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Development, Testing, and Findings of a Pediatric-Focused Trigger Tool to Identify Medication-Related Harm in US Children's Hospitals

^a Division of General Pediatrics, Department of Pediatrics, University of Southern California School of Medicine, Los Angeles, California
^b Patient Safety Program, Children's Hospital Los Angeles, Los Angeles, California
^c Division of Infectious Diseases, Department of Pediatrics, University of Southern California School of Medicine, Los Angeles, California
^d Quality Management Program, Children's Hospital Los Angeles, Los Angeles, California
^e Department of Pharmacy, Childrens Hospital Los Angeles, Los Angeles, California
^f Performance Improvement Division, Child Health Corporation of America, Shawnee Mission, Kansas
^g Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, and Department of Quality Management, Lucile Packard Children's Hospital, Palo Alto, California

	ABSTRACT

TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
OBJECTIVES. The purposes of this study were to develop a pediatric-focused tool for adverse drug event detection and describe the incidence and characteristics of adverse drug events in children's hospitals identified by this tool.

METHODS. A pediatric-specific trigger tool for adverse drug event detection was developed and tested. Eighty patients from each site were randomly selected for retrospective chart review. All adverse drug events identified using the trigger tool were evaluated for severity, preventability, ability to mitigate, ability to identify the event earlier, and presence of associated occurrence report. Each trigger and the entire tool were evaluated for positive predictive value.

RESULTS. Review of 960 randomly selected charts from 12 children's hospitals revealed 2388 triggers (2.49 per patient) and 107 unique adverse drug events. Mean adverse drug event rates were 11.1 per 100 patients, 15.7 per 1000 patient-days, and 1.23 per 1000 medication doses. The positive predictive value of the trigger tool was 3.7%. Twenty-two percent of all adverse drug events were deemed preventable, 17.8% could have been identified earlier, and 16.8% could have been mitigated more effectively. Ninety-seven percent of the identified adverse drug events resulted in mild, temporary harm. Only 3.7% of adverse drug events were identified in existing hospital-based occurrence reports. The most common adverse drug events identified were pruritis and nausea, the most common medication classes causing adverse drug events were opioid analgesics and antibiotics, and the most common stages of the medication management process associated with preventable adverse drug events were monitoring and prescribing/ordering.

CONCLUSIONS. Adverse drug event rates in hospitalized children are substantially higher than previously described. Most adverse drug events resulted in temporary harm, and 22% were classified as preventable. Only 3.7% were identified by using traditional voluntary reporting methods. Our pediatric-focused trigger tool is effective at identifying adverse drug events in inpatient pediatric populations.

Key Words: adverse drug event • trigger tool • pediatrics • harm • patient safety

Abbreviations: AE—adverse event • ADE—adverse drug event • MAR—medication administration record • IHI—Institute for Healthcare Improvement • CHCA—Child Health Corporation of America • PPV—positive predictive value • CI—confidence interval

In the report To Err Is Human,¹ the Institute of Medicine concluded that between 44000 and 98000 lives are lost per year in US hospitals as a result of error. This estimate was developed in part from the Harvard Medical Practice Study, which estimated that 3.7% of all hospitalized patients in a New York State cohort experienced an adverse event (AE) related to medical therapy.² More recent data from the Harvard group using more sophisticated detection methods revealed a 6.5% rate of adverse drug events (ADEs) alone in the adult inpatient setting, with 33% of these events described as preventable.³ Application of these methods to a pediatric population revealed a 2.3% ADE rate with 19% described as preventable.⁴ Both of these more recent studies^3,4 relied on voluntary and verbally solicited reports from hospital staff, medication administration records (MARs), and nonfocused retrospective chart review to identify medication errors and ADEs. A variety of methods have been used to identify medical errors and ADEs in both adult and pediatric patients, including chart review, voluntary reporting by health care providers, direct observation, and review of medical malpractice claims.^4–19

