Using electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting

doi:10.1016/j.ajic.2013.12.005

American Journal of Infection Control

Volume 42, Issue 3, March 2014, Pages e33-e36

https://doi.org/10.1016/j.ajic.2013.12.005 Get rights and content

Background

Streamlining health care–associated infection surveillance is essential for health care facilities owing to the continuing increases in reporting requirements.

Methods

Stanford Hospital, a 583-bed adult tertiary care center, used their electronic medical record (EMR) to develop an electronic algorithm to reduce the time required to conduct catheter-associated urinary tract infection (CAUTI) surveillance in adults. The algorithm provides inclusion and exclusion criteria, using the National Healthcare Safety Network definitions, for patients with a CAUTI. The algorithm was validated by trained infection preventionists through complete chart review for a random sample of cultures collected during the study period, September 1, 2012, to February 28, 2013.

Results

During the study period, a total of 6,379 positive urine cultures were identified. The Stanford Hospital electronic CAUTI algorithm identified 6,101 of these positive cultures (95.64%) as not a CAUTI, 191 (2.99%) as a possible CAUTI requiring further validation, and 87 (1.36%) as a definite CAUTI. Overall, use of the algorithm reduced CAUTI surveillance requirements at Stanford Hospital by 97.01%.

Conclusions

The electronic algorithm proved effective in increasing the efficiency of CAUTI surveillance. The data suggest that CAUTI surveillance using the National Healthcare Safety Network definitions can be fully automated.

Section snippets

Methods

Stanford Hospital, a 583-bed tertiary care center, assigned a multidisciplinary team of front-line providers, infection preventionists, and clinical informaticists to create an automated electronic algorithm and data reporting system to increase the efficiency of CAUTI surveillance and reporting. Using the CDC definitions published in January 2013,¹² the team designed an algorithm that provides inclusion and exclusion criteria for patients meeting the criteria for a CAUTI. The team visually

Results

Using the SHECA, we retrospectively analyzed all positive urine cultures collected at Stanford Hospital between September 1, 2012, and February 28, 2013. During this period, 6,379 positive urine cultures were recorded; of these, the SHECA identified 6,101 (95.64%) as not a CAUTI because the positive culture did not meet the NHSN criteria; 191 (2.99%) as a possible CAUTI because there were insufficient data to verify whether the culture was or was not a CAUTI; and 87 (1.36%) as a definite CAUTI (

Discussion

Infection prevention surveillance is a very time-consuming and resource-intensive process. It is estimated that 45% of infection preventionists' time is spent on surveillance and analysis.¹³ With the ever-increasing reporting requirements for infection prevention departments2, 14, 15 comes the need to maximize the efficiency of surveillance and analysis. As a result of the mass adoption of EMRs in recent years, hospitals now have the ability to leverage their data to increase the efficiency of

Conclusion

Our study suggests that health care facilities, regardless of the EMR in use, available data, or available funds, can effectively leverage their current EMR infrastructure by investing in low-cost solutions to greatly increase the efficiency and effectiveness of HAI surveillance. The health care environment is requiring hospitals to reduce costs and improve quality; infection prevention departments are focused on achieving both. However, growing surveillance requirements hinders infection

