Big Data, Big Benefits: 20 Ways Predictive Analytics Impacts Healthcare

Big Data, Big Benefits 20 Ways Predictive Analytics Impacts Healthcare

Healthcare workers are constantly challenged to make informed decisions based on a massive amount of data from multiple sources. Predictive analytics in healthcare has emerged as a powerful tool to meet this challenge. By leveraging sophisticated algorithms and machine learning techniques, predictive analytics can process and analyze data faster and more accurately than ever.

Moreover, the potential applications of predictive analytics in healthcare go beyond data processing and analysis, potentially transforming the industry in numerous ways. Let’s delve into the many benefits of predictive analytics in healthcare and explore how it can improve patient outcomes and healthcare delivery.

How Predictive Analytics Can Benefit the Healthcare Industry,

The adoption of predictive analytics in healthcare organizations has steadily risen in recent years. In 2021, most healthcare executives, specifically 60%, reported using predictive analytics. This marks a notable 13-point increase year-by-year. 

These statistics indicate a growing trend in the healthcare industry toward leveraging the benefits of predictive analytics and big data.

At its core, predictive analytics involves using complex statistical models to analyze existing data and generate insights to inform future decision-making. Healthcare professionals can take proactive measures to improve patient outcomes and minimize risks by predicting potential outcomes.

Hospitals and healthcare providers can utilize predictive analytics to:

  • Minimize the risk of patient readmissions by analyzing data from previous hospitalizations,
  • Healthcare professionals can identify factors that may increase the likelihood of readmission and take proactive measures to address these issues.
  • Reduce overhead costs &
  • Get medical research assistance.

Key Approaches Used in Predictive Analytics

Predictive Analytics Methods

1. Data Mining

In healthcare, data mining services are a method of gathering, transforming, and preprocessing relevant medical data for analysis and prediction. However, patient data must be handled with extreme care and protected according to HIPAA regulations. To ensure privacy and security data mining services should be used with an established EHR system to ensure privacy and security.

2. Data Modeling

Data modeling is a predictive analytics tool that uses statistical methods to analyze historical data and create a detailed model for predicting future events. The model needs to be regularly updated and recalculated for the best accuracy, and it can be streamlined into a real-time tool with the help of other tools.

3. AI

Artificial Intelligence (AI) combines machine learning algorithms and data processing methods that aim to replicate intelligent human behavior. In healthcare, AI is used to manage patient data and produce calculated predictions, potentially eliminating human error in the industry.

4. Machine Learning

Machine learning is a subset of AI that enables machines to achieve autonomous learning through specific algorithms that improve over time. This makes it ideal for predictive analytics in healthcare, as the algorithms can analyze large data sets and improve predictions with more experience.

5. Deep Learning Algorithms

Deep learning is a type of machine learning that uses artificial neural networks to make precise predictions. It is commonly used in healthcare to analyze medical images and detect early signs of issues. This includes MRI screenings and microscopy images.

The Latest Applications of Predictive Analytics in Healthcare: 20 Use Cases

1. Early Detection of Patients’ Deteriorating Condition

ICU is a healthcare area that necessitates swift decision-making and continuous patient monitoring. Due to the overflow of critical patients and limited intensive care specialists, the quality of care can decline. Predictive analytics can analyze patients’ vital signs and determine those at high risk of deterioration in the next hour. This enables the response team to act quickly and prevent or minimize the impact of a crisis.

2. Disease progression and comorbidities

In healthcare institutions, predictive analytics is employed to recognize patients at risk of deteriorating, such as predicting the likelihood of renal disease development in diabetes patients or identifying those progressing toward sepsis. This crucial capability has the potential to save lives since early identification of disease progression enables timely interventions, greatly enhancing patient outcomes. 

Nonetheless, the utilization of predictive analytics for forecasting the progression of a patient’s condition is restricted to specific ailments and is not yet widely implemented across the board in healthcare settings.

3. ICU Monitoring through Biosensors

The application of predictive analytics in remote critical care has greatly impacted the ICU. The use of biosensors to collect patient data, combined with advanced analytics, has enabled tele-ICUs to effectively address and respond to changes in a patient’s condition.

Employing predictive analytics has resulted in numerous advantages for remote critical care in the ICU. These include shorter response times, improved care efficacy, enhanced ICU capacity, and an added layer of protection for healthcare workers.

