6 Ways of Using Data Analytics in Healthcare to Reduce Cost

Top 6 Ways How Big Data is Efficiently Reducing Healthcare Costs?

Did you know that 57% of healthcare organizations are using their data to make decisions? 

But what if your healthcare organization is struggling to manage a huge amount of patient data? 

Driving meaningful insights from this data could help you reduce costs and save more lives. However, with the increasing complexity of medical records, treatment histories, and patient outcomes, attempting to manage the rapidly expanding patient data with a traditional approach or manual excels may end up with costly blunders. 

This is where the need for advanced data management and analysis is more critical than ever. Big data analytics has emerged as a transformative force in the healthcare industry, surprisingly changing the way we deal with data across various industries—healthcare is no exception!

Harnessing data analytics in healthcare has become a leading discipline that helps lower treatment costs, optimize processes, and save countless lives. The technology enables accurate predictions of disease outbreaks, timely identification of life-threatening conditions, and personalized treatment plans that improve outcomes. In addition, it streamlines operational processes, reduces treatment costs, and enhances overall efficiency. 

The impact of big data analytics in healthcare is becoming stronger with each passing year. The reports have projected that the growing market value will surge from $23.51 billion in 2020 to $96.90 billion by 2030, at a CAGR of 15%. 

The growth rate highlights the increasing reliance on data-driven strategies to tackle healthcare challenges. Healthcare organizations are using data analytics to save countless lives and making it a vital tool for reducing overspending on poor stock management, patient care, and staff deployment. 

In this blog, we have explained in detail what healthcare data analytics is and how it is transforming the healthcare industry. 

Understanding the Role of Data Analytics in Healthcare Industry

In the healthcare landscape, data is more than just numbers. The healthcare industry handles 5% of all data available globally. Therefore, data is the key to unlocking better patient outcomes and more efficient systems. From diagnostic imaging to administrative processing, data analytics in healthcare is revolutionizing the way organizations handle operations. 

The importance of Data analytics in healthcare has become important since the COVID-19 pandemic when healthcare was exposed to a huge gap between its systems globally. By leveraging data analytics, hospitals and clinics were able to respond more effectively to unexpected medical challenges, manage resources, and deliver better care even under immense pressure. 

Since 2019, data analytics in healthcare has become more than just a top app development trend. It empowers doctors to make better decisions by predicting patient outcomes based on a combination of factors such as age, medical history, and potential medication effects. 

Data analytics in healthcare is now considered a powerful tool that allows for more personalized treatment plans, ensuring that patients receive care tailored to their unique needs. Moreover, data analytics helps healthcare organizations manage surging patient volume by simply optimizing the use of resources like medical equipment, hospital beds, and staff. 

To incorporate AI-powered data analytics into healthcare workflows, you need to look for a data analytics consulting company that enables you to focus more on patient care rather than juggling the complexity of data.   

Why is Data Analytics in the Healthcare Industry Needed?

After the pandemic, the healthcare industry has successfully recognized the gaps they are having in their system. With the COVID-19 outbreak, the healthcare landscape is under huge pressure to prepare themselves with tools that enable them to predict future medical issues with a solution. No matter how brilliant healthcare app ideas you have adopted for your business, it requires a strategy to evolve. 

Now the question is what kind of healthcare challenges are there that are impacting the healthcare cost? What are the core attributes of increasing healthcare costs?

Before you dig deep into the process of understanding how Data Analytics in Healthcare empowers organizations with better insights and simplifies processes, let’s take a look at the factors that are affecting the increasing healthcare costs.

Increasing Percentage of Aging Population

According to the WHO report, 32 to 42% of the population falls in the age bracket of 70 -75 years. 

As people live longer, there will be a higher demand for medical care. At this time of their age, medical attention is required for chronic and complex conditions. This demographic shift causes a huge strain on healthcare resources and drives up a cost.

Technological Advancements 

To deliver enhanced patient care and improve treatment quality, it is important to adopt new technologies, which ultimately come up with a higher price tag. Apart, the cost of implementing and maintaining advanced medical equipment and treatments contributes to the overall rise in expenses. 

Inefficient Administrative System

Despite many advancements in technology, there is a major percentage of healthcare organizations that still suffer from administrative inefficiencies which lead to billing errors, redundant processes, and poor resource management. These inefficiencies contribute to higher operational costs and affect the overall cost of patient care. 

Global healthcare analytic market is projected to reach $96.90B by 2030!

Our expert team of data analytics can help you discover all new innovative opportunities using the existing healthcare data and patient’s medical records.

