Can hospital discharge diagnoses be used for surveillance of surgical-site infections?

Comparing Medical Record Abstraction (MRA) error rates in an observational study to pooled rates identified in the data quality literature

BACKGROUND

Medical record abstraction (MRA) is a commonly used method for data collection in clinical research, but is prone to error, and the influence of quality control (QC) measures is seldom and inconsistently assessed during the course of a study. We employed a novel, standardized MRA-QC framework as part of an ongoing observational study in an effort to control MRA error rates. In order to assess the effectiveness of our framework, we compared our error rates against traditional MRA studies that had not reported using formalized MRA-QC methods. Thus, the objective of this study was to compare the MRA error rates derived from the literature with the error rates found in a study using MRA as the sole method of data collection that employed an MRA-QC framework.

METHODS

A comparison of the error rates derived from MRA-centric studies identified as part of a systematic literature review was conducted against those derived from an MRA-centric study that employed an MRA-QC framework to evaluate the effectiveness of the MRA-QC framework. An inverse variance-weighted meta-analytical method with Freeman-Tukey transformation was used to compute pooled effect size for both the MRA studies identified in the literature and the study that implemented the MRA-QC framework. The level of heterogeneity was assessed using the Q-statistic and Higgins and Thompson's I2 statistic.

RESULTS

The overall error rate from the MRA literature was 6.57%. Error rates for the study using our MRA-QC framework were between 1.04% (optimistic, all-field rate) and 2.57% (conservative, populated-field rate), 4.00-5.53% points less than the observed rate from the literature (p < 0.0001).

CONCLUSIONS

Review of the literature indicated that the accuracy associated with MRA varied widely across studies. However, our results demonstrate that, with appropriate training and continuous QC, MRA error rates can be significantly controlled during the course of a clinical research study.

Comparing Medical Record Abstraction (MRA) Error Rates in an Observational Study to Pooled Rates Identified in the Data Quality Literature

Background

Medical record abstraction (MRA) is a commonly used method for data collection in clinical research, but is prone to error, and the influence of quality control (QC) measures is seldom and inconsistently assessed during the course of a study. We employed a novel, standardized MRA-QC framework as part of an ongoing observational study in an effort to control MRA error rates. In order to assess the effectiveness of our framework, we compared our error rates against traditional MRA studies that had not reported using formalized MRA-QC methods. Thus, the objective of this study was to compare the MRA error rates derived from the literature with the error rates found in a study using MRA as the sole method of data collection that employed an MRA-QC framework.

Methods

Using a moderator meta-analysis employed with Q-test, the MRA error rates from the meta-analysis of the literature were compared with the error rate from a recent study that implemented formalized MRA training and continuous QC processes.

Results

The MRA process for data acquisition in clinical research was associated with both high and highly variable error rates (70 - 2,784 errors per 10,000 fields). Error rates for the study using our MRA-QC framework were between 1.04% (optimistic, all-field rate) and 2.57% (conservative, populated-field rate) (or 104 - 257 errors per 10,000 fields), 4.00 - 5.53 percentage points less than the observed rate from the literature (p<0.0001).

Conclusions

Review of the literature indicated that the accuracy associated with MRA varied widely across studies. However, our results demonstrate that, with appropriate training and continuous QC, MRA error rates can be significantly controlled during the course of a clinical research study.

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

Background

Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency.

Objectives

To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them.

Methods

In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented.

Results

A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37–1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved.

Conclusions

Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.

Appropriateness of ICD-coded Diagnostic Inpatient Hospital Discharge Data for Medical Practice Assessment

Objectives: We performed a systematic review to investigate the quality of diagnostic hospital discharge data (DHDD) in order to gain insight in the usefulness of these data for medical practice assessment. We investigated the methods used to evaluate data quality, factors that determine data quality and its consequences for medical practice assessment.

Methods: We selected studies in which both completeness (or sensitivity: SENS) and correctness (or positive predictive value: PPV) were measured. We used the random-effects model to calculate mean SENS and PPV and to explore the effect of a number of covariates.

