Exploring the Potential of Predictive Analytics in Healthcare

Predictive analytics in healthcare uses real-time and historical data to make predictions about future trends, anticipate patient needs, and help healthcare organizations run more efficiently.

Updated 9 April 2024

Ashish Chauhan
Ashish Chauhan

Global Delivery Head at Appventurez

In a consistently evolving digital world, predictive analytics has emerged at the crossroads of healthcare and technology. This powerful tool deploys machine learning algorithms and statistical methods to analyze healthcare data and forecast future outcomes. 

The application of predictive analytics in healthcare spans from preventing diseases to managing hospital workflows, further demonstrating the significance of this tool in healthcare application development services.

However, before diving further to understand how predictive analytics is altering the healthcare landscape, it is important to understand its role in the industry, the different applications of predictive analytics in healthcare, and how it is helping the industry enhance its operations. 

What is Predictive Analytics in Healthcare?

Predictive analytics is a discipline in the data analytics world that relies heavily on modeling, artificial intelligence development services, data mining, and machine learning services.  

The tool is used to evaluate historical and real-time data to make predictions. Predictive analytics in healthcare refers to the analysis of current and historical healthcare data. The role of this advanced technology in healthcare can be understood from the following points. 

  • It allows professionals to find opportunities to make more effective and efficient operational and clinical decisions, predict trends, and even manage the spread of the disease. 
  • Healthcare data is any data that is related to the health conditions of an individual or a group of people. With the help of predictive analytics, it can be collected from administrative and medical records, health surveys, disease and patient registries, and EHRs. 
  • This data can be utilized by healthcare organizations, doctors, hospitals, pharmaceutical companies, and even healthcare stakeholders for patient welfare and for strengthening 
  • With rapid digitization in the industry, such as the integration of big data in healthcare industry, healthcare analytics has also emerged as an essential tool that anyone in the industry can use and benefit from to provide better-quality care. 

How to Use Predictive Analytics in Healthcare?

The use of predictive analytics in healthcare can be divided into three main categories: 

predictive analytics in healthcare

For Prevention & Early Diagnostic

Diagnostics is the area where the predictive models make a significant difference. Data-driven methods have taken diagnostics a few steps beyond the conventional approach, lifting the focus from the limited, traditional, test-based analytics to put it in a broader perspective. 

Hospitals using predictive analytics also allow doctors to identify the patient’s bad potential and help early enough to stop the possibilities before they become a reality and develop into a serious illness. 

For Personalized Treatment Plans

Following the diagnostics, predictive analytics in healthcare make it possible to tailor a custom treatment plan for each patient, maximizing its effect. Personalized treatment strategies are particularly effective in fighting cancer and chronic conditions where one size fits all doesn’t always yield the best results. 

A good example can be the impact of predictive data analytics on the treatment of cancer patients. Based on their history and genetic map, the oncologist can clearly understand the treatments with the highest healing potential. 

For Hospital Resource Optimization

Efficient management is one of the columns of a successful healthcare business. Predictive analysis in healthcare has an important application in this particular field, aiding in patient inflow forecasting and tactical resource allocation. 

This method of healthcare predictive analytics also facilitates the hospitals in navigating an effective response to seasonal flu outbreaks, including staff shifts, number of beds, equipment, and management of medication inventories. 

healthcare predictive analytics

Different Types of Predictive Analytics in Healthcare

Some of the common types of predictive analytics used in healthcare are:

Disease Progression Modeling

Technologies such as cloud computing in healthcare have transformed the way healthcare professionals operate and diagnose diseases. Similarly, the application of this advanced predictive analytics model helps in analyzing how a disease is likely to progress in an individual on the basis of various factors such as lifestyle, genetics, and treatment history. This information helps the healthcare professionals in tailoring the treatment plans and interventions for better outcomes. 

Predictive Model for Patient Segmentation

Segmenting patients into different groups based on their health status, risk factors, or treatment plans allows for personalized care management strategies that are tailored to each group’s specific needs. 

Medication Adherence Prediction

This type of predictive analytics in healthcare helps predict which of the patients are likely to follow their medication regimes and which are at risk of non-adherence. By following this model, which is also one of the prominent healthcare trends, professionals can intervene with adherence support strategies for at-risk patients, further improving treatment outcomes. 

Resource Demand Forecasting

This type predicts future healthcare resource demand such as hospital admissions, emergency department visits, or intensive care unit (ICU) utilization. This helps the healthcare centers plan and allocate resources efficiently to meet the patient’s needs. 

Exploring the Process of Predictive Modelling in Healthcare

The process of predictive modeling in healthcare uses statistical methods, data mining, and machine learning to identify the patterns in data and recognize the chance of particular outcomes occurring. The points mentioned below further explain the predictive modeling process in healthcare:

Data Gathering

The first stage of predictive modeling in healthcare involves the gathering of data from various resources. It also employs cleansing operations to remove noise further ensuring accurate predictions by extracting only the significant information.

Data Analysis

Before building the model, it is important to first create a basic chart of your data and thoroughly analyze it. This process also offers significant clarity regarding the problem at hand and potential solutions, further providing a solid foundation for model development.

Developing a Predictive Model

Sometimes, the data lends itself to a specific algorithm or model. As you analyze the data, run as many algorithms as you can and compare their outputs. Identify test data and apply classification rules to check the efficiency of the model.

Incorporating Models into Business Processes

To make the predictive model valuable, it is important to integrate it into the process of your healthcare organization so that it can be used to help improve patient care.

