What is Predictive Analytics in Healthcare To Manage Cost

Posted in AI Healthcare

Key Strategies to Manage Cost Through Predictive Analytics in Healthcare

Did you know the average American family spends more on healthcare than food? The healthcare industry is changing rapidly with more life-saving drugs and advanced diagnosis. Along with all the growth, cost management has become a top priority for healthcare organizations. This is where predictive analytics in healthcare comes into the picture. 

Care providers take significant steps to make healthcare a ‘right’ rather than a ‘privilege’ for better healthcare delivery. In light of that, we have covered the key strategies healthcare organizations can implement to effectively manage costs through predictive analytics. 

What Is Predictive Analytics in Healthcare Exactly?

Predictive analytics in healthcare is the use of advanced data analysis techniques. The process involves using algorithms to estimate potential medical issues before they occur. It is the identification of patterns and trends to predict future health events and outcomes. 

Moreover, it helps in reducing the risks to the population and individual health. Predictive analytics in healthcare assists the management and optimization of resources through analysis to save costs. 

Healthcare providers use it for personalized treatment according to their data, leading to improved patient care and advanced delivery.

Why Is Predictive Analytics Important for Healthcare?

How to use predictive analytics in healthcare is a common question among care providers. They want to know its impact on both individual care and the healthcare industry. 

The importance of predictive analytics lies in the use of data and machine learning in healthcare. It can analyze large volumes of data and generate actionable insights. Predictive analytics in healthcare improves the following areas for cost management:

1. Early Detection and Intervention

Healthcare professionals can use analytics to identify high-risk patients especially in chronic diseases, enabling timely intervention and potential reduction in readmissions.

2. Personalized Treatment Plans

Healthcare data empowers medical professionals with informed, evidence-based decisions and treatment plans for each patient based on the complexity of the disease.

3. Reduced Hospital Readmissions

Predictive analytics in healthcare shows the hidden areas like unnecessary tests and readmissions by identifying risk based on the patterns and symptoms to reduce costs associated with readmissions.

4. Patient Engagement

Predictive models can forecast the progression trend using analytics, which leads to better self-management and informed decision-making for personal health.

5. Efficient Staff Allocation

Healthcare organizations can utilize predictions to optimize staff schedules based on patient volume and prevent overstaffing for optimal utilization and enhanced patient care.

6. Improved Population Health

With the help of predictive analytics, government and healthcare organizations can identify the trends in chronic diseases and other epidemics for improved population health by prioritizing based on health risk.

How Does Predictive Analytics Work in the Healthcare Sector?

Healthcare costs in the US have been rising, creating a problematic situation for stakeholders. Identification of key drivers behind these costs has become increasingly important. Therefore, healthcare organizations use predictive analytics to forecast future expenses and the factors behind them with data.

The application of predictive analytics in healthcare has great potential. One of the examples of predictive analytics in healthcare is that it can be integrated with EHR and use that data to identify patterns, trends, and anomalies that are difficult for human analysis.

With predictive analytics in healthcare, organizations can manage workflows, optimize staff and physician performance, and ultimately improve patient outcomes. Furthermore, predictive analytics can improve the accuracy of health diagnoses and identify potential treatment options to offer patients the most appropriate therapy.

Predictive Analytics in Healthcare: 7 Key Insights for Success

Benefits of Predictive Analytics in Healthcare

Healthcare today is data-driven and uses predictive analytics tools to convert this data into actionable insights. Implementing personalized care, disease prevention, and cost savings in healthcare are notable predictive analytics in healthcare examples. Let’s explore the nine benefits of predictive analytics in healthcare and its applications:

1. Personalized Treatments

Research by JAMA Network found that personalized treatments lead to a 35% reduction in adverse patient outcomes and growth in remission. Personalized medicine, with the help of data, is saving lives with personalized medications.

The approach of one-size-fits-all medicine is the practice of the past. Today, treatments are personalized to meet patient’s unique genetic makeup and health history. Moreover, predictive analytics in healthcare using big data helps identify each individual’s effective medications and treatments. 

2. Population Health Management

Mitigating public health issues is one of the biggest concerns of public health officials. Analytics can predict disease outbreaks, pinpoint vulnerable communities, and target preventive measures. 

According to AJMC, hospital readmission rates can be reduced by 18% using this proactive approach to save lives and dollars spent. Predictive analytics provides insights that shape population health and create an effective intersectoral public health system that influences morbidity, mortality, and functioning.