Results: The 101 included studies were very heterogeneous. We distinguished six typical study designs. We found a mean SENS of 0.67 (95%CI: 0.62– 0.73) and PPV of 0.76 (95%CI: 0.73– 0.79); SENS was significantly lower for comorbidity and complication studies than for some single disease studies. PPV was significantly higher for Scandinavian countries than for other countries. Recoding compared to re-abstracting of the medical record as a gold standard gave a significantly lower PPV. Diagnostic data were considered appropriate by the authors of the studies for quality of care purposes when both SENS and PPV were at least 0.85. Only 13% of the studies fulfilled this criterion.

Conclusions: Variability in quality of care between settings can easily be overshadowed by variability in data quality. However, the use of DHDD by physicians to evaluate their own medical practice may be useful. But only if physicians are willing to critically interpret the meaning of the information for their medical practice assessment.

First Year of Mandatory Reporting of Healthcare-Associated Infections, Pennsylvania An Infection Control—Chart Abstractor Collaboration

Background.

In 2004, the Commonwealth of Pennsylvania mandated hospitals to report healthcare-associated infections (HAIs). The increased workload led our Infection Control staff to collaborate with Atlas, a group of chart abstractors.

Objective.

The objective of this study was to assess our first year of experience with mandatory reporting of HAIs—specifically, to assess Atlas' contribution to surveillance.

Design.

Cases were selected if they had 1 or more of the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes designated by Pennsylvania as a possible HAI. After training by the Infection Control staff, Atlas applied National Nosocomial Infection Surveillance (NNIS) system case definitions for catheter-associated urinary tract infections (UTIs) and surgical site infections (SSIs), and they applied NNIS chest imaging criteria to eliminate cases that were not ventilator-associated pneumonia (VAP). To assess Atlas' performance, Infection Control staff conducted a parallel review.

Results.

For discharges from the hospital during the fourth quarter of 2004, a total of 410 UTIs, 59 SSIs, and 56 VAPs were identified on the basis of state-designated ICD-9-CM codes; review by Atlas/Infection Control determined that 15%, 15%, and 16% of cases met case definitions, respectively. Of cases reviewed by both Infection Control and Atlas, 87% of the assessments made by Atlas were correct for UTI, and 96% were correct for SSI. For VAP, Infection Control concluded that 39% of cases could be ruled out on the basis of chest imaging criteria; Atlas correctly dismissed these 12 cases but incorrectly dismissed an additional 6 (error, 19%). Surveillance was not timely: 1-2 months elapsed between the time of HAI onset and the earliest case review.

Conclusions.

With ongoing training by Infection Control, Atlas successfully demonstrated a role in retrospective HAI surveillance. However, despite a major effort to comply with mandates, time lags and other design limitations rendered the data of low utility for Infection Control. States that are planning HAI-reporting programs should standardize an efficient surveillance methodology that yields data capable of guiding interventions to prevent HAI.

Mortality related to hospital-associated infections in a tertiary hospital; repeated cross-sectional studies between 2004-2011

BACKGROUND

Hospital-associated infections (HAIs) are reported to increase patient mortality and incur longer hospital stays. Most studies to date have focused on specific groups of hospitalised patients with a rather short follow-up period. In this repeated cross-sectional study, with prospective follow-up of 19,468 hospitalized patients, we aimed to analyze the impact of HAIs on mortality 30 days and 1 year after the prevalence survey date.

METHODS

The study was conducted at Haukeland University Hospital, Norway, a large combined emergency and referral teaching hospital, from 2004 to 2011 with follow-up until November 2012. Prevalence of all types of HAIs including urinary tract infections (UTI), lower respiratory tract infections (LRTI), surgical site infections (SSI) and blood stream infections (BSI) were recorded four times every year. Information on the date of birth, admission and discharge from the hospital, number of diagnoses (ICD-10 codes) and patient's mortality was retrieved from the patient administrative data system. The data were analysed by Kaplan-Meier survival analysis and by multiple Cox regression analysis, adjusted for year of registration, time period, sex, type of admission, Charlson comorbidity index, surgical operation, use of urinary tract catheter and time from admission to the prevalence survey date.

RESULTS

The overall prevalence of HAIs was 8.5 % (95 % CI: 8.1, 8.9). Patients with HAIs had an adjusted hazard ratio (HR) of 1.5 (95 % CI: 1.3, 1.8,) and 1.4 (95 % CI: 1.2, 1.5) for death within 30-days and 1 year, relative to those without HAIs. Subgroup analyses revealed that patients with BSI, LRTI or more than one simultaneous infection had an increased risk of death.

