#healthcare big data industry

[techdriveplay]

Why Quantum Computing Will Change the Tech Landscape

The technology industry has seen significant advancements over the past few decades, but nothing quite as transformative as quantum computing promises to be. Why Quantum Computing Will Change the Tech Landscape is not just a matter of speculation; it’s grounded in the science of how we compute and the immense potential of quantum mechanics to revolutionise various sectors. As traditional…

[ai-innova7ions]

Video Automatically Generated by Faceless.Video:

Revolutionize Your Data Security with AI Models!

Small language models are revolutionizing the tech landscape by providing a more efficient alternative to larger counterparts. Their ability to operate on modest hardware means they can run locally, making them perfect for industries like healthcare and finance where data privacy is crucial.

These models enable fast and secure processing of sensitive information, transforming how businesses manage data. By addressing privacy concerns without compromising performance, small language models are paving the way for innovative solutions in various sectors. Discover how these advancements impact our world today!

#SmallLanguageModels

#DataPrivacy

[techtoio]

Emerging Tech Trends in the Internet of Things (IoT)

Introduction

The Internet of Things (IoT) is transforming our world by connecting devices and enabling smarter, more efficient interactions. In everything from smart homes to industrial automation, the IoT is leading a revolution in our living and working environments. In this article, TechtoIO explores the emerging tech trends in IoT, highlighting the innovations and advancements that are shaping the future. Read to continue link

[123567-9qaaq9]

Big Data Healthcare Market | BIS Research

In today's world, data is king. Industries across the board are leveraging the power of data to drive decision-making, streamline processes, and innovate in ways never before imagined. Nowhere is this more evident than in the healthcare sector, where the utilization of big data is revolutionizing patient care, research, and operational efficiency. The Big Data Healthcare Market Report offers a comprehensive glimpse into this burgeoning field, exploring its current landscape, trends, challenges, and future prospects.

The global big data in healthcare market amounted to $11.45 billion in 2016 and is expected to witness a double-digit growth throughout the forecast period of 2017-2025.

Understanding the Big Data Healthcare Market

The Big Data Healthcare Market encompasses a wide array of technologies, solutions, and services aimed at harnessing the immense volume, variety, and velocity of healthcare data. This includes electronic health records (EHRs), medical imaging, wearable devices, genomics, and more. The market is driven by the increasing digitization of healthcare data, coupled with advancements in data analytics, machine learning, and artificial intelligence (AI).

Key Trends Shaping the Landscape

Several key trends are shaping the Big Data Healthcare Market:

Predictive Analytics: Healthcare providers are increasingly using predictive analytics to forecast patient outcomes, identify at-risk populations, and personalize treatment plans. By analyzing historical data and real-time inputs, predictive models can help improve clinical decision-making and preventive care.

Precision Medicine: Big data analytics is instrumental in the advancement of precision medicine, which tailors medical treatment to individual characteristics such as genetics, lifestyle, and environment. By analyzing large datasets, researchers can identify biomarkers, understand disease pathways, and develop targeted therapies.

Real-Time Monitoring: Wearable devices and remote monitoring technologies generate vast amounts of real-time health data. Big data analytics enable healthcare professionals to monitor patients remotely, detect anomalies, and intervene proactively, thereby improving patient outcomes and reducing healthcare costs.

Population Health Management: Big data analytics play a crucial role in population health management, enabling healthcare organizations to identify patterns, trends, and disparities within populations. By segmenting populations based on risk factors and healthcare needs, providers can implement targeted interventions to improve population health and reduce disparities.

Challenges and Opportunities

While the potential of big data in healthcare is vast, it is not without its challenges. Data privacy and security concerns, interoperability issues, and the complexities of data integration are among the primary hurdles facing the industry. Moreover, the sheer volume and variety of healthcare data present challenges in terms of storage, processing, and analysis.

However, these challenges also present opportunities for innovation and growth. As technology continues to advance, solutions for data interoperability, privacy, and security are emerging. Furthermore, the integration of big data analytics with emerging technologies such as blockchain and edge computing holds promise for overcoming existing challenges and unlocking new possibilities in healthcare.

