Patient safety
SYSTEMS-LEVEL QUALITY IMPROVEMENT
From Cues to Nudge: A Knowledge-Based Framework for Surveillance of Healthcare-Associated Infections
Arash Shaban-Nejad1,2 & Hiroshi Mamiya2 & Alexandre Riazanov3 & Alan J. Forster4 & Christopher J. O. Baker2,5 & Robyn Tamblyn2 & David L. Buckeridge2
Received: 3 June 2015 /Accepted: 30 September 2015 /Published online: 4 November 2015 # Springer Science+Business Media New York 2015
Abstract We propose an integrated semantic web framework consisting of formal ontologies, web services, a reasoner and a rule engine that together recommend appropriate level of patient-care based on the defined semantic rules and guide- lines. The classification of healthcare-associated infections within the HAIKU (Hospital Acquired Infections – Knowl- edge in Use) framework enables hospitals to consistently fol- low the standards along with their routine clinical practice and diagnosis coding to improve quality of care and patient safety. The HAI ontology (HAIO) groups over thousands of codes into a consistent hierarchy of concepts, along with relation- ships and axioms to capture knowledge on hospital-associated infections and complications with focus on the big four types, surgical site infections (SSIs), catheter-associated urinary tract infection (CAUTI); hospital-acquired pneumonia, and blood stream infection. By employing statistical inferencing in our study we use a set of heuristics to define the rule axioms to improve the SSI case detection. We also demonstrate how the occurrence of an SSI is identified using semantic e-triggers.
The e-triggers will be used to improve our risk assessment of post-operative surgical site infections (SSIs) for patients un- dergoing certain type of surgeries (e.g., coronary artery bypass graft surgery (CABG)).
Keywords Ontologies . Knowledgemodeling .
Healthcare-associated infections . Surveillance . Semantic framework . Surgical site infections
Introduction
Healthcare-associated Infections (HAIs) affect millions of patients around the world, killing hundreds of thousands and imposing, directly or indirectly, a significant socio- economic burden on healthcare systems [1]. According to the Centers for Disease Control (CDC) [2], hospital- acquired infections in the U.S., where the point preva- lence of HAIs among hospitalized patients is 4 %, result in an estimated 1.7 million infections, which lead to as many as 99,000 deaths and cost up to $45 billion annually [3, 4]. Similar or higher rates of HAI occur in other coun- tries as well with an estimated 10.5 % of patients in Ca- nadian hospitals having an HAI [5]. Clinical assessment and laboratory testing are generally used to detect and confirm an infection, identify its origin, and determine appropriate infection control methods to stop the infection from spreading within a healthcare institution. Failure to monitor, and detect HAI in timely manner can delay di- agnosis, leading to complications (e.g., sepsis), and allowing an epidemic to spread.
To ensure the quality of care given to the patients in healthcare settings, it is crucial to have systems that mon- itor for cases of HAI [6]. Our knowledge-based surveil- lance infrastructure enables monitoring for HAIs and
This article is part of the Topical Collection on Systems-Level Quality Improvement
* Arash Shaban-Nejad arash.shaban-nejad@berkeley.edu
1 School of Public Health, University of California at Berkeley, 50 University Hall, 94720-7360 Berkeley, CA, USA
2 Department of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
3 IPSNP Computing Inc, Suite 1000, 44 Chipman Hill, Station A, PO Box 7289, Saint John, NB E2L 4S6, Canada
4 Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada 5 Department of Computer Science, University of New Brunswick,
Saint John, NB, Canada
J Med Syst (2016) 40: 23 DOI 10.1007/s10916-015-0364-6
http://crossmark.crossref.org/dialog/?doi=10.1007/s10916-015-0364-6&domain=pdf
generates an alert when a suspect, probable, or confirmed cases of HAI is detected. In this paper we focus on sur- gical site infections (SSIs), one of the most common healthcare associated infections, accounting for about 31 % of all HAIs among hospitalized patients in 2010 in U.S [7]. Diagnosis of an SSI relies mainly on direct ob- servation of physical signs and symptoms of infection in an incisional wound and a case cannot usually be con- firmed solely by analyzing data given in laboratory re- ports. Given the diversity, complexity and heterogeneity of HAI data, availability of a reference vocabulary is a prerequisite of creating an integrated knowledge-based system. Despite several modifications and improvements to existing terminologies made by the Centers for Disease Control and Prevention (CDC) in the last decade, e.g., specifying the location of infections related to surgical operations and clarifying the criteria to identify the exact anatomic location of deep infections [8], inconsistencies, discrepancies, and confusion in the application of the criteria in different medical/clinical practices still exist, and there is a need for further improvement and clarifica- tion of the current nomenclature [9].
