients who were too lazy to check urine tests on time (which they could have done without even leaving home).


Storing and Organizing Patient Data Bases

AI, in particular machine learning, can also be used with large datasets to predict health outcomes, helping healthcare systems focus more on prevention and early detection, improve health outcomes and, over time, make health care systems financially sustainable.

The big data automation capabilities, and real-time analytics built into syndromic surveillance, provide the information you need to understand disease progression and predict its risk to patients before it occurs. In addition, track disease symptoms, better manage public population health by predicting hospital utilization, geographic leaps, and associated material and resource requirements.

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Using AI to analyze large datasets can be helpful in both healthcare settings and epidemiological research. AI models based on clinical data from a large population (e.g., patients in a healthcare region or an integrated healthcare provider system) can help identify early risk factors and initiate preventive action or early intervention at the system level. 

They can also help prioritize during times of staff shortage. Likewise, identifying an increased risk of unplanned hospitalization can help clinicians proactively intervene and avoid them.


Analysis of Digital Images

Radiologists and cardiologists make it much easier for themselves to work with images and scans, thanks to the capabilities of AI. Technological advances in this area allow you to prioritize critical cases, avoid potential errors in reading electronic health records (EHR data) and electronic medical records (EMR) and establish more accurate diagnoses.

AI algorithms can analyze big data sets quickly and compare them with other studies to uncover patterns and hidden relationships. This process allows medical imaging professionals to track critical information swiftly.

The Patient Brief examines past diagnoses and medical procedures, laboratory findings, medical history, and existing allergies and provides radiologists and cardiologists with a summary that focuses on the context of the images. The product can be integrated with any structure of the medical unit’s system, accessible from any communication workstation or some medical devices on the neural networks, and be updated without affecting the daily activities of the medical department.


AI and Pharmaceuticals

Another truly revolutionary example of the positive uses of AI in healthcare is drug research and discovery; one of the most recent AI applications in healthcare. By channeling the latest advances in AI to streamline drug discovery and repurposing processes, both the time to market for new drugs and their cost can be dramatically reduced.


Supercomputers have been used to predict, based on databases of molecular structures, which potential drugs will, or not, be effective for various diseases. AI and machine learning algorithms can identify new drugs, track their toxic potential and mechanisms of action. This healthcare technology has led to creating a drug discovery platform that allows the company to repurpose existing drugs.

Identifying new uses for known drugs is another attractive strategy for large pharmaceutical companies since it is cheaper to repurpose and relocate existing drugs than to create them from scratch.



AI and Genetics

Altered molecular phenotypes, such as protein binding, contribute to genetic diseases. Therefore, predicting these changes means predicting the likelihood of a genetic disorder. This is possible due to data collection on all identified compounds and biomarkers relevant to specific clinical trials.

This allows us to recognize genetic abnormalities in the fetus and compose an individual treatment for a person with sporadic congenital disease.


AI in the Healthcare Apps

The growing popularity of smartphones and AI technologies among patients and professionals is driving the proliferation of virtual assistants. In addition, robotic surgery has been the most promising segment in the AI healthcare market as of 2020. This is mainly because surgical robot manufacturers are entering numerous strategic partnerships with data science and analytics companies and artificial intelligence technology providers.

The leading players in the AI ​​market:

  • IBM Corporation
  • NVIDIA Corporation
  • Nuance Communications, Inc.
  • Microsoft
  • Intel Corporation
  • DeepMind Technologies Limited

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Future of AI/Deep Learning in Healthcare: Perspectives

According to The World Health Organization forecasts, the number of medical workers is steadily decreasing every year, and by 2030 there will be a shortage of almost 10 million professionals. AI, machine learning systems, and NLP can transform the way care is provided, meeting the need for better, more cost-effective care and helping to fill some of this gap in staffing. This is especially true as the population ages and health needs become more complex.

As the next step in telemedicine, Telesurgery aims to help reduce the damage caused by staff shortages. Telehealth, or virtual meeting, has become more widely used during the pandemic. This service has been used by those living in remote areas for several decades, but regularly by telephone rather than video conferencing. 


With the pandemic and the need for social distancing, telemedicine has become an integral part of healthcare services. Therefore, it has improved significantly as a result of the demand throughout the pandemic. Telesurgery is a field that is being researched and can be used in the provision of emergency care.

The current use of robotics in surgery allows physicians to perform minimally invasive surgeries and limits the impact of the procedure, improving outcomes. Expansion of surgery automation will continue to include AR and VR for increased productivity. 

Telesurgery is the next step being researched and provides access to a surgeon who does not specialize in the patient’s area of ​​residence. This saves the patient from traveling and can also be used when the patient requires immediate assistance. Problems may include delay and the need for a surgical team to support the procedure if a problem arises.


AI and automation are uniquely positioned to understand these needs and the complex interdependencies between various factors affecting public health. In addition, the extraordinary shift from symptom-based medicine, to molecular and cellular medicine, is generating ever-growing data amounts.


The pace of change in AI within healthcare has accelerated significantly over the past few years thanks to advances in algorithms, processing power, and the increasing breadth and depth of data that can be used. In response, countries, health systems, investors and innovators are now focusing their attention on the topic.


Global venture capital funding for AI, ML, and deep learning in healthcare has reached $ 8.5 billion for 50 companies as clinical trials of AI healthcare applications increase.

And although AI will not be able to replace medical personnel (especially doctors) entirely, however, with the gradual introduction of technologies, the work of doctors will change only in a positive direction:

  • More time for patients – less for paperwork (time optimization from 20 to 80%)
  • Acceleration and improvement of diagnostics (especially in such fields as radiology, ophthalmology, pathology)
  • Assistance in prioritizing the complexity of a patient’s condition (e.g., determining the likelihood of a heart attack, septic shock, respiratory failure)
  • Improving the soft skills of clinicians by changing the format of communication with patients (people with chronic diseases can be served from home thanks to telemedicine)
  • Increased educational level (while less severely ill patients can be treated remotely, the hospital will mainly admit patients with more complex cases, which requires more advanced skills from doctors)



AI bias in Healthcare: Disadvantages and Challenges

AI does not always become the optimal solution and salvation from all problems. This happens for several reasons:

  • Insufficient development of technologies (moreover, several companies can solve the problem at once, but in the end, none of them will make a high-quality product that can be immediately thrown onto the market). The solution could be the unification of diverse teams that could consider all the necessary nuances.
  • Changes in the medical education system around the world (the more technological solutions that can be offered to doctors, the more technically savvy they will have to be, and even top medical universities have not yet reorganized these new realities. Changes in patient behavior caused by AI also implies a change in the relationship between patients and practitioners, with the latter needing more attention to counseling and interpersonal skills).
  • Databases (healthcare is one of the minor digitized sectors of the economy. Healthcare providers and AI companies need to implement robust data management, ensure interoperability and standards for data formats, improve security, and clarify consent to exchange health data).
  • Regulation and risk management (defining the regulatory framework for AI in healthcare is significant for solving possible problem situations in which it is difficult to determine the degree of responsibility of all parties to the conflict).




AI in medicine still has many different stages to go through; the improvement process is only gaining momentum. But positive results are already visible. There are still fears that the excessive interference of technology will make the medical field less “human,” but only people who have not delved into the issue can speak this way. The more technologies are used in medical diagnosis, prevention, and treatment, the more time an actual doctor has directly for the patient.

Many medical and health apps help people self-diagnose their health, which ultimately allows doctors to focus on treatment. The development of such applications is carried out by companies with high expertise, including The APP Solutions. We are a highly skilled app development company who can bring your ideas to life, and we look forward to meeting you. If you have an interesting idea but are still contemplating how to implement it, contact us, we can help.

