Wouldn’t it be perfect if you could predict the future? Imagine how many mistakes you would have avoided and how much easier your life would be. Even though it’s not yet possible in our everyday life, this technology is already changing the healthcare industry. Big data and ML algorithms have merged to create a powerful predictive analysis tool. Predictive analysis allows healthcare practitioners worldwide to use healthcare data to get insights into possible favorable and adverse events and answer the question “What would happen if?”.
If you are a part of the healthcare industry but are not yet acquainted with this trend, this article is for you. IntelliSoft’s 16 years of experience in healthcare software development allow us to share valuable tips and tricks on adopting predictive analytics in your healthcare institutions, the benefits of data analytics in healthcare, and making the most out of it, so keep on reading.
Table of Contents
Predictive Analytics: What is It?
Predictive analytics has been a component of data analytics in healthcare for quite some time. With more and more industries interested in it, it’s gaining popularity, especially among healthcare professionals. Predictive analytics foretells future events based on gathered data. It helps answer the question, “What if…?”
But how does it work? Healthcare predictive analytics uses healthcare data, artificial intelligence, machine learning, and statistical algorithms to forecast the future and the likelihood of certain events happening. For example, it can involve the analysis of patient data such as health records, information from wearable devices, and patient medical imaging to identify risk factors or patterns in one’s health. Healthcare practitioners use this information to predict diseases or complications and develop preventative measures.
Nowadays, most electronic health records (EHRs) systems have numerous analytics capabilities, and their applications are growing rapidly.
The primary purpose of predictive analytics in healthcare is:
The Changing Healthcare Industry
Healthcare is one of the leading industries benefiting from AI, ML, and data analytics. The industry is not standing still; it is adopting new technologies and tools and constantly growing to meet growing demands. According to recent statistics, healthcare spending will reach 18.3 trillion US dollars by 2030.
It’s no wonder that the healthcare industry is growing at an incredibly fast pace; predictive analytics helps institutions optimize their budget, save resources, and automate a significant part of tasks. Moreover, it leads to improved individual patient care and public health.
Benefits of Predictive Analytics in Healthcare
We can’t talk about predictive analytics in healthcare without mentioning its main benefits. Thousands of companies worldwide have already experienced them, so they are tested in practice.
Improved patient outcomes
Since healthcare predictive analytics is used to identify treatment plans, possible treatment outcomes, and what medication to give patients, it positively influences patient outcomes. By predicting how certain medicine will affect patients, it is possible to intervene earlier and ensure proper treatment of diseases, especially when time is crucial.
More consistent care
Before predictive analysis was used in healthcare institutions, the quality of patient care depended solely on the experience and skills of practitioners. For example, an experienced doctor or nurse could provide better patient care and identify particular patterns better a junior practitioner. With predictive analytics, everyone has the same capabilities in treating patients, as they can access the same data and forecasts. It benefits patients who can now receive excellent care and practitioners who learn in the process.
Operations efficiency and cost savings
When healthcare practitioners can predict possible issues and diseases, they can intervene earlier and provide more effective care. It both influences operational efficiency and helps save costs and time. For example, it is possible to utilize predictive analytics to reduce unnecessary hospital overstays, saving thousands of dollars.
Fraud detection
Regrettably, patients or medical facilities might encounter charges for services that were unnecessary or never administered. Fraud, misallocation of resources, and misuse are prevalent within healthcare establishments, and predictive analytics offers a solution to address these concerns. By detecting discernible patterns indicative of fraudulent activities, these challenges can be tackled, resulting in cost reduction and an enhancement in the overall standard of healthcare provision.
Use of Predictive Analytics in Healthcare
Predictive analytics is transforming the healthcare industry in more ways than you can imagine. Here are some of the applications of this technology that are the most valuable for healthcare institutions.
