Top 10 Examples Of Big Data Analytics In Healthcare That Can Save People

The advent of big data has revolutionized how data is handled, evaluated, and utilized across various industries. The healthcare sector has also undergone significant changes due to data analytics. Healthcare analytics can reduce the cost of treatment, anticipate epidemics, prevent diseases, and enhance the overall quality of life. As life expectancy increases globally, medical practitioners face new challenges in developing effective treatment methods. Similar to entrepreneurs in the business world, healthcare professionals can collect large amounts of data and employ the best strategies to utilize it.

This article will focus on the significance of big data in healthcare and hospitals, elucidating how it can be beneficial, why it is indispensable, and the obstacles to implementing it. Additionally, we will examine ten current examples of big data in healthcare that can aid medical institutions. However, before delving into those, let’s explore the fundamental concept of healthcare analytics using big data.

What Is Big Data In Healthcare?

Big data is a term used to describe the large amounts of information generated by the digitisation of patient records and hospital management systems that traditional technologies cannot handle due to their size and complexity. Healthcare analytics that utilises big data can result in positive and life-saving outcomes by analysing large amounts of health-related data to prevent epidemics, diagnose diseases, lower costs, etc.

The healthcare industry has changed as people live longer, and data plays a crucial role in this transformation. Early disease detection is essential, and doctors want to learn as much about a patient as possible from their medical history to identify any warning signs of serious illness. This can lead to early intervention, which is less costly and less complicated. The industry has turned to key performance indicators and healthcare data analytics to provide tailored healthcare packages and address the problem of patient data silos in hospitals, clinics, and surgeries.

The healthcare industry has access to many sources that provide insights into patient health. However, the data comes in various formats and sizes, presenting a challenge for users. The current focus is on smart data management rather than data size. Technology can extract data from various sources, including patient portals, research studies, electronic health records, search engines, wearable devices, government agencies, generic databases, payer records, patient waiting rooms and staffing schedules.

In the past, collecting vast quantities of data for medical purposes was expensive and time-consuming. However, with technological advancements, gathering and creating comprehensive healthcare reports has become more manageable. These reports can then be analysed to extract critical insights to improve patient care. Healthcare data analysis aims to predict and solve potential problems before they become more severe using data-driven findings. Additionally, it can lead to the faster assessment of treatment methods, better inventory tracking, greater patient involvement in their health, and the provision of tools to empower patients to take control of their health.

10 Big Data Applications In Healthcare

Now that we understand the importance of big data in healthcare, let’s examine ten practical instances where analytical methods have been applied to improve patient care, streamline processes, and ultimately prevent loss of life.

1. Improved Staffing through Patients’ Predictions:

Hospitals face a common issue of determining the appropriate number of staff required during each shift. Having too many employees can lead to high labor costs, while having too few can result in substandard patient care. Four hospitals in the Assistance Publique-Hôpitaux de Paris system are using big data to solve this problem. They are utilizing data from various sources to predict daily and hourly patient traffic at each facility. A significant dataset used in this endeavor is ten years of hospital admission records. Time series analysis techniques were used to identify relevant patterns in admission rates, and machine learning algorithms were created to forecast future admission trends. The result is a web-based user interface that provides patient load predictions, enabling more effective resource allocation and better patient care through online data visualization. Intel has released a white paper detailing this process.

2. EHRs (Electronic Health Records)

Big data is widely used in healthcare, with Electronic Health Records (EHRs) being one of the most prevalent applications. EHRs store a patient’s medical data, including demographics, medical history, allergies, and lab test results, and can provide alerts and reminders for follow-up tests and track prescription compliance. Kaiser Permanente’s HealthConnect system is an example of successful EHR implementation in the U.S., which has improved patient outcomes and saved money through reduced office visits and lab tests. While the U.S. has made significant strides in EHR adoption, other countries, particularly the E.U., still have a long way to go.

3. Alerting in real-time

Real-time alerting is a common feature in various healthcare applications of data analytics. Clinical Decision Support (CDS) software is used in hospitals to analyze medical information and provide advice to practitioners for making decisions. However, healthcare providers are moving towards reducing in-house treatments, and wearable devices are used to collect patients’ health data continuously and transmit it to the cloud. This data is then compared with a socio-economic context to adjust delivery strategies. Sophisticated tools are used to monitor this vast data stream, and alerts are triggered in case of any concerning outcomes. For example, if a patient’s blood pressure suddenly rises, a live alert is sent to the doctor, who can take immediate action. Another example is Asthmapolis, which uses GPS-enabled inhalers to identify asthma trends and develop better treatment plans for asthmatics based on data from the CDC.