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What is the difference between Web 2.0 vs. Web 3.0?

To whatever terms, the word “web” is applied! Anything that somehow refers to the Internet can be a “web” – a site, a page, access, security, a camera… It is not surprising because the word “web” is a component of the three “great” WWW – World Wide Web – connected by websites on the network. But this prefix also applies to numbers. This is the forgotten Web 1.0. And Web 2.0, which is definitely on everyone’s lips because it occupies a dominant position. And, of course, Web 3.0, which came into use not so long ago but is widely used in certain circles, while still very timidly displacing its predecessor.


Web 1.0, Web 2.0, and Web 3.0 and their differences

Let’s deal with all these concepts to say goodbye to uncertainty once and for all! What are the differences between Web 1.0 vs. Web 2.0 vs. Web 3.0?

Web 1, 2, and 3.0 are evolutionary services that determine how users interact with and within the Internet. It all started as one-way communication from the network to users. It came to decentralized mechanisms for storing and transmitting data, which have come to a thorny path, just like silent movies turned into augmented reality movies. Each stage had its meaning and was relevant and most convenient at a particular moment in the Internet’s development.

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What is Web 1.0?

Web 1.0 is the first iteration of the Internet, dating back to the 90s with the first browsers. The most accessible and easy to understand. In this approach, the network is considered a source of information, and the user is its absorber. They were just directory, static web pages.

In those days, email was considered happiness. But, at the same time, the possibilities for creating content were very scarce – mainly read-only.


What is Web 2.0?

Web 2.0 is the second generation of interoperable Internet services. If earlier the user could only consume content, now he has the opportunity to independently produce it and exchange it with other site users (user-generated content). This became the basis for the commercialization of the Internet – entire areas of activity were en masse digitized, otherwise, they risked dying. That is; retail, banking, advertising, media, and entertainment…

It also became the basis for social networks as virtual communication platforms. This can include any interaction from written blogs to audio podcasts, from RSS to commonplace tags that allow you to find content based on your interests more efficiently. Prime examples of Web 2.0 are Apple, Amazon, Google, and other FAANG representatives.


Features of Web 2.0

  • Access to web content from mobile devices, tablets, TVs, consoles, and even a kettle connected to the Internet
  • Dynamic content (as opposed to static first-generation web pages), which is designed to work in CTA mode
  • User participation in content creation – users not only share and comment on articles and videos but also produce them themselves
  • In the process of data transmission, there is a specific “intermediary” – a controlling platform
  • Development of API for interaction between different programs

What is Web 3.0?

Web 3.0 is the third generation of Internet services that focuses on decentralizing processes and eliminating any middleman trying to take control of everyone and everything. In addition, Web 3.0 uses encryption and distributed ledger technology to address the trust issues present with Web 2.0. But decentralized Web 3.0 is not only about security but also about more effective interaction due to artificial intelligence.

This new move is sometimes called the Web 2.0 killer, although this is clearly premature. However, it cannot be denied that with the advent of this technology, many established processes will change.

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Although not everything is so cloudless in Web 3.0, with the loss of control, it will become impossible to combat negative phenomena such as cybercrime, incitement to hatred, and disinformation, which are now increasingly challenging to deal with. Not to mention the laws because it is not completely clear which country’s judicial authorities will have to be involved in disputes. And the scalability of transactions in Web 3.0 is still insignificant, which significantly slows down processing.


Features of Web 3.0

  • Artificial intelligence, which selects the most relevant options for information (search engines are actively engaged in it, reducing the role of organic search results)
  • Semantic Web or an option that allows machines to better interact with humans by understanding and interpreting the meaning of human words
  • Use of 3D images and graphics
  • A new level of security and privacy through decentralization (blockchain) – freedom from censorship and surveillance due to the lack of a control center – distributed ledger, and decentralized finance (Defi)

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Difference between Web 2.0 and Web 3.0

Web 2.0 and Web 3.0 are consistent technologies with a common background, but they solve their problems in different ways. The main difference can be described as the fact that Web 2.0 aims to read and write content, and Web 3.0 at the essence (Semantic Web). However, the latter is even better than before, applying technologies to exchange information between Internet users while also increasing security.


Content presentation principle

In other words, the main goal of Web 2.0 was to unite people around the data they were interested in, and Web 3.0 combines this data in meaning while increasing the trust in information thanks to the notorious decentralization. Thus, the communities that were naturally created with Web 2.0, with Web 3.0 disintegrate to personalize information and expand opportunities and rights. This leads to the following difference.

Content Ownership Principle

With Web 2.0, the network itself assumed responsibility for storing information, which caused specific difficulties with access and fears for the safety and confidentiality of online data. This problem was solved by Web 3.0 with the flexibility of data exchange, which can now exist at many points at once. However, Web 2.0 transfers are still faster than Web 3.0.

In Web 2.0, computers use HTTP in the form of unique web addresses to find information that is stored in a fixed location, usually on a single server. However, in Web 3.0, since information will be found based on its content, it can be stored in several places at the same time and, therefore, be decentralized; it is certainly not in the interests of the Internet giants.

The degree of centralization/decentralization of the network is in the range; no network is entirely controllable or completely independent.


Application types

For Web 2.0, these are podcasts, blogs, and video sites. In general, any type of information fits the description of self-production of content and user communication. For Web 3.0, these will be AI and ML-powered applications (dApps) such as multi-user virtual environments, 3D portals, and integrated games.

User acquisition paths

Interactive advertising works with Web 2.0, while behavioral advertising works with its “successor.” In the first case, there is a certain moderation due to the presence of a controlling body; in the second, it is impossible.


Compared to the first, the second iteration had to take a big step forward to meet new challenges, among which the main one was to stimulate the exchange of content, not just its consumption. AJAX and JavaScript, CSS3, and HTML5 are most often named among the technologies specific to Web 2.0. And then, there was a boom in the development of AI, which could not but affect Web 3.0, which was supposed to serve as a reliable “shelter” of information on the one hand and a content quality booster on the other. The leading technologies behind Web 3.0 include machine learning, deep learning, semantic web, and decentralized protocols.

Key Takeaways

Of course, Web 3.0 is an important step towards progress, but it is not perfect, so it is too early to bury Web 2.0. At the moment, the two strategies coexist perfectly. While the second still dominates, the third iteration is not far off. We at The APP Solutions are friends with both technologies and are ready to work with the projects you propose to us.

Credits to Depositphotos

Healthcare Chatbot: Improving Telemedicine & Enhancing Patient Communication

The healthcare industry is constantly evolving to meet its customers’ needs. A noteworthy trend that is emerging is the use of chatbots. These computer programs, which use artificial intelligence to automate customer service, make it easier for medical providers and patients to communicate.

Chatbots in healthcare are gaining traction, and research suggests that by 2032, the global market for healthcare chatbots will be worth $944.65 billion. The increase in internet penetration, smart device adoption, and the demand for remote medical assistance drive this market forward.

healthcare chatbot market size

In this article, we’ll cover the three main types of healthcare chatbots, how they are used, their advantages and disadvantages, and which one is right for your organization.


Primary Categories of Medical Chatbots

Chatbots can be broadly divided into three main categories: clinical support, decision support, and healthcare. Let’s take a closer look at each one.

  • Decision-support chatbots provide medical advice based on the data collected from the patient. They can be used to remind patients of drug interactions, suggested doses, and so on.
  • Clinical support chatbots are developed to offer professional medical advice to doctors, helping them make more accurate diagnoses and treatment plans.
  • Healthcare-focused chatbots are used to promote communication between health providers and patients. These chatbots are commonly employed in healthcare to respond quickly to common queries and provide general medical advice.