Recently, a different strategy, known as the trigger method, was shown to be superior to voluntary occurrence reports and conventional unfocused chart review in the identification of AEs in hospitalized adult patients^20–22 and NICU patients.²³ A trigger is defined as an "occurrence, prompt, or flag found on review of the medical chart that ‘triggers’ further investigation to determine the presence or absence of an adverse event."^20,21 An example of a trigger is the administration of naloxone to a patient, which would prompt a focused chart review for evidence of an opioid-induced AE such as respiratory depression. The use of triggers, therefore, theoretically promotes a more focused and efficient chart review than an unfocused chart review and thus may identify more ADEs. Studies using the trigger method have borne this out,^20–23 identifying AE rates as much as 50 times higher than hospital-based occurrence reporting strategies²¹ and identifying AE rates in high-risk populations as high as 112 per 100 patients, with ADE rates of 20 per 100 patients.²² The only published study to date using a trigger tool in the pediatric population identified an AE rate of 74 per 100 NICU patients when applied to 749 charts from 15 NICUs in North America.²³ Because both the Institute for Healthcare Improvement (IHI) and the Institute of Medicine have recommended a transition from measuring error to that of measuring harm,^{20–22,24,25} the trigger method is 1 promising and practical approach for effectively measuring harm associated with hospital-based health care.^20–23

Despite the ability of the trigger method to identify harm effectively, there is evidence that 1 generic ADE trigger tool will not work for every environment. For example, when the NICU AE trigger tool was constructed, the authors worked backward from the most frequent and severe AEs in the NICU setting to construct a list of triggers that would most effectively identify these high-frequency, high-severity AEs.²³ This list of AEs and resultant triggers looks substantially different from previously constructed adult-focused trigger tools. Recognizing that pediatric patients likely differ from adult inpatients in regard to relevant ADEs, we embarked on a study to develop and test a pediatric-specific trigger tool to identify ADEs. The aims of this study were

• to develop and test a pediatric population–focused trigger tool, adapted from the existing adult-focused trigger tool,²⁰ for ADE detection,
  
• to determine the rate of ADEs in hospitalized children at 12 freestanding children's hospitals in the United States, and
  
• to identify characteristics of the most frequent ADEs in children's hospitals to provide the basis for developing a cohesive strategy to prevent proactively drug-related harm in inpatient pediatric populations.
  

	METHODS

TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
This study was a cross-sectional one that used retrospective chart review in 12 children's hospitals across the United States (see "Acknowledgments" for participating sites). Patients for phase II were randomly selected from a master list of eligible patients generated at each site. Patients were eligible for phase II of the study when they were in the hospital for a minimum of 2 days and discharged, transferred out, or died between March 18, 2002, and May 28, 2002. Twenty patients were randomly selected from all eligible patients from 4 consecutive successive 2-week blocks beginning March 18, 2002, for a total of 80 patients over the 8-week time frame. Only the first 30 days of hospitalization were included in the review in an effort to maximize the efficiency of the chart review. Patients were excluded when they were in the hospital for <2 days; were in the newborn nursery or admitted through the newborn nursery; were on the obstetrics service; were in the day hospital or observation unit; or were discharged, transferred out, or died before March 18, 2002 or after May 28, 2002.

Intervention
Phase I: Applying the IHI Adult-Focused ADE Trigger Tool to Pediatric Inpatients
A collection of 12 children's hospitals from Child Health Corporation of America (CHCA) evaluated the IHI adult-focused ADE trigger tool²⁰ in pediatric inpatients. The stated aims of phase I were (1) to adapt an existing adult-focused trigger chart review tool for an inpatient pediatric population, (2) to compare the effectiveness and efficiency of the tool with standard methods of ADE reporting, and (3) to identify new triggers that are unique to the pediatric population. A total of 931 patients who met the inclusion criteria were evaluated retrospectively using the 24-trigger IHI adult-focused ADE trigger tool. Application of the adult-based tool to these 931 pediatric-aged patients identified ADE rates of 9.8 per 100 patients, 13.4 per 1000 patient-days, and 1.38 per 1000 medication doses. On the basis of frequency and efficiency criteria for trigger removal (triggers with a low positive predictive value [PPV], ambiguity, or resulting in extreme inefficiencies during chart review were removed) and trigger addition created a priori, 13 adult-based triggers were removed and 4 pediatric-specific triggers were added to establish the 15-trigger CHCA pediatric-focused ADE trigger tool (Table 1) used in our full study (phase II).