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Key Benefits of Data Analytics in the Healthcare Industry

The implementation of data analytics in healthcare industry comes with a series of benefits ranging from improved patient care to personalized treatment plans, and early detection of medical issues to mapping patient journeys. 

However, before you jump straight to integrating data analytics in the healthcare ecosystem, it is worth looking for big data analytics services to help you navigate the possibilities with data analytics…

Predictive Maintenance for Medical Equipment

Though the cost to create a healthcare app is a little expensive, but to keep delivering better care, it is worth having a digital centralized setup. But, now things have moved to the next level. To deliver better care, it is important to have medical equipment in good shape. And for that, data analysis in healthcare helps in the predictive maintenance of medical equipment.

With real-time data insights, healthcare organizations can easily predict when devices’ maintenance or replacement is due. Adopting this proactive strategy can help minimize downtime and ensure that equipment is constantly available for treatments.

Fraud Detection and Prevention

Patient data is important and it is the responsibility of healthcare professionals to keep it secure and safe. This is where adopting advanced patient data analytics is important for identifying fraudulent activities within healthcare systems. 

With constant evaluation of billing patterns and comparing them to established benchmarks, organizations can easily flag the anomalies for further investigation. This process helps protect financial resources and patient data. 

Optimizing Resource Allocation

With data analytics, healthcare organizations can create a perfect balance between rising healthcare demand and supply of resources. Healthcare providers can optimize the allocation of resources such as staff, beds, and medical supplies. 

This is where the critical role of data analytics is to help you analyze patterns and predict patient influx, and enables you accordingly plan your resources and strategically use them to resource wait times and improve patient care. 

If you are still confused about how it can be managed and implemented, then considering BI consulting services will be a worthy decision. This dynamic approach will allow for better planning and preparedness, particularly during emergencies and seasonal surges in patient volume. 

Supporting Clinical Decision-Making

One of the direct benefits of using data analytics in healthcare is it significantly enhances clinical decision-making by providing healthcare professionals with actionable derived from large volumes of patient data. 

With data insights, you can easily analyze the trends and outcomes, which allows doctors can make more informed decisions regarding diagnoses, treatment options, and care plans. 

Cost Management and Reduction

Data analytics aids in cost management by identifying high-cost-affecting areas and enabling targeted savings through detailed cost analysis.  It also supports contract optimization by analyzing claims data, helping healthcare organizations negotiate better terms with insurers and vendors, ultimately reducing overall expenses. 

These are the few major benefits of incorporating Data analytics in healthcare but what type of data analysis would you require in healthcare? 

Major Types of Data Analytics in Healthcare 

In healthcare, various types of data analytics are essential for improving patient care and operational efficiency. While choosing big data analytic consultants can help you make the best benefits of healthcare data, the choices can vary depending upon the situation and requirements.

Five types of healthcare data analytics can be applied based on the needs of healthcare professionals and organizations, which include:

• Descriptive Analytics: It provides a holistic overview of the data that allows healthcare providers and organizations to determine the practices that they are adopting to address patient issues. 

• Predictive Analytics: Predictive analytics allow organizations to forecast future healthcare events such as any disease outbreaks, and possibilities for patient readmissions, and let them proactively respond to the issues with resource planning in advance. 

• Diagnostic Analytics: It helps identify the specific outcomes or events by analyzing data relationships, aiding in understanding why such health issues occur. 

• Prescriptive Analytics: It suggests actionable recommendations based on data analysis and helps healthcare professionals make informed decisions on treatments and interventions. 

• Real-Time Analytics: It processes data immediately as it comes on the panel and allows organizations to take immediate action. It works brilliantly in case of emergencies or for monitoring patient vitals. 

The various types of data analytic models use advanced algorithms to help determine the effect of special actions and provide solutions by applying historical data to get specific results. But the question is how it can help you reduce the cost of healthcare. Let’s discuss it…

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Top Ways to Reduce Increasing Healthcare Costs Using Data Analytics

As AI-powered app development company experts say, to get the big picture of your business, it is important to optimize the data of your organization. The right way of applying data analytics can increase patient access to services, resulting in lower costs and more revenue.

Data analytics and visualization can increase a patient’s access to services and lower costs. Here are the major applications of data analytics in healthcare: 

1. Managing Patient Health Records Digitally

Hospitals or healthcare organizations that are managing the patient’s data with a manual admin system need to digitize the medical records to save on cost and time. For this Electronic Health Record (EHR) system is implemented into the existing system that helps generate a lot of data from the existing clinical information. And the best part is, all the data collected in EHRs related to patients’ diagnostic information, and administrative or patient history, will be updated in real time for immediate action. 

