Deep Learning In Healthcare

Deep learning is a part of the machine learning family. It is based on data learning representations and methodologies. In deep learning, there are three major classifications. They are supervised deep learning, unsupervised deep learning and semi-supervised deep learning. Deep learning is mainly used in recognizing speech, natural language processing, computer visions, social media networks, medical analysis, the design of the drug, bioinformatics, machine translations, game programs on board, and inspection of materials.

Data learning gets its inspiration from the processing of information and communication in medical nervous systems. The properties of data learning differ in structures and functionalities that make them incomplete. For example, the human brain. Each human brain is unique and differs in structure and functioning.

Deep learning in health care

To find out how deep learning can be used in healthcare, we must first look into the health care treatments offered by deep learning. So, Deep learning in health care is used to assist professionals in the field of medical sciences, lab technicians and researchers that belong to the health care industry.

Deep learning in health care helps to provide the doctors, the analysis of disease and guide them in treating a particular disease in a better way. So, the medical decisions made by the doctors can be made more wisely and are improving in standards.

Deep learning in various spheres of health care

  • Genomics – Deep learning technique is used to understand a genome and helps the patients undergoing treatment to get an idea about the disease that might affect them in the future. Genomics is a field which is steadily growing, and deep learning runs to have an excellent future with the help of insurance industries. Deep learning techniques are used to make medical field researchers and doctors fast and more accurate.
  • Cell scope – A Cell scope uses deep learning technology and it also helps the parents to keep monitoring the health conditions or the health status of their children. This technology can be seen on any devices and decreases the visits of parents to hospitals or health care centers to see their children or consult the doctor.
  • Insurance fraud – Deep learning is used in the analysis of medical insurance claim fraud. Deep learning uses a technique known as predictive analysis which can predict the fraud claims that are likely to happen in future. Deep learning helps the insurance industry to send offers and discounts to the target patients.
  • Medical imaging – There are a few techniques involved in the medical industry such as CT scan, ECG, MRI etc. that are used to diagnose harmful diseases. The harmful diseases include brain tumor, heart attacks, cancer, and many others. Hence, deep learning can be used to consult doctors who analyze the patient’s disease and provide them with good treatment.
  • Discovery of drug – Data learning helps in medicine discovery and is also used in developing them. A patient’s medical history is analyzed, and treatment is given according to the analysis. By the deep learning in health care, we can gain insight from the patient’s tests and reports and disease-related information regarding the symptoms.
  • Alzheimer’s disease – It is one of the most crucial challenges that medical industry clients are facing currently. Deep learning is used here to detect Alzheimer’s disease at its initial stage itself, making it more convenient for doctors to treat.

Deep learning solutions in health care and other industries

In deep learning, there are various hidden patterns and chances in helping doctors to treat their patients well. Machine learning, artificial intelligence has gained a lot of attention over the past few years. Now even deep learning has created its own path in this field and is steading growing in the market. Industries like health care, travel, and tourism, finance, retail and textiles, manufacture, health etc are relying on deep learning directly or indirectly. The first priority of any individual in today’s modern world is his or her health.

So, the health industry is one such platform that implements some technologies like deep learning for fulfilling their needs. Medical experts try to find various ways of implementing new technologies. These technologies must provide an impactful result for a better future. Deep learning puts together a bulk of data that included records of the patients, medical reports, personal data, insurance reports etc. for providing them with better treatment for a good outcome.

Current and future directions in health care and medical domains

Insights into deep learning in health care current and future applications are evident and can be clearly observed. Some of them are:

