Category: Innovation

Healthcare Transformation Using Cognitive Services

Posted on by Subhadeep Datta

Srinivasan Sundararajan

Democratization of AI in Healthcare

Organizations are becoming increasingly digital and Artificial Intelligence is being deployed in many of them. Often small tech-savvy start-ups and large firms with huge funds, like those in technology and finance businesses, are deploying sophisticated forms of AI.

But several other companies are being left behind. They may not know how or where to deploy AI, or they may not have the resources to create their own AI. Cloud technologies are filling this gap. With options from Google, AWS, Microsoft, and plenty of other vendors, companies can begin exploring how AI can help them. The more that AI becomes accessible, the more companies – and users – can leverage it for their benefit. 

Healthcare is often cited as an area that AI can help immensely. The democratization of AI in healthcare, which is being driven by cloud technologies, is leading to greater access and more predictive work in patient monitoring and smarter reactive responses to health issues. 

ML and AI have traditionally been perceived as the domain of experts and specialists with PhDs. While democratization of AI is viewed differently by different organizations, a common theme has been to make AI adoption simpler.

The following are a few democratized AI services available as part of cloud providers (most of the examples are from Microsoft Eco System as a reference, however other providers also have similar services).

Handwriting Recognition

ai-led operations consulting firm in healthcare

With Windows Ink, you can provide your doctors with the digital equivalent of almost any pen-and-paper experience imaginable, from quick, handwritten notes and annotations to whiteboard demos.

The Windows Ink platform, together with a pen device, helps create digital handwritten notes, drawings, and annotations. The platform supports capturing digitizer input as ink data, generating ink data, managing ink data, rendering ink data as ink strokes on the output device, and converting ink to text through handwriting recognition.

There are equivalent options in other platforms like iOS and Android which can be used for making similar applications for doctors.

Optical Character Recognition

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Azure’s latest OCR technology Computer Vision Read API extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents. It can extract text from text-heavy images and multi-page PDF documents with mixed languages and detect both printed and handwritten text in the same image or document.

Most hospitals have to deal with lot of documents, especially when it involves external parties like insurance companies. Healthcare organizations can increase productivity and cut down on costs by investing in OCR for managing medical documents.

Emotion APIs

The Azure Face service provides AI algorithms that can detect, recognize, and analyze human faces in images. Facial recognition software has varied applications like in security, natural user interface, image content analysis and management, mobile apps, and others.

Using this API, we can detect perceived facial expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and more. It is important to note that facial expressions alone do not represent the internal states of people.

Speech Translation

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The number of people in the U.S. who speak a language other than English is large and growing. Language barriers have been found to impede access to care, compromise quality, and increase the risk of adverse outcomes. When friends and family interpret, they are prone to omit, add, and substitute information.

The Azure Speech Translation API can translate incoming speech into more than 60 languages. This API enables real-time, multi-language speech-to-speech and speech-to-text translation of audio streams. With the Speech SDK, your applications, tools, and devices have access to source transcriptions and translation outputs for provided audio. Interim transcription and translation results are returned as speech is detected, and results can be converted into synthesized speech.

Health BOTs

AI for Health Cloud Enablement Services

  • Providers have built health bot instances that triage patient issues with a symptom checker, help patients find appropriate care, and look up nearby doctors.
  • Insurers have built health bot instances that give their customers an easy way to look up the status of a claim and ask questions about benefits and services.

Azure Health Bot empowers developers in healthcare organizations to build and deploy AI-powered, compliant, conversational healthcare experiences at scale. Combining a built-in medical database with natural language capabilities to understand clinical terminology, it can be easily customized for various clinical use cases. The service ensures alignment with industry compliance requirements and is privacy protected to HIPAA standards.

Conversational intelligence also adapts dynamically as the health bot instance learns from previous interactions.

Text Analytics for Health

The healthcare industry is overwhelmed with data. They face an incredible challenge in trying to identify and draw insights from all that information. Unlocking insights from this data has massive potential for improving healthcare services and patient outcomes.

The Key Phrase Extraction API evaluates unstructured text, and for each JSON document, returns a list of key phrases.

AI/ML Led Solutions for Life Sciences

The Text Analytics API lets you take unstructured text and returns a list of disambiguated entities, with links to more information on the web. The API supports both Named Entity Recognition (NER) for several entity categories and entity linking.

Text Analytics for health performs NER, relation extraction, entity negation, and entity linking on English-language text to uncover insights in unstructured clinical and biomedical text.

Reinforcement Learning

AI Tools for Digital Transformation in Healthcare Industry

Medical diagnoses essentially involve mapping patients’ medical history, current symptoms, and other information to the correct disease profile. It can be an incredibly complex task representing an enormous burden (in both time and cognitive energy required) for busy clinicians.

Personalizer API uses reinforcement learning to select the single best action, known as reward action ID. Azure Personalizer is a cloud-based service that helps your applications choose the best content item to show your users.

Personalizer currently uses Vowpal Wabbit as the foundation for the machine learning. This framework allows for maximum throughput and lowest latency when making personalization ranks and training the model with all events.

Anomaly Detection

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Anomaly detection in medical treatment can be used to discover deviations from regular patterns and determine whether the patient management is unusual. Detecting an anomaly from medical images including mammograms, CT, or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis.

The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data without having to know machine learning. Using your time-series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.

Detect anomalies in your streaming data by using previously seen data points to determine if your latest one is an anomaly. This operation generates a model using the data points you send and determines if the target point is an anomaly.

Conclusion

Healthcare transformation requires a great amount of AI integration and implementation.

However, most healthcare organizations don’t have enough resources and bandwidth to work on AI development and deployment. Also, AI Involvement by its very nature is iterative and more concentration is required on involving the stakeholders and arriving at a consensus. Remember, the success of AI depends on the richness of data which is the main responsibility of healthcare organizations, while implementation of AI can be taken care of by these cognitive services.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Healthcare Data Management Solutions for the post-pandemic Healthcare era, using the combination of Multi-Modal databases, Blockchain, and Data Mining. The solutions aim at Patient data sharing within Hospitals as well as across Hospitals (Healthcare Interoprability), while bringing more trust and transparency into the healthcare process using patient consent management, credentialing and zero-knowledge proofs.

Blockchain-based Platform for COVID-19 Vaccine Traceability

Posted on by Subhadeep Datta

Srinivasan Sundararajan

Over the last few weeks, several pharma companies across world have announced vaccines for COVID. The respective governments are going through rigorous testing and approval processes to roll out vaccines soon.

The massive exercise of administering vaccines to billions of people across different geographies poses various challenges. Add to this the fact that different vaccines have strict conditions for storage and handling. Also, the entire history of traceability of the vaccine should be available.

While tracking the supply chain of any commodity in general and pharmaceutical products, in particular, is always complex, the COVID-19 vaccine poses tougher challenges. The following are the current temperature sensitivity needs of various vaccine manufacturers.  

