How Big Data in Health Care Influences Patient Outcomes

Three people in lab coats look at a computer screen.

Imagine a wearable medical device that alerts you if your day-to-day activities put you at risk for developing diabetes. Imagine an app that provides immediate, personalized medical advice based on your genetic profile. Innovative technologies like these are just around the corner, as data collection tools in health care are used to transform healthcare delivery and help improve patient outcomes.

But just what is big data in health care? Learn more about the importance of data collection in health care and how data-driven healthcare solutions are revolutionizing the healthcare system.

What Is Big Data in Health Care?

Big data is a massive amount of information on a given topic. Big data includes information that is generated, stored, and analyzed on a vast scale — too vast to manage with traditional information storage systems. In health care, the move to digitize records and the rapid improvement of medical technologies have paved the way for big data to have a big impact in the field.

Many industries use big data to learn about their customers and tailor their products or services accordingly. In health care, big data sources include patient medical records, hospital records, medical exam results, and information collected by healthcare testing machines (such as those used to perform electrocardiograms, also known as EKGs).

Biomedical research on public health also provides a large portion of the big data that, if properly managed and analyzed, can serve as meaningful information for patients, doctors, administrators, and researchers alike. For example, public health researchers can generate big data to predict and prepare for future pandemics.

Why Collecting Data in Health Care Is Important

Big data collection and analysis enables doctors and health administrators to make more informed decisions about treatment and services.

For example, doctors who have big data samples to draw from may be able to identify the warning signs of a serious illness before it arises. Treating disease at an early stage can be simpler and costs less overall than treating it once it has progressed.

In other areas of the healthcare industry, administrators can use key performance indicators and data analytics to make a number of funding and resource allocation decisions. Big data amassed from health records and Google maps have been used to create critical health maps that highlight underserved locations, for example. Administrators and providers can use such information to determine where to deploy mobile health clinics and other resources.

Hospitals and other large care facilities can use big data to capture a comprehensive picture of patient experience. Big data tools allow care teams to merge data that would otherwise be archived in separate clinics, hospitals, and specialist offices and remain underutilized. Big data holds the promise of consolidating patient data, allowing for rapid and accurate communication between patients and providers that draws from a patient’s entire health history.

How Big Data Improves Patient Outcomes

For years, amassing big data for medical use has been expensive and time-consuming. Today, innovative technologies can collect data electronically and convert it into an easily readable form. Health professionals can now generate data-driven healthcare solutions to improve patient outcomes in many ways:

  • Empowering patients to engage with their own health histories with easy-to-access medical records
  • Informing providers of patients’ ongoing health status so they can in turn assess treatment methods faster
  • Saving patients time and money
  • Improving access to quality health care by streamlining administrative processes and helping administrators make informed decisions about allocating funds and resources both within and between health institutions
  • Harnessing data-driven findings to predict and solve medical issues earlier than ever before

Types of Healthcare Data

Medical records are just one type of healthcare data generated by our complex medical systems. Researchers with the Centers for Disease Control and Prevention (CDC) estimate that over 883.7 million office-based physician visits take place annually in the United States. According to the National Center for Health Statistics within the CDC, over 85 percent of office-based physicians use electronic medical record systems.

Types of patient-centered healthcare data also include:

  • Medical records
  • Dental records
  • Surgical records
  • Behavioral data (for example, a patient’s diet)
  • Biometrics (for example, a patient’s blood pressure)
  • Living conditions

Big data collection tools in health care can generate insights at the institutional level as well. Hospitals, clinics, and independent providers may track other forms of healthcare delivery data:

  • Staffing schedules (for example, to determine how many medical workers to put on staff in a given time period to care for patients)
  • Patient waiting room time
  • Insurance claim data
  • Medical referrals
  • Employee performance metrics (for example, number of patients cared for per hour)
  • Supply chain metrics (for example, for ordering the correct amounts of personal protective equipment)

Collecting High-Quality Healthcare Data with Big Data Tools

Big data allows healthcare providers and health administrators to drill down and learn more about their patients and the care they provide to them. Collecting high-quality data requires optimization of data collection tools in health care and proper use of such tools by patients and providers alike.