Drug Discovery and Development
Pharma companies widely use predictive analytics for drug discovery and development. For instance, they use this technology to anticipate a drug’s success, determine its chemical and physical attributes, and see how it behaves in various conditions. Furthermore, it is possible to use predictive analytics to anticipate how a newly developed drug will behave when tested.
Clinical Trials
Data analytics in healthcare has proven incredibly useful in clinical trials, allowing data analysts to predict patterns and correlations during trials. For instance, the technology can be used to select clinical trial participants by scanning people’s EHRs and choosing those individuals whose health records, demographics, and other factors allow them to participate in the trial.
It is also essential to predict the probability of people dropping out of the trial during clinical trials. Furthermore, doctors must know how certain drugs influence participants based on age, medical history, genetics, etc.
Chronic Disease Prediction
According to research by NCCDPHP, 6 in 10 adults in the US have a chronic disease. Thus, a significant part of healthcare costs is associated with these diseases. Healthcare practitioners must be able to predict who is more prone to developing a specific condition, such as diabetes, cancer, or heart disease. The earlier people learn about it, the higher the chance to intervene in time and prevent or postpone the development of this disease.
Predictive modeling in healthcare collects data such as a person’s age, family history, blood pressure, and other factors to assess the chance of developing a specific disease. Lifestyle and social factors should also be taken into account.
Efficient Treatment Testing
The treatment testing process does not involve predictive analytics directly. However, this technology is used to identify dead ends and streamline tasks, which allows to save a lot of time. Automating as many tasks during treatment testing is critical to prevent risks and simplify clinical trials.
Predictive algorithms are experts at predicting how specific treatment plans influence patients, helping practitioners identify risk factors. When doctors choose treatment plans for their patients, everything marked with a “Risk Factor” label is eliminated.
Precise Treatment & Personalized Healthcare
There’s no universal treatment plan for all patients, even those with the same disease. It is essential to come up with personalized healthcare solutions for each patient, considering their previous health history, genetics, age, sex, and other factors. Creating a precise, customized treatment plan is difficult, but predictive analytics simplifies this process.
Data analytics in healthcare can be used to calculate what can happen with a patient during treatment, and after, what to expect at an unprecedented turn of events, and what to do in case of disease outbreaks.
Predictive analytics can also help doctors treat patients with rare diseases, using the results from previous cases and studies, even if the practitioner has never dealt with this disease before.
Digital Twins
Industries such as manufacturing have already been using the concept of Digital Twins for some years, and now it’s the healthcare industry’s time. A Digital Twin is the exact copy of an object or system in a digital environment. It can be used to run tests or make adjustments without using the actual object to see what may happen in specific situations.
In healthcare, Digital Twins are used for medical device utilization, hospital occupancy, and even to test specific treatments or manipulations of patients’ Digital Twins.
According to research, 25% of Healthcare Delivery Organizations will include formalized digital twin initiatives by 2025.
Supply Chain Management
Predictive analytics is also used in supply chain management. It helps healthcare organizations get a more in-depth view of the state of the market and its trends, provides an opportunity to cut healthcare costs and manage the supply chain budget, and helps to use the supply chain more effectively.
Related Readings:
- Main Benefits of AI in Healthcare: How AI Brings New Solutions to the Industry
- Leveraging Data Analytics: A Guide to Revolutionizing the Pharmaceutical Industry
- Step-by-Step Guide to Developing HIPAA-compliant Medical Apps
- What Technologies Are Driving the Health Protection System?
- Telemedicine App Development: Features and Key Phases
Predictive Analytics in Healthcare: Examples
Reducing Hospital Readmissions
The higher the rate of hospital readmissions, the more likely it is that the patients did not receive proper care at the hospital. It also means that there are some serious issues with treatment effectiveness and patient care quality. Since predictive analytics helps improve the quality of care and develop personalized treatment plans for all patients, it reduces the rate of hospital readmissions. Moreover, it helps discover and warn patients with higher readmission rates due to their conditions, and data gathered from their health records.
