Defining dimensions of research readiness: a conceptual model for primary care research networks

Carr, Helen; Lusignan, Simon de; Liyanage, Harshana; Liaw, Siaw‐Teng; Terry, Amanda; Rafi, Imran

doi:10.1186/s12875-014-0169-6

Cited by 13 publications

(12 citation statements)

References 48 publications

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“…The increased use of electronic medical records (EMRs) in Canadian primary health care practice [1–3] has resulted in an expansion of the availability of EMR data. These data are being put to uses such as quality improvement activities related to patient care, and secondary purposes such as research and disease surveillance [4, 5]. This has shifted the traditional use of medical records as an aide-memoire to that of a data collection system [6].…”

Section: Introductionmentioning

confidence: 99%

A basic model for assessing primary health care electronic medical record data quality

Terry

Stewart

Cejic

et al. 2019

BMC Med Inform Decis Mak

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BackgroundThe increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs.MethodsUsing an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products.ResultsA set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight).ConclusionsThis paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.Electronic supplementary materialThe online version of this article (10.1186/s12911-019-0740-0) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

A basic model for assessing primary health care electronic medical record data quality

Terry

Stewart

Cejic

et al. 2019

BMC Med Inform Decis Mak

Self Cite

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“…When data quality presents as an antecedent, the concept of "readiness" refers to the adequate quality of data to facilitate the task at hand. For instance, several publications [14][15][16][17][18][19] state that to use data, it must pass an initial test of data quality, otherwise it should not be used to make informed decisions. On the other hand, some literature referred to data quality as a prospective goal of readiness, such as how the Nanotechnology Signature Initiative 20 posits the need to continually assess quality as a measure to predict its longevity.…”

Section: Data Qualitymentioning

confidence: 99%

“…In applied instances, interoperability seemed to be a focus of national and international infrastructure 15,19,30 and research networks. 14,33,34 In addition, a significant body of literature noted interoperability as a fundamental construct to assess for the viability of HIT applications. 26,27,35 Interoperability is often used as an umbrella term for data exchange (reading and writing); in the literature we explored, it appears that the most frequent use of interoperability (if clearly defined) is the use of data standards to facilitate data exchange.…”

Section: Interoperabilitymentioning

confidence: 99%

A Conceptual Framework of Data Readiness: The Contextual Intersection of Quality, Availability, Interoperability, and Provenance

et al. 2021

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Background Data readiness is a concept often used when referring to health information technology applications in the informatics disciplines, but it is not clearly defined in the literature. To avoid misinterpretations in research and implementation, a formal definition should be developed. Objectives The objective of this research is to provide a conceptual definition and framework for the term data readiness that can be used to guide research and development related to data-based applications in health care. Methods PubMed, the National Institutes of Health RePORTER, Scopus, the Cochrane Library, and Duke University Library databases for business and information sciences were queried for formal mentions of the term “data readiness.” Manuscripts found in the search were reviewed, and relevant information was extracted, evaluated, and assimilated into a framework for data readiness. Results Of the 264 manuscripts found in the database searches, 20 were included in the final synthesis to define data readiness. In these 20 manuscripts, the term data readiness was revealed to encompass the constructs of data quality, data availability, interoperability, and data provenance. Discussion Based upon our review of the literature, we define data readiness as the application-specific intersection of data quality, data availability, interoperability, and data provenance. While these concepts are not new, the combination of these factors in a novel data readiness model may help guide future informatics research and implementation science. Conclusion This analysis provides a definition to guide research and development related to data-based applications in health care. Future work should be done to validate this definition, and to apply the components of data readiness to real-world applications so that specific metrics may be developed and disseminated.

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“…Readiness refers to an established baseline of knowledge, competence, and capacity that will enable a community‐based organization to effectively and safely deliver and evaluate a trauma‐informed intervention, to support families within the community who are experiencing IPV. Some have similarly referred to readiness as being defined by human capital (e.g., awareness, commitment, individuals’ capacity), relationship capital (collaborations and positive relationships at all levels), and structural capital (including training, resources, and organization structure; Carr et al, 2014). We outline the need for this tool, describe the iterative process of creating and refining the tool, and report on evidence of the tool's reliability, validity, and utility.…”

Section: Introductionmentioning

confidence: 99%

Developing and testing a readiness tool for interpersonal violence prevention partnerships with community‐based projects

Andrews

Motz²,

Pepler

2020

Journal Community Psychology

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Community‐based projects that serve vulnerable families have the opportunity to identify and respond to interpersonal violence (IPV). We developed a readiness assessment tool to support selection of projects to participate in an initiative that involved implementing a community‐based IPV intervention for mothers. The overarching aim of the current study was to describe the development of this tool and examine the reliability of coding, validity, and utility of the tool. After developing and refining the tool, 41 community‐based projects completed the tool. Responses were coded and scored; scores were used to select projects for the initiative. Preliminary validation for the tool included (a) expert opinion, (b) uptake/implementation of the intervention, and (c) feedback and responses from service providers in terms of the usefulness and importance of the tool. This tool can be used by both researchers and service providers to assess community project readiness and capacity to provide trauma‐informed services for vulnerable families.

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Defining dimensions of research readiness: a conceptual model for primary care research networks

Cited by 13 publications

References 48 publications

A basic model for assessing primary health care electronic medical record data quality

A basic model for assessing primary health care electronic medical record data quality

A Conceptual Framework of Data Readiness: The Contextual Intersection of Quality, Availability, Interoperability, and Provenance

Developing and testing a readiness tool for interpersonal violence prevention partnerships with community‐based projects

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