How Data Analytics in Healthcare is Reducing Costs

Big data analytics has surprisingly changed how we deal with data across various industries, especially healthcare. When used correctly, data analytics can help lower treatment costs, give us a better understanding of patients and their conditions, and improve life overall.

Seeing how this can boost healthcare and make a difference in people’s lives is pretty cool!

Healthcare data analytics combines historical and real-time data to predict trends, reveal actionable insights, achieve medical advances, and drive long-term growth.

According to a report by Allied Market Research, the global healthcare analytics market is projected to reach $96.90 billion by 2030, growing at a CAGR of 15.3%. Various factors, such as the need for laboratory automation and efficiency and the rising prevalence of chronic diseases, have led to a surge in the adoption of data analytics in the healthcare industry.

Apart from the abovementioned factors, healthcare organizations are now turning to big data analytics to reduce overspending on poor stock management, patient care, and staff deployment.

In this article, we will focus on how healthcare data analytics is helping medical institutions and professionals assist with patient care and reduce costs. We will also study the benefits, types, challenges, and future of data analytics in the healthcare industry.

But before proceeding, let’s learn what healthcare data analytics is and how it can change the healthcare industry.

What is Data Analytics in Healthcare?

Data analytics in healthcare is all about harnessing data from various sources, like electronic health records, medical imaging, and wearables, to uncover insights that enhance patient care and improve operational efficiency. By analyzing this data, healthcare providers can spot trends, anticipate disease outbreaks, and customize patient treatments.

Additionally, hospital data analytics aids clinical decision-making with evidence-based recommendations, streamlines operations to cut costs, and supports population health management by identifying risk factors. Ultimately, utilizing data analytics allows healthcare organizations to optimize their performance, improve patient outcomes, and advance clinical research, making it an essential part of modern healthcare.

Key Benefits of Data Analytics in Healthcare

Data analytics in healthcare comes with a series of benefits, including improved patient outcomes through personalized treatment plans and early intervention, along with a host of other benefits. Let’s have a look at those:

Predictive Maintenance for Medical Equipment

Data analysis in healthcare helps in the predictive maintenance of medical equipment. Healthcare organizations can predict when devices will require maintenance or replacement by examining usage data and performance metrics. This proactive strategy minimizes downtime, ensuring essential equipment is consistently available for patient care.

Enhanced Resource Allocation

Healthcare organizations can significantly improve resource allocation by analyzing patient flow and usage patterns through healthcare data analysis. This ensures that staff, equipment, and facilities are utilized where they are needed most. Optimizing resource management reduces waste and enhances the overall efficiency of care delivery, leading to better patient satisfaction and outcomes.

Fraud Detection and Prevention

Advanced patient data analytics is crucial for identifying fraudulent activities within healthcare systems. By examining billing patterns and comparing them to established benchmarks, anomalies can be flagged for further investigation. This process helps protect financial resources and ensures funds are directed toward legitimate patient care.

A Comprehensive Guide: Financial Fraud Detection Using Machine Learning

Social Determinants of Health Analysis

Addressing social factors that impact health is essential for delivering holistic care. Data analytics in healthcare can reveal correlations between social determinants, such as income, education, living conditions, and health outcomes. This insight allows healthcare providers to tackle broader social issues, resulting in more comprehensive treatment plans for patients’ overall circumstances.

Enhanced Clinical Trials

Data analytics in healthcare can help optimize clinical trial designs and participant selection. Deploying AI in clinical trials drives actionable insights. By evaluating data from prior trials and demographics, researchers can identify suitable candidates for new studies. This increases the likelihood of successful outcomes and accelerates the development of new treatments, benefiting both patients and advancements in healthcare.

Patient Journey Mapping

By utilizing medical data analytics, healthcare providers can map the entire patient journey across various touchpoints. Understanding patients’ experiences from initial contact through treatment and follow-up enables providers to identify areas for improvement. This comprehensive perspective enhances patient engagement and helps create a more seamless healthcare experience.