CONCLUSIONS

In this long time follow-up study, we found that HAIs have severe consequences for the patients. BSI, LRTI and more than one simultaneous infection were independently and strongly associated with increased mortality 30 days and 1 year after inclusion in the study.

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

OBJECTIVE

Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI.

METHODS

Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995-2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics.

RESULTS

57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances.

CONCLUSIONS

Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.

Validation of a Modified Version of the National Nosocomial Infections Surveillance System Risk Index for Health Services Research

Objective.

To validate the National Nosocomial Infections Surveillance system risk index through administrative data to predict surgical site infections.

Design.

Retrospective cohort study.

Setting.

Population-based analysis in Ontario, Canada.

Patients.

All elderly patients who underwent elective surgery from April 1, 1992, through March 31, 2006 (n = 469,349).

Methods.

Data on procedural and patient outcomes were gathered from linked population-wide hospital discharge records and physician claims. The 75th percentile of surgical duration was estimated through anesthesiologist billing fees recorded in 15-minute increments; the American Society of Anesthesiology score of at least 3 out of 5 was estimated by diagnostic codes for severe systemic illness; and all surgeries were classified as clean or clean-contaminated because of their elective nature (thus, the maximum score on the modified index was 2).

Results.

A total of 147,216 surgeries (31%) had a score of 0;246,592 (53%) had a score of 1; and 75,541 (16%) had a score of 2 on the modified index. The 30-day risk of surgical site infection increased with each increment in the modified index (score of 0, 5.4%; score of 1, 8.0%; score of 2, 14.3%; P < .001). The association was evident for surgical site infection diagnosed during the index admission (score of 0, 2.0%; score of 1, 3.7%; score of 2, 8.9%; P < .001), as well as that associated with reoperation or death (score of 0, 0.04%; score of 1, 0.23%; score of 2, 0.73%; P < .001). The modified index predicted increases in surgical site infection risk within each of 11 surgical subgroups. In accord with past research, the modified index had modest discrimination (C statistic, 0.59), and the majority of surgical site infections (72%) occurred within lower risk strata.

Conclusions.

The modified index predicts surgical site infection in population-based analyses and is associated with incremental increases in risk.

Matching bacteriological and medico-administrative databases is efficient for a computer-enhanced surveillance of surgical site infections: retrospective analysis of 4,400 surgical procedures in a French university hospital

OBJECTIVE

Our goal was to estimate the performance statistics of an electronic surveillance system for surgical site infections (SSIs), generally applicable in French hospitals.

METHODS

Three detection algorithms using 2 different data sources were tested retrospectively on 9 types of surgical procedures performed between January 2010 and December 2011 in the University Hospital of Nantes. The first algorithm was based on administrative codes, the second was based on bacteriological data, and the third used both data sources. For each algorithm, sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were calculated. The reference method was the hospital's routine surveillance: a comprehensive review of the computerized medical charts of the patients who underwent one of the targeted procedures during the study period.

SETTING

A 3,000-bed teaching hospital in western France.

POPULATION

We analyzed 4,400 targeted surgical procedures.

RESULTS

Sensitivity results varied significantly between the three algorithms, from 25% (95% confidence interval, 17-33) when using only administrative codes to 87% (80%-93%) with the bacteriological data and 90% (85%-96%) with the combined algorithm. Fewer variations were observed for specificity (91%-98%), PPV (21%-25%), and NPV (98% to nearly 100%). Overall, performance statistics were higher for deep SSIs than for superficial infections.

CONCLUSIONS

A reliable computer-enhanced SSI surveillance can easily be implemented in French hospitals using common data sources. This should allow infection control professionals to spend more time on prevention and education duties. However, a multicenter study should be conducted to assess the generalizability of this method.

Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

This systematic review examined the current state of conceptualization and specification of data quality and the role of ontology based approaches to develop data quality based on "fitness for purpose" within the health context. A literature review was conducted of all English language studies, from January 2000-March 2013, which addressed data/information quality, fitness for purpose of data, used and implemented ontology-based approaches. Included papers were critically appraised with a "context-mechanism-impacts/outcomes" overlay. We screened 315 papers, excluded 36 duplicates, 182 on abstract review and 46 on full-text review; leaving 52 papers for critical appraisal. Six papers conceptualized data quality within the "fitness for purpose" definition. While most agree with a multidimensional definition of DQ, there is little consensus on a conceptual framework. We found no reports of systematic and comprehensive ontological approaches to DQ based on fitness for purpose or use. However, 16 papers used ontology-specified implementations in DQ improvement, with most of them focusing on some dimensions of DQ such as completeness, accuracy, correctness, consistency and timeliness. The majority of papers described the processes of the development of DQ in various information systems. There were few evaluative studies, including any comparing ontological with non-ontological approaches, on the assessment of clinical data quality and the performance of the application.

Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review

OBJECTIVE

As more electronic health records have become available during the last decade, we aimed to uncover recent trends in use of electronically available patient data by electronic surveillance systems for healthcare associated infections (HAIs) and identify consequences for system effectiveness.

METHODS

A systematic review of published literature evaluating electronic HAI surveillance systems was performed. The PubMed service was used to retrieve publications between January 2001 and December 2011. Studies were included in the review if they accurately described what electronic data were used and if system effectiveness was evaluated using sensitivity, specificity, positive predictive value, or negative predictive value. Trends were identified by analyzing changes in the number and types of electronic data sources used.

RESULTS

26 publications comprising discussions on 27 electronic systems met the eligibility criteria. Trend analysis showed that systems use an increasing number of data sources which are either medico-administrative or clinical and laboratory-based data. Trends on the use of individual types of electronic data confirmed the paramount role of microbiology data in HAI detection, but also showed increased use of biochemistry and pharmacy data, and the limited adoption of clinical data and physician narratives. System effectiveness assessments indicate that the use of heterogeneous data sources results in higher system sensitivity at the expense of specificity.

CONCLUSIONS

Driven by the increased availability of electronic patient data, electronic HAI surveillance systems use more data, making systems more sensitive yet less specific, but also allow systems to be tailored to the needs of healthcare institutes' surveillance programs.

Accuracy of hospital discharge coding data for the surveillance of drain-related meningitis

Surveillance of healthcare-associated infections is labor intensive and complex. Discharge coding is an accessible source of information that may support detection of cases. For drain-related meningitis, however, discharge coding data had low sensitivity (32%) and positive predictive value (35%) and could neither replace nor improve existing complex surveillance systems.

Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature

PURPOSE

Effective use of routine data to support integrated chronic disease management (CDM) and population health is dependent on underlying data quality (DQ) and, for cross system use of data, semantic interoperability. An ontological approach to DQ is a potential solution but research in this area is limited and fragmented.

OBJECTIVE

Identify mechanisms, including ontologies, to manage DQ in integrated CDM and whether improved DQ will better measure health outcomes.

METHODS

A realist review of English language studies (January 2001-March 2011) which addressed data quality, used ontology-based approaches and is relevant to CDM.

RESULTS

We screened 245 papers, excluded 26 duplicates, 135 on abstract review and 31 on full-text review; leaving 61 papers for critical appraisal. Of the 33 papers that examined ontologies in chronic disease management, 13 defined data quality and 15 used ontologies for DQ. Most saw DQ as a multidimensional construct, the most used dimensions being completeness, accuracy, correctness, consistency and timeliness. The majority of studies reported tool design and development (80%), implementation (23%), and descriptive evaluations (15%). Ontological approaches were used to address semantic interoperability, decision support, flexibility of information management and integration/linkage, and complexity of information models.

CONCLUSION

DQ lacks a consensus conceptual framework and definition. DQ and ontological research is relatively immature with little rigorous evaluation studies published. Ontology-based applications could support automated processes to address DQ and semantic interoperability in repositories of routinely collected data to deliver integrated CDM. We advocate moving to ontology-based design of information systems to enable more reliable use of routine data to measure health mechanisms and impacts.

Health reform: is routinely collected electronic information fit for purpose?

OBJECTIVE

Little has been reported about the completeness and accuracy of data in existing Australian clinical information systems. We examined the accuracy of the diagnoses of some chronic diseases in an ED information system (EDIS), a module of the NSW Health electronic medical record (EMR), and the consistency of the reports generated by the EMR.

METHODS

A list of ED attendees and those admitted was generated from the EDIS, using specific (e.g. angina) and possible clinical terms (e.g. chest pain) for the selected chronic diseases. This EDIS list was validated with an audit of discharge summaries, and compared with a list generated, using similar specific and possible Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), from the underlying EMR database.

