Managing Interoperability and Complexity in Health Systems

Bouamrane, Matt-Mouley; Tao, Cui; Sarkar, Indra Neil

doi:10.3414/me15-10-0001

Cited by 22 publications

(11 citation statements)

References 30 publications

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“…The UK government has recognized this issue and vowed to make fast broadband available to every home [39] but our findings suggest this will still leave much to do. Interoperability is a key aim in the United Kingdom as it strives to implement digital standards and achieve system wide interoperability but others have reported interoperability as a barrier to implementation and large scale deployments of mHealth and global health interventions [40,41]. The recent European Union (EU) Task Force on eHealth also acknowledged the need to develop EU-wide standards on interoperability and data sharing [26].…”

Section: Discussionmentioning

confidence: 99%

Readiness for Delivering Digital Health at Scale: Lessons From a Longitudinal Qualitative Evaluation of a National Digital Health Innovation Program in the United Kingdom

McGee‐Lennon¹,

Bouamrane²,

Devlin³

et al. 2017

J Med Internet Res

178

139

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BackgroundDigital health has the potential to support care delivery for chronic illness. Despite positive evidence from localized implementations, new technologies have proven slow to become accepted, integrated, and routinized at scale.ObjectiveThe aim of our study was to examine barriers and facilitators to implementation of digital health at scale through the evaluation of a £37m national digital health program: ‟Delivering Assisted Living Lifestyles at Scale” (dallas) from 2012-2015.MethodsThe study was a longitudinal qualitative, multi-stakeholder, implementation study. The methods included interviews (n=125) with key implementers, focus groups with consumers and patients (n=7), project meetings (n=12), field work or observation in the communities (n=16), health professional survey responses (n=48), and cross program documentary evidence on implementation (n=215). We used a sociological theory called normalization process theory (NPT) and a longitudinal (3 years) qualitative framework analysis approach. This work did not study a single intervention or population. Instead, we evaluated the processes (of designing and delivering digital health), and our outcomes were the identified barriers and facilitators to delivering and mainstreaming services and products within the mixed sector digital health ecosystem.ResultsWe identified three main levels of issues influencing readiness for digital health: macro (market, infrastructure, policy), meso (organizational), and micro (professional or public). Factors hindering implementation included: lack of information technology (IT) infrastructure, uncertainty around information governance, lack of incentives to prioritize interoperability, lack of precedence on accountability within the commercial sector, and a market perceived as difficult to navigate. Factors enabling implementation were: clinical endorsement, champions who promoted digital health, and public and professional willingness.ConclusionsAlthough there is receptiveness to digital health, barriers to mainstreaming remain. Our findings suggest greater investment in national and local infrastructure, implementation of guidelines for the safe and transparent use and assessment of digital health, incentivization of interoperability, and investment in upskilling of professionals and the public would help support the normalization of digital health. These findings will enable researchers, health care practitioners, and policy makers to understand the current landscape and the actions required in order to prepare the market and accelerate uptake, and use of digital health and wellness services in context and at scale.

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Section: Discussionmentioning

confidence: 99%

Readiness for Delivering Digital Health at Scale: Lessons From a Longitudinal Qualitative Evaluation of a National Digital Health Innovation Program in the United Kingdom

McGee‐Lennon¹,

Bouamrane²,

Devlin³

et al. 2017

J Med Internet Res

178

139

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“…The IDRT originally did not support the loading of special data types, such as free texts, data already present in EAV, or laboratory values and their units. These shortcomings were not mentioned in the aforesaid publication of the IDRT [12].…”

Section: Discussionmentioning

confidence: 99%

“…In our literature research, we focused on integration tools that do not require a specific source data structure in order to follow a generic approach that can be transferred to other source data models as well. Having compared different tools [10, 11, 13, 14], we decided to use the “Integrated Data Repository Toolkit” (IDRT) [12], which is an open source software and has been created with Talend Open Studio for data integration [15]. It performs a transform and load of “various formats like CSV, SQL, CDISC, ODM, or biobanks” [12] which addresses the i2b2 schema and which includes a GUI-supported ontology editor for rearranging and mapping patient data.…”