How Exactly are AI Chatbots being used in Healthcare?

Chatbots, powered by artificial intelligence, are used in various ways to improve the patient experience and simplify medical procedures. To get a better handle on the application of AI bots in healthcare, check out these examples: 

  • Appointment Booking: 

Chatbots can be integrated with online booking systems, making it a cinch for patients to set up or change visits with their medics. 

  • Virtual Health Guides: 

Chatbots use natural language processing (NLP) to comprehend and answer patient queries. For example, they can give information on common medical conditions and symptoms and even link to electronic health records so people can access their health information.


  • Clinical Studies: 

AI chatbots can assess patients for clinical trial eligibility and supply information about ongoing trials, accelerating the process of enrolling participants and collecting data.

  • Prescription Refills:

Chatbots make it quicker than ever to get refills on prescriptions – no more waiting around.

Chatbots specially designed for mental health are invaluable for those struggling with depression, anxiety, and other issues. They provide a secure outlet for communication and lessen feelings of loneliness.

  • Remote Monitoring:

Thanks to AI chatbot healthcare, remote patient health status monitoring is easier than ever. In addition, wearable devices can now supply data to healthcare providers to keep tabs on potential problems.


It’s important to note that chatbots are never meant to supplant healthcare professionals – they make their jobs more straightforward and accessible to patients.


The Role of Intelligent Chatbots in Healthcare [2023 New Applications]

Health organizations are increasingly turning to chatbots, and this tendency will continue to gain momentum in 2023 and beyond. Some of the novel and creative approaches include the following:

Making a splash in the world of telemedicine is one of the most promising areas of application. Healthcare chatbots provide patients with virtual medical consultations and advice so they can avoid leaving the coziness of their homes to get professional assistance.

Chatbots can also be handy in managing and administering medication. These bots can remind patients to take their meds, give info regarding drug interactions, and alert them if there are any issues with their treatment.


Medical data analysis is another area where chatbots can prove useful. AI bots assist physicians in quickly processing vast amounts of patient data, enabling healthcare workers to acquire info about potential health issues and receive personalized care plans.

healthcare chatbot for routine diagnostic tasks

A healthcare chatbot can link patients and trials according to their health data and demographics, boosting clinical trial participation and accelerating research.

Chatbots can manage mundane tasks like scheduling appointments and providing simple answers about treatments and insurance.

The medical chatbot can assist as an interpreter for non-English speaking patients. The bot can then interpret during consultations and appointments, eliminating language issues.

AI chatbots are also being used to uphold and teach people about their well-being. It will give advice on healthy eating, offer lifestyle modifications, and remind them of other important activities.

Suicides are a growing epidemic, so let’s tackle it head-on with technology. We can design an app and chatbot with mental health resources that deliver tailored Cognitive Behavioral Therapy. AI tech can help those in need by reminding them of appointments, offering tips for treatment, and providing invaluable assistance in tackling their mental health issues.


The Pros and Cons of Healthcare Chatbots

There are benefits and drawbacks to using chatbots in medicine, just as with any new technology. So why don’t we briefly talk about some of them below?

According to Statista, by 2022, the market size of customer service from artificial intelligence chatbots in China will amount to around 7.1 billion Yuan. AI can be a real “plus” for the healthcare industry too. 

ai market

Some of the many rewards it offers include:

Chatbots can help the health sector save an estimated $11 billion annually! Automating some tasks and quickly responding to basic questions result in reduced medical service expenses and free up doctors to tackle more complex issues.

Chatbots can be used to streamline and make healthcare services more efficient.

In addition to saving money, medical bots can offer faster access to healthcare services. According to a survey, 78% of people prefer using bots for medical services. 

AI-powered chatbots are able to provide comprehensive support and advice to patients and follow-up services.

Harnessing AI capabilities, chatbots can provide thorough aid and counsel to patients, as well as follow-up consultations and treatments.


advantages of ai chatbots in healthcare

A further benefit of a medical chatbot is that it can furnish individualized healthcare services, guidance, and assistance to patients. Utilizing the power of AI, these chatbots can provide every patient with personalized advice and reminders tailored to their requirements.

On the opposite side of the coin, there are a few obstacles to consider when contemplating the development of healthcare chatbots. Let’s take a gander at the downsides. 

Putting together an AI that can handle delicate medical information can be pretty intricate and take longer than expected.

One major disadvantage is that, for the time being, chatbots cannot deliver thorough medical counsel. Thus, these should be employed in conjunction with the direction of certified medical experts and not as a substitution.

Also, ethical and security problems may appear when bots access patient records. Some chatbots may not include the necessary safety measures to securely store and process confidential patient data, thereby risking patient privacy. Health services that employ a chatbot for medical reasons must take precautions to prevent data breaches.


The stellar performance of healthcare chatbots is only as good as the info they’re fed. Feed them incorrect details, and their misdiagnoses or shady treatments flummox you. To ensure accurate results, keep patient data up-to-date and current!

cloud computing for ai chatbots

Chatbots may not be able to provide the full scope of mental health support, so healthcare organizations must pair them with dedicated medical professionals for comprehensive aid.

How to Choose an AI Chatbot for Your Healthcare Organization

When choosing an AI chatbot for your healthcare organization, there are several factors to consider.

  • Type

The first step in developing a healthcare chatbot is determining its purpose. Specifically, do you need one that can help you make decisions and support you clinically or one that focuses on providing general medical guidance to patients?

  • Features 

Think about what the chatbot can do and what features it has. Askings questions like “can I get specific recommendations and reminders from the chatbot?” “Can patient information be safely stored and processed?” can help you make the right choice.


  • Costs

Also, take into account the cost of the chatbot. They can be expensive, so you should consider the price and make sure it fits your budget.

Costs of implementing a healthcare chatbot
  • Security

Pay close attention to the chatbot’s security settings and how to protect patient data is essential. Ensure that it has the right security measures to keep sensitive patient information from getting into the wrong hands.


Chatbots and Their Place in Healthcare

Chatbots could help improve health care by providing information, answering patients’ questions, and helping to sort out symptoms. A chatbot can tell you about general health or how to deal with a certain condition, for example. They also help healthcare providers by answering patients’ frequently asked questions and directing them to the right care. 

Healthcare facilities must use chatbots in a responsible and protected manner. They can’t replace doctors and nurses, so that’s something to remember. For the best results in patient care, hospitals, clinics, and other organizations should integrate bots with medical professionals and psychologists.



Healthcare chatbots have the potential to revolutionize the health industry. They are a powerful and cost-effective way to provide medical advice and support to patients and health providers. They also provide personalized advice and reminders tailored to the individual patient’s needs.  

Technology is still in its early stages, and chatbots still need to be built, tested, and regulated based on their usage in medical care. It is important, though, that healthcare organizations use these bots safely and responsibly. Nevertheless, we are excited about the future!

Do you need a team of specialists who will work with you to create a healthcare chatbot for your app and protect against cyberattacks?

The APP Solutions is a leading healthcare technology company that creates innovative products to improve patient outcomes and streamline healthcare processes. Our talented developers and designers work hard to give our clients the most advanced, secure, and effective solutions to improve patient outcomes and streamline healthcare processes. 

We have a proven track record of delivering high-quality, user-friendly, and scalable healthcare technology solutions. Our expertise includes developing electronic health records (EHR) systems, telemedicine platforms, patient portals, and chatbots for mobile health, among other things. Our solutions are designed to comply fully with HIPAA and HITECH. Contact us today, and you will be glad you did.

