Adoption of Cloud Computing in Healthcare to Improves Patient Care Coordination

Cloud Native, Healthcare Application Development, Healthcare IT, Software OutsourcingMindfire Solutions

The cloud has revolutionized the way we live and work. It has brought about a new era of flexibility and convenience, allowing us to access information and collaborate with others from anywhere in the world.

According to a Gartner survey, global spending on cloud services is projected to reach over $482 billion this year (2022). The numbers are much higher than those recorded last year, i.e., $313 billion.

Now, healthcare providers are taking advantage of this technology to improve patient care coordination. Adoption of Cloud Computing in Healthcare with Cloud-based applications can help healthcare organizations manage patients more effectively, share important data in a secured manner, and reduce costs.

Let’s first get on with the basics of cloud computing and move on to the benefits of cloud computing in healthcare.

What is Cloud Computing?

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. 

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). This technology allows organizations to quickly scale up or down as needed and pay only for the resources they use.

It has many advantages over traditional on-premise IT infrastructure. With cloud computing, businesses can be more agile and responsive to change because they can provision new resources in minutes or hours instead of weeks or months.

Cloud computing offers greater scalability and enables businesses to scale up or down as they please, without having to make huge capital investments as they are required to pay only for the resources they use.

Cloud Computing in Healthcare

The healthcare industry is in a state of flux. With the ever-changing landscape of regulations, the increasing costs of care and the shift to value-based reimbursement models, healthcare organizations are looking for ways to cut costs and improve efficiency. One way they’re doing this is by adopting cloud-based solutions.

Cloud computing has already transformed many industries, and healthcare is next on the list. By moving to the cloud, healthcare organizations can reduce IT costs, improve patient care, and drive innovation.

Let’s look at how cloud computing is changing healthcare for the better.

  • Reducing IT Costs

One of the biggest benefits of adoption of cloud computing in healthcare is that it can help healthcare organizations reduce their IT costs. With on-premises solutions, organizations have to pay for hardware, software, maintenance, and support. But with cloud-based solutions, they only have to pay for what they use.

Additionally, cloud providers often offer discounts for long-term contracts or for paying upfront. As cloud providers handle maintenance and upgrades, healthcare organizations can further reduce costs.

  • Improving Patient Care 

Another benefit of cloud computing in healthcare is that it can help improve patient care. By moving to the cloud, healthcare organizations can improve collaboration between care team members, ultimately leading to better patient outcomes. 

In addition, patients are increasingly expecting more personalized and convenient care. And with the help of cloud-based solutions like telemedicine and patient portals, they’re getting it.

These solutions give patients 24/7 access to their health information and allow them to book appointments, refill prescriptions, and more from the comfort of their homes.

  • Improving Patient Experience 

Doctors and hospitals now have the ability to increase patient engagement and provide them with anywhere, anytime access to their medical data, test results, and even doctor’s notes thanks to adoption of cloud computing in healthcare. This gives patients more power and control, as well as increasing their knowledge of their medical conditions. Furthermore, because doctors can access the history of medical records, it provides a new level of safety for patients, preventing them from being overprescribed or avoiding unnecessary testing. 

  • Faster Deliver of Time- Critical Medical Services & Impact of Covid19 

Covid19 proved to be a powerful driver of rapid digital transformation across industries. Cloud computing in healthcare is cost-effective and quick to deploy, among other benefits that can be extremely useful, particularly during a pandemic. In many ways, the year 2020 has been unprecedented. Time was critical in combating the pandemic and constructing new hospitals, releasing the vaccine onto the market, and arranging a safe method of mass testing. Cloud computing should be used to help bring important technological solutions to market faster for Time – Critical Medical Services.

  • Data Security and Privacy 

Security and Privacy of patient data are the two most important factors that matter to healthcare providers and payers or for that matter any other healthcare stakeholder looking at leveraging software systems. All reputed cloud services providers undertake the required measures to ascertain that vulnerability of patient data to potential breach is either negative or as negligible as possible. It is, however, a moving target and it is advisable to hire the services of a reliable and experienced tech solution provider to address this concern while adopting cloud solutions.

  • Implementation of AI/ML & Access to Analytics for Data Driven Decisions 

Large datasets of patient information from ePHI, IoT devices, and consumer health applications are processed by cloud platforms. Technology can help to promote healthier patient behavior, improve disease detection rates, and aid in advanced diagnosis and decision making. Through data insights and analytics, AI/ML enables healthcare professionals to make data-driven decisions. It has the potential to personalize medicine, improve care, and deliver real-time information to patients and staff. Data from AI/ML is being used to drive innovation. Healthcare providers make better decisions with adopting cloud computing in healthcare, which improves service operations and increases hospital efficiency. Automated analytics provide significant benefits for patient scheduling, background checks, and managing all associated medical records. Data interoperability benefits research programmes by allowing researchers to quickly collect statistics from a diverse range of patients.

Cloud Computing Market & Opportunities in Health Care

Cloud Computing Makes Patient Care Coordination Easier

There is no denying that technology has revolutionized the healthcare industry. One of the most significant changes has been the move from paper-based systems to electronic health records (EHRs). This shift has resulted in more efficient and coordinated patient care.

However, as anyone who has ever dealt with a complex health issue knows, coordinating care can still be a major challenge. Many players are involved in the care of a single patient, including doctors, nurses, specialists, pharmacists, and others. It is difficult to track all the treatments and medications a patient takes. 

That’s where cloud computing comes in.

  • Cloud computing helps in storing and accessing data online. EHRs are often stored in the cloud, allowing different patient care team members to access them from anywhere at any time. This is a major advantage in coordinating care, as it allows everyone to see a patient’s complete medical history real time and in one place.
  • In addition to making information more readily available, cloud computing also enables sharing with ease large files such as x-rays or MRI scans. This is especially important when patients consult multiple specialists or receive care at different facilities.
  • In the past, patients would have to provide their records separately to each consulting physician, which was both time-consuming and inconvenient. With cloud computing, patients can give their doctors access to their records with just a few clicks.
  • Cloud computing in healthcare also makes it easier for doctors to communicate with each other. Earlier, if two doctors needed to discuss a patient’s diagnosis or treatment plan, they would have to do so by phone or fax (if they were lucky enough to have access to each other’s contact information). With secure cloud messaging platforms, doctors can easily send referrals, consult on cases, and request test results without ever having to pick up the phone.

Conclusion

Cloud computing in healthcare enables the doctors and other members of a patient’s care team to communicate and collaborate easily, resulting in more coordinated and efficient patient care. Adoption of cloud computing in healthcare also helps in implementing latest technologies like AI/ML.

If you are stuck with on premise healthcare software systems, now is the time to switch. Not only will you be able to improve patient care coordination, but you’ll also reap all the other benefits that a cloud infrastructure can provide you, leading to increased productivity and decreased costs.

If you are looking for a software technical partner who can assist you in developing custom solutions that are cloud-based, or in cloud migration, please reach out to us. With decades of experience, we have the necessary resources to help you achieve your goals. Connect with our experts today.

