Tag: predictive analytics solutions

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.

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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 modeling

A Definitive Guide to Predictive Modeling

What is Predictive Modeling?

The process that uses statistics and data to predict results with the help of data models is said to be predictive modeling. Predictive modeling is used to predict anything from advances in technology to corporate earning. Predictive modeling methods use data mining and advanced analytics that are likely to influence results in the future. Predictive modeling techniques employ a complex network or a simple linear equation to analyze the datasets and then such data is run through algorithms to determine what kind of products or services users might be interested in.

What are the Different Predictive Modeling Techniques?

Technique #1: Behavioral Clustering

The path leading a prospective customer to convert into a buyer is incredibly valuable for a marketer who focuses on leveraging predictive modeling solutions. Behavioral clustering is one of the predictive modeling techniques that empowers marketing teams to determine trends and commonalities that help to create a new target segment. It can help marketers enhance conversions and predict the impact of marketing campaigns more precisely.

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Technique #2: Product-Based Clusters

This is a predictive modeling technique that segment customers together based on similar engagement and activity with the brand. Product-based clusters focus specifically on tracking specific buying trends among target demographics. These product-based clusters also help in forecasting the lifetime value of a customer. Such predictive modeling techniques help companies in identifying services that they can actually sell to the customer over a period of time. 

Technique #3: Share of Wallet Estimation

“Share of wallet estimation” is a predictive modeling technique that assists companies to predict how much of their customer’s budget is with competitors. Also, it unveils what specific products or services you can sell to that particular customer in order to increase your share of wallet percentage. In order to attain the desired outcome, companies must leverage this technique along with the product-based clustering techniques.

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Technique #4: Likelihood of Churn

Predictive analytics models are used by most of the marketers for lead generation. But this is a mistake because predictive modeling techniques have strong defensive capabilities that can help in measuring the likelihood of churn. For most companies, customer churn can be a big issue but predictive modeling techniques can help by measuring customer’s propensity to churn by analyzing their behavior. It can help in identifying “red flag” behaviors from previously churned customers and their troubling behavior. Such information can be followed up by the customer service teams and this can be further transitioned into a nurture campaign to reduce churn. The impact can be significant when it comes to retaining current customers and boosting revenue by minimizing churn.

customer analytics

Weekly Round-up: A Storyboard on the Benefits of Predictive modeling Techniques

LONDON: Quantzig, a leading analytics services provider, has announced the release of their latest storyboard on predictive modeling techniques and how it can help businesses in proactive decision making and pre-emptive planning.  With the availability of data in enormous volumes, it has become quite difficult for businesses to rapidly identify, objectively evaluate, and confidently pursue new market opportunities.

By using predictive modeling techniques, companies can enhance decision making and gain the ability to optimize, direct, and automate decisions, on demand, to achieve defined business goals. By applying predictive analytics models, businesses can not only better manage the present, but can also improve the probability of success in the future. In doing so, such companies become predictive enterprises.

The goal of predictive modeling techniques is to turn data into information and information into insights.

Quantzig’s analytics solutions have helped more than 55 Fortune 500 companies to make data-driven decisions and augment revenues. Below, we have rounded up some of Quantzig’s recent success stories for this week and have also highlighted ways in which predictive modeling techniques have helped businesses to become market leaders.

#1: Predictive modeling techniques improved 85% accuracy in demand forecasting: Providing seamless customer experience and complete customer satisfaction is a challenging task for any business. Don’t you agree? But predictive modeling techniques can help as it helped one of Quantzig’s clients. By utilizing predictive modeling techniques, companies can conduct analysis on historical information on sales, products, and inventory levels. Furthermore, it can reduce irregularities and streamline order management, resulting in better demand forecasting.

