Predictive analytics + healthcare = more accurate results and informed decisions about patient care.
Why so?
More and more healthcare institutions are moving towards predictive analytics since that’s one of the most effective ways to get the right outcomes from historical data.
By analyzing large amounts of data, predictive analytics in healthcare can identify specific patterns and trends. That’s how it can:
- Help doctors and other medical professionals predict and prevent potential health problems before they become serious;
- Optimize staffing levels, reduce wait times, and improve patient flow;
- Identify patients who are at high risk of being readmitted after discharge;
- Optimize the supply chain in healthcare organizations;
Help healthcare providers to personalize treatment plans and improve patient outcomes;
- and many more!
This article combines information to understand the definition of predictive analytics, how it is used with the help of healthcare development services, and what are the benefits of predictive analytics in the healthcare field.
So, let’s dig deeper and check what is predictive analytics, review market expectations, define pros and cons, and discuss the particular use cases. All in one article, to see the whole picture!
So, What is Predictive Analytics in Healthcare?
Predictive analytics is a powerful tool used in healthcare to predict future outcomes based on historical data.
Predictive analytics involves using predictive algorithms and predictive models, machine learning techniques, and data mining to identify patterns and trends in data.
In healthcare, this data can include patient demographics, medical history, treatment plans, insurance claims, medical imaging, information from electronic health records, and more.
By analyzing large amounts of data, healthcare organizations can gain insights into patient health and identify potential health risks before they become more severe. This allows healthcare providers to intervene early and provide personalized care to improve patient outcomes.
Let me give you some examples, according to the Fortune Business Inside report:
- The health operation US-based company has designed predictive models using machine learning technology to identify COVID-19 patients who have a high risk of mortality. These predictions aid in streamlining patient management.
- A US-based smart inhaler company started utilizing inhalers that come with GPS-enabled trackers, so they can see and track asthma trends among the population. This data is then combined with data from the Centers for Disease Control and Prevention (CDC) to create treatment plans for individuals with asthma.
That’s only a few examples, but it clearly describes that predictive analytics can greatly improve healthcare operations and identify risks.
Here is a picture of the latest trends in the healthcare predictive analytics market size.
As was in the examples above, new advanced technologies with machine learning and GPS can greatly improve healthcare data analysis and increase the market size.
What Are the Benefits of Predictive Analytics in Healthcare?
Some of them we discussed previously, but let’s take a look at them in more detail:
Indicate the disease before the symptoms
Identify patterns, indicate the disease, and search for risk factors. That all is possible to do with the help of predictive analytics.
Predictive analytics allows early detection, meaning improved patient outcomes and improved quality of care.
Personalized treatment plan
That’s how we can move from general treatment to more personalized.
Unique patient characteristics, medical history, risk factors, and other information can greatly improve treatment effectiveness and reduce adverse reactions.
Also, it improves the determination of the correct diagnosis, as it fits the patient’s unique situation.
Understand patient demand and optimize resources
Predictive analytics can indicate a surge in patient demand for a particular service, or identify bottlenecks and areas where patients are experiencing delays or long wait times.
Identify trends and patterns in population health management
By analyzing large datasets of population health data from medical records, health surveys, and disease registries, predictive analytics can identify patterns and trends and design interventions to improve patient health outcomes.
What About Examples of Predictive Analytics in Healthcare?
Predictive analytics helps gain actionable insights about healthcare data in different ways. Healthcare organizations can transform data into great insights, and streamline their processes and patient care at the same time.
Let’s check 7 use cases for predictive analytics in healthcare:
#1 - Predictive modeling for chronic diseases
Predictive analytics technology can identify patients with a high risk of developing chronic diseases, helping prevent the disease and improve patient outcomes.
The process begins with data collection (such as clinical history, demographic information, test results, etc.), and selection features that are associated with chronic conditions.
For example, blood glucose levels are a common feature for predicting the risk of developing diabetes.
Then, the model is being evaluated and deployed to identify patients with a high risk score of developing a chronic disease.
For example, Penn Medicine & Intel with their data science platform managed to predict sepsis and heart disease cases. The platform was able to identify nearly 85 percent of sepsis cases as much as 30 hours before the onset of septic shock (it takes two hours with traditional methods).
As for heart diseases, they managed to identify 20-30% of heart failure patients who had not been properly identified.
#2 - Predicting hospital readmissions
Hospital readmissions occur when patients are readmitted to a hospital within a certain period after their initial discharge.
By analyzing a variety of factors and healthcare data (patient age, current health status, medical history etc), advanced analytics can help predict readmissions, provide follow-up care, and adjust treatment plans.
With the help of predictive analytics tools, it is easy to identify high-risk patients or patterns which can help reduce readmissions and improve patient care.
