How Does Data Analytics Help Healthcare?
Data analytics in healthcare improves the patient experience and drives cost savings. This is best achieved using a team of cost savings experts, data scientists, technology specialists, healthcare business analysts, and healthcare executives.
Healthcare business analysts use data to:
- Find cost-cutting opportunities in major areas of spend.
- Automate cost analysis processes to free up vital resources.
- Identify utilization improvements to enhance physician engagement and OR spend.
Table of Contents
- Healthcare Analytics Definition
- The Role of Data Analytics In Healthcare
- How Does Data Analytics Help Healthcare?
- Four Types of Healthcare Data Analytics
- Three-Step Process for Healthcare Analytics Success
- Achieve Cost Savings With Data Analytics
- Key Takeaways
- FAQs About Data Analytics in Healthcare
- How We Can Help
The Role of Data Analytics In Healthcare
- Preventive Care: Identifies risk factors in patients susceptible to specific conditions or diseases.
- Predictive Care: Electronic Healthcare Records (EHRs) of millions of patients are stored in databases and used to enhance patient care and make it more efficient. Decisions around care are driven largely by analytics, which can predict the initial onset or progression of various conditions.
- Staffing: Healthcare data integration related to staffing reduces issues related to under- or over-staffing by analyzing historical hospital admission records. Patterns are identified and admissions accurately predicted in advance. The resulting data can be used to organize staffing schedules, leading to a better patient experience.
- Patient Engagement: Smart devices track numerous health metrics, providing physicians with personalized data to improve treatment plans and identify potential risk factors to patient health.
- Electronic Healthcare Records (EHRs): These digital health records store every detail related to patient health, from demographic information and family medical history to medications, test results, and previous illnesses. EHR data is used to optimize patient health.
- Value-Based Care Model: The value-based care model improves efficiencies when an analytics solution is used, leading to better quality of patient care and higher levels of satisfaction.
- Population Health Management: Places a greater focus on predictive and preventive care rather than reactive care. With predictive analytics, chronic illnesses are identified in the early stages, reducing the cost of care and repeat hospitalizations.
- Big Data: From managing an intensive care unit to an admission department, leveraging big data analytics transforms healthcare.
How Does Data Analytics Help Healthcare?
Data analytics is the key to maximizing cost savings and reducing purchased services spend.
Improving data flow in healthcare empowers industry leaders and insurers to master different systems, focus on continuous quality improvement and track key performance indicators.
It is vital for hospitals to understand the role of data analytics in healthcare to analyze financial data more accurately. The growth of healthcare data makes it impossible to understand your hospital’s performance, manage spend, or improve the quality of care without analytics.
The role of data analytics:
- Allows your hospital to uncover areas of waste.
- Empowers more informed decisions on using hospital resources.
- Delivers an accurate view of what is happening.
- Supports decisions that will lead to cost savings.
4 Types of Healthcare Data Analytics
Four different key types of data analytics used to deliver actionable intelligence (3) include
Takes historical data to identify past patterns, trends, or benchmarks and make comparisons. This helps to answer questions about what has already happened but cannot help hospitals predict future health outcomes or costs.
Extracts current and historical data to provide future insights and predictions. Predictive analytics enables healthcare leaders to decide on the best strategy, assuming that all conditions remain unchanged.
Analyzes raw data to find patterns, connections, and outliers. Discovery analytics helps healthcare organizations identify what needs to be further explored.
Prescriptive analytics uses advanced algorithms to help health systems understand what the outcome of a specific action might be. The next steps with the most potential for success are suggested in real-time.
Three-Step Process for Healthcare Analytics Success
To leverage these key areas of healthcare analytics, a proven process is required to analyze vast amounts of datasets and transform them into actionable intelligence.
That three-step process requires:
- Gathering the data: All relevant data needs to be gathered in one place.
- Data transformation: Data is cleaned, validated, and analyzed to identify issues and enable healthcare organizations to answer critical questions. On the business side of healthcare, that might mean identifying off-contract spend in your vendor contracts.
- Acting on insights: Take decisions based on real-time data that align with your hospital’s goals around patient care or cost management strategies, for example.
The Role of Data Analytics Is Key To Maximizing Cost Savings
Healthcare data integration with cost savings experts can achieve spending reductions in four ways
- Run 12 to 18 month PO detail report and benchmark each line item: Achievable with Invoice ROI™ – a patented automated process.
- Identify utilization improvement opportunities through comparing physician variations in testing, supplies, and specifically implant costs in the OR: Physician engagement is a leading (and underutilized) strategy for hospitals to deploy.
- Analyze your professional hourly rates: Identify ways to standardize and reduce these costs. Organize hourly rates by category and allow one hourly rate for each service, such as project management or technical analytics. Most hospitals can save 20% in this key area of spend with the right benchmarking partner.
- Take an area of spend in your purchased services and analyze the last 12 months of line item spend: Map invoice accuracy to agreement and identify areas for utilization improvement.
Key Takeaways for Analytics In Healthcare Industry
- Analytics in healthcare industry transforms patient care and cost management.
- Patient care is personalized to identify and monitor health risks.
- Automated processes identify savings hiding in large areas of spend.
- Data analytics is the key to maximizing cost-cutting opportunities.
Frequently Asked Questions about Healthcare Data Analytics
How VIE Can Help
- Gather large sets of complex data from multiple areas into one accessible source.
- Derive actionable insights from that data, drawing accurate conclusions for business improvements based on those insights.
- Achieve rapid implementation of key data insights, ensuring maximum business impact within a minimal timescale.
- Our healthcare analytics solutions empower your hospital to expand sourcing capabilities, benchmark agreements and purchases, and manage supplier data and contracts.