Researchers at Johns Hopkins University assert that big data, combined with biomedicine advancements, is a transformative and all-encompassing force in healthcare. It sets the stage for a Second Revolution in the industry. That is why integrating big data into medical records can alter the healthcare landscape, introducing an era of exciting changes and possibilities.
The industry’s journey to its current state has been marked by excellent tech advancements such as telemedicine, medical imaging, electronic health records (EHRs), and robotics. Tech innovation has made these developments possible, with big data playing a pivotal role. Big data in healthcare is often a matter of life and death, as it aids in saving lives and reducing costs.
According to an IDC report, the growth rate of big data in the medical domain is expected to surpass that of other industries like manufacturing, financial services, and media. However, the surge of digital tech has also uncovered many vulnerabilities, like data privacy concerns and the potential to misuse sensitive information. This article will explore the benefits of big data in healthcare alongside the key applications, real-world examples, and adoption challenges.
Table of Contents
What Makes the Adoption of Big Data in Healthcare So Crucial?
The impact of big data in healthcare is vast, and its full utilization can transform healthcare practices. Here are some key points that highlight the tech’s potential to revolutionize medical companies, a change that can significantly improve patient outcomes.
Better Patient Care
Big data is not just a prospect but an immediate tool for enhancing care delivery. Research shows that integrating big data strengthens diagnostic precision, enabling medical professionals to provide more personalized and effective treatments. By analyzing large datasets from EHRs, genomics, and wearable technology, you can ensure patients receive high-quality care promptly.
Higher Operational Performance
Big data in healthcare can reshape hospital operations. With examined data patterns and trends, the technology could increase operational efficiency by streamlining workflows, minimizing wait times, and optimizing resource allocation. A McKinsey report estimates this could save the U.S. healthcare system between $300 billion and $450 billion annually.
Reduced Costs
Big data analytics in healthcare helps identify inefficiencies and reduce waste, leading to substantial cost savings for healthcare organizations. For instance, you can analyze patient admission data to determine patterns that indicate potential inefficiencies in processes. This allows you to make adjustments to improve resource allocation. Predictive analytics can also anticipate patient admissions, empowering hospitals to manage resources more effectively and avoid unnecessary expenditures.
McKinsey underscores the potential for cost reduction, estimating savings of 12% to 17% of total healthcare costs through the strategic use of big data. This promising figure highlights the outstanding financial benefits for medical facilities.
Improved Patient Engagement
The technology allows healthcare providers to deliver more personalized patient experiences, boosting engagement and satisfaction. By analyzing patient feedback and interaction data, medical facilities can identify areas for improvement and adjust their services to meet patient needs better and streamline hospital processes.
For instance, wearable devices are one big data application in healthcare, improving patient engagement by providing real-time health monitoring and immediate feedback.
After all, the integration of big data in healthcare is a transformative force that marks a great advancement in the medical field and inspires hope for its future.
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Critical Benefits of Big Data in Healthcare
Optimization of healthcare processes is the main strength of big data techs, which benefits various medical fields, including:
- Empowering Personal Health Management. Big data does not just provide insights; it empowers patients. They better understand their conditions by comparing their health status with symptomatic cases analyzed through big data analytics. Automating this process, extracting the most relevant symptoms and diagnoses from similar cases, further enhances personal health management. That puts patients at the center of care.
- Simplifying Diagnostic Decision-Making. Big data enhances the diagnostic process by combining medical specialists’ human expertise with machine diagnostics capabilities. This synergy helps doctors make more informed diagnoses based on the computer analysis of a patient’s medical history.
- Transforming Traditional Medical Practices. Big data analytics enables the evolution of traditional healthcare practices into innovative methods for capturing institutional memory and professional expertise. By leveraging collective experience, doctors can develop and refine self-improving therapies, achieving sustainable progress in treating complex diseases. This transformation is necessary for the future of healthcare.
Big Data Advantages for Doctors
Most medical professionals acknowledge the specific benefits of big data in healthcare. Various surveys among doctors highlight the primary positive outcomes of using the technology:
- Time-Saving Workflows. Big data analytics eliminates manual tasks in data-processing routines, freeing healthcare staff from more professional activities. Additionally, big data algorithms are more precise and efficient in data processing compared to error-prone human operators.
- Enhanced Diagnostic Accuracy. The more use cases observed, the more precise the symptomatic patterns become. Accurate diagnoses rely heavily on two interdependent factors: the doctor’s expertise and the amount of data available for a particular case. Even a highly qualified doctor can provide an accurate diagnosis with relevant data.
