Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control

A near real-time electronic health record-based COVID-19 surveillance system: An experience from a developing country

CONTEXT

Access to real-time data that provide accurate and timely information about the status and extent of disease spread could assist management of the COVID-19 pandemic and inform decision-making.

AIM

To demonstrate our experience with regard to implementation of technical and architectural infrastructure for a near real-time electronic health record-based surveillance system for COVID-19 in Iran.

METHOD

This COVID-19 surveillance system was developed from hospital information and electronic health record (EHR) systems available in the study hospitals in conjunction with a set of open-source solutions; and designed to integrate data from multiple resources to provide near real-time access to COVID-19 patients' data, as well as a pool of health data for analytical and decision-making purposes.

OUTCOMES

Using this surveillance system, we were able to monitor confirmed and suspected cases of COVID-19 in our population and to automatically notify stakeholders. Based on aggregated data collected, this surveillance system was able to facilitate many activities, such as resource allocation for hospitals, including managing bed allocations, providing and distributing equipment and funding, and setting up isolation centres.

CONCLUSION

Electronic health record systems and an integrated data analytics infrastructure are effective tools to enable policymakers to make better decisions, and for epidemiologists to conduct improved analyses regarding COVID-19.

IMPLICATIONS

Improved quality of clinical coding for better case finding, improved quality of health information in data sources, data-sharing agreements, and increased EHR coverage in the population can empower EHR-based COVID-19 surveillance systems.

Development and implementation of a longitudinal clinical database for down syndrome in a large pediatric specialty clinic: Methodology and feasibility

Down syndrome (DS) is a complex condition associated with multiple medical, developmental, and behavioral concerns. A prospective, longitudinal clinical database was integrated into a specialty Down Syndrome Program, with the goals of better understanding the incidence, course, and impact of co-occurring medical, neurodevelopmental, and mental health conditions in DS. We describe the process of developing the database, including a systematic approach to data collection and database infrastructure, and report on feasibility, challenges, and solutions of initial implementation. Between March 2018 and November 2021, data from 842 patients (ages 4.8 months to 26 years) was collected. Challenges included caregiver form completion as well as time and personnel required for successful implementation. With full integration into clinical visit flow, the database proved to be feasible. The database enables identification of patterns of development and health throughout the lifespan and it facilitates future data sharing and collaborative research to advance care.

Methodological issues of the electronic health records' use in the context of epidemiological investigations, in light of missing data: a review of the recent literature

BACKGROUND

Electronic health records (EHRs) are widely accepted to enhance the health care quality, patient monitoring, and early prevention of various diseases, even when there is incomplete or missing information in them.

AIM

The present review sought to investigate the impact of EHR implementation on healthcare quality and medical decision in the context of epidemiological investigations, considering missing or incomplete data.

METHODS

Google scholar, Medline (via PubMed) and Scopus databases were searched for studies investigating the impact of EHR implementation on healthcare quality and medical decision, as well as for studies investigating the way of dealing with missing data, and their impact on medical decision and the development process of prediction models. Electronic searches were carried out up to 2022.

RESULTS

EHRs were shown that they constitute an increasingly important tool for both physicians, decision makers and patients, which can improve national healthcare systems both for the convenience of patients and doctors, while they improve the quality of health care as well as they can also be used in order to save money. As far as the missing data handling techniques is concerned, several investigators have already tried to propose the best possible methodology, yet there is no wide consensus and acceptance in the scientific community, while there are also crucial gaps which should be addressed.

CONCLUSIONS

Through the present thorough investigation, the importance of the EHRs' implementation in clinical practice was established, while at the same time the gap of knowledge regarding the missing data handling techniques was also pointed out.

Application of Telemedicine in COVID-19: A Bibliometric Analysis

Background

Telemedicine as a tool that can reduce potential disease spread and fill a gap in healthcare has been increasingly applied during the COVID-19 pandemic. Many studies have summarized telemedicine's technologies or the diseases' applications. However, these studies were reviewed separately. There is a lack of a comprehensive overview of the telemedicine technologies, application areas, and medical service types.

Objective

We aimed to investigate the research direction of telemedicine at COVID-19 and to clarify what kind of telemedicine technology is used in what diseases, and what medical services are provided by telemedicine.