3. Identify At-Risk Patients

AI predictive analytics in healthcare analyzes medical records and lifestyle factors to identify individuals at risk. Identification of patients at risk allows healthcare providers to intervene early for conditions like diabetes or heart disease. John Hopkins Medicine revealed that with data analytics, the potential of reducing type 2 diabetes is 50%.

4. Predicting Suicide Attempts

The suicide rate in the United States has been a growing concern, but it is preventable, and predictive analytics can help. Top digital mental health companies can use AI predictive analytics by analyzing social media data, search history, and medical records to identify individuals at high risk for suicide attempts. 

According to a Stanford Medicine study, there is a 93% accuracy rate in predicting suicide risk in patients with depression and a 30% reduction in suicide attempts through predictive analytics interventions. Predictive analytics has tremendous potential in identifying disease patterns, treatment targets, and potential drug interactions.

5. Chronic Disease Management

Chronic diseases like diabetes, heart disease, and cancer create challenging situations in healthcare systems. The emergence of predictive analytics leads to better patient outcomes. It potentially reduces healthcare costs—identification of high-risk patients through data patterns and future progression of complications enabling individualized interventions. 

Wearable devices and sensors can continuously collect data on vital signs, medication adherence, and lifestyle factors. Predictive models can analyze this data to identify early signs of potential problems and proactively alert healthcare providers and patients, enabling timely adjustments to treatment.

6. Healthcare Tracking & Digitalization

Healthcare today is all about data and wearables that monitor vital signs to track medication adherence. Predictive analytics can analyze this data and equip medical professionals with real-time insights. It improves adherence to medication regimens by 20-40%, as the Journal of the American Medical Informatics Association revealed.

For example, people with diabetes can monitor the rise in blood sugar at any moment without the need for finger pricks. The progression trend can be identified using analytics, which leads to better self-management and informed decision-making.

7. Fraud Detection

Healthcare fraud is a common problem and costs billions every year. Predictive analytics can analyze billing patterns and identify suspicious activity. Some people obtain subsidized or fully-covered prescription pills and sell them on the black market for a profit. They bill for a non-covered service and modify medical records to maximize payment.

Predictive analytics is instrumental in detecting and preventing healthcare fraud. The National Health Care Anti-Fraud Association reports a 50% decrease in fraudulent claims with the implementation of predictive analytics. Another example is a health insurance company that saved $12 million by implementing a fraud detection system.

8. Forecasting Appointment No-shows

Predictive analytics can reveal data about patients most likely to be no-shows when integrated with EHRs.A study found that clinical EHR could capture nearly 5,000 patient no-shows per year with greater accuracy than previous attempts to forecast patient patterns.

Predictive analytics can send frequent reminders to patients at risk of no-showing or offer individuals to make their appointments.

9. Reduces Overall Healthcare Costs

The future of predictive analytics in healthcare promises to reduce overall healthcare. It promises to do so by predicting and preventing illness, optimizing resource allocation, and reducing fraud. 

Studies estimate that predictive analytics could save the US healthcare system up to $150 billion annually. Thus, the implementation of predictive analytics results in substantial cost savings.

Use Cases of Predictive Analytics in Healthcare Space

Predictive analytics use cases in healthcare are vast and aid healthcare with powerful insights. Researchers and hospitals that have used predictive analytics, however, have been able to gain important insights and see meaningful improvements in patient outcomes.

1. Reducing Hospital Readmission Rates

Hospital readmissions are costly; in the US alone, readmissions cost Medicare $14 billion annually. Predictive models in healthcare can identify those patients at risk of readmission and enable medical professionals to timely intervention. 

At Cornwell Healthcare, AI and predictive analytics were deployed to identify patients with a probability of readmission. As soon as the predictive analytics tool spotted a potential candidate for readmission, an interdisciplinary team worked with the person to address those three aspects. This approach enabled Corewell Health to prevent the readmission of 200 patients and save $5 million in associated costs.

2. Research Into New Treatments

Research and development of new drugs is a long process. Traditional methods rely on trial and error, leading to delays and ineffective results. Predictive analytics analyzes big data sets with targeted audiences with clinical history and other data. This can streamline the research process and cut the need for inpatient groups. 

Predictive analytics can accelerate the development process and improve patients’ treatments faster. A study in Nature Genetics found that using machine learning to analyze genetic data could identify potential drug targets for complex diseases with 70% accuracy. 