What are Some of the Benefits of Predictive Analytics in Healthcare?

With the latest technological advancements, predictive analytics in healthcare has tremendously impacted the industry. Here are the most important ways through which predictive analytics has benefited healthcare institutions. 

data analytics in healthcare

Improvement in Patient Care

The most important benefit that predictive analytics brings to the industry is the accessibility to all types of data which provides the doctors with valuable insights that can also guide their decisions and lead to an overall improvement in the patient’s health. 

Population Health Management

Predictive analytics in healthcare isn’t only applicable at the individual level but also the healthcare organizations that can use it for population health management. 

It can help in identifying cohorts exposed to a possible disease outbreak and facilitate the healthcare professionals to look for treatments immediately, which can improve people’s chances of survival. 

Healthcare Tracking and Digitization

The digitization of healthcare services has completely transformed the way that patients and healthcare professionals interact with each other, further empowering the future of telemedicine. Nowadays, we can also attach devices to our bodies and can track health and body performance at any given time from our mobile phones. 

Chronic Disease Management

As per the reports, chronic diseases are one of the leading causes of death and disability in the US, as well as the main drivers of the country’s $3.5 trillion in annual health costs

Chronic disease management, as one of the benefits of predictive analytics in healthcare, depends on the ability of healthcare professionals to prevent the development of these diseases as well as to control them. Predictive analytics can also empower healthcare providers to make fact-based decisions in order to provide more effective treatments while reducing costs. 

Fraud Detection

Fraud in healthcare is a common problem, ranging from intentionally incorrect reporting of the diagnosis or the procedures to maximizing the payment. Predictive analytics in healthcare helps in identifying certain abnormalities that can flag these fraudulent actions, thus helping catch on to them early on. 

Prominent Examples of Data Analytics in Healthcare

Some of the prominent predictive analytics in healthcare examples are mentioned below:

data analytics in healthcare examples

For Reducing Hospital Readmission Rates

With the use of socioeconomic data, EHRs, and predictive analytics, patients with a high risk of readmission can be discovered, warned, and provided with additional medical care to reduce readmission rates. 

For Research into New Treatments

Predictive analytics in healthcare can be as effectively used in the research of new treatments too. 

The predictive algorithms can accurately predict the person’s response to a medication or treatment plan based on genetic information, clinical history, and other data. This is one of the predictive analytics case study examples that can also streamline the research process and cut the need for patients for groups. 

Healthcare Insurance

Among the rising technology trends in healthcare such as the use of artificial intelligence in hospital apps, predictive analytics healthcare can calculate the accurate cost of health insurance for each specific individual based on gender, age, insurance case history, heredity, etc. More so, predictive healthcare can also be used to prevent fraudulent insurance claims. 

Controlling Infectious Diseases

Controlling infectious diseases is another one of the prominent data analytics in healthcare examples. Predictive analytics has demonstrated significant prowess in controlling infectious diseases, further reshaping the approach to their prevention, management, and containment. 

This technology, which is empowered by machine learning, provides a robust mechanism to anticipate and react to infectious disease outbreaks, even before they reach epidemic and pandemic proportions. 

For Improving Patient Engagement

With predictive analytics in healthcare, the patient’s non-compliance can be detected in advance and active measures can be taken to keep patients healthy until the next appointment or treatment. 

Healthcare providers can make effective use of predictive analytics in healthcare to design patient files that incorporate specially targeted communications and techniques that help in creating a better patient-doctor relationship. 

Collaborate with Appventurez to Enhance Your Healthcare Development Services With Predictive Analytics

The healthcare industry is undeniably overflowing with data- patient records, medical images, lab tests, research studies, pharmaceutical trials- and the volume of the information generated is enormous. This data holds the potential to bolster patient care, enhance resource usage, and address global health issues. 

By collaborating with Appventurez, unlock the power of data across the health systems and the datasets with a robust healthcare data analytics solution. 

Our team holds expertise in advanced predictive analytics that provide healthcare organizations with a real-time, contextual view of their data, further assisting healthcare professionals to deliver better by empowering them to make smarter and data-driven decisions.

healthcare development services

FAQs

Q. What are some of the problems with predictive analytics in healthcare?

Some of the common predictive analytics challenges in healthcare include data privacy and ethical hazards, ensuring data quality and integrity, and integration with existing healthcare systems. Notably, these hurdles must be navigated carefully to reap the full benefits of this transformative technology.

Q. What is predictive modeling in healthcare?

Predictive modeling, in particular, allows forecasting the course of the disease, further enabling medical professionals to avoid health risks like adverse reactions to the medicine, genetically determined resistance to the treatment, and failure to adhere to the regimen.

Q. Why is predictive analytics important in healthcare?

Predictive analytics allow healthcare professionals to quickly analyze the data and plan a course of treatment that will work best for their patients, further saving time and producing better outcomes.

Q. What are the key uses of predictive analytics?

Predictive analytics can be used to streamline operations, boost revenue, and mitigate risks for almost any business or industry, including retail, banking, public sector healthcare, and manufacturing. Sometimes augmented analytics are used, which uses big data machine learning.

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    Ashish Chauhan
    Ashish Chauhan

    Global Delivery Head at Appventurez

    Ashish governs the process of software delivery operations. He ensures the end product attains the highest remarks in qualitative analysis and is streamlined to the clientele’s objectives. He has over a decade of experience as an iOS developer and teams mentorship.