Methods

Publications addressing telemedicine in COVID-19 were retrieved from the PubMed database. To extract bibliographic information and do a bi-clustering analysis, we used Bicomb and gCLUTO. The co-occurrence networks of diseases, technology, and healthcare services were then constructed and shown using R-studio and the Gephi tool.

Results

We retrieved 5,224 research papers on telemedicine at COVID-19 distributed among 1460 journals. Most articles were published in the Journal of Medical Internet Research (166/5,224, 3.18%). The United States published the most articles on telemedicine. The research clusters comprised 6 clusters, which refer to mental health, mhealth, cross-infection control, and self-management of diseases. The network analysis revealed a triple relation with diseases, technologies, and health care services with 303 nodes and 5,664 edges. The entity “delivery of health care” was the node with the highest betweenness centrality at 6,787.79, followed by “remote consultation” (4,395.76) and “infection control” (3,700.50).

Conclusions

The results of this study highlight widely use of telemedicine during COVID-19. Most studies relate to the delivery of health care and mental health services. Technologies were primarily via mobile devices to deliver health care, remote consultation, control infection, and contact tracing. The study assists researchers in comprehending the knowledge structure in this sector, enabling them to discover critical topics and choose the best match for their survey work.

Development of an Automated Hospital Infection Control Surveillance Toolkit

Background:

Infection control systems allow the healthcare facility to detect, manage, prevent and control the infection or risk of infection. It helps to improve healthcare quality by allowing hospital infection control (HIC) team to focus on prevention and intervention efforts during the outbreaks.

Objectives:

To design, develop and implement an automated HIC surveillance Toolkit for Infection Prevention and Control Department based on user requirements.

Methods:

This study was carried out in a multi-speciality hospital in Southern India. The study was carried out in four phases; the first phase included the observation of current documentation practice of Infection Prevention and Control Department. During the second phase, a formal discussion was conducted with the infection control team to collect the data to add to the HIC surveillance toolkit. The third phase included the design and development of the toolkit using ASP.NET and Standard Query Language (SQL) database server. During the fourth phase, a prospective cross-sectional survey was conducted to assess the end-user satisfaction towards the Toolkit using a structured and validated checklist based on 5-point Likert scale from extremely satisfied to not satisfied (score 5–1).

Results:

Based on the observation of existing documentation and reporting practice, a discussion was carried out with the infection control team to identify the templates to be added to the toolkit. Once identified, a web-based toolkit was designed and developed using ASP.NET and stored using SQL database server. The developed toolkit was implemented in the hospital and provided to the HIC team to use. The finding of the assessment of 15 features of HIC surveillance toolkit indicated that the end-users were very satisfied with the support of toolkit in documenting and reporting of infection-related data.

Conclusion:

HIC surveillance toolkit, allows the HIC team to capture, store, manage data, compare, and calculate the infection control rate and compliance rate. It also increases the ease of surveillance and reporting. It aids in reducing hospital-acquired infection (HAI) rates, simplifies the workflow of HIC and improves healthcare quality.

Utilizing technology to increase efficiency of infection prevention data collection: Our experience using electronic medical records for symptom surveillance

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.

How to Improve Information Technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health Informatics Experts

OBJECTIVES

To identify the ways in which healthcare information and communication technologies can be improved to address the challenges raised by the COVID-19 pandemic.

METHODS

The study population included health informatics experts who had been involved with the planning, development and deployment of healthcare information and communication technologies in healthcare settings in response to the challenges presented by the COVID-19 pandemic. Data were collected via an online survey. A non-probability convenience sampling strategy was employed. Data were analyzed with content analysis.

RESULTS

A total of 65 participants from 16 countries responded to the conducted survey. The four major themes regarding recommended improvements identified from the content analysis included: improved technology availability, improved interoperability, intuitive user interfaces and adoption of standards of care. Respondents also identified several key healthcare information and communication technologies that can help to provide better healthcare to patients during the COVID-19 pandemic, including telehealth, advanced software, electronic health records, remote work technologies (e.g., remote desktop computer access), and clinical decision support tools.

CONCLUSIONS

Our results help to identify several important healthcare information and communication technologies, recommended by health informatics experts, which can help to provide better care to patients during the COVID-19 pandemic. The results also highlight the need for improved interoperability, intuitive user interfaces and advocating the adoption of standards of care.

