The role of standardized data and terminological systems in computerized clinical decision support systems: literature review and survey

Use of Computational Phenotypes for Predicting Genetic Subgroups of Cerebral Palsy

Introduction

Emerging evidence suggests that 20-30% of cases of cerebral palsy (CP) may have a genetic cause. Our group previously identified subsets of patients with CP or CP-masquerading conditions who warrant genetic testing, including those with regression or progressive neurological symptoms (CP masqueraders) and those without any known risk factors for CP (cryptogenic CP). Recognition of these subgroups in clinical settings remains challenging.

Methods

To address this challenge, we developed and evaluated a computational phenotyping approach using ICD- 9/ICD-10 billing codes to automatically identify patients with unexplained CP or CP-masquerading conditions who may benefit from genetic testing. We applied this computational phenotyping approach to a cohort of 250 participants from the Boston Children's Hospital CP Sequencing Study, aimed at identifying genetic causes in CP and CP-masquerading conditions.

Results

Manual review served as the gold standard, identifying 8% as CP masqueraders, 42% as cryptogenic CP, and 50% as non-cryptogenic CP. Computational phenotyping based on ICD-9/10 codes achieved a sensitivity of 95%, specificity of 72%, positive predictive value of 77%, and negative predictive value of 94% in identifying cases warranting genetic testing.

Conclusions

Our findings demonstrate the feasibility of using computational phenotyping to identify patients with CP or CP- masquerading conditions who warrant genetic testing. Further studies are needed to evaluate the effectiveness and real-world application of this tool in larger healthcare systems. Nonetheless, the computational phenotyping approach holds promise as a possible clinical decision support that could be integrated into electronic health record systems, enhancing clinical workflows and facilitating actionable genetic diagnoses.

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.

Individual and contextual factors in the Swedish Nutrition Care Process Terminology implementation

BACKGROUND

Standardised terminologies and classification systems play an increasingly important role in the continuous work towards high quality patient care. Currently, a standardised terminology for nutrition care, the Nutrition Care Process (NCP) Terminology (NCPT), is being implemented across the world, with terms for four steps: Nutrition Assessment (NA), Nutrition Diagnosis (ND), Nutrition Intervention (NI) and Nutrition Monitoring and Evaluation (NME).

OBJECTIVE

To explore associations between individual and contextual factors and implementation of a standardised NCPT among Swedish dietitians.

METHOD

A survey was completed by 226 dietitians, focussing on: (a) NCPT implementation level; (b) individual factors; and (c) contextual factors. Associations between these factors were explored through a two-block logistic regression analysis.

RESULTS

Contextual factors such as intention from management to implement the NCPT (OR (odds ratio) ND 15.0, 95% Confidence Interval (CI) 3.9-57.4, NME 3.7, 95% CI 1.1-13.0) and electronic health record (EHR) headings from the NCPT (OR NI 3.6, 95% CI 1.4-10.7, NME 3.8, 95% CI 1.1-11.5) were associated with higher implementation. A positive attitude towards the NCPT (model 1 OR ND 3.8, 95% CI 1.5-9.8, model 2 OR ND 5.0, 95% CI 1.4-17.8) was also associated with higher implementation, while other individual factors showed less association.

CONCLUSION

Contextual factors such as intention from management, EHR structure, and pre-defined terms and headings are key to implementation of a standardised terminology for nutrition and dietetic care.

IMPLICATIONS FOR PRACTICE

Clinical leadership and technological solutions should be considered key areas in future NCPT implementation strategies.

AnnoDash, a clinical terminology annotation dashboard

Background

Standard ontologies are critical for interoperability and multisite analyses of health data. Nevertheless, mapping concepts to ontologies is often done with generic tools and is labor-intensive. Contextualizing candidate concepts within source data is also done in an ad hoc manner.

Methods and Results

We present AnnoDash, a flexible dashboard to support annotation of concepts with terms from a given ontology. Text-based similarity is used to identify likely matches, and large language models are used to improve ontology ranking. A convenient interface is provided to visualize observations associated with a concept, supporting the disambiguation of vague concept descriptions. Time-series plots contrast the concept with known clinical measurements. We evaluated the dashboard qualitatively against several ontologies (SNOMED CT, LOINC, etc.) by using MIMIC-IV measurements. The dashboard is web-based and step-by-step instructions for deployment are provided, simplifying usage for nontechnical audiences. The modular code structure enables users to extend upon components, including improving similarity scoring, constructing new plots, or configuring new ontologies.

Conclusion

AnnoDash, an improved clinical terminology annotation tool, can facilitate data harmonizing by promoting mapping of clinical data. AnnoDash is freely available at https://github.com/justin13601/AnnoDash (https://doi.org/10.5281/zenodo.8043943).

The impact of drug error reduction software on preventing harmful adverse drug events in England: a retrospective database study

Introduction

The use of intravenous administration systems with dose error reduction software (DERS) is advocated to mitigate avoidable medication harm. No large-scale analysis of UK data has been attempted. This retrospective descriptive study aimed to estimate the prevalence of hard limit events and to estimate the potential severity of DERS events.

Method

Twelve months of DERS data was obtained from two NHS trusts in England. Definitions for drug categories and clinical areas were standardised and an algorithm developed to extract hard maximum (HMX) events. Subject matter experts (SMEs) were asked to rate severity of all HMX events on a scale of 0 (no harm) to 10 (death). These were analysed by clinical area and drug category, per 1000 administrations.

