Category: Data Analysis

LIFE SCIENCE & PHARMA NEWS WWRAP

Life Science & Pharma News Wrap | Weekly Snippets

July 14, 2023

✅ NanoTemper Technologies launches a Biotinylated Target Labeling Kit that aims at transforming the way scientists in the pharma landscape approach challenging drug targets.https://ow.ly/Bon150P5YtC ✅ Nvidia is driving advancements for a healthier future in the pharma and healthcare landscape with Generative AI. https://ow.ly/STL850P5YtJ ✅ MeitY-nasscom CoE is all set to host an exclusive forum on the transformative power of AI in the healthcare domain.https://ow.ly/fmBH50P5YtE ✅ Scientists have developed an advanced genetic technology to combat malaria-spreading mosquitoes. This advancement brings us one step closer to eliminating malaria and saving millions of lives worldwide.https://ow.ly/GC4Y50P5YtF

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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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Sentiment Analysis

How Sentiment Analysis Can Drive Insurance Industry

July 11, 2022

Sentiment analysis in insurance is emerging as a potent tool for companies in multifarious ways. Insurance companies have tons of unstructured information that they have at hand.  Following a suitable sentiment analytics process may help insurers enhance retention of policyholders and also in the identification of opportunities pertaining to up-selling and cross-selling. Sentiment analytics have already turned into a vital aspect of strategies pertaining to customer feedback for companies of diverse sizes. Sentiment analytics in insurance fuse Machine Learning (ML) and Natural Language Processing (NLP) along with deep-text analytics for illuminating intrinsic nuances of texts.  Sentiments can be translated more easily and analyzed seamlessly than expressions. The sentiment analytics process is also known as opinion mining.  Customer data is unstructured and comes in several forms including claim data, voice messages, surveys, emails, social media posts. The entire system is tailored not only to analyze feedback and its nature, but also to put it against the right context. Benefits of Sentiment Analytics Insurers can reap multiple benefits from suitable sentiment analysis procedures. Here’s looking at some of them: 1. Detecting Fraud Reports indicate how insurers lose millions annually on account of fraud. These are estimated at anywhere around 5-10% of total compensation payouts by insurers in a year at least.  These are claims that flew under the radar. However, predictive analytics and other tools can help detect the same. A sentimental analysis dataset will help insurance companies track and assess insurance settlement and claim patterns.  It will help in quicker decision-making on the basis of crucial parameters or key performance indicators. This will help in arresting fraudulent claims and enhance the insurer’s earnings.  Text analytics also enables better decision-making through dashboards and access to other necessary data. 2. Customer Understanding  Social media sentiment analysis will help in the classification and identification of customer interactions on the basis of parameters like the services/products being provided, the marketing platforms or channels that are used, the operations in place and so on.  What sentiment analysis does is help insurers understand the voices of their customers.  It fosters superior customer understanding above everything else. Social media datasets will help in the identification of specific aspects concerning any product, process, or service.  Whenever this analysis is implemented for social media comments, it helps in clearly delineating trends in the industry and perceptions of companies along with enabling timely alerts on any reputation related issues as well. 3. Managing Claims The analysis of complaints and claims is another natural segment for using such datasets. Complaints may be automatically identified and classified on the basis of the service, product and other parameters.  This enables passing them onto suitable agents/departments in order to ensure swift action on the same. Relating those to real world Sentiment analysis in insurance reduces costs, combats fraudulent claims, helps insurance companies understand patterns, trends and customer preferences, and also lowers overall workload and the time taken to respond to customer issues.  Simultaneously, social media sentiment analysis helps in enhancing satisfaction levels of both employees and clients, while enhancing client retention, brand-building, recommendations.  It also goes a long way towards lowering indirect expenditure. Sentiment analytics can help insurance companies keep leveraging unstructured information for identification of revenue-enhancing opportunities and industry/customer trends.  Although analytics is not perfect as of yet, it is continually evolving towards the same. In this case, the sustained focus on a specific domain (insurance) can help in enhancing the overall accuracy levels as well. Indus Net Technologies offers an array of solutions tailored towards the needs of insurance companies and the industry at large, right from cutting-edge analytics and other technological tools to back-end automation, risk profiling, customizable analytics, and modernization of legacy applications.  Having worked on diverse task requirements for insurers over the years, INT has the ability to tailor industry and company-specific solutions that harness the power of data, free up company resources, and ultimately boost company revenues and growth alike. About the author: Dipak Singh is a thought leader and data cruncher, currently, he heads the Analytics Wings at INT. To know more do check out his LinkedIn profile here.

