Benefits and Challenges of Big Data in Healthcare
Lately, healthcare has undergone several transformations.
It has become more tech-driven than manual and more data-driven than instinctive.
The reasons? We can talk about many. Evolving patient needs, competition, the quest for survival, and delivering better outcomes are some primary ones.
Big data plays a significant role in the situation.
While enabling healthcare players to enhance patient care and treatment, it also helps optimize costs and efficiency.
But is implementing big data in healthcare as straightforward as writing about it is? No.
Impact of Big Data in Healthcare
Big data in healthcare refers to an extensive number of patient records, medical reports, genomic sequences, etc. Thus, the scale is massive and so are the challenges involved.
The numerical impact of big data in healthcare is also humongous.
In 2022, it was valued between 28-30 billion. But by early next decade, projections say, it will grow to fare between USD 130-USD 135 billion!
Numbers speak for big data’s gargantuan role in healthcare. However, at the individual level, healthcare providers may face innumerable issues while leveraging big data.
If you are one among them, don’t feel alone.
At big data service providers, we empathize with your situation. Thus, we’ve emphasized both sides of big data in healthcare – the benefits and challenges.
So, let’s begin with the good thing first – the advantages and then look at the common challenges and plausible solutions.
Use of Big Data in Healthcare
Big data indicates massive and diverse unstructured, semi-structured, and structured data that grow unprecedentedly over time.
These datasets are huge in every sense. Hence, it is difficult for traditional data management systems to store, process, and analyze them.
In the healthcare context, big data can comprise everything concerning the data that a particular center or the entire organization generates every moment.
It can include data pertaining to expenses, patient records, treatment data, invoicing details, insurance claims, employee salaries, doctor appointments, digital presence, etc.
Honestly, all the data is gold to a healthcare organization. It can use that data to make informed decisions, build strategic advantages, and thrive.
However, healthcare players can do it only when they gain optimal value from the data.
Merely analyzing it isn’t enough. They must cover and manage the various aspects of big data, including volume, velocity, variety, veracity, and value. It involves overcoming various challenges, which we will discuss shortly but demands expert hand-holding.
Benefits of Big Data in Healthcare
Let’s begin with the use of big data in healthcare.
Enhanced Patient Outcomes
Patients are at the core of healthcare – and they will remain so forever!
Therefore, big data must first benefit healthcare players on the patient front.
So, how does big data do it?
It helps physicians make informed decisions by providing insights from several other cases. Furthermore, doctors can use predictive models to forecast disease progression and personalized medicine recommendations based on a patient’s situation, history, etc.
These factors can help hospitals serve patients better and enhance outcomes.
Increased Operational Efficiency
Contemporary hospitals and healthcare centers generate a gigantic amount of data every day. It includes lab results, billing information, patient records, etc.
Big data tools help streamline these processes, identify bottlenecks, and help departments operate more efficiently.
Amidst rising operational costs, this can help healthcare players save money. Additionally, it can help them save time while maintaining the quality of care and service.
Early Detection and Prevention of Disease
Big data can help healthcare organizations move from reactive to proactive and prevent healthcare. How? It helps them analyze trends and risk factors and identify individuals with a higher risk of developing certain diseases before they show symptoms.
This unlocks the doors to early interventions, lifestyle changes and eventually preventing illness. It can work as an advanced early warning system for an individual’s health.
Expediting Drug Research and Discovery
This can benefit the healthcare domain as a whole.
When healthcare players can analyze extensive datasets of patient outcomes, clinical trials, and genetic information, the medical world can accelerate identifying new drug targets, innovating more effective therapies, and comprehending disease mechanisms.
It can refer to developing life-saving treatments that reach and benefit patients faster.
Improving Public Health
Analyzing population-level data can help healthcare players gain valuable insights into disease outbreaks, track how effective public health interventions are, and identify health-related disparities within communities. It can help the healthcare fraternity devise more targeted and effective public health strategies.
