Healthcare workers have to process a massive amount of data from multiple sources in every case they work on. This includes patients’ electronic healthcare records, medical images, screening results, and various administrative data. Furthermore, based on this information they often have to make informed decisions as fast as possible.
The use of predictive analytics in healthcare can help the industry in a variety of ways. One of the best benefits of predictive analytics in healthcare is the effectiveness of data processing and analysis. However, there are also other use cases for predictive analytics in healthcare that can transform the industry.
According to the survey conducted by the Society of Actuaries, 89% of healthcare providers either already use the predictive modeling methods or are planning to implement them in the future. So, let’s take a closer look at the importance of predictive analytics in healthcare and some of the most innovative use cases for it right now.
Before we describe its benefits, let’s first answer the question: what is predictive analytics in healthcare? Predictive analytics is a branch of advanced analytics aimed at making a prognosis of future events based on the available data. These forecasts can then be used to make critical decisions, identify conditions early on, and avoid the risk of complications.
Using the data from multiple sources, with the help of advanced predictive analytics methods, the healthcare industry will be able to:
As a part of the use of predictive analytics in healthcare, data mining can be described as a set of methods that help to gather relevant medical data into databases, transform it and pre-process for later modeling, analysis, and prediction formulation.
An important thing to mention is that, according to HIPAA, any data related to the patient needs to be handled with extreme care and should be protected. That’s why data mining needs to be used with an established EHR system that provides sufficient levels of privacy and security of information.
Data modeling is a predictive analytics tool that employs statistical methods to analyze historical data. As a result, it helps to create a detailed model of how the same data evolves over time, allowing to predict future events.
Being a statistical-based tool, data mining outlines the possibilities for potential future outcomes. To achieve the best fidelity, the model has to be regularly updated and recalculated. With help from other tools, this process can be streamlined into a real-time modeling tool.
Artificial Intelligence is a field of computer science that can be effectively described as a combination of complex machine learning algorithms with data processing methods. At its core, its purpose is to replicate intelligent human behavior.
As one of the examples of predictive analytics in healthcare, AI is used to manage patient data and produce calculated predictions based on it. The potential impact of AI on the industry cannot be overstated, as it might eliminate human error from it as a whole.
Machine learning is a part of the AI science field, with specific algorithms being developed to achieve autonomous learning by machines. The algorithms developed progress over time, meaning the more data sets they analyze, the better they become.
This makes machine learning a perfect tool for predictive analytics in the healthcare industry. With the large data sets available, machine learning algorithms gain experience faster and provide better predictions based on this data.
Deep learning is a subcategory of machine learning that deals with artificial neural networks. They are built to mimic the biological neural networks of the human brain. However, with modern multi-layered processing capabilities, artificial neural networks exceed the powers of the human mind and can be effectively used to make precise predictions.
One of the major use cases for predictive analytics in healthcare is its use in medical imaging analysis. It helps detect minor deviations in various medical images, from MRI screenings to microscopy images, thus helping to diagnose an issue at its early stages.
Before we start reviewing the predictive analytics use cases in healthcare, let’s determine the main fields of use for predictive analytics:
Now, let’s get back to the predictive analytics healthcare examples:
ICU is one of the healthcare branches that requires quick decision-making and constant attention to the patient’s condition. With ICU units often overflowing with critical patients (especially during the height of the COVID-19 pandemic) and the lack of intensive care specialists, the quality of care often drops.
As the vital signs of each patient are monitored constantly, this data can be used in predictive analytics. Predictive algorithms can be effectively used to determine the patients with a high risk of condition deterioration in the next 60 minutes. This allows the response team to act early on to prevent the crisis or minimize its effects.
Another effective use of predictive analytics in the ICU concerns its application in remote critical care. Tele-ICUs were only made possible by the biosensors that collect patient data and predictive analytics that analyze said data and help the teams to effectively respond to the worsening of a patient’s condition.
The use of predictive analytics reduces the response time, allows to provide more effective care, increases the capacity of the unit, and, what’s also needs mentioning, provides a way to ensure the safety of healthcare workers.
Six in ten US adults suffer from incurable or ongoing chronic diseases. Some of them are at constant risk of flare-ups and complications. To correctly determine the possibility of such a complication at any given time requires a continuous analysis of data on the patient’s condition.
Here’s where predictive analytics in healthcare using big data comes into play. By analyzing lab results, patient-generated data on their lifestyle, and their biometric data, the system can assign the person a specific risk score that signifies the possibility of a complication in the nearest future. It is also more likely to detect the early signs of deterioration and inform a physician about it.
Besides the chronically ill, there are other at-risk groups of patients that can benefit from predictive healthcare. This especially concerns older adults and patients recently discharged from the hospital after invasive manipulations.
Through the benefits of telecare and predictive analytics, these patients can avoid adverse events or get help in a crisis event as fast as possible. Through historical data processing, the software can even predict a fall event in the case of an elderly patient, thus rescuing them from a potential trauma and hospital readmission.