A digital approach in the rapid response to COVID-19 - Experience of a paediatric institution

INTRODUCTION

COVID-19 has radically changed the delivery of healthcare in Australia. Central to a tertiary paediatric institution's (The Royal Children's Hospital Melbourne (RCH) response was a digital health approach comprising a broad suite of informatics and technology solutions including optimising a fully integrated electronic medical record (EMR).

METHODS

This comprehensive approach spanned all patient care areas and encompassed a broad range of hospital operations. They included patient triage, registration, COVID-19 screening clinic operations, electronic ordering, prescribing and documentation, telehealth, reporting and analytics and research.

DISCUSSION

This paper outlines key aspects of our COVID-19 digital health strategy, highlighting the rapid transition to telehealth and the development of a remote "virtual telehealth" strategy for clinicians which proved popular and allowed true "working from home".

CONCLUSION

COVID-19 has inadvertently focussed the spotlight on the utility of digital health for clinical care. The speed and uptake of digital health within this pandemic has been remarkable and unprecedented in both an Australian and global setting. Whilst many of these changes have been beneficial, some may have been rushed or forced with minimal consideration of ongoing governance. Key stakeholders and enablers should be identified for post-pandemic consideration in future digital health implementation and adoption strategies.

Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions

OBJECTIVE

Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.

MATERIALS AND METHODS

We searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges.

RESULTS

Finally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals.

CONCLUSIONS

EHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.

Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review

BACKGROUND

COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has become a global pandemic, affecting most countries worldwide. Digital health information technologies can be applied in three aspects, namely digital patients, digital devices, and digital clinics, and could be useful in fighting the COVID-19 pandemic.

OBJECTIVE

Recent reviews have examined the role of digital health in controlling COVID-19 to identify the potential of digital health interventions to fight the disease. However, this study aims to review and analyze the digital technology that is being applied to control the COVID-19 pandemic in the 10 countries with the highest prevalence of the disease.

METHODS

For this review, the Google Scholar, PubMed, Web of Science, and Scopus databases were searched in August 2020 to retrieve publications from December 2019 to March 15, 2020. Furthermore, the Google search engine was used to identify additional applications of digital health for COVID-19 pandemic control.

RESULTS

We included 32 papers in this review that reported 37 digital health applications for COVID-19 control. The most common digital health projects to address COVID-19 were telemedicine visits (11/37, 30%). Digital learning packages for informing people about the disease, geographic information systems and quick response code applications for real-time case tracking, and cloud- or mobile-based systems for self-care and patient tracking were in the second rank of digital tool applications (all 7/37, 19%). The projects were deployed in various European countries and in the United States, Australia, and China.

CONCLUSIONS

Considering the potential of available information technologies worldwide in the 21st century, particularly in developed countries, it appears that more digital health products with a higher level of intelligence capability remain to be applied for the management of pandemics and health-related crises.

Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models

BACKGROUND

COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization.

OBJECTIVE

This study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time.

MATERIAL AND METHODS

The proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020.

RESULTS

An initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts.

CONCLUSION

This study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs.

The current scope of healthcare-associated infection surveillance activities in hospitalized immunocompromised patients: a systematic review

BACKGROUND

Immunocompromised patients are at increased risk of acquiring healthcare-associated infections (HAIs) and often require specialized models of care. Surveillance of HAIs is essential for effective infection-prevention programmes. However, little is known regarding standardized or specific surveillance methods currently employed for high-risk hospitalized patients.

METHODS

A systematic review adopting a narrative synthesis approach of published material between 1 January 2000 and 31 March 2018 was conducted. Publications describing the application of traditional and/or electronic surveillance of HAIs in immunocompromised patient settings were identified from the Ovid MEDLINE®, Ovid Embase® and Elsevier Scopus® search engines [PROSPERO international prospective register of systematic reviews (registration ID: CRD42018093651)].

RESULTS

In total, 2708 studies were screened, of whom 17 fulfilled inclusion criteria. Inpatients diagnosed with haematological malignancies were the most-represented immunosuppressed population. The majority of studies described manual HAI surveillance utilizing internationally accepted definitions for infection. Chart review of diagnostic and pathology reports was most commonly employed for case ascertainment. Data linkage of disparate datasets was performed in two studies. The most frequently monitored infections were bloodstream infections and invasive fungal disease. No surveillance programmes applied risk adjustment for reporting surveillance outcomes.