    • On gaining knowledge from complex, heterogeneous and high-dimensional bio-medical information, remains a challenge in transforming the health care industry. Different types of data are emerging in the modern world of medical sciences. Issues faced in imaging, sensor text, sensor data, electronic records etc. are solved by using deep learning algorithms. Deep learning makes the un-structured and complex examples into successful representations. The latest technology of deep learning provides new and efficient paradigms in obtaining the end to end learning models for complex data types.
    • Usage of electronic health records (EHR) promises to advance clinical research and better inform the decision-making skills clinically. Modern electronic health records can prevent the practice of predictive modeling by summarizing and representation. Patients who achieved results based on electronic health record data and an alternative feature learning strategy are being successful. From various research’s we can straight away say that deep learning framework arguments decision systems in the clinical environment.
    • Food and drug administration have finally approved the plasminogen activator streptokinase and urokinase and therapy can no longer be an approach for treating the thrombosis disease. Deep learning is used in treating acute peripheral arterial thrombosis and embolism along with acute coronary thrombosis.
    • Deep learning is a family of many computational methods that allow any algorithm that programs itself by using a learning method from a large set of examples that demonstrate the desired behavior. As an application of this method, there is a further assessment and validation to medical imaging.
    • Deep learning trained algorithms have some outcomes and measures. The specificity and the sensitive nature of the algorithm in detecting reference diabetic retinopathy (RDRs) can be defined as moderate and also as the worst ones. So deep learning trained algorithm was evaluated at two operating points which are selected from a development set out of which one handles high specificity and another one handles high sensitivity thereby providing better results.
    • Electronic health recorder or EHR with predictive modeling Dara is anticipated to solve personalized health quality in medicine. So, for constructing predictive statistical method, we require extraction of curated predictor variables from EHE data that is normalized and a process that discards the majority of information in each patient’s record.Deep learning after using this representation can be capable of predicting multiple medical events from various medical centers without any site-specific data harmonization. Data learning approaches can be used to create an accurate and scalable prediction for a variety of sceneries regarding clinical resources. In deep learning, neural networks can be used for identifying relevant information from the patient’s chart and records.
  • Data learning algorithms are convolutional networks that have become a methodology by choice. They are being used to analyze medical images. Deep learning can further be used in medical classification, segmentation, registration, and various other tasks.Deep learning is used in areas of medicine like retinal, digital pathology, pulmonary, neural etc. Deep learning is a steadily developing trend in the field of data analysis and has also been named one of the 10 breakthrough technologies in the year 2013. Deep learning is an advancement of artificial neural networks which consist of more layers at higher levels of abstraction.

    There is an improvement in the predictions from the data developed using deep learning algorithms. Deep learning is emerging as a very important machine learning tool in imaging, convolutional neural networks, computer domains vision etc.

  • Health informatics is emerging as a domain of interest among researchers all over the world. Therese researchers owe the majority of the implications on society. Right from the prediction of a disease to providing personalized services to patients, applications in deep learning range. Biomedical data in the health care industry has gained knowledge about many applications based on techniques followed by deep learning.
  • The health care field of modern era comprises various strategies that are of national importance owing to their spectrums of reach to individuals or society. We have earlier witnessed the advancements in machine learning and artificial intelligence technologies applied in various domains. Among all the domains, health care is in the primary focus of deep learning and machine learning researchers and experts of the industries owing to the veracity of data availability and high volumes.
  • The increase of health care sectors is being characterized by large data sets from clinical management systems. This provides a choice for any application of deep learning approaches on health care data sets, which may be sparse.

Benefits of deep learning

There are various benefits of deep learning in the health care industry. Some of them are:

  • Deep learning learns the important relationships in your data and records the information about past clients which can be used a future reference for the patients with similar symptoms or diseases.
  • Deep learning allows us to create a model-based on whatever source of data available when you require a risk score upon administration other than discharge.
  • Deep learning provides accurate and timely risk scores which enable the confidence and approximate allocation of resources.
  • Deep learning approaches lead to lower costs and provide improved outcomes.
  • When the deep learning algorithms interact with the training data, they become more precise and accurate allowing individuals to gain unprecedented insights into care processes, variability, and diagnostics.
  • Graphics processing units or GPU’s are getting more efficient to energy and are becoming faster.
  • Innovation is exploding as we now use deep learning algorithms at a fraction of the past costs and so algorithms are getting sophisticated.
  • Electronic health records or EHR and other digitization efforts are giving health care data the access to use trained algorithms than ever before.
  • Diagnostics are being more accurate and faster through deep learning which identifies patterns by connecting the tools.
  • Deep learning can determine whether the skin lesions are cancerous just like any other board certificated dermatologist.


Based on all the analysis done to deep learning by the different researchers, it is clear that deep learning can be an element for translating biomedical data into improved human health care. On the other hand, the latest advancements in deep learning technologies provide new useful and effective paradigms to prove the end to end learning models for uncertain and complex data structures.

Written By : Technostacks

Xcode 10

The newly released Xcode build the system with Xcode 9 by Apple is in the preview mode. The advanced features were not active during that time. The activated Xcode 10 features by default have some issues in the iOS projects since Apple is aware of these, they have separately issued new build system. They have also mentioned possible solutions to tackle those issues.