Results

A total of 745 170 infusions were administered over 644 052 patient bed days (PBDs). 45% of these (338 263) were administered with DERS enabled. HMX event incidence across the whole dataset was 17.9/1000 administrations (95% CI 17.5 to 18.4); 9.4/1000 PBDs (95% CI 9.2 to 9.7). 6067 HMX events were identified. 4604 were <2-fold deviations and excluded. HMX were identified in all drug categories. The highest incidence was antibacterial drugs (2.21%; 95% CI 2.13 to 2.29). Of the 1415 HMX events reviewed by SMEs, 747 (52.6%) were low/no harm. Drugs with greatest potential harm were antiarrhythmics (21.8/1000 administrations; 95% CI 16.3 to 29.1), parenteral anticoagulants (24.16/1000 administrations; 95% CI 15.3 to 37.9) and antiepileptics (20.86/1000 administrations; 95% CI 16.4 to 26.5). DERS has prevented severe harm or death in 110 patients in these hospitals. Medical and paediatric areas had higher prevalence of potentially harmful HMX events, but these were probably related to profile design.

Conclusion

Compliance with DERS in this study was 45%. DERS events are common, but potential harm is rare. DERS events are not related to specific clinical areas. There are some issues with definition and design of drug profiles that may cause DERS events, thus future work should focus on implementation and data standardisation for future large-scale analysis.

Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians

OBJECTIVE

To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors.

METHODS

We interviewed multidisciplinary HHC clinicians from January to March of 2021. Risk factors were mapped to standardized terminologies (eg, Omaha System). We used directed content analysis to identify risk factors for deterioration. We used inductive thematic analysis to understand HHC clinicians' response to risk factors and documentation of risk factors.

RESULTS

Fifteen HHC clinicians identified a total of 79 risk factors that were mapped to standardized terminologies. HHC clinicians most frequently responded to risk factors by communicating with the prescribing provider (86.7% of clinicians) or following up with patients and caregivers (86.7%). HHC clinicians stated that a majority of risk factors can be found in clinical notes (ie, care coordination (53.3%) or visit (46.7%)).

DISCUSSION

Clinicians acknowledged that social factors play a role in deterioration risk; but these factors are infrequently studied in HHC. While a majority of risk factors were represented in the Omaha System, additional terminologies are needed to comprehensively capture risk. Since most risk factors are documented in clinical notes, methods such as natural language processing are needed to extract them.

CONCLUSION

This study engaged clinicians to understand risk for deterioration during HHC. The results of our study support the development of an early warning system by providing a comprehensive list of risk factors grounded in clinician expertize and mapped to standardized terminologies.

Patient-centered placement matching of alcohol-dependent patients based on a standardized intake assessment: process evaluation within an exploratory randomized controlled trial

BACKGROUND

In the implementation of placement matching guidelines, feasibility has been concerned in previous research. Objectives of this process evaluation were to investigate whether the patient-centered matching guidelines (PCPM) are consistently applied in referral decision-making from an inpatient qualified withdrawal program to a level of care in aftercare, which factors affect whether patients actually receive matched aftercare according to PCPM, and whether its use is feasible and accepted by clinic staff.

METHODS

The study was conducted as process evaluation within an exploratory randomized controlled trial in four German psychiatric clinics offering a 7-to-21 day qualified withdrawal program for patients suffering from alcohol dependence, and with measurements taken during detoxification treatment and six months after the initial assessment. PCPM were used with patients in the intervention group by feeding back to them a recommendation for a level of care in aftercare that had been calculated from Measurements in the Addictions for Triage and Evaluation (MATE) and discussed with the staff on the treatment unit. As measurements, The MATE, the Client Socio-Demographic and Service Receipt Inventory-European Version, a documentation form, the Control Preference Scale, and the Motivation for Treatment Scale were administered. A workshop for the staff at the participating trial sites was conducted after data collection was finished.

RESULTS

Among 250 patients participating in the study, 165 were interviewed at follow-up, and 125 had received aftercare. Although consistency in the application of PCPM was moderate to substantial within the qualified withdrawal program (Cohen's kappa ≥ .41), it was fair from discharge to follow-up. In multifactorial multinomial regression, the number of foregoing substance abuse treatments predicted whether patients received more likely undermatched (Odds Ratio=1.27; p=.018) or overmatched (Odds Ratio=0.78; p=.054) treatment. While the implementation process during the study was evaluated critically by the staff, they stated a potential of quality assurance, more transparency and patient-centeredness in the use of PCPM.

CONCLUSIONS

While the use of PCPM has the potential to enhance the quality of referral decision making within treatment, it may not be sufficient to determine referral decisions for aftercare.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00005035 . Registered 03/06/2013.

Clinical Decision Support for Laboratory Testing

BACKGROUND

As technology enables new and increasingly complex laboratory tests, test utilization presents a growing challenge for healthcare systems. Clinical decision support (CDS) refers to digital tools that present providers with clinically relevant information and recommendations, which have been shown to improve test utilization. Nevertheless, individual CDS applications often fail, and implementation remains challenging.

CONTENT

We review common classes of CDS tools grounded in examples from the literature as well as our own institutional experience. In addition, we present a practical framework and specific recommendations for effective CDS implementation.

SUMMARY

CDS encompasses a rich set of tools that have the potential to drive significant improvements in laboratory testing, especially with respect to test utilization. Deploying CDS effectively requires thoughtful design and careful maintenance, and structured processes focused on quality improvement and change management play an important role in achieving these goals.

