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

Abstract

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.

Keywords

data quality - interoperability - governance - data management - electronic health record

Protection of Human and Animal Subjects

This research does not involve human subjects.

Publication History

Received: 25 January 2021

Accepted: 09 June 2021

Article published online:
21 July 2021

• References

• 1 Jain A, Saha D, Patel H. et al. Data readiness for AI. 2019 . Accessed June 30, 2021 at: https://researcher.watson.ibm.com/researcher/view_group.php?id=10391
• 2 General Services Administration. Data readiness services for artificial intelligence. 2020 . Accessed June 30, 2021 at: https://beta.sam.gov/opp/340d2fbf60e441bcb13ec019f0548c07/view
• 3 Tableau. Data readiness. Accessed June 30, 2021 at: https://www.tableau.com/es-es/support/consulting/data-readiness
• 4 Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; 20 (01) 144-151
• 5 Ackoff RL. From data to wisdom. J Appl Syst Anal 1989; 16 (01) 3-9
• 6 Shannon CE. The mathematical theory of information. Bell Syst Tech J 1949; 27 (03) 379-423
• 7 Richesson RL, Staes CJ, Douthit BJ. et al. Measuring implementation feasibility of clinical decision support alerts for clinical practice recommendations. J Am Med Inform Assoc 2020; 27 (04) 514-521
• 8 Peters MDJ, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid-Based Healthc 2015; 13 (03) 141-146
• 9 Hung WH, Chang LM, Lin CP, Hsiao CH. E-readiness of website acceptance and implementation in SMEs. Comput Human Behav 2014; 40: 44-55
• 10 McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 2012; 22 (03) 276-282
• 11 Page MJ, McKenzie JE, Bossuyt PM. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372 (71) n71
• 12 DAMA United Kingdom. The six primary dimensions for data quality assessment. 2013 . Accessed April 26, 2021 at: https://silo.tips/download/the-six-primary-dimensions-for-data-quality-assessment
• 13 Centers for Disease Control and Prevention. The six dimensions of EHDI data quality assessment. Accessed April 26, 2021 at: https://www.cdc.gov/ncbddd/hearingloss/documents/dataqualityworksheet.pdf
• 14 Carr H, de Lusignan S, Liyanage H, Liaw ST, Terry A, Rafi I. Defining dimensions of research readiness: a conceptual model for primary care research networks. BMC Fam Pract 2014; 15 (01) 169
• 15 de Lusignan S, Liaw ST, Krause P. et al; Contribution of the IMIA Primary Health Care Informatics Working Group. Key concepts to assess the readiness of data for international research: data quality, lineage and provenance, extraction and processing errors, traceability, and curation. Yearb Med Inform 2011; 6: 112-120
• 16 Chirkova R, Doyle J, Reutter JL. The data readiness problem for relational databases. Paper presented at: Cali, Colombia: CEUR Workshop Proceedings. May 21-25, 2018
• 17 Gibbs L, Nelson A, Dalton E, Cantor J, Shipp S, Jenkins D. IDS governance: setting up for ethical and effective use. 2017 . Accessed June 30, 2021 at: https://www.aisp.upenn.edu/wp-content/uploads/2016/07/Governance.pdf
• 18 Lu Y, Fang X, Zhan J. Data readiness level for unstructured data with a focus on unindexed text data. Paper presented at: ACM International Conference Proceeding Series. , Beijing, China; August 4–7, 2014
• 19 Wen Y-F, Hwang Y-T. The associativity evaluation between open data and country characteristics. Electron Libr 2019; 37 (02) 337-364
• 20 National Nanotechnology Initiative. NSI: nanotechnology knowledge infrastructure (NKI) data readiness levels discussion draft. 2013 . Accessed June 30, 2021 at: https://www.nano.gov/node/1015
• 21 German RR, Lee LM, Horan JM, Milstein RL, Pertowski CA, Waller MN. Guidelines Working Group Centers for Disease Control and Prevention (CDC). Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. MMWR Recomm Rep 2001; 50 (RR-13): 1-35
• 22 Canadian Institute for Health Information. The CIHI data quality framework. CIHI Ottawa; 2009. . Accessed June 30, 2021 at: https://secure.cihi.ca/free_products/dq-data_quality_framework_2009_en.pdf
• 23 Weiskopf NG, Bakken S, Hripcsak G, Weng C. A data quality assessment guideline for electronic health record data reuse. EGEMS (Wash DC) 2017; 5 (01) 14-14
• 24 Chen H, Yu P, Hailey D, Wang N. Methods for assessing the quality of data in public health information systems: a critical review. Stud Health Technol Inform 2014; 204: 13-18
