Developing the Minimum Dataset for the New Mexico Decedent Image Database

Abstract

Background A minimum dataset (MDS) can be determined ad hoc by an investigator or small team; by a metadata expert; or by using a consensus method to take advantage of the global knowledge and expertise of a large group of experts. The first method is the most commonly applied.

Objective Here, we describe a use of the third approach using a modified Delphi method to determine the optimal MDS for a dataset of full body computed tomography scans. The scans are of decedents whose deaths were investigated at the New Mexico Office of the Medical Investigator and constitute the New Mexico Decedent Image Database (NMDID).

Methods The authors initiated the consensus process by suggesting 50 original variables to elicit expert reactions. Experts were recruited from a variety of scientific disciplines and from around the world. Three rounds of variable selection showed high rates of consensus.

Results In total, 59 variables were selected, only 52% of which the original resource authors selected. Using a snowball method, a second set of experts was recruited to validate the variables chosen in the design phase. During the validation phase, no variables were selected for deletion.

Conclusion NMDID is likely to remain more “future proof” than if a single metadata expert or only the original team of investigators designed the metadata.

Keywords

minimum dataset - metadata - future uses - Delphi method - snowball sampling

Protection of Human and Animal Subjects

We received Institutional Review Board approval from the University of New Mexico Human Subjects Research Review Committee on June 10, 2013 (Human Research Protections Office13–229).

