Data Analytics Can Improve Financial Performance, Efficiency and the Patient Experience in Healthcare

VIE Healthcare Data Analytics Can Improve Financial Performance Efficiency and the Patient Experience in Healthcare

VIE Healthcare Performs Data Analytics in Healthcare so That Healthcare Providers Can Improve the Way They Manage Chronic Conditions, Improve the Efficiencies of Their Hospital Systems, Optimize Their Costs and Improve Their Revenue.

VIE Healthcare provides the following services related to Data Analytics:
VIE Healthcare Check Bullet

Spend Analysis Consulting

VIE Healthcare Check Bullet

Hospital Analytics

VIE Healthcare Check Bullet

Hospital Financial Improvement

Data analytics initiatives have the potential to transform healthcare in multiple areas, but the sector has been slow to tap into its potential. Understanding what spend data analytics in healthcare really means can encourage more hospitals to embrace its benefits.

The VIE Healthcare Advantage -
The Only Comprehensive Cost Savings Solution™

The VIE Healthcare Cost Advantage Step 1 Forensic Line Item Historical Analysis

Forensic Line-Item Historical Analysis

The VIE Healthcare Cost Advantage Step 2 Detail Contract Dissection

Detailed Contract Dissection

The VIE Healthcare Cost Advantage Step 3 Categorization Benchmarking

Categorization & Benchmarking

The VIE Healthcare Cost Advantage Step 4 Complete Cost Savings Opportunity Identification

Complete Cost
Savings Opportunity Identification

The VIE Healthcare Cost Advantage Step 5 Contract Re Negotiation VIE Healthcare


The VIE Healthcare Cost Advantage Step 6 Manage Contract Performance

Manage Contract Performance

VIE Healthcare Can your hospital benefit from data analytics

Can Your Hospital Benefit From Data Analytics?

Here are 12 ways your hospital can directly benefit from Healthcare Data Analytics:
  1. Patient care management: improving the level of care provided to a patient, leading to lower healthcare costs and better outcomes. This requires identifying groups of patients with specific characteristics who are at risk for poor outcomes and then changing the standard of care accordingly. For example, driving lifestyle changes can lead to better outcomes for patients with chronic conditions and cardiovascular disease. This leads to lower costs associated with initial treatment and maintenance, as well as a higher quality of life.
  2. Evaluate patient satisfaction: measuring how satisfied patients are with their care using such measures as patient surveys and social media engagement data. Adding analytics to these traditional qualitative tools allows for personalization of care based on feedback from the patient population.
  3. Patient Cost Efficiency: identifying how much each patient costs a hospital, as well as a cohort of patients. This allows for more targeted cost management of the overall patient population, leading to improved patient outcomes and a balance of cost optimization.
  4. Perform Utilization Analytics: measuring the level of utilization of a particular resource or service. This type of analytics help assess the productivity (or inefficiency) of different departments and physicians.
  5. Providing proactive care utilization analysis: tracking the patients who have used a service or resource and how it affected their outcomes. In this way, they can be tracked and targeted for services that would lead to better outcomes (i.e. disease management programs for those at risk of diabetes).
  6. Ad hoc reporting: providing data to key stakeholders in response to specific requests rather than as part of an on-going process or in real-time. This is useful when trying to quickly analyze data for a specific request (and in healthcare, there are many unique data requests).
  7. Population Health Data Analytics: We analyze population health data to improve the health and wellbeing of a population. An example would be using data analytics to assess the health of a population in a specific hospital, and then develop disease management programs around that information. Another example would be analyzing the data based on age, sex, race, etc, to uncover patterns that may help with targeted research or care management.
  8. Financial Analytics: financial modeling can be performed with statistical and mathematical models to predict future financial needs as well as optimizing existing budgets. These models can also give a more in-depth look at the overall cost structure of a hospital.
  9. Disease Management: grouping patients based on shared disease characteristics and then targeting them with intervention programs that can help improve their outcomes and lower costs associated with long term health problems like chronic conditions.
  10. Provide Spend Data Analytics: giving a comprehensive view of a hospital spend for supplies and services. This is helpful for identifying cost savings opportunities as well as identifying high cost product lines that need consolidation or a new pricing structure.
  11. Provide Cost Benchmarking: comparing costs between providers, hospitals, and healthcare systems in order to identify areas where costs are being incurred unnecessarily. This is done by looking at historical purchasing patterns and then comparing them with expected costs based on the population served.
  12. Managed Care Contracting Analytics: analyzing trends in managed care contracting, for example, measuring how much a health plan is spending on a particular provider or hospital. By evaluating trends over time it is possible to identify areas where increased spending is due to changes in the population being served rather than inefficient practices by the provider itself.

Learn More About Data Analytics for Your Hospital:

We Look Forward to Having a Discussion With You About How We Can Support Your Organization's Data Analytics Goals.