Participatory Design of an Electronic Cross-Facility Health Record (ECHR) System for Pediatric Palliative Care: A Think-Aloud Study

Background: Pediatric palliative care (PPC) patients experience years of multisectoral and professional care. An electronic cross-facility health record (ECHR) system can support the immediate exchange of information among PPC professionals. Based on a needs assessment, a prototype ECHR system was developed. Methods: To evaluate potential users’ perspective regarding the system, a qualitative observational study was conducted consisting of a concurrent think-aloud session and a semi-structured qualitative interview. Results: Twenty PPC professionals (nurses, physicians) from specialized outpatient PPC teams, a PPC unit, and medical offices rated the ECHR system as a helpful tool to improve the exchange and collection of information, communication between PPC professionals, and treatment planning. From the user’s point of view, the basic logic of the ECHR system should be further adapted to improve the interaction of data remirrored from patient records of outpatient and inpatient care with those entered via the system. The users wished for further functions (text search) and content (information on therapies). Some content, such as the treatment process, needs to be further adapted. Conclusion: The developed ECHR system needs to be more specific in some features by offering all available information; while for other features, be less specific to offer a quick overview. The ability to share information promptly and automatically was seen as a tremendous improvement to the quality of care for PPC patients.

An ontology-based approach for developing a harmonised data-validation tool for European cancer registration

Background

Population-based cancer registries constitute an important information source in cancer epidemiology. Studies collating and comparing data across regional and national boundaries have proved important for deploying and evaluating effective cancer-control strategies. A critical aspect in correctly comparing cancer indicators across regional and national boundaries lies in ensuring a good and harmonised level of data quality, which is a primary motivator for a centralised collection of pseudonymised data. The recent introduction of the European Union’s general data-protection regulation (GDPR) imposes stricter conditions on the collection, processing, and sharing of personal data. It also considers pseudonymised data as personal data. The new regulation motivates the need to find solutions that allow a continuation of the smooth processes leading to harmonised European cancer-registry data. One element in this regard would be the availability of a data-validation software tool based on a formalised depiction of the harmonised data-validation rules, allowing an eventual devolution of the data-validation process to the local level.

Results

A semantic data model was derived from the data-validation rules for harmonising cancer-data variables at European level. The data model was encapsulated in an ontology developed using the Web-Ontology Language (OWL) with the data-model entities forming the main OWL classes. The data-validation rules were added as axioms in the ontology. The reasoning function of the resulting ontology demonstrated its ability to trap registry-coding errors and in some instances to be able to correct errors.

Conclusions

Describing the European cancer-registry core data set in terms of an OWL ontology affords a tool based on a formalised set of axioms for validating a cancer-registry’s data set according to harmonised, supra-national rules. The fact that the data checks are inherently linked to the data model would lead to less maintenance overheads and also allow automatic versioning synchronisation, important for distributed data-quality checking processes.

More Guidelines than states: variations in U.S. lead screening and management guidance and impacts on shareable CDS development

BACKGROUND

Pediatric lead exposure in the United States (U.S.) remains a preventable public health crisis. Shareable electronic clinical decision support (CDS) could improve lead screening and management. However, discrepancies between federal, state and local recommendations could present significant challenges for implementation.

METHODS

We identified publically available guidance on lead screening and management. We extracted definitions for elevated lead and recommendations for screening, follow-up, reporting, and management. We compared thresholds and level of obligation for management actions. Finally, we assessed the feasibility of development of shareable CDS.

RESULTS

We identified 54 guidance sources. States offered different definitions of elevated lead, and recommendations for screening, reporting, follow-up and management. Only 37 of 48 states providing guidance used the Center for Disease Control (CDC) definition for elevated lead. There were 17 distinct management actions. Guidance sources indicated an average of 5.5 management actions, but offered different criteria and levels of obligation for these actions. Despite differences, the recommendations were well-structured, actionable, and encodable, indicating shareable CDS is feasible.

CONCLUSION

Current variability across guidance poses challenges for clinicians. Developing shareable CDS is feasible and could improve pediatric lead screening and management. Shareable CDS would need to account for local variability in guidance.

