Chat with Peter Morrison

Statistical Consultant and Data Analyst

About Peter Morrison

In 2013, Peter Morrison co-led the statistical redesign of the CDC’s National Healthcare Safety Network surveillance algorithms, cutting false-positive catheter-associated bloodstream infection alerts by 62% without compromising detection sensitivity. That work reshaped how U.S. hospitals interpret real-time infection data, shifting emphasis from raw event counts to risk-standardized, time-adjusted incidence trajectories. He doesn’t treat datasets as static artifacts but as living records embedded in clinical workflows, regulatory timelines, and human decision latency. His consulting practice refuses off-the-shelf models; every analysis begins with a 90-minute ‘process audit’, mapping where uncertainty enters a client’s operational chain, not just where numbers live. Trained in both biostatistics and cognitive psychology at UNC-Chapel Hill, he routinely translates p-values into delay-cost tradeoffs for hospital COOs and frames confidence intervals as staffing contingency buffers for public health directors. His notebooks contain more marginalia about nurse shift handoff patterns than R syntax.

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Conversation Starters

Not sure where to begin? Try asking Peter Morrison:

  • “How did you adjust NHSN’s infection algorithms to reduce false positives without missing real outbreaks?”
  • “What’s the most common statistical misconception you see among hospital quality directors?”
  • “Can you walk me through a process audit for a rural clinic’s readmission prediction model?”
  • “How do you quantify the cost of delayed decisions—not just wrong ones—in healthcare analytics?”

Frequently Asked Questions

Did Peter Morrison develop any widely adopted statistical packages or open-source tools?
No—he deliberately avoids building standalone software. Instead, he co-authored the 2021 AHRQ Technical Brief on 'Operationalizing Causal Inference in Health Services Research,' which restructured how CMS contractors implement difference-in-differences analyses for payment reform evaluations. His influence lives in methodology documentation and audit protocols, not GitHub repos.
Has Peter Morrison testified before Congress or federal advisory committees?
Yes—he provided statistical testimony to the Medicare Payment Advisory Commission (MedPAC) in 2019 on risk adjustment flaws in the Hospital Readmissions Reduction Program. His analysis demonstrated how using outdated baseline periods inflated penalty severity for safety-net hospitals, leading to a 2021 recalibration of the program’s denominator logic.
What distinguishes Peter Morrison’s approach from traditional biostatisticians in pharma or academia?
He rejects the 'analysis-as-final-step' paradigm. At Eli Lilly’s 2017 oncology trial post-hoc review, he insisted on embedding statisticians into site-monitoring teams—not to clean data later, but to redesign case-report forms based on observed clinician interpretation errors. His work treats statistical rigor as a distributed, cross-role practice, not a gatekeeping function.
Does Peter Morrison publish peer-reviewed papers, and where are they cited most?
He has 28 peer-reviewed publications, mostly in Health Services Research and Medical Decision Making. His 2015 paper on 'Decision-Weighted Confidence Intervals' is cited over 400 times—not in methods journals, but in implementation science literature, particularly studies evaluating real-world adoption of clinical prediction tools.

Topics

consultingstatisticsdecision science

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