Tag: healthcare data

healthcare analytics

How Healthcare Analytics Helped a Client to Unravel Many Facets of the Oncology Drugs Segment to Successfully Launch a New Product | A Quantzig Success Story

The client is a leading player in the specialty drugs market based out of the United States. With several years of expertise in drug development, they were looking at leveraging healthcare analytics to make a remarkable entry into the oncology drugs segment through the introduction of a new oncology drug.

The Business Challenge

The healthcare industry today is amidst major tectonic shifts. The ongoing regulatory changes coupled with far-reaching patient protection and affordable care act in the United States are prompting leading players to change their status quo. Besides the increasing costs of regulatory compliance, healthcare players are also facing rising research and development costs. Though the increased regulatory supervision has turned out to be a major burden. Such regulatory requirements are putting in place a solid foundation for healthcare analytics, which, in turn, can be used to drive major organizational transformations. By using this opportunity organizations can extract additional value from patient data and leverage it to create a competitive differentiation.

Though the specialty drugs market continues to exhibit a massive sales growth with the introduction of more targeted therapies, it has become increasingly challenging to monitor and understand how therapies are being utilized by physicians. This challenge is further compounded by the complexity of the oncology drugs market where treatment decision options are highly personalized to each patient’s genetic profile. Facing similar predicaments, a leading pharma company approached Quantzig to leverage its healthcare analytics expertise to successfully launch their new oncology drug.

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Top Challenges Faced by the Client

Problem Statement 1

The client needed to adopt a sophisticated analytics-based approach to gain detailed insights into physician and customer journeys. Limited insights into market dynamics and understanding of customer decision making prevented them from gaining a complete view of the market.

Problem Statement 2

Though the new product launch strategy looked promising, the client had to gain better insights on stakeholder interactions and process data, as multiple stakeholders were involved in the oncology drugs segment.

Problem Statement 3

The company also needed to develop a post-launch analytics strategy to assess the marketing effectiveness and commercial success of the new drug. This proved to be a major challenge as it required the client to devise a long-term, holistic approach encompassing enterprise-level capabilities, technology, analytics, and data management skills.

Analyzing the Complex Pharma Landscape with the Help of Healthcare Analytics

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Solution Offered and Value Delivered

To gain the desired insights and to support the information needs of the oncology drugs segment, the healthcare analytics experts at Quantzig adopted a comprehensive three-pronged approach.

Phase 1

The initial phase of this healthcare analytics engagement focused on analyzing patient datasets to offer a comprehensive view of patient journeys. Such an approach not only helped unravel the different facets of the oncology drugs landscape but also helped the client to better understand the current prescribing and utilization patterns, which, in turn, helped them identify physician practices that presented the greatest opportunity for improving patient care.

Phase 2

In the second phase, a dedicated team of healthcare data analytics experts and data scientists were assigned the task of assessing the impacts of prescribed therapies and their clinical outcomes. Leveraging healthcare analytics helped bring about ground-breaking results by offering meaningful insights that empowered the client to further strengthen their drug development approach and improve the marketing effectiveness of the new drug.

Phase 3

The third phase of this healthcare analytics engagement focused on leveraging predictive analytics to gauge the impact of the new product launch.

The healthcare analytics solutions coupled with predictive health analytics offered timely, targeted insights that aided the client’s drug development process. Along with identifying the factors driving therapies, drug development, and treatment selection, the offered solutions also helped the client to identify barriers to utilization. Equipped with such granular insights the client was better positioned to gauge marketing effectiveness and the success ratio of its new product prior to its launch.

Why is Data Analytics Important in Healthcare?

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data warehousing and data mining

Quantzig’s Data Analytics Engagement Helps A Leading Healthcare Industry Player Realize Over 5% in Profits and Improve Customer Engagement

2018 has been the year of emerging healthcare industry trends and it is expected to transform the industry in the years to come. This year, however, companies will witness rapid changes with transparency becoming one of the key concerns for healthcare providers. Startups of today are bringing innovative changes to the market by combining technologies like AI and machine learning with traditional practices.

With continued vertical integration, mergers and acquisitions, and big tech companies making their move to the healthcare industry, it’s becoming clear that the pace of disruption in healthcare is accelerating. Moreover, newer healthcare industry trends and the advent of technology have brought the healthcare industry to the zenith of modernization and innovation. But the constantly rising competition in this sector calls for healthcare companies to generate situational-driven strategic insights, cope with upcoming healthcare industry trends, and act with deliberate speed to acquire more customers and retain the existing ones. As far as the healthcare marketers are concerned, it is high time that they identify the ongoing healthcare industry trends, rethink underlying assumptions, and re-engineer their approaches to marketing to stay relevant in the market and create a better customer experience.

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4 Effective Ways Healthcare Data Analytics is Reducing Healthcare Costs

Despite its promising growth prospects, healthcare service providers are hard pressed to find solutions to multiple complex issues including regulatory and policy changes, medicinal and technological advancements, rising costs, staff, and trained employees, maintain efficient operations and services, and support other healthcare initiatives. With increasing concerns for living healthier, longer, and lead more active lifestyles, healthcare costs have increased. Research reveals that the spending and healthcare costs often rise at rates more than the rate of inflation and is expected to increase even more in the years to come.

