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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 7  |  Issue : 3  |  Page : 59-64

Experience with the two-stage (electronic detection and internal validation) health-care-associated infection tracking system in hospital infection control and prevention program


1 Department of Microbiology, Max Super Speciality Hospital, New Delhi, India
2 Department of Clinical Directorate, Max Super Speciality Hospital, New Delhi, India
3 Department of Internal Medicine, Max Super Speciality Hospital, New Delhi, India
4 Department of Lab Medicine, Max Super Speciality Hospital, New Delhi, India

Date of Submission14-Jun-2019
Date of Decision20-Sep-2019
Date of Acceptance14-Mar-2020
Date of Web Publication18-Aug-2020

Correspondence Address:
Dr. Bansidhar Tarai
Department of Microbiology, Max Super Speciality Hospital, 1, Press Enclave Road, Saket, New Delhi - 110 017
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpsic.jpsic_14_19

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  Abstract 


Background: Surveillance of health-care-associated infections (HAIs) plays a key role in the hospital infection control program and reduction of HAIs.
Objectives: To study the benefits and limitations of an innovative, two-stage surveillance method of HAIs and effectiveness of infection control measures.
Design: It is a retrospective 5-year study of electronic surveillance system to capture HAI cases with high index of suspicion and confirmation with the manual methods of HAI tracking.
Methods: The tertiary care hospital, New Delhi, India, adopted an electronic two-stage HAI surveillance system in April 2015. This system automatically detects all microbiology culture-positive cases for patients on central line, ventilator or urinary catheter for more than 48 h in the hospital along with relevant clinical information, which was further validated by infection control team. These all are suspected HAI cases. Hence, the system is screening the cases which can be potential HAI further. Daily clinical assessment to look for initial warning signs related to HAI was done in every individual case irrespective of culture results.
Results: HAI incidence rates in pre-period (before implementation of electronic detection system April 2013 to March 2015) of CLABSI, CAUTI, VAP and SSI were 1.36%, 1.41%, 2.65% and 0.16% and post-period (after implementation of electronic detection system April 2015 to March 2018) were 1.25%, 1.30%, 1.16% and 0.09%, respectively. The pre- and post-analysis depicted that rates of CLABSI, CAUTI and VAP declined significantly; however, SSI rate in post-implementation declined but not significantly.
Conclusion: The electronic tracking system acts as an early warning system for identification of suspected HAI cases and triggers an early preventive response by both clinician and hospital infection control team. Moreover, the use of electronic monitoring system also led to implementation of many additional infection control measures.

Keywords: Electronic tracking system, health-care-associated infection, infection control


How to cite this article:
Tarai B, Jain D, Sen P, Budhiraja S, Das P, Jain V. Experience with the two-stage (electronic detection and internal validation) health-care-associated infection tracking system in hospital infection control and prevention program. J Patient Saf Infect Control 2019;7:59-64

How to cite this URL:
Tarai B, Jain D, Sen P, Budhiraja S, Das P, Jain V. Experience with the two-stage (electronic detection and internal validation) health-care-associated infection tracking system in hospital infection control and prevention program. J Patient Saf Infect Control [serial online] 2019 [cited 2023 Feb 9];7:59-64. Available from: https://www.jpsiconline.com/text.asp?2019/7/3/59/292435




  Introduction Top


Health-care-associated infections (HAIs) are usually preventable causes of morbidity and mortality, hence a major threat to patient and personnel safety in hospitals. It is well known that patients can acquire serious infections while being treated in a health-care facility, which is usually due to medical intervention, invasive procedures, severity of a disease process and reduced immunity. In today's complex health-care environment, the prevalence of HAIs is increasing.[1] To improve patient outcome, it is vital to prevent these infections. Patient safety can be improved by earlier and better tracking of infections and by formulating and practising infection prevention guidelines within the health-care facility. Therefore, thorough surveillance is essential to first identify HAI, institute appropriate intervention against them with infection prevention as the ultimate target. Standardised infection control practices were first followed in the 19th century, where segregation of fever patients and those with small pox was formalised.[2] Clinical microbiology has now advanced with the introduction of systems for the management and control of HAIs. Surveillance as an essential ingredient of HAI program has resulted in a decrease in infection rates.[3],[4],[5] The purpose of surveillance is to support the controlled reduction in the risk of HAI.[6]

Surveillance methods are crucial in the design of evidence-based programs for prevention and control of HAIs. The traditional methods of surveillance are largely manual; they are limited in scope, cumbersome and time-consuming.[7] We find that for a large tertiary care hospital or for a network of hospitals, manual surveillance can be a very labour intensive process.