We are here to highlight the top 5 issues iOS developers might face and they might not be covered in the recent release notes, e.g. Xcode 10 system requirements and the system behaviour of new build with third-party tools.

Xcode 10 Features: The Newly Build System

You can now activate new build settings from Xcode Files-> Project/Workspace Settings with this toggling between legacy and new build system becomes easy. Additionally, if you are building an iOS project right from the command line using the Xcode build then it is required to pass additional parameter such as UseModernBuildSystem=YES.
The latest build system is called as the xcbuild. The new build system elevates the overall swift build by running the targets and its build phases side by side. Once it is activated you will face both its benefits and issues in regard to the new build system in your project. Here we will try to identify the issue and get solutions immediately.

1. The Info.Plist

When an iOS project is built using the new build system you will face several issues regarding the info.plist files. Here are the few things you need to keep in mind regarding New Build System and info-plist files.

  • Make sure there is no duplicate plist file in the copy bundle resources in the build phase of any target. Otherwise you will not be able to create an app with the new build system. Additionally, the files copied multiple times will hamper the functioning.
  • The new build system functions on various precedence of running info-plist step within the clean and incremental builds. In the clean build, you will find that the info.plist steps after processing assets, whereas the incremental build runs before signing.
  • If there only info.plist value and does not have the Xcode reference folders then you will face an Xcode build system failure.

2. The CocoaPods

CocoaPods bring some issues for the iOS projects.

  • With CocoaPods development pods will not be updated unless a clean build is performed. The embedded pods are not executed successfully.
  • Some of the Cocoapods build phase script doesn’t run reliably as you may see disbursement in its behaviour and you might not be able to archive the app.

Hence, this makes it clear that cocoapods and the new build system don’t get along well together.

3. Running the Script Phase

The new build system is full of flaws with the Run Script Phase started giving false results. But this too comes with a good reason.
The new Xcode 10 features have a lot of improvement in the Run Script Phase. However, it requires you to help build processes with the help of feeding files for the run script phase. You will have to specify the input files to the run script phase, this is important to determine whether the script needs to be run or not.
If Xcode build system runs parallel commands then the input for the run script phase will not be generated, this fails the build system as it gets confused. However, providing the input files to run scripts is advisable as when the inputs grow in number it gives a way to specify all the input files in the .xcfilelist format. Hence it is recommended to add the files to avoid running this phase for all the other incremental builds whenever it is not required.

4. Clean Build Folder Action

With the new build system, a clean build folder has been introduced. This introduction eliminates all the derived data of the iOS application causing the cleaner builds right from scratch. This step means if you are using cocoapods all the frameworks will be rebuilt from the scratch and you will face a delay in developing an iOS project. There may be Xcode indexing issues as well.
The new build system by Apple is introduced to improve the performance, reliability, and stability of the Swift build. This system is designed to capture the configuration errors early in the application development phase. In Xcode 10 mascos version so you don’t have a choice and you will have to update the build process to adapt to the new build system. This scenario demands a lot of configuration enhancements in the app.


There are a number of iOS developers might be using .xcconfig files to keep the Xcode build settings at one place for the appropriate goals. There are some queries that conditional variable assignment in the xcconfig files might not work as wanted, because of the build failures. To ensure your xcconfig files, Apple suggested running following command.

defaults write EnableCompatibilityWarningsForXCBuildTransition -bool YES

If this command displays any errors or warnings, we must have to solve it to get stable builds.

Moving Forward

The recent Xcode 10 release date was September 17, 2018. Let us know have you tried the Xcode code dark mode yet or migrated to the new build system? If yes, what are your experiences? If you wish to share your views or get an issue resolved, get in touch with us without any hesitation.

Written By : Technostacks

top programming languages in demand

In this blog post, you will learn about the most popular programming languages in 2020 for creating the best web applications. check its pros and cons.

Not very long ago, just a few people were considered to be computer programmers, and the general public viewed them with awe. In this digital age that we are now living in, however, a large number of IT jobs need a solid grasp of one or more programming languages. Whether one wants to develop a mobile app or get a certification for having programming knowledge, or even to learn new skills, one needs to opt for the right programming language.

Below mentioned eight most popular programming languages which are in demand for software development and web applications. This is the most used programming languages in 2019 and will be in 2020.