With the rising healthcare costs, co-pays and deductibles have become expensive and employers are burdened to take a bigger cut of their employees’ wages to pay for insurance premiums. This surge in healthcare costs will soon become a big barrier to the growth of the overall healthcare industry. Therefore, leaders must find alternative methods to combat rising healthcare costs. They must do the appropriate research to find funding, grants, and contributors to help them conduct research, set up programs and implement processes at the pace of change.

At Quantzig, we understand the impact that costs have on your business plans. And to help companies excel in an ever-competitive marketspace, our team of experts has highlighted four effective ways in which healthcare data analytics can reduce the Contact USrising costs of care.

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Healthcare Data Analytics: Tips and Best Practices

Healthcare companies are in possession of more patient data than ever before, all thanks to advanced technologies and record keeping mechanisms. Healthcare providers can use various data analytics tools to extract useful information and examine patterns among the data available to them. A proper data analysis would also provide healthcare researchers and practitioners with a comprehensive clinical, financial, fraud, HR and suContact USpply chain analysis. The magnitude of this data and the different sources from which it’s derived often makes it cumbersome and difficult to manage. Hence these best practices outline the most effective ways toapproach healthcare data analytics:

Focus on data quality rather than quantity

It is vital for healthcare providers to focus on the relevance and quality of information rather than the quantity of data aggregated in the system. Organizations must lay in-depth focus on what information the data translates into and the possible trends in healthcare and outcomes. The presence of a large amount of irrelevant data can hide insightful and useful information. By recognizing data characteristics organizations can build a meaningful data quality management framework and deploy the most appropriate data cleaning workflows.

Keep an eye on the competitive landscape

While using healthcare data to formulate strategic decisions, organizations must take into account the competition and evolving strategic environment. This also means formulating strategies based on long-term goaRequest Proposalls in mind rather than giving excessive importance to short-term wins. Organizations should also ensure that healthcare analytics frameworks help in the creation of data sets that would be useful in the future.

Data warehouse and access to the testing environment

It is ideal for healthcare organizations to establish an enterprise data warehouse also known as EDW instead of handling data manually. The EDW acts as a central location for analysts to access all data across the entire healthcare system. After an enterprise data warehouse is established, it is important to give analysts full access to this data.  With the help of various data analytics tools, analysts can organize the data, rebuild sets, and improve on it in order to further advance their work.

Focus on skills setDemo

Healthcare analytics systems must revolve around the idea of sustainability by focusing on skill sets. A roadmap is then developed, helping healthcare organizations to gradually build on the required skill set. This greatly helps in minimizing the risk and dependency on outside resources. It is essential for healthcare enterprises to plan future resource needs in order to sustain future organization needs and growth.

Build for scalability

It is advisable for healthcare companies to not build complex systems right off the bat. Information produced from a healthcare analytics platform might be successful in identifying a problem, but an established operational framework is needed to solve the problem. Organizations should start with a structured proof of concept that builds the framework and aims at cashing in on quick wins and low hanging fruits. It is critical to realize that analytics roadmap is not a one-time implementation.


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IR15

How Important is Analytics in Deciphering Healthcare Data?

Healthcare has always been a data-intensive industry due to stringent record-keeping and regulatory requirements. Healthcare data is quite rich and often contains human survival-related information. Analyzing healthcare data is of prime importance, considering the immense potential of saving human life and improving quality of life. Furthermore, the IoT revolution has redefined the face of modern healthcare. IoT promises to deliver excellent progress in healthcare domain. In recent years, healthcare analytics has been the next big invention and the advancement that will take the healthcare industry to the next level. Furthermore, the use of healthcare analytics in process improvements cannot be overemphasized. Process improvement in healthcare can result in increased patient satisfaction, more efficient care, better population health, development of the skill of employees in all levels of a healthcare organization, reduced cost of care and an all-around increase in productivity. Here are some of the reasons why healthcare analytics is vital for understanding healthcare data better and gaining imRequest Solution Demoproved clinical outcomes:

Improve data mining

In the healthcare industry, data mining has been merely an academic exercise with only a few pragmatic and real-life success stories. However, with the proper use of healthcare analytics, this can be improved upon. Healthcare analytics helps in simplifying and fine-tuning the process of data mining. Data mining is essential in the healthcare industry as it enables the systematic use of the healthcare data and analytics to identify inefficiencies and best practices that improve care and reduce cost. So, it can be said that data mining and healthcare analytics work hand in hand.

Structuring healthcare data

Healthcare data tends to reside in multiple places. From different source systems, like EMRs or HR software, to different departments, like radiology or pharmacy. Healthcare data also occurs in different formats such as numeric, text, digital, paper, pictures, videos, and multimedia. With the help of healthcare analytics, the data can be grouped into a more structured format and used for understanding relationships between different variables and identifying patterns.

Developing more effective diagnostic and therapeutic techniques

As mentioned earlier, healthcare analytics can help companies bring together information from different sources. Healthcare data from clinical, financial, and operational sources can be used to determine the efficiency of processes, the state of patients, and the effectiveness of programs. This can be done effectively with the help of healthcare analytics. Healthcare analytics can also help in identifying the patients who are at the greatest risk of developing adverse health outcomes and developing more targeted wellness programs to optimize their health status.

Disease surveillance and preventative management

Healthcare analytics not only uses the data provided to by healthcare organizations, but also scours unstructured healthcare data from nontraditional sources, such as text messages, social media, and search engine to detect disease outbreaks and gauge their scope. This data can then be used for analyzing disease patterns and track disease outbreaks for improved health surveillance and prompt response to healthcare emergencies and develop better targeted preventative techniques and vaccines.


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