Methicillin-resistant Staphylococcus aureus (MRSA) is a major public health problem primarily related to receipt of health care. As part of the VHA MRSA Initiative, all hospitalized patients are expected to be screened for MRSA carriage upon admission to the hospital, transfer between hospital units and upon discharge from the hospital. Additionally, surveillance and preventive measures for MRSA is very time intensive and technological solutions are highly desirable.
The overall goal of this project is to create and validate informatics tools which utilize data mining techniques to identify individuals with MRSA, in order to support the implementation of evidence-based practices for preventing and reducing MRSA infections in the hospital setting.
This project will be focused around five specific aims.
Specific Aim 1: Develop, review and refine an ontology for clinically- and epidemiologically-relevant concepts to enable detection of MRSA for reporting purposes and gather requirements for developing a MRSA surveillance and reporting tool.
Specific Aim 2: Index MRSA-related Concepts in Clinical Narrative.
Specific Aim 3: Clinical Inference and Analysis of MRSA-Related Information contained in the medical record
Specific Aim 4: Develop and evaluate a prototype surveillance application that uses automatically processed VA electronic health record data
Translational Aim: Evaluate algorithms for making multi-type predictions based on heterogeneous data, using MRSA as a clinical domain.
In order to develop such a system, we analyzed workflow and sections of the EHR related to the surveillance process. An ontology including the signs and symptoms of various MRSA infections was specified using National Healthcare Safety Network (NHSN) surveillance criteria. Data needed to replicate the manual review workflow was obtained from a variety of sources including microbiology, vital signs, pharmacy, admission source, ICD codes, and demographic data. This established decision points within the overall algorithm for which specific data are needed. MRSA infections were sub-classified into five major types of infections (blood stream, lower respiratory, urinary tract, and skin and soft tissue), as well as whether or not the infection was associated with the presence of an invasive device and the epidemiologic source of infection (hospital-acquired, healthcare-associated community onset, or community associated).
We have extracted and annotated text data for Bloodstream infections and urinary tract infections (UTIs). We have refined the ontology we developed for MRSA and begun applying natural language processing (NLP) to these documents. We recently received approval and have created a national VA dataset of text and microbiologic data. We have also interviewed clinical infectious diseases and infection prevention specialists to identify important workflow and desired design features for the tool. We are currently using the algorithms, ontology and annotation guidelines, to refine the rules to discover MRSA concepts within documents, and make patient level inferences. This will involve using structured and NLP-annotated unstructured data on a given patient and tying these together to create rules that allow inferences about the nature of the infection or colonization.
Based on a review of NHSN guidelines, ten infection sub-classifications were found to be relevant to the research. The NLP pipeline was piloted with a document set from one of the medical centers. For this pilot, known infections based on each site's existing surveillance data were used as a reference standard for comparison. Based on the NLP pipeline and expert review, thirty terms or abbreviations consistently indicated the presence of a MRSA infection. In order to improve the accuracy in ruling out false positives, our results indicate that sectionalization, temporality, and parsing of VA templates will need to be further developed in order to accurately achieve the goals of our research.
In order to design a user interface for the surveillance too we are using the
Theoretic Framework- Promoting Action on Research Implementation in Health Services (PARiHS) Framework which assesses evidence, context and facilitation. We consider understanding workflow to be very important. In order to assess the context of future implementation and understand workflow, we interviewed key informants.
We have conducted 7 semi-structured interviews. Using a theoretical qualitative analysis, we have determined preliminary themes to inform our dashboard design. Some of the themes include: needing to evaluate clinical data to assess whether the clinical condition matches the surveillance definitions, accommodating a semi-structured workflow, providing a process by which a synthesis by the Infection Preventionist could be provided to the hospital epidemiologist, the need to aggregate multiple sources of data, among others.
MRSA is widely considered the most important antibiotic-resistant organism that causes healthcare associated infections.. Rates of resistance among these organisms have increased significantly over the last decade. In our previous work, we found overall point estimates for MRSA rates in U.S. (including VHA) hospitals to be 36% and increasing in all types of hospitals. Two-thirds of hospitals reported increasing MRSA rates, whereas only 4% reported decreasing rates, and 24% reported MRSA outbreaks within the previous year. Most hospitals (87%) reported having implemented measures to rapidly detect resistance, however, only half reported having either provided appropriate resources for antimicrobial resistance prevention (53%) or implemented antimicrobial use guidelines (60%). Current practices to prevent and control MRSA are widely shown to be inadequate. This project will promote more effective use of information technology to better inform decision making and implement processes to prevent and control MRSA.
The team is continuing to work further on the projects within this research study and will focus on completing Aim 4 in the time remaining.
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Health Systems, Infectious Diseases
Epidemiology, Technology Development and Assessment, Research Infrastructure
Acute illness, Clinical Diagnosis and Screening, Data Management, Healthcare Algorithms, Infectious disease, Informatics, Information Management, Natural Language Processing, Patient Safety, Surveillance