Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database

The objective of this study was to define the optimal algorithm to identify patients with dyslipidemia using electronic medical records (EMRs). EMRs of patients attending primary care clinics in St. John's, Newfoundland and Labrador (NL), Canada during 2009-2010, were studied to determine the best algorithm for identification of dyslipidemia. Six algorithms containing three components, dyslipidemia ICD coding, lipid lowering medication use, and abnormal laboratory lipid levels, were tested against a gold standard, defined as the existence of any of the three criteria. Linear discriminate analysis, and bootstrapping were performed following sensitivity/specificity testing and receiver's operating curve analysis. Two validating datasets, NL records of 2011-2014, and Canada-wide records of 2010-2012, were used to replicate the results. Relative to the gold standard, combining laboratory data together with lipid lowering medication consumption yielded the highest sensitivity (99.6%), NPV (98.1%), Kappa agreement (0.98), and area under the curve (AUC, 0.998). The linear discriminant analysis for this combination resulted in an error rate of 0.15 and an Eigenvalue of 1.99, and the bootstrapping led to AUC: 0.998, 95% confidence interval: 0.997-0.999, Kappa: 0.99. This algorithm in the first validating dataset yielded a sensitivity of 97%, Negative Predictive Value (NPV) = 83%, Kappa = 0.88, and AUC = 0.98. These figures for the second validating data set were 98%, 93%, 0.95, and 0.99, respectively. Combining laboratory data with lipid lowering medication consumption within the EMR is the best algorithm for detecting dyslipidemia. These results can generate standardized information systems for dyslipidemia and other chronic disease investigations using EMRs.

The Validation of a Novel Surveillance System for Monitoring Bloodstream Infections in the Calgary Zone

Background. Electronic surveillance systems (ESSs) that utilize existing information in databases are more efficient than conventional infection surveillance methods. The objective was to assess an ESS for bloodstream infections (BSIs) in the Calgary Zone for its agreement with traditional medical record review. Methods. The ESS was developed by linking related data from regional laboratory and hospital administrative databases and using set definitions for excluding contaminants and duplicate isolates. Infections were classified as hospital-acquired (HA), healthcare-associated community-onset (HCA), or community-acquired (CA). A random sample of patients from the ESS was then compared with independent medical record review. Results. Among the 308 patients selected for comparative review, the ESS identified 318 episodes of BSI of which 130 (40.9%) were CA, 98 (30.8%) were HCA, and 90 (28.3%) were HA. Medical record review identified 313 episodes of which 136 (43.4%) were CA, 97 (30.9%) were HCA, and 80 (25.6%) were HA. Episodes of BSI were concordant in 304 (97%) cases. Overall, there was 85.5% agreement between ESS and medical record review for the classification of where BSIs were acquired (kappa = 0.78, 95% Confidence Interval: 0.75-0.80). Conclusion. This novel ESS identified and classified BSIs with a high degree of accuracy. This system requires additional linkages with other related databases.

Prospective Validation of Central Line–Days Derived From an Electronic Medical Record System

Validation of the number of central line–days by hospitals is required by the National Healthcare Safety Network. A prospective study that compared a daily report of such days generated by an electronic medical record with observational audits by nurses revealed that the report was 100% sensitive and 99.9% specific.

Infect. Control Hosp. Epidemiol. 2015;36(9):1098–1099

Electronic Documentation of Central Venous Catheter—Days: Validation Is Essential

Background.

Measurement of central line-associated bloodstream infection (CLABSI) rates outside of intensive care units is challenged by the difficulty in reliably determining central venous catheter (CVC) use. The National Healthcare Safety Network (NHSN) allows for use of electronic data for determination of CVC-days, but validation of electronic data has not been studied systematically.

Objective.

To design and validate a process to reliably measure CVC-days outside of the intensive care units that leverages electronic documentation.

Methods.

Thirty-four inpatient wards at 2 academic hospitals using a common electronic platform for nursing documentation were studied. Electronic queries were created to capture patient and CVC information, and tools and processes for tracking and reporting errors in documentation were developed. Strategies to validate electronic data included comparisons with manual CVC-day determinations and automated data validation using customized tools. Interventions included redesign of documentation interface, real-time audit with feedback of errors, and education. The primary outcome was patient-level total error rate in electronic CVC-day measurement compared with manually counted CVC-days.

