Health Care Analytics: Data-Driven Scheduling to Reduce No-Shows

health care| business | | Romain Doutriaux
The health care industry suffers from a data problem. There is an abundance of raw data in health care, but 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 white paper will provide a clear view of how the effective use of data analytics can help health care institutions and professionals gain efficiency, improve patient care, and optimize profitability.

Advanced Analytics for Efficient Healthcare: Data Driven Scheduling to Reduce No-Shows [eBook]
Click here to download the white paper!

In this white paper paper, we highlighted how a health care-specific issue—no-show appointments— can be resolved with data. Hint: the solution is predictive analytics, which can be used to craft 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 low as 5% to as high as 55%. Dealing effectively with patient no-shows has been a challenge in the health care industry especially now that reimbursement is more closely tied to performance measures surrounding physical appointments.

Annually lost due to no-show issues

What you will find in "Advanced Analytics for Efficient health care: 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 health care 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 health care 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

We hope you enjoy your read. Feel free to email me any questions or comments. You may also want to have a look at our health care page, an additional blog post on analytics in health care, or to try Dataiku Data Science Studio (DSS) for yourself!

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