Current State of Dental Informatics in the Field of Health Information Systems: Systematic Review (Preprint)

2021 ◽  
Author(s):  
Benoit Ballester ◽  
Frédéric Bukiet ◽  
Jean-Charles Dufour
Keyword(s):  
Information Systems ◽  
Patient Care ◽  
Health Information ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Health Information Systems ◽  
Clinical Decision ◽  
Data Capture ◽  
Clinical Coding ◽  
Coding Systems

BACKGROUND Over the past 50 years, dental informatics has developed significantly in the field of health information systems. Accordingly, several studies have been conducted on standardized clinical coding systems, data capture, and clinical data reuse in dentistry. OBJECTIVE The primary objective of this systematic review was to summarize studies on standardized clinical coding systems and electronic dental record (EDR) data capture in dentistry. The secondary objective was to explore the practical implications of reusing EDR data in clinical decision support systems, quality measure development, and clinical research. METHODS Based on the definition of health information systems, we divided the literature search into 3 specific sub-searches: “standardized clinical coding systems,” “data capture,” and “reuse of routine patient care data.” PubMed and Web of Science were searched for peer-reviewed articles. The review was conducted following the PRISMA protocol. RESULTS A total of 43 articles were identified for inclusion in the review. Of these, 15 were related to “standardized clinical coding systems,” 15 to “data capture,” and 13 to “reuse of routine patient care data.” Articles related to standardized clinical coding systems focused on the design and/or development of proposed systems, on their evaluation and validation, on their adoption in academic settings, and on user perception. Articles related to data capture addressed the issue of data completeness, evaluated user interfaces and workflow integration, and proposed technical solutions. Finally, articles related to reuse of routine patient care data focused on clinical decision support systems centered on patient care, institutional or population-based health monitoring support systems, and clinical research. CONCLUSIONS While the development of health information systems, and especially standardized clinical coding systems, has led to significant progress in research and quality measures, the vast majority of reviewed articles were published in the US. Clinical decision support systems that reuse EDR data have been little studied. Likewise, few studies have examined the working environment of dental practitioners or the pedagogical value of using health information systems in dentistry.

scholarly journals Coding Systems for Clinical Decision Support: Theoretical and Real-World Comparative Analysis (Preprint)

2019 ◽  
Author(s):  
Nicolas Delvaux ◽  
Bert Vaes ◽  
Bert Aertgeerts ◽  
Stijn Van de Velde ◽  
Robert Vander Stichele ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
International Classification Of Diseases ◽  
Coding System ◽  
Clinical Coding ◽  
Coding Systems

BACKGROUND Effective clinical decision support systems require accurate translation of practice recommendations into machine-readable artifacts; developing code sets that represent clinical concepts are an important step in this process. Many clinical coding systems are currently used in electronic health records, and it is unclear whether all of these systems are capable of efficiently representing the clinical concepts required in executing clinical decision support systems. OBJECTIVE The aim of this study was to evaluate which clinical coding systems are capable of efficiently representing clinical concepts that are necessary for translating artifacts into executable code for clinical decision support systems. METHODS Two methods were used to evaluate a set of clinical coding systems. In a theoretical approach, we extracted all the clinical concepts from 3 preventive care recommendations and constructed a series of code sets containing codes from a single clinical coding system. In a practical approach using data from a real-world setting, we studied the content of 1890 code sets used in an internationally available clinical decision support system and compared the usage of various clinical coding systems. RESULTS SNOMED CT and ICD-10 (International Classification of Diseases, Tenth Revision) proved to be the most accurate clinical coding systems for most concepts in our theoretical evaluation. In our practical evaluation, we found that International Classification of Diseases (Tenth Revision) was most often used to construct code sets. Some coding systems were very accurate in representing specific types of clinical concepts, for example, LOINC (Logical Observation Identifiers Names and Codes) for investigation results and ATC (Anatomical Therapeutic Chemical Classification) for drugs. CONCLUSIONS No single coding system seems to fulfill all the needs for representing clinical concepts for clinical decision support systems. Comprehensiveness of the coding systems seems to be offset by complexity and forms a barrier to usability for code set construction. Clinical vocabularies mapped to multiple clinical coding systems could facilitate clinical code set construction.

