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Big Data in Healthcare, Healthcare Big Data Analytics, Clinical Analytics

Tag: Big Data In Healthcare

pharmaceutical industry

How is Big Data Analytics Contributing to the Changing Dynamics of the Pharmaceutical Industry?

The use of big data is no longer limited to transforming customer-facing functions such as sales and marketing alone. In the pharmaceutical industry, big data analytics is helping companies to deal with declining success rates and stagnant pipelines. Big data analytics is creating immense opportunities for companies in the pharmaceutical industry to deal with complex business environments amidst the explosion of data sets. The effective utilization of these datasets can help pharmaceutical companies in drug development. Also, big data analytics has enabled companies to improve clinical trials, manage risks efficiently, and improve patient safety.

Our analytics solutions help pharmaceutical companies in medical cost management and drug development. Request a FREE proposal now to gain better insights into our portfolio of analytics solutions.

How is big data analytics revolutionizing the pharmaceutical industry?

Improving sales and marketing

Until recently sales and marketing were grey areas in the pharmaceutical industry. But by leveraging big data analytics, pharmaceutical companies can easily focus on specific geographical areas to promote their medications. Consequently, pharmaceutical companies can create targeted marketing campaigns, thereby saving both on time and effort. Therefore, the role of big data analytics has become even more critical in devising marketing strategies and sales plans.

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Enhancing the efficiency of clinical trials

Clinical trials are an integral part of the pharma industry. The patients undergoing these trials must meet some prerequisites. Big data analytics solutions help companies merge the databases from multiple sources, to filter out patients who do not meet the basic requirements. Also, these solutions help researchers monitor the patients on a real-time basis and predict the side-effects of drugs.

Helping in early detection of diseases

Predictive analytics is helping companies in the pharmaceutical industry in the early detection of drug toxicity as well as improving the chances of patient survival. The algorithms used by predictive analytics helps in gaining detailed insights into patient data and deliver personalized care to the patient.

Our pharmaceutical industry experts help companies to manage their supply chains through specialized algorithms, tools and analytics models. Get in touch with them right away.

Providing real-time feedback

Today leveraging big data analytics solutions has become imperative for the pharmaceutical industry to reach out to their end-users in a better manner. Digital apps can help companies build relations with the target audience. The data collected on these apps are linked to various verticals of the pharmaceutical and healthcare industry which provides primary data on patient compliance and instant feedback on the health of patients.

Allowing doctor-pharma collaboration

One way in which big data analytics is making improvements in the pharmaceutical industry is by predicting the best treatments for individual patients. It can help in looking through data faster than humans and find the interventions likely to cause the most significant advantages for ill patients and their caregivers. With help from big data analytics, pharmaceutical companies can reach out to physicians treating patients that fit appropriate criteria and advise on how a certain medication could and should fit within a person’s treatment plan. Also, some physicians collect real-time data about whether treatments have intended effects, especially when their patients use Internet of Things (IoT) enabled wearables.

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US Healthcare Industry

Factors Behind Canada’s Healthcare Crisis – Everything You Need to Know

The Canadian healthcare system is complex, but it’s built on the basic principle that focuses on providing healthcare access based on need rather than the ability to pay. The Canadian healthcare system deploys decentralized administration and service delivery systems that are accessible to all groups of people. However, despite the pros like universal accessibility and special care for special needs, the Canadian healthcare system is facing several obstacles due to changing healthcare landscape in the ageing society. In this article, our healthcare industry experts have highlighted a few critical challenges facing the Canadian healthcare system that healthcare companies must not overlook.

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Challenges Facing the Canadian Healthcare System

Long waiting periods

Owing to the universal accessibility of the Canadian healthcare system, the number of patients is always high leading to an increase in wait times. Canadians mostly have timely access to world-class care for chronic problems like heart attacks, strokes and cancer care. But for many problems that are not urgent, they typically wait as long as many months or even years. For example, patients who need replacements of hip or knee, shoulder or ankle surgery, cataract surgery or a visit with a specialist for a consultation tend to wait for a longer period of time than recommended. At times, this becomes frustrating and can make a non-emergency situation life-threatening due to negligence and delay. Therefore, policymakers, as well as the management, should look at ways to do away with such long waiting periods.