Grab a look at the free sample @ Big Data Healthcare Market Report 

Future Outlook

The future of the Big Data Healthcare Market is bright, with continued growth expected in the coming years. The increasing adoption of electronic health records, the proliferation of connected devices, and ongoing advancements in data analytics and AI will drive market expansion. Moreover, the COVID-19 pandemic has underscored the importance of data-driven decision-making in healthcare, further accelerating the adoption of big data technologies.

In conclusion, the Big Data Healthcare Market represents a transformative force in the healthcare industry, offering unprecedented opportunities to improve patient outcomes, enhance operational efficiency, and drive innovation. As stakeholders continue to invest in data-driven technologies and solutions, the potential for positive impact on global health outcomes is immense.

[drnic1]

Data Driven Conversations

This week I am talking to Sanjula Jain, PhD (@sanjula_jain), SVP Market Strategy & Chief Research Officer at Trilliant Health (@TrilliantHealth), a company focused on healthcare industry expertise, market research, and predictive analytics to create evidence-based direction for Healthcare. Sanjula has like many of my guests an interesting background and origin story but in her case set her on a…

[tntra]

Explore the transformative potential of big data analytics in healthcare from a new economy perspective. This insightful blog delves into the benefits and applications of big data analytics in the healthcare industry, including improved patient outcomes, personalized medicine, predictive analytics, and operational efficiency. Discover how harnessing the power of big data can drive innovation, enhance decision-making, and optimize healthcare delivery. Stay ahead of the curve and unlock the full potential of big data analytics in revolutionizing healthcare.

[d0nutzgg]

Scraper is Done but Needs Improvement

So my scraper is "done" but I realized that ffmpeg does not convert the files from mp4 to mp3 like I need for my dataset, it is a great start but now I have to delete 500 files off my computer :D

[metamatar]

$200k seems like quite a reasonable cost for a small sterile lab. It's not a plan to make it at home, it's a plan to make it in your town. As of now, there are so few insulin plants that the economies of scale aren't optimal for distribution (but they are for profits!)

did you miss the part that it was speculative? that it has never been demonstrated? also no, the economies of scale are fine for distribution cold chain distribution it is a solved problem. people aren't struggling to get insulin bc it can't be delivered, they're struggling bc its expensive.

im not sure you understand what economies of scale means, it means when you try to do things at larger scale – you are generally able to deploy productive technologies and innovations in organisation (specialisation) which make things easier to produce (less labour and capital input) on average. things become cheaper to produce. it is cheaper to weave cloth at a factory than in a loom you install in your backyard. that's why open insulin can only hypothetically get a vial down to the price of for profit insulin in the uk. big pharma is able to profit from insulin at 7 dollars a vial, ie it's even cheaper to produce. this is like, adam smith pin example.

the existence of a big factory or doing things at scale doesn't create destructive megaprofits... this is such a bizarre worldview of the world. you have to make a very sophisticated argument to prove this, which imo is immediately debunked by the reality of worker organised cooperatives in factories or even state run industrial production. profit tends to be a function of factors like labour relations + market dynamics like supply, demand and competition. us healthcare sucks bc your workers don't have rights, private insurance colludes with hospitals and competitors and the govt doesn't regulate pharma companies who are providing an inelastic good (medicine.)

also addressing this bc some people are mad at me but the only part of my argument that cites a piece hosted on RAND corp is the extremely high price of US insulin compared to every other country in the world. its like 30x. i don't think that is a fact that's a capitalist conspiracy, the data can be confirmed with other sources too, it just illustrates how dysfunctional US healthcare is. like, when your enemies agree...

[brandyschillace]

As it’s non-binary awareness week, I thought I’d share my bio—an attempt to say where I’ve been and what I’m about. And why I am she/her but mean that in a very plural sort of way.

I grew up in an underground house, next to a graveyard, in abandoned coal lands… with a pet raccoon. Oddly, this tends not to surprise people as much as I think it will. My rural community skirted the poverty line, a place of failed industry and orange rivers, poor health, and poorer access to healthcare. As a result, I spent my childhood reading a lot about disease and going to a lot of funerals. I ended up with a Ph.D. and a career in science history, which is probably a likely thing to happen when you spend your early years in a cemetery.