Improving Patient Engagement with Wearable Technology

Smart devices can record a patient’s activity levels, heart rates, sleeping habits, and many other biometrics in real time. Coupled with big data, patients’ vital information can supply doctors with more accurate medical data than patient-provided questionnaire responses alone. Wearable technologies, then, can facilitate rapid communication between doctors, patients, and their data, which could cut down on expensive hospital visits.

Getting Everyone on the Same (Electronic) Page

Questionnaire data from patients provides just one side of a patient’s health. Patients and providers alike may benefit from a holistic view supplied by standardized information from big data.

For example, doctors with patients at risk for heart disease who need to monitor their blood pressure may recommend that their patients reduce their sodium intake. In a traditional model, providers may question patients about their lifestyle and dietary habits (“How often do you add salt to the food you eat?”) and take vitals (blood pressure, blood glucose levels, etc.) to keep track of their patient’s progress.

Today, smart interactive questionnaires synced with real-time biometric technology allows providers to record information faster and in a more standardized form, leading to faster responses and individualized treatment plans.

Increasing Access to Primary and Preventive Care

Big data allows doctors to serve patients in rural areas and other locations where a robust medical infrastructure may not exist. For example, patients can use smart devices in their homes to communicate with a medical provider.

Big data also can build on and improve existing telehealth systems through automation. For example, patient questionnaire responses can be compared to a vast pool of population data and treatment plans can be automatically suggested to physicians, who can then approve outbound recommendation messages to patients, rather than write each one manually.

Addressing Concerns with Big Data in Healthcare

Privacy of patient data is crucial to protect as big data infrastructures emerge and develop in healthcare. In light of ongoing cybersecurity breaches, healthcare organizations must prioritize security. From malware to phishing attacks, healthcare data has vulnerabilities like any other collection of confidential information.

The HIPAA Security Rule offers a list of safeguards for healthcare organizations storing protected health information (PHI). These data practices include:

  • Ensuring transmission security
  • Adopting authentication protocols
  • Managing controls over data access and integrity
  • Scheduling regular data security audits

In more concrete terms, these safeguards may involve encrypting sensitive data, enabling firewalls, implementing multi-factor authentication, and ensuring anti-virus software is up-to-date.

Healthcare organizations must also remind their staff frequently that data security is critical. Staff must be willing to prioritize data security, which may mean complying with software updates, security checks, and constraints on access to data. Organizations must also consistently follow data security protocols, including reviewing who may have access to confidential data.

Learn More About Careers in Health Administration

Big data is revolutionizing health care for the better. With an advanced degree in health administration, graduates can prepare to support patients with innovative healthcare delivery systems that combine the best of medicine and technology.

Students pursuing an Online Master of Health Administration degree at Tulane University can earn an advanced education while working full time. Learn more about how students in the Online Master of Health Administration program become leaders in the health care field.

Why Community Health Is Important for Public Health

What Is Health Equity? Ensuring Access for Everyone

Why Healthcare Advocacy Is Important

Sources

American Public Health Association, Health Equity

Cancer Prevention & Control, “Evidence on Patient-Doctor Communication”

Centers for Disease Control and Prevention, DLS Strategic Framework

Centers for Disease Control and Prevention, Electronic Medical Records/Electronic Health Records (EMRs/EHRs)”

Centers for Disease Control and Prevention, Genomics, Big Data and Data Science in Public Health

Datapine, “18 Examples of Big Data Analytics in Healthcare That Can Save People”

Health Information Science and Systems, “Big Data Analytics in Healthcare: Promise and Potential”

Heredity, “Big Data in Digital Healthcare: Lessons Learnt and Recommendations for General Practice”

Journal of Big Data, “Big Data in Healthcare: Management, Analysis and Future Prospects”

Scientific Programming, Special Issue: Healthcare Big Data Management and Analytics in Scientific Programming, “A Systematic Review of Healthcare Big Data”

US Department for Health and Human Services, “Summary of the HIPPA Security Rule”