 

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Machine Learning In Banking

Utilizing Machine Learning In Banking To Prevent Fraud

Fintech, Machine Learning, Mindfire Solutions .mindfiresolutions.com

Machine Learning (ML) is a vital tool for fraud detection in banks. It can spot potential fraud by examining patterns in transactions and comparing them with known fraudulent activity. It uses algorithms to identify these patterns, which are then used to predict whether or not a transaction is fraudulent. These algorithms are trained using historical data, so they can only identify patterns in existing data and cannot learn new ways as they occur. 

This means that companies must constantly update their machine learning models with further information for continuing to use machine learning in Banking to prevent fraud.

How Does Machine Learning Overcome The Traditional Security Techniques Used By Banks?

Machine learning pushes the boundaries of what can be done with security. A traditional security strategy is to make the system as difficult to access as possible, stopping the bad guys before they get in. Banks often use biometrics and key cards to access their accounts, which are more challenging to hack than a username/password combination. 

But machine learning in banking prevents fraud even when it’s not done by someone trying to access an account. It can also be used to flag suspicious behavior so that humans can investigate it and decide whether or not it’s worth taking action on.

Machine learning algorithms can analyze data from all sources—customer transactions, social media posts, etc.—and find patterns that indicate fraudulent activity or other risks. These algorithms are trained on examples of fraud so that they know what to look for when new transactions occur.

What Are The Benefits of Machine Learning In Fraud Detection?

Machine learning has been the buzzword in the tech industry for some time. From self-driving cars to automated customer engagement, machine learning is everywhere.

But what does it mean? Let’s look at some of the benefits of using machine learning in Banking to prevent fraud.

  • Speed

Machine learning can help improve the speed of fraud detection by reducing the time it takes to detect and flag suspicious activity. Machine learning algorithms can be trained to automatically flag transactions with a high risk of fraud. This can significantly improve your ability to identify fraudulent transactions quickly so you can act on them before they become too costly to remediate.

  • Efficiency 

Machine learning also improves efficiency by automating many manual tasks that waste time and effort. For example, machine learning in banking to prevent fraud can identify known bad actors who are likely to commit fraud in the future, so you can block their access to your business immediately without having to review every transaction they make manually. 

  • Scalability 

Machine learning allows you to scale up or down your fraud detection capabilities as needed. This is important because fraud patterns change over time as criminals adapt their approach or new types of fraud emerge. Machine learning algorithms are designed with built-in flexibility to adapt quickly when new threats emerge or old threats change tactics. 

  • Accuracy 

Finally, machine learning offers increased accuracy over traditional methods because it uses data from all available sources—including humans—to learn what normal behavior looks like and spot anomalies that indicate potential problems.

What Are Some Of The Ways Machine Learning Can Be Used To Detect And Block Fraud?

There are many different techniques to detect and block suspicious cases. Some of them include the following – 

  • Classification

Classification assigns a label to an observation based on a set of observed values used as predictors. The predictors are inserted into the algorithms, which use training data to learn what labels to give. These predictions can then be used for fraud detection. This is done by identifying fraudulent transactions or users by classifying them as fraudulent or not fraudulent.

  • Regression

Regression is a supervised learning method that predicts future outcomes based on historical data. The regression algorithms can be used in fraud detection to predict the likelihood that a transaction will be fraudulent based on historical data about previous transactions that were labeled as fraudulent or not fraudulent by humans.

  • Clustering And Anomaly Detection

Clustering and anomaly detection are unsupervised learning methods that can be used for fraud detection by identifying patterns within your data that suggest fraud may occur, such as many small withdrawals from an account or many large purchases made at one store over time.

  • Anomaly Detection

Machine learning algorithms search for patterns in existing data that are not typical of what you would expect. If a new transaction is entered into your system and doesn’t fit the pattern of existing transactions, it could be an anomaly.

  • Decision Trees

A decision tree is a tree-like diagram that shows all possible paths that can take place in a decision process. A decision tree algorithm takes in data and tests each piece of information against all possible outcomes to determine if they’re true or false. If any single piece of information leads to an inaccurate result, the entire transaction is flagged as fraudulent.

  • Neural Networks

Neural networks are used to detect fraud in several ways. They can be trained to recognize patterns that indicate fraudulent transactions, such as repeated requests for withdrawals from an ATM or many purchases at one store within a short period. 

Neural networks can also monitor customer behavior over time and flag suspicious activities like sudden changes in spending habits or changes in the type of purchases being made (from low-risk items like groceries to high-risk items like jewelry).

  • Natural Language Processing (NLP)

NLP refers to technologies that use machine learning algorithms to analyze text data and extract meaningful information. 

For example, NLP software might analyze customer statements and detect instances where someone has been using their bank account number on multiple credit card applications without having applied for those cards themselves. This could indicate that they have been victims of identity theft or another fraud scheme.

Summing It Up

If you’re looking to implement machine learning in banking to prevent fraud or other systems, Mindfire Solutions has got you covered. Our goal is to take the guesswork out of it and ensure you get the most out of your investment.

We have the experience and expertise to help you implement machine-learning algorithms for your security and other needs. Our team deeply understands this technology’s potential, and we can work with you to determine the best way to use it in your organization. Contact us today to see how we can help!

 

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UPI (Unified Payments Interface) – The Game Changer

Fintech, ThoughtsMindfire Solutions .mindfiresolutions.com

Phenomenal growth may be the exact term to describe the increased volume of digital payments in India in 2022. If you inspect the March data of the current year, it shows that this payment ecosystem grew by 216% compared to the same period in 2019. The Unified Payments Interface (UPI), an initiative of the National Payments Corporation of India (NPCI) has been a critical driver of cashless transactions since its launch in 2016. The idea behind introducing UPI was to enable fast, secure, and seamless digital payments with the Immediate Payment Service (IMPS) infrastructure.

The immense popularity of this system is evident from the fact that UPI transactions exceeded USD 110 billion in January 2022.

NPCI has launched several innovative payment systems for developing the digital payment space and tied up with foreign companies like Japan Credit Bureau, China Union Pay, Discover Financial Services, etc. The international coordination would provide support by payment acknowledgment through RuPay cards.

Starting with IMPS and NFS in 2010, there have been RuPay cards, NACH, Aadhaar Payments Bridge System (APBS), Aadhaar-enabled Payments System (AePS), NETC, BBPS, UPI, and the USSD based *99#.

What makes UPI the game changer in digital payments?

Convenience

For using debit or credit cards online, you need to enter several details related to the card holder’s name, card number, CVV, and expiry dates. For UPI, you just key in the UPI ID, login, and complete the payment after putting in the PIN.

If you are using net banking, you must first add the beneficiary to your bank account. You require the beneficiary’s name, bank account number, branch address, and IFSC code. The process takes time to get activated, and then you can make the payment. To make a UPI payment, you can transfer funds to a peer account even if they do not have UPI access.

