Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Zolhavarieh, Seyedjamal; Parry, David; Bai, Quan

doi:10.2196/medinform.6169

Cited by 12 publications

(9 citation statements)

References 39 publications

(35 reference statements)

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“…Thus, if the evidence from the system's knowledge base has scientific limitations, the clinical effects generated by clinical decision support system may also be limited. There is also a difficulty in identifying, in the widely available literature, the best knowledge to be used to create this type of system [ 3 ]. In this way, clinical decision support systems will only be able to facilitate the implementation of evidence-based care when the systems can follow the literature in identifying high-quality studies and incorporate the best evidence to generate more appropriate recommendations [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

“…The clinical decision-making process in pressure ulcer care phases is complex, and its quality depends on both the professional's experience and the availability of accurate knowledge [ 3 ]. Decision making should follow evidence-based practices, represented by the management of individualized care for each patient and integrating the use of the best evidence from scientific research [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge acquisition for these systems is related to the identification and assessment of the best available knowledge [ 3 ], making their effectiveness dependent on high-quality clinical research evidence that is up-to-date, easily accessible, and interpretable by computers [ 4 ]. The use of clinical decision support systems, in addition to assisting decision makers, can increase the quality of care provided [ 6 , 8 , 10 ] and reduce errors [ 8 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

Araujo¹,

Sousa²,

Dutra³

2020

JMIR Med Inform

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Background The clinical decision-making process in pressure ulcer management is complex, and its quality depends on both the nurse's experience and the availability of scientific knowledge. This process should follow evidence-based practices incorporating health information technologies to assist health care professionals, such as the use of clinical decision support systems. These systems, in addition to increasing the quality of care provided, can reduce errors and costs in health care. However, the widespread use of clinical decision support systems still has limited evidence, indicating the need to identify and evaluate its effects on nursing clinical practice. Objective The goal of the review was to identify the effects of nurses using clinical decision support systems on clinical decision making for pressure ulcer management. Methods The systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. The search was conducted in April 2019 on 5 electronic databases: MEDLINE, SCOPUS, Web of Science, Cochrane, and CINAHL, without publication date or study design restrictions. Articles that addressed the use of computerized clinical decision support systems in pressure ulcer care applied in clinical practice were included. The reference lists of eligible articles were searched manually. The Mixed Methods Appraisal Tool was used to assess the methodological quality of the studies. Results The search strategy resulted in 998 articles, 16 of which were included. The year of publication ranged from 1995 to 2017, with 45% of studies conducted in the United States. Most addressed the use of clinical decision support systems by nurses in pressure ulcers prevention in inpatient units. All studies described knowledge-based systems that assessed the effects on clinical decision making, clinical effects secondary to clinical decision support system use, or factors that influenced the use or intention to use clinical decision support systems by health professionals and the success of their implementation in nursing practice. Conclusions The evidence in the available literature about the effects of clinical decision support systems (used by nurses) on decision making for pressure ulcer prevention and treatment is still insufficient. No significant effects were found on nurses' knowledge following the integration of clinical decision support systems into the workflow, with assessments made for a brief period of up to 6 months. Clinical effects, such as outcomes in the incidence and prevalence of pressure ulcers, remain limited in the studies, and most found clinically but nonstatistically significant results in decreasing pressure ulcers. It is necessary to carry out studies that prioritize better adoption and interaction of nurses with clinical decision support systems, as well as studies with a representative sample of health care professionals, randomized study designs, and application of assessment instruments appropriate to the professional and institutional profile. In addition, long-term follow-up is necessary to assess the effects of clinical decision support systems that can demonstrate a more real, measurable, and significant effect on clinical decision making. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42019127663; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=127663

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

Araujo¹,

Sousa²,

Dutra³

2020

JMIR Med Inform

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“…To improve the coordination efficiency among different groups of individuals within the hospital setting, IT provides a technical framework to facilitate the integration of knowledge among various individuals [12]. In this way, IT could not only promote effective knowledge exchange [13], but also provide a pathway for the mapping of knowledge to fulfill the knowledge gaps of a particular individual or group of individuals. Furthermore, the anchoring effect theory provides a theoretical background to better comprehend the beneficial aspects of fulfilling the knowledge gaps among individuals [14].…”

Section: Theoretical Foundationmentioning

confidence: 99%

Exploring the Impact of the Prescription Automatic Screening System in Health Care Services: Quasi-Experiment (Preprint)

Li¹,

Guo²,

Hsu³

et al. 2018

Preprint

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Background: Hospitals have deployed various types of merging technologies (e.g., intelligent diagnosis systems, mobile tablets). However, the impacts of these technologies on behavior change are rather vague, especially the computer-aided diagnosis systems. Although the critical roles of information systems have been demonstrated in previous research, there still remains a substantial amount of unsatisfying results after the implementation of information systems in hospitals, such as the high rate of overrides relating to alerting information generated from Prescription Automatic Screening System (PASS). Objective: The objective of this study is to systematically explore the imperative role of PASS, an aided diagnosis system, on physicians' behavior change in order to clarify the various factors relative to the effective use of PASS. Methods: In order to systematically examine the various impacts of PASS, we adopted a quasiexperiment by using a two-year observation dataset from two hospitals. We then analyzed the data related to physicians' prescription behavior both before and after the deployment of PASS and eliminated the influences from a variety of perplexing factors by utilizing a control hospital which does not use a PASS system. In total, 754 physicians were included in this experiment composed of 11,054 patients, 400 physicians in the treatment group and 354 physicians in the control group, respectively. Moreover, this study was preceded by a series of interviews, which was employed to identify the moderators and we adopted propensity score matching (PSM) integrated with the difference-in-differences (DID) so as to isolate the impacts of PASS. Results: The impacts of PASS on prescription errors and medical treatment costs were all significant (Error: 95% CI -0.40-0.11; P=0.001; Costs: 95% CI -0.75--0.12; P=0.007), and help explain physician behavior over performance. Moreover, institutional pressure decreased the impact of PASS on the error of prescription (95% CI 0.07-0.55; P=0.012) and workload, respectively, thus reducing the impact of PASS on medical treatment costs (95% CI 0.18-0.39; P<0.001). We also suspect that other pressures (e.g., title and risk of illness) also impair physicians' attention to the intelligent alerting from PASS. However, the impacts of PASS did not change among physicians having a higher title or experience or when treating diseases demonstrating high risk. The researcher posits that this is because these physicians will focus more attention on their patients in these situations, regardless of having access to an intelligent system. Conclusions: Auxiliary diagnosis systems (such as PASS) do play a critical role in physicians' behavior change. However, workload and organizational pressure remain problematic in this context, since they will impair the positive impacts of auxiliary diagnosis systems on performance. This again highlights the importance of considering both technical and organizational issues to obtain the highest level of effectiveness when deploying IT in...

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“…There is increasing interest in software frameworks and the feasibility of representing knowledge such as CPGs in semantic web technologies [ 13 - 18 ]. One such advanced system combines a formalization of CPGs using fuzzy cognitive maps (FCMs) implemented in semantic web technology [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study

Torres-Silva

Uribe

Smith³

et al. 2020

JMIR Form Res

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Background Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability. Objective This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician’s perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow. Methods We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings. Results In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable. Conclusions Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.

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Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Cited by 12 publications

References 39 publications

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

Exploring the Impact of the Prescription Automatic Screening System in Health Care Services: Quasi-Experiment (Preprint)

XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study

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