Category: AI & MI

INT. Pulse

INT. PULSE

May 26, 2023

Dear Colleague, each month, all of us at INT. Marketing dive into a dizzying research gig to write the best opening section of this newsletter (Fyi, Pulse has 35K+ monthly subscribers now 😎). Here’s this month’s winner – the Forrest Gump of tech, aka, Yahoo! And Why Is That? Sample this – Yahoo had a peak dotcom-days valuation of USD125 billion but ultimately – hold our coffees – was sold to Verison for USD4.8 billion in 2016. Here are five 🤯 Yahoo moments: In 1998 Yahoo refused to buy Google for USD1 million. 4 years later, in 2002, Google said it would sell to Yahoo for USD5 billion (but Yahoo only offered USD3 billion, meaning – no deal, sir.) ⏩ to 2006, Yahoo offered USD1 billion for Facebook but Zuckerberg turned it down. Sources said, if Yahoo had increased their bid to USD1.1 billion, Facebook’s board may have pushed for sale, but Yahoo didn’t budge. Come 2008, Microsoft offered to buy Yahoo for USD46 billion, but Yahoo said ‘Noooooo Wayyyyy!’ And finally, in 2013, Yahoo bought Tumblr for USD1.1 billion, writing it down to USD230 million just 3 years later. Psst: Also, instead of Tumblr, it considered buying Netflix for USD4 billion, now worth USD140 billion. STATS: Fastest Finger Hand First In a world where acronyms like DAU and MAU rule the roost, your mother-in-law will tell you that it would be wise to know the number of years it took each of the following to gain 50 million users, per the World of Statistics: Airlines: 68 yearsCars: 62 yearsTelephones: 50 yearsCredit Cards: 28 yearsTV: 22 yearsComputers: 14 yearsThe Internet: 7 yearsPayPal: 5 yearsYouTube: 4 yearsFacebook: 3 yearsTwitter: 2 yearsWeChat: 1 year ChatGPT: A little less than 30 days, and……🏆 PornHub: 19 days AI/ML: How Big Tech Effed Up (Major Time) ­All of us are in the know about tech layoffs, triggered by the arrival of generative AI. However, while dishing out pink slips may have made investors happy, there is another side to the story. Yeah? And What Is That? The AI Trap. Let us explain. As generative AI and coding took off, massive layoffs, led by big tech firms were triggered across the tech world. But, but, but, all these former employees are now going and building serious competition in 1/10th the time it would take biggies to get there. On the other hand, the big guys are perpetually stuck in meeting/webinar hell, arguing over use cases, tech stack, safety, and deployment methods, while solo developers knock the wind out of them, meaning, the long tail of software just grew 100X. Was It Avoidable? Probably not. You see, Covid tailwinds resulted in a huge surplus as people spent more time online and the big boys used that tailwind to hire, expecting never-ending growth. As the Covid winds died down, growth in tech crashed, leaving big tech players bloated, less agile, and ready to walk into the AI trap, with arms wide open. 💡 At INT., we have an agile AI and Advanced Analytics setup that is doing some cool work in the BFSI, Life Sciences and Retail space. Reach out to Dipak Singh to know how you can reduce costs and improve customer acquisition. ☕️ The coffee is on us! BFSI: Fintech Market Correction Is ‘Short Term’ For the last year or so, fintech exuberance has been served a super-strong shot of black coffee, with regulations clamping down hard, valuations dropping by 60% across the sector, and funding drying up by almost 43%, YoY. So, Is Fintech Dying? In one word – NO WAY! Per this BCG-QED report, the fintech growth story is only in its initial stage and is expected to grow to a USD1.5 trillion industry by 2030. Here are some key takeaways from that report. Sit back and get a hold of this. Where Does Fintech Stand Today? Word on the street is that the fintech journey is still at infancy and will continue to disrupt the financial services industry over time. Basis of that belief is; customer experience remains poor and with over 50% of the global population remaining unbanked or underbanked, financial technology (FinTech) is the only means to unlock new use cases, resulting in growth going up by leaps and bounds. Deepak Goyal, MD, BCG, opines that all stakeholders must therefore seize the moment. Regulators need to be proactive and lead from the front. Incumbents should partner with fintechs to accelerate their own digital journeys. APAC To Lead The Fintech Show Asia-Pacific is this big unserviced market, with almost USD4 trillion in financial services revenue pools, and is slated to outpace the US to become the world’s top fintech market by 2030. This growth will be driven primarily by Emerging APAC (e.g. China, India, and Indonesia) at a projected CAGR of 27%. 🔥 What’s Hot & Happening In Fintech? While payments led the last leg, B2B2X and B2b (serving small businesses) will lead the next. B2B2X is made up of B2B2C (enabling other players to better serve consumers), B2B2B (enabling other players to better serve businesses), and financial infrastructure players. The B2B2X market is expected to grow at a 25% CAGR to reach USD440 billion in annual revenues by 2030. 💡 Need to create and implement your B2B2X strategy? Souvik Chaki is your go to person, so feel free. Stuff We Are Watching ­📌 Are Credit Cards Dying? Because from now on, you can get easy credit on UPI as well. Here’s how this disruptive feature can boost the Indian Economy, or turn into a recovery nightmare, depending on who’s reading. 📌 Big Tech Work Cultures: Sample this and guess which company these people hail from – Super thoughtful, similar to Microsoft, platform mindset, but sometimes too slow to act. All Big Tech work cultures, summed up by one observer here… 📌 Why Optimise Code Anymore: Remember the old times when most software installation was done via 1.4 MB floppy disks? With storage space restrictions dead, why should developers optimise code?