Besides, there are advantages over other existing payment methods. For instance, UPI allows you to make payments directly from your bank account without any third-party wallet or a card. Thus, making it much more convenient and secure than using a debit or credit card for online payments.

Moreover, UPI transactions happen in real-time. There is no waiting period for the funds to be credited or debited from the user’s account. This feature makes UPI an ideal choice for digital payments.

Simple

UPI is also very simple to use. You just need a smartphone with an active internet connection. You can then download a UPI-enabled app from your bank’s website or the Google Play Store. After installing the app, simply link your bank account and start making payments.

Versatile

You can also use UPI to request money from others or to send money to them. All in all, UPI is a very convenient and secure way to make digital payments. It is fast and easy to use.

UPI is particularly beneficial as a non-cash payment instrument when it involves person-to-person (P2P) fund transfers and transactions related to small-value person-to-merchant (P2M) payments.

Interoperable

With interoperability technology between different payment systems and UPI, and more people using digital payments, the costs of transactions are expected to decrease further.

India’s most prominent technology firms like TCS, Infosys, Wipro, etc., and fintech companies in various fields like insurance, payment, agriculture, microloans, crowdfunding, and wealth management are some of the major factors behind the growth of UPI. The role of an open Application Programming Interface (API) is also worth mentioning. 

How does UPI’s rise affect the Indian economy?

All the above factors have pegged the volume of UPI transactions at approximately 9 times that of debit and credit card transactions in FY22. But this is just the initial phase. UPI is forecasted to grow manifold and comprise around 73% of the total volume of digital transactions by the financial year 2026.

UPI transactions beyond borders

The tie-up between NIPL (NPCI International Payments Limited) and others like Liquid Group – Singapore, Mashreq Bank – UAE, Lyra Network – France, and PayXpert – UK, are significant steps toward enabling QR-based UPI payments in countries across the globe. Nepal and Bhutan became the first countries to adopt UPI.

RBI’s latest move entails linking RuPay credit cards with UPI. The decision to implement such a change has come at the perfect hour since credit card transactions are rising. A Goldman Sachs report states that the volume of transactions was around $130 Bn in FY22. The forecast is that it may reach $285 Bn by FY26 while the UPI volume growth in August 2022 was more than Rs. 6500 Mn with 346 banks going live on the platform.

An important point is the MDR (merchant discount rate) on transactions by RuPay credit cards. Like other credit cards, the rate will likely be around 2%, as specified by the National Payments Corporation of India (NPCI).

What can be the impact of this MDR on BFSI?

Payment companies like PayU, Razorpay, Billdesk, etc., would be able to acquire more transactions as credit cards would be an additional payment instrument on the UPI platform.

The proposed MDR of 2% (1.5% for the issuing bank and 0.5% for the payment company) on credit cards for payments through the Unified Payments Interface (UPI), would generate a revenue stream for the payment companies.

Currently, the MDR on UPI merchant transactions is zero, which is a deterrent for the payment companies as there is no direct income available for them on UPI transactions. This MDR would help the payment companies to manage and maintain the infrastructure for UPI volume growth.

For smaller merchants, providing a subsidy for MDR will mean the payment companies would lose their revenue percentage by an amount equivalent to the subsidy amount. In that scenario, the Government can incentivize the payment companies to maintain their payment infrastructure (technology, etc.) and operating costs.

Conclusion

With the RBI’s decision to allow credit cards for payments through UPI, the central bank is enabling access to digital payments for those who do not have a debit card or a bank account. It would help to expand the reach of digital payments to a wide demographic besides bringing in higher revenue for the payment companies. If you are looking for a game changer in the digital payments space, UPI it is.

If you want to develop a mobile payment solution application that offers a top-notch user experience and data protection, team up with Mindfire Solutions.

Mindfire Solutions is a trusted software service provider that can assist you in developing robust and secure fintech products. With our two decades of experience, we have learned to leverage modern technologies to offer customized solutions for your business needs. To know more about us, visit Mindfire Solutions.

 

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AI in critical care

Role of AI in Chronic Care Management

Healthcare Application Development, Healthcare IT, Wellness SolutionsMindfire Solutions .mindfiresolutions.com

Effectively managing chronic diseases such as asthma, diabetes, cancer, and several others have been one of the biggest challenges for healthcare providers worldwide. According to a study, chronic diseases are responsible for 70% of deaths and about 50% of the disease burden globally. Owing to this, we will discuss how AI in chronic care management can change these numbers.

As the pandemic unleashed a global health crisis, many countries faced a shortage of healthcare professionals and medical resources. This caused several hurdles for patients with chronic conditions to get treatment at the right time. Such conditions forced chronic patients to adapt to remote and digital treatment, which is the new normal in the current world.

For medical professionals to deliver quality remote healthcare, it has become essential to leverage fast-emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML).

Both AI and ML have already shown how they can significantly improve the efficiency of operations in various other industries like e-commerce, manufacturing, automotive, etc. In this article, we will take a look at the role of AI in Chronic Care Management (CCM).

Key Benefits of Chronic Care Management

Before we understand the importance of AI in Chronic Care Management, let us look at some key benefits of Chronic Care Management: 

  • Chronic Care Management or CCM offers an organized approach to dealing with chronic conditions, making the process more coordinated for patients as well as the healthcare provider.
  • Healthcare providers can build long-term relationships with patients through their CCM services, which can result in increased revenue.
  • It can prevent unnecessary visits to partitioners.
  • Researchers have proved that CCM can offer quality healthcare to patients at a lower cost.

What is the Role of AI in Critical Care Management?

Here is how AI in chronic care management can turn around the situation:

●     Medical Data Analysis

An extensive medical data set will be required to utilize AI in chronic care management at its full potential. In today’s digital world, it can be easily gathered throughout the patient life cycle via mobile applications, IoT devices, and patient portal software. An Al-based algorithm can analyze this pool of data and generate new insights and opportunities for both patients and medical professionals in CCM. This can streamline the overall chronic care management processes.

●     Prognosis and Prevention of the Disease

One aspect of CCM is to prevent diseases from happening. With the help of AI, practitioners can identify the choric disease to which the patient is susceptible. This allows the doctors to take the correct preventive measures and circumvent the chronic condition.

In a study published by Yannis Paschalidis in Harvard Business Review, healthcare professionals were able to forecast hospitalizations due to diabetes and heart disease a year in advance using machine learning and Electronic Health Records (EHRs) with an accuracy of 82%. 

●     Diagnosis of Diseases

Many recent studies have proved that AI can also be indispensable when it comes to the diagnosis of diseases. Al-algorithms can easily detect diseases based on data points like medical imagery, ECG data, patients’ demography, and their medical history. Such developments have led to time and cost-effective CCM service. It also has significantly improved remote diagnosis.

●     Treatment

The healthcare data can be used to create AI/ML models that can assist physicians in medication modeling and treatment suggestions. These models can be further applied to suggest appropriate dosage and treatment plans for the patient.

A personalized plan and treatment for individual patients can also be created by AI. The personalized treatment can enable clinicians to intervene before a patient’s condition becomes critical, making CCM more effective.