For each, there is little information about the language, benefits and its complexity, as well as about its usage. One must decide for themselves which are the best programming languages for their own usage based on the below mentioned most utilized development languages.

About GitHub

GitHub has the knowledge about what’s going on in the world of coding. It has almost 24 million users located in 200 countries who are currently working on 337 different programming languages. These users include employees from some of the leading tech companies, such as Apple, Google, and Facebook. These employees rely on GitHub for the purpose of spreading their open source software to the world. GitHub community is continuously growing, contributing, and collaborating at a pace more than before.

Some interesting facts about It.

  • GitHub has been including more and more new developers as users in the year 2018 as compared to in the first six years combined.
  • There are almost 2.1 M+ organization’s employees on GitHub. This helps in bringing people together. This year there are 40% more organizations on GitHub as compared to last year.
  • There are almost 96 M+repositories that are hosted on GitHub. This is 40% more as compared to the previous year. More than one-third of all the repositories had been created in this last year.
  • There are almost 200 M+ pull requests that are created, ever. And more than one-third of them had been in the last 12 months.

Below mentioned are the most in demand programming languages of 2020 according to GitHub.

1. Python


Python is considered to be one of the most popular programming languages and It is said to be an easy language for the beginners to learn due to its readability.

It is a free, open-source programming language that has extensive support modules as well as community development. It has easy integration with the web services. It is equipped with user-friendly data structures and has a GUI-based desktop application. It is one of the most popular programming languages for Machine Learning along with deep learning applications. Also check,
How to apply machine learning in an android app.

Python has been used to develop 2D imaging as well as 3D animation packages such as Inkscape, Blender, and Autodesk. It has even been used to create famous video games, such as Civilization IV, Vegas Trike and the famous Toontown. Python is also used for scientific and computational applications such as FreeCAD and Abacus. Python is also considered to be one of the Trending Programming Languages 2020 by popular websites such as Quora, Pinterest, YouTube, and Instagram. As per GitHub, it is one of the most used programming languages.

Benefits of Python –

  • Naturally/Intuitively readable
  • Flexible
  • Scripted as opposed to compiled
  • Contains official tutorials as well as documentation

Drawbacks Python –

  • The language doesn’t begin with the programming basics
  • It is known to abstract too many of the important basic concepts

2. Java


Java is also one of the most common, in-demand Best Programming Languages in use today. It is owned by the Oracle Corporation. Java is a general-purpose programming language. It has an object-oriented structure, which has become a standard of applications which can be used irrespective of any platform (e.g. Mac, Android, Window, iOS, etc.) due to its Write Once, Run Anywhere (WORA) feature. It is as a consequence of this feature that Java is well known for its portability across a wide range of platforms ranging right from the mainframe data centers to the smartphones.

Presently there are more than almost 3 billion devices that are running applications that are built with the help of the Java. No wonder that Java is considered in the list of most popular Programming Languages 2020 and currently one of the top programming languages in demand.

Java has been widely used for the purpose of web and application development along with Big Data. It is also used at the backend of many of the popular websites, such as Amazon, Google, Twitter, and YouTube.

Java is also used in hundreds of applications. The New Java frameworks such as Spring, Struts, and Hibernate have also become very popular. Having millions of Java developers across the globe, there are a number of texts available in order to learn Java programming.

Benefits of Java –

  • It is considered to be a good start for the purpose of beginning to think like a programmer
  • Easy to access/manipulate the extremely important computer parts such as the file system, graphics, and the sound for any kind of fairly sophisticated or modern program which can run on any kind of operating system

Drawback of Java –

  • A large number of new vocabularies to learn

3. JavaScript


JavaScript is said to be an object-oriented computer programming language, that is commonly used for the purpose of creating interactive effects within the internet browsers.

Typescript is considered as a superset of JavaScript and it adds optional static typing features to the language software. Alongside with HTML and CSS, JavaScript is also seen as one of the three core existing technologies of the Web. It is even used at the front end of many of the popular websites such as YouTube, Facebook, Google, Wikipedia, and Amazon.

JavaScript is also used in a number of popular web frameworks such as AngularJS, Node.js as well as React.js. As per GitHub, it is one of the top Programming Languages 2020.

Benefits of Javascript –

  • Swiftness, simplicity, interoperability, rich interfaces versatility and extended functionalities

Drawbacks of Javascript –

  • Issues with client-side security, client-side scripts and browser support interpretations

4. PHP


Image Source:-

PHP is programmed with the help of built-in web development capabilities. The programmers can embed the code written in server-side programming language flawlessly into HTML code throughout the Script tag. It is considered as one of the most in demand programming languages in 2020.