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Insurtech Revolutionises Insurance with Personalised, Faster, and Affordable Solutions

May 23, 2023

Insurtech is the latest phenomenon that is revolutionising insurance across the spectrum. The insurance industry is innovating with the use of technology with an aim towards making products, services and solutions more affordable, personalised and quicker for customers. Here are some of the digital technology offerings that are playing a major role in this space: AI (Artificial Intelligence)- This is one of the biggest innovations contributing towards automating the processing of claims, enabling better detection of frauds and also enhancing customer service. AI enables more accurate and improved pricing and assessments of risks. It helps insurance companies manage risks better while lowering costs simultaneously. It also ensures that customers get more personalised and cost-effective insurance offerings. 2. IoT- The Internet of Things is another aspect which enables cost reduction and personalisation alike. It also boosts customer experiences greatly. The insurance industry is leveraging IoT devices for collecting information on consumer behaviour and environments, including home security, driving habits, health, and so on. This is facilitating accurate assessments of risks and pricing, while helping develop new products tailored to customer needs. For example, IoT devices may be used to develop insurance products where customers are charged on actual driving distance and usage. 3. Blockchain– This digital technology functions through distributed ledgers, enabling transparent and secure transactions without centralised intermediaries. It is being used in insurtech for streamlining the processing of claims and lowering frauds along with enhancing overall data security too. 4. Mobile Apps- Insurtech also functions through new-age mobile apps for boosting customer experience and making claims processing simpler. Customers are getting more personalised recommendations and higher control over their policies. Mobile apps are also being used for tracking the status of claims, managing policy data, and getting personalised advice on products based on their behaviour and specific requirements. 5. Telematics- It is already being used for gathering data on customer driving behaviour and performance, enabling more accurate assessments of risks along with better pricing strategies. Products are thus tailored to meet the needs of customers in a more personalised manner. Why insurtech is gaining ground in the insurance industry These are some of the chief reasons behind the rising popularity of insurtech solutions throughout the mainstream insurance sector. FAQs 1. Can Insurtech solutions replace traditional insurance providers? Insurtech solutions can be replacements for conventional insurance offerings. However, they will not replace traditional providers completely. Rather, these companies will work closely with insurtech players to come up with better products and services for their customers. 2. Are Insurtech solutions regulated? The insurance industry is one of the highest-regulated sectors in the world. Insurtech is also similarly regulated since it is used by insurance companies for carrying out many of their functions. 3. How does Insurtech impact the insurance industry? Insurtech positively impacts the insurance industry by helping it reduce costs, automating manual and repetitive tasks, personalising customer experiences, scaling up overall efficiency, and making products/services more affordable for customers. Customers get more control over their journey with the insurance company and wait times are reduced considerably as well. 4. How can Insurtech solutions improve claims processing? Insurtech solutions can automate claims processing, thereby saving time and money for the company. They can gather data and verify the same minutely in quick time, while also eliminating frauds alongside. This leads to more accurate processing of claims without any risks of losses/fraud.