Integration of Genomic Data

Combining data analytics and healthcare with traditional health data and genomic information paves the way for personalized medicine. By analyzing an individual’s genetic profile alongside their medical history, healthcare providers can develop customized treatment plans. This precision medicine approach leads to more effective therapies and improved patient outcomes.

Clinical Workflow Optimization

Data analytics in hospitals is essential for improving clinical workflows by identifying bottlenecks and inefficiencies in care delivery. By analyzing workflow data, hospitals can streamline operations, reduce administrative burdens on staff, and enhance the patient experience through shorter wait times. This data-driven approach boosts operational efficiency and helps ensure that improvements are sustained over time, ultimately leading to higher patient satisfaction.

Integration of Healthcare Business Intelligence

Integrating healthcare business intelligence revolutionizes patient care by enabling medical professionals to make decisions based on complex data analysis. This innovative approach has improved patient care standards. Business intelligence in healthcare translates intricate data into actionable insights, shaping treatment plans and enhancing operational efficiency.

Telemedicine Effectiveness

The growing prominence of telehealth services requires a thorough evaluation of their effectiveness. Data analytics in medicine can assess patient outcomes, levels of satisfaction, and engagement in virtual care settings. This information allows healthcare providers to refine their telemedicine offerings, ensuring they effectively meet patients’ needs and improve overall access to care.

What are the Types of Data Analytics in Healthcare?

Four types of healthcare data analytics can be applied based on the goals and needs of healthcare professionals and institutions. Depending on the situation, each type is crucial for making the most of healthcare data.

Descriptive analytics: Descriptive analytics provides a historical view of data that allows healthcare providers and management to determine if current practices are efficient and make necessary recommendations.

Predictive analytics: Predictive analytics uses modeling and forecasting to determine what is likely to happen next. It helps healthcare providers calculate patients’ risk scores and identify which patients require additional attention. However, predictive analytics does not indicate preventing adverse events such as hospitalization.

Diagnostic analytics: Diagnostic analytics helps understand why something happened so that actions can be taken to address the problem. This type of advanced analytics for healthcare is useful in figuring out what events and factors led to a specific outcome. Just like descriptive analytics, diagnostic analytics also involves investigating historical data.

Prescriptive analytics: This type of data analytics in the healthcare industry allows healthcare providers to understand what actions are needed to change the prediction. It uses advanced algorithms to help determine the effects of specific actions and provide solutions by applying historical data to get a specific result. It is the most useful and powerful type of analytics, providing guiding recommendations to healthcare professionals.

Top Ways to Reduce Costs in Healthcare Using Data Analytics

Applying data analytics and visualization in the right way can increase patient access to services, resulting in lower costs, more revenue, and improved patient satisfaction. In this section, we will delve into different ways to reduce costs in the healthcare sector using data analytics tools and solutions.

Electronic Health Records (EHRs)

One of the major benefits of data analytics in healthcare is digitizing medical records, which can result in substantial savings. EHRs generate a lot of data from the clinical information they contain. The data collected in EHRs comprises administrative and diagnostic patient information updated in real-time for each encounter.

In particular, EHR provides information on procedures, demographics, length of stay, and fees. Additionally, EHRs improve the quality of care since they can trigger warnings and reminders for diagnostics.

They also enhance performance by streamlining routine tasks, reducing errors, and speeding data access/entry, thus significantly cutting healthcare costs.

Kaiser Permanente, a leading healthcare company in the US, has implemented an integrated system that shares data across all their facilities and makes it easier to use EHRs. This improved outcomes in cardiovascular disease while saving the healthcare company $1 billion from reduced office visits and lab tests.

Thus, it means a shared system of digitized patient records can save hospitals and healthcare centers substantial sums of money.