RESULTS

Of the 33,115 ED attendees, 2559 had diabetes mellitus (DM), cardiovascular disease or asthma/chronic obstructive pulmonary disease; of these 2559, 876 were admitted. Discharge summaries were missing for 12-15% of patients. Only three-quarters or fewer of the diagnoses were confirmed by the discharge summary audit, best for DM and worst for cardiovascular disease. Proportion of agreement between the lists generated from the EDIS and EMR was best for DM and worst for asthma/chronic obstructive pulmonary disease. Possible reasons for this discrepancy are technical, such as use of different extraction terms or system inconsistency; or clinical, such as data entry, decision-making, professional behaviour and organizational performance.

CONCLUSIONS

Variations in information quality and consistency of the EDIS/EMR raise concerns about the 'fitness for purpose' of the information for care and planning, information sharing, research and quality assurance.

Validity of ICD-9-CM coding for identifying incident methicillin-resistant Staphylococcus aureus (MRSA) infections: is MRSA infection coded as a chronic disease?

BACKGROUND AND OBJECTIVE

Investigators and medical decision makers frequently rely on administrative databases to assess methicillin-resistant Staphylococcus aureus (MRSA) infection rates and outcomes. The validity of this approach remains unclear. We sought to assess the validity of the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for infection with drug-resistant microorganisms (V09) for identifying culture-proven MRSA infection.

DESIGN

Retrospective cohort study.

METHODS

All adults admitted to 3 geographically distinct hospitals between January 1, 2001, and December 31, 2007, were assessed for presence of incident MRSA infection, defined as an MRSA-positive clinical culture obtained during the index hospitalization, and presence of the V09 ICD-9-CM code. The κ statistic was calculated to measure the agreement between presence of MRSA infection and assignment of the V09 code. Sensitivities, specificities, positive predictive values, and negative predictive values were calculated.

RESULTS

There were 466,819 patients discharged during the study period. Of the 4,506 discharged patients (1.0%) who had the V09 code assigned, 31% had an incident MRSA infection, 20% had prior history of MRSA colonization or infection but did not have an incident MRSA infection, and 49% had no record of MRSA infection during the index hospitalization or the previous hospitalization. The V09 code identified MRSA infection with a sensitivity of 24% (range, 21%-34%) and positive predictive value of 31% (range, 22%-53%). The agreement between assignment of the V09 code and presence of MRSA infection had a κ coefficient of 0.26 (95% confidence interval, 0.25-0.27).

CONCLUSIONS

In its current state, the ICD-9-CM code V09 is not an accurate predictor of MRSA infection and should not be used to measure rates of MRSA infection.

Developing information technology for infection prevention surveillance

The potential to automate at least part of the surveillance process for health care-associated infections was seen as soon as hospitals began to implement computer systems. Progress toward automated surveillance has been ongoing for the last several decades. But as more information becomes available electronically in the healthcare setting, the promise of electronic surveillance for healthcare-associated infections has become closer to reality. Although true fully automated surveillance is not here yet, significant progress is being made at a number of centers for electronic surveillance of central catheter-associated bloodstream infections, ventilator-associated pneumonia, and other healthcare-associated infections. We review the progress that has been made in this area and issues that need to be addressed as surveillance systems are implemented, as well as promising areas for future development.

Estimating the annual burden of health care-associated infections in Finnish adult acute care hospitals

BACKGROUND

We estimated the burden of health care-associated infections (HAIs) occurring in Finnish adult acute care hospitals using national hospitalization data and estimates of HAI based on a recent national prevalence survey.

METHODS

A total of 7531 non-HAI patients and 703 HAI patients (8.5%) identified in the prevalence survey were included in the study. Using the patients' national identity numbers and the prevalence survey date, we obtained data on hospitalizations, including discharge diagnoses from the National Hospital Discharge Registry (NHDR), and the dates and causes of death from the National Population Information System. We converted the prevalence of HAI into incidence using the Rhame-Sudderth formula, assessed the 28-day case fatality of the HAI patients, and then extrapolated the annual estimates of HAI burden from the total number of hospitalizations in adult acute care hospitals in 2005 (n = 804,456). We also assessed the sensitivity of the NHDR diagnoses in identifying HAIs.

