How Big Data in Healthcare Encourages Value-Based Care

big data in healthcare

An insightful survey by the PwC reveals that 91% of surveyed patients underwent video virtual care and are likely to use it again. In addition, 66% of all individuals would use remote clinical trials. Thus, it’s evident that the healthcare world is becoming used to technologies and using them to their benefit.

Given the complex nature of the healthcare world, medical facilities need powerful software and in-depth databases. Today, organizations acquire cutting-edge technology to enhance performance and productivity.

It also means that technical devices and software’s gather increases volumes of patient and doctor data- which translates to a dire need for big analytics in the healthcare industry.

Big data has successfully transformed how healthcare specialists manage, operate, and analyze data. Here we discuss the importance of big data and its applications:

A Quick Glance at Big Data in Healthcare

According to an in-depth study, big data will likely grow faster than other industries- experiencing a compound annual growth rate of 23% between 2020 and 2025.

Another insightful research reveals that the big data analytics industry will likely reach $67.82 billion by 2025. It indicates an impressive growth rate of 19.1% between 2018 and 2025.

More importantly, McKinsey and Company’s research shows that big data can help individuals save up to $300 to $450 billion annually.

Thus, it’s evident that big data is transforming the healthcare industry, boosting productivity, and improving value-based care.

What is Big Data In Healthcare?

Big data refers to the volume of data amassed from various sources. Big data refers to electronic health records, genomic sequencing, medical imaging, payor records, wearable medical devices, and more in the healthcare world.

Users often confuse big data with traditional electronic medical health data. Here’s what makes big data stand out:

  • It helps analyze high volumes
  • It moves at an incredible velocity
  • It is highly variable since it derives from various sources

Types of Healthcare Data

Healthcare data collection is of various types, including:

1. Administrative Data

When providing top-notch medical services, institutions need to collect data about the most profitable services.

Healthcare providers may collect such information by pulling data from individual patients, including their appointments, registration in programs, billing, and more.

Healthcare organizations can allocate their resources and improve value-based care armed with this information.

2. Electronic Health Records

Electronic health records comprise confidential data relevant to a patient’s medical history. It includes their past illnesses, treatment plans, results, and more.

Doctors and staff members create EHRs and update them according to need. It helps diagnose, develop treatment plans, and transmit data from one specialist to another.

3. Clinical Data

Healthcare facilities like nursing homes have to provide standardized healthcare data. Medical institutions can determine the best way to allocate resources with this data.

Big data analytics also allows healthcare institutions to assess the performance and productivity of a company.

Applications of Big Data in Healthcare

Up to 97.2% of all organizations invest in big data and AI. Why? It’s because big data offers users a plethora of benefits and uses, including:

1. Improve Health Tracking

Big data analytics and IoT offer new and innovative ways for tracking user statistics and vitals. Conventional wearables that can detect your heart rate, sleep time, beats per minute, distance walked, and more

Apart from them, revolutionary medical equipment can now track glucose levels, blood pressure, pulse oximeters, and more. With these state-of-the-art techs, healthcare providers can monitor the health and safety of patients without keeping them in hospitals. They can also identify potential risks and intervene before the situation worsens.

2. Streamline Hospital Administration

Big data helps streamline and smoothen healthcare administration. It achieves this by effectively reducing the cost of patient care, providing incredible clinical support, and managing the number of at-risk patients.

It also allows healthcare providers to analyze data from various sources. In turn, healthcare providers can conclude the best treatment plans and their effects on a particular patient’s health.

3. Integrate Big Data with Medical Imaging

Medical imaging is an essential part of the healthcare world- mainly since specialists perform more than 600 million imaging procedures annually.

Analyzing and storing these images can be expensive and time-consuming since radiologists examine each image individually.

Big data analytics solve this problem by efficiently analyzing thousands of images and converting them into numbers. This way, big data can help streamline workflow within medical institutions.

4. Eliminate Prescription Errors

Research by the Network for Excellence in Health Innovation reveals that prescription errors cost healthcare institutions approximately $21 billion annually. In addition, it affects 7 million patients and causes more than 7,000 deaths.

Big data helps reduce prescription errors which, in turn, boosts outcomes and saves live.

5. Reduce Overall Costs

Big data is an excellent way of reducing costs in medical institutions experiencing over or understaffing. Big data’s predictive analytical nature solves this problem by effectively predicting admission rates.

Moreover, the insurance company can save costs by providing wearables and health trackers. This way, patients don’t have to stay in hospitals for prolonged periods, which helps reduce wait times for other consumers.

6. Fight Cancer with Big Data

Cancer rates are increasing dramatically across the world. But big data can help combat cancer effectively and appropriately.