3. Health Insurance

Health insurance companies are facing severe strain with rising healthcare costs. Predictive analytics can assist insurance companies in calculating the accurate cost of health insurance for each individual based on age, gender, medical history, insurance case history, and heredity. 

Insurers can use machine learning algorithms for risk assessment; traditional methods often miss the mark, leading to inaccurate premiums and financial strain. Moreover, it can be used to prevent fraudulent insurance claims, reduce losses, and prevent future scamming attempts. 

4. Resource Allocations and Acquisitions

Healthcare organizations often encounter situations where patient flow is under or over-estimated. Predictive analytics can analyze historical data and identify patients likely to miss their appointments to prevent that time slot from going to waste. 

It proves the importance of predictive analytics in healthcare by optimizing resource allocation, reducing costs, and ensuring patients receive the care they need.

Effective allocation of healthcare resources based on data reduces burnout and maximizes capacity with the proper allocation. For example, a hospital increased room utilization by 9% after using predictive analytics. 

5. Assessing Patient Engagement and Behavior

Understanding patients is crucial for healthcare providers to improve outcomes. Predictive analytics analyzes EHR wearable device data to gain insights into patient behavior. This allows healthcare organizations to tailor interventions, improve communication, and ultimately empower patients to take control of their health.

Challenges and Hurdles in Implementing Predictive Analytics in Healthcare

Implementing predictive analytics in healthcare has the potential for improved patient outcomes. However, healthcare organizations need to navigate some unique challenges to maximize the potential of this technology. We’ve listed some roadblocks to consider:

• Healthcare data is usually stored in an unstructured way without a centralized format in legacy software. Cleaning, standardizing, and integrating vast data sets into useable formats is a significant challenge.
• Patient data is sensitive, and protecting it during collection, transmission, and analysis is crucial. Predictive analytics in healthcare must meet HIPAA compliance requirements for data governance.
• Implementing AI in healthcare is a challenge, due to biases in training data. AI in quality assurance is important to identify and mitigate risks beforehand.
• AI chatbots in healthcare offer more personalized solutions; however, AI chatbots lack explainability and transparency in terms of healthcare patterns and may produce inaccurate results.
• Implementation cost of AI in healthcare with predictive analytics tools with existing systems is complex and costly. Legacy data structures can hinder the efficiency of the analytics.
• Adopting a predictive approach in a sensitive domain like healthcare is complex, and organizations may face resistance to adapt this change by medical professionals

Closing Thoughts

Predictive analytics is a great tool to take care of complexities and utilize data to predict future issues and prevent them beforehand.  Analyzing medical records, patient behavior, and health patterns, predictive models identify the risk of chronic diseases, hospital readmissions, and even suicide attempts. With analysis of personalized treatment plans, early intervention leads to improved patient care.

Healthcare analytics automates the workflows for hospitals and allocates resources for optimal utilization. From forecasting appointment no-shows, streamlining staff scheduling, and even predicting equipment failures, reducing waste and improving efficiency predictions are for everything involved.

There are pros and cons of every method used, but the benefits of AI healthcare are great. when utilized properly it has proven to be a great success like saving millions just by analyzing claims. Predictive analytics is a cost-effective solution for healthcare that enables accessible care under budget for all. When the cost of healthcare is skyrocketing and underserved populations are struggling.

Frequently Asked Questions

What Predictive Models Are Used in Healthcare?

The most commonly used models in predictive analytics are regression, classification, clustering, and survival analysis models.

What Are Predictive Models?

A predictive model is a statistical tool that uses data to predict future outcomes.

Why Do Data Silos Occur in Healthcare for Hospitals?

Data is recorded in different segments and lacks unification, leading to no information flow across departments.

What Is an Example of Predictive Analytics in Healthcare?

Patients with chronic diseases like diabetes are at high risk of strokes and predictive analytics identify complications early for timely prevention.

What Is the Difference Between Predictive Analytics and Data Science?

Data science is a whole bunch of valuable tools; however, predictive analytics uses one model for specific actions.

How Is AI Currently Used in Healthcare?

Top AI applications in healthcare are assisting doctors in diagnosis, personalized treatment, healthcare chatbots, administrative tasks, and even helpful in drug discovery.

What Are the Three Types of Analytics Used in Healthcare?

Descriptive analytics is for what happened, predictive analytics is for what might happen, and prescriptive analytics is for what to do about it.

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