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Hackathon Diaries #7 - The Third Eye

Hackathon Diaries #7 – The Third Eye

May 5, 2023

Greetings, fellow coders and tech enthusiasts. It’s the 7th and final edition of the INT. Hackathon Diaries V1.0. But don’t shed a tear just yet, we will be back soon with the next edition as our in-house masterminds are up and running with new and innovative ideas all the time. We’ve saved one of the bests for last, and it’s a project sure to keep you wide awake: The Third Eye – Driver’s Drowsiness and Mobile Distraction Detection Solution. The Third Eye We all know how dangerous driving is when we’re tired or distracted. It’s like playing Russian roulette with our lives, and those of everyone else on the road. But fear not, as The Third Eye team has come up with a solution that’s so clever, it’ll make you wonder why nobody thought of it before. Using the latest computer vision and machine learning technology, The Third Eye system monitors drivers in real time, watching for telltale signs of drowsiness or distraction. It’s like having a personal wake-up call or a stern aunt, reminding you to keep your eyes on the road and your hands on the wheel. It is to create the leeway to a sustainable and protected world while driving. The Team Arijit Datta Arnab Kanti Ghosh Pabitra Bhunia Nitesh Kumar Singh Rahul Lohar Suva Samanta Explosive Growth in the Market  Per recent reports, the global market for drowsiness monitoring systems was valued at a staggering $2.2 billion in 2019 and is projected to grow to $3.3 billion by 2027 with a CAGR of 5.2% during the forecast period.  But that’s not all, folks. The global market for distracted driving prevention technology is expected to explode from $1.27 billion in 2019 to a whopping $2.9 billion by 2025, with a CAGR of 12.7% during the forecast period.  These numbers speak volumes about the urgent need for cutting-edge solutions that keep drivers alert and focused behind the wheel. So get ready to join the race to the top as we explore the latest developments in driver safety technology that are taking the market by storm. Resolution Realms Scope 1 – Drowsiness Detection Prepare the dataset: Collect and prepare data for drowsiness detection. Augment the data: Improve the model’s performance by data augmentation techniques. Split the dataset: Divide the prepared dataset into training and testing sets. Configure the model: Customise the YOLOv5 model for drowsiness detection by modifying configuration files to specify hyperparameters, input image size, and a number of classes. Train the model: Train the YOLOv5 model using the prepared training set and the configured model. Evaluation: Measure the model’s performance on the testing set using evaluation metrics such as precision, recall, and F1 score. Fine-tune: Adjust the hyperparameters and retrain the model on the entire dataset or a subset of it to fine-tune the model. Deployment: Integrate the trained model into a mobile or web application for real-world drowsiness detection. Scope 2 – Mobile Phone Distraction Data collection: Gather a dataset of images depicting instances of mobile phone distraction. Data Preparation: Transform the annotations into a format that is compatible with YOLOv5, such as COCO or YOLO. Model configuration: Set up the YOLOv5 model to recognize mobile phone distractions. Model training: Employ a deep learning framework like TensorFlow or PyTorch to train the YOLOv5 model on the training set. Evaluation: Test the trained model on the test set to determine its accuracy and performance. Deployment: Deploy the trained model onto our device. Alert mode: Once our device detects a driver using a mobile phone while driving, it will emit a continuous alert message until the driver puts down the phone. Tech Stack AI/ML Azure Map Service Smart Band Edge Computing  IoT Wow Factors Scene 1: Driver wearing sunglasses Infrared (IR) cameras detect the heat signatures of objects, including human eyes, even when they are partially obstructed by sunglasses. Scene 2: Driving at night Night Vision Camera tracks the driver’s face and eye movements. Scene 3: Presence of multiple faces before the camera Detection of only the front face. Conclusion Stay tuned and keep your eyes peeled for the next edition, where we’ll bring you more cutting-edge solutions and innovations that are driving the industry forward. See you there.

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Digital Asset Management - SharePoint Syntex to the Rescue