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The information is from publicly available sites and should not be treated as a guideline for vaccine storage.

Blockchain to the Rescue

Even before the pandemic, Blockchain with its built-in ability to provide transparency across stakeholders has been a major platform for pharmaceutical traceability. The criticality for providing COVID-19 vaccine traceability has only strengthened the cause of utilizing blockchain for the supply chain in the pharma industry.

Blockchain networks with its base attributes like de-centralized ownership of data, single version of truth across stakeholders, the ability to ensure the data ownership based on cryptography-based security, and the ability to implement and manage business rules, will be a default platform handling the traceability of COVID-19 vaccines across multiple stakeholders.

Going beyond, Blockchain will also play a major role in the Identity and Credentialing of healthcare professionals involved, as well as the Consent Management of the patients who will be administered the vaccine. With futuristic technology needs like Health Passport, Digital Twin of a Person, Blockchain goes a long way in solving the current challenges in healthcare beyond streamlining the supply chain.

GAVS Blockchain Based Prototype for COVID-19 vaccine Traceability

GAVS has created a prototype of Blockchain-based network platform for vaccine traceability to demonstrate its usability. This solution has a much larger scope for extending to various healthcare use cases.

The below is the high-level process flow of the COVID-19 vaccine trial and various stakeholders involved.

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Image Source – www.counterpointresearch.com

Based on the use case and the stakeholders involved. GAVS prototype first creates a consortium using a private blockchain network. For the sake of simplicity, Distributors are not mentioned, but in real life, every stakeholder will be present. Individuals who receive the vaccine from hospitals are not part of the Network at this stage. But in future, their consent can be tracked using Blockchain.

Using Azure Blockchain Service, we can create private consortium blockchain networks where each blockchain network can be limited to specific participants in the network. Only participants in the private consortium blockchain network can view and interact with the blockchain. This ensures that sensitive information about vaccines are not exposed or misused.

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The following smart contracts are created as part of the solution with assigned ownership to the individual stake holders.

Blockchain solution and services

A glimpse of few of the smart contracts are listed for illustration purposes.

pragma solidity ^0.5.3;

pragma experimental ABIEncoderV2; 

contract Batch {

    string  public BatchId;

    string  public ProductName;

    string  public ProductType;

    string  public TempratureMaintained;

    string  public Efficacy;

    string  public Cost;

    address public CurrentOwner;

    address public ManufacturerAddr;

    address public AirLogAddr;

    address public LandLogAddr;

    address public HospAdminAddr;

    address public HospStaffAddr;

    string[] public AirTemp = new string[](10);

    string[] public LandTemp = new string[](10);

    string[] public HospTemp = new string[](20);

    string  public receiptNoteaddr;

    constructor  (string memory _batchId, string memory _productName,  string memory _productType,  string memory _TemperatureMaintained,  string memory _Efficacy,  string memory _Cost) public {

        ManufacturerAddr = msg.sender;

        BatchId = _batchId;

        ProductName = _productName ;

        ProductType = _productType;

        TemperatureMaintained = _TemperatureMaintained;

        Efficacy = _Efficacy;

        Cost = _Cost;

    }   

    modifier onlyOwner()    {

        require (msg.sender == CurrentOwner, “Only Current Owner Can Initiate This Action”);

        _;

    }      

    function updateOwner(address _addr) onlyOwner public{

       CurrentOwner = _addr;

    }        

    function retrieveBatchDetails() view  public returns (string memory, string memory, string memory, string memory, string memory, address, address, address, address, address, string[] memory, string[] memory, string[] memory, string memory) {

        return (BatchId,ProductName,TemperatureMaintained,Efficacy,Cost,ManufacturerAddr,AirLogAddr,LandLogAddr,HospAdminAddr,HospStaffAddr,AirTemp,LandTemp,HospTemp,receiptNoteaddr);  

    }

}  

The front end (Dapp) through which the traceability of the COVID-19 vaccine can be monitored is also developed and the following screenshots show certain important data flows.

Vaccine Traceability System Login Screen

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Traceability view for a particular batch of Vaccine

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Details of vaccinated patients entered by hospital

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Advantages of The Solution

  • With every vaccine monitored over the blockchain, each link along the chain could keep track of the entire process, and health departments could monitor the chain as a whole and intervene, if required, to ensure proper functioning.
  • Manufacturers could track whether shipments are delivered on time to their destinations.
  • Hospitals and clinics could better manage their stocks, mitigating supply and demand constraints. Furthermore, they would get guarantees concerning vaccine authenticity and proper storage conditions.
  • Individuals would have an identical guarantee for the specific vaccine they receive.
  • Overall this technology-driven approach will help to save the lives in this critical juncture.

 Extensibility to Future Needs

Gartner’s latest hypercycle for emerging technologies highlight several new technologies and notably Health Passport. As the travelers used to travel with a physical passport pandemic has created the need for a health passport, which is more like a digital health record that passengers can carry on their phones. Ideally, it should show the passengers past exposure to diseases and the vaccine records. By properly deploying health passports, several industries can revive themselves by allowing free-flowing movement of passengers across the globe.

The above blockchain solution though meant for COVID-19 traceability can potentially extended to a health passport once the patient also becomes part of it by a wallet based authentication mechanism, at GAVS we plan to explore the health passports on Blockchain in the coming months.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Healthcare Data Management Solutions for the post-pandemic Healthcare era, using the combination of Multi Modal databases, Blockchain and Data Mining. The solutions aim at Patient data sharing within Hospitals as well as across Hospitals (Healthcare Interoperability) while bringing more trust and transparency into the healthcare process using patient consent management, credentialing, and zero knowledge proofs.

Zero Knowledge Proofs in Healthcare Data Sharing

Posted on by Subhadeep Datta

Srinivasan Sundararajan

Recap of Healthcare Data Sharing

In my previous article (https://www.gavstech.com/healthcare-data-sharing/), I had elaborated on the challenges of Patient Master Data Management, Patient 360, and associated Patient Data Sharing. I had also outlined how our Rhodium framework is positioned to address the challenges of Patient Data Management and data sharing using a combination of multi-modal databases and Blockchain.

In this context, I have highlighted our maturity levels and the journey of Patient Data Sharing as follows:

  • Single Hospital
  • Between Hospitals part of HIE (Health Information Exchange)
  • Between Hospitals and Patients
  • Between Hospitals, Patients, and Other External Stakeholders

In each of the stages of the journey, I have highlighted various use cases. For example, in the third level of health data sharing between Hospitals and Patients, the use cases of consent management involving patients as well as monetization of personal data by patients themselves are mentioned.

In the fourth level of the journey, you must’ve read about the use case “Zero Knowledge Proofs”. In this article, I would be elaborating on:

  • What is Zero Knowledge Proof (ZKP)?
  • What is its role and importance in Healthcare Data Sharing?
  • How Blockchain Powered GAVS Rhodium Platform helps address the needs of ZKP?

