Healthcare industry is experiencing a significant leap forward due to the growing adoption of big data and machine learning algorithms. The tools are becoming more powerful, and the results are becoming more informative. One of the most useful machine learning tools is predictive analytics algorithms.

The steady supply of information feeds the healthcare system. The patient shares his or her feelings with the doctor; the doctor gets more data from the machines and equipment; the researchers receive the compiled data from various hospitals, and in turn, can work on creating a treatment that would help the initial patient (and all the others). As such, this is a perfect playground for technology like predictive analytics.

In this article, we will talk about how predictive analytics can bring healthcare to a new level.

What is Predictive Analytics?

The term “Predictive analytics” describes a methodology of getting an insight into the possible future events based on the available data and statistical analysis, answering the question "What might happen?"

The purpose of predictive algorithms in healthcare is:

To find the correlations in the patient’s data;

To find associations of the symptoms;

To find familiar antecedents of the symptoms;

To explore the impact of different factors (genome structure, clinical variable, et al.) on the course of treatment;

To examine the possible influence of past and current diseases.

How Predictive Analytics helps in Healthcare

The researchers, as well as doctors, can benefit from predictive analytics to see what can happen. Here is a simplified process:

Descriptive analytics algorithms are the first to the scene. They take the incoming data from electronic health record and present it in an understandable format. The information includes clinical documentation, claims data, patient surveys, lab tests and so on - everything that already happened.

The processed information is sorted into various datasets by various criteria (for example, drug reaction dataset and genomics dataset.)

Predictive analytics algorithms start their work. Depending on the goal of the analysis, a predictive algorithm can produce assumptions based either on available data directly from a given patient or general medical data from the public health datasets.

The assumptions are usually grouped by their probability - from the most likely to the least likely to happen. It's important to remember that predictions are, in fact, nothing more than assumptions and probabilities. The more data you have, the more accurate and detailed result you will get (like a trend line or risk score.)

All these insights give a foundation for prescriptive analytics, which also calculates probabilities. The difference is that predictive analytics answers the question "What can happen?" and prescriptive analytics answer "What can we do about it?"

Healthcare Predictive Analytics Examples

Precise Treatment & Personalized Healthcare - Make Better Decisions

Predictive analytics' most significant contribution to healthcare is personalized and accurate treatment options.

Getting the treatment strategy right requires going through a lot of data and taking a lot of factors into consideration. In addition to that, the process is time-consuming, which can be detrimental to the treatment as the patient’s condition may worsen in-between the tests and results.

The predictive analytics algorithms can:

calculate what can happen,

say what to expect in certain turns of events,

tell how to map out the treatment of the disease.

Even without implementing the prescriptive algorithms, the doctors can use the results from predictive analytics to treat the patient right (especially in cases of rare diseases that the docs did not have enough experience with before.)

Analytics streamline the process - all you technically need is input data and a clear understanding of what are you looking for.

Efficient Treatment Testing - Reduce Risks

Predictive analytics aren't directly involved in the treatment testing process, but it is used to cut out the apparent dead ends and streamline the other tasks that will contribute to the treatment. Considering the amount of information to sift through, any functions that can be done automatically simplify the trial runs and reduce potential risks.

Structured patient data is a treasure trove of information. Based on this information, the predictive algorithm can assess how various types of treatments might affect the organism.

The results of the analysis are processed with the assistance of public health datasets and then interpreted as risk factors for the specific scenarios. The criteria are usually symptom-based, time-based and treatment type-based.

Risk Factor intelligence is a set of filters, which is utilized during treatment testing and scenario simulation.

When the time comes to select the proper treatment, the elements that don't fit the Risk Factor filters are eliminated.

Disease Control and Management - Avoid Sepsis

Sepsis is when the body starts to attack its own organs and tissues in attempts to fight off the bacteria or other causes. It is one of the most dangerous threats during any course of treatment. According to the recent Sepsis Alliance study, harmful bacterias and toxins in the tissues kill one person every two minutes.

In the case of a septic shock, doctors need to act quickly and understand the patient's needs and reactions. Predictive algorithms can help to avoid fatal outcomes.

Real-time analytics provide doctors with a big picture of what is going on with the patient.

The incoming information is analyzed to detect any kind of anomalies.

In case of any suspicious symptoms, early warning system informs the doctors and they can prevent the condition from harming the patient.

Such applications as DNA Nanopore sequencers can detect pathogens and toxins in the DNA samples and calculate possible courses of action that avoid the mere possibility of sepsis.

Our developers from the APP Solutions have worked alongside medical researchers and Google team on a proof of concept for genomics researchers and bioinformatics developers to fight such sepsis danger, using the breadth and depth of Google Cloud. Read the case study on Google Blog.

Workflow Optimization - Predict Patient Utilization Patterns

Besides treating patients, predictive analytics can also help to manage the hospital and other medical institutions' workflows.

Managing healthcare institution, especially on the day-to-day operation level, is a significant undertaking. The predictive algorithm can streamline some of its elements and boost the services' efficiency by avoiding operational downtime and stalling.

Predictive algorithms can be used to analyze Patient Utilization Patterns. For instance, it can detect the peak highs and lows as well as the weak points of the workflow. Predictive algorithms can also provice a big picture of the working process and its effectiveness.

On the other hand, predictions can be used to optimize the workflow of various departments:

Build an effective schedule that will avoid extreme workload and avoid needless downtime;

Manage personnel allocation;

Predict supply chain demands and refill/maintenance schedule.

All this can help to flatten the bell curve and even out the workflow of each department (unless we're talking about ER, where the flow is pretty much unpredictable.)

Supply Chain Management

The supply chain management is an important part of the healthcare workflow. Predictive algorithms in hospital analytics can solve a few issues here:

provide a more in-depth view into the state of the market and its possibilities;

give hospital administrative managers an opportunity to cut costs and use supply chain budget more effectively;

can help to better utilize the supply chain according to the demands of the healthcare operation.

In other words, Predictive Analytics put things into perspective. A combination of the current trends and history can show what the optimal decision can be in the current situation.

It is a variation of e-commerce market basket analysis with additional inventory management tools. Predictions are based on associations between the items and their consumption and the results can streamline the workflow. As a result, you get a much more cost-effective operation and much less headache.

In conclusion

Healthcare industry is bound by the need for making the right decisions and the key to this is understanding what the future holds.

Predictive analytics with its handy sets of predictions and estimates provide a competitive advantage and lets you think to through the course of action a couple of steps ahead. Predictive analytics help to act instead of react.