Hackathon Diaries #6-Digital Asset Management

April 20, 2023

The Hackathon Diaries are back, and they’re better than ever. Are you ready for an exhilarating ride? The 6th edition of Hackathon Diaries is here, and we’re taking on a challenge that’s sure to get your heart racing: digital asset management using Sharepoint Syntex. With its advanced capabilities, Syntex is transforming the way businesses manage their valuable digital assets. But the journey to mastering this technology won’t be easy. We’ll need to put our skills to the test and unleash our creativity to solve complex problems. So, get ready to witness innovation in action as we dive deep into this exciting new project. Digital Asset Management Digital assets are a critical component of any modern business, but managing them can be a daunting task. That’s where SharePoint Syntex comes in – an AI-powered engine that can transform the way organizations manage their digital assets. With Syntex, you can create a powerful Digital Asset Library system without any coding efforts, making it easy for your team to store, access, and analyse your most valuable information. By capturing the information in your business documents and transforming that information into working knowledge, Syntex enables your organisation to make quick data analyses and insights. It can extract key data points, classify documents, and even automate workflows with its advanced capabilities – all with just a few clicks. So why wait? Start unlocking the power of your digital assets today with Syntex and take your business to the next level. The Techie Meet the mastermind behind the magic – Aniruddho Kodali, the developer who brought this project to life. Problem Statement In today’s fast-paced business world, data is king. But with the sheer volume of information available, finding what you need can feel like searching for a needle in a haystack. The average worker spends a staggering 20% of their time searching for information, leading to lost productivity and missed opportunities.  But what if there was a solution that could cut that time by as much as 35%? Imagine a world where knowledge was easily searchable, accessible, and organized. That’s the challenge we’re taking on with our latest project: digital asset management using Sharepoint Syntex. We believe that with the right tools, managing overwhelming amounts of data can be a breeze. And with Sharepoint Syntex, we’re taking that belief to the next level. Our goal is to create a system that makes it easy for employees to find the information they need when they require it. Business Solution Syntex Content AI – Digital Asset Management In today’s fast-paced business world, information is king. But with the sheer volume of content available, managing it all can feel like an impossible task. That’s where Syntex Content AI for Digital Asset Management comes in – an innovative solution that transforms how content is created, processed, and discovered. By utilising the latest advancements in cloud and AI technology, Syntex empowers people and automates workflows at scale. It automatically reads, tags, and indexes high volumes of content, making it easy to find and connect information where it’s needed – in search, in applications, and as reusable knowledge. But Syntex is more than just a search engine. It manages your content throughout its lifecycle, providing robust analytics, security, and automated retention. And with features like auto classification, zero-touch information management, and reporting and visualisation, It modernises the way businesses approach information management and governance. Impacts Are you tired of your business spending countless hours and resources managing overwhelming amounts of content? Syntex Content AI is here to revolutionise the way you approach digital asset management – and save you money in the process. With Syntex’s advanced content classification and curation capabilities, businesses can save between $1.2 million to $3.3 million, reducing the need for costly professional services and streamlining content management. But that’s not all – Syntex’s improved discovery capabilities can save your business between $42 million to $127 million by making it easier to find and connect the information you need, when you need it. And with reduced reliance on legacy tools and professional services, businesses can save between $864,482 to $1.2 million – freeing up resources for other critical projects. With Syntex Content AI, businesses can unlock the power of their content and save money in the process. Don’t let inefficient content management hold you back – it’s time to discover the new possibilities of the future. 

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Hackathon Diaries #5 - Fraud Detection in Discharge Summary

Hackathon Diaries #5 – Fraud Detection in Discharge Summary

April 13, 2023

It’s time for the 5th edition of Hackathon Diaries. This time around, we are thrilled to present a project that is both timely and crucial – Fraud Detection in Discharge Summary. With healthcare fraud on the rise, there is a pressing need for innovative solutions that can identify and prevent fraudulent activities in the healthcare sector.  Our team of talented developers and data scientists have come together to tackle this challenge head-on and develop a system that can effectively detect fraudulent activities in discharge summaries. So, fasten your seatbelts and get ready for an exciting journey as we take you through our journey of building this game-changing solution. Fraud Detection in Discharge Summary Are you ready for a game-changer in the world of healthcare fraud detection? Our team of talented developers has come up with an innovative solution that will leave fraudsters shaking in their boots – the Fraud Detection in Discharge Summary project. By analysing a patient’s discharge summary, our system can quickly identify any suspicious or fraudulent activity and flag it for further investigation.  Say goodbye to financial losses due to fraudulent healthcare practices – our cutting-edge technology will help ensure that healthcare remains transparent and trustworthy for all.  The Team (Data Wizards) Arghya Chakraborty Anirban Bhattacharya Arindam Mukherjee Sourav Mukherjee Saurav Mandal Problem Statement Calling all healthcare warriors. Are you ready to take on one of the biggest problems facing insurance companies today? Providers falsifying or exaggerating hospital discharge summaries to receive higher reimbursement rates from insurers is costing them a fortune – and compromising the integrity of medical records. Besides financial Losses for insurance companies, the overall procedure consumes a good amount of time and also demands extensive levels of manual interventions. Proposed Solution The proposed solution is based on cutting-edge AI technology and is divided into two phases, each designed to accurately identify fraudulent activity and notify the relevant stakeholders. In Phase 1, the team developed an AI model that analyses contextual data and structural patterns within discharge summaries to identify any signs of fraud. This model is designed to identify inconsistencies and irregularities that may indicate fraudulent activity, and immediately notify the necessary parties to take action. Phase 1.1 focuses on the development phase, where the AI model will be trained to recognise and prevent fraud in handwritten discharge summaries. The implementation of AI-based content prevention techniques will be done to identify and flag any fraudulent activity. In Phase 2 (our future scope), the solution was taken to the next level by identifying discrepancies between discharge summaries and medical billings. The AI model is further trained on historic data to identify inconsistencies between provided discharge summaries and ideal discharge summaries. This would help identify fraudulent medical billing practices and notify the relevant stakeholders, helping to prevent further financial losses due to healthcare fraud. With this innovative solution, insurance organisations can rest assured that fraudulent activity in healthcare will be quickly and accurately identified to take appropriate actions to prevent further losses. Tech Stack Front-end: Django/Flask, HTML, CSS, and JS Back-end: Core Python, OCR, OpenCV, Database The Workflow How We Stand Out The solution is a first-of-its-kind that collects different fraud detection models under one umbrella, making it easier and more efficient. It boasts a blazing-fast processing time, taking an average of just 2.5 seconds per analysis. That means you can quickly identify and prevent fraudulent activity in real-time, without having to wait hours or days for results. Don’t settle for outdated and inefficient fraud detection methods – upgrade to our innovative solution today and take control of the fight against healthcare fraud.