With big data and its analytical tools, healthcare providers can effectively detect and diagnose diseases in their early stages. In addition, they can curate holistic treatment plans based on a patient’s unique genetic makeup and minimize side effects to maximize effectiveness.

It also provides better support for mapping billions of DNA base pairs.

7. Prevent Suicide and Self-Harm Attempts

Each year, approximately 800,000 people commit suicide which translates to one death per 40 seconds. In addition, more than 17% of the world’s population self-harms during their lifetime. While these statistics are alarming, they’re only growing.

Suicide and self-harm are complex areas to tackle; however, big data steps in as the perfect solution to help make a positive change. Big data analytics helps identify which individuals are more likely to self-harm or attempt suicide.

In an insightful study by KP and the Mental Health Research, researchers provided individuals with a mix of standard depression questionnaires and EHR data. With this data, researchers were able to identify people at a greater risk of attempting suicide and self-harm.

8. Facilitate Telemedicine

Integrating big data with telemedicine poses several advantages, including allowing doctors to perform operations from miles away.

Big data also facilitates early diagnosis, virtual nursing assistance, remote patient monitoring, and more. Telemedicine and big data make the lives of patients and doctors easier in the following ways:

  • Doctors don’t have to fill unnecessary paperwork and consultations
  • Healthcare experts can monitor and consult 24/7
  • Patients help reduce waiting in lines
  • Reduce unnecessary hospitalizations and re-admissions
  • Clinicians can effectively predict medical events at the right time
  • Effectively reduce costs and boost the quality of service

9. Facilitate Preventive Care

Preventing diseases is far better than curing them for hospitals, patients, and insurance companies.

With big data, healthcare providers can effectively predict the chances of someone getting sick based on their current health metrics and behaviors.

In this way, doctors can identify warning signs before symptoms evolve into a severe illness.

10. Prevent Opioid Abuse in the U.S.

Studies reveal that overdoses from opioids cause more accidental deaths than road accidents—big data analytics in healthcare steps in as the ideal solution.

With big data, healthcare organizations can determine, with high accuracy, which patients are at risk for abusing opioids.

11. Enhance the Use of Electronic Health Records

Electronic Health Records provide healthcare organizations access to big data, meaning most healthcare institutions are already using them. According to insightful research, more than 94% of all U.S. hospitals leverage EHRs to execute healthcare practices.

Electronic health records provide doctors and patients access to complete patient medical history. Since documents are shared through cutting-edge information systems, all patient-doctor data is secure.

EHRs also trigger warnings when a patient doesn’t follow the doctor’s orders and reminders when a patient should get tested.

12. Improve Supply Chain Management

Weak and fragmented supply chains can impact all components of a medical institution, including patient care, long-term finances, patient care, and more. Luckily, big data in healthcare leverages insightful analytics to streamline the supply chain and ensure efficiency.

By leveraging analytics tools, healthcare providers can track supply chain performance metrics. This way, medical institutions can make data-driven and accurate decisions and save up to $10 million per year.

With predictive and descriptive models, you can enhance decision-making skills to reduce supply variations, negotiate prices, and optimize the overall order process. Ultimately, HCOs can thrive while delivering unparalleled customer service without delays or snags.

13. Develop New Therapies and Innovations

Extensive data analysis promises a bright future in the medical industry by facilitating the discovery of innovative drugs and new therapies.

By combining historical and real-time metrics with data visualization techniques, medical institutions can identify strengths and weaknesses in processes.

In addition, with the help of knowledge-based genetic analysis and reactionary predictions, big data analytics can play a pivotal role in developing groundbreaking drugs and therapies

Overall, data analytics can help innovate, streamline, and enhance security, give confidence, and save lives.

14. Detect and Prevent Fraud

According to research, up to 93% of healthcare organizations experience data breaches. Big data is a valuable tool for preventing various errors in the healthcare administration, including wrong medicines, faulty devices, wrong dosages, and more.

It also helps prevent potential fraudulent insurances claims.

Why is Big Data Important in Healthcare?

Here’s a glance at the importance of big data in healthcare:

  • Better customer service
  • Cost reduction
  • Detects diseases at the right time
  • Ensure accurate treatment
  • Facilitates real-time alerting
  • Forecast the risk of a particular treatment
  • Personalize patient care and treatment plans
  • Predict the cost of treatment
  • Prevents mass diseases
  • Reduces medical errors
  • Streamlined hospital operations
  • Suicide and self-harm prevention

Now, let’s discuss the main benefits of big data in healthcare:

1. Boost Patient Care

Knowledge-based big data offers healthcare providers more valuable clinical insights. With the help of these cutting-edge analytics, specialists can effectively improve patient care.