The role of AI and machine learning in digital biology

Digital biology, also known as biotechnology and digital biotech, gives bioengineers, medication producers, agricultural companies, and industrial businesses excellent opportunities. Biotechnicians can turn biomaterials – living systems and organisms – into a digital data format, organize it, discover hidden patterns, and store it in databases. 

Why does it matter? 

Such an approach streamlines the research, development, and test stages of biology projects that previously took bio technicians months or years. Moreover, medical specialists apply digital biology to diagnose health conditions, such as cancer and sepsis, within several hours and suggest the most appropriate treatment based on patient samples. 

Digital biology took a leap in development by applying Artificial intelligence and machine learning algorithms that automate biological data analysis and research. Thus, bioengineers generate more data in shorter terms, compared with the analog study methods they used previously.

In this article, you’ll find the current state of digital biology and the fields it serves. You’ll also read about biotechnology areas that benefit the most from other intelligent technologies, such as AI, machine learning, and cloud computing. 

The current state of the Digital Biology market 

Digital biology is a cross-disciplinary field that combines both biological and technological components. It includes exploring and analyzing living organisms with new intelligent tools. 

Recognizing the considerable potential of biotechnology, governmental organizations, such as the National Institute of Biomedical Imaging & Bioengineering in developed nations and the National Center for Biotechnology Information, increased their investments into the research and development activities in biotechnology fields. 

The market research from Global Market Insights (GMI), a global market research and management consulting company, says an increased interest from governmental organizations is expected to make biotechnology the largest and the fastest-growing market, projected to reach $729 billion by 2025, compared with $417 billion in 2018.

digital biology market overview

The research also includes a forecast of revenue increase for the following technology segments:

  • Fermentation 
  • Tissue engineering and regeneration 
  • PCR technology 
  • Nanobiotechnology 
  • Chromatography 
  • DNA sequencing 
  • Cell-based assay

And others. 

In particular, the fermentation segment is the most prominent sub-niche of biotechnology that received an 11% revenue share of the whole biotechnology market in 2018. 

The report predicts substantial progress for fermentation technology during the next few years. Fermentation is a process that changes organic substrates on the chemical level by enzyme action and micro-organisms. 

Such growth of fermentation technology is explained by excessive use in the food and beverage industry. The food and beverage industry’s key business players will increase investments in biotechnology to improve the research and development activities to produce more fermented products. 

The expected growth of biotechnology opens new opportunities for biotech startups, well-established companies, and research institutions. Another reason for biotechnology’s rise is various applications in medication, agriculture, and other industries. 

Biotechnology Application Outlook

Digital biology, or biotechnology, includes several categories of applications. Biological technicians and other scientists apply digital biotechnology for solving scientific problems with living organisms across various industries – from healthcare and agricultural to industrial processing and bioinformatics. 

digital biology applications in food and agriculture

Let’s see how each category benefits from artificial intelligence and machine learning. 


In medical biotechnology, scientists receive information from living cells to get a clearer picture of human health, thus producing the most appropriate drugs and antibiotics. 

Bio technicians dig into the smallest details to achieve these goals – study DNA and manipulate cells to predict beneficial and vital characteristics. 

The most useful tech solutions used in medical biotechnology are Artificial Intelligence and machine learning that enable scientists to improve their drug discovery process by reaching small molecules and their target structures they need to treat. 

Machine learning algorithms also perform great for patient testing and diagnostics. The algorithm can detect damaged tissues and other abnormalities via medical images, patient samples, and even sounds. For example, intelligent algorithms can detect cancer cells in X-rays, sepsis via DNA sequencing, and define whether the patient has COVID after hearing one’s cough. 

In this way, doctors provide more timely and accurate treatment for better outcomes. 

Moreover, artificial intelligence and machine learning are used in electronic health record (EHR) systems and clinical decision support systems to help doctors suggest a patient’s personalized medical treatment and accurate medication management. 

Food and agriculture

Agricultural biologists apply biotechnology to increase crop yields, genetically modified plants, and identity infected crops before the harvest. For these purposes, scientists use DNA sequencing devices and databases with DNA samples of already sequenced genes. Once new DNA samples are sequenced, scientists can change their structure, learn more about the plant origins and potential issues typical for one or other plant. 

biotechnology applications growth

[Increasing application of cell line engineering will drive the overall market expansion]

Food and agriculture biotechnology companies apply AI-algorithms to harvest crops, watch crop health, and find AI-powered tools more effective than humans. 

Such an application requires food and agriculture businesses to integrate autonomous robots or drones, computer vision algorithms, and deep learning technologies. While drones and robots carry cameras, algorithms analyze crop pictures they receive, compare data captured with crop images in their database, and define whether crops and soil are healthy or not.   

Industrial processing

Industrial biotechnology includes research on biopolymer substitutes, inventions of vehicle parts, alternative fuels, new chemicals, and the process of production. In this area, intelligent technologies and the Internet of Things (IoT) devices help industrial producers analyze their machinery to predict outages, optimize equipment, and even reduce human worker numbers with automated warehouse management. 

One example is Ocado Technology, an online grocery retailer that automated its warehouse with 3500 robots to process 220,000 online orders a week for grocery delivery.

To learn more about AI and machine learning applications in industrial processing and supply chain, check out our previous article about top AI applications in supply chain optimization. 


Bioinformatics is a subdiscipline of digital biology that combines biology and computer science to acquire, store, analyze, and disseminate biological data, such as DNA and amino acid sequences. Scientists understand biological information using mathematics, data science, and different digital biology tools by organizing it in large biological pools. 

Bioinformatics also receive benefits from AI and machine learning. Artificial intelligence and machine learning help biologists sequence DNA from the massive data crunch, classify proteins, protein catalytic roles, and their biological functions. Leveraging intelligent technologies, scientists can automate gene expressions and gene annotation and identify the location of genes required for computer-aided medication design. 

In digital biology, biotechnologists base their research on digital data, generated from life samples or DNA sequencing devices and stored in a thousand databases, both private and public.  

So we can conclude that the growing biotechnology industry will heavily rely on AI-algorithms, machine learning, and data analytics. But the development of biotechnology across all segments depends on biotechnology researchers’ ability to master their skills for the useful contribution of their findings and researcher results. Not only AI makes biotech engineers more efficient, but also for a bunch of other reasons.

Let’s check them out.  

Top 3 advantages of using AI in the biotechnology industry

PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution says AI will contribute to the global output of $15.7 trillion by 2030. By this time, 44% of pharma and life sciences experts expect to adopt Artificial Intelligence in their laboratories and R & D centers and replace analog tests. 

But why do scientists prefer digital biology to the old-but-gold analog approach?

Development and research projects often require scientists to deal with numerous amounts of data and large sample sizes, such as genome sequencing. In such cases, biological test digitalization allows researchers to produce more data than analog study methods. And by applying digital biology, scientists can receive real-time insights into biological functions which have taken them days and weeks when using an analog approach. 

The adoption of AI and machine learning by biology specialists make the digital biology approach even more useful. And here is how: 

Crucial predictions 

Artificial intelligence and machine learning algorithms help bio technicians make more precise predictions than standard approaches used for decades. Successfully applied in supply chain and logistics, predictive analytics drastically reduce the time biotech companies spend to launch new products to market. 

To make data-based decisions and forecast outcomes, data scientists train algorithm models with historical databases. Then, such algorithms are effectively used for pattern recognition, despite the data type. 

As Nature online resource highlighted, intelligent algorithms’ ability to analyze large amounts of data in datasets helps drug-producing companies make new pharmaceuticals quicker and more effective. Soon, medication specialists will provide more personalized treatments, based on the disease’s cause, hidden deeply in biological structures. In this way, pharmaceutical companies can reduce the medicine development process from the $2.6 billion price tag and decrease the 90% failure rate of new medication created. 