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Unrealistic fees

Healthcare providers are mandated by law to charge a certain fee or amount of money for specific treatments and medical interventions. Unless the fees are practical and they ensure that the healthcare providers can keep operating, the Canadian Medicare system will remain flawed. Most welfare schemes end up burdening the average taxpayer. However, healthcare players bear the brunt of the financial fallout with low fees which is unviable in the long run.

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Accessibility in rural areas

Basically, the funding of the Canadian healthcare system is done based on the location and the demographics. Cities and towns that are more populated get adequate funding, but sparsely populated regions get fewer funds. Due to the scarcity of funds, the scope of growth and development is confined. As a result, people living in rural areas travel to cities for adequate healthcare. Several complicated health conditions cannot be attended to by rural healthcare providers and the pace at which the rural healthcare scenario is changing is not encouraging either.

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US Healthcare Industry

Predictive Analytics in Healthcare: Benefits and Challenges

The healthcare industry is going through transformations as it moves from a volume-based business to a value-added business. Organizations in the healthcare industry are facing the heat to provide value-based care with optimum outcomes to the patients. With the huge influx of data in the healthcare systems, it is really becoming very difficult for healthcare organizations to draw meaningful insights from it and utilize it to treat patients and improve the quality of services. This is where predictive analytics in healthcare comes into the picture. Also, with the emergence of value-based reimbursement, many healthcare systems have now realized the importance of predictive analytics in healthcare. It has become an important key to manage population health and deliver care more cost-effectively. At the same time, it helps healthcare organizations in reducing readmission rates and predict different types of healthcare trends. In this article, our team of healthcare analytics experts has highlighted a few benefits of predictive analytics in healthcare. Also, they have discussed the challenges that healthcare organizations face in implementing predictive analytics in healthcare.

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Benefits of Predictive Analytics in Healthcare

Benefit #1: Predictive analytics in healthcare can increase the accuracy of diagnoses

By leveraging predictive analytics solutions, physicians can utilize predictive algorithms to make more accurate diagnoses. Predictive modeling and machine learning can provide real-time information to doctors that can fuel the accuracy of predictions and can lead to better patient outcomes. Also, with the use of predictive analytics in healthcare, multiple datasets from hundreds of patients can be analyzed to build tools that find patterns in patient journeys to facilitate early diagnosis and predict negative outcomes. Furthermore, predictive analytics solutions can help in monitoring diagnosed patients and assessing how their disease status progresses in real-time. Such alerts can facilitate early intervention that can make a real difference in helping a patient avoid complications or long-term physical damage.

Benefit #2: Predictive analytics will help preventive medicine and public health

Predictive analytics in healthcare facilitates early intervention that can help in the prevention or amelioration of many chronic diseases. Predictive modeling, particularly within the realm of genomics, can also help primary care physicians to identify at-risk patients within their practice. With that knowledge, patients can be advised to make changes in their lifestyle to avoid future risks or complications. Also, predictive analytics solutions help to develop a profile for patients at risk of substance abuse and help providers anticipate how their prescription decisions might affect those patients.

Are you finding it difficult to reap clinical and financial rewards from predictive analytics? We can help. Get in touch with our experts now.

Benefit #3: Predictive analytics can provide employers and hospitals with predictions concerning insurance product costs

Healthcare organizations providing healthcare benefits to employees can input characteristics of their workforce into a predictive analytic algorithm to predict future medical costs. Such predictions can be based upon the own data of the organization or the organization may even collaborate with insurance providers to generate the predictive algorithms. Furthermore, healthcare organizations working with insurance providers can synchronize databases and actuarial tables to build predictive models and subsequent health plans. Organizations might also use predictive analytics in healthcare to determine which providers may give them the most effective products for their specific needs.

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Challenges in the implementation of predictive analytics in healthcare

Challenge #1: Developing a comprehensive patient profile

Most healthcare providers already possess the data assets of their patients that are required to build a predictive model. But utilizing such information can certainly be a challenge. The difficulty increases even more when patients move between providers, health systems, or even geographical regions. As a result, healthcare organizations find it difficult to map the profile of such patients. Therefore, implementing predicting analytics solutions becomes challenging.

Challenge #2: The challenge of clinical application

As predictive analytics in healthcare has become increasingly available for real-world applications, healthcare professionals diagnosing and treating patients encounter multiple data elements apart from classical clinical data. When new data is added into the patient narrative, this information needs to be put into a framework that physicians can understand and recognize as credible. Therefore, it is important that an alert not only states that a patient likely has a certain disease, but also includes a detailed rationale as to why the analysis is making a specific prediction.