CONCLUSIONS

Targeted, tailored monitoring of HAIs in high-risk immunocompromised settings is infrequently reported in current hospital surveillance programmes. Standardized surveillance frameworks, including risk adjustment and timely data dissemination, are required to adequately support infection-prevention programmes in these populations.

Rapid response to COVID-19: health informatics support for outbreak management in an academic health system

OBJECTIVE

To describe the implementation of technological support important for optimizing clinical management of the COVID-19 pandemic.

MATERIALS AND METHODS

Our health system has confirmed prior and current cases of COVID-19. An Incident Command Center was established early in the crisis and helped identify electronic health record (EHR)-based tools to support clinical care.

RESULTS

We outline the design and implementation of EHR-based rapid screening processes, laboratory testing, clinical decision support, reporting tools, and patient-facing technology related to COVID-19.

DISCUSSION

The EHR is a useful tool to enable rapid deployment of standardized processes. UC San Diego Health built multiple COVID-19-specific tools to support outbreak management, including scripted triaging, electronic check-in, standard ordering and documentation, secure messaging, real-time data analytics, and telemedicine capabilities. Challenges included the need to frequently adjust build to meet rapidly evolving requirements, communication, and adoption, and to coordinate the needs of multiple stakeholders while maintaining high-quality, prepandemic medical care.

CONCLUSION

The EHR is an essential tool in supporting the clinical needs of a health system managing the COVID-19 pandemic.

The impact mechanism of the controlling system in hospitals on quality of care: A study on clinical practice in China

BACKGROUND

Quality control system is one of the hospital information systems. The adoption of quality control system increases the work efficiency; however, to some extent, it also increases the workload for physicians.

OBJECTIVE

The purpose of this study is to investigate the impacts of the quality control system on quality of care (e.g., process and outcome performance).

METHODS

Our study collected physicians' behavior information from a large urban hospital in China. We constructed the fixed-effect model to examine the relationship between the quality control system adoption and quality of care.

RESULTS

Using the quality control system has a significant (p< 0.001) and negative effect on patients' stay length in the hospital (process performance). Furthermore, using the quality control system has a significant (p< 0.001) and positive effect on the trends of cure rate in the hospital (outcome performance). The coefficient of the dependent variable from the patients' stay length (process performance) is lower than the trends of cure rate (outcome performance).

CONCLUSIONS

The controlling system can improve medical quality even though it limits physician behavior to some extent. The controlling system improves both the process performance and outcome performance, and it brings more benefits to outcome performance rather than process performance which means the reflection of the new technology may have more evident on outcome variables.

Evaluation of a web-based tool for labelling potential hospital outbreaks: a mixed methods study

BACKGROUND

Labelling outbreaks in surveillance data is necessary to train advanced analytical methods for outbreak detection, but there is a lack of software tools dedicated to this task.

AIM

To evaluate the usability of a web-based tool by infection control practitioners for labelling potential outbreaks.

METHODS

A mixed methods design was used to evaluate how 25 experts from France and Canada interacted with a web-based application to identify potential outbreaks. Each expert used the application to retrospectively review 11-12 1-year incidence time series from 23 different types of micro-organism. The interactions between the users and the application were recorded and analysed using mixed effect models. The users' comments were analysed via qualitative methods.

FINDINGS

From the 240 reviews completed, 439 potential outbreaks were labelled, approximately half with a high probability. Significant heterogeneity was observed between users regarding their answers and behaviours (evaluation time, usage of the different options). A significant learning effect was also observed for the experts' interactions with the tool, but this did not seem to impact their answers. The content analysis of the comments highlighted the difficulty of early outbreak identification for practitioners, but also the potential utility of web applications such as that evaluated for routine surveillance.

CONCLUSION

The interactive web application was both usable and useful for infection control practitioners. Its implementation in routine practice could help professionals to identify potential outbreaks while creating data to train automated detection algorithms.

An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

BACKGROUND

Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement.

OBJECTIVE

We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems.

METHODS

First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics.

RESULTS

Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables-TMUHcS-RITP and TMUHcS-IMU-showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports.