While the Centers for Disease Control and Prevention (CDC) has provided a certain criteria as a guideline [8] to prevent, control and reduce HAIs, in the HAIKU project [10] we have brought together expertise in artificial intel- ligence, knowledge modeling, epidemiology, medicine, and infection control to explore how advances in semantic technology can improve the analysis and detection of HAIs. To develop a common understanding about the do- main of infection control and to achieve data interopera- bility in the area of healthcare-associated infections, we present the HAI Ontology as part of the HAIKU (Hospital Acquired Infections – Knowledge in Use) project. The formal HAI ontology assists researchers and health pro- fessionals in analyzing medical records to identify and flag potential cases of HAIs among patients who could be at risk of acquiring an SSI.
In this paper we discuss the role and importance of the HAIKU semantic infrastructure to improve the detection of HAI using semantic web technologies. The paper is organized as follows: BExisting methods for detecting HAI^ section pre- sents an overview of existing tools and systems for managing nosocomial infections. The HAIKU ontology design and im- plementation along with the related semantic rules and axioms designed for intelligent alerting are presented in BThe HAI ontology: an overview^ and BTheHAIKU framework for case de tec t ion and repor t ing^ sec t ion , respec t ive ly. BAxiomatization using semantic and statistical analysis^ sec- tion presents our axiomatization process informed by statisti- cal analysis of existing datasets. The paper concludes in BConclusion^ section with a general discussion, a summary of findings, and anticipated future work.
Existing methods for detecting HAI
Healthcare-associated Infections have been considered an im- portant healthcare quality outcome since Florence Nightingale reduced mortality rates through the application of septic tech- niques in field hospitals during the Crimean war [11]. HAIs continue to be costly to individual patients and to the health system. Although there are several different types of HAIs, five of them account for nearly all cases. These HAI types are: pneumonia, surgical site infections, urinary tract infections, bloodstream infections, and gastrointestinal infections [3, 5]. The recognition that specific syndromes represent the majority of infections was an important advancement in efforts to re- duce the incidence and impact of HAIs. While general ap- proaches to reduce infections have been employed since the 1800s – including encouraging hand hygiene [12, 13] and environment cleaning [14, 15] – evidence-based preventive measures specifically designed for each of the five HAI syn- dromes now exist [16–20].
A cornerstone of HAI prevention and control is disease surveillance. The Centers for Disease Control and Prevention has specified explicit criteria and cohort definitions to support the surveillance of various HAI syndromes [6]. Their efforts in this domain began in the 1970s and led to the conduct of the SENIC Project [6], which evaluated the impact of infection surveillance on HAI incidence [21]. This study demonstrated that systematic tracking of HAIs coupled with physician-level feedback significantly reduces infection risk [21]. Other re- searchers [22, 23] have also described the use of electronic systems for the surveillance of hospital acquired infections, mainly through monitoring microbiology lab reports. As a result of the SENIC Project, hospital based infection control programs have become a standard practice; and surveillance is a primary function of these programs. The task of surveil- lance, however, is not trivial. It is instructive to consider sur- veillance for surgical site infections as an example. Each day, patients undergoing surgery must be identified, baseline infor- mation recorded, and a method of follow-up identified. Then, practitioners must follow patients for 30 days following the surgery to identify specific criteria indicative of infection [24]. This monitoring requires extensive review of medical records and possibly a telephone interview with the patient. This man- ual process is time consuming and is expensive, requiring highly skilled personnel. Due to the expense, hospitals may forego surveillance or focus only on a subset of patients. Nei- ther of these alternatives is optimal and in spite of many years of experience and research, the detection and control of HAIs remains as a challenge.
However, many of the steps in the surveillance of HAI could, in theory, be automated. The cohort identification could be simplified by taking advantage of information contained in information systems used tomanage operating rooms.Most of the criteria specifying…