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How the Large Language Models like GPT are revolutionising the AI space in all domains (BFSI, Pharma, and HealthCare)

How the Large Language Models like GPT are revolutionising the AI space in all domains (BFSI, Pharma, and HealthCare)

April 6, 2023

Large language models or LLMs are ushering in a widespread AI revolution throughout multiple business and industry domains. DALL-E-2 set the cat amongst the pigeons in the AI segment in July 2022, developed by OpenAI, before ChatGPT came into the picture. This has put the spotlight firmly on the invaluable role increasingly played by LLMs (large language models) across diverse sectors. Here’s examining the phenomenon in greater detail.  LLMs make a sizeable impact worldwide With natural language processing, machine learning, deep learning, and predictive analytics among other advanced tools, LLM neural networks are steadily widening the scope of impact of AI across the BFSI (banking, financial services, and insurance), pharma, healthcare, robotics, and gaming sectors among others.  Large language models are learning-based algorithms which can identify, summarise, predict, translate, and generate languages with the help of massive text-based datasets with negligible supervision and training. They are also taking care of varied tasks including answering queries, identifying and generating images, sounds, and text with accuracy, and also taking care of things like text-to-text, text-to-video, text-to-3D, and digital biology. LLMs are highly flexible while being able to successfully provide deep domain queries along with translating languages, understanding and summarising documents, writing text, and also computing various programs as per experts.  ChatGPT heralded a major shift in LLM usage since it works as a foundation of transformer neural networks and generative AI. It is now disrupting several enterprise applications simultaneously. These models are now combining scalable and easy architectures with AI hardware, customisable systems, frameworks, and automation with AI-based specialised infrastructure, making it possible to deploy and scale up the usage of LLMs throughout several mainstream enterprise and commercial applications via private and public clouds, and also through APIs.  How LLMs are disrupting sectors like healthcare, pharma, BFSI, and more Large language models are increasingly being hailed as massive disruptors throughout multiple sectors. Here are some aspects worth noting in this regard:  Pharma and Life Sciences:  Healthcare:  The impact of ChatGPT and other tools in healthcare becomes even more important when you consider how close to 1/3rd of adults in the U.S. alone, looking for medical advice online for self-diagnosis, with just 50% of them subsequently taking advice from physicians.  BFS:  Insurance:  The future should witness higher LLM adoption throughout varied business sectors. AI will be a never-ending blank canvas on which businesses will function more efficiently and smartly towards future growth and customer satisfaction alike. The practical value and potential of LLMs go far beyond image and text generation. They can be major new-gen disruptors in almost every space.  FAQs What are large language models? Large language models or LLMs are specialised language frameworks that have neural networks with multiple parameters that are trained on vast amounts of unlabelled text with the usage of self-supervised learning.  How are they limited and what are the challenges they encounter? LLMs have to be contextual and relevant to various industries, which necessitates better training. Personal data security risks, inconsistencies in accuracy, limited levels of controllability, and lack of proper training data are limitations and challenges that need to be overcome.  How cost-effective are the Large Language Models? While building an LLM does require sizeable costs, the end-savings for the organisation are considerable, right from saving costs on human resources and functions to automating diverse tasks.  What are some potential ethical concerns surrounding the use of large language models in various industries? Some concerns include data privacy, security, consent management, and so on. At the same time, there are concerns regarding these models replicating several stereotypes and biases since they are trained using vast datasets. This may lead to discriminatory or inaccurate results at times in their language. 

