Predictive Analytics in Healthcare: 5 Future Use Cases

By definition, predictive analytics is the ability to use historical data to forecast future events. Today, this is a game-changer in many industries, including insurance, marketing, and manufacturing.

Healthcare is another field that hugely benefits from adopting predictive analytics algorithms. During a hospital stay, patients share a lot of information with physicians, and hospitals can leverage this data to improve standards of care, the precision of diagnosis, and increase recovery speeds.

In this post, we will share a list of promising predictive analytics solutions in healthcare, outline the advantages of implementing these algorithms, and discuss the risks involved in the process.

One of the goals of predictive analytics adoption in healthcare is to facilitate decision-making for physicians and make sure they don't shoulder full responsibility for the outcomes. Offering healthcare professionals a source of reliable data eliminates the guesswork when it comes to making medical calls, thus decreasing the possibility of human error.

The range of examples of predictive analytics in healthcare is vast: from monitoring patients in ICUs to express analysis of medical images or improving the quality of telemedicine. Here are the top five high-value use cases for predictive analytics in healthcare:

Today (Dec, 2020), ICU wards all over America are at full capacity. Working in such a strenuous environment creates more challenges for critical care physicians and calls for rapid and error-free decision-making.

Without the help of data science, generating patient deterioration algorithms can be challenging — however, predictive algorithms help physicians identify which patients are at risk of deterioration in the next 60 minutes. By using biosensors, hospital managers can help physicians detect small changes in oxygen saturation and other stats, preventing further deterioration of the patient's condition.

Coupled with tele-ICUs, the use of biosensors and predictive analytics helps physicians fight the COVID-19 pandemic and improve the quality of critical care.

Since the power of predictive analytics is widely praised online, a lingering question inevitably arises: "Could artificial intelligence have predicted the COVID outbreak?". The answer is: it actually did.

On December 30, 2019, a Canadian company BlueDot that builds AI projects sent an alert to governmental institutions regarding a surge of atypical pneumonia cases in the zone of Wuhan. Nine days later, the WHO released an official statement regarding the emergence of the novel Coronavirus.

How did BlueDot build a system for early outbreak recognition? The company used publicly available data to pinpoint patterns and derive conclusions based on their findings. Here are the triggers that signaled the spread of the pandemic:

• Number of international and domestic flights worldwide
  
• Areas with increased consumption of health supplies
  
• Movements of emergency physicians and healthcare practitioners
  
• Change of social media sentiments
  
• Retail patterns
  

Other than anticipating the outbreak of COVID, predictive analytics algorithms have been credited using patients' personal and clinical data to forecast surges of flu cases.

Preventing a surge of COVID cases by identifying the groups at risk for contracting the disease and educating them on self-care is another way to slowly lift the burden of the pandemic.

Predictive analytics systems can determine who, among urban and rural residents, is at a greater risk for contracting COVID-19 based on the following points:

• Finding correlations between the state of the environment (air pollution rates, water quality) and the rate of COVID-19 case growth.
  
• Determining which populations are affected by factors that can increase the rate of hospital admissions.
  
• Identifying groups with pre-existing chronic conditions and reducing the risk of COVID-19 exposure.

Armed with data-driven insights on COVID-19 risk groups, physicians and public officials can focus on preventative care delivery that aims to keep patients healthy, as opposed to dealing with the consequences of high-risk groups contracting the disease.

The range of use cases of predictive analytics in healthcare goes way beyond clinical decision-making. For instance, one of the ways that technology optimizes hospital management is failure prediction machine learning solutions, and allowing personnel to replace or repair the machinery before it breaks down and causes downtime.

Hospital managers can monitor the performance of MRI scanners, oxygen ventilators, and CT scanners by using sensors. In addition, technicians can oversee equipment stats in real-time from any location by creating a "digital twin" for all scanners.

These measures help pinpoint the early signs of equipment deterioration and replace faulty machines before they impact the institution's productivity.

Genetics and neonatal care are the fields that are largely impacted by predictive analytics. Genetic testing, for one, relies on forecast algorithms to predict the probability of a congenital disease manifesting in a fetus.

Offering explicit data regarding the development process of a baby gives parents the opportunity to prevent the deterioration of their future child's health, and allows them to make informed decisions regarding pregnancy termination.

With the rate at which predictive analytics and data science are growing, we are nearing a future in which all children will have "genetic reports" that determine the likelihood of being diagnosed clearly and precisely with the most popular diseases.

In the future, parents will have a clear idea of whether their child is in a risk group for heart conditions, cancers, and other pathologies.

The applications of predictive analytics are highly helpful to physicians and hospital managers. When it comes to patients, what care improvements can they anticipate once predictive analytics models become commonplace in healthcare?

Here's a rundown of the ways in which predictive analytics impacts patient care:

Healthcare is a field where errors have an extremely high impact. The medical community is often conservative about adopting emerging technologies — and rightfully so. Before predictive healthcare analytics becomes commonplace in healthcare, physicians, hospital managers, and legal professionals need to collectively discuss ethical and responsibility concerns.

