Forty years of SNOMED: a literature review

BACKGROUND: Over a period of 40 years, SNOMED has developed from a pathology-specific nomenclature (SNOP) into a logic-based health care terminology. In spite of its long existence and continuous evolvement, it is yet unknown to what extent SNOMED is used in clinical practice, and what benefits were achieved. The aim of this paper is to investigate this by providing an overview of published studies in which a version of SNOMED was studied or applied. METHODS: This paper analyzes the use of SNOMED over time, as reflected in scientific publications, by means of Medline literature search in PubMed. The search included papers from 1966 until June 2006. For each selected paper the following characteristics were classified: version, medical domain, coding moment (during or after the care process), usage, and type of evaluation (e.g., does SNOMED work, does SNOMED help). RESULTS: 250 papers were included in this research. The number of annual publications has increased, as has the number of domains in which SNOMED is being used. Theoretical studies mainly concern comparison of SNOMED to other terminological systems and SNOMED as an illustration of a terminological systems' theory. Few studies are available on the usage of SNOMED in clinical practice, largely involving coding information and retrieval/aggregation based on SNOMED codes. CONCLUSION: The clinical application of SNOMED is broadening beyond pathology. The majority of studies concern proving the value of SNOMED in theory. Fewer studies are available on the usage of SNOMED in clinical practice. Literature gives no indication of the use of SNOMED for direct care purposes such as decision suppor

Protégé: A Tool for Managing and Using Terminology in Radiology Applications

The development of standard terminologies such as RadLex is becoming important in radiology applications, such as structured reporting, teaching file authoring, report indexing, and text mining. The development and maintenance of these terminologies are challenging, however, because there are few specialized tools to help developers to browse, visualize, and edit large taxonomies. Protégé (http://protege.stanford.edu) is an open-source tool that allows developers to create and to manage terminologies and ontologies. It is more than a terminology-editing tool, as it also provides a platform for developers to use the terminologies in end-user applications. There are more than 70,000 registered users of Protégé who are using the system to manage terminologies and ontologies in many different domains. The RadLex project has recently adopted Protégé for managing its radiology terminology. Protégé provides several features particularly useful to managing radiology terminologies: an intuitive graphical user interface for navigating large taxonomies, visualization components for viewing complex term relationships, and a programming interface so developers can create terminology-driven radiology applications. In addition, Protégé has an extensible plug-in architecture, and its large user community has contributed a rich library of components and extensions that provide much additional useful functionalities. In this report, we describe Protégé’s features and its particular advantages in the radiology domain in the creation, maintenance, and use of radiology terminology

Artificial Intelligence in Clinical Decision Support : Challenges for Evaluating AI and Practical Implications

OBJECTIVES This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. METHOD A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. CONCLUSION Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application

Quality indicators for patients with traumatic brain injury in European intensive care units: a CENTER-TBI study

Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measurement and improvement.Methods: Our analysis was based on 2006 adult patients admitted to 54 ICUs between 2014 and 2018, enrolled in the CENTER-TBI study. Indicator scores were calculated as percentage adherence for structure and process indicators and as event rates or median scores for outcome indicators. Feasibility was quantified by the completeness of the variables. Discriminability was determined by the between-centre variation, estimated with a random effect regression model adjusted for case-mix severity and quantified by the median odds ratio (MOR). Statistical uncertainty of outcome indicators was determined by the median number of events per centre, using a cut-off of 10.Results: A total of 26/42 indicators could be calculated from the CENTER-TBI database. Most quality indicators proved feasible to obtain with more than 70% completeness. Sub-optimal adherence was found for most quality indicators, ranging from 26 to 93% and 20 to 99% for structure and process indicators. Significant (p Conclusions: Overall, nine structures, five processes, but none of the outcome indicators showed potential for quality improvement purposes for TBI patients in the ICU. Future research should focus on implementation efforts and continuous reevaluation of quality indicators.</p

The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview

Aziz Sheikh and colleagues report the findings of their systematic overview that assessed the impact of eHealth solutions on the quality and safety of health care

The relation between TISS and real paedeatric ICU costs: a case study with generalizable methodology

To determine the quantitative relation between the Therapeutic Intervention Scoring System (TISS) in combination with other relevant clinical variables and the real costs of (paediatric) intensive care. A prospective, observational study. A Ten-bed paediatric intensive care unit in a university children's hospital. In a 17-months registration period we collected patient- and treatment-related data for all 611 consecutive admissions. A 21-day calibration period was used to collect detailed data to calculate the real costs of 33 consecutive admissions, in addition to the same data as in the registration period. We used the Multi Moment Measurement method to measure time spent by nurses and physicians and medication used in the 21-day calibration period. The calibration period data set with explanatory variables including TISS was used to build a regression model to estimate nurse and physician time, which were converted to personnel costs, and to estimate medication costs. The regression models built from the calibration period were subsequently used to estimate the total costs per day and per admission in different patient groups in the registration period. It was feasible to calculate total direct medical costs based on a limited number of readily available clinical variables related to patient characteristics and treatment, of which TISS was the most important determinant. The proposed methods provide further tools for assessment of (paediatric) intensive care unit performanc