Introduction to Zero Knowledge Proof

As the name suggests, Zero Knowledge Proof is about proving something without revealing the data behind that proof. Each transaction has a ‘verifier’ and a ‘prover’. In a transaction using ZKPs, the prover attempts to prove something to the verifier without revealing any other details to the verifier.

Zero Knowledge Proofs in Healthcare 

In today’s healthcare industry, a lot of time-consuming due diligence is done based on a lack of trust.

  • Insurance companies are always wary of fraudulent claims (which is anyhow a major issue), hence a lot of documentation and details are obtained and analyzed.
  • Hospitals, at the time of patient admission, need to know more about the patient, their insurance status, payment options, etc., hence they do detailed checks.
  • Pharmacists may have to verify that the Patient is indeed advised to take the medicines and give the same to the patients.
  • Patients most times also want to make sure that the diagnosis and treatment given to them are indeed proper and no wrong diagnosis is done.
  • Patients also want to ensure that doctors have legitimate licenses with no history of malpractice or any other wrongdoing.

In a healthcare scenario, either of the parties, i.e. patient, hospital, pharmacy, insurance companies, can take on the role of a verifier, and typically patients and sometimes hospitals are the provers.

While the ZKP can be applied to any of the transactions involving the above parties, currently the research in the industry is mostly focused on patient privacy rights and ZKP initiatives target more on how much or less of information a patient (prover) can share to a verifier before getting the required service based on the assertion of that proof.

Blockchain & Zero Knowledge Proof

While I am not getting into the fundamentals of Blockchain, but the readers should understand that one of the fundamental backbones of Blockchain is trust within the context of pseudo anonymity. In other words, some of the earlier uses of Blockchain, like cryptocurrency, aim to promote trust between unknown individuals without revealing any of their personal identities, yet allowing participation in a transaction.

Some of the characteristics of the Blockchain transaction that makes it conducive for Zero Knowledge Proofs are as follows:

  • Each transaction is initiated in the form of a smart contract.
  • Smart contract instance (i.e. the particular invocation of that smart contract) has an owner i.e. the public key of the account holder who creates the same, for example, a patient’s medical record can be created and owned by the patient themselves.
  • The other party can trust that transaction as long the other party knows the public key of the initiator.
  • Some of the important aspects of an approval life cycle like validation, approval, rejection, can be delegated to other stakeholders by delegating that task to the respective public key of that stakeholder.
  • For example, if a doctor needs to approve a medical condition of a patient, the same can be delegated to the doctor and only that particular doctor can approve it.
  • The anonymity of a person can be maintained, as everyone will see only the public key and other details can be hidden.
  • Some of the approval documents can be transferred using off-chain means (outside of the blockchain), such that participants of the blockchain will only see the proof of a claim but not the details behind it.
  • Further extending the data transfer with encryption of the sender’s private/public keys can lead to more advanced use cases.

Role of Blockchain Consortium

While Zero Knowledge Proofs can be implemented in any Blockchain platform including totally uncontrolled public blockchain platforms, their usage is best realized in private Blockchain consortiums. Here the identity of all participants is known, and each participant trusts the other, but the due diligence that is needed with the actual submission of proof is avoided.

Organizations that are part of similar domains and business processes form a Blockchain Network to get business benefits of their own processes. Such a Controlled Network among the known and identified organizations is known as a Consortium Blockchain.

Illustrated view of a Consortium Blockchain Involving Multiple Other Organizations, whose access rights differ. Each member controls their own access to Blockchain Network with Cryptographic Keys.

Members typically interact with the Blockchain Network by deploying Smart Contracts (i.e. Creating) as well as accessing the existing contracts.

Current Industry Research on Zero Knowledge Proof

Zero Knowledge Proof is a new but powerful concept in building trust-based networks. While basic Blockchain platform can help to bring the concept in a trust-based manner, a lot of research is being done to come up with a truly algorithmic zero knowledge proof.

A zk-SNARK (“zero-knowledge succinct non-interactive argument of knowledge”) utilizes a concept known as a “zero-knowledge proof”. Developers have already started integrating zk-SNARKs into Ethereum Blockchain platform. Zether, which was built by a group of academics and financial technology researchers including Dan Boneh from Stanford University, uses zero-knowledge proofs.

ZKP In GAVS Rhodium

As mentioned in my previous article about Patient Data Sharing, Rhodium is a futuristic framework that aims to take the Patient Data Sharing as a journey across multiple stages, and at the advanced maturity levels Zero Knowledge Proofs definitely find a place. Healthcare organizations can start experimenting and innovating on this front.

Rhodium Patient Data Sharing Journey

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Healthcare Industry today is affected by fraud and lack of trust on one side, and on the other side growing privacy concerns of the patient. In this context, the introduction of a Zero Knowledge Proofs as part of healthcare transactions will help the industry to optimize itself and move towards seamless operations.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Data Management Solutions for new-age enterprises using the combination of Multi Modal databases, Blockchain, and Data Mining. The solutions aim at data sharing within enterprises as well as with external stakeholders.

Healthcare Data Sharing

Posted on by Site Admin

Srinivasan Sundararajan

Patient Care Redefined

The fight against the novel coronavirus has witnessed transformational changes in the way patient care is defined and managed. Proliferation of telemedicine has enabled consultations across geographies. In the current scenario, access to patients’ medical records has also assumed more importance.

The journey towards a solution also taught us that research on patient data is equally important. More the sample data about the infected patients, the better the vaccine/remedy. However, the growing concern about the privacy of patient data cannot be ignored. Moreover, patients who provide their data for medical research should also benefit from a monetary perspective, for their contributions.

The above facts basically point to the need for being able to share vital healthcare data efficiently so that patient care is improved, and more lives are saved.

The healthcare industry needs a data-sharing framework, which shares patient data but also provides much-needed controls on data ownership for various stakeholders, including the patients.

Types of Healthcare Data

  • PHR (Personal Health Record): An electronic record of health-related information on an individual that conforms to nationally recognized interoperability standards and that can be drawn from multiple sources while being managed, shared, and controlled by the individual.
  • EMR (Electronic Medical Record): Health-related information on an individual that can be created, gathered, managed, and consulted by authorized clinicians and staff within one healthcare organization. 
  • EHR (Electronic Health Record): Health-related information on an individual that conforms to nationally recognized interoperability standards and that can be created, managed and consulted by authorized clinicians and staff across more than one healthcare organization. 

In the context of large multi-specialty hospitals, EMR could also be specific to one specialist department and EHR could be the combination of information from various specialist departments in a single unified record.

Together these 3 forms of healthcare data provide a comprehensive view of a patient (patient 360), thus resulting in quicker diagnoses and personalized quality care.

