You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Detecting adverse events for patient safety research: a review of current methodologies 
Chapter Title:  Single data source 
1. Abstract of Cited Paper (BIB001):  An automatic surveillance system to detect changes in the incidences of microorganisms diagnosed in the department of clinical microbiology has been developed. The program is incorporated into the laboratory computer system and gives a weekly list of microorganisms whose isolation rates compared with those of a previous period exceed a chosen limit. The system uses time series analysis with moving weighted averages, and the detection limit is based on the distribution of the residuals. Output from the system included information about potential outbreaks of gastroenteritis, nosocomial infection with Corynebacterium jeikeium, and a seasonal epidemic of respiratory syncytial virus. The system also listed organisms not commonly isolated in the laboratory and detected incorrect reports. We conclude that continuous surveillance of laboratory data with time series analysis is a valuable tool for epidemiologic surveillance and quality control. Large quantities of data may be screened. 
2. Abstract of Cited Paper (BIB002):  OBJECTIVE:To describe and validate a computer-based quality assurance method that detects narcotic overdoses associated with patient-controlled analgesia (PCA) use.SETTING:Two acute care teaching hospitals.PATIENTS:4669 patients who received PCA.INTERVENTIONS:The following patient lists were obtained during a two-year period from both hospital information systems: those who received PCA and (1) received naloxone, a narcotic antagonist, (2) were transferred to an intensive care unit, (3) had a cardiac or respiratory arrest, or (4) died. Possible overdoses were defined as patients who appeared on the PCA list and one of the other lists. Charts were reviewed if the patient's name appeared on the PCA and one of the other lists. Patients were judged to have experienced a narcotic overdose if there was an immediate improvement in blood pressure, respiratory rate, or mental status after the administration of naloxone.RESULTS:The search strategy identified 294 possible overdoses in 1499 patients who received PCA.... 
3. Abstract of Cited Paper (BIB003):  OBJECTIVE ::: To implement and measure the effects of automatic computerized laboratory signals (ALS) as a detection support tool of adverse drug reactions (ADRs) in hospital. ::: ::: ::: METHODS ::: This was a prospective observational study of a total of 192 patients (199 sequential medical admissions) during a 2-month period in a 34-bed medical ward at the Hadassah University Hospital, Jerusalem, Israel. The study involved the routine (daily) distribution to staff physicians of lists of automatic signals generated from computerized laboratory data as potential indicators of ADRs. Patient charts were reviewed by the clinical pharmacology team for ADRs and to see whether these were recognized by the staff physicians. ::: ::: ::: RESULTS ::: Seventy-one ADRs were detected in 64 of the 199 (32%) admissions. Twenty-seven per cent of the ADRs were serious, 9% of the admissions were due to ADRs. Two hundred and ninety-five ALS were generated involving 69% of the admissions. Sixty-one per cent of the ADRs were identified by ALS. ALS were present in 58% of the ADR negative admissions. Eighty-five per cent of the ADRs were recognized as such and 19% of the ALS-positive ADRs were not recognized by the staff physicians. ::: ::: ::: CONCLUSIONS ::: The routine implementation of ALS doubled the number of ADRs recognized by the physicians while patients were hospitalized in the medical ward. The use of the system appeared valid, simple and potentially cost-effective. 
4. Abstract of Cited Paper (BIB004):  Objective: To implement a computer-based adverse drug reaction monitoring system and compare its results with those of stimulated spontaneous reporting, and to assess the excess lengths of stay and costs of patients with verified adverse drug reactions. 
5. Abstract of Cited Paper (BIB005):  Aims Liver damage remains the most frequent type of adverse drug reaction (ADRs) that can lead to the withdrawal of a drug from the market. The abnormal laboratory data identified by computerized hospital information systems can be used in order to improve the detection of ADRs. Our objectives were to assess the detection and incidence of drug-induced liver abnormalities in a university hospital inpatient population and to evaluate the underreporting rate of drug-induced liver injury. Methods We conducted a prospective study performed 1 week per month from June to October 1997. We selected patients by a computerized process using biochemistry laboratory data, based on serum enzyme values, alanine aminotransferase (over 2 fold normal) and alkaline phosphatase (over 1.5 fold normal). Results Among 1976 ALT and 1814 AP assays performed during the period of the study, 156 (7.9%) and 159 (8.8%) tests, respectively, fell into the selected criteria. These conceme 147 patients. Among these patients, 13 (8.8%) cases of drug-induced liver injuries were suspected. Seven cases were asymptomatic. Six cases were classified as serious by these criteria: hospitalization to investigate the cause of health status impairment (4 patients), prolongation of hospitalization (1 patient) and life-threathening (1 patient). Using the hospitalization database, the incidence of drug-induced liver injuries was estimated as 6.6 per 1000 inpatients a week. Only 1 case was reported by physicians in the same period. Conclusions Computerization of biochemical data would allow the development of systems to improve detection of drug-induced injury. Moreover, underreporting remains important for such potentially serious ADRs, even in a university hospital.

Literature Review:
Most described nosocomial infection detection systems have utilized multiple data sources and a rule-base to generate alerts. Dessau et al. BIB002 have described a system that relies solely on laboratory data. This surveillance system detects changes in the incidences of microorganisms identified within the hospital laboratory databases. Isolated microorganisms identified over a weekly period are compared to isolation rates detected during a preceding period. Detection limits are determined based on the distribution of the differences between the expected event count and the observed event count. The authors note several nosocomial infectious outbreaks identified by this system. While this system appears successful at identifying nosocomial outbreaks, it would only be able to capture a small subset of all nosocomial infections. Computerized adverse drug event monitors have used a variety of signals to detect adverse drug events. Signals include drug orders  BIB001 , laboratory abnormalities BIB004 BIB005 BIB006 , and billing codes BIB003 (Table 2) . Generally predefined criteria are specified, such as a platelet count of less than 70,000/lL, and once this threshold has been reached, an alert is generated for a clinical pharmacist to review. The major limitation to these systems has been numerous false positives. False positive rates for some of these single data source detection systems have ranged from 77 to 96%  BIB004 BIB005 BIB006 BIB003 . To reduce this rate of false positives, event monitors have been designed that incorporate multiple data sources and screening algorithms.