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Hackathon Diaries #4 FaceFinder: Face Recognition Application

Hackathon Diaries #4 FaceFinder: Face Recognition Application

April 4, 2023

Hey there tech enthusiasts. Welcome to the fourth edition of Hackathon Diaries, where we present to you the latest and greatest innovations created by the brilliant minds at INT. Hackathon 2023. Hold on to your hats because this time we’re taking things up a notch with our cutting-edge solution, FaceFinder. It’s all about securing today for a safer tomorrow, and we’re thrilled to share all the exciting details with you. FaceFinder Picture this: You arrive at your workplace, but instead of fumbling around with keys or access cards, you just stand in front of the gate and let FaceFinder work its magic.  It is an application to open gates securely and automatically through facial recognition technology. It facilitates users to upload/register images of their faces, which can be used to recognise them. The stored image in the database will be used to verify any new entry request. It grants access to matching, otherwise,  the gate won’t open and physical intervention will be needed. The Techie  V Sweta Working Flowchart But let’s get into the nitty-gritty of how it all works. Our brilliant tech-savvy superstar has designed an innovative flowchart that seamlessly integrates various tools to make FaceFinder a robust and reliable solution.  Tech Stack We’re talking about:  ASP.NET Core at the backend Azure Cognitive Service Computer Vision and Face API for detecting and recognising people Azure Storage account to store all your pretty faces And of course, Entity Framework Core is there to make sure everything is stored in our trusty SQL server Now, let’s talk benefits Enhanced Security: Using facial recognition technology to open gates can enhance the security of the premises by ensuring access to only authorised individuals Convenience: With this application, users can open gates without having to manually unlock them Efficiency: Automatic gate opening saves time and effort for individuals who frequently access the premises Enhanced User Experience: The intuitive UI/UX of the application makes it hassle-free for users accessing the gateway Cost Savings: Reduces the need for security personnel, thereby cutting costs associated with staffing and training. It also eliminates the requirement for physical access controls such as keys or access cards, which can be expensive to produce and maintain. Potential Challenges Of The Prototype and The Future Opportunities But we’re not going to shy away from potential challenges. FaceFinder has a few limitations, like being unable to recognise identical twins, finding it difficult to identify individuals with facial injuries, and struggling to identify those wearing a cap or scarf.  Hey, we’re not giving up on these. We’re already working on ways to improve FaceFinder, such as implementing IoT devices, maintaining a block list to restrict specific individuals, initiating breaching alerts, enhancing scalability, reducing response time, and exploring more use cases. So, there you have it. FaceFinder is the future of secure and convenient gate access, and we’re excited to take this technology to new heights. Stay tuned for more exciting developments from Hackathon Diaries.

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Addressing drug shortages with advanced analytics