I’ve worked in an English Department, a History Department, and for a Medical Anthropology journal. I spent five years as a research associate in a medical museum among amputation saws, surgery kits, and smallpox vaccines—and now, in addition to being an author, I’m Editor-in-Chief for BMJ’s Medical Humanities Journal. I tend to fall outside the borders and binaries on every side. It’s not that I eschew being a ‘woman’ but that the box isn’t big enough to contain the selves that are me.

I always liked the line by Walt Whitman: I contain multitudes. Each of us are completely unique sets of data and DNA, blood and bones, bits and pieces of ancient stardust (and some microplastics). We don’t just have fingerprints. We are fingerprints — completely unique phenomenon in the universe, never before and never to be again. I am a truck, a train, a bulldog in a wind-tunnel; I’m also autistic. I live in the middle spaces where the contradictions are, containing bits of astral matter, aspects of both genders and possibly some dragons and vampires. I do history the way most people climb mountains–I get my hands dirty–I end up in catacombs, archives, basements. As you can imagine, this sort of thing doesn’t fit in a box very well. Then again, life is more interesting at the intersections.

[tech-insides]

What are the skills needed for a data scientist job?

It’s one of those careers that’s been getting a lot of buzz lately, and for good reason. But what exactly do you need to become a data scientist? Let’s break it down.

Technical Skills

First off, let's talk about the technical skills. These are the nuts and bolts of what you'll be doing every day.

Programming Skills: At the top of the list is programming. You’ll need to be proficient in languages like Python and R. These are the go-to tools for data manipulation, analysis, and visualization. If you’re comfortable writing scripts and solving problems with code, you’re on the right track.

Statistical Knowledge: Next up, you’ve got to have a solid grasp of statistics. This isn’t just about knowing the theory; it’s about applying statistical techniques to real-world data. You’ll need to understand concepts like regression, hypothesis testing, and probability.

Machine Learning: Machine learning is another biggie. You should know how to build and deploy machine learning models. This includes everything from simple linear regressions to complex neural networks. Familiarity with libraries like scikit-learn, TensorFlow, and PyTorch will be a huge plus.

Data Wrangling: Data isn’t always clean and tidy when you get it. Often, it’s messy and requires a lot of preprocessing. Skills in data wrangling, which means cleaning and organizing data, are essential. Tools like Pandas in Python can help a lot here.

Data Visualization: Being able to visualize data is key. It’s not enough to just analyze data; you need to present it in a way that makes sense to others. Tools like Matplotlib, Seaborn, and Tableau can help you create clear and compelling visuals.

Analytical Skills

Now, let’s talk about the analytical skills. These are just as important as the technical skills, if not more so.

Problem-Solving: At its core, data science is about solving problems. You need to be curious and have a knack for figuring out why something isn’t working and how to fix it. This means thinking critically and logically.

Domain Knowledge: Understanding the industry you’re working in is crucial. Whether it’s healthcare, finance, marketing, or any other field, knowing the specifics of the industry will help you make better decisions and provide more valuable insights.

Communication Skills: You might be working with complex data, but if you can’t explain your findings to others, it’s all for nothing. Being able to communicate clearly and effectively with both technical and non-technical stakeholders is a must.

Soft Skills

Don’t underestimate the importance of soft skills. These might not be as obvious, but they’re just as critical.

Collaboration: Data scientists often work in teams, so being able to collaborate with others is essential. This means being open to feedback, sharing your ideas, and working well with colleagues from different backgrounds.

Time Management: You’ll likely be juggling multiple projects at once, so good time management skills are crucial. Knowing how to prioritize tasks and manage your time effectively can make a big difference.

Adaptability: The field of data science is always evolving. New tools, techniques, and technologies are constantly emerging. Being adaptable and willing to learn new things is key to staying current and relevant in the field.

Conclusion

So, there you have it. Becoming a data scientist requires a mix of technical prowess, analytical thinking, and soft skills. It’s a challenging but incredibly rewarding career path. If you’re passionate about data and love solving problems, it might just be the perfect fit for you.

Good luck to all of you aspiring data scientists out there!