At the moment, these are the most pressing challenges for integrating big data in healthcare.

On clinical decision-making. Numerous studies suggest that, when provided with a "safety net," people tend to be less diligent in performing cognitive tasks. For healthcare, introducing predictive analytics could be the starting point that encourages physicians to be more lax in decision-making and enable them to make riskier choices. Other than that, at the moment, the point at which a physician can delegate decision-making to a computer isn't well-defined. Finally, there's the question of who takes responsibility for a failed medical decision — a data scientist, a physician, or both.

Human connections have a huge part to play in a safe recovery — which is why it's impossible to assume physicians can be removed from patient care and replaced by artificial intelligence. This is why healthcare professionals can rely on predictive analytics and machine learning software only to a particular extent, carrying out most tasks manually for the sake of their patients' well-being.

In most countries, there aren't official documents regulating the use of predictive analytics. To an extent, these technologies are self-regulated by physician associations; however, in order to protect both patients and caretakers legally, clear regulations on data gathering, processing, and clinical decision-making are vital.

A growing awareness of the impact of personal data has been one of the major trends among healthcare customers and consumers. Governments and medical professionals need to find a way to keep large sets of data undisclosed and ensure that the means for gathering information are compliant with GDPR and other active legislations. It's important to understand how to get data for machine learning projects without violating privacy rules.

While machines are partially responsible for clinical decision-making, the underlying algorithms are developed by humans. Due to cultural influences, we are not immune to biases — conscious or unconscious. This is why relying on machine-made decisions as 100% objective isn't an intelligent strategy. Instead, physicians and data scientists need to collaborate in order to pinpoint and bypass algorithm bias. Analyzing data sets thoroughly, for one, helps reduce their impact.

Once the medical and tech community join forces and find ways to settle regulatory and moral concerns, they will have access to a wide range of opportunities, and be capable of transforming and optimizing the industry. For example Fayrix digital healthcare platform connects healthcare workers with those who need their guidance within one online health platform and helps to monitor, analyse and act in time on received patient's data. It can be a stand-alone solution or implemented on existing digital health platforms.

Adopting predictive analytics is a considerable investment for healthcare facility managers; however, it is also high-yielding, as it:

• Eliminates or reduces the downtime associated with equipment malfunctions;
  
• Saves resources (electricity, water, etc.) by analyzing consumption data and optimizing usage;
  
• Optimizes supply chain costs by analyzing supply order patterns (on its own, supply chain is responsible for 30% of hospital spending); and,
  
• Reduces the number of patient no-shows.
  

Predictive analytics in healthcare is no exception when it comes to recognizing predictive analytics as the cornerstone of business intelligence. There are plenty of ways to optimize and improve internal processes using intelligent forecasting models, including:

• Evaluating staff performance in real time;
  
• Predicting surges in patient numbers and hiring more staff according to data-driven insights; and,
  
• Assessing the competencies of newly onboarded healthcare professionals and managing physician training.
  

Predictive analytics is a game-changer for pharma companies, where low-level effectiveness of drugs is an issue that restricts 90% of pharmaceutical products from receiving FDA approval.
Once adopted, prediction models will help pharma professionals assess the effectiveness of a drug before it goes into production, saving both money and human resources.

Predictive analytics provide a reliable tool to assist physicians, removing the pressure of having to make life-altering decisions with no data to back them up. Once data analytics algorithms are implemented, physicians will be able to get a "second opinion" on a diagnosis or a scan reading from a computer, ensuring that not a single detail goes unnoticed.

As a result, by the time caregivers make a final diagnosis, they can be highly confident in their decisions.

Predictive analytics contribute to research in new ways. For example, rather than focusing on individual cases, community leaders can pay attention to the collective health of a population, as well — the well-being of the entire group.

By now, there are plenty of examples of researchers using predictive analytics to get real-time insights about the health of large groups of people. For example, the Spanish Rare Diseases Network uses predictive analytics to collect data on psych admissions and readmissions all over the country and applies it to clinical research.

Predictive analytics represents a new frontier in healthcare. As shown in the healthcare predictive analytics examples, this technology is already on a steady path towards implementation, assisting in ICU management, predicting viral outbreaks, and analyzing the likelihood of developing neonatal conditions.

Once the risks associated with the implementation of predictive analytics are settled, even more powerful predictive analytics in healthcare use cases will open up for healthcare professionals.

Fayrix is an experienced team of developers who has a wide technology competence in Predictive analytics. We have successfully completed a handful of healthcare-related projects using AI and ML — a cardiac excitation detector, a tele healthcare platform, predictive maintenance optimization, an X-ray reader, AI social monitoring platform and hr predictive analytics software.

Take a look at the services we offer. To discuss a project idea, contact us — we will get in touch as soon as possible!