4. Improving Patient Engagement

Consumers are increasingly using smart devices to track various aspects of their daily lives, including heart rate, sleep patterns, and more. By combining this data with other trackable information, potential health risks can be identified. For example, chronic insomnia and a high heart rate may indicate a future risk of heart disease. Patients can take an active role in monitoring their health, and health insurance companies can incentivize healthy habits by offering rewards for using smartwatches. New wearable devices that track specific health trends are currently being developed and can transmit data to the cloud, where physicians can monitor it. This technology is particularly useful for patients with conditions such as asthma or high blood pressure, allowing them to be more self-sufficient and reducing the need for doctor visits.

5. Combating Opioid Misuse in the U.S.

Big data is being used to tackle the misuse of opioids, which has surpassed car accidents as the leading cause of accidental death in the United States. Blue Cross Blue Shield and Fuzzy Logix have analyzed insurance and pharmacy data to identify 742 risk factors indicating the likelihood of opioid misuse. This information can help in reaching out to high-risk individuals and preventing addiction, offering hope for mitigating the problem that is taking lives and costing the healthcare system significant amounts of money.

6. Leveraging Health Information to Make Strategic Decisions

The healthcare sector can use extensive data to plan their strategies more effectively. By analyzing health data across different demographics, care managers can identify what factors keep people from seeking medical treatment. For instance, the University of Florida used open-source health data and Google Maps to generate maps illustrating the growth of population and chronic illnesses. By comparing this information with the accessibility of healthcare services in the areas with the highest illness rates, they could improve their delivery strategy by adding more care facilities in the most underserved regions.

7. Big Data Might Just Heal Cancer

The use of big data in healthcare is exemplified by the Cancer Moonshot program, an initiative launched by President Obama, aimed at accelerating progress towards curing cancer. By analyzing large amounts of data on cancer patients, researchers can identify trends and treatments that have higher success rates. They can also study tumour samples and patient records to identify connections between specific mutations, cancer proteins, and treatments, leading to unexpected benefits like using Desipramine, an antidepressant that can help cure some types of lung cancer. However, integrating patient databases from various institutions is a challenge due to incompatible data systems, patient confidentiality, and reluctance to share data. Nonetheless, data analysis has the potential to hasten progress towards finding a cure for cancer.

8. Leveraging Data Insights To Anticipate Health Issues

Predictive analytics has the potential to go beyond the business world and make significant contributions to healthcare. Optum Labs has collected electronic health records from over 30 million patients to create predictive analytics tools that can improve healthcare delivery. Healthcare business intelligence tools can assist physicians in making quick and informed decisions to improve patient care, which is especially beneficial for patients with complex medical histories or multiple conditions. By leveraging these new tools, it is possible to predict which individuals are at risk of developing diabetes and recommend additional screenings or weight management.

9. Decrease Fraud And Improve Security

Healthcare organizations use analytics to detect suspicious behavior and prevent data breaches by implementing security measures. Analytical tools also help reduce fraud and streamline the insurance claim process, leading to better reimbursements and faster payment. The Centers for Medicare and Medicaid Services claim to have prevented over $210.7 million in fraud in a year thanks to these tools.

10. Telemedicine

Telemedicine, which utilizes technology to provide remote clinical services, has become fully functional with the emergence of online video conferencing, wireless devices, smartphones, and wearables. It can be used for primary consultations, remote patient monitoring, medical education, and even telesurgery. With healthcare data analytics, healthcare professionals can personalize treatment plans, anticipate medical events, and avoid hospitalization. Telemedicine reduces costs, improves service quality, and provides care that can be accessed from anywhere and anytime.

Why We Need Big Data Analytics In Healthcare

Upon examining the list of significant applications of big medical data, you may already be aware of the benefits of employing analytics in the healthcare industry. To summarize the essential points of importance, here are some advantages of big data in healthcare. The healthcare industry has a tremendous requirement for big data, especially with the rising expenses in countries such as the United States. As per a report by McKinsey, healthcare costs currently constitute 17.6% of the GDP, which is nearly $600 billion more than the anticipated benchmark for a country of the United States’s size and wealth. This quote underscores the primary benefit.

• Decreasing costs:

The healthcare expenses have been rising for the past two decades, and it has become necessary to have more intelligent, data-informed strategies to tackle this problem. Numerous insurance providers are shifting their focus from fee-for-service to outcome-based policies, emphasising patient outcomes rather than the quantity of patients treated rapidly with costly and superfluous treatments.This shift in incentives encourages healthcare providers to share patient information to utilise the power of analytics, resulting in better patient care and lower costs for insurance companies. The financial incentives for prioritising patient health are becoming more apparent, causing a positive change in the healthcare industry.

