AHRQ, Author Interviews, Infections, University of Pennsylvania / 15.10.2014

Craig A Umscheid, MD, MSCE, FACP Assistant Professor of Medicine and Epidemiology Director, Center for Evidence-based Practice Medical Director, Clinical Decision Support Chair, Department of Medicine Quality Committee Senior Associate Director, ECRI-Penn AHRQ Evidence-based Practice Center University of Pennsylvania, Philadelphia, PA 19104MedicalResearch.com Interview with: Craig A Umscheid, MD, MSCE, FACP Assistant Professor of Medicine and Epidemiology Director, Center for Evidence-based Practice Medical Director, Clinical Decision Support Chair, Department of Medicine Quality Committee Senior Associate Director, ECRI-Penn AHRQ Evidence-based Practice Center, University of Pennsylvania Philadelphia, PA 19104 Medical Research: What are the main findings of the study? Dr. Umscheid: We developed an automated early warning and response system for sepsis that has resulted in a marked increase in sepsis identification and care, transfer to the ICU, and an indication of fewer deaths due to sepsis. Sepsis is a potentially life-threatening complication of an infection; it can severely impair the body’s organs, causing them to fail. There are as many as three million cases of severe sepsis and 750,000 resulting deaths in the United States annually. Early detection and treatment, typically with antibiotics and intravenous fluids, is critical for survival. The Penn prediction tool, dubbed the “sepsis sniffer,” uses laboratory and vital-sign data (such as body temperature, heart rate, and blood pressure) in the electronic health record of hospital inpatients to identify those at risk for sepsis. When certain data thresholds are detected, the system automatically sends an electronic communication to physicians, nurses, and other members of a rapid response team who quickly perform a bedside evaluation and take action to stabilize or transfer the patient to the intensive care unit if warranted. We developed the prediction tool using 4,575 patients admitted to the University of Pennsylvania Health System (UPHS) in October 2011.  We then validated the tool during a pre-implementation period from June to September 2012, when data on admitted patients was evaluated and alerts triggered in a database, but no notifications were sent to providers on the ground.  Outcomes in that control period were then compared to a post-implementation period from June to September 2013.  The total number of patients included in the pre and post periods was 31,093. (more…)
AHRQ, Author Interviews, BMJ, Outcomes & Safety / 11.09.2014

Yves A. Lussier, MD, Fellow ACMI Professor of Medicine Associate Vice President for Health Sciences (Chief Knowledge Officer) The University of ArizonaMedicalResearch.com Interview with: Yves A. Lussier, MD, Fellow ACMI Professor of Medicine Associate Vice President for Health Sciences (Chief Knowledge Officer) The University of Arizona Medical Research: What are the main findings of the study? Dr. Lussier:  The main finding is that reporting patient safety using ICD-10-CM coding schema rather than ICD-9-CM will change the reported percentage of adverse events reported for half the specific "patient safety indicators" (PSIs), even with a true unaltered frequency of reported events in the medical center. For some patient safety indicators, the reported frequency will appear to increase substantially and for others, it will appear to decrease.  The latter is particularly  worrisome as it may erroneously appease administrators and prospective clients (patients) as their apparent trend is improving, while their institution may inadvertently be under-reporting adverse events. (more…)
AHRQ, Author Interviews, Electronic Records, Urinary Tract Infections / 26.08.2014