A doctor can use healthcare databases to prescribe better treatments, make accurate clinical decisions, diagnose appropriately, and eliminate ambiguity.

With big data on our side, the healthcare industry is slowly moving closer to enhancing patient outcomes.

2. Efficiently Predict Patients at High Risk

Big data facilitates aggregate data which, in turn, provides predictive analytics helpful for pinpointing which patients are at a higher risk for experiencing particular diseases.

With these valuable hints, healthcare providers can intervene at the right time to protect them. It is crucial for spotting chronic diseases.

To predict analytics, healthcare providers need to aggregate data related to various factors, including a patient’s medical history, socio-economic profile data, demographic area data, patient’s comorbidities, and more.

3. Reduce Overall Healthcare Costs

Big data provides more significant insights and analytics, translating to more efficient patient care. It also ensures shorter hospital stays, fewer admissions and eliminates the need for re-admission. Ultimately, it helps patients save costs due to less hospitalization.

In addition, predictive analytics helps healthcare providers estimate individual patient costs to maximize patient care by effectively creating a holistic treatment plan.

4. Improved Patient Diagnostics with EHRs

Electronic Health Records (EHRs) are typical applications for data sets and are crucial for achieving an efficient patient diagnosis.

EHRs comprise in-depth patient history, including allergies, medical history, family history, a treatment plan of past illness, previous diagnoses, and more.

EHRs make it easy for doctors to access patient files, update them with new information about illness or treatment plans, and notify patients about appointments.

5. Deliver Better Patient Insights

Analyzing big data helps you identify which segments are at a higher risk of getting infected or experiencing a particular disease. In this way, healthcare providers can take appropriate preventive measures at the right time.

Moreover, this insightful data can help inform and educate patients to take care of their well-being. It also helps boost the effectiveness of treatment plans to deliver better patient care outcomes.

Future Trends for Big Data in Healthcare

Big data helps analyze workforce needs, streamline patient care, and boost customer experience.

Undoubtedly, big data will continue to expand and become increasingly important for the healthcare world. Here we’ve listed the top trends for big data in healthcare:

1. Expanding Categories

Typically, users categorize big data within the three V’s:

  • Volume
  • Velocity
  • Variety

But with big data expanding, those categories have evolved into five, including ‘value’ and ‘veracity.’ Here’s we discuss each ‘V’:

  • Volume- In today’s tech-savvy world, there is more data than ever
  • Velocity- Healthcare providers don’t pull info only from HER; instead, they also use apps, portals, and various other fast and efficient methods
  • Variety- Healthcare organizations collect data from several different sources to create holistic plans
  • Value- Refers to the result of the data a healthcare provider collects. It includes what benefits or uses the information brings to the industry or organization
  • Veracity- Healthcare companies need to focus on increasing the trustworthiness and quality of data to ensure the security of health information and to produce better patient outcomes

2. Predictive Analytics

Another trend for big data in healthcare is leveraging predictive analytics to make the best treatment and diagnostic decisions. It also ensures significant cost-saving potential and improves outcomes for patients.

Predictive analytics are helpful for clinicians, HR teams, finance departments, and virtually everyone within the healthcare world. Your healthcare company may create software for use of predictive analytics to:

  • Determine which patients require re-admission
  • Predict quarterly finances
  • Identify sepsis rates
  • Track which patients require additional care
  • Track the well-being of nurses, doctors, and other staff members
  • Create weekly or monthly schedules

Critical Challenges of Big Data in Healthcare

Let’s discuss the most pressing issues in healthcare:

1. Data Quality

Development of AI and ML algorithms need reliable input data free of inaccuracies to develop high-quality and trustworthy insights. If the data quality is poor, doctors may misdiagnose a patient or prescribe the wrong medicines.

It means that healthcare organizations need to work on data governance and data management to improve data quality effectively.

2. Management Issues

To fully benefit from big data analytics, organizations need to adjust how they do business. Data scientists need to connect with IT staff to run analytics effectively.

3. Data Mining

Data exploration tools used in Business Intelligence help solve data mining problems. Data engineers and data scientists can leverage data mining for improving healthcare services.

4. Policy and Process Challenges

Once you validate and aggregate data, healthcare organizations need to address various processes and policies.

According to HIPAA regulations, medical institutions need to follow stringent guidelines to protect confidential health data.

The Bottom Line

Big data is revolutionizing the healthcare world, one treatment plan at a time. However, this is only the beginning. The evolution in medicine and financing that big data is sure to bring offers tangible solutions to improve value-based healthcare.

Today, big data analytics empowers actionable insights on patients’ history, predicts overall health costs, reduces overall healthcare costs, and generates real-time alerting.

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