4. Resource Acquisitions

Healthcare organizations’ magnitude, extent, and intricacy have posed challenges for administrators in efficiently and effectively allocating resources. However, predictive analytics has the potential to alleviate this issue by identifying patterns in resource allocations and forecasting future requirements. This empowers administrators to procure or relocate resources appropriately, ensuring they are positioned correctly at the optimal time.

Patient utilization patterns, organizational capacity, and resource management were once distinct areas but are now converging in a highly beneficial manner to assist healthcare organizations in effectively managing their operations.

5. Risk Scoring for Chronic Illnesses

Approximately six out of every ten adults in the United States suffer from chronic diseases that are either incurable or ongoing. Certain individuals face a constant risk of experiencing flare-ups and complications. To accurately assess the likelihood of such complications occurring at any given time, it is crucial to continuously analyze the patient’s health data.

This is where applying predictive analytics in healthcare, utilizing big data, becomes essential. By examining factors such as laboratory test results, patient-generated lifestyle data, and biometric information, the system can assign a specific risk score to each individual, indicating the probability of experiencing a complication shortly. Furthermore, this technology is adept at detecting early signs of deterioration and promptly notifying healthcare providers.

6. Providing Predictive Care to High-Risk Patients

In addition to individuals with chronic illnesses, other at-risk patient groups can also benefit from predictive healthcare. This includes older adults and patients recently discharged from the hospital following invasive procedures.

Utilizing telecare and predictive analytics, at-risk patients can receive timely assistance to prevent adverse events or respond quickly during a crisis. By analyzing historical data, the software can even predict fall events in elderly patients, effectively preventing potential injuries and subsequent hospital readmissions.

7. Preventing Patient Suicide and Self-Harm

Mental health concerns require equal attention to other chronic illnesses. Predictive algorithms can identify patterns related to suicide, self-harm, and violent tendencies, which may initially appear random or unprovoked.

Timely provision of professional assistance can effectively prevent mental health crises, even in highly unstable patients. Thus, predictive analytics enhances patients’ quality of life and saves lives.

8. Forestalling Appointment No-Shows

Unanticipated gaps in the daily schedule can lead to financial implications for the organization and disrupt a clinician’s workflow.

Applying predictive analytics to identify patients at risk of missing appointments without prior notice can enhance provider satisfaction, reduce revenue losses, and enable organizations to offer vacant slots to other patients, thus improving timely access to care.

Alternatively, providers can utilize this data to send extra reminders to at-risk patients, provide transportation or other assistance to ensure appointment attendance or propose alternative settings and time options that better accommodate their needs.

9. Reducing Rate of Readmissions in Hospitals 

In 2018, the average readmission rate for adults stood at 14%, with 20% of those cases attributed to four specific conditions: septicemia, heart failure, diabetes, and COPD.

Under Medicare’s Hospital Readmissions Reduction Program (HRRP), hospitals and health systems face substantial penalties, creating a financial motivation to prevent unplanned readmissions.

In conjunction with enhancing care transitions and implementing care coordination strategies, predictive analytics can alert providers when a patient’s risk factors indicate a significant probability of readmission within the 30-day timeframe.

10. Hospital Overstays

Healthcare organizations use predictive analytics to identify inpatients at risk of exceeding the average length of stay for their conditions. By analyzing patient, clinical, and departmental data, clinicians can adjust care protocols to keep treatments and recoveries on track. This helps patients avoid overstays, which drive up costs, strain hospital resources and may expose them to secondary infections.

11. Genetic Research-based Predictions

Approximately 10% of adults have genetic irregularities, and identifying them early can assist in their management and prevent future complications. However, analyzing genetic information is a challenging task due to the intricate nature of the human genome.

Predictive analytics can be leveraged to analyze an individual’s genetic data and compare it with a database of known defects and illnesses associated with specific genetic markers. This approach can be employed from early neonatal stages, providing parents with early warnings about potential conditions their child may have.

12. Investigating Innovative Treatments and Precision Medicine through Research

In addition to patient care, predictive analytics finds practical applications in the research field of healthcare. By analyzing data such as genetic information, clinical history, and responses from previously studied patient groups, algorithms can accurately predict an individual’s response to medications or treatment plans.

This approach significantly reduces the reliance on large patient groups and streamlines the research process. Moreover, it enables researchers to focus on individual patients and tailor custom healthcare software development for their cases.

13. Enhancing Patient Engagement and Satisfaction Levels

Building a trusting and personal relationship is equally vital in healthcare as selecting the appropriate treatment plan. It serves as a catalyst for patient compliance with their prescribed treatments, follow-up appointments, and the development of healthier habits, ultimately preventing complications and more serious health issues.

