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A model for increasing patient safety in the intensive care unit: increasing the implementation rates of proven safety measures
  1. W S Krimsky1,
  2. I B Mroz2,
  3. J K McIlwaine2,
  4. S D Surgenor2,
  5. D Christian2,
  6. H L Corwin2,
  7. D Houston2,
  8. C Robison2,
  9. N Malayaman1
  1. 1
    Critical Care Medicine, Franklin Square Hospital, Baltimore, Maryland, USA
  2. 2
    Critical Care Medicine, Department of Anesthesiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
  1. Dr W S Krimsky, Franklin Square Hospital, 9103 Franklin Square Drive, Suite 300, Baltimore, MD 21237, USA; william.krimsky{at}


Background: Few published data exist with respect to current implementation of interventions that increase patient safety in intensive care units (ICUs) Furthermore, even less published data exist that address implementation of outcome-related methodologies of patient safety interventions in ICUs.

Objective: The purpose of this study was threefold: (1) to increase implementation rates of known, evidence-based interventions in the Dartmouth Hitchcock Medical Center (DHMC) ICU that have been demonstrated to reduce morbidity and mortality in critically ill patients; (2) to develop a durable and reproducible intervention model that can be applied not only to various aspects of ICU medicine but to any healthcare microsystem that is process oriented; and (3) to design an “ICU-specific” value compass.

Design: Using a before/after study design, the interventions involved: (1) establishing a systematic approach to integrate the delivery of proven ICU safety measures; (2) using the design of the various tools to develop a method for team communication and team building; (3) incorporating prompts into a ICU progress note for the healthcare team to address three evidence-based measures on a daily basis; and (4) using a data wall to demonstrate progress and to provide “real-time” feedback for error correction.

Setting and participants: In the before and after study, two groups of 40 consecutive patients admitted to DHMC’s Intensive Care Unit were evaluated. The first group of patients was admitted between April and May of 2003. The second group of 40 patients was admitted between May and June of 2004. To ensure process stability, control data were also collected on patients at an interval time point between these two groups.

Main outcome measures: Three evidence-based interventions were identified that reduce the likelihood of adverse events resulting simply from an ICU stay: (1) prophylaxis against venous thrombo-embolic disease (venous thromboembolism or deep vein thrombosis); (2) prophylaxis against ventilator-associated pneumonia (VAP); and (3) prophylaxis against stress-ulcers (SU). Two data points were obtained per patient per day corresponding to the work shift schedule in the ICU. The unit of measure was patient-shift observation. A limited data set was collected before implementing the change package to ensure system stability.

Results: Both traditional statistical analysis and statistical process control (SPC) were used to evaluate the results. For each metric, it was possible to demonstrate an increase in the measure of the mean, reduced point-to-point variation as well as a substantial narrowing of the control limits indicating improved process control.

Limitations: By virtue of the involvement of the researcher in the data collection for the control group, the potential existed for methodological bias by acting on the information collected. There was also the lack of a cohesive data structure from which to collect information (ie, the hospital computer speaks one language, the ventilator a second and the monitoring systems a third).

Conclusions: A model for changing the ICU microsystem at DHMC was created that enabled successful implementation of evidence-based measures by maximising the natural flow of work and fostering a team-based culture to improve patient safety. Unique to this method and unlike currently available methods that define only the delivery of the appropriate intervention as success, system success was defined in terms of both true positives, namely delivering care when it is indicated, as well as true negatives, not delivering care when there is none indicated, to offer a more comprehensive system review. Additionally, the method of data collection allowed simplified defect analysis, thereby eliminating a resource-consuming audit of data after the fact. This approach, therefore, provides a basis for adapting and redesigning the PDSA cycle so as to specifically apply this type of “disciplinary” work.

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  • Funding: Supported by a grant from the Quality Research Grant Program.

  • Competing interests: None.

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