Addressing drug shortages with advanced analytics

April 3, 2023

Drug shortages have become a part and parcel of modern healthcare systems due to several reasons. While there is a sizeable economic impact of drug shortages for manufacturers and pharmacies alike, there are widespread community and social disadvantages as well. Pharmacies or clinics running out of medicine stocks are representatives of a scenario that is often witnessed worldwide and with frightening consequences.  For example, Europe is already seeing shortages of commonly-used medicines. A survey by the Pharmaceutical Group of the European Union (EU) had 100% of 29 member nations reporting shortages of medicines amongst community pharmacists. 76% also stated how shortages had worsened than the earlier year (the survey was implemented between 14th November and 31st December 2022). The UK is also witnessing HRT shortages according to reports, while hospitals in the U.S. are also reporting issues with procurement for liquid ibuprofen, while ADHD diagnoses have gone up in the U.S. as well, leading to shortages of vital drugs for the same. Mexico is witnessing chronic shortages and unfulfilled prescriptions and supply fluctuations and disruptions have been seen widely throughout Asia too.  What are the reasons for medicine shortages?  Wondering about the reason for drug shortages? There are quite a few that can be noted in this context:  Higher seasonal illness outbreaks in the aftermath of COVID-19, leading to skyrocketing average annual demand for medicines that is higher than normal in several categories.  The inability of pharmaceutical companies to meet such unprecedented demand, with excess capacity restricted for cost control.  Global supply chain impact along with higher energy costs and inflation have impacted global drug manufacturers who have to contend with pricing measures.  Stockpiling by customers due to sudden drug shortages.  Over-prescribing by the system.  Reports estimate that the National Health Service in the UK loses a whopping 300 million pounds annually owing to partially-used or unused medication which cannot be reused or recycled.  Lack of systems for forecasting and identifying supply shortages, while ensuring proper inventory management.  Drug Shortage Solutions That May Work  There are a few drug shortage solutions that may be effective for combating and reducing shortages.  Data and analytics are enabling better access towards medicines worldwide while enabling superior supply and demand management for individual patients and pharmacies alike.  Real-time pharmacy, hospital, and clinical data will enable a proper understanding of the demand for specific drugs/medical products.  Leveraging electronic and public health records for enabling healthcare stakeholders to report demand figures for drugs, without revealing confidential patient data.  Opportunities for better inventory and supply chain management with AI (artificial intelligence) and machine learning (ML).  Generic entities may leverage smarter technologies for lowering manufacturing costs by up to 20% while enhancing production. Smarter and connected factories with proper insights and data analysis can enable higher savings and reliable deliveries.  Companies may look at higher procurement of local active ingredients while depending on go-to nations for the same. Boosting supply and production levels, along with harnessing real-time data analytics will enable tackling this scenario.  Supervised machine learning and analytics models can help in forecasting/predicting shortages for most drugs used throughout various categories, price points, and age groups.  Modelling can enable healthcare stakeholders to understand more about the issues behind drug shortages while analytics can also help predict demand for specific drugs based on historical data and current trends.  Pharmacies and other players may not have access to data on the supply side, although they have demand-side information. They will be able to gain more visibility into the supply chains of manufacturers with an integrated information-sharing system.  Data analytics-driven insights for optimizing orders and eventually lowering the effect of drug shortages on pharmaceutical and healthcare operations.  Systems for tracking and reporting drug shortages, including aspects like the frequency, drugs involved, period, causes, duration, managing strategies, impacts, and future shortages too.  Real-time identification and tracking of patients receiving shorter supplies of drugs by hospitals, clinics, and pharmacies. Immediate patient identification regulations for capturing present drug utilization across multiple categories.  Real-time identification and addressing situations along with finding out drugs in shorter supply. Predictive abilities enable higher time for researching material for alternative agents or making suitable arrangements for drug acquisition from other sites or facilities.  Once supply levels normalize for a drug, pharmacists and healthcare stakeholders may discontinue their surveillance regulations without waiting for technical assistance. Real-time data-filtering and reporting abilities are leveraged for viewing drug usage trends and prescription patterns throughout healthcare systems. These insights may enable higher standardization of drug management across institutions, while also facilitating better training of clinicians for lowering care variations.  Advanced data analytics will help address drug shortages and enable better inventory management simultaneously. However, suitable implementation, technological integration, and awareness are necessary for the same.  FAQs How can advanced analytics be used to address drug shortages? Advanced analytics can be deployed for tackling drug shortages through real-time tracking and surveillance of prescription trends and drug demand, forecasting shortages, and enabling better drug supply management.  What are the benefits of using advanced analytics to address drug shortages? Advanced analytics goes a long way towards helping tackle drug shortages, enabling forecasting future demand and shortages, identifying patterns for better management, and also enabling better global medicine access.  What are the challenges of using advanced analytics to address drug shortages? Challenges include technological integration, legacy systems integration, awareness regarding best practices, quality data generation, and more.  What are the best practices for implementing advanced analytics for drug shortage management? Best practices include unified and integrated public databases, suitable data modelling systems, suitable protocols for data security and privacy, and swift reporting mechanisms for demand and shortages.

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Data analytics plays a crucial role in clinical trial design and analysis by providing valuable insights into the effectiveness of new treatments and therapies.

