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Do pneumonia readmissions flagged as potentially preventable by the 3M PPR software have more process of care problems? A cross-sectional observational study
  1. Ann M Borzecki1,2,3,
  2. Qi Chen4,
  3. Joseph Restuccia4,5,
  4. Hillary J Mull4,6,
  5. Michael Shwartz4,5,
  6. Kalpana Gupta3,7,
  7. Amresh Hanchate3,4,
  8. Judith Strymish7,8,
  9. Amy Rosen4,6
  1. 1Center for Healthcare Organization and Implementation Research, Bedford VAMC Campus, Bedford, Massachusetts, USA
  2. 2Department of Health Policy and Management, Boston University School of Public Health, Boston, Massachusetts, USA
  3. 3Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
  4. 4Center for Healthcare Organization and Implementation Research, Boston VA Campus, Boston, Massachusetts, USA
  5. 5School of Management, Boston University, Boston, Massachusetts, USA
  6. 6Department of Surgery, Boston University School of Medicine, Boston, Massachusetts, USA
  7. 7Department of Infectious Disease, VA Boston Healthcare System, Boston, Massachusetts, USA
  8. 8Department of Medicine, Harvard University School of Medicine, Boston, Massachusetts, USA
  1. Correspondence to Dr Ann M Borzecki, Center for Healthcare Organization and Implementation Research, Bedford, MA 01730, USA; amb{at}


Background In the USA, administrative data-based readmission rates such as the Centers for Medicare and Medicaid Services’ all-cause readmission measures are used for public reporting and hospital payment penalties. To improve this measure and identify better quality improvement targets, 3M developed the Potentially Preventable Readmissions (PPRs) measure. It matches clinically related index admission and readmission diagnoses that may indicate readmissions resulting from admission- or post-discharge-related quality problems.

Objective To examine whether PPR software-flagged pneumonia readmissions are associated with poorer quality of care.

Methods Using a retrospective observational study design and Veterans Health Administration (VA) data, we identified pneumonia discharges associated with 30-day readmissions, and then flagged cases as PPR–yes or PPR–no using the PPR software. To assess quality of care, we abstracted electronic medical records of 100 random readmissions using a tool containing explicit care processes organised into admission work-up, in-hospital evaluation/treatment, discharge readiness and post-discharge period. We derived quality scores, scaled to a maximum of 25 per section (maximum total score=100) and compared cases by total and section-specific mean scores using t tests and effect size (ES) to characterise the clinical significance of findings.

Results Our abstraction sample was selected from 11 278 pneumonia readmissions (readmission rate=16.5%) during 1 October 2005–30 September 2010; 77% were flagged as PPR–yes. Contrary to expectations, total and section mean quality scores were slightly higher, although non-significantly, among PPR–yes (N=77) versus PPR–no (N=23) cases (respective total scores, 71.2±8.7 vs 65.8±11.5, p=0.14); differences demonstrated ES >0.30 overall and for admission work-up and post-discharge period sections.

Conclusions Among VA pneumonia readmissions, PPR categorisation did not produce the expected quality of care findings. Either PPR–yes cases are not more preventable, or preventability assessment requires other data collection methods to capture poorly documented processes (eg, direct observation).

  • Quality measurement
  • Health services research
  • Performance measures

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