However, web developers cannot write bulky and multifaceted websites and web applications swiftly without executing PHP code all the way through an assortment of web frameworks. There is a plenty of PHP development tools available which makes this language more efficient.

In accumulation to promoting brisk web application development, the PHP frameworks even make it straightforward to work with web application development solutions by enabling a basic structure. Some of the examples of these frameworks are Laravel, Codeigniter, Symfony etc.

The features and tools offered by these web frameworks even facilitate better development by adding useful features and functionalities to the web application and carry on web development tasks without writing prolonged and composite code.

The web developers even have alternatives to pick from an extensive range of PHP frameworks. Most of these frameworks are open source and can be utilized without paying any of the licensing fees. Some of these PHP frameworks fall in the categories of full-stack web frameworks, while others are micro frameworks. But it is also significant for the web developers to comprehend the benefits and drawbacks going with PHP and its different frameworks in the coming years.

Benefits of PHP –

  • Speed up tailored app development and simplify maintenance
  • No require to write added code and handle databases resourcefully
  • Automate customary development activities and reduce overall costs

Drawbacks of PHP –

  • Need to learn PHP frameworks in place of PHP
  • Quality of PHP frameworks be at variance
  • Lack of choices to transform core behavior

5. Swift

Swift programming language

During March 2017, Swift had made it to the top 10 of the monthly TIOBE Index rank of the most popular programming languages. It had developed by Apple in the year 2014 for the usage in Linux and Mac applications.

Swift is an open source programming language that is easy to learn. It supports almost everything that is ranging from the programming language to the Objective-C. Swift requires less coding as compared to other popular programming languages, and it can be easily used with the IBM Swift Sandbox and the IBM Bluemix.

The software is used in a large number of popular iOS apps such as Mozilla Firefox, WordPress, SoundCloud and also in the game of Flappy Bird.

Benefits of Swift –

  • Open source language and simple to work on
  • It is the future of Apple’s development
  • Safe, secure and uses modern programming conventions

Drawbacks of Swift –

  • Considered to be unstable due to major alterations in each release
  • Reduced interoperability with the 3rd party tools and IDEs

6. C#

c sharp

Image source:-

Created by Microsoft, C# rose to fame in the 2000s for supporting the concepts of object-oriented programming. C is considered to be one of the most powerful programming languages in the Dot NET framework.

Anders Hejlsberg, the creator of the C# language, says that the language is much more like the C++ than it is like Java. The language is best suited for the applications that are based on Windows, Android as well as iOS since it takes the aid of an integrated development environment product that is Microsoft Visual C++.

The C# is used in a number of backend operations of many popular websites such as Dell, Bing, Visual Studio and Market Watch.

Benefit of C# –

  • Easy to find supplementary developers whether it’s for a contract basis or full-time working model

Drawback of C# –

  • Often tough to work with as your code needs to be compiled every time you make even a small change

7. C Progamming (and C++)

C and C++ programming

Image Source:-

The C language is probably considered to be the oldest and the most commonly used programming language. This language is said to be the root of the other programming languages like C#, Java, as well as JavaScript.

The C++ language is said to be an enhanced version of the C language. A number of developers, in the current scenario, skip the process of learning the C language on its own, whereas there are others who think of the aspect that learning C first gives a valuable foundation for the C++ development. Both of these languages are being widely used in the area of computer science along with programming.

The C and the C++ developers can make the use of compilers for a large variety of platforms, making the applications that are developed in these languages easily transportable. Both the languages, C and C++, are considered to be high-performance languages and also the most used programming languages as per GitHub.

These software languages are widely used for the purpose of developing applications where the performance is considered to be a critical issue, for example, the client/server applications, the commercial products such as Adobe and Firefox, as well as in the video games.

Benefits of C –

  • C – It is used to learn the basics of programming at the starting (hardware) level
  • C++ – Allows the user for a much better “control” as compared to the other languages

Drawbacks of C –

  • C – The coding in C is stricter, and not a very beginner-friendly language, it has a steeper learning curve
  • C++ – it is a little more difficult to learn and become productive with it than C

8. Ruby


Ruby is considered to be a language that is well – known for being comparatively simple to learn and implement. It was developed in the 1950s. Ruby was designed to possess a more human-friendly syntax and still be flexible from a standpoint of the object-oriented architecture that it supports in the procedural and the functional programming notation. The web-application framework which is implemented in Ruby is called as Ruby on Rails (“RoR”).