RESULTS

The estimated incidence of HAIs was 5.7% (95% confidence interval = 5.0% to 6.5%), and the 28-day case fatality was 9.8%. Thus, >8500 hospitalizations per million population annually would result in at least 1 HAI and approximately 270 HAI-associated deaths within 28 days. The sensitivity of the NHDR diagnoses was 34% (range by infection type, 0% to 67%).

CONCLUSION

Our disease burden estimates can be used in health care planning and resource allocation. The NHDR was not a reliable source for case finding of HAIs.

Quantifying data quality for clinical trials using electronic data capture

Background

Historically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes.

Methods and Principal Findings

The National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC trials. We characterize the average source-to-database error rate (14.3 errors per 10,000 fields) for the first year of use of the new evaluation method. This error rate was significantly lower than the average of published error rates for source-to-database audits, and was similar to CRF-to-database error rates reported in the published literature. We attribute this largely to an absence of medical record abstraction on the trials we examined, and to an outpatient setting characterized by less acute patient conditions.

Conclusions

Historically, medical record abstraction is the most significant source of error by an order of magnitude, and should be measured and managed during the course of clinical trials. Source-to-database error rates are highly dependent on the amount of structured data collection in the clinical setting and on the complexity of the medical record, dependencies that should be considered when developing data quality benchmarks.

Who is steering the ship? External influences on infection control programs

Over the past decade, hospital infection control programs have begun to face new external influences that affect day-to-day practice. The mainstream media's interest in hospital-acquired infection sparked consumer interest, which resulted in more legislative activity and government regulation. Industry's influence is also increasing. To meet the increased demands of external agencies, infection control programs will need additional resources, and the infection control community will need to be more proactive in educating the public and defining the priorities for practice and research.

Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections

BACKGROUND

ICD-9-CM coding alone has been proposed as a method of surveillance for health care-associated infections (HAIs). The accuracy of this method, however, relative to accepted infection control criteria is not known.

METHODS

Retrospective analysis of patients at an academic medical center in 2005 who underwent surgical procedures or who were at risk for catheter-associated bloodstream infections or ventilator-associated pneumonia was performed. Patients previously identified with HAIs by Centers for Disease Control and Prevention's National Healthcare Safety Network surveillance methods were compared with those of the same risk group identified by secondary infection ICD-9-CM codes. Discordant cases identified by only coding were all rereviewed and adjusted prior to final analysis. When coding and surveillance were both negative, a sample of patients was used to estimate the proportion of false negatives in this group.

RESULTS

The positive predictive values (PPVs) ranged from 0.14 to 0.51 with an aggregate of 0.23, even after adjustment for additional cases detected on subsequent medical record review. The negative predictive values (NPVs) ranged from 0.91 to 1.00, with an aggregate of 0.96. The estimates of the true variance of PPVs and NPVs across surgical procedures were small (0.0129, standard error, 0.009; 0.000145, standard error, 0.00019, respectively) and could be mostly explained by variation in prevalence of surgical site infections.

CONCLUSION

Administrative coding alone appears to be a poor tool to be used as an infection control surveillance method. Its proposed use for routine HAI surveillance, public reporting of HAIs, interfacility comparisons, and nonpayment for performance should be seriously questioned.

Mandatory public reporting in the USA: an example to follow?

Increasingly states in the USA are enacting laws mandating reporting and disclosure of hospital-acquired infections (HAIs). The rapid development of legislation has occurred in response to increased coverage of HAIs in the mainstream media coupled with active involvement of consumer advocacy organizations. The transformation of healthcare in the USA into a commodity has fostered a strong role for consumer advocacy to which state legislative bodies have shown willingness to respond.

An enhanced benchmark for prosthetic joint replacement infection rates

BACKGROUND

The National Nosocomial Infection Surveillance System (NNIS) has historically provided the infection control community with the most accurate benchmark for healthcare-associated infections. However, NNIS does not require postdischarge surveillance. For medical centers where comprehensive postdischarge surveillance is possible, the efficiency of surgical site infection (SSI) detection is enhanced and rates may be higher than those provided by NNIS.

METHODS

From 1999 to 2004, a large integrated healthcare system (IHCS) used a standard surveillance methodology inclusive of the postdischarge period. This article compares IHCS and NNIS SSI data.