#3 - Healthcare utilization forecasting
Predictive analytics can help to predict the future utilization of healthcare services by patients using historical data and statistical methods.
Let’s take a look into example: a healthcare organization needs to forecast a number of patients that will need hospitalization in the next month. Here comes the predictive analytics! Using the data about the patients, the models can predict the likelihood of hospitalization for the patients.
Using this information, the healthcare organization can predict how many staff will be needed in the next month, how many medical supplies should be prepared, medications, and so on. With correct data it is much easier to plan a budget and provide efficient patient care.
Another example is how predictive analytics and big data helped Wake Forest Baptist Health in North Carolina to anticipate peak utilization times and adjust its scheduling practices accordingly.
#4 - Fraud detection
Healthcare organizations can use predictive analytics to flag potential fraud cases.
Let’s take a look at the example of an insurance company. They can use predictive analytics to detect fraudulent claims by analyzing claims data and identifying patterns that may indicate fraudulent activity.
#5 - Patient engagement
With predictive analytics software it becomes possible to increase patient engagement.
For instance, using predictive analytics healthcare organizations can identify patients with chronic diseases that are most likely to become disengaged with their treatment. That can help improve chronic disease management strategy.
#6 - Clinical trials optimization
Predictive analytics can be used to identify patients that can benefit from the particular trial of particular treatment.
Let’s imagine: the clinic needs to prepare for its new trial of medicines, and the first step is to select patients. So, the predictive analytics model can help to identify patients who have some patterns that make them more likely to respond to a particular treatment.
This can help trial designers recruit the right patients, design more effective trials and make right clinical decisions.
#7 - Prevent mental health illnesses
Here predictive analytics can help to prevent mental illness diseases by collecting data of patients who are at high-risk of developing such diseases. For example:
- Depression / bipolar disorder. A predictive model can identify patients who are at risk of developing depression based on their medical history. For example, the model could identify patients who have a family history of such disease, or who have recently experienced trauma.
- Suicide risk. Healthcare providers can develop predictive models that identify patients who are at risk of suicidal ideation or behavior, such as those who have a history of suicide attempts or those who have been diagnosed with a mental health condition.
For example, the study conducted by KP and the Mental Health Research Network states that the combination of EHR data and a standard depression questionnaire helped to identify high-risk patients with the suicide attempts.
Overall: healthcare cost reduction, prevention of diseases, staff optimization…and it doesn’t end there.
There are more and more use cases regarding the use of predictive analytics in healthcare using big data, that help improve patient care and optimize workflow in the healthcare industry.
But, Are There Some Challenges?
To see the whole picture, we need to describe both pros and cons of predictive analytics in healthcare.
Despite the fact that predictive analytics can greatly enhance the work of healthcare organizations, there are some challenges that should be taken into account.
For example, these are:
Data quality
Healthcare data can come from different sources. As a result: it may face a lack of uniformity in data formats and definitions. Also, the data may be inaccurate or inconsistent due to errors in coding on transmission.
That’s why it is important to make sure the data you rely on is accurate since poor data quality can lead to wrong results and predictions.
Security
Healthcare data should be protected to ensure the protection of patient data. As predictive analytics require access to vast amounts of data, it may raise some concerns about data security.
It’s vital to stick to privacy regulations (such as HIPAA in the US), and protect data from thefts or misuses. If healthcare organizations want to use predictive analytics in the healthcare field, they should implement security measures to securely store the data of patients.
For example, our team recently enhanced the security for the dental system, which is now reliable for preserving and managing the patients’ sensitive data.
Wrap Up
Predictive analytics is not a magic bullet, and there are still challenges to overcome. However, its potential benefits are too significant to ignore.
Understanding patient demands, optimizing treatments, recognizing diseases before they appear, improving workflow, and many other essential benefits that can enhance patient care.
We know how to carry software development with care and keep the patient data protected at all times.
Do not hesitate to reach out for custom healthcare solutions regarding your needs.
Q&A: Predictive Analytics in Healthcare
We know you’re here to find answers. We’ve got you covered.
It’s a method of using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
In other words, it is the practice of using data to make predictions about what might happen in the future.
It is mainly used to identify patterns and insights in healthcare data. This way, healthcare organizations can improve patient care and patient satisfaction, and improve supply chain management.
For example, it becomes easy to find high risk patients for some particular diseases, understand patient demand and optimize resources in the hospitals/clinics/organizations. And many more!
For example:
- Chronic diseases prediction
- Hospital readmissions prediction
- Healthcare utilization forecasting
- Fraud detection
- Patient engagement increase
- Clinical trials optimization
- Mental health illnesses prevention
- ML & artificial intelligence algorithms
- Data mining
- Predictive modeling
- NLP