[Also Read: How to build an EHR system? Features and cost breakdown]

Forecasts Operating Room Demands

Operating rooms are expensive to build, operate, and staff. So, optimizing operating room use without compromising patients’ health is in every hospital’s best interest. To achieve this goal, several healthcare providers and administrators are utilizing data analytics to help hospitals understand the relationships between the numerous operating room variables that tend to ruin effective scheduling. These variables include surgeon availability, operating hours, and equipment functionality and availability.

With so many factors to consider, creating a surgical schedule isn’t easy. Fortunately, data analytics can help streamline operating room management. Hospitals are now awash in staff, patients, and facilities data. So, the best way is to mine the right data, study patterns of operating room utilization, and use predictive modeling to match the most appropriate human and support resources with the right operating rooms.

For instance, hospitals like UCHealth in Colorado have switched to mobile apps that use cloud computing and predictive analytics to optimize operating room utilization and eliminate schedule conflicts. Since the switchover, UCHealth saw an increase of 4% or $15 million in revenue from surgeries per year.

Optimizes Staffing

In many healthcare facilities, on-the-fly scheduling is done without considering other factors, which can sometimes lead to a staff shortage that will ultimately affect patient care. Accurate staffing is extremely important because half of a hospital’s budget is spent on labor costs. Data analytics in healthcare is now helping administration and managers better cope with staffing challenges at least 30 days in advance.

Data analytics for healthcare ventures uses intelligence to forecast and analyze historical staffing numbers, local weather trends, seasonal infections, holidays, and more available for each organization. This leads to reduced labor costs and efficient, cost-effective shift management.

Prevents 30-day Hospital Readmissions

Unnecessary readmissions are rampant in the US healthcare system. They also place an excessive cost burden on hospitals with few resources. Reducing readmissions promises to lower hospital costs. Healthcare data analytics tools can identify patients with specific symptoms and diseases that lead to their readmission. This helps healthcare providers take additional measures to prevent patients from returning within the 30-day window.

Analytics tools can also be used to develop a heat map for each patient who has stayed out of the hospital for the past 30-day period. Healthcare professionals can look at easy-to-understand visual representations of the data to identify where exactly in this 30-day window the patient is most at risk. This information helps them plan further actions.

For instance, data scientists at NYU Langone Medical Center have developed a predictive analytics algorithm based on a wide range of clinical factors. This patient data analysis helps identify patients likely to spend less than two nights in a hospital. The tool also allows physicians to know when to place a patient on observation.

Forestalls No-Show Appointments

When patients do not arrive for scheduled appointments, the unexpected gaps in a practitioner’s daily calendars can have financial ramifications and throw off the workflow. Using Medtech data analytics to identify patients who are likely to skip appointments without advance notice can significantly lower revenue loss, allow medical professionals to offer free slots to other patients, and enhance customer experience. A Duke University study found that data analytics could capture 4,800 patient no-shows a year for higher accuracy.

Medical data analysis tools can also predict when patients may appear at facilities such as urgent care centers or emergency departments, which do not have fixed schedules. This, in turn, could help improve staffing levels and minimize wait times.

Enhances Supply Chain Cost Management

As for all businesses, hospitals rely on the supply chain for efficient operations. If the chain experiences disconnection, patient care and treatment suffer, and hospitals lose revenue. This is where business analytics in the healthcare industry comes in.

Analytical tools maintain efficiency and track supply chain metrics, resulting in cost savings of up to $10 million yearly. To reduce documentation errors, these tools also help automate requisitions, purchase orders, invoices, and other processes.

Prevents Fraud And Enhances Security

Hospitals are no strangers to data breaches and fraud claims. In June 2020, the US Department of Health and Human Services witnessed an increase in data breaches as cybercriminals took advantage of the distractions from the Covid-19 pandemic. Such cybercrimes are costly to hospitals, resulting in an average revenue loss of nearly $3 million. To combat data breaches, healthcare units use hospital data analytics to identify pattern changes in network traffic and other suspicious online behaviors.