Publication History

Received: 25 January 2021

Accepted: 30 April 2021

Article published online:
02 June 2021

• References

• 1 How much data is created every day?. [27 powerful stats]. Accessed November 24, 2020 at: https://seedscientific.com/how-much-data-is-created-every-day/
• 2 Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 2018; 5 (01) 180178
• 3 Bielefeld RA, Yamashita TS, Kerekes EF, Ercanli E, Singer LT. A research database for improved data management and analysis in longitudinal studies. MD Comput 1995; 12 (03) 200-205
• 4 National Library of Medicine. MedPix. Accessed 2021 at: https://medpix.nlm.nih.gov/home
• 5 Cancer Imaging Archive. Accessed 2021 at: https://www.cancerimagingarchive.net/
• 6 Tagare HD, Jaffe CC, Duncan J. Medical image databases: a content-based retrieval approach. J Am Med Inform Assoc 1997; 4 (03) 184-198
• 7 Greenberg J. Metadata generation: processes, people and tools. Bull Am Soc Inf Sci Technol 2005; 29 (02) 16-19
• 8 Sarah C, Jane B, Rónán O, Ben R. Quality assurance for digital learning object repositories: issues for the metadata creation process. ALT J 2004; 12 (01) 5-20
• 9 Sicilia M-Á. Metadata, semantics, and ontology:providing meaning to information resources. Int J Metadata Semant Ontol 2006; 1 (01) 83-86
• 10 Malaxa V, Douglas I. A Framework for metadata creation tools. Interdiscip J E Learning Learn Objects 2005; 1 (01) 151-162
• 11 2019 CT Market Outlook Report. Accessed 2019 at: https://imvinfo.com/product/2020-ct-market-outlook-report/
• 12 Choplin RH, Boehme II JM, Maynard CD. Picture archiving and communication systems: an overview. Radiographics 1992; 12 (01) 127-129
• 13 DICOM. Accessed Accessed November 24, 2020 at: https://www.dicomstandard.org/
• 14 Greenspan L, McLellan BA, Greig H. Abbreviated injury scale and injury severity score: a scoring chart. J Trauma 1985; 25 (01) 60-64
• 15 Annamalai M, Guo D, Susan M, Sep JS. 2009 U. Oracle database 11 g DICOM medical image support. Accessed 2009 at: https://download.oracle.com/otndocs/products/multimedia/pdf/oow2009/mm_oow09_dicom_S311474.pdf
• 16 Health at a Glance 2017: OECD Indicators. Accessed 2017 at: https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-2017_health_glance-2017-en
• 17 Health Information Policy Council. Background paper: uniform minimum health data sets. Accessed 1983 at: https://link.springer.com/chapter/10.1007/978-1-4757-4160-5_18
• 18 Werley HH, Devine EC, Zorn CR, Ryan P, Westra BL. The nursing minimum data set: abstraction tool for standardized, comparable, essential data. Am J Public Health 1991; 81 (04) 421-426
• 19 Domensino AF, Winkens I, van Haastregt JCM, van Bennekom CAM, van Heugten CM. Defining the content of a minimal dataset for acquired brain injury using a Delphi procedure. Health Qual Life Outcomes 2020; 18 (01) 30
• 20 Tee JW, Chan CHP, Gruen RL. et al. Inception of an Australian Spine Trauma Registry: The Minimum Dataset. Accessed 2012 at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3864422/
• 21 Abbasi M, Ahmadian L, Amirian M, Tabesh H, Eslami S. The Development of a Minimum Data Set for an Infertility Registry. Perspect Heal Inf Manag; 2018
• 22 McCann LJ, Kirkham JJ, Wedderburn LR. et al. Development of an internationally agreed minimal dataset for juvenile dermatomyositis (JDM) for clinical and research use. Trials 2015; 16 (01) 268
• 23 Ranegger R, Hackl WO, Ammenwerth E. A proposal for an Austrian nursing minimum data set (NMDS): a delphi study. Appl Clin Inform 2014; 5 (02) 538-547
• 24 Werley HH, Lang NM, Westlake SK. Brief summary of the nursing minimum data set conference. Nurs Manage 1986; 17 (07) 42-45
• 25 Meaney FJ, Cunningham GC, Riggle SM. Development of a national genetic services database. Proc Symp Comput Appl Med Care. . Accessed 1991 at: Published online 1991: 424-428 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2247567/
• 26 Porock D, Oliver DP, Zweig S. et al. Predicting death in the nursing home: development and validation of the 6-month minimum data set mortality risk index. J Gerontol A Biol Sci Med Sci 2005; 60 (04) 491-498
• 27 Rubinstein YR, Groft SC, Bartek R. et al. Creating a global rare disease patient registry linked to a rare diseases biorepository database: Rare Disease-HUB (RD-HUB). Contemp Clin Trials 2010; 31 (05) 394-404
• 28 Jenders R, McDonald C, Rubinstein Y, Groft S. Applying standards to public health: an information model for a global rare-diseases registry. Accessed 2011 at: Published online 2011: 1819 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900177/
• 29 Greenberg J, Robertson WD. Semantic Web Construction: An Inquiry of Authors' Views on Collaborative Metadata Generation. Vol 0.; 2002
• 30 Bagley Thompson C, Schaffer J. Minimum data set development: air transport time-related terms. Int J Med Inform 2002; 65 (02) 121-133
• 31 Hillmann DI. Metadata quality: From evaluation to augmentation. Cat Classif Q 2008; 46 (01) 65-80
• 32 Zumwalt RE, Aurelius M, Brooks E. et al. 2010 Annual report office of the medical investigator state of New Mexico. Accessed 2010 at: https://hsc.unm.edu/omi/_docs/pdfs/ar2010.pdf
• 33 2010 Census: new Mexico profile. Accessed August 5, 2020 at: https://www2.census.gov/geo/maps/dc10_thematic/2010_Profile/2010_Profile_Map_New_Mexico.pdf
• 34 New Mexico office of the medical investigator annual report. Accessed 2017 at: https://hsc.unm.edu/omi/_docs/pdfs/ar2018.pdf
• 35 Yousuf MI. Using experts‘experts’opinions through Delphi technique. Pract Assess, Res Eval 2007;12(04):
• 36 Hsu C-C, Sandford BA. The Delphi technique: making sense of consensus. Pract Assess, Res Eval 2007; 12: 10
• 37 Goossen WTF, Epping PJMM, Feuth T, Dassen TWN, Hasman A, van den Heuvel WJA. A comparison of nursing minimal data sets. J Am Med Inform Assoc 1998; 5 (02) 152-163
• 38 Berry SD, Edgar HJH. Standardizing data from the dead. Stud Health Technol Inform 2019; 264: 1427-1428
• 39 Leo A. Goodman. Snowball Sampling. Ann Math Stat 1961; 32 (01) 148-170
• 40 Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42 (02) 377-381
• 41 Edgar H, Daneshvari Berry S, Moes E, Adolphi N, Bridges P, Nolte K. New Mexico decedent image database. Office of the Medical Investigator; University of New Mexico: 2020
• 42 Hou J, Chen Z, Qin X, Zhang D. Automatic image search based on improved feature descriptors and decision tree. Integr Comput Aided Eng 2011; 18 (02) 167-180
• 43 Robards J, Evandrou M, Falkingham J, Vlachantoni A. Marital status, health and mortality. Maturitas 2012; 73 (04) 295-299
• 44 Verbrugge LM. Marital Status and Health. Vol 41.; Accessed 2021 at: https://psycnet.apa.org/record/1980-27843-001
• 45 Umberson D. Gender, marital status and the social control of health behavior. Soc Sci Med 1992; 34 (08) 907-917
• 46 Berry SD, Edgar HJH. Extracting and standardizing medical examiner data to improve health. AMIA Jt Summits Transl Sci proceedings AMIA Jt Summits. Transl Sci 2020; 2020: 63-70