Achieving Effective Health Information Systems

2009 ◽  
pp. 560-583
Author(s):  
Jim Warren ◽  
Karen Day ◽  
Martin Orr
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Health Informatics ◽  
Clinical Decision Support Systems ◽  
Health Information Systems ◽  
Clinical Decision ◽  
Commodity Culture ◽  
Domain Specific ◽  
Quantitative And Qualitative Research ◽  
Hierarchy Of Evidence

In this chapter we aim to promote an understanding of the complexity of healthcare as a setting for information systems and how this complexity influences the achievement of successful implementations. We define health informatics and examine its role as an enabler in the delivery of healthcare. Then we look at the knowledge commodity culture of healthcare, with the gold standard of systematic reviews and its hierarchy of evidence. We examine the different forms of quantitative and qualitative research that are most commonly found in healthcare and how they influence the requirements for health information systems. We also examine some domain-specific issues that must be considered by health information systems developers, including those around clinical decision support systems and clinical classification and coding systems. We conclude with a discussion of the challenges that must be balanced by the health systems implementer in delivering robust systems that support evidence-based healthcare processes.

scholarly journals Coding Systems for Clinical Decision Support: Theoretical and Real-World Comparative Analysis

10.2196/16094 ◽  
2020 ◽  
Vol 4 (10) ◽  
pp. e16094
Author(s):  
Nicolas Delvaux ◽  
Bert Vaes ◽  
Bert Aertgeerts ◽  
Stijn Van de Velde ◽  
Robert Vander Stichele ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
International Classification Of Diseases ◽  
Coding System ◽  
Clinical Coding ◽  
Coding Systems

Background Effective clinical decision support systems require accurate translation of practice recommendations into machine-readable artifacts; developing code sets that represent clinical concepts are an important step in this process. Many clinical coding systems are currently used in electronic health records, and it is unclear whether all of these systems are capable of efficiently representing the clinical concepts required in executing clinical decision support systems. Objective The aim of this study was to evaluate which clinical coding systems are capable of efficiently representing clinical concepts that are necessary for translating artifacts into executable code for clinical decision support systems. Methods Two methods were used to evaluate a set of clinical coding systems. In a theoretical approach, we extracted all the clinical concepts from 3 preventive care recommendations and constructed a series of code sets containing codes from a single clinical coding system. In a practical approach using data from a real-world setting, we studied the content of 1890 code sets used in an internationally available clinical decision support system and compared the usage of various clinical coding systems. Results SNOMED CT and ICD-10 (International Classification of Diseases, Tenth Revision) proved to be the most accurate clinical coding systems for most concepts in our theoretical evaluation. In our practical evaluation, we found that International Classification of Diseases (Tenth Revision) was most often used to construct code sets. Some coding systems were very accurate in representing specific types of clinical concepts, for example, LOINC (Logical Observation Identifiers Names and Codes) for investigation results and ATC (Anatomical Therapeutic Chemical Classification) for drugs. Conclusions No single coding system seems to fulfill all the needs for representing clinical concepts for clinical decision support systems. Comprehensiveness of the coding systems seems to be offset by complexity and forms a barrier to usability for code set construction. Clinical vocabularies mapped to multiple clinical coding systems could facilitate clinical code set construction.

scholarly journals Implementing Black Box Warnings (BBWs) in Health Information Systems

2012 ◽  
Vol 03 (01) ◽  
pp. 124-134 ◽  
Author(s):  
E. Yang ◽  
A. Daghstani ◽  
D. C. Kaelber ◽  
M. Ikezuagu
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Improve Patient Safety ◽  
Health Information Systems ◽  
Clinical Decision ◽  
Black Box ◽  
Third Parties ◽  
Black Box Warnings ◽  
Types Of Information ◽  
Improve Patient