Our hospital had been practicing infection control practices based on the manual review of medical records and ongoing traditional inpatient surveillance. A drawback of this approach is that it could be applied only to a limited patient population and the surveillance confidence was lower due to strong operator dependence. Keeping these drawbacks in mind, the hospital initiated an electronic surveillance system to capture HAI cases with high index of suspicion.[8] We expected the automation of data extraction, analysis and presentation to make the surveillance more efficient and allow us to redirect our limited resources towards prevention, rather than identification, measurement and monitoring of infections.[9]

Objectives

  1. To study the benefits and limitations of an innovative, two-stage surveillance method including electronic detection and internal validation of HAIs
  2. To understand the cumulative effectiveness of infection control measures through 5-year HAI trend analysis in pre- and post-period.



  Methods Top


The study hospital is one of the largest private health-care providers in New Delhi. The hospital has more than 550 beds, including more than 150 beds for critical care. It has approximately 30 speciality and super-speciality departments including the microbiology department and dedicated infection control team/committee to overlook the infection control practices. The hospital implemented an electronic health record system in 2011, following which patient medical records were being maintained in a digital format for all the patients. During this period, HAI tracking was manual, in which all culture-positive reports were correlated with patient's clinical features, other laboratory parameters and radiological investigations. This was performed by members of infection control team. Monitoring of post-surgical wound was done by infection control nurses (ICNs), which includes follow-up in post-surgical dressing register for outpatient department (OPD) patients and assessment of surgical site of infection along with clinicians in admitted patients. This resulted in the creation and ongoing enrichment of an extensive clinical data repository. Subsequently, the electronic tracking system for HAI was launched in April 2015.

The health-care information ecosystem includes patient demographic characteristics such as gender, date of birth, residential address, patient ID, length of stay, admission and discharge status, medical history, clinical diagnosis, diagnostic results, medical procedures, disease progression details, date of service, payer groups, hospital locations, billing charges, vital parameters and details of culture tests performed.

The patients were tracked from the point of Computerised Physician Order Entry onwards for the placement of invasive lines and/or the application of procedures in the wider context of the hospital admission and stay. The electronic system first identifies a case of HAI based on a defined algorithm as per the CDC guidelines and subsequently picks up the relevant patient details, hospital administrative details, microbiology culture test result and organism along with the information on invasive procedures.[9] The authorised person from infection control team has a user ID and password for login in electronic system. After login, one can select the type of HAI and asses the relevant reports according to date and area selected.

This new process for tracking the HAI cases by the infection control team was put in place and integrated with the new tracking mechanism for an end-to-end surveillance and improvement of HAI case incidence. The infection control team used a real-time HAI tracking status to investigate high suspicion cases for confirmation. Root cause analysis for each confirmed HAI case was done.

The hospital infection control team validates the electronic device counts with daily round feedback provided by treating physicians. High suspicion HAIs identified through the electronic system are evaluated by the infection control team. Review of initial culture reports and cross-checking is done to differentiate a true pathogen from a coloniser. This is followed by a check on the colony counts in respiratory and urine samples. A patient's condition is examined by the infection control team for fever, clinical signs and symptoms for CAUTI, CLABSI and SSI or chest findings for VAP, as per the CDC guidelines. Finally, the CDC guideline criteria were applied on each suspected case in discussion with the treating physician and the infection control team for confirmation of respective HAIs.


  Results Top


The study tracked and analysed HAI rate trends from April 2013 to March 2015 (pre-analysis) and from April 2015 to March 2018 (post-analysis) for the super-speciality tertiary care hospital, overlapping the transition to two-stage hybrid HAI tracking system. The rates of individual HAI in pre- and post-period on quarterly basis are shown in [Table 1] and [Figure 1].
Table 1: Health-care-associated infection rates for the hospital from April 2013 to March 2018

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Figure 1: Quarterly HAI rates for the 5-year period