CONCLUSIONS

Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations.

Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System

BACKGROUND

Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases.

OBJECTIVE

The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness.

METHODS

We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity.

RESULTS

The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients' diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively.

CONCLUSIONS

The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases' clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.

Challenges with Surveillance of Healthcare-Associated Infections in Intensive Care Units in South Africa

Background

The incidence of healthcare-associated infections (HAIs) in the public health sector in South Africa is not known due to the lack of a surveillance system. We report on the challenges experienced in the implementation of a surveillance system for HAIs in intensive care units (ICUs).

Methods

A passive, paper-based surveillance system was piloted in eight ICUs to measure the incidence of ventilator-associated pneumonia, catheter-associated urinary tract infection, and central line-associated bloodstream infection. Extensive consultation with the ICU clinical and nursing managers informed the development of the surveillance system. The Plan-Do-Study-Act method was utilized to guide the implementation of the surveillance.

Results

The intended outputs of the surveillance system were not fully realized due to incomplete data. The organizational culture did not promote the collection of surveillance data. Nurses felt that the surveillance form added to their workload, and the infection control practitioners were unable to adequately supervise the process due to competing work demands.

Conclusions

A manual system that adds to the administrative workload of nurses is not an effective method of measuring the burden of HAIs. Change management is required to promote an organizational culture that supports accurate data collection for HAIs.

Development of trigger-based semi-automated surveillance of ventilator-associated pneumonia and central line-associated bloodstream infections in a Dutch intensive care

Background

Availability of a patient data management system (PDMS) has created the opportunity to develop trigger-based electronic surveillance systems (ESSs). The aim was to evaluate a semi-automated trigger-based ESS for the detection of ventilator-associated pneumonia (VAP) and central line-associated blood stream infections (CLABSIs) in the intensive care.

Methods

Prospective comparison of surveillance was based on a semi-automated ESS with and without trigger. Components of the VAP/CLABSI definition served as triggers. These included the use of VAP/CLABSI-related antibiotics, the presence of mechanical ventilation or an intravenous central line, and the presence of specific clinical symptoms. Triggers were automatically fired by the PDMS. Chest X-rays and microbiology culture results were checked only on patient days with a positive trigger signal from the ESS. In traditional screening, no triggers were used; therefore, chest X-rays and culture results had to be screened for all patient days of all included patients. Patients with pneumonia at admission were excluded.

Results

A total of 553 patients were screened for VAP and CLABSI. The incidence of VAP was 3.3/1,000 ventilation days (13 VAP/3,927 mechanical ventilation days), and the incidence of CLABSI was 1.7/1,000 central line days (24 CLABSI/13.887 central line days). For VAP, the trigger-based screening had a sensitivity of 92.3%, a specificity of 100%, and a negative predictive value of 99.8% compared to traditional screening of all patients. For CLABSI, sensitivity was 91.3%, specificity 100%, and negative predictive value 99.6%.

Conclusions

Pre-selection of patients to be checked for signs and symptoms of VAP and CLABSI by a computer-generated automated trigger system was time saving but slightly less accurate than conventional surveillance. However, this after-the-fact surveillance was mainly designed as a quality indicator over time rather than for precise determination of infection rates. Therefore, surveillance of VAP and CLABSI with a trigger-based ESS is feasible and effective.

Improving the work efficiency of healthcare-associated infection surveillance using electronic medical records

In this study, we developed an integrated hospital-associated urinary tract infection (HAUTI) surveillance information system (called iHAUTISIS) based on existing electronic medical records (EMR) systems for improving the work efficiency of infection control professionals (ICPs) in a 730-bed, tertiary-care teaching hospital in Taiwan. The iHAUTISIS can automatically collect data relevant to HAUTI surveillance from the different EMR systems, and provides a visualization dashboard that helps ICPs make better surveillance plans and facilitates their surveillance work. In order to measure the system performance, we also created a generic model for comparing the ICPs' work efficiency when using existing electronic culture-based surveillance information system (eCBSIS) and iHAUTISIS, respectively. This model can demonstrate a patient's state (unsuspected, suspected, and confirmed) and corresponding time spent on surveillance tasks performed by ICPs for the patient in that state. The study results showed that the iHAUTISIS performed better than the eCBSIS in terms of ICPs' time cost. It reduced the time by 73.27 s, when using iHAUTISIS (114.26 s) and eCBSIS (187.53 s), for each patient on average. With increased adoption of EMR systems, the development of the integrated HAI surveillance information systems would be more and more cost-effective. Moreover, the iHAUTISIS adopted web-based technology that enables ICPs to online access patient's surveillance information using laptops or mobile devices. Therefore, our system can further facilitate the HAI surveillance and reduce ICPs' surveillance workloads.