[saetoru]

I just read your post about Ai and can I just share that I work in archeology and I'm absolutely terrified of the way AI is becoming normalized within research. The University I work for recently granted our department a large budget for this research project, and instead of hiring new people, or taking even taking in undergrad students as assistant researchers, my superiors have been using ChatGPT. It's absolutely insane to me that it is being recommended for us to use it to create surveys and organize our collected data. we are even being told to use it if we're having trouble writing the summary reports that help us continue getting funding.

I use it almost everyday, and it is not 100% accurate. Its actually wrong a lot of the time when you're using it to do more advanced math so it's very worrisome that it's being used for quantitative research. it is so easy for things to be wrong and no one to even notice.

YES ITS SO SCARY !!! im in healthcare management and the company i’m working with is a supplier for hospitals and the way they use chat gpt to compare pricing and break down data and even just write emails for summaries is so scary because the calculations aren’t always correct and they admit that !! and they still use it !! means for breaking down data aren’t always the most effective. and it’s especially scary bc my friend who is a pharm tech has been consistently complaining that more and more drugs have become hard to order in larger quantities and they are having loads of shortages. they’ve had to limit how much they can give at a time significantly in a few pharmacies in my area and it’s like. if you chat gpt things like calculating when it comes to supplying things for the healthcare industry, you heavily risk accidentally miscalculating how you split things up or wtv the case may be and already scarce items will be even more scarce. it’s just a very very nauseating thing to hear — and young ppl especially !! the company i’m working with has also cut back so many interning spots with data analytics bc they rely on ai to do it !! there are so many young individuals that are slowly being robbed of experience they desperately need to build their careers and the economy isn’t exactly helping with the case either, and :,) it’s just. a rly rly big area for concern bc there seems to be no regulation whatsoever and i can’t wrap my head around it.

and besides the professional aspect, there’s also the social aspect you have to consider. ai generated images are becoming more and more accurate and we’re living in a time where we can start to make anyone’s voice and faces be generated to say / look however we want. there are so many dangers to that i don’t even think i need to go into but from every standpoint all ai screams to me is a way to make life more and more miserable instead of efficient :,)

[upcomingtradera]

[healthcaremarketanalysis]

Revolutionizing Healthcare: The Role of Cloud Computing in Modern Healthcare Technologies

In today’s digital era, cloud computing is transforming industries, and healthcare is no exception. The integration of cloud computing healthcare technologies is reshaping patient care, medical research, and healthcare management. Let’s explore how cloud computing is revolutionizing healthcare and the benefits it brings.

What is Cloud Computing in Healthcare?

Cloud computing in healthcare refers to the use of remote servers to store, manage, and process healthcare data, rather than relying on local servers or personal computers. This technology allows healthcare organizations to access vast amounts of data, collaborate with other institutions, and scale operations seamlessly.

Download PDF Brochure

Key Benefits of Cloud Computing in Healthcare

Enhanced Data Storage and Accessibility Cloud technology allows healthcare providers to store massive volumes of patient data, including medical records, images, and test results, securely. Clinicians can access this data from anywhere, ensuring that patient information is available for timely decision-making.

Improved Collaboration Cloud-based healthcare platforms enable easy sharing of patient data between healthcare providers, specialists, and labs. This facilitates better collaboration and more accurate diagnoses and treatment plans, especially in multi-disciplinary cases.

Cost Efficiency The cloud reduces the need for expensive hardware, software, and in-house IT teams. Healthcare providers only pay for the resources they use, making it a cost-effective solution. Additionally, the scalability of cloud systems ensures they can grow as healthcare organizations expand.

Better Data Security Protecting sensitive patient information is critical in healthcare. Cloud computing providers invest heavily in data security measures such as encryption, multi-factor authentication, and regular audits, ensuring compliance with regulatory standards like HIPAA.

Telemedicine and Remote Patient Monitoring Cloud computing powers telemedicine platforms, allowing patients to consult with doctors virtually, from the comfort of their homes. It also enables remote patient monitoring, where doctors can track patients' health metrics in real time, improving outcomes for chronic conditions.

Advanced Data Analytics The cloud supports the integration of advanced data analytics tools, including artificial intelligence (AI) and machine learning (ML), which can analyze large datasets to predict health trends, track disease outbreaks, and personalize treatment plans based on individual patient data.

