As a data-rich sector, healthcare can potentially gain a lot from implementing analytics solutions. So far, we have seen many different examples of how healthcare institutions and providers are using novel technologies to make better decisions, accelerate their operations, and ultimately deliver a better experience to patients.
But what about predictive analytics? In this article, we take a closer look at the advanced predictive analytics tools used in healthcare today.
Read on to explore the most important use cases and challenges healthcare organizations experience when implementing predictive analytics solutions.
What is predictive analytics?
Predictive analytics is a type of technology that combines machine learning and business intelligence with historical as well as real-time data to make projections about future events.
The success of predictive analytics and healthcare lies in identifying the most promising use cases, capturing quality data, and applying the best model to uncover meaningful insights that can improve various areas of healthcare.
Healthcare providers are using such tools to develop decisions and processes that improve patient outcomes, reduce spending, and increase operational efficiency. The potential benefits of predictive analytics include everyone: hospitals and patients but also insurance providers and product manufacturers.
Predictive analytics in healthcare – use cases
1. Improving operational efficiency
Healthcare organizations are currently investing in Business Intelligence and analytics tools to improve their operations and deliver more value. For example, real-time reporting helps to get timely insights into various operations and react accordingly by assigning more resources into areas that require it.
Healthcare providers are also using such tools to analyze both historical and real-time patient data to better understand the flow and analyze staff performance in real time. Moreover, they can prepare for situations when the surge in incoming patients might cause shortages. Equipped with such a solution, hospitals can react to such shortages in real time by adding extra beds and deploying more staff.
Healthcare organizations can also achieve an optimal patient to staff ratio with predictive analytics. Such solutions help hospitals and healthcare institutions to plan how many staff members should be located in a given facility by using historical data, overflow data from nearby facilities, demographic data, and seasonal sickness patterns.
2. Personal medicine
In the field of personal medicine, predictive analytics will allow doctors to use prognostic analytics to find cures for particular diseases. That is true even for diseases that are not known at the time. Predictive analytics allows hospitals to introduce more accurate modeling for mortality rates for individuals. But this is just the tip of the iceberg.
We have known for a long time that some types of medicines work better for specific groups of people but not others. That’s because human bodies are complex, and we still don’t know many things about them. It’s impossible for a single health practitioner to manually analyze all of the detailed information.
That’s where predictive analytics tools can help. They can discover correlations and hidden patterns when examining large data sets and then create predictions. Such tools can be applied efficiently at an individual level and allow caregivers to come up with the best treatment options.
3. Population health and risk scoring
Prediction and prevention go hand in hand for a reason. This is especially true in the field of population health management.
Healthcare organizations can use predictive analytics to identify individuals with a higher risk of developing chronic conditions early in the disease progression. That way, patients can avoid developing long-term health problems.
This can be achieved by creating risk scores with the help of big data and predictive analytics. Such scores are based on patient-generated health data, biometric data, lab testing, and many others.
Healthcare companies can use predictive modeling to proactively identify patients at the highest risk, who would benefit most from intervention. This improves risk management for providers and helps deliver better care to patients.
4. Outbreak prediction
Machine learning and AI tools are now used by governments to understand the spread of contagious diseases throughout societies. They’re essential for implementing the best measure to curb the outbreaks.
Predictive models can use historical as well as real-time data to help authorities understand the scale of the outbreak and its possible development within different regions, cities, or even continents.
An example of such a tool is BlueDot, which identified the coronavirus outbreak before the Chinese government issued an official warning about it to WHO and the world.
5. Controlling patient deterioration
While at the hospital, patients face various threats such as the acquisition of infection, development of sepsis, or sudden downturn due to the existing clinical conditions.
Doctors equipped with data analytics tools can predict the possible deterioration on the basis of the changes in the patient’s vitals. Most importantly, they can do that before the symptoms clearly manifest themselves.
Machine learning is a technology that has proven to be effective in predicting clinical events at the hospital — for example, the development of an acute kidney injury or sepsis. At the University of Pennsylvania, doctors leverage a predictive analytics tool that helps to identify patients who might fall victim to severe sepsis or septic shock 12 hours before the onset of the condition.