A thematic analysis to examine the feasibility of EHR-based clinical decision support for implementing Choosing Wisely ® guidelines

Objective

To identify important barriers and facilitators relating to the feasibility of implementing clinical practice guidelines (CPGs) as clinical decision support (CDS).

Materials and Methods

We conducted a qualitative, thematic analysis of interviews from seven interviews with dyads (one clinical expert and one systems analyst) who discussed the feasibility of implementing 10 Choosing Wisely^® guidelines at their institutions. We conducted a content analysis to extract salient themes describing facilitators, challenges, and other feasibility considerations regarding implementing CPGs as CDS.

Results

We identified five themes: concern about data quality impacts implementation planning; the availability of data in a computable format is a primary factor for implementation feasibility; customized strategies are needed to mitigate uncertainty and ambiguity when translating CPGs to an electronic health record-based tool; misalignment of expected CDS with pre-existing clinical workflows impact implementation; and individual level factors of end-users must be considered when selecting and implementing CDS tools.

Discussion

The themes reveal several considerations for CPG as CDS implementations regarding data quality, knowledge representation, and sociotechnical issues. Guideline authors should be aware that using CDS to implement CPGs is becoming increasingly popular and should consider providing clear guidelines to aid implementation. The complex nature of CPG as CDS implementation necessitates a unified effort to overcome these challenges.

Conclusion

Our analysis highlights the importance of cooperation and co-development of standards, strategies, and infrastructure to address the difficulties of implementing CPGs as CDS. The complex interactions between the concepts revealed in the interviews necessitates the need that such work should not be conducted in silos. We also implore that implementers disseminate their experiences.

The iCRF Generator: Generating interoperable electronic case report forms using online codebooks

Semantic interoperability of clinical data is essential to preserve its meaning and intent when the data is exchanged, re-used or integrated with other data. Achieving semantic operability requires the use of a communication standard, such as HL7, as well as (functional) information standards. Manually mapping clinical data to a medical thesaurus, such as SNOMED CT, is complicated and requires expert knowledge of both the dataset, including its context, and the thesaurus. As an alternative, the (re-)use of codebooks, data definitions which may already have been mapped to a thesaurus, can be a viable approach.

We’ve developed the iCRF Generator, a Java program that can generate the core of an interoperable electronic case report form (iCRF) for several of the major electronic data capture systems (EDCs). To build their CRFs, users can select one or more items from established codebooks, available from an online system called ART-DECOR. By providing an easy to use method to create CRFs for multiple EDCs based on the same codebooks, interoperability can be more easily attained.

Machine Learning and Other Emerging Decision Support Tools

Emerging applications of machine learning and artificial intelligence offer the opportunity to discover new clinical knowledge through secondary exploration of existing patient medical records. This new knowledge may in turn offer a foundation to build new types of clinical decision support (CDS) that provide patient-specific insights and guidance across a wide range of clinical questions and settings. This article will provide an overview of these emerging approaches to CDS, discussing both existing technologies as well as challenges that health systems and informaticists will need to address to allow these emerging approaches to reach their full potential.

An argument for reporting data standardization procedures in multi-site predictive modeling: case study on the impact of LOINC standardization on model performance

Objectives

We aimed to gain a better understanding of how standardization of laboratory data can impact predictive model performance in multi-site datasets. We hypothesized that standardizing local laboratory codes to logical observation identifiers names and codes (LOINC) would produce predictive models that significantly outperform those learned utilizing local laboratory codes.

Materials and Methods

We predicted 30-day hospital readmission for a set of heart failure-specific visits to 13 hospitals from 2008 to 2012. Laboratory test results were extracted and then manually cleaned and mapped to LOINC. We extracted features to summarize laboratory data for each patient and used a training dataset (2008–2011) to learn models using a variety of feature selection techniques and classifiers. We evaluated our hypothesis by comparing model performance on an independent test dataset (2012).

Results

Models that utilized LOINC performed significantly better than models that utilized local laboratory test codes, regardless of the feature selection technique and classifier approach used.

Discussion and Conclusion

We quantitatively demonstrated the positive impact of standardizing multi-site laboratory data to LOINC prior to use in predictive models. We used our findings to argue for the need for detailed reporting of data standardization procedures in predictive modeling, especially in studies leveraging multi-site datasets extracted from electronic health records.

Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database

Objective

Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes.

Materials and Methods

Across 130 sites in the Department of Veterans Affairs Corporate Data Warehouse, we selected the 150 most commonly used laboratory tests with numeric results per site from 2000 through 2016. Using source data text and numeric fields, we developed a machine learning model and manually validated random samples from both labeled and unlabeled datasets.

Results

The raw laboratory data consisted of >6.5 billion test results, with 2215 distinct LOINC codes. The model predicted the correct LOINC code in 85% of the unlabeled data and 96% of the labeled data by test frequency. In the subset of labeled data where the original and model-predicted LOINC codes disagreed, the model-predicted LOINC code was correct in 83% of the data by test frequency.

Conclusion

Using a completely automated process, we are able to assign LOINC codes to unlabeled data with high accuracy. When the model-predicted LOINC code differed from the original LOINC code, the model prediction was correct in the vast majority of cases. This scalable, automated algorithm may improve data quality and interoperability, while substantially reducing the manual effort currently needed to accurately map laboratory data.