RESULTS

IHCS infection rates, stratified and weighted average (hip, 1.7; knee, 2.1) for the study period are higher than the corresponding NNIS rates (hip, 1.4; knee, 1.2) (hip, P = .006; knee, P = .012) when infections detected by the IHCS during the postdischarge period are included.

CONCLUSIONS

The data from the study period show that when comprehensive postdischarge surveillance is used by the IHCS, SSI rates are higher than those reflected in the NNIS database.

Impact of postdischarge surveillance on the rate of surgical site infection after orthopedic surgery

OBJECTIVE

To evaluate the impact of postdischarge surveillance on surgical site infection (SSI) rates after orthopedic surgery.

SETTING

Nine hospitals participating in the Finnish Hospital Infection Program.

PATIENTS

All patients who underwent hip or knee arthroplasty or open reduction of a femur fracture during 1999-2002.

RESULTS

The date of discharge was available for 11,812 procedures (90%). The median length of hospital stay was 8 days (range per hospital, 6-9 days). The overall SSI rate was 3.3% (range, 0.8%-6.4%). Of 384 SSIs detected, 216 (56%; range, 28%-90%) were detected after discharge: 93 (43%) were detected on readmission to the hospital, 73 (34%) at completion of a postdischarge questionnaire, and 23 (11%) at a follow-up visit. For 27 postdischarge SSIs (13%), the location of detection was unknown. Altogether, 32 (86%) of 37 of organ/space SSIs, 57 (80%) of 71 deep incisional SSIs, and 127 (46%) of 276 superficial incisional SSIs were detected after discharge. Most SSIs (70%) detected on readmission were severe (organ/space or deep incisional), whereas most SSIs (86%) detected at follow-up visits or at completion of a postdischarge questionnaire were superficial. Of all SSIs, 78% (range, 48%-100%) were microbiologically confirmed. Microbiologic confirmation was less common after discharge than during postoperative hospital stay (66% vs 93%; P<.001).

CONCLUSIONS

Postdischarge surveillance had a large impact on the rate of SSI detected after orthopedic surgery. However, postdischarge surveillance conducted by means of a questionnaire detected only a minority of deep incisional and organ/space SSIs.

Linkage of microbiology reports and hospital discharge diagnoses for surveillance of surgical site infections

Surveillance of surgical site infections (SSIs) with feedback to surgical personnel is pivotal in decisions regarding infection control. Prospective surveillance is time and resource consuming, so we aimed to evaluate a method based on data collected routinely during care delivery. The study was carried out at three acute hospitals in North-eastern Italy, from 1 January 2001 to 31 December 2001. Hospital discharge diagnoses (selected codes from the International Classification of Diseases, 9th Revision--Clinical Modification) and electronic microbiology reports (positive cultures from surgical wounds and drainages) were linked to identify suspected SSIs. A random sample of tracked events was submitted to total chart review in order to confirm the presence of SSIs retrospectively according to Centers for Disease Control and Prevention definitions. Of 865 suspected SSIs, 64.5% were identified from the microbiological database, 27.1% from discharge codes, and 8.4% from both. Four hundred and three admissions were sampled for review; the overall positive predictive value was 72% (95%CI=69-76%). Since inpatient individual antibiotic exposure is not registered in Italy, the combined use of discharge codes and microbiology reports represents the most feasible automated method for surveillance of SSIs developing during hospital stay.

Costs associated with hospital-acquired bacteraemia in a Belgian hospital

Studies from around the world have shown that hospital-acquired infections increase the costs of medical care due to prolongation of hospital stay, and increased morbidity and mortality. The aim of this study was to determine the extra costs associated with hospital-acquired bacteraemias in a Belgian hospital in 2001 using administrative databases and, in particular, coded discharge data. The incidence was 6.6 per 10000 patient days. Patients with a hospital-acquired bacteraemia experienced a significantly longer stay (average 21.1 days, P<0.001), a significantly higher mortality (average 32.2%, P<0.01), and cost significantly more (average 12853 euro, P<0.001) than similar patients without bacteraemia. At present, the Belgian healthcare system covers most extra costs; however, in the future, these outcomes of hospital-acquired bacteraemia will not be funded and prevention will be a major concern for hospital management.