R. Scott Evans

Detecting Adverse Events Using Information Technology

CONTEXT Although patient safety is a major problem, most health care organizations rely on spontaneous reporting, which detects only a small minority of adverse events. As a result, problems with safety have remained hidden. Chart review can detect adverse events in research settings, but it is too expensive for routine use. Information technology techniques can detect some adverse events in a timely and cost-effective way, in some cases early enough to prevent patient harm. OBJECTIVE To review methodologies of detecting adverse events using information technology, reports of studies that used these techniques to detect adverse events, and study results for specific types of adverse events. DESIGN Structured review. METHODOLOGY English-language studies that reported using information technology to detect adverse events were identified using standard techniques. Only studies that contained original data were included. MAIN OUTCOME MEASURES Adverse events, with specific focus on nosocomial infections, adverse drug events, and injurious falls. RESULTS Tools such as event monitoring and natural language processing can inexpensively detect certain types of adverse events in clinical databases. These approaches already work well for some types of adverse events, including adverse drug events and nosocomial infections, and are in routine use in a few hospitals. In addition, it appears likely that these techniques will be adaptable in ways that allow detection of a broad array of adverse events, especially as more medical information becomes computerized. CONCLUSION Computerized detection of adverse events will soon be practical on a widespread basis.

Opioid-Related Adverse Drug Events in Surgical Hospitalizations: Impact on Costs and Length of Stay

Background: Opioid analgesics remain a mainstay in the treatment of pain associated with surgical procedures. Such use is associated with adverse drug events (ADEs). Objective: To investigate the impact of opioid-related ADEs on total hospital costs and length of stay (LOS) in adult surgical patients. Methods: This was a retrospective matched cohort study using data from computerized medical records. ADE cases were prospectively detected using computerized surveillance and verified by pharmacists. Surgical patients treated at LDS Hospital in Salt Lake City from January 1, 1998, to December 31, 2003, were included. The primary outcomes were costs and hospital LOS associated with opioid-related ADEs and the relationship of opioid dose to ADE events. Results: Patients experiencing opioid-related ADEs had significantly increased median total hospital costs (7.4% increase; 95% CI 3.83 to 10.96; p < 0.001) and increased median LOS (10.3% increase; 95% CI 6.5 to 14.2; p < 0.001) compared with matched non-ADE contrals. The increased costs attributable to ADEs, by surgery type, were general surgery (

Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports

676.51; 95% CI 351.50 to 1001.50), orthopedics (

Preventing Adverse Drug Events in Hospitalized Patients

861.50; 95% CI 448.20 to 1274.80), and obstetrics/gynecology (

Hospital Workload and Adverse Events

540.90; 95% CI 281.40 to 800.40). Similarly, increased LOS attributable to ADEs, by surgery type, were general surgery (0.64 days; 95% CI 0.40 to 0.88), orthopedics (0.52 days; 95% CI 0.33 to 0.71), and obstetrics/gynecology (0.53 days; 95% CI 0.33 to 0.72). Higher doses of opioids were associated with increased risk of experiencing ADEs (OR 1.3; 95% CI 1.07 to 1.60; p = 0.01). Conclusions: Opioid-related ADEs following surgery were associated with significantly increased LOS and hospitalization costs. These ADEs occurred more frequently in patients receiving higher doses of opioids.

Risk of Symptomatic DVT Associated With Peripherally Inserted Central Catheters

OBJECTIVE To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports. METHODS DESIGN Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured. OUTCOME MEASURES Recall, precision, specificity, and agreement (using Finns R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia. RESULTS Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference. CONCLUSION In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.

Therapeutic antibiotic monitoring: Surveillance using a computerized expert system☆

OBJECTIVE: To use computerized adverse drug event (ADE) surveillance to help identify methods to reduce the number of ADEs in hospitalized patients. DESIGN: Prospective study of 79 719 hospitalized patients during a 44-month period. SETTING: LDS Hospital, a 520-bed tertiary care center affiliated with the University of Utah School of Medicine, Salt Lake City. INTERVENTION: Sequential study periods of at least one year each were compared. In the first period, data were collected but not reported to physicians, pharmacists, or nurses. In the subsequent study periods, three interventions (computerized alerts of drug allergies, standardized antibiotic administration rates, and timely physician notification of all ADEs) were made to reduce the number of type B (allergic or idiosyncratic reactions) and severe ADEs. RESULTS: In the first study period, we identified 56 type B ADEs during 120 213 patient days. During two subsequent study periods that included alerts to physicians of known drug allergies and standardized antibiotic administration rates, 8 type B events were identified during 113 237 patient days and 18 during 107 868 patient days, respectively (p<0.OO2). Early notification of physicians of all confirmed ADEs regardless of severity was associated with asignificant reduction of ADEs classified as severe from 41 during 113 859 patient days in the first study period to 12 during 103 071 patient days and 15 during 108 320 patient days in two subsequent study periods, respectively (p<0.00 1). CONCLUSIONS: Prospective surveillance of computer-based medical records for known drug allergies and appropriate drug administration rates can reduce the number of type B ADEs. Early ADE detection and notification of physicians permit drug and dosage changes that reduce the progression of mild and moderate ADEs to more severe conditions.