Recently, a company named IBM Watson has achieved some remarkable results by utilizing AI in the field of oncology. The organization performed genetic data analysis and was successfully able to identify the rare secondary leukemia caused by myelodysplastic syndromes.

●     Remote Patient Monitoring (RPM)

With an AI-powered CCM, healthcare professionals can constantly monitor a patient’s vitals like blood pressure, pulse rate, temperatures, etc., from the comfort of their home. The AI can also send an alert to health professionals if there is any sudden or critical change in the vitals of a patient.

●     Virtual Assistant

Chronic diseases generally last for a lifetime. As the patient gets older, it gets difficult for them to keep track of medication and appointments, which causes disturbance in their chronic care management. In such cases, an AI-powered virtual assistant can come in handy for the patient. It can assist them by ensuring drug adherence and also monitor their vital data.

For example, a Meditech company created AI-embed nurse avatars that send notifications to the patient each morning for a check-in routine, record their vitals, and sends alerts for timely intake of medicine.

Another aspect of chronic care management is measuring and managing the patient’s chronic pain. Here again, AI can assist in detecting chronic pain by monitoring the facial muscle movements of patients who are unable to self-report the pain to their physicians.

Conclusion

Artificial Intelligence can offer actionable insights to guide clinical decisions and allow physicians to diagnose, treat and handle chronic conditions remotely, thus making chronic care management more efficient, accessible, and affordable.

That’s why many healthcare organizations have begun to utilize AI in their chronic care management services. If you are also looking to leverage AI to improve your chronic care management, you will need to hire or work with people who have relevant expertise and skill set.

Mindfire Solutions is an IT-service provider that has worked with several healthcare organizations over the years to provide customized healthcare tech solutions that are highly cost-effective, secure, and scalable. Visit Mindfire Solutions to learn more about us.

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Vulnerabilities of IoMT

Top 6 Vulnerabilities of IoMT devices to look out for

Healthcare Application Development, Wellness Solutions, , Mindfire Solutions .mindfiresolutions.com

The internet of medical things (IoMT) is an innovative solution transforming the healthcare industry. With IoMT, healthcare providers can gather data from various medical devices and create a comprehensive view of a patient’s health. The solution enables healthcare professionals to offer more proactive patient care and better treatment outcomes while reducing expenses.

That’s why many healthcare providers are keen on leveraging the internet of medical things. In 2020, as the pandemic caused global turmoil and remote solutions became a necessity, the global market capitalization for the internet of medical things (IoMT) reached a value of USD 41.7 billion.

By the time 2028 arrives, the global internet of medical things (IoMT) market size is projected to be worth USD 187.6 billion, growing at a CAGR of 29.5%.

While there are several benefits of utilizing the IoMT, there are also certain risks associated with the technology. In this blog, we will bring forward some of the vulnerabilities of IoMT.   

Vulnerabilities of IoMT

Here are some of the major vulnerabilities of IoMT:

Multiple Entry Points

The interconnectedness of medical devices makes IoMT a groundbreaking solution for healthcare; however, it also makes systems more vulnerable to cyberattacks. Medical devices are difficult to patch. As the number of devices and sensors increases, the possible entry points for attacks also increase. Even a single point of a breach can be catastrophic for the entire system.

That’s why it becomes essential to deploy anti-malware mechanisms to ensure that devices are not susceptible to attacks.

Privacy and Security Issues

IoMT systems gather large amounts of data from patients. This data is often sensitive, and if it falls into the wrong hands, it could be used for malicious purposes. For example,  passive attacks such as traffic analysis can allow attackers to gather confidential information on patients like their medical conditions, medications, and treatments.

Active attacks are also possible. For example, an attacker could use a denial-of-service (DoS) attack to disable a device or system, preventing it from being used. Such incidents can cause serious consequences for patients who rely on the device or system for care.

To protect patient privacy and security, healthcare organizations must implement strong security measures like encryption of stored data and data-in-transit.  

Poor Authentication

Most healthcare devices are not equipped with proper authentication mechanisms. Research that aimed to understand the vulnerabilities of IoMT devices observed that many healthcare organizations adhered to default passwords and settings, which were available on the manual online. 

This makes it easy for unauthorized individuals to access the network and tamper with it. In such cases, cybercriminals can remotely take control of devices and use them to carry out hostile activities.

Therefore, your organization should have security protocols such as Multi-Factor Authentication in place to improve the safety of IoMT devices.

Unsecured Internet Connection 

Usually, healthcare enterprises run IoMT systems on the same network which is used for managing their infrastructure. This not only poses a threat to your IoMT system but also to your entire organization’s infrastructure.

Hence, it is advisable to segment the company’s network. Operate IoMT devices on a separate network. This way, even if there is any breach, the damage will be limited to a small portion of your entire network, which can be countered quickly.

Lack of Visibility 

Another major issue with IoMT is the lack of visibility into the network. With so many devices and sensors collecting data, it becomes difficult to track all the activity occurring within the system. According to a study, about 80% of IoMT devices are used frequently in a month. Therefore, it is difficult for the IT team to identify anomalies and potential threats.

Outdated Systems

A study has shown that many healthcare organizations leveraging IoMT are still using outdated versions of operating systems that are not designed to deal with modern cybersecurity threats. This leaves systems vulnerable to sophisticated attacks that can easily bypass traditional security measures.

Even if the organization is up to date with its operating systems, it is equally important to update the devices in the IoMT system regularly. 

Conclusion

IoMT systems offer several transformational benefits to the healthcare organization. However, healthcare providers also need to be aware of these vulnerabilities and take steps to mitigate them.

One of the ways to secure your IoMT system is to conduct risk assessments, implement security measures, and keep up with the latest trends in cybersecurity. By doing so, you can ensure that your IoMT system is protected and can be used to improve patient care.

As testing any system can be an overwhelming task, you can also collaborate with an experienced IT service provider to reduce the workload.

Mindfire Solutions is one of the leading agencies in the IT sector that has assisted several world-renowned organizations with IoT Testing. With our team of industry experts, we offer solutions that are tailored to your organization. If you are also looking to test your IoT or IoMT system, visit Mindfire Solutions to learn more about our services.

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Hybrid Cloud Strategy with Kubernetes

Develop a Hybrid Cloud Strategy With Kubernetes

Mindfire Solutions, , Mindfire Solutions .mindfiresolutions.com

Initially developed by Google, Kubernetes or K8 is an open-source container orchestration platform that automates several application development processes, including day-to-day procedures like upgrading, monitoring, and logging.

Kubernetes allows its users to utilize Google’s expertise in distributed systems consisting of highly complex clusters, making it one of the leading container orchestration platforms that support organizations in developing applications cost-effectively.

In 2021, 50% of the organizations globally adopted Kubernetes for application development.

Kubernetes is a fundamentally resilient platform that offers myriad benefits in terms of rapid scaling, deployment, and automated container rollouts. One of the critical benefits of K8 is hybrid deployments. In this blog, we will look into how to develop a hybrid cloud strategy with Kubernetes.