Use Cases of Cloud Computing in Healthcare

Electronic Health Records (EHRs): Cloud-based EHRs allow healthcare providers to access and update patient records instantly, improving the quality of care.

Genomics and Precision Medicine: Cloud computing accelerates the processing of large datasets in genomics, supporting research and development in personalized medicine.

Hospital Information Systems (HIS): Cloud-powered HIS streamline hospital operations, from patient admissions to billing, improving efficiency.

Challenges in Cloud Computing for Healthcare

Despite its numerous benefits, there are challenges to implementing cloud computing in healthcare. These include:

Data Privacy Concerns: Although cloud providers offer robust security measures, healthcare organizations must ensure their systems are compliant with local and international regulations.

Integration with Legacy Systems: Many healthcare institutions still rely on outdated technology, making it challenging to integrate cloud solutions smoothly.

Staff Training: Healthcare professionals need adequate training to use cloud-based systems effectively.

Request Sample Pages

The Future of Cloud Computing in Healthcare

The future of healthcare will be increasingly cloud-centric. With advancements in AI, IoT, and big data analytics, cloud computing will continue to drive innovations in personalized medicine, population health management, and patient care. Additionally, with the growing trend of wearable devices and health apps, cloud computing will play a crucial role in integrating and managing data from diverse sources to provide a comprehensive view of patient health.

Conclusion

Cloud computing is not just a trend in healthcare; it is a transformative force driving the industry towards more efficient, secure, and patient-centric care. As healthcare organizations continue to adopt cloud technologies, we can expect to see improved patient outcomes, lower costs, and innovations that were once thought impossible.

Embracing cloud computing in healthcare is essential for any organization aiming to stay at the forefront of medical advancements and patient care.

Content Source:

[uthra-krish]

From Curious Novice to Data Enthusiast: My Data Science Adventure

I've always been fascinated by data science, a field that seamlessly blends technology, mathematics, and curiosity. In this article, I want to take you on a journey—my journey—from being a curious novice to becoming a passionate data enthusiast. Together, let's explore the thrilling world of data science, and I'll share the steps I took to immerse myself in this captivating realm of knowledge.

The Spark: Discovering the Potential of Data Science

The moment I stumbled upon data science, I felt a spark of inspiration. Witnessing its impact across various industries, from healthcare and finance to marketing and entertainment, I couldn't help but be drawn to this innovative field. The ability to extract critical insights from vast amounts of data and uncover meaningful patterns fascinated me, prompting me to dive deeper into the world of data science.

Laying the Foundation: The Importance of Learning the Basics

To embark on this data science adventure, I quickly realized the importance of building a strong foundation. Learning the basics of statistics, programming, and mathematics became my priority. Understanding statistical concepts and techniques enabled me to make sense of data distributions, correlations, and significance levels. Programming languages like Python and R became essential tools for data manipulation, analysis, and visualization, while a solid grasp of mathematical principles empowered me to create and evaluate predictive models.

The Quest for Knowledge: Exploring Various Data Science Disciplines

A. Machine Learning: Unraveling the Power of Predictive Models

Machine learning, a prominent discipline within data science, captivated me with its ability to unlock the potential of predictive models. I delved into the fundamentals, understanding the underlying algorithms that power these models. Supervised learning, where data with labels is used to train prediction models, and unsupervised learning, which uncovers hidden patterns within unlabeled data, intrigued me. Exploring concepts like regression, classification, clustering, and dimensionality reduction deepened my understanding of this powerful field.

B. Data Visualization: Telling Stories with Data

In my data science journey, I discovered the importance of effectively visualizing data to convey meaningful stories. Navigating through various visualization tools and techniques, such as creating dynamic charts, interactive dashboards, and compelling infographics, allowed me to unlock the hidden narratives within datasets. Visualizations became a medium to communicate complex ideas succinctly, enabling stakeholders to understand insights effortlessly.