Current Challenges in Sharing Healthcare Data

  • Lack of unique identity for patients prevents a single version of truth. Though there are government-issued IDs like SSN, their usage is not consistent across systems.
  • High cost and error-prone integration options with provider controlled EMR/EHR systems. While there is standardization with respect to healthcare interoperability API specifications, the effort needed for integration is high.
  • Conflict of interest in ensuring patient privacy and data integrity, while allowing data sharing. Digital ethics dictate that patient consent management take precedence while sharing their data.
  • Monetary benefits of medical research on patient data are not passed on to patients. As mentioned earlier, in today’s context analyzing existing patient information is critical to finding a cure for diseases, but there are no incentives for these patients.
  • Data stewardship, consent management, compliance needs like HIPAA, GDPR. Let’s assume a hospital specializing in heart-related issues shares a patient record with a hospital that specializes in eye care. How do we decide which portions of the patient information is owned by which hospital and how the governance is managed?
  • Lack of real-time information attributing to data quality issues and causing incorrect diagnoses.

The above list is not comprehensive but points to some of the issues that are plaguing the current healthcare data-sharing initiatives.

Blockchain for Healthcare Data Sharing

Some of the basic attributes of blockchain are mentioned below:

  • Blockchain is a distributed database, whereby each node of the database can be owned by a different stakeholder (say hospital departments) and yet all updates to the database eventually converge resulting in a distributed single version of truth.
  • Blockchain databases utilize a cryptography-based transaction processing mechanism, such that each object stored inside the database (say a patient record) can be distinctly owned by a public/private key pair and the ownership rights carry throughout the life cycle of the object (say from patient admission to discharge).
  • Blockchain transactions are carried out using smart contracts which basically attach the business rules to the underlying data, ensuring that the data is always compliant with the underlying business rules, making it even more reliable than the data available in traditional database systems.

These underlying properties of Blockchain make it a viable technology platform for healthcare data sharing, as well as to ensure data stewardship and patient privacy rights.

GAVS Rhodium Framework for Healthcare Data Sharing

GAVS has developed a framework – ‘Rhodium’, for healthcare data sharing.

This framework combines the best features of multi-modal databases (relational, nosql, graph) along with the viability of data sharing facilitated by Blockchain, to come up with a unified framework for healthcare data sharing.

The following are the high-level components (in a healthcare context) of the Rhodium framework. As you can see, each of the individual components of Rhodium play a role in healthcare information exchange at various levels.

GAVS’ Rhodium Framework for Healthcare

GAVS has also defined a maturity model for healthcare organizations for utilizing the framework towards healthcare data sharing. This model defines 4 stages of healthcare data sharing:

  • Within a Hospital 
  • Across Hospitals
  • Between Hospitals & Patients
  • Between Hospitals, Patients & Other Agencies

The below progression diagram illustrates how the framework can be extended for various stages of the life cycle, and typical use cases that are realized in each phase. Detailed explanations of various components of the Rhodium framework, and how it realizes use cases mentioned in the different stages will be covered in subsequent articles in this space.

Rhodium Patient Date Sharing Journey

Benefits of the GAVS Rhodium Framework for Healthcare Data Sharing

The following are the general foreseeable benefits of using the Rhodium framework for healthcare data sharing.

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Healthcare Industry Trends with Respect to Data Sharing

The following are some of the trends we are seeing in Healthcare Data Sharing:

  • Interoperability will drive privacy and security improvements
  • New privacy regulations will continue to come up, in addition to HIPAA
  • The ethical and legal use of AI will empower healthcare data security and privacy
  • The rest of 2020 and 2021 will be defined by the duality of data security and data integration, and providers’ ability to execute on these priorities. That, in turn, will, in many ways, determine their effectiveness
  • In addition to industry regulations like HIPAA, national data privacy standards including Europe’s GDPR, California’s Consumer Privacy Act, and New York’s SHIELD Act will further increase the impetus for providers to prioritize privacy as a critical component of quality patient care

The below documentation from the HIMSS site talks about maturity levels with respect to healthcare interoperability, which is addressed by the Rhodium framework.

Source: https://www.himss.org/what-interoperability

This framework is in its early stages of experimentation and is a prototype of how a Blockchain + Multi-Modal Database powered solution could be utilized for sharing healthcare data, that would be hugely beneficial to patients as well as healthcare providers.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Data Management Solutions for new-age enterprises using the combination of Multi-Modal databases, Blockchain, and Data Mining. The solutions aim at data sharing within enterprises as well as with external stakeholders.

RASA – an Open Source Chatbot Solution

Posted on by Site Admin

Maruvada Deepti

Ever wondered if the agent you are chatting with online is a human or a robot? The answer would be the latter for an increasing number of industries. Conversational agents or chatbots are being employed by organizations as their first-line of support to reduce their response times.

The first generation of bots were not too smart, they could understand only a limited set of queries based on keywords. However, commoditization of NLP and machine learning by Wit.ai, API.ai, Luis.ai, Amazon Alexa, IBM Watson, and others, has resulted in intelligent bots.

What are the different chatbot platforms?

There are many platforms out there which are easy to use, like DialogFlow, Bot Framework, IBM Watson etc. But most of them are closed systems, not open source. These cannot be hosted on our servers and are mostly on-premise. These are mostly generalized and not very specific for a reason.

DialogFlow vs.  RASA

DialogFlow

  • Formerly known as API.ai before being acquired by Google.
  • It is a mostly complete tool for the creation of a chatbot. Mostly complete here means that it does almost everything you need for most chatbots.
  • Specifically, it can handle classification of intents and entities. It uses what it known as context to handle dialogue. It allows web hooks for fulfillment.
  • One thing it does not have, that is often desirable for chatbots, is some form of end-user management.
  • It has a robust API, which allows us to define entities/intents/etc. either via the API or with their web based interface.
  • Data is hosted in the cloud and any interaction with API.ai require cloud related communications.
  • It cannot be operated on premise.

Rasa NLU + Core

  • To compete with the best Frameworks like Google DialogFlow and Microsoft Luis, RASA came up with two built features NLU and CORE.
  • RASA NLU handles the intent and entity. Whereas, the RASA CORE takes care of the dialogue flow and guesses the “probable” next state of the conversation.
  • Unlike DialogFlow, RASA does not provide a complete user interface, the users are free to customize and develop Python scripts on top of it.
  • In contrast to DialogFlow, RASA does not provide hosting facilities. The user can host in their own sever, which also gives the user the ownership of the data.

What makes RASA different?

Rasa is an open source machine learning tool for developers and product teams to expand the abilities of bots beyond answering simple questions. It also gives control to the NLU, through which we can customize accordingly to a specific use case.

Rasa takes inspiration from different sources for building a conversational AI. It uses machine learning libraries and deep learning frameworks like TensorFlow, Keras.

Also, Rasa Stack is a platform that has seen some fast growth within 2 years.