What is a Hybrid Cloud Strategy?

Many companies are migrating toward public cloud infrastructure. However, businesses do not want to abandon their on-premises infrastructure due to the critical data available on it. Hence, opting for a hybrid cloud strategy becomes an ideal option for businesses, where they can utilize on-premises resources with cloud resources.

A hybrid cloud strategy is a plan to deploy and manage applications and services on multiple clouds, i.e., public and private. Creating a hybrid cloud strategy is essential for enterprises undergoing digital transformations. With this strategy, organizations can easily bifurcate what data goes on on public cloud infrastructure and private cloud infrastructure.

Benefits of a Hybrid Cloud Strategy

Here is a list of the benefits that enterprises can get by developing a hybrid cloud strategy:

  • Scalability

With a hybrid cloud strategy, businesses can achieve true application portability. Meaning the application can be shifted instantaneously from one cloud infrastructure to another as per the requirement. It can distribute and manage the load without performing any complex configuration.

  • Data Safety

By replicating your on-premise data on cloud infrastructure, you can provide a backup for your database in case your on-premise infrastructure faces some technical disaster and you lose all your crucial information.

  • Innovation

A hybrid cloud strategy enables the IT team to develop and test new updates of an application on the private cloud infrastructure before making it available on the public cloud infrastructure. This allows the IT team to be more innovative and eliminates the need for any downtime required for developing new features.

  • Cost-effectiveness

Businesses using a hybrid cloud strategy require lesser time and resources in developing and deploying an application. Hence, making the overall process cost-effective. 

  • High Performance

By adapting to a hybrid cloud strategy, you can distribute the application globally, hosting critical services and data per the requirement, and provide high-performance to the users.

Benefits of Kubernetes

Kubernetes comes with several features that are very beneficial for organizations:

  • Auto Scaling is one of the most prominent benefits of Kubernetes. K8 has three different autoscaling capabilities- Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler. It allows the enterprises to run the workloads cost-effectively and efficiently.
  • Kubernetes acts as an orchestration system and enables the IT team to leverage a containerized environment.
  • Another impressive benefit of Kubernetes is its self-healing ability. If a container fails, K8 can replace it or restart it automatically. The platform also constantly monitors the health of nodes and containers.
  • With Kubernetes, IT teams can manage the network of multiple containers through a single console.
  • One can also build a micro-services-based application with Kubernetes. It allows the companies to scale the parts of the application that have received high traffic.

Challenges with Kubernetes

Even though there are numerous advantages of Kubernetes, there are also some drawbacks of the platform.

  • There are too many moving parts involved in Kubernetes; hence, new users would have to go through a steep learning curve while adapting to the platform.
  • The initial step-up and configuration of Kubernetes is complex and can be overwhelming.
  • Kubernetes provide limited automation during the restoration of the containerized application environment.
  • The platform can be more expensive than its alternatives.

Kubernetes-Based Hybrid Cloud Strategy

Kubernetes offers an ideal foundation for hybrid cloud strategy because of its consistency. It does not matter if you are using Kubernetes as an on-premise infrastructure or a cloud infrastructure; they both work on the same commands and in similar ways. As Kubernetes is open-source, you can also deploy it in clusters of machines without licensing or contracts.

With Kubernetes, you can develop a hybrid cloud strategy using three different methods:

  • Clusters Without Direct Interaction: In this method, there are multiple clusters in a system, with each one having a different task to perform, and there isn’t any direct interaction among them. It is a beneficial strategy if you are looking for environment segregation to develop applications on one cloud platform and deploy them on another.
  • Cluster Federation: This is the opposite of clusters without a direct interaction method. Cluster federation provides a single point of view for all clusters. The method comes in handy when looking for a centralized configuration of data centers and a single API. It establishes a standardized Kubernetes deployment for all clusters.
  • Serverless Architectures: This method can be considered one of the popular trends for adopting a Kubernetes-based hybrid cloud strategy. The purpose of a serverless architect is to offload the cluster. It allows Kubernetes to be integrated as a virtual machine (VM) in case your physical machines are exhausted.  

Conclusion

In conclusion, a Kubernetes-based hybrid cloud strategy provides a holistic solution for deployment, management, and operational concerns. However, there are a lot of moving parts involved in the process. Coming up with a hybrid cloud strategy can be a challenging task. That’s why many companies prefer outsourcing this job to expert IT professionals.

To create a perfect Kubernetes-based hybrid cloud strategy for you, Mindfire Solutions has assembled a team of passionate individuals who are well-equipped with industry knowledge and have been working in this domain for a long time. Over the years, Mindfire Solutions has worked with several world-renowned companies like SAP, AsianPaints, and DHL. 

Visit Mindfire Solutions to know more about our services.

 

 

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AI for healthcare

How Do AI Applications Improve Healthcare And Wellness?

Healthcare Application Development, Wellness Solutions, , Mindfire Solutions .mindfiresolutions.com

The COVID-19 pandemic overwhelmed the existing healthcare infrastructure. It has been a rude reality check for clinical administrators worldwide. Now, as the contagion subsumes, the persisting rise in the global burden for non-communicable ailments like lifestyle disorders is likely to keep medical practitioners on their toes in the days ahead. With this, the demand for preventive measures is increasing. By using AI for healthcare, we may step ahead in this crisis.

A Looming Crisis In Healthcare

Today, the demand for preventative intervention is skyrocketing. But limited growth in clinical ranks indicates an ever-widening talent gap with cascading implications. WHO assessed a projected shortfall of 18 million healthcare workers by 2030. This gap is likely to be manifested primarily in low and lower-middle-income geographies. Medvocation, in one of its recent studies, found that nearly 44% of doctors worldwide are already breaking under the immense workload and are unable to live happy and healthy lives.

The existing state of affairs can portend a crippling impediment as the global population ages. Reactive healthcare becoming more costly adds to the worries of the patients and clinical professionals.

AI For Healthcare: Reimagining Wellbeing

Fortunately, advancements in data-driven technologies like AI and machine learning have brought preventive medicine much closer to high-risk and healthy individuals. It has improved the possibilities of self-care and wellness like never before. For instance, today, AI applications can monitor every heartbeat and predict congestive heart failure (CHF) with remarkable accuracy. It permits the prospective patient a significant head start in seeking expert advice long before ending up on a gurney in an ICU.

Due to this the global market for intelligent self-care medical devices ($13 billion in 2020) is expected to reach a valuation of more than $30 billion by 2027, with a CAGR of over 8%.  

AI in Medicine: A Comprehensive Approach

For some years, AI-powered applications have made significant inroads across various clinical procedures and treatments, from automating medical front desk services, drug repurposing, vaccine development, building Smart EHR systems, and improving pathology to successfully predicting drug reactions. Today, at least 90% of healthcare establishments have an AI strategy. Indeed the benefits are substantial as cognitive computing allows practitioners to delegate repetitive tasks like clinical data extraction, assimilation, and report creation to the machines. Effectively AI for healthcare professionals can help with better decisions and focus on what they do best: care for their patients. 