Section: Methodsmentioning

confidence: 99%

“…However, these approaches expect the source data to be present in a specific format so that they are normally not transferable to different source data schemas. Instead of making yet another proprietary approach or using an integration tool that requires a specific source data format, we decided to evaluate the use of the Integrated Data Repository Toolkit (IDRT), which promises to be a “platform for easy integration and administration of data from heterogeneous sources” [12] and offers the possibility to provide a configuration with which the schema of the source data can be defined.…”

Section: Introductionmentioning

confidence: 99%

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Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools

Maier

Christoph

Schmidt

et al. 2019

Journal of Healthcare Engineering

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The secondary use of data from electronic medical records has become an important factor to determine and to identify various causes of disease. For this reason, applications like informatics for integrating biology and the bedside (i2b2) offer a GUI-based front end to select patient cohorts. To make use of those tools, however, clinical data need to be extracted from the Electronic Health Record (EHR) system and integrated into the data schema of i2b2. We used TBase, a documentation system for nephrologic transplantations, as a source system and applied the Integrated Data Repository Toolkit (IDRT) for the Extract, Transform, and Load (ETL) process to load the data into i2b2. Since i2b2 uses an entity-attribute-value (EAV) schema, which is a fundamentally different way of modeling data in comparison to a standard relational schema in TBase, we evaluated if (a) the data relationship of the source system entities can still be represented in the i2b2 schema and if (b) the IDRT is a suitable solution for loading the data of a comprehensive data schema like TBase into i2b2. For that reason, we identified entities in the TBase data schema which were relevant for answering questions on cohort identification. By doing so, we found out that the entities had different structures that needed to be handled differently for the ETL process. Furthermore, the use of IDRT revealed shortcomings with regard to large input data and specific data structures that are part of most modern EHR systems. However, this project also showed that our way of modeling the TBase data in i2b2 has been proven to be successful in terms of answering the most common questions of clinicians on cohort identification.

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“…In addition, adverse sensitivity data are also documented as free text in providers' notes and clinical reports [8,9]. Although free-text narratives can help clinicians make better sense of a patient's clinical status [10], using unstructured data limits the use of computer aided applications (such as clinical decision support), thus potentially impacting the quality and safety of patient care [11]. There is a paucity of up-to-date research on describing and evaluating the existing adverse sensitivity information models.…”

Section: Introductionmentioning

confidence: 99%

Standard Information Models for Representing Adverse Sensitivity Information in Clinical Documents

Topaz¹,

Seger²,

Goss³

et al. 2016

Methods Inf Med

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SummaryBackground: Adverse sensitivity (e.g., allergy and intolerance) information is a critical component of any electronic health record system. While several standards exist for structured entry of adverse sensitivity information, many clinicians record this data as free text. Objectives: This study aimed to 1) identify and compare the existing common adverse sensitivity information models, and 2) to evaluate the coverage of the adverse sensitivity information models for representing allergy information on a subset of inpatient and outpatient adverse sensitivity clinical notes. Methods: We compared four common adverse sensitivity information models: Health Level 7 Allergy and Intolerance Domain Analysis Model, HL7-DAM; the Fast Healthcare Interoperability Resources, FHIR; the Consolidated Continuity of Care Document, C-CDA; and OpenEHR, and evaluated their coverage on a corpus of inpatient and outpatient notes (n = 120). Results: We found that allergy specialists' notes had the highest frequency of adverse sensitivity attributes per note, whereas emergency department notes had the fewest attributes. Overall, the models had many similarities in the central attributes which covered between 75% and 95% of adverse sensitivity information contained within the notes. However, representations of some attributes (especially the value-sets) were not well aligned between the models, which is likely to present an obstacle for achieving data interoperability. Also, adverse sensitivity exceptions were not well represented among the information models. Conclusions: Although we found that common adverse sensitivity models cover a significant portion of relevant information in the clinical notes, our results highlight areas needed to be reconciled between the standards for data interoperability.

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Managing Interoperability and Complexity in Health Systems

Cited by 22 publications

References 30 publications

Readiness for Delivering Digital Health at Scale: Lessons From a Longitudinal Qualitative Evaluation of a National Digital Health Innovation Program in the United Kingdom

Readiness for Delivering Digital Health at Scale: Lessons From a Longitudinal Qualitative Evaluation of a National Digital Health Innovation Program in the United Kingdom

Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools

Standard Information Models for Representing Adverse Sensitivity Information in Clinical Documents

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