RASA terminologies

  • Intent: Consider it as the intention or purpose of the user input. If a user says, “Which day is today?”, the intent would be finding the day of the week.
  • Entity: It is useful information from the user input that can be extracted like place or time. From the previous example, by intent, we understand the aim is to find the day of the week, but of which date? If we extract “Today” as an entity, we can perform the action on today.
  • Actions: As the name suggests, it’s an operation which can be performed by the bot. It could be replying something (Text, Image, Video, Suggestion, etc.) in return, querying a database or any other possibility by code.
  • Stories: These are sample interactions between the user and bot, defined in terms of intents captured and actions performed. So, the developer can mention what to do if you get a user input of some intent with/without some entities. Like saying if user intent is to find the day of the week and entity is today, find the day of the week of today and reply.

RASA Stack

Rasa has two major components:

  • RASA NLU: a library for natural language understanding that provides the function of intent classification and entity extraction. This helps the chatbot to understand what the user is saying. Refer to the below diagram of how NLU processes user input.
RASA Chatbot

  • RASA CORE: it uses machine learning techniques to generalize the dialogue flow of the system. It also predicts next best action based on the input from NLU, the conversation history, and the training data.

RASA architecture

This diagram shows the basic steps of how an assistant built with Rasa responds to a message:

RASA Chatbot

The steps are as follows:

  • The message is received and passed to an Interpreter, which converts it into a dictionary including the original text, the intent, and any entities that were found. This part is handled by NLU.
  • The Tracker is the object which keeps track of conversation state. It receives the info that a new message has come in.
  • The policy receives the current state of the tracker.
  • The policy chooses which action to take next.
  • The chosen action is logged by the tracker.
  • A response is sent to the user.

Areas of application

RASA is all one-stop solution in various industries like:

  • Customer Service: broadly used for technical support, accounts and billings, conversational search, travel concierge.
  • Financial Service: used in many banks for account management, bills, financial advices and fraud protection.
  • Healthcare: mainly used for fitness and wellbeing, health insurances and others

What’s next?

As any machine learning developer will tell you, improving an AI assistant is an ongoing task, but the RASA team has set their sights on one big roadmap item: updating to use the Response Selector NLU component, introduced with Rasa 1.3. “The response selector is a completely different model that uses the actual text of an incoming user message to directly predict a response for it.”

References:

https://rasa.com/product/features/

https://rasa.com/docs/rasa/user-guide/rasa-tutorial/

About the Author –

Deepti is an ML Engineer at Location Zero in GAVS. She is a voracious reader and has a keen interest in learning newer technologies. In her leisure time, she likes to sing and draw illustrations.
She believes that nothing influences her more than a shared experience.

Demystifying Digital Transformation

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Sri Chaganty, CTO, GAVS Technologies

What is Digital Transformation?

Digital Transformation is the change associated with the use of digital technologies to enable an organization to improve and evolve. It implies using digital technologies to do something better or to create something that did not exist previously.  Digital Transformation has been in place since digital computers came into existence.  For example, when mechanical cash registers were replaced with computerized cash registers, that was a digital transformation.  But the technologies available today are far advanced that such transformation has shifted from evolution to revolution. A revolution to provide the most satisfying experience to the customer – either internal or external.

To accomplish Digital Transformation, existing process and operations need to transform. New and enhanced processes and operations leverage emerging technologies to get better insights into how well the customer is being served. Enhanced operations will positively impact the productivity of the organization and empower employees to innovate, which demands transparency. Naturally, innovation within an organization translates into new products and services and more engaged customers. Google stands as a shining example.  Just think how many products and services we use from Google today – ostensibly a search engine company!

What is driving this need for digital transformation?

Simple answer – us.  We want every transaction we are involved in to be as “smart phone like” as possible.  We embraced Uber, Lyft, AirBnB, Online banking, and anything and everything that met our demand – “I want it now”.  We are now turning to our workplaces and demanding such customer experience with all transactions within our organizations because we want to be more productive. Our organizations are asking us to be more productive in order provide solutions to our customers that will encourage customer engagement and loyalty.

For us to be more productive, we need to be empowered to innovate, fail fast and recover, be transparent in pushing the envelope without sacrificing the security aspects of those being served and those that are serving. All of this requires not just new hardware or software.  This requires optimized, enhanced, and new business processes and operations that will maximize the return on investment in those technologies.

Click here to watch our webinar on Demystifying Digital Transformation by Sri Chaganty, CTO, GAVS Technologies

Enablers of Digital Transformation

Digital Transformation today is not evolving like it used to in the past.  Digital Transformation is a revolution today.  The massive amounts of cheap compute power and storage combined with ubiquitous connectivity driven by machine learning and machine autonomy is driving Digital Transformation.

Between Google, Amazon, Microsoft, Facebook, Oracle, Salesforce – just to name a few – the magnitude of new compute power provided to the word annually is staggering.  The massive amount of compute power that resides in your hand or in your pocket as your cell phone or tablet is millions of times more powerful than all of NASA’s combined computing in 1969, the year we landed on the Moon.

Along with massive amounts of cheap compute power, staggering amounts of “almost-free” storage space is being deployed as well in these global server farms and data centers.  All the data stored is ready to be analyzed with this compute power.

If a device can have a microprocessor embedded in it, it will. If it has a microprocessor, it will be connected to the Internet and if it’s connected to the Internet, it’s going to collect and stream (lots of) data.  Everything will be connected. With the Internet of Things (IoT) we are wiring up the entire planet (and everything on it). Data can be ingested from almost every corner of the planet – transforming the entire planet into a living organism of data.

All the data stored is ready to be analyzed with this massive amount of compute power.

Impact of Digital Transformation on ITOps

Digital Transformation touches all aspects of the business.  The most impact is felt in the IT Operations.  The reason is obvious.  For reinventing and creating new processes in operations, we need to adopt new technologies.

Let us see how IT Operations is affected by the plethora of technological advances that facilitate digital transformation.

First, there is a revolution in application architectures to support quick turnaround of solution development.

Second, we are moving into a continuous integration and delivery model for new code to get into production and cut short the long QA cycles.

Third, there are languages that evolved to support the continuous integration and delivery.

Fourth, the packaging of the applications is revolutionized with containerization.

Everything now is virtualized to make sure that the resources are available for continuous delivery.

Finally, cloudification became the norm of our business processes.

All these advancements on how we develop software have a direct impact on IT Operations that need to support such fundamental technology shifts. As you can rightly imagine, IT Operations is constantly involved in issues and struggles to support the shifts we saw in software development. And those struggles have direct impact on the bottom-line of the business.

You hear about outages in Amazon, on Azure or security breaches at Target or other big organizations because those stories draw media attention. But within organizations, application downtimes in this digital age have significant impact on the bottom-line. 

Outages put organizations in a crisis mode.

Repetitive problems and issues leave IT organizations with no room to innovate and be proactive in adopting new technologies. The bigger the organization, the longer the arch of adoption.  The length of the arch of adoption is a reflection on the time it takes for them to be fully digitally transformed.

Modern enterprise IT operations should focus on zero outages.  The target should be to eliminate outages because they cannot afford to have any if they want to be digitally transformed.