Using this system can help organizations get information on procedures, demographics, length of stay, and fees. Additionally, EHRs improve the quality of care by sending constant reminders and warnings for diagnosing and reducing operational costs by simply digitizing data management.

2. Forecasting Demand for Operating Rooms

Operating rooms are one of the most budget-effective areas of hospitals that can’t be easily expanded with increasing requirements. By carefully analyzing the use of operating rooms without compromising on the patient’s healthcare is one of the prime concerns in most healthcare institutions. 

To attain this goal, several providers and administrators are utilizing healthcare data analytic consulting services to integrate the data analytic tools into their existing systems. With expert data analysts, hospitals will get to know the availability of the operating rooms and predict the future scheduling of operating rooms. These predictions will include the availability of surgeons, operating hours, and equipment functionality and availability. 

There are so many factors to consider when creating a surgical schedule and that won’t be an easy task for administration with manual processes. So implementing data analytics in the process can help healthcare organizations streamline operating room management and make sure that staff, patients, and rooms remain available at the right time of need. 

So the core role of data analytics in reducing cost is to help mine the data, study the pattern of operating room utilization, and use predictive modeling methods to match the most appropriate patient and surgeons and support resources with the right operating rooms to avoid blunders. 

In the US, one of the top hospitals has decided to switch to mobile apps that use cloud computing services and predictive analysis to optimize operating room utilization. With this practice, they have successfully eliminated schedule conflicts. This smart move to switchover right technology has saved them $15 million in revenue from scheduled surgeries annually. 

3. Prevent 30-Day Hospital Readmissions

Readmissions within a 30-day window are costly and often indicate inadequate care. Reducing readmissions not only reduces hospital costs but also minimizes the burden on hospitals to manage resources.

At this point, healthcare data and analytics tools can be used to identify the patients in need of special care or with special symptoms that lead to readmissions. With the implementation of systems powered by data analytics, healthcare organizations can manage and take additional measures to prevent patients from coming back for readmissions within a 30-day window.

For instance, the leading data scientist at a medical center has analyzed the overall issues aligned with readmissions and developed predictive analytics algorithms that analyze all clinical factors. This patient data analysis makes patients likely to spend less than two nights in a hospital. In addition, this practice helps physicians know the right time to provide treatment or place a patient under observation. 

4. Predicting No-Show Appointments

One of the major challenges that healthcare providers come across is dealing with patient’s no-shows which leads to lost revenue, inefficiencies, and disturbing schedules. These missed appointments not only waste valuable time of medical staff but also prevent patients from seeking timely care. 

Data analytics plays a pivotal role in mitigating this issue by predicting the likelihood of no-shows through various data points and analyzing the data trends. But how do data analytic services work and predict no-shows?

Here are the key highlights:

• Analyzing the patient’s historical attendance records, data analytic tools can help identify the pattern of whether the patient will be punctual, likely to miss visits, cancel lately, or inconsistent with appointments.

• Incorporating various external factors like weather conditions, public holidays, or local events that may affect a patient’s ability to attend their appointment. For example, in case of a snowstorm or major public events, there will be a high chance of encountering no-shows.

• Evaluating demographic and behavioral insights like where travel distance, appointment times, and demographics of patients also affect attendance. For example, patients traveling long distances and having early morning slot schedules are more likely to be canceled. 

By leveraging predictive analytics, healthcare providers can proactively address the issue of no-shows, enhancing both operating efficiency and improving patient satisfaction. 

5. Managing Supply Chain Costs

The cost and quality of treatment in healthcare organizations are heavily depending upon the availability of essential medical supplies, equipment, and medications. Any disruption or inefficiency in the supply chain will have a direct impact on patient care and operational costs. 

Healthcare organizations using systems powered by data analytics can enhance supply chain management through real-time monitoring and insightful predictions. Here’s how data analytics optimizes supply chain costs:

Data analytic tools keep tracking the status of inventories and supplies in real time and allow healthcare providers to access the real-time data of stock. It helps prevent shortages and delays that make healthcare providers to compromise on patient care.

With data analytics, healthcare providers can easily forecast demand by analyzing historical data and current trends. This enables hospitals to maintain optimal inventory levels, reducing excess stock and minimizing waste.

Data analytics allow healthcare providers to evaluate suppliers’ performance by analyzing delivery times, quality of goods, and contract adherence. Extracting this information helps hospitals partner with a consistent supply chain. 

With data analytics tools, service providers can identify cost-saving supply chain opportunities like bulk purchasing or looking for alternative suppliers offering lower prices without compromising on quality. 