C. Big Data: Mastering the Analysis of Vast Amounts of Information

The advent of big data challenged traditional data analysis approaches. To conquer this challenge, I dived into the world of big data, understanding its nuances and exploring techniques for efficient analysis. Uncovering the intricacies of distributed systems, parallel processing, and data storage frameworks empowered me to handle massive volumes of information effectively. With tools like Apache Hadoop and Spark, I was able to mine valuable insights from colossal datasets.

D. Natural Language Processing: Extracting Insights from Textual Data

Textual data surrounds us in the digital age, and the realm of natural language processing fascinated me. I delved into techniques for processing and analyzing unstructured text data, uncovering insights from tweets, customer reviews, news articles, and more. Understanding concepts like sentiment analysis, topic modeling, and named entity recognition allowed me to extract valuable information from written text, revolutionizing industries like sentiment analysis, customer service, and content recommendation systems.

Building the Arsenal: Acquiring Data Science Skills and Tools

Acquiring essential skills and familiarizing myself with relevant tools played a crucial role in my data science journey. Programming languages like Python and R became my companions, enabling me to manipulate, analyze, and model data efficiently. Additionally, I explored popular data science libraries and frameworks such as TensorFlow, Scikit-learn, Pandas, and NumPy, which expedited the development and deployment of machine learning models. The arsenal of skills and tools I accumulated became my assets in the quest for data-driven insights.

The Real-World Challenge: Applying Data Science in Practice

Data science is not just an academic pursuit but rather a practical discipline aimed at solving real-world problems. Throughout my journey, I sought to identify such problems and apply data science methodologies to provide practical solutions. From predicting customer churn to optimizing supply chain logistics, the application of data science proved transformative in various domains. Sharing success stories of leveraging data science in practice inspires others to realize the power of this field.

Cultivating Curiosity: Continuous Learning and Skill Enhancement

Embracing a growth mindset is paramount in the world of data science. The field is rapidly evolving, with new algorithms, techniques, and tools emerging frequently. To stay ahead, it is essential to cultivate curiosity and foster a continuous learning mindset. Keeping abreast of the latest research papers, attending data science conferences, and engaging in data science courses nurtures personal and professional growth. The journey to becoming a data enthusiast is a lifelong pursuit.

Joining the Community: Networking and Collaboration

Being part of the data science community is a catalyst for growth and inspiration. Engaging with like-minded individuals, sharing knowledge, and collaborating on projects enhances the learning experience. Joining online forums, participating in Kaggle competitions, and attending meetups provides opportunities to exchange ideas, solve challenges collectively, and foster invaluable connections within the data science community.

Overcoming Obstacles: Dealing with Common Data Science Challenges

Data science, like any discipline, presents its own set of challenges. From data cleaning and preprocessing to model selection and evaluation, obstacles arise at each stage of the data science pipeline. Strategies and tips to overcome these challenges, such as building reliable pipelines, conducting robust experiments, and leveraging cross-validation techniques, are indispensable in maintaining motivation and achieving success in the data science journey.

Balancing Act: Building a Career in Data Science alongside Other Commitments

For many aspiring data scientists, the pursuit of knowledge and skills must coexist with other commitments, such as full-time jobs and personal responsibilities. Effectively managing time and developing a structured learning plan is crucial in striking a balance. Tips such as identifying pockets of dedicated learning time, breaking down complex concepts into manageable chunks, and seeking mentorships or online communities can empower individuals to navigate the data science journey while juggling other responsibilities.

Ethical Considerations: Navigating the World of Data Responsibly

As data scientists, we must navigate the world of data responsibly, being mindful of the ethical considerations inherent in this field. Safeguarding privacy, addressing bias in algorithms, and ensuring transparency in data-driven decision-making are critical principles. Exploring topics such as algorithmic fairness, data anonymization techniques, and the societal impact of data science encourages responsible and ethical practices in a rapidly evolving digital landscape.

Embarking on a data science adventure from a curious novice to a passionate data enthusiast is an exhilarating and rewarding journey. By laying a foundation of knowledge, exploring various data science disciplines, acquiring essential skills and tools, and engaging in continuous learning, one can conquer challenges, build a successful career, and have a good influence on the data science community. It's a journey that never truly ends, as data continues to evolve and offer exciting opportunities for discovery and innovation. So, join me in your data science adventure, and let the exploration begin!