Surveillance for Quality Assessment: IV. Surveillance Using a Hospital Information System

Dc, Classen; Jp, Burke; Sl, Pestotnik; Evans, Robert M.; Le, Stevens

doi:10.1086/646331

Cited by 22 publications

(1 citation statement)

References 24 publications

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“…10 " 16 Hownually in the United States. 1 In recent studies, the incidence ever, traditional surveillance methods based on manual review of BSI ranged from 1.3 to 18.4 episodes per 1,000 hospital of clinical and microbiological data are time-consuming and admissions, depending on the study population, the presence costly, 15 and the automated detection of hospital-acquired of invasive devices, and the length of hospital stay. 1,4 " 7 The infections on the basis of electronic data is a promising alcrude mortality rate also varied widely for patients with nosternative that can facilitate this task.…”

Section: Infect Control Hosp Epidemiol 2007; 28:1030-1035mentioning

confidence: 99%

Comparison of Automated Strategies for Surveillance of Nosocomial Bacteremia

Bellini¹,

Petignat²,

Francioli³

et al. 2007

Infect. Control Hosp. Epidemiol.

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OBJECTIVE. Surveillance of nosocomial bloodstream infection (BSI) is recommended, but time-consuming. We explored strategies for automated surveillance.METHODS. Cohort study. We prospectively processed microbiological and administrative patient data with computerized algorithms to identify contaminated blood cultures, community-acquired BSI, and hospital-acquired BSI and used algorithms to classify the latter on the basis of whether it was a catheter-associated infection. We compared the automatic classification with an assessment (71% prospective) of clinical data.SETTING. An 850-bed university hospital.PARTICIPANTS. All adult patients admitted to general surgery, internal medicine, a medical intensive care unit, or a surgical intensive care unit over 3 years.RESULTS. The results of the automated surveillance were 95% concordant with those of classical surveillance based on the assessment of clinical data in distinguishing contamination, community-acquired BSI, and hospital-acquired BSI in a random sample of 100 cases of bacteremia. The two methods were 74% concordant in classifying 351 consecutive episodes of nosocomial BSI with respect to whether the BSI was catheter-associated. Prolonged episodes of BSI, mostly fungemia, that were counted multiple times and incorrect classification of BSI clinically imputable to catheter infection accounted for 81% of the misclassifications in automated surveillance. By counting episodes of fungemia only once per hospital stay and by considering all cases of coagulase-negative staphylococcal BSI to be catheter-related, we improved concordance with clinical assessment to 82%. With these adjustments, automated surveillance for detection of catheter-related BSI had a sensitivity of 78% and a specificity of 93%; for detection of other types of nosocomial BSI, the sensitivity was 98% and the specificity was 69%.

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Section: Infect Control Hosp Epidemiol 2007; 28:1030-1035mentioning

confidence: 99%

Comparison of Automated Strategies for Surveillance of Nosocomial Bacteremia

Bellini¹,

Petignat²,

Francioli³

et al. 2007

Infect. Control Hosp. Epidemiol.

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Linking Laboratory and Pharmacy

Schiff¹,

Klass²,

Peterson³

et al. 2003

Arch Intern Med

122

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A myriad of errors and lost improvement opportunities result from failure of clinical laboratory and pharmacy information systems to effectively communicate. Pharmacotherapy could benefit from enhanced laboratory-pharmacy linkage with respect to (1) drug choice (laboratory-based indications and contraindications), (2) drug dosing (renal or hepatic, blood level-guided adjustments), (3) laboratory monitoring (laboratory signals of toxicity, baseline and ongoing monitoring), (4) laboratory result interpretation (drug interfering with test), and (5) broader quality improvement (surveillance for unrecognized toxicity, monitoring clinician response delays). Linkages can be retrospective or real-time. Many organizations could benefit now by linking existing pharmacy and laboratory data. Greater improvement is possible through implementation of electronic order entry with real-time decision support incorporating linked laboratory and pharmacy data. While many guidelines, admonitions, and rules exist regarding drugs and the laboratory, substantial new knowledge and evidence in this area are needed. Focusing on these unmet needs and accompanying logistical challenges is a priority.

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Computerized Surveillance of Adverse Drug Events in Hospital Patients

Classen¹

1991

JAMA

494

203

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Objective: To develop a new method to improve the detection and characterization of adverse drug events (ADEs) in hospital patients. Design: Prospective study of all patients admitted to our hospital over an 18 month period. Setting: LDS Hospital, Salt Lake City, Utah, a 520-bed tertiary care center affiliated with the University of Utah School of Medicine, Salt Lake City. Patients: We developed a computerized ADE monitor, and computer programs were written using an integrated hospital information system to allow for multiple source detection of potential ADEs occurring in hospital patients. Signals of potential ADEs, both voluntary and automated, included sudden medication stop orders, antidote ordering, and certain abnormal laboratory values. Each day a list of all potential ADEs from these sources was generated, and a pharmacist reviewed the medical records of all patients with possible ADEs for accuracy and causality. Verified ADEs were characterized as mild, moderate, or severe and as type A (dose-dependent or predictable) or type B (idiosyncratic or allergic) reactions, and causality was further measured using a standardized scoring method. Outcome measure: The number and characterization of ADEs detected. Results: Over 18 months we monitored 36 653 hospitalized patients. There were 731 verified ADEs identified in 648 patients, 701 ADEs were characterized as moderate or severe, and 664 were classified as type A reactions. During this same period only nine ADEs were identified using traditional detection methods. Physicians, pharmacists, and nurses voluntarily reported 92 of the 731 ADEs detected using this automated system. The other 631 ADEs were detected from automated signals, the most common of which were diphenhydramine hydrochloride and naloxone hydrochloride use, high serum drug levels, leukopenia, and the use of phytonadione and antidiarrheals. The most common symptoms and signs were pruritus, nausea and/or vomiting, rash, and confusion-lethargy. The most common drug classes involved were analgesics, anti-infectives, and cardiovascular agents. Conclusion: We believe that screening for ADEs with a computerized hospital information system offers a potential method for improving the detection and characterization of these events in hospital patients.

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Surveillance for Quality Assessment: IV. Surveillance Using a Hospital Information System

Cited by 22 publications

References 24 publications

Comparison of Automated Strategies for Surveillance of Nosocomial Bacteremia

Comparison of Automated Strategies for Surveillance of Nosocomial Bacteremia

Linking Laboratory and Pharmacy

Computerized Surveillance of Adverse Drug Events in Hospital Patients

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