6. Efficient Staffing Solution

Staffing accounts for a major portion of healthcare expenditures and often consumes nearly half of a hospital’s budget. Ensuring that the right number of staff is crucial to maintaining high-quality patient care while controlling costs is one of the challenging tasks. However, choosing data analytics and consulting services can provide you with a holistic view of your staffing and provide tools to predict and manage staffing needs with precision.

Here’s how data analytics help enhancing staffing efficiency:

• Forecast predictions by analyzing various factors, including historical staffing data, trends in patient admissions, weather patterns, and seasonal health issues. Data analytics can accurately predict hospital staffing needs. With this broad overview, hospitals can prepare for periods of high demand, such as flu season. 

• Data analytics help you allocate resources mindfully and more strategically. For example, during anticipated surges in patient volume, additional nurses or support staff can be scheduled in advance, ensuring continuous care without overburdening the existing workforce.

• Another major staffing challenge hospitals often encounter is managing staff shift schedules. With data-driven insights, healthcare providers can develop more effective shift schedules that seamlessly align with patient volume and staff availability. 

In a nutshell, data-driven staffing can help you save a huge cost on overstaffing appointments, minimize unnecessary overtime, and prevent the need for expensive last-minute hires. 

Looking to transform your healthcare operations with data analytics?

With the healthcare analytics market growing at a CAGR of 15.3%, our experts can help you tap into the booming industry by offering tailored data analytics services.

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How can Xicom Help You Implement Data Analytics in Healthcare System?

In today’s dynamic healthcare environment, the strategic use of data analytics is key to driving superior patient outcomes, improving operational efficiency, and reducing costs. Xicom stands at the forefront of this transformation, helping healthcare providers unlock the full potential of their data through innovative and tailored solutions.

Tailored Data Analytics Solutions

Understanding that no two healthcare organizations are the same, Xicom offers bespoke data analytics solutions that cater to your specific needs. Our experts work closely with you to develop and implement strategies that seamlessly integrate with your existing systems, ensuring you can harness the power of data without disrupting your operations.

Seamless Data Integration

Healthcare providers often face challenges with data silos across electronic health records (EHR), diagnostic imaging, administrative systems, and more. Xicom excels in integrating these diverse data sources into a unified platform, enabling comprehensive insights that lead to more informed decision-making and enhanced patient care.

Predictive Analytics for Proactive Care

With Xicom’s predictive analytics, healthcare organizations can shift from reactive to proactive care. By analyzing historical data and identifying trends, our solutions help forecast patient needs, optimize treatment plans, and anticipate potential health issues before they become critical, ultimately improving patient outcomes and reducing hospital readmissions.

Resource Optimization

Efficient resource management is critical in healthcare. Xicom’s data analytics solutions provide actionable insights into resource utilization, from staffing and equipment to inventory management. This ensures that healthcare facilities can allocate resources effectively, reducing waste and ensuring availability where it’s needed most.

Enhanced Patient Experience

Personalized care is the future of healthcare. Xicom leverages advanced analytics to create personalized treatment plans based on patient history, preferences, and real-time health data. This not only improves patient satisfaction but also enhances treatment efficacy, fostering better health outcomes.

Fraud Detection and Data Security

Protecting patient data is paramount. Xicom’s analytics solutions include advanced fraud detection capabilities that help identify and prevent fraudulent activities. Our focus on data security ensures that sensitive patient information remains protected, fostering trust and compliance with regulatory standards.

Cost Reduction and Efficiency

Xicom helps healthcare providers identify cost-saving opportunities by analyzing operational data. From optimizing supply chain logistics to improving billing accuracy, our solutions streamline processes and reduce unnecessary expenses, ensuring financial sustainability while maintaining high-quality care.

With Xicom, healthcare organizations can leverage the power of data analytics to drive innovation, improve patient care, and achieve operational excellence. If you are ready to unlock new opportunities with Xicom, then it is worth booking an appointment with our experts.

Conclusion

At the end of this blog, it is fair enough to say that incorporating data analytics into the healthcare system is no longer a modern trend – it has become a necessity to deliver improved patient care. By leveraging patient medical data and other healthcare records, Data Analytical tools provide you with a broad picture of your workflow and help you plan your resources to reduce costs and streamline resources and operations. Our Big data analytic consultants can help you transform your healthcare ecosystem with data-driven decisions.

Xicom, being a top software development company is dedicated to empowering healthcare organizations with cutting-edge analytics solutions that drive innovation and improve decisions. Whether it be predictive analytics, resource optimization, or enhanced patient experience, our tailored healthcare app development services enable you to keep evolving with the changing patient requirements. For more details, you can directly contact us!

FAQs

Here we have gathered some additional information related to data analytics in healthcare industry and how it can help in transforming patient care and medical costs.