Results.

At baseline, there were a mean (± standard deviation) of 0.32 ± 0.25 electronic CVC-day errors (omission and commission errors summed and counted equally) per manually counted CVC-day. After several process improvement cycles over 7 months, the error rate decreased to <0.05 errors per CVC-day and remained at or below this level for 2 years.

Conclusions.

Baseline electronic CVC-day counts had a high error rate. Stepwise interventions reduced errors to consistently low levels. Validation of electronic calculation of CVC-days is essential to ensure accuracy, particularly if these data will be used for interinstitutional comparison.

Electronic Documentation of Central Venous Catheter—Days: Validation Is Essential

Background.

Measurement of central line-associated bloodstream infection (CLABSI) rates outside of intensive care units is challenged by the difficulty in reliably determining central venous catheter (CVC) use. The National Healthcare Safety Network (NHSN) allows for use of electronic data for determination of CVC-days, but validation of electronic data has not been studied systematically.

Objective.

To design and validate a process to reliably measure CVC-days outside of the intensive care units that leverages electronic documentation.

Methods.

Thirty-four inpatient wards at 2 academic hospitals using a common electronic platform for nursing documentation were studied. Electronic queries were created to capture patient and CVC information, and tools and processes for tracking and reporting errors in documentation were developed. Strategies to validate electronic data included comparisons with manual CVC-day determinations and automated data validation using customized tools. Interventions included redesign of documentation interface, real-time audit with feedback of errors, and education. The primary outcome was patient-level total error rate in electronic CVC-day measurement compared with manually counted CVC-days.

Results.

At baseline, there were a mean (± standard deviation) of 0.32 ± 0.25 electronic CVC-day errors (omission and commission errors summed and counted equally) per manually counted CVC-day. After several process improvement cycles over 7 months, the error rate decreased to &lt;0.05 errors per CVC-day and remained at or below this level for 2 years.

Conclusions.

Baseline electronic CVC-day counts had a high error rate. Stepwise interventions reduced errors to consistently low levels. Validation of electronic calculation of CVC-days is essential to ensure accuracy, particularly if these data will be used for interinstitutional comparison.

Diagnostic performance of electronic syndromic surveillance systems in acute care: a systematic review

CONTEXT

Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making.

OBJECTIVE

To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems.

DATA SOURCES

All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012.

STUDY SELECTION

Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values.

DATA EXTRACTION

Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created.

RESULTS

The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%.

CONCLUSIONS

There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

Automated surveillance for healthcare-associated infections: opportunities for improvement

Surveillance of healthcare-associated infections is a cornerstone of infection prevention programs, and reporting of infection rates is increasingly required. Traditionally, surveillance is based on manual medical records review; however, this is very labor intensive and vulnerable to misclassification. Existing electronic surveillance systems based on classification algorithms using microbiology results, antibiotic use data, and/or discharge codes have increased the efficiency and completeness of surveillance by preselecting high-risk patients for manual review. However, shifting to electronic surveillance using multivariable prediction models based on available clinical patient data will allow for even more efficient detection of infection. With ongoing developments in healthcare information technology, implementation of the latter surveillance systems will become increasingly feasible. As with current predominantly manual methods, several challenges remain, such as completeness of postdischarge surveillance and adequate adjustment for underlying patient characteristics, especially for comparison of healthcare-associated infection rates across institutions.

CMS' Hospital-Acquired Conditions for the Neurohospitalist

In 2008, the Centers for Medicare and Medicaid Services (CMS) implemented a provision that denies Medicare payment for hospital-acquired conditions (HACs). This provision brings attention to the quality of patient care and the financial impact associated with "never-events" occurring during a patient's hospitalization. Our review of HACs focuses on the 5 which are most pertinent to the neurohospitalist: stages III and IV pressure ulcers, catheter-associated urinary tract infection, vascular catheter-associated infection, manifestations of poor glycemic control, and falls resulting in fractures, dislocations, and/or intracranial injuries. We address why CMS came up with them, their impact on quality patient care and hospital finances, and how the neurohospitalist can continue to participate in the future of HAC prevention and management as they relate to one's patients, hospital, and community.