Promising Use-cases for Predictive Analytics in Healthcare

HIMMS report predicts predictive analysis will support population health management, aid towards healthcare financial success, and produce better results across the service industry in 2019. Sophisticated big data analytics capabilities have enabled healthcare organizations to take big steps towards predictive insights from basic descriptive analytics.

Predictive analytics does not simply present information about past events to the user. In fact, it delivers a likelihood of a future outcome based on structures and patterns of past data. This estimation allows healthcare providers, including clinicians, administrative staffs, and financial people to be on alert about notified events even before they take place. Their systematic approach allows them to make informed decisions while proceeding with choices.

Predictive analytics is extremely important concerning incentive care, surgical procedures, and emergency care, where it becomes necessary to stay one step ahead of events, where patients’ lives often rely on a fast and accurate decision, and where a fine sense and reflex is required when something goes out of order.

10 Use cases for Predictive Analytics in Healthcare

Listed below are 10 use cases on how healthcare facilities implement predictive analytics to retrieve actionable and scalable insights from their historical data.

Create Risk Scoring for Chronic Diseases

Organizations can identify individuals with increased risk of developing chronic conditions early in the disease’s progression by creating risk scores and risk stratification techniques. Predictive models based on biometric data, lab testing, claims data, and patient-generated health data will give healthcare providers better insights using which they possibly have the best chance of helping patients avoid long-term health problems that are both expensive and difficult to treat.

Avoid 30-day Readmissions

Predictive analytics can notify hospitals when a patient’s risk factors stand a chance for readmission within a 30-day window. Healthcare providers are subject to severe penalties under HRRP, adding a financial incentive for preventing emergency returns of patients.

According to a 2016 study by the University of Texas Southwestern, certain occurrences during longer hospital stays, such as vital signs instability upon discharge, longer length of stay and C. difficile infection, increased the chance of 30-day readmissions. These patients’ traits can be identified through analytical tools, assisting providers to plant systems to prevent quick returns to the hospital.

Prevent Appointment No-Shows

Predictive analytics can be used to identify a patient’s likelihood to skip appointments without advanced notice, eliminating the creation of unexpected gaps that create financial ramifications for healthcare providers.

EHR technology and other predictive models can improve provider satisfaction, bring down revenue losses, and help hospitals to serve other patients in need with open slots. EHR data can reveal no-shows –a better and precise way to forecast patients’ patterns. EHR data can also be used to send reminders to patients, offer transportation facilities, and suggest alternative services.

Prevent Patient Suicide and Self-Harm

EHR can once again play the role of a commando by supporting detection of suicide risks. The combined use of EHR data and a standard depression questionnaire can exactly identify individuals with elevated risk of suicide attempt, shows a 2018 study conducted by Mental Research Network.

The research team used a predictive algorithm and found that suicide attempts and commits were 200 times more amid the one percent of patients flagged in the risk zone.

Substance abuse, mental health diagnoses, use of psychiatric medicines, past suicide attempts, and high scores on the questionnaire were the strongest contributors for the prediction.

Avert Patient Health Deterioration

Patients often succumb to potential threats while hospitalized, such as the development of sepsis, an infection that is hard to treat, or sudden health deterioration due to their existing condition. However, with the help of data analytics and predictive analytics tools, such as machine learning, providers can quickly react to changes in patient’s vitals and recognize any imminent symptoms of deterioration as early as 12 hours before the onset of the condition.

Predicting Out-Patients’ Flow Pattern

Care sites often operate without fixed schedules and their staffing fluctuates a lot due to this. While predictive analytics help care centers, such as emergency units and urgent care, to predict available beds for patients who need to be admitted and keep wait time low for patients, visualization tools and analytics strategies can predict patient patterns to ensure optimal staffing levels, highlight opportunities to plan schedule changes and workflow adjustments, and ultimately, model patient flow pattern.

Regulate Supply Chain

The supply chain is the most crucial center, which is also the largest cost center for the providers. Healthcare providers can significantly cut down unnecessary spending and improve efficiency by utilizing predictive tools. Hospital executives who are looking to reduce variations and gain actionable insights into the supply chain can use analytical tools to make productive, proactive, and data-driven decisions. According to a survey, by monitoring supply chain, hospitals can save up to $10 million per year. However, the survey also found that only 17% to 20% of the hospitals currently use automated solutions to manage the supply chain.

Develop New Therapies and Precision Medicine

“FDA’s Center for Drug Evaluation and Research (CDER) is currently using modeling and simulation to predict clinical outcomes, inform clinical trial designs, support evidence of effectiveness, optimize dosing, predict product safety, and evaluate potential adverse event mechanisms,” said FDA Commissioner Scott Gottlieb, MD, after the passing of the 21st Century Cures Act.

As precision medicine and genomics gain steam, in silico models are used to supplement traditional business trials related to degenerative conditions, such as Alzheimer’s, Parkinson’s Disease, and Huntington’s Disease. Predictive analytics is used massively to translate new drugs into precision therapies. This will enable researchers to better understand the associations between the impact of particular therapies and genetic variants.

Improve Data Security

As the cyber-attacks increase in terms of sophistication and complexity, it is found that predictive analytics and AI could be the ultimate answer to anticipate real-time risk scores for specific transactions or requests.

“Once the risk score has been determined in real-time, the system can use this during a login event to either grant the access for a low-risk event or to challenge for Multi-Factor Authentication (MFA) or possibly block the access for high-risk events,” explained David McNeely from the Institute for Critical Infrastructure Technology (ICIT), in an ICIT report.

Analytical tools can give organizations early warning when something changes in data access, sharing, and utilization, especially if they indicate a presence of intruders.

Reinforce Patient Engagement

Apart from the aforementioned aspects of patient-provider association, predictive analytics can keep patients engaged in other areas as well, mainly in Consumer Relationship Management. It has become the most important skill for both providers and insurance companies. Predictive analytics tools predict patients’ behaviors – the only key to developing effective communication and technology adherence.

Lillian Dittrick, a member of Society of Actuaries affirms, “Both payers and providers have a wealth of information that they can use to build models. Healthcare providers can also acquire some other sources, like the social determinants of health, for example, that will really help the strength and accuracy of their models. When we use predictive models to look at all the variables, it helps us prioritize those patients who are really going to be receptive to changing something in their lifestyles, such as nutrition or exercise.”

Although use-cases for predictive analytics are present in the healthcare environment, not all involve fast and real-time alerts that require a team of healthcare professional spring to action almost instantly. Evenset’s analytics solutions can be applied in finance, administration, and data security challenges, where providers can actually see a surprising gain in patient satisfaction and care efficiency. We also offer custom software solutions in New York and Toronto.


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