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A monthly database of HAI rates was created on Microsoft Excel 2007 (v12.0.4518.1014). This monthly HAI rate database has been used to carry out an analysis where individual HAI rate is the dependent variable, while time (in months) and post (indicator with value 0 for pre-implementation and value 1 for post-implementation) are the independent variables. The data analysis was carried out on the software SPSS 16 (SPSS Inc, Chicago, IL). The analysis shows that in the case of CLABSI, CAUTI and VAP, there is a significant change in the rates both with time and post-variables. This, however, is based on overall trend that can be affected by few low values towards the end of the observation period. VAP rate had started to decline even before the system implementation which consolidated the downwards trend thereafter. However, in the case of SSI, the post-implementation change is not significant, even though SSI rate declined post-implementation but showed sudden rise between 20 and 30 months. This has caused the overall SSI trend to appear stationary on average [Figure 2]. A close look at the plot reveals that it took almost 20 months post-implementation for CAUTI and CLABSI rates to be significantly lower. We believe that the sustained decline achieved post 20 months marks the successful culmination of change management in hospital and clinical practices described further in discussion.
Figure 2: Data analysis of CLABSI, CAUTI, VAP and SSI rates (pre- and post-period)

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  Discussion Top


A systematic review by Freeman et al. was performed to assess the utility of electronic surveillance systems for monitoring and detecting HAIs.[10] The review documented that the implementation of an electronic surveillance system is feasible when integrated into the hospital information systems and routine surveillance practices. In our study, a joint team of experts from the fields of microbiology, internal medicine, health quality, data analysis and information technology contributed to the surveillance criteria for the development of a HAI-detection algorithm, system and process design. The team reviewed clinical guidelines from CDC and WHO for identification criteria for HAI. CDC guidelines were found to be most commonly accepted by the experts. The algorithm development (based on the CDC guidelines) followed multiple iterations of testing and correction to eventually achieve the desired results. Once developed, these were piloted in a close group of infection control teams, who evaluated and validated the performance of the algorithms over a period of 3 months. Our approach looked for an agreement between the electronic surveillance and manual case identification to develop the best-fit algorithm for a highly sensitive surveillance system. The pilot ensured that the HAI case detection and confirmation were within 5% of manual counts.[11] On successful completion of the pilot, the system was rolled out across the network of hospitals. To date, our experience suggests that the electronic surveillance adopted by the hospital has enhanced the robustness of HAI monitoring. Improving reliability of human interpretation has been the focus of system implementation and validation, since judging “infection” may be biased and certainly varies from person to person.[12],[13]

CAUTI, CLASBI and VAP rates in the present study are lower as compared to general data from the International Nosocomial Infection Control Consortium (INICC). However, these rates are slightly higher than those calculated by the National Healthcare Safety Network (NHSN).[14] The CLABSI rate was 0.33–2.99 per 1000 central line days in this study, which is less than the INICC report rate of 4.8–5.1, and slightly more than the NHSN rate of 0.8–0.9. Similarly, the CAUTI rate (0.10–2.53) is lower in our study than INICC report of 5.2–5.8 and slightly higher than NHSN rate of 1.1–1.3. Finally, the VAP rate was 0.34–5.61 and hence much lower than the INICC report rate of 16.1–16.8 but higher than the NHSN rate of 0.98–1.2.

The present findings also highlight the need for strengthening of infection control policies, related to catheters and lines, along with training of the staff. A common challenge in our hospital scenario is frequent staff change, which requires repeated training in aspects of infection control practices. HAI detection through electronic system led to additional infection control measures in prevention. The fact that it took almost 20 months before any significant change can be attributed to better pick up of cases in initial post-implementation period and gradual implementation of additional infection control practices for all HAI.

The pulmonologists and intensive care doctors increased the ventilator and expiratory cassettes ratio to 1:1.5; sterilisation of expiratory cassette before a new patient is put on ventilator and again every week for long-duration ventilated patients; a practice of closed suction and placement of sub-glottic endotracheal tubes for all ventilated patients was introduced as well.

Measures were also undertaken to reduce CLABSI rates after implementation of online tracking system such as experience staff was handling the dressing of central line, use of 2% chlorhexidine for asepsis was started instead of 0.5, educational material related to care of line was provided to patients on peripherally inserted central catheter line and Hickman line at the time of discharge and at last bundle care monitoring by the ICN was also included.