In her article, Melanie Matheu, Ph.D. and founder at Prellis Biologics, Inc. the human tissue engineering company predicts the new generation of therapeutics entering drug pipelines empowered by AI screening for selecting targets will reduce clinical trial failure rates for small molecules by 86%. 

Effective decision-making 

Clinical trials used to be manual and a very time-consuming process – they included inviting participants to the clinic during the in-person visit, recording their symptoms, prescribing them treatments, and analyzing side effects. Moreover, to get the right sample size, medication companies heavily invested in marketing resources for recurring right patients and treating rare conditions.  

Now, intelligent algorithms and cloud technologies digitized clinical trials and enabled biotech organizations to test medication on more patients within less time. 

One example is Invitae, a medical genetic company. In November 2019, the company launched a trial in collaboration with Apple Watch to bring together biometric data from wearables and genetic tests and determine genes that cause cardiovascular disease. In this way, the company made the trial available to many people and excluded Apple Watch users who didn’t meet the trial criteria. 

Biotech companies make clinical trials even more effective by leveraging machine learning algorithms that analyze data from current trials and use it for forecasting treatment effectiveness in the future, down to a molecular level. ML also helps scientists revise information from previous tests to find gaps and new applications for existing medications. 


Modern devices, cloud databases, data analytics pipelines, and machine learning algorithms reduced the cost of genome sequencing from $2.7 billion for the Human Genome Project to less than $300 by now. It is expected to cost even less – $100 in the future. Bioengineers receive more extensive screening of trial participants and targeting of interventions. They also see the future in personalized treatment plans and targeted therapies that provide therapies at genetic and molecular levels of patient genes. 

The main area for targeted therapy is cancer treatment – the treatment of blood cancer such as leukemia, where a treatment called CAR T-cell therapy, according to the National Cancer Institute, the immune system will “attack tumors,” so we’ll soon witness more cancer survivors. 

Biotech organizations also use cloud computing to host and run computations and no longer need to buy expensive computer hardware for their research. This fact is a substantial benefit for early-stage startups with limited funding to enter the market with their research and medications. Cloud computing is also handy for established medical corporations, allocating resources for new projects cheaper and more manageable. 

What is the future of AI and machine learning in the biotechnology industry?

Biotechnology is an innovative industry that effectively solves scientific problems with living organisms. But new issues continuously arise and require biotechnologists applying modern methods to be solved. 

Thus, to remain relevant, biotech specialists must make room for improvements. Fortunately, there are many solutions they can apply – AI, data analytics, deep learning, and others we’ve already listed in this article. 

Thus, AI, machine learning, and robotics play critical roles in pushing the boundaries of possibilities in medical, industrial, or agricultural biotechnologies, and will remain relevant for subsequent decades. 

The APP Solutions has experience developing and integrating AI functionality into biotech projects. You can learn more about our expertise in creating a real-time DNA sequence analysis application during our partnership with the Google Cloud Platform and the Queensland University of Technology. Don’t hesitate to contact us if you need experts to advise and develop intelligent software for your biotech project.

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What is EHR (electronic health record), and how does it work?

Healthcare and data science are something of a perfect pair. Healthcare operations require insights into patient data to function at a practical level. At the same time, data science is all about getting deep into data and finding all sorts of interesting things. 

The combination of these two resulted in the adoption of Electronic Health Records (EHR) that use a data science toolkit for the benefit of medical procedures.

In addition to this, healthcare is the perfect material for various machine learning algorithms to streamline workflows, modernize database maintenance, and increase the accuracy of results.

In this article, we will explain what EHR is and how machine learning makes it more effective.


What is EHR?

Electronic Health Record (aka EHR) is a digital compendium of all available patient data gathered into one database. 

The information in EHR includes medical history, treatment record data such as diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results.

  • The adoption of EHR in the industry kickstarted in the late 90s after the enacting and signing of HIPAA (Health Insurance Portability and Accountability Act) in 1996. 
  • However, due to technological limitations, things proceeded slowly. 
  • The technology received a significant boost after the passing of the HITECH (Health Information Technology for Economic and Clinical Health) Act in 2014 which specified the whats, whys, and hows of EHR implementation.

The main goal of implementing EHR is to expand the view of patient care and increase the efficiency of treatment.


In essence, EHR is like a good old patient’s paper chart which expands into a full-blown, interactive, data science dashboard, with real-time updates where you can examine the information and also perform various analytical operations. 

  • Think about it as a sort of Google Account type of thing, where your data is gathered into one place and you can use it for multiple purposes with tools like Office 365 or the likes.

The critical characteristics of Electronic Health Records are:

  1. Availability – EHR data is organized and updated in real-time for further data science operations, such as diagnostics, descriptive analytics, predictive analytics, and, in some cases, even prescriptive analytics. It is available at all times and shared with all required parties involved in a patient’s care – such as laboratories, specialists, medical imaging labs, pharmacies, emergency facilities, etc. 
  2. Security – the information is accessed and transformed by authorized users. All patient data is stored securely by extensive access management protocols, encryption, anonymization, and data loss protection routines.
  3. Workflow optimization – EHR features can automate such routine procedures as recurrent Automate and streamline provider workflow. In addition to this, EHR automation can handle healthcare data processing regulations such as HITECH, HIPAA (USA), and PIPEDA (Canada) by implementing required protocols during data processing.

Electronic Health Records vs. Electronic Medical Record – What’s the Difference?

There is also another type of electronic record system used in healthcare operations – Electronic Medical Records AKA EMR. 

The main difference between EHR and EMR is the focus on different persons involved in medical procedures. 

  • EMR is a digital version of the dataflow in the clinician’s office. It revolves around a specific medical professional and contains treatment data of numerous patients within the specialist’s practice.
  • In contrast, EHR data revolves around the specific patient and his medical history. 

In one way or another, EHR intertwines with numerous Electronic Medical Records within its workflow. There is a turnaround of data going back and forth – medical histories, examination data, test results, time-based data comparison, and so on.

Read a more detailed overview of EHR/EMR differences in the article EHR, EMR and PHR Differences

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How AI/ML fits into Electronic Health Record?

As was previously mentioned, the availability of data is one of the primary benefits of implementing Electronic Health Records into medical proceedings. 

Aside from data being available for medical professionals at all times, the way medical data features in EHR makes it perfectly fitting for various machine learning-fueled data science operations.


Overall, machine learning is a viable option in the following aspects of Electronic Health Record:

  • Data Mining
  • Natural Language Processing 
  • Medical Transcription
  • Document Search
  • Data Analytics
  • Data Visualization
  • Predictive Analytics
  • Privacy and regulatory compliance

Let’s look at them one by one.

Data mining 

Gathering valuable insights is one of the essential requirements for providing efficient medical treatment. One of the challenges that come with gaining insights is that, in order to do that, you need to go through a lot of data. This process takes a lot of time.

With the increasing scope of data generated by medical facilities and its growing complexity – the use of machine learning algorithms to process and analyze information during data mining becomes a necessity. 

Overall, the use cases for Data mining in Electronic Health Record revolve around two approaches with different scopes:

  • Finding data about the patient and his treatment. In this case, ML is used to round up relevant information in the medical history and treatment record to assist further in the decision-making process. 
  • On the other hand, patient-centered data mining is used to assess different types of treatment and outcomes by studying similar cases from the broader EHR database.
  • Data extraction for medical research across multiple EHR/EMR, and also public health datasets. In this case, a machine learning application is used to gather relevant data based on specific terms and outcomes across the EHR database. For example, to determine which types of medication for particular ailments were proven to be active and under what circumstances.
  • On the other hand, the same tools apply for exploratory research that reshapes available data according to specific requirements — for example, examining test result patterns of annual lipid profiles.