SummaryObjective: To develop a practical approach for implementing clinical decision support (CDS) for medication black box warnings (BBWs) into health information systems (HIS).Methods: We reviewed all existing medication BBWs and organized them into a taxonomy that identifies opportunities and challenges for implementing CDS for BBWs into HIS.Results: Of the over 400 BBWs that currently exist, they can be organized into 4 categories with 9 sub-categories based on the types of information contained in the BBWs, who should be notified, and potential actions to that could be taken by the person receiving the BBW. Informatics oriented categories and sub-categories of BBWs include – interactions (13%) (drug-drug (4%) and drug-diagnosis (9%)), testing (21%) (baseline (9%) and on-going (12%)), notifications (29%) (drug prescribers (7%), drug dispensers (2%), drug administrators (9%), patients (10%), and third parties (1%)), and non-actionable (37%). This categorization helps identify BBWs for which CDS can be easily implemented into HIS today (such as drug-drug interaction BBWs), those that cannot be easily implemented into HIS today (such as non-actionable BBWs), and those where advanced and/ or integrated HIS need to be in place to implement CDS for BBWs (such a drug dispensers BBWs).Conclusions: HIS have the potential to improve patient safety by implementing CDS for BBWs. A key to building CDS for BBWs into HIS is developing a taxonomy to serve as an organizing roadmap for implementation. The informatics oriented BBWs taxonomy presented here identified types of BBWs in which CDS can be implemented easily into HIS currently (a minority of the BBWs) and those types of BBWs where CDS cannot be easily implemented today (a majority of BBWs).

scholarly journals Clinical Decision Support for Immunization Uptake and Use in Immunization Health Information Systems

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Lauren Shrader ◽  
Stuart Myerburg ◽  
Eric Larson
Keyword(s):  
United States ◽  
Decision Support ◽  
Information Systems ◽  
Health Information ◽  
Clinical Decision Support ◽  
Health Information Systems ◽  
Clinical Decision ◽  
The United States ◽  
Precise Integration ◽  
Vaccine Recommendations

Context: In the United States, immunization recommendations and their associated schedules are developed by the Advisory Committee on Immunization Practices (ACIP). To assist with the translation process and better harmonize the outcomes of existing clinical decision support tools, the Centers for Disease Control and Prevention (CDC) created clinical decision support for immunization (CDSi) resources for each set of ACIP recommendations. These resources are continually updated and refined as new vaccine recommendations and clarifications become available and will be available to health information systems for a coronavirus disease 2019 (COVID-19) vaccine when one becomes available for use in the United States. Objectives: To assess awareness of CDSi resources, whether CDSi resources were being used by immunization-related health information systems, and perceived impact of CDSi resources on stakeholders’ work.Design: Online surveys conducted from 2015–2019 including qualitative and quantitative questions.Participants: The main and technical contact from each of the 64 CDC-funded immunization information system (IIS) awardees, IIS vendors, and electronic health record vendors. Results: Awareness of at least one resource increased from 75% of respondents in 2015 to 100% in 2019. Use of at least one CDSi resource also increased from 47% in 2015 to 78% in 2019. About 80% or more of users of CDSi are somewhat or very highly satisfied with the resources and report a somewhat or very positive impact from using them. Conclusion: As awareness and use of CDSi resources increases, the likelihood that patients receive recommended immunizations at the right time will also increase. Rapid and precise integration of vaccine recommendations into health information systems will be particularly important when a COVID-19 vaccine becomes available to help facilitate vaccine implementation.

TANCLICO: Tools for the Analysis of Inter-departmental Clinical Communications

2000 ◽  
Vol 39 (01) ◽  
pp. 44-49
Author(s):  
F. Crippa ◽  
C. Combi ◽  
G. Reni ◽  
D. Fava ◽  
F. Pinciroli
Keyword(s):  
Information Systems ◽  
Patient Care ◽  
Health Information ◽  
Information Exchange ◽  
Care Management ◽  
Health Information Systems ◽  
New Method ◽  
Care Providers ◽  
Work Processes ◽  
Healthcare Organizations

Abstract:Patient care management provided by healthcare organizations is complex, involving many different care providers. The information exchange between providers concerns a varying and considerable number of actors and a high transmission load. Based on models, used to characterize specific features of work processes, we propose a new method able to analyze and represent clinical communications inside hospitals. Software has been developed, providing tools for storing and retrieving information resulting from clinical communications. The method, together with data collected in actual situations, may constitute useful tools for health information systems developers.

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