Patient facing decision support system for interpretation of laboratory test results

Background

In some healthcare systems, it is common that patients address laboratory test centers directly without a physician’s recommendation. This practice is widely spread in Russia with about 28% of patients who visiting laboratory test centers for diagnostics. This causes an issue when patients get no help from the physician in understanding the results.

Computer decision support systems proved to efficiently solve a resource consuming task of interpretation of the test results. So, a decision support system can be implemented to rise motivation and empower the patients who visit a laboratory service without a doctor’s referral.

Methods

We have developed a clinical decision support system for patients that solves a classification task and finds a set of diagnoses for the provided laboratory tests results.

The Wilson and Lankton’s assessment model was applied to measure patients’ acceptance of the solution.

Results

A first order predicates-based decision support system has been implemented to analyze laboratory test results and deliver reports in natural language to patients. The evaluation of the system showed a high acceptance of the decision support system and of the reports that it generates.

Conclusions

Detailed notification of the laboratory service patients with elements of the decision support is significant for the laboratory data management, and for patients’ empowerment and safety.

Electronic supplementary material

The online version of this article (10.1186/s12911-018-0648-0) contains supplementary material, which is available to authorized users.

The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support

BACKGROUND

Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed. Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully.

METHODS

We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist.

RESULTS

We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy.

CONCLUSIONS

The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed.

Electronic health record with computerized decision support tools for the purposes of a pediatric cardiovascular heart disease screening program in Crete

BACKGROUND AND OBJECTIVE

Early detection of cardiovascular (CV) disease or associated risk factors during childhood is of paramount importance, allowing for early treatment or lifestyle modifications, respectively. The objective of this study was to describe the development of an electronic health record (EHR), with integrated computerized decision support system (CDSS), specifically designed for supporting the needs of a pilot pediatric CV disease screening program applied on primary school students of a Mediterranean island.

METHODS

Evidence-based knowledge, national and international practice guidelines regarding sport preparticipation CV screening of children and young athletes has been used for the design of the designated EHR. A CDSS, capable for providing alerts for further cardiology evaluation need, has been incorporated into the EHR, based on normative anthropometric and electrocardiographic data as well as predefined positive history responses.

RESULTS

We developed a designated EHR with integrated CDSS supporting pediatric CV disease screening, capable for documenting CV-related personal and family history responses, physical evaluation data (weight, height, blood pressure), allowing for entering electrocardiogam (ECG) measurements and for uploading of multimedia files (including ECG images and digital phonocardiogram audio files). The EHR incorporates clinical calculators and referral alerts for the presence (and degree) of adiposity, hypertension, ECG abnormalities and positive history responses indicative of high CV disease risk. In a preliminary EHR validation, performed by entering data from 53 previously available paper-based health records, the EHR was proven to be fully functional.

CONCLUSIONS

The pediatric cardiology EHR with CDSS features which we developed might serve as a model for EHR for primary health care purposes, capable to document and early detect CV disease and associated risk factors in pediatric populations.

DMTO: a realistic ontology for standard diabetes mellitus treatment

BACKGROUND

Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge.

RESULTS

This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients' current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology's BioPortal at http://bioportal.bioontology.org/ontologies/DMTO . The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs.

CONCLUSION

The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.

Iterative Development and Evaluation of a Pharmacogenomic-Guided Clinical Decision Support System for Warfarin Dosing

OBJECTIVE

Pharmacogenomic-guided dosing has the potential to improve patient outcomes but its implementation has been met with clinical challenges. Our objective was to develop and evaluate a clinical decision support system (CDSS) for pharmacogenomic-guided warfarin dosing designed for physicians and pharmacists.

METHODS

Twelve physicians and pharmacists completed 6 prescribing tasks using simulated patient scenarios in two iterations (development and validation phases) of a newly developed pharmacogenomic-driven CDSS prototype. For each scenario, usability was measured via efficiency, recorded as time to task completion, and participants' perceived satisfaction which were compared using Kruskal-Wallis and Mann Whitney U tests, respectively. Debrief interviews were conducted and qualitatively analyzed. Usability findings from the first (i.e. development) iteration were incorporated into the CDSS design for the second (i.e. validation) iteration.

RESULTS

During the CDSS validation iteration, participants took more time to complete tasks with a median (IQR) of 183 (124-247) seconds versus 101 (73.5-197) seconds in the development iteration (p=0.01). This increase in time on task was due to the increase in time spent in the CDSS corresponding to several design changes. Efficiency differences that were observed between pharmacists and physicians in the development iteration were eliminated in the validation iteration. The increased use of the CDSS corresponded to a greater acceptance of CDSS recommended doses in the validation iteration (4% in the first iteration vs. 37.5% in the second iteration, p<0.001). Overall satisfaction did not change statistically between the iterations but the qualitative analysis revealed greater trust in the second prototype.

CONCLUSIONS

A pharmacogenomic-guided CDSS has been developed using warfarin as the test drug. The final CDSS prototype was trusted by prescribers and significantly increased the time using the tool and acceptance of the recommended doses. This study is an important step toward incorporating pharmacogenomics into CDSS design for clinical testing.

Recommendations for antiarrhythmic drugs based on latent semantic analysis with fc-means clustering

In this paper, we propose a novel model for the appropriate recommendation of antiarrhythmic drugs by introducing a fusion of a latent semantic analysis and k-means clustering. Our model not only captures the latent factors between the types of arrhythmia and patients but also has the ability to search a group of patients with similar arrhythmias. The performance studies conducted against the MIT-BIH arrhythmia database show that clinicians accepted 66.67% of the drugs recommended from our model with a balanced f-score of 38.08%. Comparative study with previous approach also confirms the effectiveness of our model.

Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

ODMedit: uniform semantic annotation for data integration in medicine based on a public metadata repository

BACKGROUND

The volume and complexity of patient data - especially in personalised medicine - is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare.

METHODS

Semantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don't provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository.

RESULTS

A web-based tool called ODMedit ( https://odmeditor.uni-muenster.de/ ) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal.

CONCLUSION

Uniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository.

Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework

Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.

Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems

BACKGROUND AND OBJECTIVES

The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications.

METHODS

A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data.

RESULTS

The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders.

CONCLUSIONS

The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach.

Reducing diagnostic errors in primary care. A systematic meta-review of computerized diagnostic decision support systems by the LINNEAUS collaboration on patient safety in primary care

BACKGROUND

Computerized diagnostic decision support systems (CDDSS) have the potential to support the cognitive task of diagnosis, which is one of the areas where general practitioners have greatest difficulty and which accounts for a significant proportion of adverse events recorded in the primary care setting.

OBJECTIVE

To determine the extent to which CDDSS may meet the requirements of supporting the cognitive task of diagnosis, and the currently perceived barriers that prevent the integration of CDDSS with electronic health record (EHR) systems.

METHODS

We conducted a meta-review of existing systematic reviews published in English, searching MEDLINE, Embase, PsycINFO and Web of Knowledge for articles on the features and effectiveness of CDDSS for medical diagnosis published since 2004. Eligibility criteria included systematic reviews where individual clinicians were primary end users. Outcomes we were interested in were the effectiveness and identification of specific features of CDDSS on diagnostic performance.

RESULTS

We identified 1970 studies and excluded 1938 because they did not fit our inclusion criteria. A total of 45 articles were identified and 12 were found suitable for meta-review. Extraction of high-level requirements identified that a more standardized computable approach is needed to knowledge representation, one that can be readily updated as new knowledge is gained. In addition, a deep integration with the EHR is needed in order to trigger at appropriate points in cognitive workflow.

CONCLUSION

Developing a CDDSS that is able to utilize dynamic vocabulary tools to quickly capture and code relevant diagnostic findings, and coupling these with individualized diagnostic suggestions based on the best-available evidence has the potential to improve diagnostic accuracy, but requires evaluation.

Developing a Web-Based Nursing Practice and Research Information Management System: A Pilot Study

Many hospital information systems have been developed and implemented to collect clinical data from the bedside and have used the information to improve patient care. Because of a growing awareness that the use of clinical information improves quality of care and patient outcomes, measuring tools (electronic and paper based) have been developed, but most of them require multiple steps of data collection and analysis. This necessitated the development of a Web-based Nursing Practice and Research Information Management System that processes clinical nursing data to measure nurses' delivery of care and its impact on patient outcomes and provides useful information to clinicians, administrators, researchers, and policy makers at the point of care. This pilot study developed a computer algorithm based on a falls prevention protocol and programmed the prototype Web-based Nursing Practice and Research Information Management System. It successfully measured performance of nursing care delivered and its impact on patient outcomes successfully using clinical nursing data from the study site. Although Nursing Practice and Research Information Management System was tested with small data sets, results of study revealed that it has the potential to measure nurses' delivery of care and its impact on patient outcomes, while pinpointing components of nursing process in need of improvement.

A Multistep Maturity Model for the Implementation of Electronic and Computable Diagnostic Clinical Prediction Rules (eCPRs)

Introduction:

The use of Clinical Prediction Rules (CPRs) has been advocated as one way of implementing actionable evidence-based rules in clinical practice. The current highly manual nature of deriving CPRs makes them difficult to use and maintain. Addressing the known limitations of CPRs requires implementing more flexible and dynamic models of CPR development. We describe the application of Information and Communication Technology (ICT) to provide a platform for the derivation and dissemination of CPRs derived through analysis and continual learning from electronic patient data.

Model Components:

We propose a multistep maturity model for constructing electronic and computable CPRs (eCPRs). The model has six levels – from the lowest level of CPR maturity (literaturebased CPRs) to a fully electronic and computable service-oriented model of CPRs that are sensitive to specific demographic patient populations. We describe examples of implementations of the core model components – focusing on CPR representation, interoperability, electronic dissemination, CPR learning, and user interface requirements.

Conclusion:

The traditional focus on derivation and narrow validation of CPRs has severely limited their wider acceptance. The evolution and maturity model described here outlines a progression toward eCPRs consistent with the vision of a learning health system (LHS) – using central repositories of CPR knowledge, accessible open standards, and generalizable models to avoid repetition of previous work. This is useful for developing more ambitious strategies to address limitations of the traditional CPR development life cycle. The model described here is a starting point for promoting discussion about what a more dynamic CPR development process should look like.

Data visualization for truth maintenance in clinical decision support systems

BACKGROUND AND OBJECTIVES

The goal is to inform proactive initiatives to expand the knowledge base of clinical decision support systems.

DESIGN AND SETTING

We describe an initiative in which research informationists and health services researchers employ visualization tools to map logic models for clinical decision support within an electronic health record.

MATERIALS AND METHODS

We mapped relationships using software for social network analysis: NodeXL and CMAP. We defined relationships by shared observations, such as two Arden rules within medical logic modules that consider the same clinical observation, or by the presence of common keywords that were used to label rules according to standardized vocabularies.