GAVS’ Role in Facilitating Digital Transformation

GAVS Zero Incident Framework TM (ZIF) – an AI powered platform, can ingest streaming events from various IT operations monitoring tools and process them to identify the underlying sub sequences. Patterns are self-learned and unlearned by algorithms to provide a high accuracy in correlations. Supervised, Reinforced and Unsupervised machine learning algorithms auto-tune parameters based on the enterprise’s data streams to derive higher accuracy in correlating events generated from siloed IT operations monitoring tools. ZIF removes the alert fatigue in an enterprise. ZIF enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident EnterpriseTM.  ZIF can play a significant role in eliminating outages thereby empowering them to be digitally transformed.

Healthcare benefiting from automation

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What would someone decipher by the term ‘automation in healthcare’? Does it appear to you as robots operating human limbs or machines empathizing with patients? Well, not exactly! Automation in the healthcare sector focusses on improving the overall service that patients deserve since healthcare is a basic right and should not be mistaken as a privilege.

Healthcare industry – How is the transition shaping up?

The healthcare industry has made a massive shift; from the paper-based method of maintaining records to compiling digital records and practicing online information sharing. This industry, in particular, is critical and cannot afford even an iota of downtime. The medical practitioners must have consistent access to medical records and information of patients. Thus, the IT team needs to ensure a smooth functioning of the machines. In this regard, automation is truly an amazing solution that assists in enhancing the capabilities of data centers and ensured seamless operation. It also provides constant monitoring and real-time alerts, further preventing life-threatening system downtimes.

Automation in Healthcare: Still a mile apart

Currently, the two key focus areas of the healthcare industry include cost reduction and efficiency boosting. Highlighting trends, it is worthwhile to mention that the Japanese Lean approach has been adopted by the healthcare industry with basic focus on waste reduction and improvement of performance and efficiency through automation of manual tasks. Now, automation is reduction of human intervention by increasing dependence on artificial intelligence (AI). Infact, automation has engulfed our daily lives with ATMs, self-checkout in stores and auto-park assistance in connected vehicles etc. However, automation in the healthcare sector is comparatively a new concept, yet to be explored in entirety!

Need for automation is a priority

Population explosion has directly impacted the growth in ageing population. With age, people require greater medical attention and care. This increases the demand for healthcare professionals and care givers and in order to meet that growing demand, automation is the best possible solution. The rise in demand for healthcare solutions has been addressed by healthcare organizations by hiring more care givers. However,  secondary research reveals that the nursing shortage in the US is predicted to increase and the current number will come down to 260,000 registered nurses by 2025, thus indicating a steep fall in the volume of care givers. This shortage can be aptly handled by adoption of technology, further maximizing efficiency with minimal human resources. Although this may look like an ideal scenario, yet it is a massive challenge to motivate employees to imbibe technological changes and embrace the evolving landscape.

The acceptance of automation in healthcare industry transformed the concept of medical care and facility. According to Institute for Health Technology Transformation, “Automation makes population health management feasible, scalable and sustainable.” Similar to other sectors, initially automation was considered a negative aspect even in the healthcare sector. Pharmacists and medical professionals feared unemployment with the onset of automation and robotics. The thought here is not to replace a doctor by a robot, rather blend automation with the workflow of a medical professional to enhance efficiency and productivity of the medical attention procedure.

Engaging patients for an effective care

Medical care flourished and improved through empowering patients with gadgets and apps, that can engage and initiate awareness to participate in the healthcare process. For example, automated health monitoring reminders can create a great impact on the patient’s health, creating consciousness.

Garnering benefits of automation in healthcare

Introduction of automation in the healthcare sector has revolutionized the concept of medical facility and care. It has evolved from complete dependence on humans to sophisticated merger of both, humans and machines. The following are the advantages of leveraging automation in the healthcare domain.

  • Saving considerable time

Automation of manual tasks saves time. This, however, does not indicate firing of employees, rather, it focuses on elevating employee efficiency and support to successfully manage higher-functioning roles. Costly and repetitive individual tasks and complex workflows are usually time-consuming and can easily be automated.

  • Connecting medical facilities

Automation ensures end to end processing of customer information and reports for easy access and creation of a synergy between all healthcare activities conducted by the care center. A patient ideally is not well-versed with the series of medical services and fail to understand the correlation of one with the other. Here, automation helps to create that connect, extending a level of comfort to patients.

  • Enhancing quality and reliability

Automation in healthcare can improve quality and consistency to a great extent since, it reduces the chance of human error and fatigue. Thus, patients can expect consistency in care and service.

  • Data storage and access

As per market research, automation eases the process of storing medical data and order entries. This is useful during emergencies when doctors need quick access of patient’s reports and medical history, acting promptly to save a life.

  • Supporting system for decision-making

Automation ensured data-driven insight on patient’s health conditions. This impacted the decision making and choose the correct course of treatment which is reliable and efficient. Also, this enhanced decision-making capabilities of medical professionals, reduced deaths, minimized surgery complications and brought down medical expenses. Hence, the doctors have started relying on automation to support complex clinical decisions.  

  • Enhancing customer support

Automation empowered patients with self-service options and customer support for seamless self-service. This optimized the process with innovative technology and improved the course of patient care.

  • Improving understanding of patients  

Patients are unaware of the medical processes and its complexity hence, automation connected the medical team with the patients efficiently, helping them to understand the entire process of medical attention. It reduced the time and effort wasted in bridging the gap between a doctor and the patient.

  • Monitoring critical patients

The healthcare sector also implemented automation in post-operative care and Intensive Care Unit (ICU). This ensured automation of the patient’s lifecycle increasing visibility of the treatments given and its consequent result.

  • Managing outcome

For any industry to operate seamlessly, outcomes should be predictable. This is so true for the healthcare industry as well. It is convenient for patients to follow a standardized care path through automation due to its monitoring advantages.

  • Reducing wait-time for medical attention

Automation of healthcare tools can handle larger patient population with efficiency and satisfaction. This also enhanced the patient’s experience of the medical facility.

  • Changing the payment structure

The concept of health insurance has improved due to automation. Treatment was made possible without making any advance payment. This is a revolution in the field of medical attention.

  • Merging of technology

The forte of automation is its ability to integrate old legacy systems with the new evolving technology. The hospitals in United States of America maintains an integrated patient management system that contains reports, information and details of all patients. This combination improved the efficiency of the healthcare sector massively.

  • Ensuring security and compliance

The healthcare industry deals with sensitive information about patients, which is why, the data is critical and requires protection from hackers. Automation, here, plays an important role in safeguarding the data through stringent security regulations.

All the above are constructive steps towards improving the healthcare sector through adoption of automation technology.

Areas to Automate

Healthcare and automation in developing countries

Investments in the healthcare industry will not yield any direct and immediate gain. However, both Government and private investments in this sector will strengthen the automation that will ensure both, qualitative and economic advantages. Infact, the healthcare sectors in developing nations need a thrust of automation to meet the global standard of medicine and healthcare.