The Ruby developers have touted it for being a simple language to write in and even for the comparatively short learning time required. Such attributes have contributed to building up a large community of Ruby developers along with a growing interest in this language among the beginning level of developers.

Benefits of Ruby –

  • Straightforward and swift creation of web applications
  • Refined, sturdy and easy to decipher

Drawbacks of Ruby –

  • Low runtime speed affects performance
  • Less boot speed affects stability

Key Takeaways

Thus, it can be seen that there are a number of languages available for the purpose of programming. GitHub provides us with extensive knowledge of the popular languages that are available for the purpose of coding and programming. One can decide for oneself based on the information provided which language they wish to opt for based on the convenience and need of the project at hand. Each programming language comes with a set of benefits and drawbacks. Thus, the choice is totally individualized.

If you have any question or planning to develop a web or mobile application then you can contact us. We at Technostacks (leading web and mobile development company in India) here to offer quality app development solution for your business. We have experienced team of web developers who are able to satisfy your requirements.

Written By : Technostacks

AR Quick Look
At WWDC 2018, Apple released ARKit 2.0 with fresh APIs, functionalities and features for Augmented Reality (AR) development. One of these key features was an accumulation to their quick look APIs.
Quick look overall is a framework that enables users to get a preview of a complete range of file formats such as PDFs, images, and much more. For an instance, the mail application in iOS utilizes quick look to preview the needed attachments.
With iOS 12 AR quick look, 3d models of products in the .usdz file format can be uploaded straight away to the online stores and observed in AR right within the safari version, without the need of downloading a different application.
In Object Mode, the device you are utilizing does not require to be AR proficient if it is operating the iOS 12 or the safari version on the macOS. It is in fact a studio viewer where the object is rendered on a flat white background and is all set to be interrelated with. You can pan around your object viewing it at diverse angles, you can zoom in to display finer details, and get access to see animations if your model supports the same.

In AR Mode, you can always place your 3D object straightforwardly into the physical space. The AR Viewer smartly collects lighting information from the real world and renders the object to go with the environment it’s in. A grounding shadow is functional to your object to make it feel as though the object is in fact sitting right on the surface in the direct front side of the given viewer.

AR quick look is a latest technology that could alter the way we can view the media on the web and in our mobile apps.

1) First add .usdz file in your app, ensure that you check the target box as shown below.

AR quick look

2) Add models list array.

check the target box

3) Open QLPreviewController for seeing the 3d model in the scene by directly putting it into the physical world.

Finally, build and run your application. Ensure that the app runs on a real device running iOS 12. As while running the app on a simulator won’t showcase the needed Quick Look preview.

Quick Look preview

Moving Forward

It works as projected by integrating AR Quick Look into your apps. However, that’s not all as AR Quick Look even offers the required web support. So, you can build a web portal utilizing both the HTML and the AR Quick Look.

Here is a video presensentation for AR quick look.

If you have any doubt, then you can contact us. We will give you the right solution for your query. Technostacks Infotech is a leading AR app development company which has vast experience in developing AR based apps for both Android and iOS. Hire us for App development project.

Written By : Rahul - iOS Team lead

blockchain technology in healthcare

Blockchain was created in the year 2008 by Satoshi Nakamoto, a pseudonymous person and was utilized as a major element of the digital currency, ‘bitcoin’.

What is Blockchain Technology?

The blockchain is the technology that took the industry by storm by its groundbreaking work in data handling and its exchange. Its enormous success across diverse industries has left even the healthcare space to fluster with queries. People are considering it the ‘solution to interoperability,’ and the technology that can resolve healthcare’s alarming issues,’- however, if the perplexity looms, blockchain will take more time than the required to make a difference in the healthcare space.

Benefits of Using Blockchain Technology in Healthcare

  • Single, longitudinal patient records
  • Longitudinal patient records can better compile episodes, disease registries and lab outcomes. The treatments can be attained via blockchain, comprising inpatient, ambulatory and wearable data assisting enablers, also coming up with improved ways of delivering healthcare services.

  • Master the patient indices
  • Time and again when dealing with healthcare information and data, records get incompatible or turn duplicated. Even diverse EHRs have altered schema for each single field – coming up with ways of inflowing and influencing the simplest of required data sets.