The full pediatric-focused ADE trigger tool trial (phase II) was launched with a Webcast in May 2002. The final ADE trigger tool, an instruction manual with definitions, a chart audit tool, and instructions for using a standard random selection strategy to identify patients were sent to each site. Specifically, all eligible patients at each site were included in a master list from which 20 patients per 2-week period were randomly selected using the randomization function in Excel (Microsoft Inc, Redmond, WA). We defined an ADE as "an injury, large or small, caused by the use (including nonuse) of a drug."^20–23,26 Preventability was defined on the basis of the initial reviewer's interpretation and the second reviewer's confirmation of whether the ADE could have been prevented.

A physician, nurse, or pharmacist who was trained in chart review methods was designated locally to be the site's initial chart reviewer. We encouraged the use of senior physicians, nurses, or pharmacists who were experienced in chart review to carry out the initial chart review, although no formal criteria were established. Conference calls, e-mail, and telephone access to the investigators were provided to the chart reviewers to promote a more clear understanding of the definitions and methods. Selected charts were reviewed locally for the presence or absence of each of the 15 triggers. Each trigger identified prompted an in-depth review for the presence of an associated ADE. In addition, the chart reviewer was instructed to identify any ADE that was not associated with a trigger. Each identified trigger and any ADE (regardless of whether it was associated with a trigger) were recorded in a customized personal digital assistant with screens based on a standard data collection sheet. Sections of the charts were reviewed in the following order: discharge summary, International Classification of Diseases, Ninth Revision codes (if available in the chart), laboratory reports, physician orders and MAR, nursing flow sheets, and nursing/multidisciplinary progress notes. All ADEs were evaluated for severity using categories based on the system used for classifying medication errors by the National Coordinating Council for Medication Error Reporting and Prevention (Table 2). ²⁷ All ADEs were evaluated by the local chart reviewer using local definitions to determine whether these ADEs could have been prevented, identified earlier, or mitigated more effectively and whether an associated occurrence report was filed. In addition, each ADE was evaluated for the stages of the medication process in which a medication error occurred (for preventable ADEs), medication class, the principle diagnosis at discharge of the patient who incurred the ADE, and the hospital location of the event. Each ADE and associated characteristics identified by the primary chart reviewer were then reviewed for accuracy by a local physician (secondary reviewer), who was provided a summary of the event and any relevant clinical information that would allow him or her to confirm (or reject) that an ADE occurred. In case of discrepancy, the second reviewer's interpretations of the preventability, earlier identification, more effective mitigation, medication stages, medication class, principle diagnosis at discharge, and location of event were considered final.

Outcomes
Analysis of outcomes included the following:

• ADEs per 100 patients, per 1000 patient-days, and per 1000 medication doses;
  
• triggers per patient;
  
• trigger PPVs (defined as the number of times a specific trigger independently identified an ADE divided by the number of times a trigger was identified) individually and for the trigger tool in total; an ADE could have been identified by 1 trigger;
  
• severity of ADEs (defined as the highest level of harm applicable using the National Coordinating Council for Medication Error Reporting and Prevention severity scale)²⁷;
  
• percentage of ADEs that were preventable, could have been identified earlier, could have been mitigated more effectively, or were associated with a hospital occurrence report;
  
• stages (ordering, transcribing, dispensing, administering, or monitoring) of the medication management process during which the medication error (for preventable ADEs only) was believed to occur;
  
• class of medication resulting in the ADE;
  
• principle diagnosis of patient on discharge;
  
• hospital unit where the ADE occurred.
  