Predictive Analytics

EHR is all about data analytics and making it more efficient. One of the most important innovations brought by Electronic Health Record is streamlining the data pipeline for further transformations.

The thing is – EHR machine learning-fueled data processing provides a foundation to identify patterns and detect certain tendencies occurring throughout numerous tests and examinations of a specific patient across multiple health records. 

  • With all patient data and respective reference databases intertwined into a single sprawling system – one can leverage the available data to predict possible outcomes based on existing data. 
  • Predictive analytics assist the doctor’s decision-making process by providing more options while considering possible courses of action.
  • On the other hand, machine learning predictive analytics reduces the time required to pro.  

Predictive analytics models are trained case-by-case on the EHR databases. The accumulation of diverse data allows them to identify common patterns and outliers regarding certain aspects of disease development or a patient’s reaction to different treatment methods.

Let’s take DNA Nanopore Sequencing as an example. 

  • The system combines input data (coming from the patient) with data about the illness and ways of treating it. 
  • The predictive algorithm determines whether a particular match of treatment will result in a positive outcome and to which extent. (you can read more about Nanostream in our case study).

Natural Language Processing

In one way or another, natural language processing is involved in the majority of EHR-related operations. The reason for that is simple: most medical record documentation is in a textual form combined with different graphs and charts to illustrate points.

  • Why not use a simple text search instead? Well, while the structure of the document is more or less uniform across the field, the manner of presentation may vary from specialist to specialist. NLP solution provides more flexibility in that regard.

The main NLP use cases for Electronic Health Record are the following:

  • Document Search – both as part of the broader data mining operation and simply as an internal navigation tool. In this case, the system uses a named-entity recognition model trained on a set of specific terms and designations related to different types of tests and examinations. As a result, doctors can save time on finding relevant information in the vast scopes of data. Depending on the purpose, the search results form via the following methods:
  • By context – locating information within the document – vanilla document search. For example, you can perform a comparison of physical examination reports criteria by criteria.
  • Terms/Topics/Phrases – extracting instances of specific terms used or topics mentioned. For example, a doctor can obtain all blood test results and put them into perspective.
  • Search across multiple documents;
  • One of the most prominent current applications is the Linguamatics I2E platform which also provides data visualization features.
  • Medical transcription – in this case, NLP is used to recognize speech, and subsequently, format it in an appropriate way (for example, break down into segments by context).
  • The speech-to-text component operates with a set of commands like “new line” or “new paragraph.”
  • Nuance Communications make one of the most prominent products of this category. Their tools, Nuance Dragon, augments EHR with a conversational interface that assists with filling data into the record.
  • Report generation – in this case, NLP functions as a form of data visualization in a textual form. These models are trained on existing reports and operate on specific templates (for example, for blood test results). Due to the highly formalized language of the reports, it is relatively easy to train a generative model based on term and phrase collocation and correlation. 
  • In this case, the correct verbiage is analyzed out of the habitual juxtaposition of a particular word with another word or words with a frequency higher than chance (collocation) and the extent to which two or more variables fluctuate together (correlation). 


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Data Visualization

Data visualization is another important aspect of data analytics brought to its full extent with the implementation of Electronic Health Records. 

Visualization is one of the critical components that make Electronic Health Record more effective in terms of accessibility and availability of data for various data science operations. 

  • The thing is – as an electronic health record is basically a giant graph with lots of raw data regarding different aspects of the patient’s state, as such, it is not practical to use it in this state. The role of visualization, in this case, is to make data more accessible and understandable for everyday purposes. That has to be obvious, right?

However, you can’t use the same data visualization template for every EHR. While the framework remains the same, it requires room for customization to visualize patient data on the EHR dashboard adequately. 

The role of machine learning in this operation parallels its role in data mining. However, in the case of data visualization, it is about interpreting data in an accessible form. 

At the current moment, one of the most frequently used visualization libraries in Electronic Health Record is d3. For example, we have used its sunburst and pie charts in the Nanostream project. 


Regulatory compliance, privacy, and patient data confidentiality

Healthcare is an industry that mostly operates with sensitive data through and through. Pretty much every element of healthcare operation, in one way or another, touches certain aspects of privacy and confidentiality. 

The fact is that integrated systems like EHR are vulnerable to breaches, data loss, and other unfortunate things that may happen to data in the digital realm. 

In addition to that, healthcare proceedings are bound by government regulations that detail the ins and outs of personal data gathering, processing, and storing in general, and specifically in the context of healthcare.

Such regulations as the European Union’s GDPR, Canada’s PIPEDA, and United States’ HIPAA describe how to handle sensitive personal data and what the consequences are of its mishandling.

The implementation of EHR makes compliance with these regulations much more convenient as it allows us to automate much of the compliance workflow. Here’s how:

  • Anonymization during data processing – in this case, patient data is prepared for testing, but non-crucial identifiable elements, such as names, are concealed.
  • Access management – EHR structure allows limiting access to patient data only for those involved in a patient’s treatment. 
  • A combination of encryption for data-at-rest and data-in-transit – the goal is to avoid any outside interference into data processing.


In Conclusion

The adoption of electronic health records and the implementation of machine learning elevates healthcare operations to a new level.

On the one hand, it expands the view on patient data and puts it into the broader context of healthcare proceedings.

On the other hand, machine learning-fueled EHR provides doctors with a much more efficient and transparent framework for data science that results in more accurate data and deeper insights into it.

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Best chatbot development trends and business applications

Predictions suggest that 80% of businesses will use chatbots by 2020. If you still haven’t integrated a chatbot to your business operations, you may be falling behind the competition. However, you have an opportunity to develop one in 2020. But before hiring chatbot development companies you need to be aware of the most popular chatbot trends for 2020. 

Below we have gathered industries that apply chatbots, benefits chatbots bring to business operations, and main trends to build a conversation interface for your business. 

Let’s start. 

Chatbot Overview: adoption across different industries 

Many industries currently apply chatbots however, the effectiveness of user interfaces varies from one industry to another. To find out whether a chatbot will suit your particular industry, check out the top industries profiting from chatbots:

Real estate

Every person who is looking for a house or apartment to buy has unique requirements. Real estate chatbots help businesses to gather customers’ needs for more personalized recommendations and validate leads, which helps sales managers spend less time answering questions. 


Apart from more personalized product recommendation chatbot usage, online retailers use chatbots to streamline the sales process. Now, chatbots help customers search for a product, place an order and pay for it, and even track the delivery of the order. 


Travel agencies use chatbots to help travelers to find the best trip, book a hotel, and even buy tickets. Besides this, chatbots are handy for providing travelers with local insights, weather forecasts and booking tables, and restaurants. 


Artificial Intelligence-powered chatbots perform as intelligent tutoring systems, providing a personalized learning environment for students. Chatbots analyze a student’s response and how well they learn new material. Moreover, an AI chatbot can teach students by sending them lecture material in the form of messages, like in a chat.

Related readings: 

HR and recruiting

In this industry, chatbots can automate each stage of communicating with a candidate. Recruitment agency chatbots can perform as advisors, automate the search for candidates, evaluate their skill set, and give feedback on whether or not a candidate qualifies for a particular job. 


Healthcare chatbots help patients to book appointments, refill prescriptions, and remind patients to take medications on time. Moreover, more advanced chatbots can monitor a patient’s health periodically, make diagnoses, and give advice on treatment plans. 

