Development, implementation and evaluation of an information model for archetype based user responsive medical data visualization

Kopanitsa, Georgy; Veseli, Hasan; Yampolsky, Vladimir

doi:10.1016/j.jbi.2015.04.009

Cited by 20 publications

(14 citation statements)

References 34 publications

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“…By keeping vital signs registered under specified keywords and consistent data formats the data will be easier to retrieve and connect between systems. Preferably these keywords should be based on reference EHR models and connected to standardized reference terminology and coding systems [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

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How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments

Skyttberg

Vicente

Chen

et al. 2016

BMC Med Inform Decis Mak

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BackgroundVital sign data are important for clinical decision making in emergency care. Clinical Decision Support Systems (CDSS) have been advocated to increase patient safety and quality of care. However, the efficiency of CDSS depends on the quality of the underlying vital sign data. Therefore, possible factors affecting vital sign data quality need to be understood.This study aims to explore the factors affecting vital sign data quality in Swedish emergency departments and to determine in how far clinicians perceive vital sign data to be fit for use in clinical decision support systems. A further aim of the study is to provide recommendations on how to improve vital sign data quality in emergency departments.MethodsSemi-structured interviews were conducted with sixteen physicians and nurses from nine hospitals and vital sign documentation templates were collected and analysed. Follow-up interviews and process observations were done at three of the hospitals to verify the results. Content analysis with constant comparison of the data was used to analyse and categorize the collected data.ResultsFactors related to care process and information technology were perceived to affect vital sign data quality. Despite electronic health records (EHRs) being available in all hospitals, these were not always used for vital sign documentation. Only four out of nine sites had a completely digitalized vital sign documentation flow and paper-based triage records were perceived to provide a better mobile workflow support than EHRs. Observed documentation practices resulted in low currency, completeness, and interoperability of the vital signs. To improve vital sign data quality, we propose to standardize the care process, improve the digital documentation support, provide workflow support, ensure interoperability and perform quality control.ConclusionsVital sign data quality in Swedish emergency departments is currently not fit for use by CDSS. To address both technical and organisational challenges, we propose five steps for vital sign data quality improvement to be implemented in emergency care settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0305-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

“…Interoperability with medical devices is considered as a way of increasing complete, current and connectable registrations [ 45 , 46 ]. If medical devices are able to deliver vital signs into the EHR the delay of entry may decrease thereby increasing the currency.…”

Section: Discussionmentioning

confidence: 99%

How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments

Skyttberg

Vicente

Chen

et al. 2016

BMC Med Inform Decis Mak

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show abstract

“…This study has the potential to inform the growing domain of research in integrating visualizations into informatics solutions that support personalized patient care [ 45 , 62 - 71 ]. This includes work on integrating home monitoring data for individuals engaging in cancer care [ 27 ] as well as health-related quality of life data for prostate cancer care [ 72 ].…”

Section: Discussionmentioning

confidence: 99%

“…For visual encoding and interaction design , mock-up encodings and designs were guided by literature on (1) visualizing data [ 14 - 17 , 35 - 44 ]; (2) health data visualization [ 23 , 45 - 47 ]; and (3) a mock patient persona and scenario [ 48 , 49 ]. The mock patient was a 56-year-old woman with uterine cancer and type 1 diabetes.…”

Section: Methodsmentioning

confidence: 99%

Data Visualizations to Support Health Practitioners’ Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study

2018

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BackgroundThere exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care. Data visualization has the potential to address this challenge and support personalized cancer care.ObjectiveThe aim of the study was to assess the health care practitioners’ perceptions of and feedback regarding visualizations developed to support the care of individuals with multiple chronic conditions engaging in cancer care.MethodsMedical doctors (n=4) and registered nurses (n=4) providing cancer care at an academic medical center in the western United States provided feedback on visualization mock-ups. Mock-up designs were guided by current health informatics and visualization literature and the Munzner Nested Model for Visualization Design. User-centered design methods, a mock patient persona, and a scenario were used to elicit insights from participants. Directed content analysis was used to identify themes from session transcripts. Means and SDs were calculated for health care practitioners’ rankings of overview visualizations.ResultsThemes identified were data elements, supportive elements, confusing elements, interpretation, and use of visualization. Overall, participants found the visualizations useful and with the potential to provide personalized care. Use of color, reference lines, and familiar visual presentations (calendars, line graphs) were noted as helpful in interpreting data.ConclusionsVisualizations guided by a framework and literature can support health care practitioners’ understanding of data for individuals with multiple chronic conditions engaged in cancer care. This understanding has the potential to support the provision of personalized care.

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“…These clinics use the same HIS, which facilitate the mapping project. HIS Avrora is a complex archetype based hospital information system that automates most of the clinical processes from appointments to billing [18,19]. It can natively process XML definitions of ISO 13606 archetypes, so after the mapping has been completed the LOINC codes were added to the ontology sections of the archetypes.…”

Section: Methodsmentioning

confidence: 99%

Application of a Regenstrief RELMA V.6.6 to Map Russian Laboratory Terms to LOINC

Kopanitsa¹

2016

Methods Inf Med

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SummaryBackground: Manual mapping of laboratory data to Logical Observation Identifiers Names and Codes (LOINC) requires a major effort. Application of the LOINC mapping assistant RELMA V.6.6 can reduce the effort required for mapping. The goal of the paper is to perform a semi-automated mapping of Russian laboratory terms to LOINC. Methods: A semi-automated mapping of the 2563 terms from two clinics in Russia was performed. The first step was automatic mapping using RELMA V.6.6 and LOINC V.2.48 Russian translation by Yaroslavl state medical academy. The second step was a manual expert mapping. To evaluate the correctness of mapping all the mapped terms were reviewed by two experts. Results:The paper presents the results of semi-automatic mapping of Russian laboratory terms to LOINC. Two clinics (A and B) and a laboratory service participated in the project. The following results were achieved: mapping of 86% terms from Clinic A and 87% from Clinic B. It has to be mentioned that 99% of terms used in 2014 were mapped. In total 2398 out of 2563 were mapped. Discussion: The required effort was reasonable and the price of mapping and maintenance was considered as relatively low in comparison to manual methods. Conclusion: RELMA V.6.6 and LOINC V.2.48 offer the opportunity of a low effort LOINC mapping even for non-English languages. The study proved that the mapping effort is acceptable and mapping results are on the same level as the manual mapping.

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Development, implementation and evaluation of an information model for archetype based user responsive medical data visualization

Cited by 20 publications

References 34 publications

How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments

How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments

Data Visualizations to Support Health Practitioners’ Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study

Application of a Regenstrief RELMA V.6.6 to Map Russian Laboratory Terms to LOINC

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