Statistical Analysis
Descriptive statistics, including 95th percentile confidence intervals (CIs), were calculated for patient and hospitalization characteristics as appropriate. The exact binomial distribution was used for proportions, and the Poisson distribution was used for rates. Statistical analysis was performed by using Stata 9.0 (Stata Corp LP, College Station, TX).

	RESULTS

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Aggregate Data
A total of 960 randomly selected charts from 12 children's hospitals reflecting a total of 6806 patient-days were evaluated. Relevant patient and hospital characteristics for the study population are listed in Table 3. The mean length of stay was 7.1 days with a median length of stay of 4 days. Thirty (3.1%) of the 960 charts represented admissions that lasted >30 days (range: 31–160 days). A total of 2388 triggers were detected, resulting in a mean rate of 2.49 triggers per patient (95% CI: 2.39–2.59). A total of 107 ADEs were identified, resulting in a mean rate of 11.1 ADEs per 100 patients (range: 0–45; 95% CI: 9.13–13.5; Table 4), 15.7 ADEs per 1000 patient-days (range: 0–55; 95% CI; 12.9–19.0), and 1.23 ADEs per 1000 medication doses (range: 0–8.3; 95% CI: 1.01–1.49). Eight (7.5%) of these 107 ADEs were determined to be the result of a medication's being omitted (order omission: 5; dose omission: 3). A total of 70 patients (7.29%; 95% CI: 5.73%–9.12%) had 1 ADEs during their hospitalization. The mean PPV of the trigger tool overall was 3.7% (range of PPVs for each individual independent trigger: 0–20; Table 5).

View larger version (8K): [in this window] [in a new window]   FIGURE 1 Stage of the medication management process in which a medication error occurred (preventable ADEs only; n = 24). Note that >1 stage could be identified for each preventable ADE.

View larger version (14K): [in this window] [in a new window]   FIGURE 2 Medication classes associated with identified ADEs (n = 107 total ADEs).

View larger version (12K): [in this window] [in a new window]   FIGURE 3 Percentage of patients with an ADE according to principle diagnosis category at discharge.

View larger version (12K): [in this window] [in a new window]   FIGURE 4 Percentage of patients with ADEs according to hospital location. HEMONC indicates hematology/oncology unit.

View larger version (12K): [in this window] [in a new window]   FIGURE 5 Most frequent types of ADEs. "Other" (n = 39) was defined as any ADE type with fewer than 3 occurrences.

	DISCUSSION

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Our study of ADE rates in inpatient pediatric populations is consistent with recent studies concluding that the trigger tool methods seem to be more robust than the traditional methods of occurrence reports,^7,20–23,28 nontriggered chart review,^2–4,16 and administrative data analysis.^5,6 For example, the 2 most frequently cited previously published estimates of ADE rates in pediatrics^4,19 used unfocused (ie, nontrigger focused) chart review methods to identify ADEs. Kaushal et al⁴ used a combination of "voluntary and verbally solicited reports from house officers, nurses and pharmacists and by medication order sheet, medication administration records and chart review of all hospitalized patients" to identify ADE rates, whereas Holdsworth et al¹⁹ used a clinical pharmacist to "search each medical chart for evidence of ADEs and potential ADEs by reviewing physician and nursing notes, pharmacy records, medication administration records, and laboratory data." In the first study, Kaushal et al⁴ reported ADE rates in children on the inpatient wards at 2 urban teaching hospitals to be 2.3 per 100 admissions (26 events), with an additional potential ADE rate of 10 per 100 admissions (115 events). Of the 26 true ADEs, 5 (19%) were determined to be preventable. In the second study, Holdsworth et al reported an ADE rate in pediatric inpatients (PICU and general care unit at a university hospital) of 6 per 100 admissions (76 events), with 61% judged as preventable, and a potential ADE rate of 8.0 per 100 patient-days (94 events).¹⁹ Our data, using the trigger tool method, identified 11.1 ADEs per 100 admissions, thus identifying between 1.8 and 4.8 times more ADEs per discharge than the estimates of Kaushal et al⁴ and Holdsworth et al.¹⁹ Similarly, when comparing the pediatric ADE trigger tool method with occurrence reports, our study showed that only 4 of the identified 107 ADEs were identified by the occurrence report system. Although the trigger tool specifically identified only 89 of the 107 total ADEs in this study, it did identify all 4 found via occurrence reporting. Thus, the trigger tool method identified 22 times more ADEs than the frequently used but flawed occurrence report methods.²⁸ These findings are consistent with other trigger tool occurrence report comparisons as well.^7,20–23 The higher rate identified by the trigger tool is likely attributable to the ability of the tool to direct focus on specific circumstances associated with ADEs, on specific chart elements, and on specific ADE types identified a priori to be of interest.