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Adopted by banks, chatbots can provide the user with information on their current account balance, report on expenditures, calculate taxes, and make money transfers to other bank accounts. 

chatbot adoption across industries

Consider that there are different types of chatbots. Rule-based or scripted chatbots answer simple questions, and smart agents help the user solve particular tasks via voice commands.

Accenture chatbot statistics show that rule-based bots bring the most benefits for such industries as: 

  • Healthcare (64%), 
  • Telecommunications (59%)
  • Banking (50%)

At the same time, voice assistants are handy in:

  • Food (56%), 
  • Banking (44%), 
  • And retail (35%)

Chatbots are also handy in the following business areas:

  • Customer service (95%)
  • Sales and marketing (55%) 
  • Order processing (48%)

Business benefits from using a chatbot 

Many businesses that have adopted chatbots are already receiving advantages from this technology. But how exactly do chatbots improve business operations? Let’s find out. 

  • Reduce customer support costs by 30%. A vast amount of chatbots are used for customer service by answering simple questions. In this way, by 2020, 85% of all customer interactions will be handled without a human agent, helping businesses to cut costs by $8 billion.
  • Increase income by 40%. Research shows that customers who interact with brands via social media networks spend 20-40% more than average customers. In this way, to receive the same benefits, you can integrate a chatbot into the social media accounts of your company. 
  • Increase lead generation. Thanks to their proactive nature, chatbots can start communication with your clients, taking them through the sales funnel. Besides this, chatbots can event capture customer’s details, thus, generate more leads.  
  • Increase user retention rate. As you may know, some chatbots are powered by Artificial Intelligence and machine learning. Thus, they can learn from each interaction and remember a client’s preferences. Since customers receive more personalized product recommendations, they become loyal to your brand. 

Top 5 chatbot predictions for 2021

Now, let’s find out what the future holds for the chatbot industry. 

1. Voice recognition chatbot technology 

Voice recognition chatbots will become more widespread in 2020. Why? Because this year, Google and Amazon, recognized tech industry giants, continue driving the “smart speakers” market. For example, Amazon alone has sold 100 million devices with built-in voice assistant Alexa. Moreover, 110 million Americans use voice assistants at least once a month. This market trend shows us that voice-based chatbots, driven by tech industry leaders and voice-powered chatbot platforms, such as PullString, will become even more popular in 2020. 

voice assistant chatbots

2. Smarter Bots

Rule-based chatbots no longer satisfy the needs of modern business, especially in terms of personal recommendations and customer engagement. Thus, it is expected that most companies that want to automate processes, will choose AI-based chatbots over scripted ones. As well as this, the high adoption of AI-based chatbots is also expected in mass media and live news. In this way, readers will no longer search for relevant news but instead receive personalized news recommendations. 

how ai chatbot works

[AI chatbot working logic]

3. Banking and insurance chatbots 

Chatbots for the banking sphere allow delivering more personalized customer service. According to statistics, 43% of online banking users want to solve their issues via a chatbot. 

A great example is Erica, a Bank of America chatbot that handles customer queries, anticipates customer needs by applying predictive analysis, and guides clients through complicated banking procedures. 

[Erica, a Bank of America chatbot ]

As for the insurance sector, businesses will continue to adopt AI chatbots since they have proven their effectiveness. Chatbots help insurance companies to educate clients in various fields, including inspections, submissions, documentation claim adjustments, and update them on the status of their claims. 

4. Data analysis 

Chatbots are becoming, not only a new form of communication but also a sales channel. Moreover, AI chatbots are able to, not only provide users with more personalized and relevant results but also help in data mining and analytic activities. Thus, by analyzing customer data received from interaction with clients, businesses can get even more valuable insights. 

5. Chatbot call centers 

Chatbots are no longer only conversation interfaces. In the year 2020, chatbots are expected to be used for automated call centers. Call centers will use this technology to receive information about an issue, as well as personal details. Thus, based on the information received, chatbots can switch a customer to the most qualified human agent. In this way, chatbots reduce the waiting time and improve the quality of customer care. 

The future of chatbots

chatbot matket prediction

[US chatbot market prediction]

In 2021, the chatbot market is expected to grow as over 80% of businesses will adopt chatbots. AI chatbots help businesses across different industries to automate sales, marketing, and customer care. 

With the high adoption of voice assistants produced by Amazon and Google, in 2020, there will be even more which are voice-based. Furthermore,  chatbots that participate in the sales funnel will become the new source of valuable customer information for online businesses. Next year, it is also expected that chatbots for banking and insurance companies will become even more popular. 

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Medical Imaging Explained

Healthcare is an industry permanently aimed at future technologies. It is one of those sectors eager to embrace emerging tech to see if it can make a difference in its quest to cure diseases and save people’s lives. 

Given the fact that healthcare proceedings are data-heavy by design, it seemed evident that sooner than later machine learning, in all its variety, would find its way to the healthcare industry. 

In that context, medical imaging is one of the most prominent examples of effective deep learning implementation in healthcare operations.

In this article, we will:

  • Explain the basics of medical imaging;
  • Explain how deep learning makes medical imaging more accurate and useful;
  • Describe primary machine learning medical imaging use cases;

What is medical imaging?

The term “medical imaging” (aka “medical image analysis”) is used to describe a wide variety of techniques and processes that create a visualization of the body’s interior in general, and also specific organs or tissues. 

Overall, medical imaging covers such disciplines as:

  • X-ray radiography;
  • magnetic resonance imaging (MRI);
  • ultrasound;
  • endoscopy; 
  • thermography; 
  • medical photography in general and a lot more.

The main goal of medical image analysis is to increase the efficiency of clinical examination and medical intervention – in other words, to look underneath the skin and bone right into the internal organs and discover what’s wrong with them.

  • On the one hand, medical imaging explores the anatomy and physical inner-workings. 
  • On the other hand, medical image analysis helps to identify abnormalities and understand their causes and impact. 

With that out of the way, let’s look at how machine learning and deep learning, in particular, can make medical imaging more efficient.

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Why deep learning is beneficial for medical imaging?

One of the defining features of modern healthcare operation is that it generates immense amounts of data related to a variety of intertwined processes. Amongst different healthcare fields, medical images generate the highest volume of data. And, it grows exponentially because the tools are getting better at capturing data. 

Deep inside that data are valuable insights regarding patient condition, the development of the disease/anomaly, and the progress of the treatment. Each piece contributes to the whole and, it is critical to put it all together into a big picture as accurately as possible. 

However, the scope of data often surpasses the possibilities of traditional analysis. Doctors can’t take into consideration so much data. 

This aspect is a significant problem given the fact that data Interpretation is one of the most crucial factors in such fields as medical image analysis. The other issue with human interpretation is that it is limited and prone to errors due to various factors (including stress, lack of context, and lack of expertise). 

Because of this, deep learning is a natural solution to the problem.

Deep learning applications can process data and extract valuable insights at higher speeds with much more accuracy. This can help doctors to process data and analyze test results more thoroughly. 

The thing is – with that much data at hand, the training of deep learning models is not a big challenge. On the other hand, the implementation of deep learning in healthcare proceedings is an effective way to increase the efficiency of operation and accuracy of results. 

The primary type of deep learning application for medical image analysis is a convolutional neural network (you can read more about them here). CNN uses multiple filters and pooling to recognize and extract different features out of input data. 

How deep learning fits into medical imaging?

The implementation of deep learning into medical image analysis can improve on the main requirements for the proceedings. Here is how: 

  • Provide high accuracy image processing; 
  • Enable input image analysis with an appropriate level of sensibility to certain field-specific aspects (depends on the use case. For example, bone fracture analysis).