A novel tool for standardizing clinical data in a semantically rich model

Standardizing clinical information in a semantically rich data model is useful for promoting interoperability and facilitating high quality research. Semantic Web technologies such as Resource Description Framework can be utilized to their full potential when a model accurately reflects the semantics of the clinical situation it describes. To this end, ontologies that abide by sound organizational principles can be used as the building blocks of a semantically rich model for the storage of clinical data. However, it is a challenge to programmatically define such a model and load data from disparate sources. The PennTURBO Semantic Engine is a tool developed at the University of Pennsylvania that transforms concise RDF data into a source-independent, semantically rich model. This system sources classes from an application ontology and specifically defines how instances of those classes may relate to each other. Additionally, the system defines and executes RDF data transformations by launching dynamically generated SPARQL update statements. The Semantic Engine was designed as a generalizable data standardization tool, and is able to work with various data models and incoming data sources. Its human-readable configuration files can easily be shared between institutions, providing the basis for collaboration on a standard data model.

Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification

BACKGROUND

Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records. Knowledge of the frequent lexical variations of these numerical concepts, and their accurate identification, is important for many information extraction tasks. This paper describes an analysis of the variation in how numbers and numerical concepts are represented in clinical notes.

METHODS

We used an inverted index of approximately 100 million notes to obtain the frequency of various permutations of numbers and numerical concepts, including the use of Roman numerals, numbers spelled as English words, and invalid dates, among others. Overall, twelve types of lexical variants were analyzed.

RESULTS

We found substantial variation in how these concepts were represented in the notes, including multiple data quality issues. We also demonstrate that not considering these variations could have substantial real-world implications for cohort identification tasks, with one case missing > 80% of potential patients.

CONCLUSIONS

Numbering within clinical notes can be variable, and not taking these variations into account could result in missing or inaccurate information for natural language processing and information retrieval tasks.

The Emergence of Openness in Open-Source Projects: The Case of OpenEhR

The meaning of openness in open source is both intrinsically unstable and dynamic, and tends to fluctuate with time and context. We draw on a very particular open-source project primarily concerned with building rigorous clinical concepts to be used in electronic health records called openEHR. openEHR explains how openness is a concept that is purposely engaged with, and how, in this process of engagement, the very meaning of open matures and evolves within the project. Drawing on rich longitudinal data related to openEHR we theorise the evolving nature of openness and how this idea emerges through two intertwined processes of maturation and metamorphosis. While metamorphosis allows us to trace and interrogate the mutational evolution in openness, maturation analyses the small, careful changes crafted to build a very particular understanding of openness. Metamorphosis is less managed and controlled, whereas maturation is representative of highly precise work carried out in controlled form. Both processes work together in open-source projects and reinforce each other. Our study reveals that openness emerges and evolves in open-source projects where it can be understood to mean rigour; ability to participate; open implementation; and an open process. Our work contributes to a deepening in the theorisation of what it means to be an open-source project. The multiple and co-existing meanings of ‘open’ imply that open-source projects evolve in nonlinear ways where each critical meaning of openness causes a reflective questioning by the community of its continued status and existence.

TrhOnt: building an ontology to assist rehabilitation processes

Background

One of the current research efforts in the area of biomedicine is the representation of knowledge in a structured way so that reasoning can be performed on it. More precisely, in the field of physiotherapy, information such as the physiotherapy record of a patient or treatment protocols for specific disorders must be adequately modeled, because they play a relevant role in the management of the evolutionary recovery process of a patient. In this scenario, we introduce TrhOnt, an application ontology that can assist physiotherapists in the management of the patients’ evolution via reasoning supported by semantic technology.

Methods

The ontology was developed following the NeOn Methodology. It integrates knowledge from ontological (e.g. FMA ontology) and non-ontological resources (e.g. a database of movements, exercises and treatment protocols) as well as additional physiotherapy-related knowledge.