Cost of Opioid-Related Adverse Drug Events in Surgical Patients

Context:Hospitals are under pressure to increase revenue and lower costs, and at the same time, they face dramatic variation in clinical demand. Objective:We sought to determine the relationship between peak hospital workload and rates of adverse events (AEs). Methods:A random sample of 24,676 adult patients discharged from the medical/surgical services at 4 US hospitals (2 urban and 2 suburban teaching hospitals) from October 2000 to September 2001 were screened using administrative data, leaving 6841 cases to be reviewed for the presence of AEs. Daily workload for each hospital was characterized by volume, throughput (admissions and discharges), intensity (aggregate DRG weight), and staffing (patient-to-nurse ratios). For volume, we calculated an “enhanced” occupancy rate that accounted for same-day bed occupancy by more than 1 patient. We used Poisson regressions to predict the likelihood of an AE, with control for workload and individual patient complexity, and the effects of clustering. Results:One urban teaching hospital had enhanced occupancy rates more than 100% for much of the year. At that hospital, admissions and patients per nurse were significantly related to the likelihood of an AE (P < 0.05); occupancy rate, discharges, and DRG-weighted census were significant at P < 0.10. For example, a 0.1% increase in the patient-to-nurse ratio led to a 28% increase in the AE rate. Results at the other 3 hospitals varied and were mainly non significant. Conclusions:Hospitals that operate at or over capacity may experience heightened rates of patient safety events and might consider re-engineering the structures of care to respond better during periods of high stress.

Risk Factors for Adverse Drug Events: A 10-Year Analysis

BACKGROUND Previous studies undertaken to identify risk factors for peripherally inserted central catheter (PICC)-associated DVT have yielded conflicting results. PICC insertion teams and other health-care providers need to understand the risk factors so that they can develop methods to prevent DVT. METHODS A 1-year prospective observational study of PICC insertions was conducted at a 456-bed, level I trauma center and tertiary referral hospital affiliated with a medical school. All patients with one or more PICC insertions were included to identify the incidence and risk factors for symptomatic DVT associated with catheters inserted by a facility-certified PICC team using a consistent and replicated approach for vein selection and insertion. RESULTS A total of 2,014 PICCs were inserted during 1,879 distinct hospitalizations in 1,728 distinct patients for a total of 15,115 days of PICC placement. Most PICCs were placed in the right arm (76.9%) and basilic vein (74%) and were double-lumen 5F (75.3%). Of the 2,014 PICC insertions, 60 (3.0%) in 57 distinct patients developed DVT in the cannulated or adjacent veins. The best-performing predictive model for DVT (area under the curve, 0.83) was prior DVT (odds ratio [OR], 9.92; P < .001), use of double-lumen 5F (OR, 7.54; P < .05) or triple-lumen 6F (OR, 19.50; P < .01) PICCs, and prior surgery duration of > 1 h (OR, 1.66; P = .10). CONCLUSIONS Prior DVT and surgery lasting > 1 h identify patients at increased risk for PICC-associated DVT. More importantly, increasing catheter size also is significantly associated with increased risk. Rates of PICC-associated DVT may be reduced by improved selection of patients and catheter size.

Forecasting daily patient volumes in the emergency department.

STUDY OBJECTIVE To develop and evaluate a computerized system to monitor therapeutic antibiotics in a hospital setting. MATERIAL AND METHODS From November 1986 through October 1987, we prospectively monitored 1,632 hospitalized patients who had 2,157 microbiology specimens sent for culture and sensitivity testing. During the study period, computer algorithms were used to identify patients whose antibiotic therapy was inappropriate in relation to microbiology culture and sensitivity data. When inconsistencies occurred between antibiotic therapy and in vitro sensitivity data, computer algorithms generated therapeutic antibiotic monitor (TAM) alerts. A clinical pharmacist then notified the attending physician of the alert. RESULTS Antibiotic therapy was identified by the computer as inappropriate in 696 instances (32%). After we eliminated false-positive alerts, 420 evaluable TAM alerts remained. Physicians responded to the TAM alerts by either changing or starting antimicrobial therapy in 125 cases (30%). Moreover, physicians were previously unaware of the relevant susceptibility test results in 49% of the alerts. CONCLUSION Computer-assisted monitoring is an efficient and promising method to identify and correct errors in antimicrobial prescribing and to assure the appropriate use of therapeutic antibiotics.