Data Quality in Health Care: Main Concepts and Assessment Methodologies

Mehrnaz Mashoufi; Haleh Ayatollahi; Davoud Khorasani-Zavareh; Tahere Talebi Azad Boni

doi:10.1055/s-0043-1761500

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2023; 62(01/02): 005-018
DOI: 10.1055/s-0043-1761500

Original Article for Focus Theme

Data Quality in Health Care: Main Concepts and Assessment Methodologies

Mehrnaz Mashoufi

¹Department of Health Information Management, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran

,

Haleh Ayatollahi

²Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran

³Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

,

Davoud Khorasani-Zavareh

⁴Department of Health in Emergencies and Disasters, Safety Promotion and Injury Prevention Research Center, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

,

Tahere Talebi Azad Boni

³Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

⁵Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran

› Institutsangaben

Abstract

Introduction In the health care environment, a huge volume of data is produced on a daily basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health data usually face with numerous challenges that lead to producing incomplete, inaccurate, and untimely data. As a result, data quality issues have received more attention than before.

Objective The purpose of this article is to provide an insight into the data quality definitions, dimensions, and assessment methodologies.

Methods In this article, a scoping literature review approach was used to describe and summarize the main concepts related to data quality and data quality assessment methodologies. Search terms were selected to find the relevant articles published between January 1, 2012 and September 31, 2022. The retrieved articles were then reviewed and the results were reported narratively.

Results In total, 23 papers were included in the study. According to the results, data quality dimensions were various and different methodologies were used to assess them. Most studies used quantitative methods to measure data quality dimensions either in paper-based or computer-based medical records. Only two studies investigated respondents' opinions about data quality.

Conclusion In health care, high-quality data not only are important for patient care, but also are vital for improving quality of health care services and better decision making. Therefore, using technical and nontechnical solutions as well as constant assessment and supervision is suggested to improve data quality.