Hospitals and automation: Real-life scenario

Secondary market research on hospitals in Texas US, that adopted and implemented automation, showcased exemplary results. Patients treated in those hospitals had lower death rates, very few complications and manageable treatment cost. Government initiatives made it simple for the hospitals to adopt the automation technologies such as electronic medical records, computerized order entry systems and clinical decision support systems. This on one hand, reduced waste of manpower and time while on the other hand, improved its service quality. Automation of the hospital’s clinical information is categorized into four segments viz. medical notes, test results, order entry and decision support.

Healthcare reformed with GAVS

A need to improve patient care has led to the coalesce of technology and healthcare industry. In an attempt to do so, the industry eagerly embraced cloud computing, data analytics and security. GAVS Technologies is one such prominent company which empowered the healthcare sector with technology-led solutions and SMART delivery. Infact, GAVS successfully enabled many hospitals and healthcare centers to improve the quality of care offered to the patients.

GAVS revolutionized the healthcare sector with strategic and cost-effective healthcare technology solutions that blend conventional clinical approach with modern technologies. Needless to say, the world is witnessing a transformed healthcare facility through automation.

5 ways Machine Learning is Shifting Health-Care Industry towards better future

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Machine Learning as a service is all poised to shift healthcare towards a healthier future. It is the next big thing in the healthcare industry as providers start to adopt advanced data analytics capabilities.

The development of machine learning and predictive analytics is likely to reach $19.5 billion by 2025, as per Research and Markets. As the Internet of Things begin to bring huge data into the healthcare environment, providers will start to use this information to drive down costs and improve the patient experience.

Due to the high variety, variable, velocity and unstandardized nature of IoT data, advanced machine learning capabilities will be required to generate multiple data streams and actionable insights that can be used to directly improve results and streamline the process of delivering patient care.

Implementing Machine Learning in a Medical ecosystem.

Machine Learning provides methods, techniques, and tools that can help solve diagnostic and prognostic problems in a variety of medical domains. It is being used to analyze important clinical parameters, prediction of disease progression, data mining of medical research, and for overall patient management.

Successful implementation of ML methods provides opportunities to facilitate and enhance the work efficiency of medical experts and quality of medical care.

Below are some major ML application areas in medicine.

1. Personalized medical treatment

Predictive analytics and Machine learning is offering a wide range of applications in the future that will change the dynamics of healthcare.
Individual and personalized healthcare treatments are based on their medical history, genetic lineage, past conditions, diet, stress levels, and more.
This will impact in high stake treatments like cancer, chemotherapy etc. where every patient’s treatment is based on their age, gender, stage of advancement of the disease etc.

2. Autonomous treatment or recommendations

Still in the trial stages, autonomous treatment will be an opportunity for providers to monitor, adjust and disperse the medications by tracking the patient’s data about their blood, sleep, diet etc. It will automatically regulate and take corrective actions like calling the doctors in case of emergencies.

Take the example of insulin pumps that work autonomously, constantly monitor blood sugar levels and inject insulin as needed, without disturbing the user’s daily life.
The legal constraint of having so much power based on algorithms are not trivial, and like any other innovation in healthcare, autonomous treatments of any kind will be scrutinized to prove their viability, safety, and advantage over other treatment methods.

3. Improving performance (beyond amelioration)

While contemporary medicine is primarily focused on treatment and prevention of disease, the need is for proactive healthcare prevention and intervention. Driving this change is the IoT devices, notably the wearable technologies like Fitbit, smart watches etc. that push for this initiative.

Machine learning and artificial intelligence can be leveraged to not only monitor health related parameters like BP, blood sugar etc. but also to track work related aspect like job stress levels and seek positive improvement in high risk groups.

4. Autonomous robotic surgery

At present, robotic surgical system like the da Vinci Surgical System are still in the nascent stages of complex assisted surgery using minimally invasive approach and is controlled by a surgeon from a console. They have the dexterity and trained ability of a surgeon and are commonly used for cardiac valve repair and gynecologic surgical procedures.

Machine learning, in the future could be used to combine visual data and motor patterns within devices such as the da Vinci to allow machines to master surgeries. These machine learning algorithms could learn as many surgical procedures with enough training to better perform medical procedures.

5. Virtual Assistants and Machine Learning

Apple’s Siri, Google’s Assistant and Microsoft’s Cortana have successfully made voice-enabled devices popular with the smartphone users, including healthcare professionals and patients. Powered by speech and text recognition software, machine learning and artificial intelligence (AI), these virtual assistants can interact with users and help them with day-to-day tasks.
These voice-based assistant devices collect data through verbal interaction with the patients. Various platforms such as Echo Dot incorporate these capabilities and features to collect the necessary data that can benefit patients and healthcare professionals.

Smart Machines Transforming Business Outcomes

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Smart machines will redefine and transform business processes by detecting, classifying and locating faults and recommend actions.

It must have the capacity to automatically repair and correct processing errors and avoid failures by developing capabilities to increase accuracy and power. It should be able to use basic knowledge which includes both practical and science-based theory considering present and past research models.

Current trends in the smart machines industry:

Trend #1: Automated safety systems

The system combines standard and safety control methods into a single platform using artificial intelligent which will collect safety system data and automatically raises alerts. The data is also used to increase productivity, monitor downtime and report compliance issues.

Trend #2: Access to secure information

Operational, business and transactional data are connected with different performance dashboards which will support to develop actionable insights.

Trend #3: Real time diagnostics

Embedded intelligence devices are used to predict maintenance issues using real-time data which will help to quickly troubleshoot and repair problems. It remotely monitors critical parameters and repairs the problems before they reach a point of failure.

Trend #4: Intelligent personal assistance

The tasks are automatically performed by taking to consideration user input data, location, weather and traffic conditions. It also manages emails, calendar events and work schedules.

Trend #5: Smart integration

Replacing a multi-tiered networking strategy with a single network which can simplify network information platform by easing the collection, transfer and analysis of real-time operations data.

Trend #6: Smart data discovery

Provides highly complex results by using the most tailored use cases. It deploys a variety of data types and provide a detailed outcome which can be applied to all the departments of the business.

Trend #7: Cognitive computing

Involves human thoughts and creates a self-learning system using data mining, pattern recognition and natural language processing technologies with an aim to automate IT systems to solve problems without human assistance.

Trend #8: Machine to Machine communication

Helps to implement large-scale remote monitoring systems. It uses the concept of internet of things to manage warehouse scheduling, controls traffic, tracks logistic services and integrates supply chain.

Trend #9: Data analytics helps process excellence

Companies are recording data at different set points which provide information on the complete manufacturing and production work flows. This analyzed data is used to automate shop floor and take management level decisions.

Trend #10: Mobile robotic device

It helps to cover multiple locations by using navigation devices which allows it to travel a pre-defined route and automatically manage inventory, pick items on the shop floor and deliver orders to different departments.