RESULTS

We studied the Child Health Improvement through Computer Automation (CHICA) system, an electronic medical record that contains 170 unique variables representing discrete clinical observations. These variables were used in 300 medical logic modules (MLM's) that prompted health care providers to deliver preventive counseling or otherwise served as clinical decision support. Using data visualization tools, we generated maps that illustrate connections, or lack thereof, between clinical topics within CHICA's MLMs.

CONCLUSIONS

The development of such maps may allow multiple disciplines commonly interacting over EMR platforms, and various perspectives (clinicians, programmers, informationists) to work more effectively as teams to refine the EMR by programming logic routines to address co-morbidities or other instances where domains of medical knowledge should be connected.

Development and Implementation of the Clinical Decision Support System for Patients With Cancer and Nurses' Experiences Regarding the System

PURPOSE

The purpose of this study was to develop and implement the clinical decision support system (CDSS) for oncology nurses in the care of patients with cancer and to explore the nurses' experiences about the system.

METHODS

The study was conducted using a mixed-methods research design with 14 nurses working at a gynecological oncology clinic at a university hospital in Turkey.

FINDINGS

The nurses stated that they did not experience any problems during the implementation of the CDSS, and its usage facilitated the assessment of patients' needs and care management.

CONCLUSIONS

The results indicated that the CDSS supported the nurses' decision-making process about patients' needs and preparation of individual care plans.

PRACTICE IMPLICATIONS

The CDSS should be developed and implemented by the nurses working with patients with cancer. AMAÇ: Amaç kanser hastalarının bakımına yönelik klinik karar destek sistemi oluşturmak, uygulamak (KKDS) ve sistemi kullanan hemşirelerin deneyimlerini incelemektir. YÖNTEM: Çalışma kalitatif ve kantitatif araştırma yöntemleri kullanılarak Türkiyede'ki bir üniversite hastanesinin jinekolojik onkoloji servisinde çalışan 14 hemşire ile yürütülmüştür.

BULGULAR

Hemşireler KKDS'ni kullanırken herhangi bir sorun yaşamadıklarını ve sistemin hasta gereksinimlerini değerlendirmeyi ve bakım yönetimini kolaylaştırdığını belirtmişlerdir. SONUÇ: Bulgular hastanın gereksinimlerine karar verme sürecinde ve bireysel bakım planları hazırlamada KKDS'nin hemşireleri desteklediğini göstermektedir. HEMŞIRELIK UYGULAMALARI IÇIN ÖNERILER: Kanserli hastaların bakımına yönelik KKDS geliştirilebilir ve hemşireler tarafından klinikte kullanılabilir.

Evaluation methods used on health information systems (HISs) in Iran and the effects of HISs on Iranian healthcare: a systematic review

OBJECTIVES

The most important goal of a health information system (HIS) is improvement of quality, effectiveness and efficiency of health services. To achieve this goal, health care systems should be evaluated continuously. The aim of this paper was to study the impacts of HISs in Iran and the methods used for their evaluation.

METHODS

We systematically searched all English and Persian papers evaluating health information systems in Iran that were indexed in SID, Magiran, Iran medex, PubMed and Embase databases until June 2013. A data collection form was designed to extract required data such as types of systems evaluated, evaluation methods and tools.

RESULTS

In this study, 53 out of 1103 retrieved articles were selected as relevant and reviewed by the authors. This study indicated that 28 studies used questionnaires to evaluate the system and in 27 studies the study instruments were distributed within a research population. In 26 papers the researchers collected the information by means of interviews, observations, heuristic evaluation and the review of documents and records. The main effects of the evaluated systems in health care settings were improving quality of services, reducing time, increasing accessibility to information, reducing costs and decreasing medical errors.

CONCLUSION

Evaluation of health information systems is central to their development and enhancement, and to understanding their effect on health and health services. Despite numerous evaluation methods available, the reviewed studies used a limited number of methods to evaluate HIS. Additionally, the studies mainly discussed the positive effects of HIS on health care services.

Investigation of Data Representation Issues in Computerizing Clinical Practice Guidelines in China

Objectives

Methods

Results

Conclusions

Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data

BACKGROUND

The necessity to translate eligibility criteria from free text into decision rules that are compatible with data from the electronic health record (EHR) constitutes the main challenge when developing and deploying clinical trial recruitment support systems. Recruitment decisions based on case-based reasoning, i.e. using past cases rather than explicit rules, could dispense with the need for translating eligibility criteria and could also be implemented largely independently from the terminology of the EHR's database. We evaluated the feasibility of predictive modeling to assess the eligibility of patients for clinical trials and report on a prototype's performance for different system configurations.

METHODS

The prototype worked by using existing basic patient data of manually assessed eligible and ineligible patients to induce prediction models. Performance was measured retrospectively for three clinical trials by plotting receiver operating characteristic curves and comparing the area under the curve (ROC-AUC) for different prediction algorithms, different sizes of the learning set and different numbers and aggregation levels of the patient attributes.

RESULTS

Random forests were generally among the best performing models with a maximum ROC-AUC of 0.81 (CI: 0.72-0.88) for trial A, 0.96 (CI: 0.95-0.97) for trial B and 0.99 (CI: 0.98-0.99) for trial C. The full potential of this algorithm was reached after learning from approximately 200 manually screened patients (eligible and ineligible). Neither block- nor category-level aggregation of diagnosis and procedure codes influenced the algorithms' performance substantially.

