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Examples of Predictive Modelling in Healthcare Applications

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Examples of Predictive Modelling in Healthcare Applications

Published: 2024/01/29

5 min read

In today’s business world, hyper personalization is the name of the game, meaning healthcare providers must implement more examples of predictive modelling than ever. But what does this mean, what benefits will it deliver and how can it be implemented effectively?

Over the last few years the healthcare sector has seen a huge uptick in biotech software development, generative AI development services and even more custom software development that does not fit into these first two areas.

All this data collection in healthcare has been done to make customer offerings more personal and reflect a world where they can always get what they want from social media sites and streaming giants in just a few clicks. But what are the actual benefits of all this data collection in the healthcare space?

Honestly, there are too many to go into in this blog alone. But this article will examine one of the benefits of AI in healthcare – leveraging predictive models to help healthcare providers make more informed decisions that significantly improve patient and customer experiences.

But what is predictive modeling, what is it used for and what benefits can it deliver to modern healthcare providers and insurers?

What is predictive modeling?

Predictive modeling is a form of advanced analytics which many industries, including healthcare, leverage to predict future events with the help of customer data they already have stored in their database.

This popular form of data analytics is driven by data mining, machine learning and artificial intelligence (AI). It is used to detect any correlations and/or patterns present in the data being analyzed to provide actionable insights connected to a company’s patients or customers.

Within the healthcare industry, this form of predictive analytics was initially built using medical records, demographical data, socioeconomic characteristics and other information to identify which patients were most at risk from chronic ailments, such as diabetes, that put a strain on hospitals and insurers.

However, in today’s ultra-personalized healthcare market, the use of predictive analytics does not just identify high-risk patients or predict trends before they happen – it is also used in a variety of other key areas to ensure patients receive the best care possible. Some are outlined below.

Predictive modeling applications in healthcare – examples

It is important to note here that as the technology becomes more advanced in the coming years, more examples of predictive modeling in healthcare will become popular. But for now, this blog will highlight six key areas where it is making a real difference in the industry. These are:

  • Predicting patient flow – predictive analytics can integrate with any hospital’s management system to analyze patient behavior and recognize patterns, so a hospital can highlight upcoming check-ins with patients who frequently miss appointments. This helps to optimize wait times and staffing, while improving patient satisfaction while avoiding staff overload.
  • Lowering hospital readmission rates – through socioeconomic data, electronic health records (EHRs) and predictive analytics, healthcare providers can identify patients with an elevated risk of readmission before they arrive at their hospital. This gives staff the opportunity to provide better medical advice to those patients in particular, which will significantly reduce readmission rates across the board.
  • Improving medical imaging – predictive modeling can automate medical image analysis and save both time and staff resources as it can identify disease-specific anatomical changes caused by, for example, COVID-19, breast cancer and lung diseases on X-ray images. This, in turn, helps practitioners proactively treat the most at-risk patients.
  • Producing better clinical trials – predictive analytics-based algorithms can accurately predict a person’s response to a medication or treatment plan based on genetic information, clinical history and other data. This streamlines the entire research process around clinical trials and eliminates the need for inpatient groups.
  • Strengthening data security – predictive analytics can be used to effectively protect against cyberattacks. AI-enabled software can monitor and analyze hospital systems in real time, which helps identify uncommon patterns, excessive information exchange, or suspicious access at speed, enabling the alert to be raised much earlier than in other more traditional security setups.
  • Protecting insurance/healthcare providers against fraud – predictive analytics helps insurance and healthcare companies develop and train machine-learning algorithms to determine whether there is any malicious intent behind a case that is perceived to be suspicious early on. This helps to significantly reduce losses and prevent future scamming attempts.

Adoption of predictive modeling in healthcare

There can be no doubt then, that these examples of predictive modeling in healthcare bring a lot to the table for both insurers and healthcare providers. However, making these examples a reality comes with their own challenges, including ensuring that:

  • The data used to build these models is of the highest quality possible
  • Any patient or customer data used in the model building process is secure 24/7
  • Data gathering processes are kept as streamlined as possible.

Therefore, before organizations get excited over the benefits of predictive modeling, they need to get align strategies when it comes to assessing data quality, gathering information and implementing security processes that will ensure the model they build will give them the results they want.

What is more, if they do not give these challenges their full attention during the model building process itself, they may face a lack of trust with their customers and patients, and even potential lawsuits down the line as a result.

However, while putting these processes in place, and the consequences of failing to do so properly, may sound daunting, the benefits are worth it.

Benefits of predictive modeling in healthcare

This form of data analytics gives any organization the ability to:

  • Understand the marketplace better and identify key competitors at speed
  • Leverage data-driven strategies to gain a tangible advantage over competitors
  • Optimize products or services to ensure customers get what they want
  • Understand what customers want through data-driven, actionable insights that can be built upon moving forward
  • Forecast and identify financial risks and external factors that could impact productivity or workflow, well before they become an issue
  • Reduce time, effort and costs across the board by eliminating the need to estimate outcomes and move towards a model that promises more accurate results
  • Forecast inventory or resource management processes which will ensure any faulty equipment, such as CAT scanner parts for example, are replaced well before they become deficient
  • Identify trends before they take off in order to get ahead of competitors and anticipate customer needs before everyone else
  • Plan for workforce churn ahead of time, which will help reduce the likelihood of staff shortages and lead to happier staff who ensure they provide the best possible experiences to customers and/or patients.

Predictive analytics in healthcare – key takeaways

In conclusion, predictive analytics in healthcare is here to stay, but implementing examples of this in any organization need not be difficult, so long as steps are taken to create something that works from day one.

At Software Mind, we know that doing this can be challenging. But our experts understand the benefits these analytics can deliver, what they can do for you and how to implement them in your company efficiently and effectively. Our dedicated software team is happy to talk about the types of analytics they can create for your business – get in touch by using this contact form.

About the authorSoftware Mind

Software Mind provides companies with autonomous development teams who manage software life cycles from ideation to release and beyond. For over 20 years we’ve been enriching organizations with the talent they need to boost scalability, drive dynamic growth and bring disruptive ideas to life. Our top-notch engineering teams combine ownership with leading technologies, including cloud, AI, data science and embedded software to accelerate digital transformations and boost software delivery. A culture that embraces openness, craves more and acts with respect enables our bold and passionate people to create evolutive solutions that support scale-ups, unicorns and enterprise-level companies around the world. 

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