Challenge #3: Data aggregation challenges

Data of patients are often spread across many file cabinets, servers, hospitals, and government agencies. Pulling all these data together and collaborating them all for the use in the future requires a lot of planning. Every participating organization must agree and understand upon the types and formats of big data they intend to analyze.  Also, the quality and accuracy of such data of patients need to be established. This requires not only data cleansing but also a review of data governance processes.

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Predictive Analytics in Healthcare: A Pathway to Personalized Patient Care

Every industry today is transformed virtually by predictive analytics and the healthcare industry is no exception. Predictive analytics in healthcare is advancing patient care significantly by offering detailed insights into the medical history of patients. However, there are many healthcare organizations that are yet to tap into the full potential of predictive analytics in healthcare. Deploying predictive capabilities in extracting actionable, forward-looking insights from the growing pool of data is still a daunting task for many healthcare companies.

However, realizing the power of predictive analytics in healthcare is highly essential as predictive analytics platforms can help healthcare companies to transform medical wisdom into real-world insights. Leveraging predictive analytics in healthcare can help companies to move into the new era of medicine. Consequently, this can improve hospital care, help in the management of chronic diseases and enhance supply chain processes.

At Quantzig, we understand the difference that leveraging predictive analytics solutions can create in the healthcare industry. And to help companies in the healthcare sector stay ahead of the curve, our team of analytics experts have highlighted the ways in which predictive analytics can improve healthcare and also have explained a few use cases for predictive analytics in healthcare.

Are you looking for ways to gain consolidated insights to mitigate risks and resolution of issues for medical cost management and improved patient care? Get in touch with our experts now!

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Weekly Round-Up: A Storyboard on Healthcare Analytics and its Importance in a Fast-Changing World

LONDON: Quantzig, a leading analytics services provider, has announced the release of their new storyboard on the growing importance of healthcare analytics. These downloadable resources explain how healthcare analytics have helped healthcare players to reduce inefficiencies, minimize cost, and forecast future healthcare plans.

Today the healthcare industry is facing the heat of skyrocketing cost of care. Companies in the healthcare sector are trying to address the critical challenges by enhancing data sharing across the value chain. This is the reason why they are looking to leverage healthcare analytics solutions to improve business operations, patient experience, and healthcare outcomes. Analytics in healthcare can help companies to build multidimensional predictive models, enhance healthcare outcomes, and reduce the cost of care.

Quantzig’s analytics solutions have helped companies in the healthcare sector including medical devices, pharma, diagnostics, and healthcare delivery to gain actionable insights into patient care, mitigate risks, medical cost management, and improve payment accuracy. Below, we have summarized some of Quantzig’s free resources that highlight ways in which healthcare analytics have solutions helped healthcare businesses to gain a competitive market advantage.

Digitization has empowered healthcare businesses to gauge the impact of analytics on patient experience and outcomes. Wondering how?  Get in touch with us right now!

#1. How is healthcare analytics transforming the healthcare sector? Do you think maximizing profit by reducing healthcare costs is a herculean task? If yes, then it is time for you to leverage healthcare analytics solutions. Healthcare analytics can help businesses to implement a new model of payment on the basis of performance. Here the healthcare providers can receive the value determined by outcomes. Also, customized analytics tools and real-time dashboards can help in scrutinizing the supply chain to optimize healthcare service and patient care.

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Are you wondering how to optimize healthcare service and maintenance operations? Analytics in healthcare and real-time dashboard can help. Request a free proposal to know more about our healthcare analytics solutions.

#2. Learn how healthcare analytics can modernize the patient journey?  Today healthcare companies may question the value of improving patient experience amidst multiple priorities. But with analytics in healthcare, it seems to be a simpler task. Healthcare analytics can also help map the patient’s journey and assist healthcare companies in bridging gaps that result in leakages in the patient experience.

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Maximizing sales and marketing insights that can be obtained out of every patient interaction is a tough nut to crack and we understand that. Our healthcare analytics solutions provide best-in-class frameworks to assist healthcare organizations in enhancing patient care and outcomes. Request a free demo for more insights.

#3. Customer lifetime value models hold the solution to rising consumerism in the healthcare sector: Customer lifetime value models have always been of great importance for the retail sector, but these models have the potential to solve critical challenges in the healthcare sector too. You must be wondering how? Healthcare analytics by leveraging customer lifetime value models can help companies to enhance their relationship with patients and can lead to more compassionate patient experience.