• Decreasing medical mistakes:

Medical practitioners now increasingly depend on evidence-based decisions, which are based on comprehensive research and clinical data, rather than relying solely on their education and professional experience. Nonetheless, doctors are only human, and there is still a chance of human error. This can result in prescribing the wrong medication or treatment, which can be dangerous to a person’s life. By employing big data and the various tools mentioned in this article, healthcare professionals can be rapidly alerted if there is a mistake in the medication, test, treatment, or any other area, and rectify it immediately. This can significantly decrease medical errors and improve the reputation of the facility. Data gathering and management are increasing in many other industries, and healthcare professionals need assistance. This fresh approach to treatment has created a higher demand for big data analytics in healthcare facilities than ever before, and the advent of SaaS B.I. tools is addressing this need.

• Improving how an organisation is managed, including the management of personnel:

Although data is essential to provide quality patient healthcare, big data can also assist the healthcare industry in other operational areas. Ensuring that healthcare facilities work optimally to provide quality care is crucial. With the help of big data, this can be achieved. With the aid of appropriate B.I. software, healthcare workers can collect and assess up-to-date data about their facility’s operations, finances, and staff management performance. Predictive analytics can provide valuable information on admission rates, allowing healthcare facilities to determine staffing schedules to meet demand and stock inventory for medical supplies. This enables care facilities to be proactive and ensure that patients receive the best possible care. Managers can use these findings to redirect resources where they are most required and improve underperforming areas, ensuring the best return on investment.

Challenges to Implementing Big Data in Healthcare on a Large Scale

The benefits of utilising big data in healthcare were previously discussed, but it is also important to acknowledge the challenges that come with this technology. Some of the challenges that organisations may face when integrating analytics into their processes include the following:

1. Data integration and storage: The integration of medical data from various sources can be challenging. To utilise big data in healthcare, data providers must work together to develop new infrastructure that can accommodate the various sources of data.
2. Data sharing: The healthcare industry needs to catch up with other industries that have adopted more advanced analytics methods. To achieve this, online reporting software and business intelligence strategies must be implemented to enable relevant users to access the data.
3. Security and privacy: Hospitals and care centres deal with sensitive patient data that requires utmost privacy and security. Healthcare organisations must comply with regulations, conduct regular audits, and train employees on data protection best practices.
4. Data literacy: The use of big data in healthcare requires a certain level of knowledge and skills that can be a limitation for average users. However, self-service B.I. tools have been developed to enable any user to access and utilise analytics without requiring technical knowledge.

In conclusion, new healthcare analytics applications can benefit patients, and organisations should prioritise overcoming the challenges and obstacles discussed above. Other industries can provide inspiration and ideas for coping with these challenges.

10 Big Data Examples In Healthcare – A Summary

The healthcare industry is evolving, and, similar to other industries, big data has a significant impact. However, incorporating new technologies to push the industry forward and enhance decision-making and operations is still much to be accomplished. To summarise the examples mentioned in this article, healthcare data analytics can assist in the following:

• Improved Staffing through Patients’ Predictions
• Electronic Health Records (EHRs)
• Alerting in real-time
• Improving Patient Engagement
• Combating Opioid Misuse in U.S.
• Leveraging Health Information to Make Strategic Decisions
• Big Data Might Just Heal Cancer
• Leveraging Data Insights To Anticipate Health Issues
• Decrease Fraud And Improve Security
• Telemedicine

The application of data analytics in healthcare, as demonstrated by the ten examples discussed earlier, highlights its importance for healthcare professionals in saving lives. Through data-driven analytics, early detection and timely interventions can be made, leading to faster, safer, and more precise patient care. As technology continues to evolve, these critical functions will become even more effective, signalling that the future of healthcare is already here and dependent on data.

Author bio: Mark Edmonds is a highly experienced writer and researcher who specializes in the field of healthcare. He has worked as a content creator and editor for several years for Academic Assignments, a leading provider of high-quality assignment writing services. Mark is passionate about helping students achieve their academic goals and has provided expert guidance and support to countless students pursuing careers in healthcare. With his in-depth knowledge of the healthcare industry and exceptional writing skills, Mark has become a trusted source of healthcare assignment help for students worldwide. In his free time, he enjoys reading about new developments in healthcare and spending time with his family.