Trend #11: Connected enterprise

It automates manufacturing and industrial processes uses internet of things and helps to share information across people, plants and supply chains. It reduces process complexities and minimizes risks related to information security.

Different ways by which smart machine helps to create a successful business model:

  • Provides scope to remain globally competitive by using relevant business model which helps to easily shift from mass production to mass customization.
  • Automates workforce issues like creating a process to knowledge transfer and retaining skills of retiring workers.
  • Uses latest inputs which helps to address risks related to security threats and complex regulations.
  • Provides information on different type of technologies for different processes.
  • Automated analysis and repairing capabilities which will help to create latest decision support system.
 

GAVS’ ZIF is an AIOps based TechOps platform that enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident Enterprise™ . Visit www.zif.ai to know more

Let’s go Blockchain

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Every business is thinking, talking, and onto digital transformation, and we know how its time has arrived when we witness even traditional manufacturing industries, say, like the diamond industry, is keeping with the times and digitized operations – their stones are being traced right from the diamond mines up till the time it is bought by consumers. It helps identify conflict diamonds and other frauds that might creep into the system. Digitization helps organizations to be better equipped to track their products through complex networks of supply chain.

Blockchain technology was introduced in 2008 as part of the proposal of bitcoin, a virtual currency that does not need a central authority to issue currency. Blockchain functions as an open, shared ledger which records transactions between parties in complete transparent, instantaneous, and verifiable ways. Changes entered into one copy simultaneously gets updated in all the other copies, with no need for intercessors at any point in time. It works as a sole version of truth for various users across nations and communities. Governments and nations are built by contracts and records, and transactions are the backbone of our economic systems, and we have a steadfast technology in blockchain to regulate and establish advanced administrative control as we keep up with digital transformation. The technology could be the answer to the near gridlocks faced by organizations and financial systems in maintaining and coordinating transactions while maintaining scrupulous functioning. It would bring unprecedented standards to various systems bringing rigor and discipline into trading and other cross border activities.

The technology’s architecture is open and shared; volunteers across the globe maintains its core software. Blocks get created which contains the entire sequence of previous transactions, and miners with massive computing powers compete to validate the block. Each block is contained in a secure and synchronized ledger and is time stamped with a digital block seal. The technology is particularly resilient to hacking because it demands having to go through all the blocks previously used, and across computers spread throughout the world. It avoids disparate ledger systems vulnerable to errors and misinterpretation, reduces cost of transactions and eliminates problems of reconciling transactions across ledgers. In a traditional scenario, for e.g. the banking sector, they charge anywhere between 10 to 20% to send money to another country, whereas with blockchain, these service charges are entirely done away with, and trust is established by clever codes! Let’s take another example – a stock transaction executed within seconds online takes perhaps as long as a week for the settlement; for the stock ownership transfer. This is because the parties are working with different ledgers and establishing that an asset is owned and eligible to be transferred takes time, it does not happen automatically. The system calls for intermediaries to act as guarantors of assets, and the transaction record has to move from one organization to the other for the ledgers to be updated individually.

Blockchain would impact our economic system and create a whole new foundation for the society once the technology is completely adopted. All contracts and data would be stored digitally, available to users across shared databases with the advantage of it being entirely tamper proof. The technology brings forth a system with improved security and data portability. It enables smart contracts, change and transfer of ownership, transactions in real-time, and for funds to be released to the seller as soon as the transfer is completed.

There are companies governed completely by blockchain; all of its operations like corporate governance, investments, payments and so on are all done through code. DAO (Decentralized Autonomous Org) is one such company. These companies have their operations functioning thru a series of contracts in motion by a software that sits on blockchain technology. In such organizations, anyone can join, participate, raise funds… The transformation brought by the technology is truly revolutionary to say the least, there is no Board of Directors or Chairman or CEO who rules, it is the code that rules! The Wall Street Journal has called the DAO one of the best capitalized fin-tech startups due to the $100 million it has raised so far.

Let’s take the example of autonomous cars as they are becoming a reality and a part of IoT, we would want it to be completely tamper proof, right? This is where a technology like blockchain again becomes a powerful tool, its distributed ledger can be used to monitor a vehicle’s operating system and its multiple sensors that go to make an autonomous car. It’s configuration can be checked for tampering. In the entertainment industry, ‘music making’ has bit into the technology, as removing middlemen makes it more viable for artists. It’s song tracking, digital registration, data back to artists increases earnings for artists.

The technology needs to be adopted by everyone in the chain, coordinating the ecosystem – governments, consumers, legal organizations, etc., and that by itself would be a drawn out process. It’s success will of course be dependent on its business use cases, the higher the number and collaboration among the diverse businesses across the ecosystem, the higher its value as a technology. Even as bitcoin transactions were expected to do around $ 92 billion, global payments were expected to be $411 trillion. Some of the big names in the financial sector like NYSE, Bank of America, Standard Chartered are in the process of testing the technology to replace manual transactions in foreign exchange, trade finance, etc. The Bank of Canada is testing CAD coin as its digital currency for interbank transfers. Nasdaq is working with a blockchain infrastructure provider for processing and validating their financial transactions. The technology needs to be adopted by everybody in the chain, calling for governments, consumers and organizations to implement it.

There are several blockchain applications currently, for eg. ‘smart contracts’. The application automates transfer of currency and payments once negotiated conditions between parties are met. It can transfer payment to a supplier once the committed delivery is completed. Deliveries are tracked and payments triggered in the most efficient and fastest manner. Adopting the technology would eventually do away with many third party bodies our society currently holds as intermediaries of trust – like bankers, lawyers and so on. Currently companies are using the technology in relatively small packages, as single-use applications that minimize risk. Tech experts suggest one way of adopting the technology is to introduce virtual currency into the payment mechanism. It will result in Finance and Accounting, sales and marketing and other functions subsequently build blockchain capability. Amazon, Microsoft and even some startups provide blockchain services, which makes testing single use applications within organizations easy as they struggle to reconcile multiple internal databases.

Blockchain will reshape economies and reward tremendous value once the technology is completely adopted. And it would call for new expertise in skillsets, jobs and software. It has the capability to penetrate and resolve deep rooted societal and economic problems like money laundering, among others. If the technology scales up to the tremendous volumes of trade and can handle the massive updates that would be required, we can store more and more value on it. What is needed then would be a more even and conscientious response from the regulators as the higher the value stored in these vaults of the technology, it becomes more attractive for the hackers.

With IoT, AR and more and more technologies integrating into our smart phones, we will have to collectively depend more on a technology like blockchain to serve as a strong and cohesive source of support. Blockchain is believed to be the biggest invention after the internet. The technology implies honor in its very design and is democratic by nature. And like all the techno futurists of today, the technology prescribes to the concept of the algorithm being better than any system built by humans through its sheer efficiency and effectiveness.