So, that was about the advantages of big data in healthcare.
However, healthcare organizations can confront various challenges while adopting big data and making it work in their specific context.
Let’s look at some common ones and the solutions at the broader level.
Challenges of Healthcare Big Data Analytics
Here are some common challenges of big data in healthcare and potential solutions.
Data Privacy and Security
Medical data is highly sensitive. Thus, ensuring it stays secure and confidential is fundamental to every healthcare organization.
Accordingly, hospitals, healthcare centers, large-sized clinics, etc., must implement robust systems and set stringent regulations to prevent breaches and information misuse.
Some measures in this regard can include;
- Robust encryption and anonymization techniques
- Solid regulatory frameworks and compliance
- Seek patient consent and provide control over data
- Secure data storage and access controls
Data Silos and Interoperability
Imagine your data is locked in systems that cannot interact or are disconnected.
While impeding the use of data, it can prove to be an operational hurdle, particularly when you depend on data for running routine operations and making decisions.
In this case, healthcare establishments must ensure their systems can seamlessly share and exchange data to achieve the interoperability levels they intend to achieve.
Some actions can include;
- Standardized data formats and APIs
- Using data integration platforms and middleware
- Adopting cloud-based to achieve scalability and interoperability
- Taking collaborative data-sharing initiatives
Data Quality and Accuracy
Technologies like big data, predictive analytics, AI, etc., are only as good as the data they receive, they are trained on, and they process. So, if your data lacks quality, so would your insights! It will consequently affect decision-making and outcomes.
Healthcare providers must ensure data accuracy, completeness, and accuracy across different sources. Of course, it is challenging. However, here are some measures to implement.
- Establish comprehensive and clear data governance frameworks
- Automate data cleansing, error detection, and standardization
- Audit data regularly and implement continuous monitoring mechanisms
- Ensure data professionals receive regular training and updates
Data Analysis and Interpretation
Having abundant data but no one to handle or process is like having all the ingredients on the table but no chef to cook them!
Thus, healthcare players must hire in-house resources or outsource them to big data service providers. The latter is a better strategy given that the providers provide data scientists, analysts, healthcare experts, etc., to use the data and derive meaningful insights.
Some additional initiatives can include;
- Developing intuitive analytical platforms
- Leveraging the power of AI and ML
- Ensuring collaboration between data scientists and clinicians
Data Infrastructure and Technology
Of course, data and related technology come at a significant cost. Many healthcare providers do not have the financial muscle to invest in them.
Remember, consulting big data experts can prove helpful. They help make the right choices and invest in what matters to the organization from the commercial, strategic, and patient perspectives. Some recommendations can include;
- Using scalable and affordable cloud computing services
- Adopting high-performance computing infrastructure
- Partnering with big data experts and service providers
- Scaling phase-by-phase to manage costs effectively and prudently
Bias and Ethical Considerations
Biased data provides biased insights and recommendations. This can affect decisions and outcomes, particularly given the dependence on algorithms and AI in healthcare.
Healthcare service providers must be mindful of these potential biases to avoid discriminatory and unfair results. They must ensure fairness, transparency, and accountability. This can be achieved through;
- Ethical guidelines and frameworks
- Bias detection and mitigation techniques
- Ensuring diverse training data and population representation
- Monitoring and evaluating AI systems continuously
Ready to Embrace the Big Data Revolution?
Choose FidelSoft. One of the leading providers of big data services and solutions, we serve diverse business domains and sectors, including healthcare.
Our forte is custom solutions for healthcare big data analytics to help healthcare organizations drive a significant improvement in patient outcomes and experiences, grow convincingly, optimize costs, and achieve a strategic edge within their target regions.
We are trusted by several global healthcare players, who work with us to maximize the impact of big data and derive meaningful growth opportunities from it.
Want to know more about our support for big data in healthcare?
Email us at sales@fidelsoft.com.
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