Let’s break it down in an understandable term example, an x-ray of bones: 

  • Shallow layers identify broad elements of an input image. In this case – bones. 
  • Deeper layers identify specific aspects – like fractures, their positions, severity, and so now. 

The primary operations handled by deep learning medical imaging applications are as follows:

  • Diagnostic image classification – involves the processing of examination images, comparison of different samples. It is primarily used to identify objects and lesions into specific classes based on local and global information about the object’s appearance and location.
  • Anatomical object localization – includes localization of organs or lesions. The process often involves 3D parsing of an image with the conversion of three-dimensional space into two-dimensional orthogonal planes. 
  • Organ/substructure segmentation – involves identifying a set of pixels that define contour or object of interest. This process allows quantitative analysis related to shape, size, and volume.
  • Lesion segmentation – combines object detection and organ / substructure segmentation.  
  • Spatial alignment – involves the transformation of coordinates from one sample to another. It is mainly used in clinical research.
  • Content-based image retrieval – used for data retrieval and knowledge discovery in large databases. One of the critical tools for navigation in numerous case histories and understanding of rare disorders.
  • Image generation and enhancement – involves image quality improvement, image normalizing (aka cleaning from noise), data completion, and pattern discovery. 
  • Image data and report combination. This case is twofold. On the one hand, report data is used to improve image classification accuracy. On the other hand, image classification data is then further described in text reports.

Now let’s look at how medical image analysis uses deep learning applications.

Deep Learning in Medical Imaging Examples

Deep learning cancer detection

At the time of writing this piece, cancer detection is one of the major applications of deep learning CNNs. This particular use case makes the most out of deep learning implementation in terms of accuracy and speed of operation.

This aspect is a big deal because some forms of cancer, such as melanoma and breast cancer, have a higher degree of curability if diagnosed early. 

On the other hand, deep learning medical image analysis is practical at later stages. 

For instance, it is used to track and analyze the development of metastatic cancer. One of the most prominent deep learning models in this field is LYmph Node Assistant (LYNA) developed at MIT. The LYNA model trained on pathological slides datasets. The model reviews sample slides and recognizes characters of tumors and metastases in a short time span with a 99% rate of accuracy. 

In the case of skin cancer detection, deep learning is applied at the examination stage to identify anomalies and track its development. To do that, it compares sample data with available datasets such as T100000. (You can read more about it in our recent case study).

Breast cancer detection is the other critical use case. In this case, a deep learning neural network is used to compare mammogram images and identify abnormal or anomalous tissues across numerous samples.

Tracking tumor development

One of the most prominent features of convolutional neural networks is its ability to process images with numerous filters to extract as many valuable elements as possible. This feature comes in handy when it comes to tracking the development of the tumor.

One of the main requirements for tracking tumor development is to maintain the continuity of the process i.e., identifying various stages, transition points, and anomalies. 

The training of tumor development tracking CNN requires a relatively small number of clinical trials in comparison with other use cases. 

The resulting data reveal critical features of the tumor with various image classification algorithms. The features include tumor location, area, shape, and also density. 

In addition to that, such CNN can: 

  • track the changes of the tumor over time; 
  • tie this data with the impacting factors (for example, treatment or lack thereof). 

In this case, the system also uses predictive analytics to analyze tumor proliferation. One of the most common methods for this is tumor probability heatmap that classifies the state of the tumor based on the tissue patch overlap.

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Deep learning medical image analysis – MRI image processing acceleration

MRI is one of the most complicated types of medical imaging. The operation is both resource-heavy and time-consuming (which is why it benefits so much from cloud computing). The data contains multiple layers and dimensions that require contextualization for accurate interpretation.

Enter deep learning. The implementation of the convolutional neural network can automate the image segmentation process and streamline its proceedings with a wide array of classification and segmentation algorithms that sift through data and extract as many things of note as required.

The operation of MRI scan alignment takes hours of computing time to complete. The process involves sorting millions of voxels (3D pixels) that constitute anatomical patterns. In addition to this, the same process is required for numerous patients time after time. 

Here’s how deep learning can make it easier. 

  • Image classification and pattern recognition are two cases in which neural networks are at best. 
  • The convolutional neural network can train to identify common anatomical patterns. The data goes through multiple CNN filters that sift through it and identify relevant patterns.
  • As a result, CNN will be capable of spotting anomalies and identify specific indications of different diseases.

The segmentation process may involve 2d/3d convolution kernels that determine the segmentation patterns. 

  • 2D CNN slices the data one-by-one to construct a pattern map;
  • 3D CNN uses voxel data that predicts segmentation maps for volumetric patches.

As such, segmentation is a viable tool for diagnosis and treatment development purposes across multiple fields. In addition to that, it contributes significantly to quantitative studies and computational modeling, both of which are crucial in clinical research.

One of the most prominent implementations of this approach is MIT’s VoxelMorph. This system used several thousand different MRI brain scans as training material. This feature enables the system to identify common patterns of brain structure and also spot any anomalies or other suspicious differences from the norm.

Retinal blood vessel segmentation

Retinal blood vessel segmentation is one of the more onerous medical imaging tasks due to its scale. The thing is – blood vessels take just a couple of pixels contrasting with background pixels, which makes them hard to spot, not to mention analyze at the appropriate level.

Deep learning can make it much more manageable. However, it is one of the cases where deep learning takes more of an assisting role in the process. Such neural networks use Structured Analysis of the Retina, aka STARE dataset. This dataset contains 28 999×960 annotated images. 

Overall, there are two ways deep learning improves retinal blood vessel segmentation operation:

  1. Image enhancement can improve the quality of an image.
  2. Substructure segmentation can correctly identify the blood vessels and determine their state. 

As a result, the implementation of neural networks significantly compresses the time-span of workflow. The system can annotate the samples on its own as it already has the foundational points of reference. Because of that, the specialist can focus on the case-specific operations instead of manually reannotating samples every time. 

Deep learning cardiac assessment

Cardiac assessment for cardiovascular pathologies is one of the most complicated cases that require lots of data to spot the pattern and determine the severity of the problem. 

The other critical factor is time, as cardiovascular pathologies require swift reaction to avoid lethal outcomes and provide effective treatment. 

This is something deep learning can handle with ease. Here’s how. Deep learning fits into the following operations:

  • Blood Flow Quantification – to measure rates and determine features.
  • Perform anomaly detection in the accumulated quantitative data. 
  • Data Visualization of the results. 

The implementation of deep learning in the process increases the accuracy of the analysis and allows doctors to gain much more insight in a shorter time. The speed of delivery can positively impact the course of treatment.

Musculoskeletal Radiograph’s Abnormality Detection 

Bone diseases and injuries are amongst the most common medical causes of severe, long-term pain and disability. As such, they are a high testing ground for various image classification and segmentation CNN use cases.

Let’s take the most common method of bone imaging – X-rays. While the interpretation of images is less of a problem in comparison with other fields, the workload in any given medical facility can be overwhelming for the resident radiologist. 

Enter deep learning: 

  • CNN is used to classify images, determining their features (i.e., bone type, etc.) 
  • After that, the system segments the abnormalities of the input image (for example, fractures, breaks, spurs, etc.).

As a result, the implementation of deep learning CNN can make the radiologist’s job more accessible and effective.

In Conclusion

From the data volume standpoint, medical image analysis is one of the biggest healthcare fields. This alone makes the implementation of machine learning solutions a logical decision. 

The combination is beneficial for both.

  • On one hand, medical imaging gets a streamlined workflow with faster turnaround and higher accuracy of the analysis.
  • On the other hand, such an application contributes to the overall development of neural network technologies and enables their further refinement.

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