The role of data analytics in clinical trial design and analysis

March 31, 2023

What is the role of data analysis in clinical trials? Can there be better clinical trial data analysis using R and other technologies? Is there a case for using big data analysis in clinical trials? Experts would certainly say Yes to all these questions. Clinical trials themselves have gone through sweeping changes over the last decade, with several new developments in immunotherapy, stem cell research, genomics, and cancer therapy among numerous segments. At the same time, there has been a transformation in the implementation of clinical trials and the process of identifying and developing necessary drugs.  To cite a few examples of the growing need for clinical trial data analysis, researchers gain quicker insights through the evaluation of databases of real-world patient information and the generation of synthetic control arms, while identifying drug targets alongside. They can also evaluate drug performance post-regulatory approvals in this case. This has lowered the cost and time linked to trials while lowering the overall burden on patients and enabling faster go-to-market timelines for drugs too.  What is driving data analysis in clinical trials?  Clinical trial data analysis is being majorly driven by AI (artificial intelligence) along with ML (machine learning), enabling the capabilities of collection, analysis, and production of insights from massive amounts of real-time data at scale, which is way faster than manual methods. The analysis and processing of medical imaging data for clinical trials, along with tapping data from other sources is enabling innovation of the entire process while being suitable for supporting the discovery procedure in terms of quickening the trials, go-to-market approaches, and launches.  The data volumes have greatly increased over the last few years, with more wearable usage, genomic and genetic understanding of individuals, proteomic and metabolomic profiles, and detailed clinical histories of patients derived from electronic health records. Reports indicate 30% of the data volumes of the world are generated by the global healthcare industry. The CAGR (compound annual growth rate) for healthcare data will touch 36% by the year 2025 as well. The volume of patient data in clinical systems has already grown by a whopping 500% to 2020 from 2016.  Data analysis in clinical trials- What else should you note?  Here are a few factors that are worth noting:  Synthetic control arm development  The role of data analysis in clinical trials is even more evident when one considers the development of synthetic control arms. Clinical drug discovery and trials may be fast-tracked while enhancing success rates and designs of clinical trials. Synthetic control arms may help in overcoming challenges linked to patient stratification and also lower the time required for medical treatment development. It may also enable better recruitment of patients through resolving concerns about getting placebos and enabling better management of diverse and large-sized trials.  Synthetic control arms tap into both historical clinical trials and real-world data for modelling patient control groups and doing away with the requirement for the administration of placebo treatments for patients which may hinder their health. It may negatively impact patient outcomes and enrolment in trials. The approach may work better for rare ailments where populations of patients are tinier and the lifespan is also shorter owing to the disease’s virulent nature. Using such technologies for clinical trials and bringing them closer to end-patients may significantly lower the overall inconveniences of travelling to research spots/sites and also the issue related to consistent tests.  ML and AI for better discovery of drugs ML and AI may enable a quicker analysis of data sets gathered earlier and at a swifter rate for clinicians, ensuring higher reliability and efficiency in turn. The integration of synthetic control arms in mainstream research will offer new possibilities in terms of transforming the development of drugs.  With an increase in the count of data sources including health apps, personal wearables and other devices, electronic medical records, and other patient data, these may well become the safest and quickest mechanisms for tapping real-world data for better research into ailments with sizeable patient populations. Researchers may achieve greater patient populations which are homogenous and get vital insights alongside. Here are some other points worth noting:  The outcomes of clinical trials are major metrics with regard to performance, at least as far as companies and investors are concerned. They are also the beginning of collaborations between patients, groups, and the healthcare sector at large. Hence, there is a clearly defined need for big data analysis in clinical trials as evident through the above-mentioned aspects.  FAQs How can data analytics be used in clinical trial design and analysis? Data analytics can be readily used for clinical trial design and analysis, expanding patient selection criteria, swiftly sifting through various parameters and helping researchers better target matching patients who match the criteria for exclusion and inclusion. Data analysis methods also enable better conclusions from data while also improving clinical trial design due to better visibility of the possible/predicted risk-reward outcomes.  What are the benefits of using data analytics in clinical trial design and analysis? The advantages of using data analytics in clinical trial design and analysis include the integration of data across diverse sources, inclusive of third parties. Researchers get more flexibility in terms of research, finding it easier to analyze clinical information. Predictive analytics and other tools are enabling swifter disease detection and superior monitoring.  What are the challenges of using data analytics in clinical trial design and analysis? There are several challenges in using data analytics for the analysis and design of clinical trials. These include the unavailability of skilled and experienced resources to implement big data analytics technologies, data integration issues, the uncertainty of the management process, storage and quick retrieval aspects, confidentiality and privacy aspects and the absence of suitable data governance processes.  What are the best practices for implementing data analytics in clinical trial design and analysis? There are numerous best practices for the implementation of data analytics for the analysis and design of clinical trials. These include good clinical data management practices, clinical practices, data governance

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