6. Supply chain management
This area isn’t directly related to healthcare service delivery, but it’s an essential part of it. The supply chain is one of the most expensive areas of healthcare. But it also represents one of the most exciting opportunities for organizations to reduce their spendings and improve efficiency.
Hospital executives who want to reduce variation and gain more actionable insights into their ordering patterns and supply utilization are now investing in predictive analytics. Using such tools to monitor the supply chain allows making data-driven, proactive decisions about spending. This could save hospitals almost $10 million per year, according to a survey.
Both predictive and descriptive analytics can support decision-making for price negotiation, optimizing the ordering process, and reducing the variation in supplies.
7. Potential in precision medicine
Healthcare organizations have access to millions of records they can use to uncover patients who had a similar response to a specific medication.
Only machine learning-based predictive analytics solutions can uncover such insights because the data sets in question are massive. They include data such as age, gender, location, and all the relevant healthcare data.
Predictive analytics can lead to improved precision medicine outcomes and make it easier for doctors to customize medical treatments, products, and practices to individual patients.
8. Cost reductions from eliminating waste and fraud
Fraud, waste, and abuse cost the healthcare system in the United States more than $234 billion each year.
By analyzing billing records and patient data, organizations will be able to identify treatment or billing anomalies that include duplicate claims, medically unnecessary treatments, or doctors prescribing unusually high rates of tests.
By identifying such issues, providers will be able to eliminate waste, fraud, and abuse in their systems to reduce the losses and invest the money gained into mission-critical areas.
Challenges of implementing predictive analytics solutions
Many organizations want to embrace the newest technologies, cloud infrastructure, and data science solutions that implement predictive analytics. But to do it successfully, they need to be aware of several key challenges.
1. Integrating separate data sources
Success in predictive analytics is based on the quality and accessibility of data. To implement successful use cases, organizations need to integrate data quickly and reliably from many disparate sources (both internal and external). Then they need to find a way to store and process these massive volumes of data before they’re fed into their predictive analytics solutions.
Here’s an example. One of the main sources of healthcare data in the United States is Electronic Health Records. This resource poses many integration challenges. You will find many different vendors on the market and an average hospital using as many as 16 different platforms.
Moreover, medical and health records are kept separate from purchasing, HR, and finance. Such data siloization makes it very difficult to gain a comprehensive view of patient costs, care, and treatment.
2. Adoption of cloud technologies
Even if cloud adoption is growing within the healthcare industry, privacy and security concerns are still significant blockers. According to Gartner, CIOs working at healthcare organizations often see the cloud as an extension of their internal infrastructure.
This means that healthcare data environments are often hybrid. Predictive analytics tools will need to be designed to use data from both on-premises and cloud infrastructures easily and securely.
3. Privacy and security
Even if major cloud providers are diligent about their security measures, healthcare is a highly regulated industry. Organizations need to be extra careful about patient privacy. Their solutions need to secure data at all stages of their lifecycle.
This is particularly relevant for hybrid environments. Healthcare organizations need to store data behind a firewall and keep a close track of data, which is in motion between the on-premises and cloud infrastructures.
4. The fast pace of technology changes
We all know that technology is always changing. With healthcare data up in the cloud, organizations need to be careful about updating their technology stack. They also should become more flexible about adopting new technologies, new data sources, and making organizational changes. A scalable technology stack is a must-have for healthcare organizations that want to be adaptable.
Predictive analytics has a bright future in healthcare. An increasing number of healthcare organizations implement machine learning and AI-based tools to predict future trends and analyze their data better.
In the near future, healthcare providers who embrace data and think carefully about their investments in technology will be able to provide the best care for their patients and optimize their operational costs.
At Codete, we have ample experience in working with healthcare organizations to help them improve their infrastructures and build new products that deliver better services.
If you’d like to get more insights about how healthcare organizations are using technology today, keep a close eye on our blog.
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