There are several limitations of this study. The most important limitation is the lack of a gold standard for ADE detection with which we can compare our results. We therefore made the assumption that the ADEs identified in this study were the sum total of all ADEs that occurred in these patients. This is the only way that we could calculate a PPV for each of the triggers and the tool itself. Second, the use of the trigger tool, in particular the determination of an event as being an ADE, is subjective and susceptible to certain biases that could affect the outcomes in uncertain ways.²⁹ For efficiency purposes, we did not require a second primary reviewer or a second secondary reviewer (as used in other trigger tool trials^21,22); however, we attempted to standardize the use of the tool and the interpretations of the findings by requiring review of 10 standard practice scenarios; discussion of the findings via conference call; clear definitions for triggers, ADEs, and severity ratings; a detailed instruction manual with specific instructions, processes, and definitions; open and frequently used access to the primary investigator for questions; and a local second reviewer to confirm all ADEs. Despite these safeguards, there remains some subjectivity in the identification and interpretation of these triggers and events.²⁹ Additional studies on the interrater reliability of this and other trigger tools is warranted. Third, the classification of preventability (as well as the ability to identify sooner and ability to mitigate more effectively) of an event is subjective²⁹ and was left to local sites to determine. Although we suspect substantial variability in a chart reviewers' interpretation of preventability, the assignment of preventability in aggregate remains critical to obtaining buy-in to use of the tool and ultimately toward redesigning systems to minimize risk for ADEs in the future. Fourth, we did not evaluate the time required to review each chart, which would help to determine the efficiency of this method of ADE detection. We purposely limited the chart reviews to the first 30 days of hospitalization as a strategy to enhance efficiency, a strategy different from the IHI's recommendation of 20 minutes maximum per chart review.^20,21 This strategy had little effect in our study, however, because only 30 (3.1%) of the 960 patient charts reviewed had admissions of >30 days. Previous studies consistently found that trigger tool–based chart reviews require between 15 and 20 minutes per chart.^20–23 Finally, we did not evaluate interrater reliability between trigger tool reviewers in this study. This may have particular relevance because we allowed several disciplines, including physicians, nurses, and pharmacists, to function as primary reviewers. At least 1 previous trigger tool in pediatrics, using the same study method as used in this study, showed the reviewers to identify accurately NICU-related triggers 88.8% and NICU-related AEs 92.4% of the time.²³