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customer analytics in retail

Healthcare Analytics: How is It Improving Healthcare Operations Globally?

What is Healthcare Analytics?

Healthcare analytics aims at offering insights into patient records, hospital management, costs, and diagnoses. It covers a broad swath of the healthcare industry, offering insights on both the micro and macro level. Healthcare analytics, when combined with data visualization tools and business intelligence suites, helps healthcare companies operate better by offering real-time information that can support decision making and deliver actionable insights.

Are you facing difficulties in dealing with regulatory pressures in the healthcare industry and constantly changing market dynamics? Get in touch with us to know how healthcare analytics can help you deal with all your issues.

Why is Data Analytics Important in Healthcare?

With the adoption of digital tools like electronic health records (EHRs), data has become more structured. Furthermore, it has improved the analysis to a large extent. Moreover, the emergence of machine learning and artificial intelligence have made inferences and predictions easier than ever before.  The dramatically positive impact data analytics can have on the pressure health systems is more efficient and promises to enhance clinical outcomes. Ideas such as doctor-on-demand, video calls, and Wi-Fi-enabled blood pressure monitors were a fantasy a decade ago. But today they are mainstream and real. This is encouraging both consumers and healthcare providers to adopt healthcare analytics rapidly to lower costs, improve efficiency and enhance patient satisfaction.

Healthcare analytics provides consolidated and actionable insights and helps in risk mitigation and resolution of issues facilitating efficiency and accuracy in the process.

Benefits of Health Analytics

Benefit #1: Identifying and targeting the right people

Health plan serves a diverse group of people who may be at any point along the continuum of health and wellness. Healthcare analytics helps the healthcare companies to identify people who are at risk and could benefit from weight management, additional screenings or smoking cessation programs. Furthermore, healthcare analytics aids in providing care to those who need it best. This starts by analyzing multiple sources such as claims data, health risk assessments and a lot more. Without healthcare analytics, health plans would have to wait to find who requires careful coordination. Additionally, health plans can leverage healthcare analytics to analyze what are the factors that motivate people and how to change their behavior. Also, it helps in taking a closer look at screening rates among people in different demographic groups. This can further help in identifying barriers to screening and determining the best way to encourage target groups to complete the recommended screenings.

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Benefit #2: Increasing productivity with real-time healthcare analytics

Healthcare analytics offers relevant data that elevates operational efficiency. This is the reason why proactive healthcare organizations are turning to healthcare analytics for right-time data feeds to track costs and outcomes and improve decision-making. Furthermore, it also facilitates the standardization of staffing with real-time data that allows physicians to focus on one patient-centered activity at a time. Top healthcare organization has realized an improvement of 50 percent in its variance from benchmark lengths of stay and a reduction of 10 percent in readmissions for heart attacks and heart failure.

Benefit #3: Reducing inefficiencies in the supply chain

Healthcare companies accumulate a huge amount of data which is stored in silos across the entire enterprise. Data silos make it difficult to spot the opportunities of savings in the thousands of daily supply chains. Healthcare analytics helps in connecting these islands of information which leads to smarter and wiser buying decisions. Additionally, it provides the ability to gather and blend data on a common field. By leveraging healthcare analytics, companies can create dashboards with data from multiple sources. Furthermore, this will offer 360-degree assessments of supply chains and help in more accurate visuals, eliminating excess and obsolete inventory while reducing the involvement of clinical staff in replenishment and ordering.

Benefit #4: Automating ad hoc visual analysis

Healthcare analytics helps in automating spreadsheets for ad hoc financial reporting in healthcare. This rule out the tendency of mistakes that are made at the expense of accuracy, efficiency, and time. Data visualization thus allows to quickly automate processes that used to consume a large amount of time. Furthermore, by leveraging healthcare analytics and artificial intelligence, providers can see a significant improvement in revenue, patient access, lower cost, patient experience, and increased asset utilization.

We understand the challenges companies face in data-driven decision making and solving operational problems. Our analytics solutions provide best-in-class frameworks to introduce innovations frequently and strive for better clinical outcomes. Request a free demo below for more insights.