- Effective Therapies. Widely sharing progressive treatment methods is crucial for advancing clinical practice. Each use case matters: a specific laboratory finding or a report from an ambulance crew can save lives. Healthcare information systems based on globally collected big data help doctors choose the proper therapy for each patient.
- Reduced Disease Risks. Analyzing pandemic patterns and addressing common and chronic diseases like diabetes, cardiovascular disease, seasonal colds, and flu are impossible without big data. Big data analytics is critical in understanding and mitigating these health risks, providing a reassuring layer of protection for the public.
- Monitoring of Health Social Factors. Big data provides insights into social factors that impact health. These insights help address broader health determinants indirectly but effectively:
- nutritional quality (indications of obesity or malnutrition)
- drug regulation and control (availability of opioid analgesics in pharmacies)
- local hygiene and preventive measures
- climate change and weather conditions
Therefore, big data processing technologies improve healthcare delivery and pave the way for more efficient, accurate, and personalized medical care.
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The Most Common Big Data Use Cases in Healthcare
When utilized by medical professionals, big data analysis empowers them with valuable knowledge, transforming all aspects of care delivery. From conducting medical research to enhancing patient experience, big data in healthcare is crucial in improving patient care and outcomes. That helps foster a sense of empathy and connection among healthcare experts.
Improving Diagnostics
By leveraging the power of big data, you can swiftly and accurately diagnose diseases. This efficient process saves time and equips doctors and physicians with knowledge and high efficiency. It departs from the traditional method of discussing symptoms, comparing them to known ones, and consulting medical literature or colleagues for complex cases. That makes the diagnostic process more productive and efficient.
Big data allows physicians to gather patient information and use algorithms to suggest the most likely diagnoses. These algorithms also recommend high-value tests, reducing the need for unnecessary procedures. That helps save time and resources and ensures a more considerate approach to patient treatment. Moreover, computer vision tech in healthcare is also extensively used for diagnostics, like early anomaly detection in the retina to prevent diseases.
Modeling & Predicting Outcomes
Big data and predictive analytics are indispensable tools, providing you with the confidence to make informed clinical decisions. Predictive modeling, a widely used tool, is key for predicting disease or treatment outcomes and identifying at-risk patients. Also, it helps forecast disease spread within populations, giving healthcare professionals a sense of preparedness and control.
Monitoring Health Conditions in Real-Time
Wearables and other IoT devices, now widely produced by healthcare technology companies, are at the forefront of the industry’s trends. These devices automatically collect health metrics such as heart rate, pulse, blood pressure, temperature, oxygen concentration, and blood sugar levels. This empowers patients to take control of their health. Such automation removes the need for them to visit providers or manually collect data. After all, the vast amounts of valuable information generated by these solutions can aid doctors in diagnostics and treatment planning.
Creating Treatment Plans for Serious Diseases
Analyzing patient data on various treatment plans can reveal trends and patterns, identifying those with the highest success rates. That reassures healthcare professionals that they are providing the most effective treatments, optimizing patient care, and enhancing the quality of life for those with serious diseases. This approach makes them feel impactful in their roles.
Increasing Population Health Quality
Big data enhances the quality of life by offering the potential to predict and prevent the spread of infectious diseases. Although such tools were unavailable during past pandemics, big data became instrumental in enhancing epidemic surveillance and response during the coronavirus pandemic. Now, nations globally utilize big data and data analytics to deliver real-time statistics, monitor the spread of viruses, and assess the potential impact of outbreaks.
Implementing Preventive Care
Preventing diseases is preferable to treating them for patients, hospitals, and insurance providers. Physicians aim to keep patients healthy and out of hospitals, and big data is crucial in this effort. By analyzing behaviors, the tech can predict the likelihood of illness and identify early warning signs of severe conditions.
Leveraging Telehealth
Big data is highly relevant in telehealth, enabling doctors to perform surgeries remotely with robots and high-speed real-time information. The tech is crucial in robot-assisted surgery, initial diagnosis, remote patient monitoring, and virtual nursing assistance. Telehealth and big data simplify the lives of doctors and patients:
- Patients can avoid waiting in lines
- Doctors save time by reducing unnecessary consultations and paperwork
- Patients receive monitoring and consultation anywhere, anytime
- Big data helps prevent hospitalization or re-admission
- Clinicians can predict acute medical events and prevent patient condition deterioration
- Telehealth reduces costs and enhances service quality
Analyzing Imaging Information
Analyzing imaging data such as CT, MRI, or PET scans is complex, but big data analytics streamlines this process for radiologists. Algorithms identify specific patterns in the pixels and convert them into numbers, aiding healthcare specialists in diagnosis.