• 25 University of Delaware. Managing data availability. 2020 . Accessed April 26, 2021 at: https://www1.udel.edu/security/data/availability.html
• 26 Austin CC. A path to big data readiness. Paper presented at: Proceedings - 2018 IEEE International Conference on Big Data (Big Data 2018). , Seattle, Washington, United States; December10–13, 2018
• 27 Digital Curation Centre. 5 steps to research data readiness - a guide for IT managers. Accessed June 30, 2021 at: https://www.dcc.ac.uk/sites/default/files/documents/resource/5%20Steps%20to%20Research%20Data%20Readiness.pdf
• 28 Klievink B, Romijn BJ, Cunningham S, de Bruijn H. Big data in the public sector: uncertainties and readiness. Inf Syst Front 2017; 19 (02) 267-283
• 29 Richesson R. Quantifying system and data readiness for automated clinical decision support. National Library of Medicine; 2016. . Accessed June 30, 2021 at: https://grantome.com/grant/NIH/R15-LM012335-01A1
• 30 The World Bank Group. Readiness assessment tool. 2015 . Accessed June 30, 2021 at: http://opendatatoolkit.worldbank.org/en/odra.html
• 31 United Nations Office for Disaster Risk Reduction. Sendai Framework data readiness review 2017 - global summary report. 2017 . Accessed June 30, 2021 at: https://www.undrr.org/publication/sendai-framework-data-readiness-review-2017-global-summary-report#:~:text=Sendai%20Framework%20data%20readiness%20review%202017%20%2D%20Global%20summary%20report,-Documents%20and%20publications&text=Effective%20monitoring%20of%20progress%20in,and%20applicability%20of%20multiple%20datasets
• 32 Health Information Management Systems Society. What is interoperability?. 2019 . Accessed June 30, 2021 at: https://www.himss.org/library/interoperability-standards/what-is-interoperability
• 33 Jennings E, De Lusignan S, Michalakidis G. et al. An instrument to identify computerised primary care research networks, genetic and disease registries prepared to conduct linked research: TRANSFoRm International Research Readiness (TIRRE) survey. J Innov Health Inform 2018; 25 (04) 207-220
• 34 Campbell W, Campbell J, Reich C, Belenkaya R. Research data network ontologies for precision cancer medicine supporting i2b2 and OMOP. Paper presented at: 2020 AMIA Inform Summit. ; November 14–18, 2020
• 35 Lawrence ND. Data readiness levels. arXiv preprint arXiv:170502245. 2017
• 36 Wang J, Crawl D, Purawat S, Nguyen M, Altintas I. Big data provenance: challenges, state of the art and opportunities. Paper presented at: Proc IEEE International Conference on Big Data. , October 29–November 1, 2015: 2509-2516
• 37 Cheriath K. Data governance 2.0. 2018 (Journal, Electronic). Accessed June 30, 2021 at: https://www.infoworld.com/article/3268054/data-governance-2-0.html
• 38 Ellaway RH, Topps D, Pusic M. Data, big and small: emerging challenges to medical education scholarship. Acad Med 2019; 94 (01) 31-36
• 39 Cheah Y-W, Plale B. Provenance quality assessment methodology and framework. J Data Inform Qual 2014; 5 (03) 9
• 40 Cheah Y, Plale B. Provenance analysis: Towards quality provenance. Paper presented at: 2012 IEEE 8th International Conference on E-Science, Chicago, Illinois, United States. ; October 8–12, 2012
• 41 Karvounarakis G, Ives ZG, Tannen V. Querying data provenance. Paper presented at: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, Indianapolis, Indiana, United States. ; June 6–11, 2010
• 42 Mulissa Z, Wendrad N, Bitewulign B. et al. Effect of data quality improvement intervention on health management information system data accuracy: an interrupted time series analysis. PLoS One 2020; 15 (08) e0237703-e0237703
• 43 Gass Jr JD, Misra A, Yadav MNS. et al. Implementation and results of an integrated data quality assurance protocol in a randomized controlled trial in Uttar Pradesh, India. Trials 2017; 18 (01) 418-418
• 44 Health Level 7. FHIR overview. 2019 . Accessed June 30, 2021 at: https://www.hl7.org/fhir/overview.html
• 45 Health Level 7. Resource provenance - content. 2019 . Accessed June 30, 2021 at: https://www.hl7.org/fhir/provenance.html
• 46 Wilkinson MD, Dumontier M, Aalbersberg IJ. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3: 160018
• 47 Ivers AM, Byrne J, Byrne PJ. Analysis of SME data readiness: a simulation perspective. J Small Bus Enterprise Dev 2016; 23 (01) 163-188
• 48 Vorhees Group. Institutional data readiness assessment tool. 2007 . Accessed June 30, 2021 at: http://www.voorheesgroup.org/voorheesgroup-tools/Institutional%20Data%20Readiness%20Assessment%20Tool.pdf