Data in healthcare is crucial for tracking incorrect or fraudulent payments, enabling organizations to enhance their financial integrity. Healthcare providers can streamline the claims process by leveraging data analytics, ensuring more expedient reimbursements and identifying and addressing incorrect payments.

Also Read: Key Strategies for Ensuring Cybersecurity in Healthcare

Reduces Medical Errors

Medical errors arising from surgical, diagnostic, medication, and other failures affect approximately 400,000 patients a year and cost hospitals billions of dollars in lost revenue. Many errors occur due to staff negligence or insufficient information. Big data can help reduce these medical errors by analyzing the patient’s medical records with all prescribed treatments and flagging anything that seems out of place.

Now that we have seen how data analytics is used in healthcare for reducing costs and the benefits of data analytics in healthcare, let’s see what the future of data analytics looks like in the healthcare industry.

Applications of Data Analytics in Healthcare With Real-Life Examples

Data analytics is widely applied in healthcare to enhance patient care and operational efficiency. These applications highlight how data-driven insights are transforming healthcare practices and outcomes. Let’s check out the top examples of data analytics in healthcare:

Geisinger Health System’s Population Health Management

Data analytics empowers healthcare providers to discern health trends within specific populations, facilitating targeted interventions. By examining demographic data, socioeconomic factors, and health outcomes, providers can pinpoint high-risk groups and allocate resources more effectively.

Geisinger Health System utilizes data analysis to uncover health trends among its patient populations. This results in tailored community health initiatives that tackle prevalent issues like diabetes and heart disease. These initiatives enhance individual patient outcomes and contribute to the community’s overall well-being.

Teladoc Health’s Telemedicine Enhancement

Analytics evaluate the effectiveness and utilization of telehealth services, enhancing virtual care. By analyzing usage patterns, types of appointments, and demographic information, healthcare providers can pinpoint service gaps and tailor their offerings to better address patient needs.

Teladoc Health leverages data analytics to assess patient satisfaction and health outcomes from telemedicine consultations, enabling it to refine its services and elevate the patient experience. By continuously analyzing feedback, it can implement enhancements that make virtual care more efficient and effective for its patients.

Mount Sinai’s Utilization of Predictive Analytics

Predictive analytics forecasts patient outcomes by analyzing historical data and identifying risk factors. By identifying patterns in patient data, healthcare providers can anticipate complications and address them proactively.

Mount Sinai Health System in New York utilizes predictive analytics to identify patients at risk for readmission. By analyzing data from EHRs, they implement targeted interventions that reduce readmission rates and enhance patient care. This proactive approach leads to better health outcomes and more efficient resource use.

Pfizer’s Clinical Research Optimization

Data analytics in healthcare streamlines clinical trials by identifying suitable participants and analyzing trial data. Researchers can refine trial designs and improve participant selection efficiency by employing advanced algorithms and machine learning techniques. This accelerates the drug development timeline and increases the chances of trial success by ensuring the right candidates are matched with the appropriate studies.

Pfizer leverages data analytics to optimize its clinical trial processes, utilizing patient data to pinpoint the most suitable trial candidates. This approach expedites drug development and enhances outcomes.

Navigating the Challenges of Data Analytics Implementation in Healthcare

Implementing data analytics in healthcare presents several challenges. By addressing these issues, healthcare organizations can unlock the full potential of data analytics. Let’s examine the top implementation challenges and solutions to overcome them.

Data Integration Issues

Healthcare organizations often rely on many disparate data sources, complicating effective integration and analysis.

Solution: Invest in interoperable systems and standardized data formats to enable seamless data exchange. Utilize robust data integration tools to consolidate information from various platforms.

Data Quality Concerns

Inaccurate, incomplete, or outdated data can significantly hinder analytics initiatives.

Solution: Establish a strong data governance framework with routine quality assessments and validation processes. Promote consistent data entry practices among staff to enhance accuracy.

Complexity of Healthcare Data

The intricate nature of healthcare data can complicate analysis efforts.