However, the role of AI on the demand side of the story is equally spectacular! Proper self-care, yet elusive, is now feasible as wearable devices embedded with machine intelligence enable individuals to listen to their vital signs better. As algorithms operating on the edge get more intelligent and more emphatic, they will only expand the chances of proactively and precisely diagnosing physiological parameters and assessing the likelihood of acute events for individuals with chronic conditions. It, in turn, will preempt health risks, transform clinical deliveries and ease the pressure on the existing medical infrastructure worldwide.

Factors Fueling The Influx Of AI For Healthcare

The trend is in no way isolated and wholly synced with the cognitive technology maturity curve. Several factors advocate making personal devices like Blood Glucose monitors, Insulin Pumps, Sleep Apnea Devices, Blood Pressure Monitors, and Smart Watches intelligent enough to improve self-care and wellness for their owners. None is more telling than the dichotomy that although there has been an explosive growth of health data collected institutionally in recent years, it may still fall short in enabling patient outcomes.

For instance, an asthma patient typically visits the physician every three months and spends over 2,100 hours in between when the symptoms are not actively monitored, undermining realistic assessment. Now, data continuously ingested through smart devices can bridge this gap. Other factors include:

– Advancement in cloud computing: 

The computational power available for training AI models and algorithms has grown exponentially in recent years with the GPU revolution. Today, with easy access to bare metal servers from cloud infrastructure providers, it is easy to configure systems for running high-performance healthcare workloads.

– Development of Deep Neural Networks: 

The development of Artificial Neural Networks today is supplementing ML capabilities, providing for much better and more precise modeling. ML procedures like Capsule Neural Networks and Transfer Learning can transform how ML models are built and deployed, leading to far more accurate predictions even when trained with limited datasets. It will indeed make self-care medical devices smarter and more cost-effective.

– Shift in the healthcare delivery philosophy: 

As AI systems become more readily available, institutions worldwide are unmistakably considering how care is delivered and how precious healthcare resources and infrastructure are utilized. For instance, the National Institute of Health in the United Kingdom has launched an initiative to encourage the use of AI for healthcare of individuals to self-diagnose the onset of chronic conditions. The broader objective is to eliminate unnecessary outpatient visits and save operating costs, optimizing the resources available to the frontline care workers.

AI In Self-care: Use Cases

This research paper published by Nature.com manifests the overall apprehensions of patients around the role of AI in healthcare. However, advancements in cognitive technologies can bring in early and reliable insights and even formulate an effective response to some of the most prevalent chronic health conditions worldwide. These includes: 

– Diabetes: 

While the retroactive insights on blood sugar levels are currently derived from lab tests like A1C and self-service glucometer readings, AI-enabled devices can completely upend the diabetes treatment pathway. Intelligent insulin pumps can monitor blood glucose levels and other health metrics in real-time and auto-administer appropriate insulin doses based on the patient’s health condition and symptoms.

– Hypertension: 

Collecting blood pressure readings periodically through cuff-based devices is only half the game, pending further diagnosis. However, with Smart wearable devices connected to the cloud, blood pressure data can be assimilated with multimodal data sets like genomics and behavioral to pinpoint anomalies and preempt acute instances.

– Asthma:  

Asthma patients must regularly visit clinics for pulmonary function tests. They are monitored for environmental variables like air quality and moisture profile that can adversely impact their health. AI algorithms can extrapolate heart rate and blood oxygen level data from pulse oxymeters. Utilizing other pointers like pathophysiological analysis, natural history, seasonality, phenotypes, genetics, environmental monitoring, disease biomarkers, etc., AI predicts the possibilities of an asthma attack.

– Congestive Heart Failure (CHF): 

Conventional detection for CHF is done through a clinical diagnosis like ECG and studying factors like hereditary prevalence and lifestyle choices. Nevertheless, AI algorithms can accurately predict heart health through raw electrocardiograms to predict the possibilities of CHF, almost with 100% accuracy.

– Depression: 

Screening for mental health conditions depends on subjective evaluations to detect the root cause and respond to symptoms. However, AI algorithms can eliminate this subjectivity and clinical bias from the equation by evaluating symptoms through facial expressions, voice patterns, and online habits. Moreover, they can objectively assess treatment progress by interpreting brainwave profiles unique to patients with depression.

Final Thoughts

Bringing AI to improve the state of self-care and wellness is an idea whose time has come. Cognitive technologies can play a pivotal role in preventing catastrophic health events and saving lives. However, considering the wide range of variables and the risks involved, expert implementation becomes as crucial as the technology to ensure the first-time-right outcomes. Therefore alongside investment in technology, it becomes a strategic necessity to find an experienced technology partner who can aptly demonstrate the viability of self-care through AI.

Mindfire Solutions is one such leader in AI/ML, well-acclaimed in the global health tech market. Get in touch with one of their AI consultants to discover how the company simplifies self-care and wellness for millions worldwide.

 

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Brand Loyalty

Spoilt for choice – Is Brand loyalty of Buyers a thing of the past?

Digital Marketing Strategies, Mindfire Solutions .mindfiresolutions.com

The Covid 19 pandemic has seen variants across the entire spectrum of human behavior, including consumers’ brand loyalty. The latest McKinsey reports, “In the US, more than 60% of consumers who tried a new behavior plan to stick with it post-crisis.” A second study agreed, saying that 62% of people who changed their preferred brand pre-pandemic will most likely make it a permanent change. 

Research says during the pandemic, consumers have changed their shopping choices. When they didn’t find a particular product with a specific retailer, they moved on to a different brand. This causes supply chain disruptions globally. Availability, convenience, and comfort took precedence during times of scarcity. 

We cannot overlook Post pandemic changes in consumer behavior as temporary. It can shape the future trajectory of brands and customer relationships. 

Buying Behaviour Pre-Pandemic 

The change in buying approach is not entirely a post-pandemic trend. In 2019, a study of more than 34000 customers by Verint® Systems Inc. stated that brand loyalty has steadily declined for years. More than 60% of the people interviewed in the study said that it was very likely for them to switch to a competitor with better customer service. A report by Accenture Strategy found nearly 90% of buyers were quicker to shift to a new brand than they were three years back. 

Brand Loyalty Is A Thing Of The Past

Brand loyalty is not just declining. It is quickly diminishing. In the era of e-commerce and social media, buyers have plenty of choices. There has been a mass migration of buyers onto the internet. Billboards and television ads are almost dead.  Hence, consumers today tend to be more versatile and less loyal than ever. Research on consumer satisfaction indexes has long proven that despite having a positive experience with an existing brand, satisfied consumers are less likely to buy the same car, shampoo, or washing machine again. 

Previous research indicates that customers are invested in a brand for reasons like consistency, increase in value, quality, reward points, personalized gifts, etc. Today, brands are struggling to recall value. Between times of loyalty to no brand loyalty, the world around us has gradually but significantly shifted.