Implementation of electronic surveillance of catheter use and catheter-associated urinary tract infection at Nurses Improving Care for Healthsystem Elders (NICHE) hospitals

BACKGROUND

Manual surveillance of indwelling urinary catheters (IUCs) and catheter-associated urinary tract infections (CAUTIs) is resource intense.

METHODS

We implemented electronic surveillance in nonintensive care units of Nurses Improving Care for Healthsystem Elders (NICHE) hospitals. Capacity was created centrally to analyze data collected electronically or manually at each site. We measured the average IUC duration and proportion of patients with IUC duration <3 days. CAUTIs were identified using a validated algorithm based on the Centers for Disease Control and Prevention definition and used to calculate rates and standardized incidence ratios (SIRs).

RESULTS

Electronic surveillance was implemented in 25 units at 20 NICHE hospitals. Full automation was achieved at 15 of 16 sites with electronic health records (EHRs). Electronic surveillance challenges included EHR data element formats and IUC documentation. Study units reported on 4,574 patients for 16,105 IUC days over a 6-month period. The mean of the unit-level average IUC duration was 3.2 ± 2.6 days, mean proportion of patients with IUC duration <3 days was 52.4% ± 50%, and mean CAUTI SIR was 0.14 ± 0.31.

CONCLUSION

A centralized electronic surveillance strategy for CAUTI is feasible and sustainable. Baseline performance of participating sites was exemplary, with very low SIRs at baseline.

Incidence of Nosocomial Infections in a Big University Affiliated Hospital in Shiraz, Iran: A Six-month Experience

BACKGROUND

Nosocomial infections (NIs) are one of the most important health issues, particularly in developing countries, because these infections cause high mortality and morbidity, and economic and human resource loss as a consequence. To date, most surveillance studies have been conducted in developed countries, and only a few have been performed in Iran. All of the few Iranian studies have been performed using paper-based collection forms, and none was conducted with the aid of an electronic patient data retrieving and collecting tool. The aim of this study is to determine the incidence of NIs in a big university hospital of Shiraz, with the help of specifically programmed surveillance software merging electronically the available patient data and the infection results input manually.

METHODS

The study was conducted prospectively through 6 months from 21(st) March up to 22(nd) September 2006, in a 374-bedded educational hospital. All patients admitted during this period were included in the study and examined everyday for detecting four types of NIs: surgical site infection (SSI), urinary tract infection (UTI), pneumonia (PNEU), and blood stream infection (BSI). Centres for Disease Control and Prevention National Nosocomial Infection Surveillance system criteria were applied.

RESULTS

4013 patients were admitted in the hospital. The overall infection rate was 4.14, and UTI, SSI, BSI, and PNEU rates were 1.82, 1.22, 0.5, and 0.5, respectively, per 1000 patient days of admission.

CONCLUSIONS

The results of this study showed that the frequency of NI in the investigated hospital was not higher than in many other reported surveillance results from other countries. This, however, might be a bias as the administration of antibiotics was very high in this study and the quality of microbiological investigation might have influenced significantly, resulting in more false-negative results than expected. Overall, the use of the Iranian National Nosocomial Infection Surveillance System Software proved to be useful and allowed both rapid data collection and detailed data analysis.

Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections

In this study, we propose an approach to build a detection model for surveillance of healthcare-associated urinary tract infection (HA-UTI) based on the variables extracted from the electronic medical records (EMRs) in a 730-bed, tertiary-care teaching hospital in Taiwan. Firstly we mapped the CDC's HA-UTI case definitions to a set of variables, and identified the variables whose values could be derived from the EMRs of the hospital automatically. Then with these variables we performed discriminant analysis (DA) on a training set of the EMRs to construct a discriminant function (DF) for the classification of a patient with or without HA-UTI. Finally, we evaluated the sensitivity, specificity, and overall accuracy of the function using a testing set of EMRs. In this study, six surveillance variables (fever, urine culture, blood culture, routine urinalysis, antibiotic use, and invasive devices) were identified whose values could be derived from the EMRs of the hospital. The sensitivity, specificity and overall accuracy of the built DF were 100 %, 94.61 %, and 94.65 %, respectively. Since most hospitals may adopt their EMRs piece-by-piece to meet their functional requirements, the variables that are available in the EMRs may differ. Our approach can build a detection model with these variables to achieve a high sensitivity, specificity and accuracy for automatically detecting suspected HA-UTI cases. Therefore, our approach on one hand can reduce the efforts in building the model; on the other hand, can facilitate adoption of EMRs for HAI surveillance and control.

Hospital adoption of automated surveillance technology and the implementation of infection prevention and control programs

BACKGROUND

This research analyzes the relationship between hospital use of automated surveillance technology (AST) for identification and control of hospital-acquired infections (HAI) and implementation of evidence-based infection control practices. Our hypothesis is that hospitals that use AST have made more progress implementing infection control practices than hospitals that rely on manual surveillance.

METHODS

A survey of all acute general care hospitals in California was conducted from October 2008 through January 2009. A structured computer-assisted telephone interview was conducted with the quality director of each hospital. The final sample includes 241 general acute care hospitals (response rate, 83%).

RESULTS

Approximately one third (32.4%) of California's hospitals use AST for monitoring HAI. Adoption of AST is statistically significant and positively associated with the depth of implementation of evidence-based practices for methicillin-resistant Staphylococcus aureus and ventilator-associated pneumonia and adoption of contact precautions and surgical care infection practices. Use of AST is also statistically significantly associated with the breadth of hospital implementation of evidence-based practices across all 5 targeted HAI.

CONCLUSION

Our findings suggest that hospitals using AST can achieve greater depth and breadth in implementing evidenced-based infection control practices.

Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction

BACKGROUND

The use of electronic surveillance systems (ESSs) is gradually increasing in infection prevention and control programs. Little is known about the characteristics of hospitals that have a ESS, user satisfaction with ESSs, and organizational support for implementation of ESSs.

METHODS

A total of 350 acute care hospitals in California were invited to participate in a Web-based survey; 207 hospitals (59%) agreed to participate. The survey included a description of infection prevention and control department staff, where and how they spent their time, a measure of organizational support for infection prevention and control, and reported experience with ESSs.

RESULTS

Only 23% (44/192) of responding infection prevention and control departments had an ESS. No statistically significant difference was seen in how and where infection preventionists (IPs) who used an ESS and those who did not spend their time. The 2 significant predictors of whether an ESS was present were score on the Organizational Support Scale (odds ratio [OR], 1.10; 95% confidence interval [CI], 1.02-1.18) and hospital bed size (OR, 1.004; 95% CI, 1.00-1.007). Organizational support also was positively correlated with IP satisfaction with the ESS, as measured on the Computer Usability Scale (P = .02).

CONCLUSION

Despite evidence that such systems may improve efficiency of data collection and potentially improve patient outcomes, ESSs remain relatively uncommon in infection prevention and control programs. Based on our findings, organizational support appears to be a major predictor of the presence, use, and satisfaction with ESSs in infection prevention and control programs.

The electronic medical record as a tool for infection surveillance: successful automation of device-days

BACKGROUND

Manual collection of central venous catheter, ventilator, and indwelling urinary catheter device-days is time-consuming, often restricted to intensive care units (ICU) and prone to error.

METHODS

We describe the use of an electronic medical record to extract existing clinical documentation of invasive devices. This allowed automated device-days calculations for device-associated infection surveillance in an acute care setting.

RESULTS

The automated system had high sensitivity, specificity, and positive and negative predictive values (>0.90) compared with chart review. The system is not restricted to ICUs and reduces surveillance efforts by a conservative estimate of over 3.5 work-weeks per year in our setting. Eighty percent of urinary catheter days and 50% of central venous catheter-days occurred outside the ICU.

CONCLUSION

Device-days may be automatically extracted from an existing electronic medical record with a higher degree of accuracy than manual collection while saving valuable personnel resources.