We believe the healthcare industry suffers from a data problem as there is an abundance of raw data in healthcare and no one really knows what to do with it. The good news is that all of this data can be used to solve a multitude of common, day-to-day problems using predictive analytics.
This ebook will provide a clear view of how the effective use of data analytics can help healthcare institutions and professionals gain in efficiency, improve patient care, and optimize profitability.
In this paper, we highlighted how a healthcare specific issue—no-show appointments— can be resolved with data. Our research led us to show how predictive analytics can be used to discover real-world solutions to a multi-billion-dollar problem.
The unfortunate reality is that no-shows have become extremely common — one study reported that the no-show rate in U.S. primary care practice can vary from as little as 5% to as much as 55%. Dealing effectively with patient no-shows has been a challenge in the healthcare industry, especially now that reimbursement is more closely tied to performance measures surrounding physical appointments.
What you will find in "Advanced Analytics for Efficient Healthcare: Data Driven Scheduling to Reduce No-Shows":
We will start by having a look at what is wrong with the current implementation of data analytics in the healthcare ecosystem and how it applies to the no-show issue. We will then offer an alternative approach to addressing no-show appointments that makes use of predictive analytics. Lastly, we will discuss how this method could be applied to the healthcare industry.
I) Data Fragmentation & Limited Skills Deteriorate the Data Analysis Process
- Multiplicity of Data Sources Makes Collection & Use Difficult
- Data Diversity Hinders Data Integration
- Limited Human Skills Inhibit Effective Data Analysis
II) Data Management Challenges Illustrated by the No-Show Issue
- The Financial and Human Cost Behind No-Shows
- The 3 Main Barriers Keeping Institutions from Solving the No-Show Issue
- 4 Quick Fixes... That Don't Work
III) Step by Step Methodology to Build Your Scheduling Data Product
- Order Out of Chaos: Collecting & Making Sense of Data
- A Predictive Model to Test Your Hypothesis
- From Theory to Practice: Deploying Your Data Product
IV) Creating a Proper Data Structure for a Complete Analytics Methodology
- To Know Before You Go
- Developing System Interoperability
- Fostering the Distribution of Skills & Knowledge
- A Shift from Retrospective to Prospective Analytics
- Engaging with Patients: Now or Never