Reasons For Changing Consumer Behavior

Some of the reasons that can explain the changing consumer behavior are:

Product Availability  –  With shutdowns happening since 2019, many products went off the shelves. Despite preferences, consumers found more options – similar and sometimes better in quality. 

Exposure to online reviews – Customers used to listen to brands. Now, they have the option to read online reviews and assess the product’s quality. We see people reading an online review and saying, “I am going to try it,” now more than ever. 

Customer service – More than 85% of customers say that excellent customer service is a ‘make it or break it moment’ for them when selecting a service provider. 

Convenience – When a diverse range of products is available at a  screen swipe, an average buyer wants maximum comfort. 68% of consumers say they intend to engage with a brand that makes it easy for them. 

Technology – Close to 50% of customers say that they are more likely to stay loyal to brands with improved technology than their counterparts. We often associate good tech with better customer engagement. 

Human contact – In sharp contrast to the previous point, while many customers prefer self-service, they also find it comfortable to engage with a person across phone and digital platforms. Non-availability of people in customer service proved to be a firm no-no for buyers. 

While customer service has improved across services, some outperforming brands and retailers have set the bar high. Today’s world is fast evolving to provide the best shopping experiences online. Buyers don’t have to be on their feet all day hopping from one store to another. It is not enough to be good or better. Be the best to stand a chance at winning the buyer’s brand loyalty. 

Value for Money

“Value for money” is the primary reason to switch brands more often. Increasing global awareness of ethical, sustainable, and value-based brands (e.g., supporting local businesses) are some reasons why buyers choose to move away from certain brands or products.  

Another challenge for retailers when retaining loyal customers on the internet is competing for the buyer’s attention, engagement and consistency.  While it takes time to nurture such loyalty, in the face of the bombardment of information on the internet, people who want to buy do not have the attention span for one particular brand. Therefore retailers investing at least 50% of their budget in improving customer experience are set to gain in the long run because research says loyal customers are more likely to spend 67% more than new customers. It is always easier to sell to someone ‘again’ than for the first time. Technology can make personalized products and services for all buyers a reality. Brands can reap rich profits through early investments in robust IT. 

Conclusion

The soar in online shopping is unlikely to decline after the pandemic abates. For brands to maintain a solid relationship with their valuable customers, they must keep a firm grip on their brand loyalty programs. Instead of running isolated programs around marketing, companies should integrate brand loyalty goals into all departments such as operation, finance, and technology. It is crucial to ensure seamless coalescing of operation, marketing, and the tech footprint of the company to build brand loyalty. Swift and smooth returns, replacements, and refunds, with efficient and polite customer care, are essential success factors for a brand. Couple it with a user-friendly online interface. And you have the success mantra for customer retention in the long run.

Mindfire Solutions provides robust IT solutions and a user-friendly online interface that can help you bridge the gap with your customers. Connect with us to know more about solutions that can help grow your business. 

 

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IoT in Automotive Industry - Smart Cars

How IoT In Automotive Industry Is Transforming Smart Cars

Transportation Application Development, , , Mindfire Solutions .mindfiresolutions.com

The Internet of Things is rapidly influencing every sphere of our lives. IoT in Automotive Industry is seeing one such rapid growth. As of 2020, an article by Deloitte cites that over 20 billion IoT devices are in use. 

Overtly, the connected vehicle is the most recent embodiment of IoT technology. While automotive engineers and software developers both claim responsibility for this success, the real power behind the wheels lies with the IoT service providers. 

Our cars can link to our smartphones and stream our favorite playlists for hours. But today it is equipped to provide us with emergency roadside assistance and real-time traffic alerts. With a simple click, IoT devices can evaluate engine performance, monitor and control air temperature, and measure physical indicators. Hence, automotive companies can no longer afford to overlook the contribution and role of IoT. 

Internet Of Things Will Be The Backbone Of The Future Automotive Industry

The Internet of Things is ushering transformative across sectors; the automotive industry will continue to change rapidly under its influence. It will be the backbone of crucial industry changes. And the connected car will be a critical player in determining the future economy, besides providing an enjoyable user experience on the roads. 

We see examples of power struggles between the software and the automotive industries. Evidently, the industry is evolving and reinventing itself. We are moving from the age of products to the age of experiences, from hardware to software, from information to value creation. The customer mindset is transitioning from siloed products to inclusive ecosystems.

As the automotive industry undergoes this overhaul in its position, IoT in automotive industry can fortify using the following methods:

  1. Align the vision with the automotive industry – IoT, software service providers, and the automotive industry will need to be better aligned to understand the market needs. 
  2. Accountability of data is of paramount importance – Who owns the data, how one uses it, and how one ensures its privacy will need to be determined. 
  3. Adopt a customer and service-centric approach for the entire automotive lifecycle.
  4. Accept and adopt newer capabilities in-vehicle software development. 
  5. Address production, manufacturing, and lifecycle challenges with the help of technology, ensuring a hassle-free and integrated development. 
  6. Form strategic partnerships to create a value chain for smart-device manufacturers. 

The Changes Brought Forward By Iot In Automotive Industry:

Wireless technology lies at the heart of the Internet of Things. IoT devices collect, share, store and exchange data with other devices, making it a connected ecosystem. It is then powered by the ability to communicate without any human involvement. 

IoT in automotive manufacturing is the most significant adoption – to reduce human involvement, increased time management, and improve productivity. While the cross-industry and multi-sectoral dependence on it grows, consumer use of IoT outpaces that of business.  

Connected vehicles are likely to increase by 67% over the next two years with consumers’ spending on in-vehicle connectivity anticipated to double by 2030. Thus, drivers worldwide are constantly expecting their vehicles to resemble intuitive smartphones on wheels. And IoT indeed has already proven its ability to fulfill demands around this futuristic technology. 

So how can the IoT developers keep pace with the demands of the automotive industry?

Predictive repairing

IoT connectivity comes with many advantages, predictive maintenance technology being one of them. A Smart car will collect performance data of the different parts and transfer it to the cloud. Then insights based on the data can help with a risk evaluation and prevention of a potential malfunction to avoid future incidents. Both private car owners and dealers of larger fleets can adopt predictive maintenance. In the future, IoT in automotive industry will play a prominent role in preventing unplanned stops or breakdowns. Also affecting the insurance and risk industry – with customized vehicle insurance premiums powered by vehicle and driver performance.

IoT in Automotive Industry - Predictive repairing

Automatic updates

IoT-powered automatic updates in connected vehicles can improve traffic management by increasing the accuracy of real-time traffic data services, enabling millions of drivers with a more comfortable and supported driving experience. As a result, many manufacturers in the automotive industry are redesigning vehicle software and hardware to comply with NDS (Navigation Data Standard) and improve vehicles’ connectivity. 