Despite these design weaknesses, this study reinforces the notion that trigger tools represent the most robust method developed to date to assess ADEs. First, their ability to identify ADEs is substantially greater than previously developed strategies such as administrative database analysis, hospital-based occurrence reports, and nontriggered chart review. The ability to measure harm effectively is becoming increasingly important as the IHI embarks on its 5 Million Lives campaign, a campaign to reduce iatrogenic harm in 5 million inpatients in a 2-year time frame.²⁵ Trigger tools, a concept established by Roger Resar, MD, and David Classen, MD, both of whom work with the IHI to establish measurement strategies,^20–23,26 are the clear choice for accurately measuring these rates of harm. Second, trigger tools provide a consistent method that allows routine determination of ADE rates over time. The ability to track ADE rates over time in a methodologically consistent manner is critical to quality improvement efforts at a local level. Third, trigger tools are associated with a standardized instruction manual with standard definitions and therefore can potentially be used to identify best practice sites for benchmarking purposes. Finally, trigger tools can potentially be automated, which could allow ADE identification in real time. The heavy weighting of laboratory and medication triggers in the pediatric ADE trigger tool suggests that it could form the basis of an efficient electronic strategy for tracking ADE rates as well as identifying ADEs in real time. This strategy is being explored aggressively at several children's hospitals, with promising results (Brian Jacobs, MD, written personal communication, 2008). Ideally, this real-time identification could be used to mitigate ADEs before they fully evolve. An example of this is identification of a rise in serum creatinine (possibly as a result of a medication error) that is still in the normal creatinine range for age but is flagged automatically by a computer-based preprogrammed "trigger" before extensive renal damage occurs. Several studies successfully used an automated identification system with a trigger tool in the adult setting.^20,30–32 The national movement toward electronic medical charts should enhance this effort.

	CONCLUSIONS

TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
This study is the first to develop and evaluate a trigger tool to detect ADEs in an inpatient pediatric population. We identified an ADE rate of 11.1 per 100 admissions (15.7 per 1000 patient-days), the most common stage in the medication management process for a preventable ADE to be the monitoring phase, and the most common class of medications associated with ADEs to be analgesics-opioids (causing 51% of all inpatient ADEs). The unit with the highest ADE rate per patient was the hematology/oncology unit, and the most frequently identified ADE throughout the 12 study hospitals was pruritis. Twenty-two percent of all identified ADEs were classified as preventable, with 2.8% of ADEs falling into the more severe harm categories of F through I. Compared with other detection strategies such as administrative databases, hospital-based occurrence reports, and nontriggered chart review, the trigger method seems superior in identifying ADEs in multiple settings, including an inpatient pediatric population. Our data support this claim, comparing the pediatric-focused ADE trigger tool with occurrence reporting within the inpatient setting. These data should provide the groundwork for aggressive, evidence-based prevention strategies to decrease the substantial risk for medication-related harm to our pediatric inpatient population.

	ACKNOWLEDGMENTS

 
We appreciate the encouragement and advice extended to us by David Classen, Roger Resar, and Carol Haraden, who, on behalf of IHI and Premier, granted permission to use the IHI/Premier trigger tool for adaptation in this study. Participants and associated hospitals include Children's Hospital of Buffalo, Buffalo, NY: Sandy McDougal, Mike Cimino; Charleston Area Medical Center, Inc, Charleston, WV: Nicole Passerrello, Karen Goldsteen; Children's Hospital Medical Center, Cincinnati, OH: Mike McKibben, Terri Byczkowski; Cook Children's Hospital, Fort Worth, TX: Cindy Guess, Doreen Walo; Childrens Hospital Los Angeles, Los Angeles, CA: Ronald Ferdman; Le Bonheur Children's Medical Center, Memphis, TN: Debbie Hannah, Kelley Lee; Children's Hospital of Wisconsin, Milwaukee, WI: Krista Colpaert, Matt Scanlon; Children's Hospital of Orange County, Orange, CA: Susan Feidner, Camille Bauer; Lucile Salter Packard Children's Hospital at Stanford, Palo Alto, CA: Nancy Abel; Children's Hospital of Pittsburgh, Pittsburgh, PA: David Kazimer, Jean White; All Children's Hospital, St Petersburg, FL: Dipti Amin, Sandy Wismer; Children's National Medical Center, Washington, DC: Michelle Mercado, Karl Gumpper.