For reducing CAUTI rates, many measures were undertaken such as reminder for removal of urinary catheter after 10th and 14th days of insertion after which system notification comes for checking urine routine microscopy and it asks for justified reason of putting catheter again. Extra care was given during transport of patients having catheter in situ, i.e., keeping the urobag below bladder level. Urinary catheter care was started from once a day to thrice a day along with routine bundle care. For sample collection in ICU, especially in long-term catheterised patients, UnoMeter (with fixed bag and universal sampling port) was used so that the sample collection was more aseptic.

For reducing SSI rates, chlorhexidine bath before major surgeries was initiated. Better tracking of SSI was expected following the adoption of the two-stage hybrid tracking system. Earlier, the hospital used a manual register in the OPD and clinics to capture the SSI cases.

Electronic system is reflecting positive culture reports of cases with invasive procedures, so bundle care is given at earlier stage and with more precautions in such patients. That is how, with two-step approach, the system is preventing HAI or any HAI outbreak to happen before time. With the earlier approach, cases were often missed. However, now, the electronic tracking system captures every culture report in the system to identify probable SSI cases. Trend analysis for SSI presents a unique challenge due to a combination of expected improvement in case identification, incomplete post-discharge surveillance and continual improvement in hospital infection control practices.

After implementation of electronic surveillance system, tracking was easier and less time-consuming. The digital accessibility of data helped create a more effective and transparent system with the clinicians and infection control team. Robust online tracking helped prevent unchecked outbreaks. Although there were certain demerits of online tracking system that, even after removal of line it used to reflect in the system which was further manually detected and if line is not billed the online system was not able to capture it. The results of the present study demonstrate a much improved tracking system which is able to monitor changes in HAI rate with shift in surveillance from conventional to electronic method. It has also helped in reducing the substantial amount of time needed for an infection control expert to identify and categorise recognised infections. Although the present study has not measured improvement in resource utilisation, other studies have reported improved resource utilisation, saving about 10 weeks of infection control time annually with only 1/6–1/3 of the time required for standard manual surveillance.[15],[16] Even though the present study has shown a declining trend in all the four HAIs tracked over the study period of 5 years, it is still incorrect to attribute the reason entirely to the two-stage hybrid tracking system.[17] We find the tracking system is a useful support system to measure and monitor HAIs, which likely contributes to the overall success of hospital program on control and prevention of HAIs.[18],[19]

However, there are limitations and challenges with the hybrid tracking method. The success or failure of electronic tracking is dependent on user involvement, successful navigation of learning curve and administrative support. Besides that, system up-grading, maintenance and team trainings incur cost, requiring additional financial support. Previous studies have demonstrated that electronic surveillance systems are efficient and effective in screening for potential outbreaks and recognising endemic HAIs. However, it is important to understand the data that is generated electronically, which is influenced by the critical thinking skills of the infection preventionist.[20] All data that were generated electronically were further confirmed within treating physician and microbiologist/infection control officer as per the CDC definition.


  Conclusion Top


This work suggests an improvement in resource utilisation, robustness of case identification with the adoption of the two-stage hybrid HAI tracking system in a tertiary care hospital. The hybrid system promotes early identification of suspected HAI cases, which help trigger an immediate response from the care provider team. The follow-up with root cause analysis and early corrective action by the hospital infection control team prevents cross infection to other patients in the same unit and thereby prevents potential outbreaks. We believe that a combination of all these benefits is responsible for improvements in HAI cases in the hospital. An additional benefit of the system to a multi-location group of hospitals is its advantage in standardisation of approach to identification and confirmation of hospital-acquired infections. This helps with a more effective comparison of individual hospital HAI rate within the group.

Acknowledgement

The authors thankfully acknowledge Alex van Belkum, Global Director of R and D Microbiology, BioMerieux for his helpful comments during the preparation of the manuscript. We also acknowledge Dr Abhaya Indrayan for conducting the HAI rate-time analysis for the study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Charrier L, Argentero PA, Farina EC, Serra R, Mana F, Zotti CM. Surveillance of healthcare-associated infections in Piemonte, Italy: Results from a second regional prevalence study. BMC Public Health 2014;14:558.  Back to cited text no. 1
    
2.
Woodward J. To do the Sick no Harm: A Study of the British Voluntary Hospital System to 1875. London, United Kingdom: Routledge and Kegan Paul; 1974.  Back to cited text no. 2
    