CONCLUSIONS

Our results indicate that predictive modeling is a feasible approach to support patient recruitment into clinical trials. Its major advantages over the commonly applied rule-based systems are its independency from the concrete representation of eligibility criteria and EHR data and its potential for automation.

FindZebra: a search engine for rare diseases

BACKGROUND

The web has become a primary information resource about illnesses and treatments for both medical and non-medical users. Standard web search is by far the most common interface to this information. It is therefore of interest to find out how well web search engines work for diagnostic queries and what factors contribute to successes and failures. Among diseases, rare (or orphan) diseases represent an especially challenging and thus interesting class to diagnose as each is rare, diverse in symptoms and usually has scattered resources associated with it.

METHODS

We design an evaluation approach for web search engines for rare disease diagnosis which includes 56 real life diagnostic cases, performance measures, information resources and guidelines for customising Google Search to this task. In addition, we introduce FindZebra, a specialized (vertical) rare disease search engine. FindZebra is powered by open source search technology and uses curated freely available online medical information.

RESULTS

FindZebra outperforms Google Search in both default set-up and customised to the resources used by FindZebra. We extend FindZebra with specialized functionalities exploiting medical ontological information and UMLS medical concepts to demonstrate different ways of displaying the retrieved results to medical experts.

CONCLUSIONS

Our results indicate that a specialized search engine can improve the diagnostic quality without compromising the ease of use of the currently widely popular standard web search. The proposed evaluation approach can be valuable for future development and benchmarking. The FindZebra search engine is available at http://www.findzebra.com/.

Description and comparison of quality of electronic versus paper-based resident admission forms in Australian aged care facilities

PURPOSE

To describe the paper-based and electronic formats of resident admission forms used in several aged care facilities in Australia and to compare the extent to which resident admission information was documented in paper-based and the electronic health records.

METHODS

Retrospective auditing and comparison of the documentation quality of paper-based and electronic resident admission forms were conducted. A checklist of admission data was qualitatively derived from different formats of the admission forms collected. Three measures were used to assess the quality of documentation of the admission forms, including completeness rate, comprehensiveness rate and frequency of documented data element. The associations between the number of items and their completeness and comprehensiveness rates were estimated at a general level and at each information category level.

RESULTS

Various paper-based and electronic formats of admission forms were collected, reflecting varying practice among the participant facilities. The overall completeness and comprehensiveness rates of the admission forms were poor, but were higher in the electronic health records than in the paper-based records (60% versus 56% and 40% versus 29% respectively, p<0.01). There were differences in the overall completeness and comprehensiveness rates between the different formats of admission forms (p<0.01). At each information category level, varying degrees of difference in the completeness and comprehensiveness rates were found between different form formats and between the paper-based and the electronic records. A negative association between the completeness rate and the number of items in a form was found at each information category level (p<0.01), i.e., more data items designed in a form, the less likely that the items would be completely filled. However, the associations between the comprehensiveness rates and the number of items were highly positive at both overall and individual information category levels (p<0.01), suggesting more items designed in a form, more information would be captured.

CONCLUSION

Better quality of documentation in resident admission forms was identified in the electronic documentation systems than in previous paper-based systems, but still needs to be further improved in practice. The quality of documentation of resident admission data should be further analysed in relation to its specific content.

Auditing consistency and usefulness of LOINC use among three large institutions - using version spaces for grouping LOINC codes

OBJECTIVES

We wanted to develop a method for evaluating the consistency and usefulness of LOINC code use across different institutions, and to evaluate the degree of interoperability that can be attained when using LOINC codes for laboratory data exchange. Our specific goals were to: (1) Determine if any contradictory knowledge exists in LOINC. (2) Determine how many LOINC codes were used in a truly interoperable fashion between systems. (3) Provide suggestions for improving the semantic interoperability of LOINC.

METHODS

We collected Extensional Definitions (EDs) of LOINC usage from three institutions. The version space approach was used to divide LOINC codes into small sets, which made auditing of LOINC use across the institutions feasible. We then compared pairings of LOINC codes from the three institutions for consistency and usefulness.

RESULTS

The number of LOINC codes evaluated were 1917, 1267 and 1693 as obtained from ARUP, Intermountain and Regenstrief respectively. There were 2022, 2030, and 2301 version spaces among ARUP and Intermountain, Intermountain and Regenstrief and ARUP and Regenstrief respectively. Using the EDs as the gold standard, there were 104, 109 and 112 pairs containing contradictory knowledge and there were 1165, 765 and 1121 semantically interoperable pairs. The interoperable pairs were classified into three levels: (1) Level I - No loss of meaning, complete information was exchanged by identical codes. (2) Level II - No loss of meaning, but processing of data was needed to make the data completely comparable. (3) Level III - Some loss of meaning. For example, tests with a specific 'method' could be rolled-up with tests that were 'methodless'.

CONCLUSIONS

There are variations in the way LOINC is used for data exchange that result in some data not being truly interoperable across different enterprises. To improve its semantic interoperability, we need to detect and correct any contradictory knowledge within LOINC and add computable relationships that can be used for making reliable inferences about the data. The LOINC committee should also provide detailed guidance on best practices for mapping from local codes to LOINC codes and for using LOINC codes in data exchange.

Mapping local laboratory interface terms to LOINC at a German university hospital using RELMA V.5: a semi-automated approach

OBJECTIVE

Logical Observation Identifiers Names and Codes (LOINC) mapping of laboratory data is often a question of the effort of mapping compared with the benefits of the structure achieved. The new LOINC mapping assistant RELMA (version 2011) has the potential to reduce the effort required for semi-automated mapping. We examined quality, time effort, and sustainability of such mapping.