When employed to assess an individual’s behavioral patterns, predictive analytics enables the system to determine the most effective approach. Furthermore, it can identify the healthcare professional with the highest likelihood of establishing a personal connection, enhancing the patient-provider relationship.

14. Supply Chain Management in Healthcare

The supply chain within a hospital is a complex system, with the required supplies varying based on patient volume and specific cases.

The application of predictive analysis enables hospitals to make data-driven decisions for future procurement. This facilitates more efficient and cost-effective purchasing by reducing unnecessary acquisitions and minimizing equipment waste.

15. Staff Management in Healthcare

Through predictive modeling, hospitals can identify patterns in patient care, enabling them to determine the optimal number of individuals in the facility at any given time. This predictive capability extends to identifying the specific need of certain specialists, allowing for efficient scheduling and allocation of their time off.

Optimizing staff occupancy has the potential to significantly reduce operational costs, leading to the reorganization of the hospital budget and ultimately enhancing patient care.

16. Predictive Maintenance Planning for Medical Equipment,

The inevitability of wear and failure due to friction and resistance is an undeniable aspect of physics. As a result, predictive analytics has been employed across diverse industries to anticipate the potential wear-out and breakdown of specific components.

The healthcare sector can also reap the advantages of such analyses. For instance, by scrutinizing data from sensors within an MRI machine, the system can forecast an impending failure before it transpires. This foresight enables proactive measures such as partial repairs or replacing specific components to address the issue.

17. Insurance reimbursements

Predictive analytics offers significant support in the realm of billing. Healthcare organizations can leverage this technology to analyze the documentation they submit to insurers, enabling them to identify claims that have a high likelihood of being declined and those that have the potential for higher payments.

18. Insurance Fraud Detection

According to the National Healthcare Anti-Fraud Association, healthcare fraud results in financial losses ranging from 3% to 10% of healthcare expenditure, amounting to potentially $300 billion. While insurance companies have made efforts to combat these losses, only a few have implemented predictive analytics on a significant scale to address the issue.

By leveraging extensive data on fraudulent insurance claims and mismanagement of insurance funds, customized machine-learning algorithms can be developed and trained to detect early signs of malicious intent. This proactive approach aims to minimize financial losses and deter scammers from future fraudulent attempts.

19. Patient engagement and behavior.

Predictive analytics not only aids in chronic disease management, reducing wait times, and optimizing therapy outcomes but also plays a crucial role in keeping patients engaged in various aspects of their care.

Consumer relationship management has become essential for providers and insurance companies to promote wellness and minimize long-term expenses. Predicting patient behaviors is critical to developing effective communication strategies and adherence techniques, enabling healthcare entities to foster patient engagement and enhance treatment outcomes.

20. Centralized command center capabilities

Healthcare institutions now have the opportunity to manage various predictive analytics use cases together through integrated solutions offered by health IT vendors. Gartner analysts refer to these capabilities as real-time health system command centers, comparable to air traffic control systems that provide continuous monitoring and situational awareness. 

This comprehensive solution empowers health systems to make informed decisions by predicting available ICU beds, supply needs, and in-patient bed availability. 

For instance, the system can proactively alert administrators about potential shortfalls in bed availability or nursing staff based on anticipated service demands. This enables administrators to take preemptive action, enhancing the overall patient experience and improving outcomes.

Final Thoughts

Predictive analytics holds immense potential to revolutionize the healthcare industry by leveraging data-driven insights to drive better patient outcomes, improve operational efficiency, and enhance resource management. Predictive analytics offers numerous benefits across various healthcare domains, from early detection of deteriorating conditions to personalized patient care. 

Healthcare organizations can proactively identify risks, optimize treatments, and improve decision-making by utilizing advanced technologies such as data mining, AI, and machine learning.

Moreover, predictive analytics enables healthcare providers to engage patients effectively, reduce costs, prevent fraud, and streamline operations. As the healthcare industry continues to embrace digital transformation, the integration of predictive analytics will play a pivotal role in shaping the future of healthcare.

Embrace the power of predictive analytics in healthcare to enhance patient outcomes, optimize resource utilization, and drive operational excellence.

Contact us today to explore how predictive analytics can transform your organization.

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Big Data, Big Benefits: 20 Ways Predictive Analytics Impacts Healthcare
John Ogden
Published on
June 20, 2023

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