Fleet Management 

Truck transportation is often disregarded when adopting innovations for the automotive industry. However, there have been some breakthrough innovations. One of the most significant examples is the Tesla Classic 8 electric truck which can go 500 miles on a single charge. Using IoT, the vehicle can call 911 if the driver isn’t responding. IoT can tap into large fleets that can give them information on routes, fuel consumption, performance data, and traffic. Integrated cloud-based platforms can gain multiple benefits, such as tracking vehicle positions to monitor time and efficiency. 

Smart manufacturing 

Smart infrastructure is powering Smart cities and revolutionizing the automotive industry. It will not take long for street signs and traffic signals to become a thing of the past. Our connected cars in the future will be smart enough to take twists and turns on busy roads with Smart technology. For this, we require a drastic change in how automotive manufacturers approach a Smart car. As a result, software and hardware development will need to be combined to create that perfect car, straight from the future. 

Braking Point

The Independent points out that by 2030, the standard cars will have evolved from merely assisting drivers to fully taking control. IoT technology will power tomorrow’s car with sophisticated sensors and cameras and the ability to communicate with road signages and markings. Most payments will happen online, indicating that drivers will no longer have to carry cash. Consequently, vehicles pay their insurance themselves! 

In conclusion, with the influence of IoT in automotive industry, millions of drivers can connect with over-the-air software to receive updates, information, and bug fixes. V2V, V2I, and V2X connectivity will launch a new era in fleet management. With IoT, a Wifi connection will be enough to make the drive smooth, safe, and 100% hassle-free. So, there’s no braking point but full throttle with max safety and much more than you might think. 

Looking for more information?

Check out our white papers on technology that can help improve your transportation and logistics business –

  1. Business efficiency management
  2. Product Tracking Solution

Get more insights on how we make it easy for IoT solutions in any business like healthcare with our automated testing solutions.

 

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geospatial navigation

Future of Geospatial Navigation System in Transportation

Geospatial TargetingMindfire Solutions .mindfiresolutions.com

Introduction:

Geospatial data, also known as geomatics, is adopted by several industries and technologies for the collection, analysis, processing, and distribution of geographic information.

Most people are familiar with GPS, or Global Positioning Systems, the powerhouse behind our online maps and navigation. Another popular acronym is GIS, or Geographic Information Systems, which is a geographic repository of categorised information.

With evolving times, both these systems contributing to the overarching geospatial navigation system form the backbone of our lives. Everyday, industries ranging from healthcare, retail and finance to critical functionalities such as defense, food and water security, risk assessment and urban development are all dependent on it. Information technology has been instrumental in rapidly expanding its influence in all aspects of our lives.

There are enormous benefits of geospatial navigation in the current world. One can use it to pinpoint the location of their customers, design targeted marketing, predict weather patterns, aid local businesses with inventory stocking and predict areas where vaccines will be most in demand, ensuring their efficient and effective distribution. From a local truck driver to the hospital management, everyone depends on geospatial navigation today.

With recent advances in data mining, information is available faster and in greater quantity than ever before. The widespread adoption of geospatial data has impacted companies and organizations across the globe. The following section is a deep dive on how geospatial data is revolutionising the transportation industry.

The application of Geospatial data:

The first thing that comes to our mind when we travel is navigation and maps. While many among us use it only when we are lost or in a new place, one would be surprised to know how geo-spatial data is reorienting the transportation industry. With more than 155 million people using online maps, it has evolved to become one of those industries through the constantly developing digital maps and navigation systems.

While the overarching use of geospatial data is to primarily give drivers useful information on routes, traffic patterns, distance, etc. This is also extremely useful for businesses in the transportation sector. Eg – logistics, shipping and supply chain. Geospatial data helps to improve delivery schedules to track and expedite shipments. This ensures an increase in efficiency when it comes to the management and delivery of goods, as a result of mapping transit routes and warehouse locations. The overall result being business productivity, customer satisfaction and cost efficiency.

Geospatial navigation system has also paved the way for targeted expansion efforts for effective business growth. All industries can now gain access to information on land use, population density, geography and customer income. This indicates factoring in more parameters that can contribute to business performances due to transportation conditions in a new retail location.

Geospatial navigation opening new vistas in transportation:

Beyond the obvious spectrums of life where GIS has enabled industries to achieve both competence and excellence, it has played a huge role in the day to day transportation space. Geospatial navigation has been useful to oversee and manage complex systems around transportation planning and management with effectiveness. It is instrumental in determining if capacities are to be enhanced, operations strengthened and strategic investments identified, to keep running systems cost-effectively. There are several ways in which GIS is aiding transportation. The major areas of its intervention includes highway maintenance, traffic and route planning and accident analysis.

Traffic modelling:

With the pressure of a growing population, traffic management has become a crisis almost everywhere, primarily in countries with a large urban populace. Most countries struggle with traffic management, especially during the rush hours. However, if used correctly, GIS data can be transformed to bring about large scale traffic simulation. Interactive visualization of virtual roadmaps helps in real time traffic modelling. Hence, they can help with navigating through highways, overpasses, intersections and ramps.

Accident Analysis:

As per the World Health Organization, between 20 and 50 million more people suffer non-fatal injuries, with many incurring a disability as a result of road accidents. Traffic accidents have large scale social, economic and political consequences for the country. With spatial analytics, through GIS, traffic management systems are today able to get a better understanding of the patterns behind these accidents, helping them form an intelligent accident analysis.

Route Planning:

Route planning is an important aspect within transportation. Road blockages and other hurdles can contribute to large scale economic losses. It becomes easy for everyone if people know which road to travel in advance. Since managing modern roadways becomes a complex affair, transportation planners must draw on a wide range of technologies to effectively manage it. This includes computerized traffic control systems, accident and safety management systems and so on. GIS can aid transportation officials to improve operational efficiencies through a responsible route planning system.

Final Thoughts:

The future of mobility is going to be determined by 5 themes – multi-modal, shared, autonomous, connected and electrified. It cites that LI is going to play a vital role in ensuring all 5 areas function at optimum efficiency. Precision in data and technology will be a key determinant to the future of transportation technology. It can effectively transform a vehicle to a thinking and self-operating IoT device.

Location Intelligence or LI is the collection and analysis of geospatial data gathered from different sources. The strategic insights from this data help to solve a variety of business challenges for the transportation industry. This is done by providing access to spatial data, trends, patterns and relationships between vehicles and infrastructure. In this process, GIS becomes a means to an end, which is to shape global businesses, which adds critical context to the decision making process.

Geospatial data is a vast arena that continues to expand. Gone are the days where a team would be sent “on the ground” to investigate a business opportunity. Geospatial data can do all this and more, while providing rich insights that help the decision making. Companies all over the world are utilizing geospatial data in diverse ways. It will be interesting to see how the transportation industry expands using it, to create an even greater impact in the future.

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Like other businesses, if you too are looking for geospatial navigation solutions, Mindfire Solutions can be your partner of choice. We have a team of highly skilled and certified software professionals, who have developed many custom transportation and logistics solutions for our global clients over the years.

Here are a few interesting projects we have done. Click here to know more:

Product Tracking Solution

Logistics Monitoring Solution

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