Mental health issues deserve just as much attention as other chronic conditions. Suicide, self-harm, and other violent tendencies might seem to occur at random and even without any provocation, yet predictive algorithms can detect specific patterns.
Professional help provided at the right time can help to prevent the mental health crisis even in the most unstable patients. So, predictive analytics can be used not only to improve patients’ quality of life but also to save their lives.
While the Hospital Readmission Reduction Program has implemented measures to limit the unplanned 30-day patient readmission, it still happens all across the country. In 2018, an average adult readmission rate reached 14%, with 20% of them referring to one of four conditions – septicemia, heart failure, diabetes, and COPD.
With the help of predictive analytics, patients with a high risk of readmission can be discovered, warned, and provided better preventive care. For predictive analytics in healthcare real examples, take a look at this Texas hospital that has managed to cut its readmission rates by 5%.
Genetic irregularities are present in at least 10% of adults. Catching some of them at the early stages can help manage them and avoid complications later in life. However, analyzing genetic information is a complicated process, as the human genome is a complex system.
Predictive analytics can be used to analyze and compare the person’s genetic data with a database of possible defects and illnesses connected to them. Furthermore, it can be used as early as the neonatal stages, warning the parents of the condition their child might have.
Besides patient care, predictive analytics can be effectively used in the research branch of healthcare. The algorithms can accurately predict the person’s response to the medication or treatment plan based on their data (genetic information, clinical history, etc.) and the responses of the earlier studied patient groups.
This can effectively cut the need inpatient groups and overall streamline the research process. Besides, it allows one to narrow the focus to a single patient and develop a precise solution for their particular case.
Establishing a trusting personal relationship is as important in healthcare as choosing the right treatment plan. It can help incentivize patients to follow their treatment, return for the second examination, and overall form healthier habits, thus avoiding complications and more severe issues.
Using predictive analytics to evaluate the person’s behavioral patterns allows the system to determine the best approach to them. It can also select the most suitable professional that has the best chance at establishing a personal connection.
A supply chain for any hospital is a complicated system, as the supplies needed to depend on the patient load and the specifics of each patient case.
Using predictive analysis can help the hospital to make an utterly data-driven decision for future purchasing. This makes it easier to make purchasing more efficient and cost-effective by reducing unnecessary buys and waste of equipment.
By identifying the patterns inpatient care, a hospital, with the help of predictive modeling, can determine how many people should be in the hospital at any given moment. This can also help to correctly identify the need in certain specialists and allow them to have to time off at any other moment.
Optimizing the staff occupation can greatly reduce the operational costs, thus helping to reorganize the hospital budget and provide better patient care.
Physics is merciless – no machine can work forever due to friction and resistance. That’s why, for some time now, predictive analytics have been used in various industries to predict the potential wear-out and failures of certain components.
Healthcare tools can benefit from such analysis as well. For example, by analyzing the data from sensors in an MRI machine, the system might predict its failure before it occurs. This allows for an issue to be fixed with a partial repair or a separate detail replacement.
The National Healthcare Anti-Fraud Association estimates that the financial losses due to healthcare fraud amount from 3% up to 10% of funds spent on healthcare, which adds to a sum of up to $300 billion. Insurance companies have spent a lot of time and effort to reduce these sums, but only a few of them employ predictive analytics to tackle the problem on a large scale.
With enough data on fraudulent insurance claims and insurance fund mismanagement, tailored machine learning algorithms can be developed and trained to determine whether there is any malicious intent behind the case early on. Thus, they will help to reduce the funds lost and dissuade the scammers from future attempts.
One of the projects we’ve been working on at NIX United is a heart failure prediction system using data mining and an artificial neural network. The challenge of the project lies in the improvement of the existing system based on a complex mathematical model.
As a part of our solution, we’ve employed the CNN deep learning technique to analyze the visual imagery. We’re also planning to use MRI 4D data to form a better prediction model of the temporal evolution of the blood flow patterns.
Big data and predictive analytics in healthcare have an incredible potential to change the industry for the better. Not only can it help to improve the quality and effectiveness of the treatment, but also to reduce its costs, achieve better patient engagement, and even develop new methods and medication.
If you want to employ the predictive modeling methods, but don’t have enough knowledge or experience in the area, contact us at NIX United. We’ll share our expertise and help you develop unique solutions to your challenges.
Predictive modeling methods in healthcare are used to detect the early signs of patient deterioration and risk-scoring for chronic patients.
Predictive analytics in healthcare can be used for diagnosis, prognosis, treatment course design, clinical decision support, remote monitoring, adverse events reduction, care quality improvement, and care cost reduction.
Some opportunities for predictive analytics include genetic-based predictive modeling, improvement of patient engagement, and staff management.
With more than 3 years of practical experience, Natalia helps CIOs of Medical companies, CTOs and Founders of agile Healthtech startups build technology solutions that make medical practice better and leverage digital transformation to meet patients expectations.
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