    With blockchain, the intact data set is being hashed to a required ledger, and not mere the primary key. The user would look for the address – there can be manifold addresses and numerous keys, but they will all capitulate to a solitary patient recognition.

  • Claims adjudication
  • Since blockchain operates on a validation-based exchange, the claims can be automatedly verified where the network accepts upon the way a contract is carried out. Even since there is no centralized authority, there would be lesser errors and frauds.

  • Supply chain management
  • Blockchain contracts can help healthcare organizations in tracking supply-demand cycles via its complete lifecycle – how is the transaction being executed, whether the contract is flourishing, or if there are any further delays.

  • Interoperability
  • Interoperability, the assurance of blockchain, can be comprehended by the utilization of APIs to make EHR interoperability and data storage a steadfast procedure. With a blockchain network being shared with authorized services providers in a secure and uniform way that would get rid of the cost and burden connected with data reconciliation.

    Other than these benefits, blockchain can even transform drug supply management, the revenue cycle management, required clinical trials and put off the frauds.

How to Implement Blockchain Technology in Healthcare with its Applications

Having explored the significance of blockchain technology in the healthcare sector along with the alterations that it could bring, here are some of the use cases that use the latent of the technology and could make the healthcare industry easier to get to, secure and trustworthy.

  • Managing the Population Health Data
  • Population health data refers to the medical data of a specific demographic. For instance, it may be health risk data for women suffering from thyroid in the group of 25 to 40 years. To comprehend the risks across a diverse population, the data is typically offered in an anonymized form and no names are publicized in these cases.

    Blockchain provides a consistent solution to this precise challenge. When applied accurately, blockchain will allow enhanced Security, data sharing, interoperability, data integrity and Real-time modernization with required access.

    Utilizing blockchain technology can enable people to contribute in population health studies to and monetize their data in the medium of tokens. Moreover, superior data and sharing of population health data can perk up healthcare delivery transversely via diverse populations. With more data sets, the use of new technologies like ML and AI would be possible which will lead to discovering extensive risks of population health.

  • Securing the Healthcare Setups
  • The present healthcare system and organizations work via one single central database. This database is handled by one entity in the organization. In this way, the point of failure comes to one sole point. In such scenarios, if a hacker or an anti-social component attacks the system, the concerned user can way in the on the whole database and would put the patients as well as the organization in risk.

    Blockchain can be used to safeguard the internal infrastructure of a setup. A large-sized organization with numerous independent actors having diverse levels of a way in on a blockchain ledger along with the encryption embedded on the blocks will protect organizations from outside attacks. If a blockchain network is directed precisely in a healthcare organization, it would avert such ransom attacks and other problems such as data corruption and hardware failures.

  • Making Patient Payments via Cryptocurrencies
  • Another tempting application of Blockchain in healthcare is the utilization of cryptocurrencies as payments instead of cash or fiat money. The cash healthcare practices are in occurrence however, the health care expenses are not defined as such.

    By having blockchain systems and applications implemented the possibility of offering the precise solutions and eliminating frauds has amplified. Bill processing automation removes the 3rd parties from the complex chain and has reduced the overall administrative costs. In addition, when larger institutions will adopt payment processing through cryptocurrencies, a major reallocation would take place. Each penny paid to the Medicare would be monitored and make sure that no frauds are made during the procedure.

  • The Drug Traceability
  • Counterfeiting of drugs along with forged drugs in the supply chain leads to a big loss of billions of dollars every year. As per the Health Research Funding Organization (HRFO), report, 10 % to 30 % of drugs in developing nations are not original.

    The key feature of blockchain technology that can be applied in medicine traceability is its precise security. With every new transaction which are added to a block will be irreversible as well as time stamped. This will make it trouble-free to trail a product and make sure that the data in the block cannot be distorted.

    Likewise, the attribute of data transparency in a blockchain system can lend a hand to find the entire path of origin, thus, serving in getting rid of the exchange of fake drugs.

Moving Forward

There is a rapid development of blockchain in financial applications. However, the blockchains latent for healthcare relies on how willing healthcare organizations are to build the needed technical infrastructure. The blockchain is expensive, there is some apprehension concerning its integration with the existing technology, and there surely is some critical assumption about its cultural adoption in healthcare space.

Written By : Technostacks

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