Keywords

health - data quality - documentation - assessment

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Referenzen

References
1 Pezoulas VC, Kourou KD, Kalatzis F. et al. Medical data quality assessment: on the development of an automated framework for medical data curation. Comput Biol Med 2019; 107: 270-283
2 Lemma S, Janson A, Persson LÅ, Wickremasinghe D, Källestål C. Improving quality and use of routine health information system data in low- and middle-income countries: a scoping review. PLoS One 2020; 15 (10) e0239683
3 Zhang Y, Koru G. Understanding and detecting defects in healthcare administration data: toward higher data quality to better support healthcare operations and decisions. J Am Med Inform Assoc 2020; 27 (03) 386-395
4 Sirgo G, Esteban F, Gómez J. et al. Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: the importance of data quality assessment. Int J Med Inform 2018; 112: 166-172
5 Ehsani-Moghaddam B, Martin K, Queenan JA. Data quality in healthcare: a report of practical experience with the Canadian Primary Care Sentinel Surveillance Network data. HIM J 2021; 50 (1–2): 88-92
6 Kurniati AP, Rojas E, Hogg D, Hall G, Johnson OA. The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database. Health Informatics J 2019; 25 (04) 1878-1893
7 Ehrlinger L, Wöß W. A survey of data quality measurement and monitoring tools. Front Big Data 2022; 5: 850611
8 Nikiforova A. Definition and evaluation of data quality: user-oriented data object-driven approach to data quality assessment. Balt J Mod Comput. 2020; 8 (03) 391-432
9 Naik S, Voong S, Bamford M, Smith K, Joyce A, Grinspun D. Assessment of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database using a data quality index. J Am Med Inform Assoc 2020; 27 (05) 776-782
10 International Organization for Standardization. ISO 8000–8:2015-Data quality—Part 8: Information and data quality: Concepts and measuring. [Internet] 2015 [Updated, 2022, Cited 27 September 2022]. Accessed January 18, 2023 at: https://www.iso.org/standard/60805.html
11 de Medeiros MM, Maçada ACG, Junior JCdSF. The effect of data strategy on competitive advantage. Bottom line (NYNY) 2020; 33 (02) 201-216
12 Terry AL, Stewart M, Cejic S. et al. A basic model for assessing primary healthcare electronic medical record data quality. BMC Med Inform Decis Mak 2019; 19 (01) 1-11
13 Liaw S-T, Pearce C, Liyanage H, Liaw GS, de Lusignan S. An integrated organisation-wide data quality management and information governance framework: theoretical underpinnings. Inform Prim Care 2014; 21 (04) 199-206
14 Rumisha SF, Lyimo EP, Mremi IR. et al. Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania. BMC Med Inform Decis Mak 2020; 20 (01) 340
15 Papadiamantis AG, Klaessig FC, Exner TE. et al. Metadata stewardship in nanosafety research: community-driven organisation of metadata schemas to support FAIR nanoscience data. Nanomaterials (Basel) 2020; 10 (10) 2033
16 Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005; 8 (01) 19-32
17 Tricco AC, Lillie E, Zarin W. et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018; 169 (07) 467-473
18 Adeleke IT, Adekanye AO, Onawola KA. et al. Data quality assessment in healthcare: a 365-day chart review of inpatients' health records at a Nigerian tertiary hospital. J Am Med Inform Assoc 2012; 19 (06) 1039-1042
19 Greiver M, Barnsley J, Glazier RH, Harvey BJ, Moineddin R. Measuring data reliability for preventive services in electronic medical records. BMC Health Serv Res 2012; 12 (01) 116
20 Laberge M, Shachak A. Developing a tool to assess the quality of socio-demographic data in community health centres. Appl Clin Inform 2013; 4 (01) 1-11
21 Singer A, Kroeker AL, Yakubovich S, Duarte R, Dufault B, Katz A. Data quality in electronic medical records in Manitoba: do problem lists reflect chronic disease as defined by prescriptions?. Can Fam Physician 2017; 63 (05) 382-389
22 Kahn MG, Raebel MA, Glanz JM, Riedlinger K, Steiner JF. A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Med Care 2012; 50 (Suppl 0): S21-S29
23 Kahn MG, Callahan TJ, Barnard J. et al. A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. EGEMS (Wash DC) 2016; 4 (01) 1244
24 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
25 Feder SL. Data quality in electronic health records research: quality domains and assessment methods. West J Nurs Res 2018; 40 (05) 753-766
26 Harrington L. Quality of electronic health record data on which we stand. AACN Adv Crit Care 2019; 30 (02) 105-109
27 Keny A, Biondich P, Grannis S, Were MC. Adequacy and quality of immunization data in a comprehensive electronic health record system. J Health Inform Afr. 2013; 1(01):
28 Muthee V, Bochner AF, Osterman A. et al. The impact of routine data quality assessments on electronic medical record data quality in Kenya. PLoS One 2018; 13 (04) e0195362
29 Alipour J, Ahmadi M. Dimensions and assessment methods of data quality in health information systems. Acta Med Mediter 2017; (33) 313-320
30 Mashoufi M, Ayatollahi H, Khorasani-Zavareh D. A review of data quality assessment in emergency medical services. Open Med Inform J 2018; 12: 19-32
31 Mashoufi M, Ayatollahi H, Khorasani-Zavareh AD. Data quality assessment in emergency medical services: what are the stakeholders' perspectives?. Perspect Health Inf Manag 2019; 16 (Winter): 1c
32 Daneshkohan A, Alimoradi M, Ahmadi M, Alipour J. Data quality and data use in primary health care: a case study from Iran. Inform Med Unlocked. 2022; 28: 100855
33 Almeshari M, Khalifa M, El-Metwally A, Househ M, Alanazi A. Quality and accuracy of electronic pre-anesthesia evaluation forms. Comput Methods Programs Biomed 2018; 160: 51-56
34 Zan S, Zhang X. Medical data quality assessment model based on credibility analysis. Paper presented at: Proceedings of IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC): December 14–16, 2018, Chongqing, China. 2018: 940-944
35 Lodge W, Menon G, Kuchukhidze S. et al. Assessing completeness of patient medical records of surgical and obstetric patients in Northern Tanzania. Glob Health Action 2020; 13 (01) 1765526
36 Endashaw N, Birhanu B, Teka M, Abrham G. Quality of medical records in public health facilities of Jimma zone, Oromia regional state, Southwest, Ethiopia. Research Square 2021
37 Kaloyanova K, Naydenova I, Kovacheva Z. Addressing data quality in healthcare. Paper presented at: Proceedings of the Information Systems and Grid Technologies (ISGT 2021) Online, May 28–29, 2021
38 Thuraisingam S, Chondros P, Dowsey MM. et al. Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment. BMC Med Inform Decis Mak 2021; 21 (01) 297
39 McClean K, Cross M, Reed S. An audit of obesity data and concordance with diagnostic coding for patients admitted to Western Australian country health service hospitals. Aust J Adv Nurs 2021; 38 (01) 45-52
40 Chen H, Hailey D, Wang N, Yu P. A review of data quality assessment methods for public health information systems. Int J Environ Res Public Health 2014; 11 (05) 5170-5207
41 Mohammed SA, Yusof MM. Towards an evaluation framework for information quality management (IQM) practices for health information systems–evaluation criteria for effective IQM practices. J Eval Clin Pract 2013; 19 (02) 379-387
42 Ayatollahi H, Bath PA, Goodacre S. Information needs of clinicians and non-clinicians in the Emergency Department: a qualitative study. Health Info Libr J 2013; 30 (03) 191-200
43 Ayatollahi H, Bath PA, Goodacre S. Accessibility versus confidentiality of information in the emergency department. Emerg Med J 2009; 26 (12) 857-860
44 Ayatollahi H, Bath PA, Goodacre S. Factors influencing the use of IT in the emergency department: a qualitative study. Health Informatics J 2010; 16 (03) 189-200
45 Wang Z, Talburt JR, Wu N, Dagtas S, Zozus MN. A rule-based data quality assessment system for electronic health record data. Appl Clin Inform 2020; 11 (04) 622-634