Inside Angle

From 3M Health Information Systems

Tag: PPRs

To lower readmission rates, focus on the intersection of cost and quality

August 28th, 2019 / By L. Gordon Moore, MD

A 55-year-old male is admitted to the hospital for an exacerbation of their chronic obstructive pulmonary disease (COPD). An 8-year-old child visits an emergency department with an asthma exacerbation. A […]

Infographic: Can hospitals reduce readmissions?

Hospitals face Medicare penalties for excess readmissions. What can be done? Here are some results from across the country indicating sustained effort pays off.


Webinar: Preventing Readmissions: PPRs and ACRs

With Cheryl Manchenton, RN

Cheryl Manchenton, addresses potentially preventable readmissions (PPRs) and all cause readmissions (ACRs) […]

Budget Neutral Payment for Pharmaceuticals – Tying Value to Outcomes

May 18th, 2015 / By Norbert Goldfield, MD, Richard Fuller, MS

We believe there are two core principles that should be adhered to when implementing payment reform initiatives. First, that measurement of performance change should be directly quantifiable in dollars where […]

Case Study: Auburn Community Hospital

Using the power of data, Auburn Community Hospital implemented initiatives to reduce avoidable readmissions, increase case mix index, and improve documentation.

Video: How to reduce potentially preventable readmissions

Allina Health uses the 3M Potenially Preventable Readmissions (PPRs) methodology to stratify patient groups, guide care transitions, and prevent readmissions.

Want to Improve Safety? Choose the Right Metrics for Avoidable Readmissions and Complications

March 5th, 2014 / By Kristine Daynes

In January the Minnesota RARE campaign received the prestigious Eisenberg Award for reducing avoidable readmissions. Over an 18-month period, the campaign helped hospitals and community partners prevent more than six […]

Part 2: Clinical Categorical vs. Regression Based Classification Systems

October 9th, 2013 / By Norbert Goldfield, MD, Richard Fuller, MS

In our last blog we discussed the fundamental aspects of patient classification systems, specifically categorical clinical and regression based models. While any classification system has its limitations, we’ve found that […]