This process also allows you to build image history catalogs. They are comprehensive databases of past imaging data for each patient and use computer vision and data science techniques for quick analysis. Such catalogs enable doctors to compare current images with past ones, aiding in detecting subtle changes or patterns that may indicate a health issue.
Integrating Electronic Health Records
EHRs are one of the primary sources of big data in healthcare. According to Hi-Tech and Global research, many organizations have adopted EHRs, with 94% of US hospitals implementing them. EHRs provide a complex and detailed view of a patient’s medical history, securely shared through information systems available to public and private sector providers.
They enable doctors to make timely updates without paperwork or data replication issues. These solutions also generate alerts for necessary lab tests and track prescriptions to ensure patients adhere to their treatment plans.
Providing Robust Security
Big data analytics in healthcare is not just a buzzword. The tech plays a crucial role in fraud prevention and detection. One critical application is identifying changes in network traffic or behaviors indicative of cyber-attacks. Advanced analytical tools help healthcare organizations detect unusual access patterns, abnormal data transfers, and irregular usage times. Once these potential threats are identified, you can take immediate measures to prevent harmful activities, enhancing the security of sensitive patient information.
Managing Hospital Operations
Big data platforms for healthcare can improve hospital operations and reduce costs. Through data-driven analytics, you may predict when you will need staff in particular departments at peak times while distributing skilled personnel to other areas during quiet periods. Also, by keeping track of employee performance across the board, you can use healthcare data analysis to gain insight into who needs support or training and when.
Building Strategic Plans
Technology allows medical specialists to understand better and spot problems and opportunities that derive from it. You can identify which areas suffer the most and which care units are more prevalent. Also, big data analytics assists in the discovery of new therapies and drugs. With a mix of historical, real-time, predictive analytics, and data visualization techniques, healthcare experts can determine potential strengths and weaknesses in trials or processes.
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Major Challenges of Adopting Healthcare Big Data
Big data offers innovative solutions to prevent diseases, predict healthcare outcomes, and reduce medical errors. Its integration can enhance the quality and affordability of care. However, many healthcare organizations have yet to embrace big data fully. A recent PwC survey reports that 95% of healthcare CEOs actively seek better ways to utilize and manage the tech, but only 36% have made meaningful progress.
So, what are the primary obstacles hindering the widespread adoption of big data in healthcare? Let’s explore the most pressing issues and understand why it is crucial to address them.
Integrating and Storing the Information
The big data ecosystem resolves the challenges of ingesting and storing vast and diverse data. Concepts like data lakes provide solutions for storing various types of medical information, such as images, document files, and exports from older health systems.
Standardizing Data
While healthcare applies multiple data standards, you can standardize the diverse information from data lakes into structured forms like data warehouses. Combining information from various sources, including EHRs and lab reports, is essential. In most healthcare organizations, data is dispersed across multiple departments, leading to fragmentation. Therefore, cross-departmental cooperation is vital for successful data integration. Adopting a data-driven mindset, suitable data collection mechanisms, and employee training are also necessary.
Ensuring Proper Data Quality
AI and ML algorithms require high-quality input data, free from duplications and inaccuracies, to generate reliable insights. The need for such insights in healthcare cannot be overstated. Poor data quality can lead to patient misidentification and incorrect treatments. Thus, modern medical practices must implement data governance and master data management solutions to enhance data quality. Automated checks for incremental pipelines and thorough data preparation and cleaning are critical in this case.
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Collecting Data
Data exploration tools from the big data ecosystem, commonly used in healthcare business intelligence (BI), are vital for data mining. However, data engineers and scientists are the unsung heroes, playing a key role in extracting valuable insights from healthcare data.
Sharing the Information
Sharing healthcare data across multiple organizations poses tremendous challenges due to the need for more standardization. However, the importance of timely and accurate data sharing is indeed underscored during public health emergencies. Every second counts and the information shared at the right time can make a life-saving difference.
