Many industries are now singing the praises of Big Data, and healthcare is no exception. This article explores both the challenges and benefits of data-driven healthcare decision-making. We explain how big data, combined with cutting-edge technologies, can improve patient outcomes, decrease patient care costs, streamline operations, and increase overall value.
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What Is Value in Healthcare?
With all this mention of increasing value through Big Data, let’s take a moment to understand what value actually means in healthcare. When we talk about value, we’re actually talking about two distinct but interconnected things: economic value and ethical value. Though the former is more easily quantified, the latter reflects the ultimate goal of most organizations.
- Economic Value
Economic value is objective and quantifiable. It deals with concepts like utility, resourcefulness, and practicality. It can be calculated and described by various metrics, such as the financial implications of patient outcomes, pay-for-performance (P4P) adherence, the cost savings of preventing hospital-acquired complications (HACs), and other numerical indicators.
When reviewing historic descriptive data, insightful trends can emerge. For instance, a cost reduction combined with improved outcomes points to an increase in value; the reverse indicates a decrease in value.
Source: Maryville University
- Ethical Value
Ethical value is more subjective and abstract than economic value. It deals with higher-order concepts like fairness, compassion, and honesty. These values typically align with cultural and/or personal values and are reflected in an organization’s core values or mission statement.
Ethical values manifest in various ways, from value-based leadership and quality of care to patient and staff satisfaction. Audio/written commentary, social media posts, surveys, and focus groups can all shed light on the ethical values of an organization. However, up until recently, these forms of unstructured data could not be parsed or interpreted by computers for use in predictive or prescriptive analytics.
Big Data allows healthcare organizations to reliably produce evidence-based strategies—whether in the realm of reimbursement rates or patient satisfaction. It’s therefore true that data-driven decisions can optimize both economic and ethical values.
What Is Big Data?
The term ‘Big Data’ gets thrown around a lot. But it’s important to understand that Big Data refers to more than just a large amount of data. It is larger, more complex data sets, most often from new data sources. Big Data contains data sets that are so large that traditional data processing tools cannot process and manage the data. This is measured using the three V’s: volume, velocity, and variety.
- Volume – the sheer quantity of data that’s generated and collected.
- Velocity – the rate at which data is generated or collected.
- Variety – the types of data sources, such as structured, relational data (like electronic health records (EHR)) and unstructured data (like medical imaging, genomic sequencing, etc.).
This unprecedented volume of data contains valuable potential to reveal patterns, trends, and associations that can help us solve problems we have not been able to solve in the past. In addition, it requires new tools to process that data to realize that value.
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Types of Data Analytics (and How Data Analytics Helps Make Data-Driven Decisions in Healthcare)
Traditional relational databases—that most healthcare organizations continue to rely on—can only make sense of 10% – 20% of captured data. New strides in the fields of artificial intelligence (AI) and machine learning (ML) have finally made it possible to parse, analyze, and interpret Big Data.
Today, sophisticated software applications can centralize and store the remaining 80% – 90% of unstructured data. Not only that, analysis can be performed to visualize outcomes and operational trends, extract actionable business intelligence (such as key performance indicators (KPI)), and provide evidence-based treatment plans for patients.
There are three general categories that Big Data analytics can fall into.
- Descriptive Analytics
Descriptive analytics organize and present raw data, usually in the form of a dashboard or scorecard. It can provide you with an overview of historical and current patterns but does not provide insight into what caused the patterns.
- Predictive Analytics
In predictive analytics, historic data is collected and analyzed to generate projections. Predictive analytics can be used to model a variety of future outcomes, such as when patients have a higher chance of hospital readmission.
- Prescriptive Analytics
Prescriptive analytics interpret raw data to uncover the best course of action. It can be used to establish both short-term and long-term strategies. For instance, A/B testing which drugs to prescribe to a patient for a particular illness.
Opportunities of Big Data in Healthcare
Many healthcare organizations already use some form of data analysis. But, while other industries prioritize predictive and prescriptive analytics, healthcare professionals limit the scope of their insights to descriptive data.
An effective data strategy combines all three analytic approaches.
By expanding your analytic tool belt, healthcare practices can benefit from:
- Actionable business intelligence
- Increased positive outcomes
- Clinical decision support (CDS)
- Insight for thought leadership and research topics
- Forecasting and predictive modelling
Challenges of Data-Driven Healthcare
Healthcare is one of the last industries to adopt Big Data technologies. This reluctance comes as no surprise. Even today, electronic health records (EHR) have still not been completely standardized across healthcare organizations.
There are also a handful of specific challenges that stand in the way of Big Data. Things like data capture, cleaning, storage, and security. However, the most powerful obstacle is the culture itself.
Technological shifts require cultural ones. In order to succeed in establishing a new healthcare paradigm, senior managers must move away from instinct-based decision-making and toward evidence-based decision-making. They must be open-minded, ready to concede their own stance in favor of data-driven conclusions, and able to bring healthcare professionals and data analysts together to solve problems.
Bring Value to Your Organization With Data-Driven Health Care
Data-driven decision-making is all about using evidence-based strategies to optimize patient-perceived value and reduce spending. With the help of sophisticated machine learning technologies, you’ll be able to:
- Improve patient outcomes
- Improve public health surveillance
- Improve health care policy decisions
- Increase performance visibility
- Decrease care costs
- Increase patient and staff satisfaction
Though it can feel like a leap of faith, True North will help you navigate your technological transition and data management. We are industry leaders in managed IT and cloud services for healthcare organizations.
The future of medicine belongs to data-driven healthcare organizations. Contact us today to discuss how we can strategize, implement, and manage all of your data platforms.
Frequently Asked Questions About Data-Driven Healthcare
Data-driven healthcare empowers organizations to leverage Big Data to systematically address multiple concerns, like:
- Improving patient outcomes
- Reducing the cost of patient care
- Streamlining operations
- Increasing overall value
As we mentioned earlier in the blog, Big Data in the healthcare space is thought about through the three Vs:
- Volume: The quantity of data generated and collected.
- Velocity: The rate at which data is collected.
Variety: The types of data sources, including structured data like EHR and unstructured data like medical imaging.
Some of the data your organization might collect in a data-driven healthcare environment include:
- Patient information: Personal details, medical history, and diagnostic information
- Operational data: Staffing, equipment usage, and supply chain management
- Clinical data: EHRs and unstructured data like genomic sequencing
Ethical considerations around data-driven healthcare are largely centered around fairness, compassion, and honesty.
Think along the lines of:
- Patient privacy and data security: Keep patient data confidential. Prevent unauthorized access and maintain regulation compliance.
- Fairness in treatment: Data-driven decisions should be without bias. All patients should be treated equally.
- Transparency and honesty: Maintain transparency in how your organization uses and analyzes data.
- Quality of care: All insights should lead to better quality and more compassionate care that aligns with the patient’s best interests.
- Big Data analytics: Organizations are using Big Data analytics to organize, analyze, and interpret large and complex data sets. Think Predictive Analytics to generate projections.
- Evidence-based strategies: Use machine learning technologies that provide actionable business intelligence and support clinical decision-making.
- Centralized data storage: With applications that centralize data, organizations are tapping into visualization of outcomes and operational trends and much more.
Healthcare organizations are keeping data clean and accurate with tools like:
- Validation checks and data quality assessments
- Standardizing EHRs and adhering to common data formats
- Encryption, access controls, and regular security audits
- Continuous monitoring and optimization of data sets