Research Into New Treatments
One of the most common examples of predictive analytics used in healthcare is treatment research. The algorithms can help discover new ways of dealing with specific diseases and treating patients with rare conditions. A person’s clinical history, genetic data, and other information are analyzed to create personalized and accurate treatment plans.
Health Insurance
The cost of health insurance for everyone differs based on age, gender, insurance case history, medical history, and more. Thus, these costs might not be calculated accurately, either on purpose or not. To eliminate fraud and miscalculations, healthcare organizations utilize predictive analytics algorithms.
A Four-Step Framework to Identify the Best Use Cases for Predictive Analytics in Healthcare
Implementing predictive analytics algorithms will differ from organization to organization based on their capabilities, needs, and requirements. Yet, there is a four-step framework that will help you navigate through the process and know what steps to take.
1. Project Intake and Prioritization
During this first step, you need to ask yourself some questions. What do you expect from your system in future? What issues do you need to resolve, and what processes to optimize? That’s when you meet with the data scientists, discuss your questions, the challenges your organization is dealing with, and make a list of these problems. For instance, you might discuss the type of available data, resources, and the business value the algorithms will have. You need to set priorities straight during this step or it will cause confusion later.
2. Project Kickoff
During this step, the data science team should decide what use cases need to be solved by predictive analytics, and they need to communicate with the clients about the organization’s data quality and accessibility. This is the final step before the model development, so all the details and nuances must be covered.
3. Model Development
Now the team has started building the predictive analytics model. It happens in an iterative way since the team discovers additional data and needs to adjust the processes accordingly. It is essential to not skip the same questions asked during the first step, but rather, cover all the data available, or it will affect the end product. It is also possible that the data science team will look at the existing data in a totally new way and reframe it.
4. Operationalizing the Predictive Model
Now it is critical to shift focus from looking at the data coming into the model, to the data coming out. Predictive analytics results help data science teams discuss and understand new insights gathered from the model and how to use them in practice.
Risks Associated with Predictive Analytics
It’s already evident that data analysis in healthcare will revolutionize how healthcare organizations work and offer patient care. However, with the numerous benefits, there also come risks that you should be aware of if you decide to implement these algorithms into your practice.
Gaining Doctors’ Acceptance
Working with predictive analytics tools is not easy; it adds a new responsibility to a practitioners’ workload, as they have to capture and process huge amounts of patient data. Thus, it can be challenging to combine it with patient care and appointments.
Moreover, if doctors have never dealt with such advanced software before, it can take a lot of time and effort for them to learn how to use it, and some can even be reluctant to learn, preferring a more traditional way of work.
To solve this issue, it is essential to involve doctors in the process of model creation, asking them what needs the algorithm should address. It will help practitioners learn during this process and ensure the models are tailored specifically for them.
Ethics and Moral Hazards
Some practitioners may have too much trust in predictive modeling in healthcare, letting it make all the decisions and sticking to them no matter what. However, you need to understand that these tools are still developing and improving, and their decisions are just suggestions for practitioners, not a guide they need to follow from A to Z. A doctor should always make the final decision and discuss everything with colleagues and patients. This is especially important when it comes to moral or ethical questions.
Algorithm Bias and Lack of Regulations
Currently, there are no strict regulations on developing predictive analytics algorithms, which can lead to algorithm bias. All the responsibility is on the vendor who develops these models, so it’s vital to choose the data analytics provider thoroughly, or else you can end up with an algorithm that does not meet regulations.
Conclusion
Healthcare industry practitioners need to be able to predict the future and understand how their current actions will influence what will happen next. Predictive analytics is the magic wand for all healthcare providers who want to improve the quality of patient care, optimize their operations, and save costs.
Even though these models are still only gaining popularity in the healthcare industry, the future is promising, and utilizing them will become a must in the near future. If you have been considering using predictive analytics in your healthcare organization and need a team of experienced and trustworthy developers, we’re right here: contact us.