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Business Intelligence & Data Analysis – The Next Big Thing

August 31, 2021

33.3 billion dollars is what global business intelligence (BI) is targeting in the next five years. The report suggests in 2021 itself recorded a jump from 21% to 26% of the adoption rate of BI. Therefore business intelligence is going to be the next big thing in the business space. But it is not BI only that is taking over the world, and it has become imperative to extract insights from the data. Thus, refocussing on data analysis is also one of the big things we will see in the near future. Business Intelligence is a technology-driven process to collect data from different sources, analyze them & finally deliver an ‘Actionable Information‘ that helps the company to make important predictive business decisions. This is possible by using various BI tools such as Power BI, Tableau and many more. Some of the important features of BI tools are : Reporting Analytics and Interactive Dashboard Development Data mining and Process Mining Complex Event Processing Benchmarking Predictive and Perspective Analytics Data gaining popularity in 2022 For businesses to reach the strategic endpoint, data analysis plays a vital role. Here are a few ways by which we know why Data Analysis is so popular in 2021 No-Code Process: BI tools are so easy to use & require no coding knowledge, thus attracting both technical & non-technical individuals. Anyone can pull data from various sources, modify & create visualizations – all without writing a single line of code. This encourages everyone to be data-driven and more interested in pursuing a career in Data Analysis. Easy Collaboration: One of the main reasons for data analysis using BI tools getting popular in 2021 is because of its ‘Collaborative’ nature. The process is called ‘Collaborative BI’, which merges the BI tools with other collaboration tools. This allows the data visualizations/ reports to be shared with co-workers in the same organization so that they can understand. This method allows everyone in the team (even the non-technical ones) to be on the same page & help them make wise decisions about the business. Collaborative BI promotes : Knowledge sharing Faster Decision-Making Better Teamwork More transparency & Visibility Wide range of Data Sources:  Data Source, in BI, refers to the location from where the information or raw data is originated. Our modern BI tools are designed so that they can pull data from various sources, such as Excel Workbook, SharePoint folder, Pdf, XML, JSON and even from the databases (SQL, Oracle & a lot more). Power BI, as a BI tool, has the ability to be connected with a MySQL database, and one can run SQL queries for more refined analytics. This ability to connect with more platforms makes Data Analysis more reachable for today’s professionals.  Top 5 Benefits of Business Intelligence (BI) : Today, businesses can collect data along with every point of the customer journey. This data may include different attributes, like system usage, no. of clicks, interactions with other platforms and a lot more. The organizations have the ability to pull this data from various sources & transform it into a meaningful insight that is easily understandable by everyone in the team. Following are some of the key benefits of adopting Business Intelligence: Fast & Accurate Reporting: Companies can create customized reports based on the data pulled from different data sources, including financial, operational & sales data. These reports are generated in real-time in the form of graphs, tables, charts etc. and can be shared easily within the same organization so that the team can make decisions quickly. Most of the visualizations created with BI tools are so interactive that anyone can play with the data by changing the variables. Valuable Business Insights: The reports generated from the BI tools help the organization understand what’s working and what isn’t. Hence, they can take necessary actions regarding the business process. Improved Decision Making: In today’s competitive business world, where customer satisfaction is paramount, it is required to identify the failures or business problems accurately and take necessary steps to stay on top of the industry. Hence, Business Intelligence comes into the picture, which helps to visualize the data rather than manual calculations using thousands of records. So, definitely, BI tools come in handy when it comes to better decision making. Identifying Market Trends: Analyzing new opportunities & building out strategies with supportive data can give organizations a competitive edge, thus impacting the long-term profitability. The companies can leverage market data with internal data & detect new opportunities by analyzing market trends & also by spotting business problems. Increased Revenue: Undoubtedly, this is the ultimate goal for any business. Data visualizations help organizations dig deeper into business problems by asking questions about what went wrong & how to make impactful changes in the business. When organizations take care of customer satisfaction, watch their competitors, & improving their own operations, revenue is more likely to increase. Popular BI Tools in 2021: Here are some popular BI tools which are trending in the market right now : Microsoft Power BI Tableau Board Domo Oracle Analytics Cloud Tibco Qlik SAS Business Intelligence Vs Business Analytics : Business Analytics & Business Intelligence are very similar and somewhat connected. Pat Roche, Vice President of Engineering at Magnitude Software believes, “BI is needed to run the business while Business Analytics are needed to change the business.” Although it’s a debatable topic, most people in the modern business world still believe that Business Analytics & Business Intelligence tend to work well when paired together. The main usage of BI is to present the data in front of the team in the form of various visualizations, thus helping them make the right business decision, whereas the role of business analytics is to ‘analyze the business’ & think of ways to improve a company’s future performance. Generally, both BI & BA requires analytical skills which ultimately helps the business to succeed. However, despite the similarities & differences between Business Intelligence & Business Analytics, we can certainly agree that both

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