3.
Haley RW, Culver DH, White JW, Morgan WM, Emori TG, Munn VP, et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985;121:182-205.  Back to cited text no. 3
    
4.
Curran ET, Coia JE, Gilmour H, McNamee S, Hood J. Multi-centre research surveillance project to reduce infections/phlebitis associated with peripheral vascular catheters. J Hosp Infect 2000;46:194-202.  Back to cited text no. 4
    
5.
Brown KL, Ridout DA, Shaw M, Dodkins I, Smith LC, O'Callaghan MA, et al. Healthcare-associated infection in pediatric patients on extracorporeal life support: The role of multidisciplinary surveillance. Pediatr Crit Care Med 2006;7:546-50.  Back to cited text no. 5
    
6.
Horan T, Gaynes R. Surveillance of nosocomial infections. In: Mayhall CG, editor. Hospital Epidemiology and Infection Control. 4th ed. Philadelphia: Lippincott Williams and Wilkins; 2004. p. 1659-702.  Back to cited text no. 6
    
7.
Peterson LR, Brossette SE. Hunting health care-associated infections from the clinical microbiology laboratory: Passive, active, and virtual surveillance. J Clin Microbiol 2002;40:1-4.  Back to cited text no. 7
    
8.
Woeltje KF, McMullen KM, Butler AM, Goris AJ, Doherty JA. Electronic surveillance for healthcare-associated central line-associated bloodstream infections outside the intensive care unit. Infect Control Hosp Epidemiol 2011;32:1086-90.  Back to cited text no. 8
    
9.
Woeltje KF, Lin MY, Klompas M, Wright MO, Zuccotti G, Trick WE. Data requirements for electronic surveillance of healthcare-associated infections. Infect Control Hosp Epidemiol 2014;35:1083-91.  Back to cited text no. 9
    
10.
Freeman R, Moore LS, García Álvarez L, Charlett A, Holmes A. Advances in electronic surveillance for healthcare-associated infections in the 21st Century: A systematic review. J Hosp Infect 2013;84:106-19.  Back to cited text no. 10
    
11.
Tejedor SC, Garrett G, Jacob JT, Meyer E, Reyes MD, Robichaux C, et al. Electronic documentation of central venous catheter-days: Validation is essential. Infect Control Hosp Epidemiol 2013;34:900-7.  Back to cited text no. 11
    
12.
Mayer J, Greene T, Howell J, Ying J, Rubin MA, Trick WE, et al. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55:364-70.  Back to cited text no. 12
    
13.
Lin MY, Hota B, Khan YM, Woeltje KF, Borlawsky TB, Doherty JA, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:2035-41.  Back to cited text no. 13
    
14.
Leblebicioglu H, Erben N, Rosenthal VD, Atasay B, Erbay A, Unal S, et al. International Nosocomial Infection Control Consortium (INICC) national report on device-associated infection rates in 19 cities of Turkey, data summary for 2003-2012. Ann Clin Microbiol Antimicrob 2014;13:51.  Back to cited text no. 14
    
15.
Hebden JN, Wright M, Fuss EP, Standiford HC. Leveraging surveillance technology to benefit the practice and profession of infection control. Am J Infect Control 2008;34:S7-11.  Back to cited text no. 15
    
16.
Atreja A, Gordon SM, Pollock DA, Olmsted RN, Brennan PJ, Healthcare Infection Control Practices Advisory Committee. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control. Am J Infect Control 2008;36:S37-46.  Back to cited text no. 16
    
17.
Palumbo AJ, Loveless PA, Moll ME, Ostroff S. Evaluation of healthcare-associated infection surveillance in Pennsylvania hospitals. Infect Control Hosp Epidemiol 2012;33:105-11.  Back to cited text no. 17
    
18.
Wright MO, Perencevich EN, Novak C, Hebden JN, Standiford HC, Harris AD. Preliminary assessment of an automated surveillance system for infection control. Infect Control Hosp Epidemiol 2004;25:325-32.  Back to cited text no. 18
    
19.
Furuno JP, Schweizer ML, McGregor JC, Perencevich EN. Economics of infection control surveillance technology: Cost-effective or just cost? Am J Infect Control 2008;36:S12-7.  Back to cited text no. 19
    
20.
Grota PG, Stone PW, Jordan S, Pogorzelska M, Larson E. Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction. Am J Infect Control 2010;38:509-14.  Back to cited text no. 20
    


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