METHODS

To verify the mapping quality, two samples of 100 laboratory terms were extracted from the laboratory system of a German university hospital and processed in a semi-automated fashion with RELMA V.5 and LOINC V.2.34 German translation DIMDI to obtain LOINC codes. These codes were reviewed by two experts from each of two laboratories. Then all 2148 terms used in these two laboratories were processed in the same way.

RESULTS

In the initial samples, 93 terms from one laboratory system and 92 terms from the other were correctly mapped. Of the total 2148 terms, 1660 could be mapped. An average of 500 terms per day or 60 terms per hour could be mapped. Of the laboratory terms used in 2010, 99% could be mapped.

DISCUSSION

Semi-automated LOINC mapping of non-English laboratory terms has become promising in terms of effort and mapping quality using the new version RELMA V.5. The effort is probably lower than for previous manual mapping. The mapping quality equals that of manual mapping and is far better than that reported with previous automated mapping activities.

CONCLUSION

RELMA V.5 and LOINC V.2.34 offer the opportunity to start thinking again about LOINC mapping even in non-English languages, since mapping effort is acceptable and mapping results equal those of previous manual mapping reports.

Implementing Black Box Warnings (BBWs) in Health Information Systems: An Organizing Taxonomy Identifying Opportunities and Challenges

OBJECTIVE

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).

Development and implementation of an advanced coronary angiography and intervention database system

The 'evidence' in evidence-based medicine (EBM) is often limited to knowledge obtained from randomized controlled clinical trials (RCT). Most RCTs, however, have strict enrollment criteria which make patient background characteristics and clinical histories significantly different from those encountered in actual practice. Thus it is important to accumulate and analyze data obtained in daily practice to gain insight into a larger clinical picture. Recent developments in information technology and its lowered cost have enabled us to record clinical activity in much greater detail at a lower cost. These factors prompted us to design and develop a coronary angiography and intervention reporting system (CAIRS) to collect data and analyze outcomes of coronary intervention. The resulting advanced CAIRS can record detailed data on coronary angiographic and interventional procedures.To date, data on 10,025 cases of coronary angiography, of which 3,574 were interventional, have been collected over a 5.5 year period. There were 4,343 unique patients, 3,115 (71.7%) of which were male. The overall mean age was 67.0 ± 11.5. The mean age of males was 66.3 ± 11.4 and that of females was 69.0 ± 11.4. About one-third of the patients never underwent a PCI procedure at our institution. For patients that underwent at least one PCI procedure at our institution, the prescription rate of statin increased from 50.8% in 2005 to 80.3% in 2011, while those of nitrate and ticlopidine decreased from 36.7% and 90.8% in 2005 to 21.3% and 0.8% in 2011, respectively. We have also implemented the same system at another institution and compared the data on stent usage between the two institutions, which revealed vastly different stent usage profiles.In conclusion, we have successfully developed and implemented an advanced coronary angiography and intervention reporting system which we call CAIRS. Implementing the same system at multiple institutions and analyzing data collected from several institutions will provide detailed and timely insight into the 'real world' of coronary angiography and interventional procedures and their outcome.

Enabling international adoption of LOINC through translation

Interoperable health information exchange depends on adoption of terminology standards, but international use of such standards can be challenging because of language differences between local concept names and the standard terminology. To address this important barrier, we describe the evolution of an efficient process for constructing translations of LOINC terms names, the foreign language functions in RELMA, and the current state of translations in LOINC. We also present the development of the Italian translation to illustrate how translation is enabling adoption in international contexts. We built a tool that finds the unique list of LOINC Parts that make up a given set of LOINC terms. This list enables translation of smaller pieces like the core component "hepatitis c virus" separately from all the suffixes that could appear with it, such "Ab.IgG", "DNA", and "RNA". We built another tool that generates a translation of a full LOINC name from all of these atomic pieces. As of version 2.36 (June 2011), LOINC terms have been translated into nine languages from 15 linguistic variants other than its native English. The five largest linguistic variants have all used the Part-based translation mechanism. However, even with efficient tools and processes, translation of standard terminology is a complex undertaking. Two of the prominent linguistic challenges that translators have faced include: the approach to handling acronyms and abbreviations, and the differences in linguistic syntax (e.g. word order) between languages. LOINC's open and customizable approach has enabled many different groups to create translations that met their needs and matched their resources. Distributing the standard and its many language translations at no cost worldwide accelerates LOINC adoption globally, and is an important enabler of interoperable health information exchange.

An approach to improve LOINC mapping through augmentation of local test names

Mapping medical test names into a standardized vocabulary is a prerequisite to sharing test-related data between health care entities. One major barrier in this process is the inability to describe tests in sufficient detail to assign the appropriate name in Logical Observation Identifiers, Names, and Codes (LOINC®). Approaches to address mapping of test names with incomplete information have not been well described. We developed a process of "enhancing" local test names by incorporating information required for LOINC mapping into the test names themselves. When using the Regenstrief LOINC Mapping Assistant (RELMA) we found that 73/198 (37%) of "enhanced" test names were successfully mapped to LOINC, compared to 41/191 (21%) of original names (p=0.001). Our approach led to a significantly higher proportion of test names with successful mapping to LOINC, but further efforts are required to achieve more satisfactory results.