Decision support systems for adoption in dental clinics: A survey

Goh, Wee Pheng; Tao, Xiaohui; Ji, Zhang; Yong, Jianming

doi:10.1016/j.knosys.2016.04.022

Cited by 28 publications

(17 citation statements)

References 69 publications

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“…The survey questions were designed following an extensive literature review of similar studies [15][16][17][18][19][20]. The questionnaire comprised of 15 questions covering four sections: demographics; availability of electronic prescribing systems and the perceived impact; and barrier and facilitators for use of CDSS.…”

Section: Questionnaire Instrumentmentioning

confidence: 99%

Factors that impact the adoption of clinical decision support systems (CDSS) in healthcare settings for evidence-based decision making

Laka¹,

Milazzo²,

Merlin³

2020

Preprint

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Background: Clinical decision support systems (CDSS) are designed to promote evidence-based patient care and shared-decision making in healthcare settings. Despite these benefits, adoption and long-term use of the systems remain limited. There is a need to identify different factors that influence CDSS adoption in healthcare settings.Objective: The Unified Theory of Acceptance and Use of Technology (UTAUT) model was applied to determine different factors that influence the adoption of CDSS in healthcare settings.Methods. A cross-sectional online survey examining clinicians’ perceptions about CDSS implementation in hospital and primary care settings in Australia was undertaken from June - October 2019. Multivariate logistic regression was used to examine the association of UTAUT moderators (age, gender and experience) and care settings (hospital and primary care) with perceived benefits, barriers and facilitators to CDSS adoption.Results: Access to information required for evidence-based decision making, and improvements in quality and safety of patient care were the most common perceived benefits of CDSS. Mapping of different barriers and facilitators to the UTAUT model indicated that ease of use (effort expectancy), perceived benefit (performance expectancy) and a facilitating environment greatly influence the adoption of CDSS. Respondents indicated that systems providing a better fit between relevance, content and timeliness greatly facilitates uptake. Adoption of CDSS also depends on the ability of an organisation to create a facilitating environment that can help address the lack of users’ trust in these systems. The type of healthcare setting was found to be a significant predictor of lack of confidence in the content within CDSS, threat to professional autonomy, and time constraints as barriers to CDSS implementation. Therefore, setting dynamics as well as user-specific requirements need to be considered to improve the acceptability and use of CDSS. Conclusion: Our study has explored different factors that may help address implementation challenges for CDSS. By combining internal factors of users’ inclination and perceptions about the system’s perceived benefits, coupled with external factors of system design requirements, training and support, and stakeholders’ consultations, our findings highlight the need for a holistic implementation framework to enable effective CDSS adoption.

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Section: Questionnaire Instrumentmentioning

confidence: 99%

Factors that impact the adoption of clinical decision support systems (CDSS) in healthcare settings for evidence-based decision making

Laka¹,

Milazzo²,

Merlin³

2020

Preprint

View full text Add to dashboard Cite

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“…Moreover, the word embedding method is also adopted which uses features that relate the similarity of a drug-pair in terms of how closely the words are related to each drug of the drug-pair. This approach distinguishes from our earlier work where feature vectors were constructed based on term similarities within the drug corpus [11].…”

Section: Drug Interactionsmentioning

confidence: 99%

Drug prescription support in dental clinics through drug corpus mining

Goh

Tao

et al. 2018

Int J Data Sci Anal

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The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug-pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug-pairs, by using feature vectors generated from term similarities and word embeddings of bio-medical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model's association of the similarity ratio between two drugs yielded a superior F score of 89%.Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors and thereby increase the health outcomes of patients.

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“…Wooden furniture for storage is being replaced by digital media. Hence a decision support system is important for the health practitioner to deliver the service efficiently [10].…”

Section: Introductionmentioning

confidence: 99%

Feature-Based Learning in Drug Prescription System for Medical Clinics

Goh

Tao

Zhang

et al. 2020

Neural Process Lett

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Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles. Within our three-tiered frameworkthe prediction layer, the knowledge layer and the presentation layer-we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes. Keywords Feature vector • Similarity ratio • Word embedding • Adverse network model • Personalised drug prescription This work is partially supported by Glory Dental Surgery Pte Ltd and undertaken collaboratively with their panel of dentists. We would like to thank Dr. Xueling Oh and Dr. Elizabeth Goh for enriching the authors understanding of drug prescription within the medical domain.

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Decision support systems for adoption in dental clinics: A survey

Cited by 28 publications

References 69 publications

Factors that impact the adoption of clinical decision support systems (CDSS) in healthcare settings for evidence-based decision making

Factors that impact the adoption of clinical decision support systems (CDSS) in healthcare settings for evidence-based decision making

Drug prescription support in dental clinics through drug corpus mining

Feature-Based Learning in Drug Prescription System for Medical Clinics

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