Results

We demonstrate how the ontology fulfills the purpose of providing a reference model for the representation of the physiotherapy-related information that is needed for the whole physiotherapy treatment of patients, since they step for the first time into the physiotherapist’s office, until they are discharged. More specifically, we present the results for each of the intended uses of the ontology listed in the document that specifies its requirements, and show how TrhOnt can answer the competency questions defined within that document. Moreover, we detail the main steps of the process followed to build the TrhOnt ontology in order to facilitate its reproducibility in a similar context. Finally, we show an evaluation of the ontology from different perspectives.

Conclusions

TrhOnt has achieved the purpose of allowing for a reasoning process that changes over time according to the patient’s state and performance.

The influence of system quality characteristics on health care providers' performance: Empirical evidence from Malaysia

BACKGROUND

The Ministry of Health Malaysia initiated the total hospital information system (THIS) as the first national electronic health record system for use in selected public hospitals across the country. Since its implementation 15 years ago, there has been the critical requirement for a systematic evaluation to assess its effectiveness in coping with the current system, task complexity, and rapid technological changes. The study aims to assess system quality factors to predict the performance of electronic health in a single public hospital in Malaysia.

METHODS

Non-probability sampling was employed for data collection among selected providers in a single hospital for two months. Data cleaning and bias checking were performed before final analysis in partial least squares-structural equation modeling.

RESULTS AND CONCLUSIONS

Convergent and discriminant validity assessments were satisfied the required criterions in the reflective measurement model. The structural model output revealed that the proposed adequate infrastructure, system interoperability, security control, and system compatibility were the significant predictors, where system compatibility became the most critical characteristic to influence an individual health care provider's performance. The previous DeLone and McLean information system success models should be extended to incorporate these technological factors in the medical system research domain to examine the effectiveness of modern electronic health record systems. In this study, care providers' performance was expected when the system usage fits with patients' needs that eventually increased their productivity.

Semantic processing of EHR data for clinical research

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.

A Generic Data Harmonization Process for Cross-linked Research and Network Interaction. Construction and Application for the Lung Cancer Phenotype Database of the German Center for Lung Research

OBJECTIVE

Joint data analysis is a key requirement in medical research networks. Data are available in heterogeneous formats at each network partner and their harmonization is often rather complex. The objective of our paper is to provide a generic approach for the harmonization process in research networks. We applied the process when harmonizing data from three sites for the Lung Cancer Phenotype Database within the German Center for Lung Research.

METHODS

We developed a spreadsheet-based solution as tool to support the harmonization process for lung cancer data and a data integration procedure based on Talend Open Studio.

RESULTS

The harmonization process consists of eight steps describing a systematic approach for defining and reviewing source data elements and standardizing common data elements. The steps for defining common data elements and harmonizing them with local data definitions are repeated until consensus is reached. Application of this process for building the phenotype database led to a common basic data set on lung cancer with 285 structured parameters. The Lung Cancer Phenotype Database was realized as an i2b2 research data warehouse.

CONCLUSION

Data harmonization is a challenging task requiring informatics skills as well as domain knowledge. Our approach facilitates data harmonization by providing guidance through a uniform process that can be applied in a wide range of projects.

Designing an architecture for monitoring patients at home: ontologies and web services for clinical and technical management integration

This paper presents the design and implementation of an architecture based on the combination of ontologies, rules, web services, and the autonomic computing paradigm to manage data in home-based telemonitoring scenarios. The architecture includes two layers: 1) a conceptual layer and 2) a data and communication layer. On the one hand, the conceptual layer based on ontologies is proposed to unify the management procedure and integrate incoming data from all the sources involved in the telemonitoring process. On the other hand, the data and communication layer based on REST web service (WS) technologies is proposed to provide practical backup to the use of the ontology, to provide a real implementation of the tasks it describes and thus to provide a means of exchanging data (support communication tasks). A case study regarding chronic obstructive pulmonary disease data management is presented in order to evaluate the efficiency of the architecture. This proposed ontology-based solution defines a flexible and scalable architecture in order to address main challenges presented in home-based telemonitoring scenarios and thus provide a means to integrate, unify, and transfer data supporting both clinical and technical management tasks.

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.