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IR27

Top 3 Benefits of Data Analytics That Can Take Wearable Technology to the Next Level

From fitness and activity trackers to “smart clothing” and smartwatches, the possibilities are practically limitless within the industry for wearable technology. And this growth is further enhanced by the promise of high monetary rewards. But design and data collection are only a small component of the potential that wearables offer to both companies and consumers. Taking wearable technology to the next level of usefulness requires companies to build in additional analysis features that increase engagement levels, improves the usefulness of the wearable technology, and provides an unparalleled experience to the consumer. In other words, the benefits of data analytics can help companies to effectively determine what the wearable technology is actually doing for their consumers. But the question that arises here is, what are the benefits of data analytics and how can it help wearable technology to become even more popular. This article has answers to this question. Here, we have discussed in detail some of the significant benefits of data analytics that have potential to take wearable technology to the next level in 2019.

Benefits of Data Analytics

Detailed insights from collected data

One of the most crucial benefits of data analytics is that it can turn collected data into the foundation needed for actionable insights, and in doing so provides additional company and consumer benefits. For example, a sleep tracking device might collect data on how and when a consumer is sleeping. But without the analysis of the collected data, the device only helps in identifying the hours that a consumer sleeps or fails to reach a REM cycle. Does this knowledge help consumers in any way? The answer is simple, no! It is in this context that the real benefits of data analytics are realized.

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Personalized services based on habits 

By leveraging the benefits of data analytics, wearable devices can provide personalized offers to consumers. By analyzing the data captured by wearable technologies, companies can create marketing offers that are customized to each consumer. For example, a step-tracker can reveal that a consumer takes the most steps around lunch time. By leveraging this information viaRequest Proposal personalized offers, companies can encourage the consumer to take more steps throughout the day. 

Improved employee health and productivity

One of the key benefits of data analytics is their ability to improve the health and productivity of employees. Wearable technology can collect the data needed for a company to analyze the productive hours of the day of their employees. Additionally, the devices can also monitor employees’ health, so that the company can mitigate risks associated with lack of sleep, high levels of stress, and other health symptoms that contribute to an ineffective and unhappy workforce.


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3 Common Mistakes to Avoid in Big Data Analytics

Today, big data analytics is one of the most crucial processes for any business, big or small. For data scientists, it acts as a pair of glasses that helps them see the actual reality of a business’ performance, beyond scattered numbers in graphs. A proper, solid, and reliable analysis allows you to make fact-based and rational decisions, but if mistaken, advanced data analytics can lead you astray and you might end suffering a huge loss. So, it can be safely presumed that it is not enough to have good quality data unless you use the datasets efficiently. However, there are many hurdles that businesses might encounter along the way. While implementing a new strategy to strengthen your business with advanced data analytics, mistakes can prevent you from realizing its complete potential. So, in this article, we have summed up some of the common blunders businesses should avoid while developing a big data analytics strategy.

Mistakes to Avoid in Big Data Analytics

Being rigid in processes and products

If you are rigid with your process and product, you are committing a big mistake. You should begin your project in a way that is both strategic in vision and agile in execution. Therefore, you need to pick technologies that are open and expandable. For example, you must avoid vendor lock-in by using open source tools. For obtaining optimum results from advanced data analytics, it is important to foster a culture that fosters failing fast and learning from mistakes. You must avoid letting egos drive your project and understand that if your team experiments on ten things, eight of them might not work. You should get people on board in your data project team who can thrive in this sort of DevOps style of work.

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Neglecting security and governance at the beginning

Today, security and governance are extremely important, as privacy is one of the major concerns in almost every industry. Businesses still tend to start big data analytics projects as pilots, with just a handful of people working on them, and without security and governance baked in. This is a huge mistake when it comes to big data analytics. You must get compliance, governance, and security conversations started on the very first day of the project. You must carefullRequest Proposaly choose the right governance strategies, as well as the right technology of governance.

Relying on the same KPI’s always

As things are constantly changing and your business is subjected to constant dynamics, so you must learn to adapt to the ever-changing environment. This is how you can prosper. So, try not to hold on to the old performance indicators that are used to measure your success in the past. You need to use newer and more suitable tools to make advanced data analytics tools reflect the current performance of your business and identify what really drives your business forward.

Quantzig’s Advantage

Being a leader in offering big data analytics services, Quantzig helps businesses to manage, store, and integrate huge datasets. Also, we help businesses to gain predictive insights that facilitate proactive business decisions and pre-emptive planning. Additionally, Quantzig promises to deliver best-in-class frameworks for multi-dimensional data aggregation and utilizes visualization-based data discovery tools for insight generation.


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