85% accuracy in demand forecasting achieved by a leading computer hardware manufacturer through predictive modeling techniques

#2: Predictive modeling techniques precisely forecasted the roll-out of a new initiative: Predictive analytics models are the best way to improve business operations and predict future trends. This is clearly evident in Quantzig’s latest success story, where the client managed to make accurate predictions about the success and failure points of their new initiative. Moreover, it enabled optimal budget allocation.  

Leveraging Predictive Analytics to Forecast the Roll-out of a New Initiative for a Leading Grocery Retail Chain

#3: How can clustering algorithms uncover relations between invisible variants? If you are looking to upsell your products, this predictive modeling technique can certainly help. Clustering algorithms use data mining techniques to find out the relation between different invisible variants. Furthermore, this helps businesses to obtain desired outcomes.

Top 3 Predictive Modeling Techniques That You Must Know

#4: Predictive modeling techniques optimized inventory management: Optimizing inventory for better customer service is very essential for the success of any business. By leveraging predictive modeling solutions, companies can build better inventory and can discover capabilities to drive continuous market excellence. Furthermore, predictive modeling techniques can be successfully implemented, either independently or in tandem, to offer actionable insights into the performance of the inventory.

Improved inventory management achieved by a leading oil and gas company through predictive modeling techniques

#5: How predictive modeling techniques can boost business outcomes? : Changing customer dynamics poses critical challenges before businesses such as managing customer demands. Don’t you feel the same? This is where predictive analytics plays the role of a savior. By leveraging predictive analytics solutions and utilizing predictive modeling techniques, companies can deal with a humongous set of data, which can aid decision-making.

3 Ways Predictive Analytics Solutions Can Help Businesses Derive Successful Outcomes

#6: Utilizing predictive modeling techniques to boost customer retention dramatically: Managing the huge amount of customer data is not an easy task for businesses and we understand that. This success story is an excellent example of that as one of our clients faced the same issue and approached Quantzig to leverage its expertise in offering predictive analytics solutions to reduce customer churn rate.  Also, our predictive modeling techniques proved beneficial for the client in integrating traditional and digital data sources to correlate the data and identify potential churners.

Telecom company improves customer retention through predictive churn model

#7: Why you can’t afford to ignore predictive modeling techniques?  No matter which industry you are in, retail, transport, or healthcare, you cannot ignore the importance of predictive modeling. It helps you to gain a comprehensive understanding of the market trends, customer behavior, or competitor’s approach. Furthermore, by utilizing predictive analytics models, companies can easily track the volatility of different categories, brands, and products. This can result in better business outcomes.

Is Predictive Analytics All Set to Drive Success in the Transportation Industry?

#8: Predictive modeling models identified inefficiencies and improved ROI: Analyzing customer lifecycles to devise better marketing strategies and improve customer service is one of the common issues that every business faces, so don’t be surprised if you are facing the same challenge. If you have a proper predictive modeling technique in place, you have nothing to worry about. A leading firm in the healthcare industry was facing the same issue but witnessed a great change in their approach to risk assessment after leveraging Quantzig’s predictive modeling solutions. Moreover, it reduced inefficiencies drastically and improved their ROI.

#9: Predictive modeling techniques enhanced fraud detection and customer satisfaction: For some businesses, anticipating fraud risk is a herculean task. This impacts their customer service  and the customer ends up having a bad experience. So, for such companies, utilizing predictive modeling techniques is a necessity as these techniques can help anticipate potential suspicious claims and fraud risks.

Claims predictive analysis delivers reduction in frauds and enhanced customer satisfaction for insurance company

#10: How can predictive modeling increase accuracy and improve efficiency of marketing strategies?: Accuracy in forecasting the impact of several factors on the business outcomes is essential if you want your business to grow. Predictive models, in this context, hold the key to success. These models have the potential to assess and predict the performance of different components and their tentative impact on the future of the business. Furthermore, this can allow companies to make smarter decisions and exit declining markets.

How Healthcare Industry is Leveraging Predictive Analytics to Improve Patient Outcome

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