We thank Frederick Dorey for statistical advice. Project management was provided by CHCA staff members Ginger Morse, Kelsi Currier, and Tina Logsdon. Database design and data management were provided by Dan Dawdy of CHCA.

	FOOTNOTES

 
Accepted Sep 26, 2007.

Address correspondence to Paul J. Sharek, MD, MPH, Lucile Packard Children's Hospital, 700 Welch Rd #227, Palo Alto, CA 94304. E-mail: psharek{at}lpch.org

The authors have indicated they have no financial relationships relevant to this article to disclose.

What's Known on This Subject Data using the established Harvard Medical Practice Study methodology revealed a 2.3% rate of adverse drug events (ADEs) in the pediatric inpatient population.

 

What This Study Adds A pedatric-focused ADE trigger tool was built and tested. Use of this more sophisticated detection tool identified an 11.1% rate of ADEs in pediatric inpatients.

 

	REFERENCES

TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 

1. Kohn LT, Corrigan JM, Donaldson MS, eds. To Err Is Human: Building a Safer Health System. National Academies Press: Washington, DC; 1999
2. Brennan TA, Leape LL, Laird NM, et al. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. N Engl J Med. 1991;324 (6):370 –376[Abstract]
3. Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events. JAMA. 1995;274 (1):29 –34[Abstract]
4. Kaushal R, Bates DW, Landrigan C, et al. Medication errors and adverse drug events in pediatric inpatients. JAMA. 2001;285 (16):2114 –2120[Abstract/Free Full Text]
5. Slonim AD, LaFleur BJ, Ahmed W, Joseph JG. Hospital-reported medical errors in children. Pediatrics. 2003;111 (3):617 –621[Abstract/Free Full Text]
6. Miller MR, Zhan C. Pediatric patient safety in hospitals: a national picture in 2000. Pediatrics. 2004;113 (6):1741 –1746[Abstract/Free Full Text]
7. Suresh G, Horbar JD, Plsek P, et al. Voluntary anonymous reporting of medical errors for neonatal intensive care. Pediatrics. 2004;113 (6):1609 –1618[Abstract/Free Full Text]
8. Aranda JV, Portuguez-Malavasi A, Collinge JM, Germanson T, Outerbridge EW. Epidemiology of adverse drug reactions in the newborn. Dev Pharmacol Ther. 1982;5 (3–4):173 –184[ISI][Medline]
9. Tisdale JE. Justifying a pediatric critical-care satellite pharmacy by medication-error reporting. Am J Hosp Pharm. 1986;43 (2):368 –371[Abstract]
10. Folli HL, Poole RL, Benitz WE, Russo JC. Medication error prevention by clinical pharmacists in two children's hospitals. Pediatrics. 1987;79 (5):718 –722[Abstract/Free Full Text]
11. Raju TN, Kecskes S, Thornton JP, Perry M, Feldman S. Medication errors in neonatal and paediatric intensive-care units. Lancet. 1989;2 (8659):374 –376[CrossRef][ISI][Medline]
12. West DW, Levine S, Magram G, MacCorkle AH, Thomas P, Upp K. Pediatric medication order error rates related to the mode of order transmission. Arch Pediatr Adolesc Med. 1994;148 (12):1322 –1326[Abstract]
13. Wilson DG, McArtney RG, Newcome RG, et al. Medication errors in paediatric practice: insights from a continuous quality improvement approach. Eur J Pediatr. 1998;157 (9):769 –774[CrossRef][ISI][Medline]
14. Ross LM, Wallace J, Paton JY. Medication errors in a paediatric teaching hospital in the UK: five years of operational experience. Arch Dis Child. 2000;83 (6):492 –496[Abstract/Free Full Text]
15. Kozer E, Scolnik D, Macpherson A, et al. Variables associated with medication errors in pediatric emergency medicine. Pediatrics. 2002;110 (4):737 –742[Abstract/Free Full Text]