Visualizing Data
Projects that involve developing big data platforms for healthcare require high visibility. It means providing real-time monitoring, operational dashboards, and relevant periodic business/report dashboards. However, visualizing health data is not a simple task. This process is a complex challenge that relies on specific tools and expertise.
Providing Scalability
Some healthcare enterprise data warehouse systems lack horizontal scalability and support only vertical scaling. Migrating to a massively parallel processing (MPP) data warehouse or the big data ecosystem can address the emerging scalability issues.
Guaranteeing Information Security and Privacy
Security is a top priority in healthcare, a highly regulated industry with stringent laws governing the storage and sharing of sensitive data. Despite these regulations, data breaches and leaks are common. Setting up configurations, conducting regular audits, performing risk assessments, and training employees on security best practices are vital. Meanwhile, healthcare experts, IT specialists, and administrators are critical in ensuring information security and privacy.
Looking for Big Data Skills
Leveraging big data in healthcare requires robust infrastructure, advanced analytical tools, and skilled personnel. Collecting, cleaning, processing, managing, and analyzing vast medical information can be daunting. However, partnering with experts is a key strategy to overcome these issues while implementing and using big data and big data analytics.
Complying with HIPAA
Big data solutions must comply with HIPAA rules to protect personal information against data breaches. However, government agencies must provide a specific roadmap for compliance, leaving healthcare organizations to create frameworks to ensure that for big data applications.
Addressing these challenges will enable medical practices to fully leverage big data, improving patient care, operational efficiency, and overall healthcare outcomes.
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Real-Life Examples of Adopting Big Data in Healthcare
Researchers, medical facilities, and physicians are increasingly collaborating to utilize a vast healthcare data network. This collective effort allows them to gain insights into clinical contexts, prevent future health problems, and discover new treatment approaches. We have highlighted five critical areas where big data is being used to tackle significant challenges in healthcare.
Here are examples of companies and organizations that are making strides in these fields.
Cancer Research
Cancer has touched nearly all of our lives, driving an intense search for new methods to combat the disease. Researchers across the private and public sectors are committed to finding cures and creating more effective treatments. Big data has revolutionized cancer research by offering unprecedented access to patient information, trends, and patterns.
Tempus
Tempus is creating the world’s most extensive molecular and clinical data library. This resource provides medical professionals with a deep clinical context for each cancer case, instilling a sense of security in the depth of cancer research. The Tempus platform aggregates and organizes data from lab reports, clinical notes, radiology scans, and pathology images, accelerating oncology research. Also, it assists physicians in crafting more personalized and informed treatment plans.
Flatiron Health
Flatiron Health is a beacon of collaboration, leveraging billions of data points from cancer patients to enhance research and uncover new insights for patient care. Their solutions connect oncologists, hospitals, academics, and life science researchers, fostering a collaborative environment where all parties can learn from each patient’s experience.
Oncora Medical
Oncora Medical’s platform is about more than just integrating machine learning, automation, and big data. The tool streamlines workflows for oncologists, allowing them to compile and quickly update patient information. This efficiency reassures us about the speed and accuracy of cancer treatment, instilling confidence in the future of cancer care and enabling oncologists to deliver more timely and personalized treatment plans.
Disease Detection
Early detection of diseases and complications is vital for effective treatment. Screenings and exams are crucial in staying ahead of conditions like cancer and multiple sclerosis.
Pieces
Pieces is a cloud-based software company that gathers data throughout the patient journey to improve care quality and cost. Their flagship product, Pieces Decision Sciences, is a clinical engine that makes decisions and recommendations based on various data types, such as lab results, vitals, and structured and unstructured data. The platform continuously identifies potential interventions, offering hope for improved patient outcomes.
PeraHealth
PeraHealth developed the Rothman Index, a peer-reviewed, universal scoring system for assessing a patient’s health. It integrates data from EHRs, vitals, lab results, and nursing assessments. The scores are presented in a visual graph and updated in real-time, identifying changes and tracking details and ultimately aiding in preventing complications.
Prognos
Prognos uses AI to analyze clinical data and manages Prognos Factor, a hub for multi-sourced diagnostic data. Their AI platform assists physicians in applying treatments earlier, displaying clinical trial opportunities, suggesting therapies, and identifying care gaps for over 30 conditions.
Population Health
Population health focuses on studying patterns and conditions that affect the overall health of groups, differing from public health, which aims to ensure healthier populations. Big data is critical for understanding population health, as identifying patterns without data is challenging.