Solution: Leverage advanced analytics tools with machine learning capabilities to handle complex datasets and uncover valuable insights effectively. Simplify data presentations for stakeholders to enhance understanding.

Siloed Departments

Different departments may operate in silos, leading to a lack of collaboration and shared insights.

Solution: Foster cross-departmental collaboration by forming interdisciplinary teams focused on analytics projects. Implement integrated reporting systems to promote the sharing of insights across the organization.

Shortage of Skilled Personnel

There is often a lack of healthcare professionals proficient in data analytics and data science.

Solution: Invest in training programs for current staff and collaborate with educational institutions to develop a talent pipeline. Hiring specialized data analysts or working with external consultants can also help bridge skill gaps.

The Future of Data Analytics in Healthcare

Analyzing healthcare data is already transforming the sector by enabling providers to deliver quality care while reducing overall costs for patients and medical institutions. This data-driven approach helps identify trends, optimize resources, and improve patient outcomes.

As data analytics capabilities continue to evolve, the impact of analyzing healthcare data is expected to grow even more substantially in the coming years, further enhancing the efficiency and effectiveness of care delivery.

Big data analytics in healthcare will increasingly be used to help predict the probability of future scenarios to make informed and better decisions.

Technological and organizational advancements will make increasing quantities of data available suitable for developing and deploying data analytical tools and systems. Processing power will increase in the future, and more off-the-shelf analytical tools will be available, making this development easier and commercially realizable.

Also, in the coming years, there will be a significant increase in the use of data analytics tools to predict patient compliance and engagement, which is particularly important in successfully managing long-term conditions.

Empower Your Business with Appinventiv’s Data Analytics Solutions

As we have seen, the impact of data analytics in healthcare is tremendous. Data analytics is vital in reducing hospital expenses and enhancing patients’ overall quality of care. This is the reason why there has been a significant surge in the adoption of big data analytics in the healthcare sector.

As a premier healthcare software development company, Appinventiv boasts a skilled team of professionals dedicated to implementing data analytics solutions tailored for healthcare organizations.

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So, in case you want to implement data analytics solutions in your business or learn the importance of data analytics in healthcare, you can discuss with our IT healthcare consulting experts. Our team will offer you the best-in-class data analytics services, with all your needs covered!

FAQs

Q. How is data analytics used in healthcare?

A. Data analytics in healthcare is utilized efficiently to improve patient outcomes and streamline operations. It helps identify trends in patient populations, predict disease outbreaks, and manage chronic conditions more effectively. By analyzing patient flow and staffing needs, analytics can optimize resource allocation, resulting in shorter wait times and enhanced care delivery.

Moreover, it supports personalized medicine by examining genetic and lifestyle information, allowing healthcare providers to tailor treatments to meet each patient’s unique needs.

Q. What is the future of data analytics in healthcare?

A. Here are some of the key data analytics future transformations expected in the healthcare sector:

Integration of AI and Machine Learning: Integrating artificial intelligence and machine learning will enhance healthcare data analysis, making it quicker and more precise.

Personalized Healthcare: As healthcare becomes increasingly personalized, analytics will be essential in customizing treatments based on comprehensive patient data, ultimately leading to better health outcomes.

Increased Data Availability: Due to greater data availability and technological innovations, the future of data analytics in healthcare is set for major advancements.

Evolving Predictive Analytics: Predictive analytics will develop, allowing for earlier interventions and improved risk assessments.

Q. Why is data important in healthcare?

A. Data is vital in healthcare because it informs decision-making and enhances patient care. It offers valuable insights into how effective treatments are, ensures patient safety, and improves operational efficiency. By analyzing data, healthcare providers can spot trends, optimize resource use, and create targeted interventions for specific patient groups.

Data also helps ensure compliance with regulations and boosts the overall quality of care by supporting evidence-based practices. Using data effectively leads to better health outcomes, cost savings, and increased patient satisfaction.

THE AUTHOR

Sudeep Srivastava

Co-Founder and Director