Craig A Umscheid, MD, MSCE, FACP Assistant Professor of Medicine and Epidemiology Director, Center for Evidence-based Practice Medical Director, Clinical Decision Support Chair, Department of Medicine Quality Committee Senior Associate Director, ECRI-Penn AHRQ Evidence-based Practice Center University of Pennsylvania, Philadelphia, PA 19104MedicalResearch.com Interview with: Craig A Umscheid, MD, MSCE, FACP Assistant Professor of Medicine and Epidemiology Director, Center for Evidence-based Practice Medical Director, Clinical Decision Support Chair, Department of Medicine Quality Committee Senior Associate Director, ECRI-Penn AHRQ Evidence-based Practice Center University of Pennsylvania, Philadelphia, PA 19104 Medical Research: What are the main findings of the study? Dr. Umscheid: We found that targeted automated alerts in electronic health records significantly reduce urinary tract infections in hospital patients with urinary catheters. In addition, when the design of the alert was simplified, the rate of improvement dramatically increased. Approximately 75 percent of urinary tract infections acquired in the hospital are associated with a urinary catheter, which is a tube inserted into the bladder through the urethra to drain urine.  According to the Centers for Disease Control and Prevention, 15 to 25 percent of hospitalized patients receive urinary catheters during their hospital stay. As many as 70 percent of urinary tract infections in these patients may be preventable using infection control measures such as removing no longer needed catheters resulting in up to 380,000 fewer infections and 9,000 fewer deaths each year. Our study has two crucial, applicable findings.  First, electronic alerts do result in fewer catheter-associated urinary tract infections. Second, the design of the alerts is very important. By making the alert quicker and easier to use, we saw a dramatic increase in the number of catheters removed in patients who no longer needed them. Fewer catheters means fewer infections, fewer days in the hospital, and even, fewer deaths. Not to mention the dollars saved by the health system in general. In the first phase of the study, two percent of urinary catheters were removed after an initial “off-the-shelf” electronic alert was triggered (the stock alert was part of the standard software package for the electronic health record). Hoping to improve on this result in a second phase of the study, we developed and used a simplified alert based on national guidelines for removing urinary catheters that we previously published with the CDC. Following introduction of the simplified alert, the proportion of catheter removals increased more than seven-fold to 15 percent. The study also found that catheter associated urinary tract infections decreased from an initial rate of .84 per 1,000 patient days to .70 per 1,000 patient-days following implementation of the first alert and .50 per 1,000 patient days following implementation of the simplified alert. Among other improvements, the simplified alert required two mouse clicks to submit a remove-urinary-catheter order compared to seven mouse clicks required by the original alert. The study was conducted among 222,475 inpatient admissions in the three hospitals of the University of Pennsylvania Health System between March 2009 and May 2012. In patients’ electronic health records, physicians were prompted to specify the reason (among ten options) for inserting a urinary catheter. On the basis of the reason selected, they were subsequently alerted to reassess the need for the catheter if it had not been removed within the recommended time period based on the reason chosen. (more…)
AHRQ, Author Interviews, Electronic Records, Hospital Readmissions, University of Pennsylvania / 28.11.2013

Craig A Umscheid, MD, MSCE, FACP Assistant Professor of Medicine and Epidemiology Director, Center for Evidence-based Practice Medical Director, Clinical Decision Support Chair, Department of Medicine Quality Committee Senior Associate Director, ECRI-Penn AHRQ Evidence-based Practice Center University of Pennsylvania Philadelphia, PA 19104MedicalResearch.com Interview with: Craig A Umscheid, MD, MSCE, FACP Assistant Professor of Medicine and Epidemiology Director, Center for Evidence-based Practice Medical Director, Clinical Decision Support Chair, Department of Medicine Quality Committee Senior Associate Director, ECRI-Penn AHRQ Evidence-based Practice Center University of Pennsylvania Philadelphia, PA 19104 MedicalResearch.com: What are the main findings of the study? Dr. Umscheid: We developed and successfully deployed into the electronic health record of the University of Pennsylvania Health System an automated prediction tool which identifies newly admitted patients who are at risk for readmission within 30 days of discharge.  Using local data, we found that having been admitted to the hospital two or more times in the 12 months prior to admission was the best way to predict which patients are at risk for being readmitted in the 30 days after discharge. Using this finding, our automated tool identifies patients who are “high risk” for readmission and creates a “flag” in their electronic health record (EHR). The flag appears next to the patient’s name in a column titled “readmission risk.” The flag can be double-clicked to display detailed information relevant to discharge planning.  In a one year prospective validation of the tool, we found that patients who triggered the readmission alert were subsequently readmitted 31 percent of the time. When an alert was not triggered, patients were readmitted only 11 percent of the time.  There was no evidence for an effect of the intervention on 30-day all-cause readmission rates in the 12-month period after implementation. (more…)