1upHeallth
1upHealth offers data solutions to promote interoperability across healthcare. Their 1up Population Connect product simplifies the acquisition and sharing of clinical and claims data, reducing manual workloads and informing decision-making with patient population data.
Arcadia
Arcadia provides a big data platform that offers actionable insights across the healthcare landscape. It enables organizations to make strategic decisions that support their financial, clinical, and operational objectives. Arcadia’s analytics capabilities in population health management help identify and overcome care gaps, enhancing overall health outcomes.
Amitech Solutions
Amitech Solutions, a company that applies information to healthcare through modern data management and analytics, takes a patient-centric approach. Their population health management solutions not only combine physical and behavioral health data to identify risks but also engage patients in their own care, making them feel valued. This ultimately leads to improved health outcomes, reinforcing the importance of patient involvement in healthcare.
Pharma Research
Pharmaceutical research relies heavily on data to discover and create new medications, from vaccines and synthetic insulin to antihistamines. Data is essential for assessing the viability and effectiveness of new treatments.
Evidation
Evidation offers a mobile app that rewards users for healthy behaviors, provides health insights, and allows them to contribute to health research. Through Evidation, researchers can access everyday health data to inform their work, leading to new ways of diagnosing, treating, and managing various medical conditions. The company ensures that users have control over their data by seeking consent before accessing it.
IQVIA
IQVIA connects analytics, data, and tech to facilitate faster and more effective clinical research for pharmaceutical leaders. With a comprehensive healthcare database, IQVIA uses AI and ML to identify ideal patients for specific trials. That allows pharmacists to conduct decentralized trials, compile data with IQVIA’s devices, and accelerate the R&D process.
Kalderos
Kalderos is addressing the cost of pharmaceuticals with its drug discount management platform, which aggregates data from multiple sources and stakeholders to enhance transparency. Drug manufacturers, covered entities, and payers can use the platform to collaborate, promoting trust and equity within the industry.
Patient Health Records
Risk is essential in healthcare and health insurance, influencing how patients access care. Thus, many reliable companies leverage big data to gain deeper insights into risk and ensure accuracy in adjustments.
Blubyrd
Blubyrd helps surgical facilities and clinical practices compile and exchange data efficiently and securely. The platform manages data such as appointment schedules, procedure codes, and equipment inventory, streamlining operations and enhancing data accuracy.
Avaneer Health
Avaneer Health aims to improve the efficiency of data flow in healthcare by providing network participants with access to administrative support and secure transactions. Founded in 2020 by top healthcare industry leaders, including CVS, Anthem, and Cleveland Clinic, Avaneer Health’s platform relies on blockchain technology to ensure data security and integrity.
Particle Health
Particle Health offers an API platform that consolidates patient records into a single, secure location. With a simple query, developers can access clean and actionable data sets, enabling healthcare providers to make more meaningful patient recommendations. The goal is to use this consolidated data to enhance patient care and decision-making.
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Final Words
Big data transforms healthcare, from improving cancer treatments to optimizing hospital operations. Remarkably, its adoption in the medical sector is still early, with significant progress expected throughout the second half of 2020s.
With the increasing integration of big data in healthcare, the focus is shifting towards enhancing predictive analytics and tailoring treatments to individual patients. This shift is primarily driven by AI’s growing capabilities, enabling more healthcare organizations to depend on real-time data analysis, which is crucial for swift clinical decision-making.
Ultimately, to ensure a smooth transition of your healthcare facility into the era of big data, you should consider utilizing the expertise of a remote IT team proficient in medical data analytics.
From predictive analytics to real-time patient monitoring, our big data solutions help reduce costs, improve patient outcomes, and increase efficiency!
FAQ
What is big data in healthcare?
Big data in healthcare refers to the vast volumes of health-related information generated from various sources like EHRs, medical imaging, genomic sequencing, wearables, and IoT devices. This data is analyzed using advanced analytics and ML techniques to uncover patterns, trends, and insights. The goal is to improve patient care, enhance operational efficiency, enable early disease detection and personalized treatment plans, and support medical research.
What are the key advantages of big data in healthcare?
Big data offers numerous benefits in the medical domain. These include improved patient care through more accurate diagnoses and personalized treatments and increased operational efficiency by optimizing workflows and resource allocation. The technology also offers significant cost reductions by identifying inefficiencies. It also enables early disease detection and better population health management and supports medical research, leading to a more efficient and patient-centered healthcare system.