16. Moore TJ, Weiss SR, Kaplan S, Blaisdell CJ. Reported adverse drug events in infants and children under 2 years of age. Pediatrics. 2002;110 (5). Available at: www.pediatrics.org/cgi/content/full/110/5/e53
17. Weiss J, Krebs S, Hoffman C, et al. Survey of adverse drug reactions on a pediatric ward: a strategy for early and detailed detection. Pediatrics. 2002;110 (2 pt 1):254 –257[Abstract/Free Full Text]
18. Fortescue EB, Kaushal R, Landrigan CP, et al. Prioritizing strategies for preventing medication errors and adverse drug events in pediatric inpatients. Pediatrics. 2003;111 (4 pt 1):722 –729[Abstract/Free Full Text]
19. Holdsworth MT, Fichtl RE, Behta M, et al. Incidence and impact of adverse drug events in pediatric inpatients. Arch Pediatr Adolesc Med. 2003;157 (1):60 –65[Abstract/Free Full Text]
20. Rozich JD, Haraden CR, Resar RK. Adverse drug event trigger tool: a practical methodology for measuring medication related harm. Qual Saf Health Care. 2003;12 (3):194 –200[Abstract/Free Full Text]
21. Resar RK, Rozich JD, Classen DC. Methodology and rationale for the measurement of harm with trigger tools. Qual Saf Health Care. 2003;12 (suppl 2):ii39 –ii45[Abstract/Free Full Text]
22. Resar RK, Rozich JD, Simmonds T, Haraden CR. A trigger tool to identify adverse events in the intensive care setting. Jt Comm J Qual Saf. 2006;32 (10):585 –590
23. Sharek PJ, Horbar JD, Mason W, et al. Adverse events in the neonatal intensive care unit: development, testing, and findings of an NICU-focused trigger tool to identify harm in north American NICUs. Pediatrics. 2006;118 (4):1332 –1340[Abstract/Free Full Text]
24. Institute of Medicine Report. Patient safety: achieving a new standard of care. Washington, DC: National Academy Press; 2004. Available at: www.iom.edu/CMS/3809/4629/16663.aspx. Accessed April 2, 2007
25. Institute for Healthcare Improvement. The 5 million lives campaign: protecting 5 million lives from harm. Available at: www.ihi.org/IHI/Programs/Campaign. Accessed April 2, 2007
26. Classen DC, Pestotnik SL, Evans RS, Burke JP. Computerized surveillance of adverse drug events in hospital patients. JAMA. 1991;266 (20):2847 –2851[Abstract]
27. National Coordinating Council for Medication Error Reporting and Prevention. Taxonomy of medication errors. Available at: www.nccmerp.org. Accessed April 2, 2007
28. Cullen DJ, Bates DW, Small SD, Cooper JB, Nemeskal AR, Leape LL. The incident reporting system does not detect adverse drug events: a problem for quality improvement. Jt Comm J Qual Improv. 1995;21 (10):541 –548[Medline]
29. Thomas EJ, Lipsitz SR, Studdert DM, Brennan TA. The reliability of medical record review for estimating adverse event rates. Ann Intern Med. 2002;136 (11):812 –816[Abstract/Free Full Text]
30. Raschke RA, Gollihare B, Wunderlich TA, et al. A computer alert system to prevent injury from adverse drug events. JAMA. 1998;280 (15):1317 –1320[Abstract/Free Full Text]
31. Jha AK, Kuperman GJ, Teich JM, et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc. 1998;5 (3):305 –314[Abstract/Free Full Text]
32. Levy M, Azaz-Livshits T, Sadan B, Shalit M, Geisslinger G, Brune K. Computerized surveillance of adverse drug reactions in hospital: implementation. Eur J Clin Pharmacol. 1999;54 (11):887 –892[CrossRef][ISI][Medline]

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