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Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning
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Postoperative infections constitute more than half of all postoperative complications. Surveillance of these complications is primarily done through manual chart review, which is time consuming, expensive, and typically only covers 10% to 15% of all operations. Automated surveillance would permit the timely evaluation of and reporting of all operations.
The goal of this study was to develop and validate parsimonious, interpretable models for conducting surveillance of postoperative infections using structured electronic health records data. This was a retrospective study using 30,639 unique operations from 5 major hospitals between 2013 and 2019. Structured electronic health records data were linked to postoperative outcomes data from the American College of Surgeons National Surgical Quality Improvement Program. Predictors from the electronic health records included diagnoses, procedures, and medications. Infectious complications included surgical site infection, urinary tract infection, sepsis, and pneumonia within 30 days of surgery. The knockoff filter, a penalized regression technique that controls type I error, was applied for variable selection. Models were validated in a chronological held-out dataset.
Seven percent of patients experienced at least one type of postoperative infection. Models selected contained between 4 and 8 variables and achieved >0.91 area under the receiver operating characteristic curve, >81% specificity, >87% sensitivity, >99% negative predictive value, and 10% to 15% positive predictive value in a held-out test dataset.
Surveillance and reporting of postoperative infection rates can be implemented for all operations with high accuracy using electronic health records data and simple linear regression models.
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Utilizing technology can increase efficiency for infection prevention data collection. The purpose of this article is to explain our experience using electronic medical record (EMR) reports for symptom surveillance to increase understanding of how technology enhances infection prevention abilities. The new EMR report method took on average just over 8 minutes a week to run compared to the traditional manual method which took 7 hours a week indicating a major time decrease for data collection.
Identification of postoperative complications using electronic health record data and machine learning
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Although the NSQIP data are considered to be of high quality, the data collection methods greatly limit the number of patients who can be assessed (∼15% of those patients undergoing surgery at most large hospitals) because the process is time-consuming and costly, and participating hospitals must pay to participate. There is a large literature regarding the use of statistical models applied to the electronic health record (EHR) to identify surgical complications: surgical site infections,1–4 urinary tract infections,5–15 sepsis,16 bleeding,17,18 and any type of complication.19–21 Most work on the identification of postoperative complications using EHR data has used structured data for the identification of specific types of complications, but because of the chosen statistical models for these analyses, the authors only explored a small number of explanatory variables.1,2,22,23
Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR).
We used an elastic-net model to estimate regression coefficients and carry out variable selection. International classification of disease codes (ICD-9), common procedural terminology (CPT) codes, medications, and CPT-specific complication event rate were included as predictors.
Of 6840 patients, 922 (13.5%) had at least one of the 18 complications tracked by NSQIP. The model achieved 88% specificity, 83% sensitivity, 97% negative predictive value, 52% positive predictive value, and an area under the curve of 0.93.
Using machine learning on EHR postoperative data linked to NSQIP outcomes data, a model with 163 predictors from the EHR identified complications well at our institution.
Identification of urinary tract infections using electronic health record data
2019, American Journal of Infection Control
Citation Excerpt :
Although we did not include catheterization in our final model reported in Table 3, we suggest including it when applying similar methodology to different UTI datasets, especially given the continued high risk of CAUTIs reported in the literature.5,18-20 However, as we pointed out in the introduction, catheter identification from EHR data can be difficult,2,5-8,21-23 and it was not clear whether the EHR data used in this study properly captured the catheter variable. It is also important to point out that we were not specifically looking for CAUTIs in this analysis.
Population ascertainment of postoperative urinary tract infections (UTIs) is time-consuming and expensive, as it often requires manual chart review. Using the American College of Surgeons National Surgical Quality Improvement Program UTI status of patients who underwent an operation at the University of Colorado Hospital, we sought to develop an algorithm for identifying UTIs using data from the electronic health record.
Data were split into training (operations occurring between 2013-2015) and test (operations in 2016) sets. A binomial generalized linear model with an elastic-net penalty was used to fit the model and carry out variables selection. International classification of disease codes, common procedural terminology codes, antibiotics, catheterization, and common procedural terminology-specific UTI event rates were included as predictors. The Youden's J statistic was used to determine the optimal classification threshold.
Of 6,840 patients, 134 (2.0%) had a UTI. The model achieved 92% specificity, 80% sensitivity, 100% negative predictive value, 16% positive predictive value, and an area under the curve of 0.94 using a decision threshold of 0.03.
A model with 14 predictors from the electronic health record identifies UTIs well, and it could be used to scale up UTI surveillance or to estimate the impact of large-scale interventions on UTI rates.
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Detecting the presence of an indwelling urinary catheter and urinary symptoms in hospitalized patients using natural language processing
2017, Journal of Biomedical Informatics
Citation Excerpt :
Attempts at automating the surveillance process for detecting infections have had mixed success as the data required to make the inference usually reside in disparate hospital information systems and are not always amenable to mining [5]. There have been attempts to automate the detection of CAUTI, also with mixed success [6–9]. The most important data element is the presence of an IUC in the patient and, arguably, this is the most challenging to determine using available medical records.
To develop a natural language processing pipeline to extract positively asserted concepts related to the presence of an indwelling urinary catheter in hospitalized patients from the free text of the electronic medical note. The goal is to assist infection preventionists and other healthcare professionals in determining whether a patient has an indwelling urinary catheter when a catheter-associated urinary tract infection is suspected. Currently, data on indwelling urinary catheters is not consistently captured in the electronic medical record in structured format and thus cannot be reliably extracted for clinical and research purposes.
We developed a lexicon of terms related to indwelling urinary catheters and urinary symptoms based on domain knowledge, prior experience in the field, and review of medical notes. A reference standard of 1595 randomly selected documents from inpatient admissions was annotated by human reviewers to identify all positively and negatively asserted concepts related to indwelling urinary catheters. We trained a natural language processing pipeline based on the V3NLP framework using 1050 documents and tested on 545 documents to determine agreement with the human reference standard. Metrics reported are positive predictive value and recall.
The lexicon contained 590 terms related to the presence of an indwelling urinary catheter in various categories including insertion, care, change, and removal of urinary catheters and 67 terms for urinary symptoms. Nursing notes were the most frequent inpatient note titles in the reference standard document corpus; these also yielded the highest number of positively asserted concepts with respect to urinary catheters. Comparing the performance of the natural language processing pipeline against the human reference standard, the overall recall was 75% and positive predictive value was 99% on the training set; on the testing set, the recall was 72% and positive predictive value was 98%. The performance on extracting urinary symptoms (including fever) was high with recall and precision greater than 90%.
We have shown that it is possible to identify the presence of an indwelling urinary catheter and urinary symptoms from the free text of electronic medical notes from inpatients using natural language processing. These are two key steps in developing automated protocols to assist humans in large-scale review of patient charts for catheter-associated urinary tract infection. The challenges associated with extracting indwelling urinary catheter-related concepts also inform the design of electronic medical record templates to reliably and consistently capture data on indwelling urinary catheters.

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: This study was funded through hospital quality improvement initiatives.

: Conflict of interest: None to report.

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Online-only articleMajor articleUsing electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting

Background

Methods

Results

Conclusions

Section snippets

Methods

Results

Discussion

Conclusion

Am J Infect Control

Am J Infect Control

Am J Infect Control

First year of mandatory reporting of healthcare-associated infections, Pennsylvania: an infection control-chart abstractor collaboration

Infect Control Hosp Epidemiol

Laws pertaining to healthcare-associated infections: a review of 3 legal requirements

Infect Control Hospital Epidemiol

Effect of nonpayment for hospital-acquired, catheter-associated urinary tract infection: a statewide analysis

Ann Intern Med

Healthcare Infection Control Practices Advisory Committee. Guideline for prevention of catheter-associated urinary tract infections 2009

Infect Control Hosp Epidemiol

Online-only article
Major article
Using electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting