Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.
data quality
dental informatics
electronic health record
health care disparities
public health dentistry
Journal
Journal of public health dentistry
ISSN: 1752-7325
Titre abrégé: J Public Health Dent
Pays: United States
ID NLM: 0014207
Informations de publication
Date de publication:
24 Apr 2024
24 Apr 2024
Historique:
revised:
27
03
2024
received:
09
12
2023
accepted:
11
04
2024
medline:
25
4
2024
pubmed:
25
4
2024
entrez:
25
4
2024
Statut:
aheadofprint
Résumé
This work describes the process by which the quality of electronic health care data for a public health study was determined. The objectives were to adapt, develop, and implement data quality assessments (DQAs) based on the National Institutes of Health Pragmatic Trials Collaboratory (NIHPTC) data quality framework within the three domains of completeness, accuracy, and consistency, for an investigation into oral health care disparities of a preventive care program. Electronic health record data for eligible children in a dental accountable care organization of 30 offices, in Oregon, were extracted iteratively from January 1, 2014, through March 31, 2022. Baseline eligibility criteria included: children ages 0-18 with a baseline examination, Oregon home address, and either Medicaid or commercial dental benefits at least once between 2014 and 2108. Using the NIHPTC framework as a guide, DQAs were conducted throughout data element identification, extraction, staging, profiling, review, and documentation. The data set included 91,487 subjects, 11 data tables comprising 75 data variables (columns), with a total of 6,861,525 data elements. Data completeness was 97.2%, the accuracy of EHR data elements in extracts was 100%, and consistency between offices was strong; 29 of 30 offices within 2 standard deviations of the mean (s = 94%). The NIHPTC framework proved to be a useful approach, to identify, document, and characterize the dataset. The concepts of completeness, accuracy, and consistency were adapted by the multidisciplinary research team and the overall quality of the data are demonstrated to be of high quality.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : National Institutes of Health, Agency for Health Care Research and Quality
ID : R01MD013719
Informations de copyright
© 2024 The Authors. Journal of Public Health Dentistry published by Wiley Periodicals LLC on behalf of American Association of Public Health Dentistry.
Références
Hoffman S, Podgurski A. Big bad data: law, public health, and biomedical databases. J Law Med Ethics. 2013;41(1_suppl):56–60. https://doi.org/10.1111/jlme.12040
Botsis T, Hartvigsen G, Chen F, Weng C. Secondary use of EHR: data quality issues and informatics opportunities. Summit Transl Bioinform. 2010;2010:1–5.
Song M, Liu K, Abromitis R, Schleyer TL. Reusing electronic patient data for dental clinical research: a review of current status. J Dent. 2013;41(12):1148–1163. https://doi.org/10.1016/j.jdent.2013.04.006
Richesson RL, Horvath MM, Rusincovitch SA. Clinical research informatics and electronic health record data. Yearb Med Inform. 2014;9(1):215–223. https://doi.org/10.15265/iy-2014-0009
Honeyford K, Expert P, Mendelsohn EE, Post B, Faisal AA, Glampson B, et al. Challenges and recommendations for high quality research using electronic health records. Front Digit Health. 2022;4:940330. https://doi.org/10.3389/fdgth.2022.940330
Kruse CS, Stein A, Thomas H, Kaur H. The use of electronic health records to support population health: a systematic review of the literature. J Med Syst. 2018;42(11):214. https://doi.org/10.1007/s10916-018-1075-6
Casey JA, Schwartz BS, Stewart WF, Adler NE. Using electronic health Records for Population Health Research: a review of methods and applications. Annu Rev Public Health. 2016;37:61–81. https://doi.org/10.1146/annurev-publhealth-032315-021353
Kukafka R, Ancker JS, Chan C, Chelico J, Khan S, Mortoti S, et al. Redesigning electronic health record systems to support public health. J Biomed Inform. 2007;40(4):398–409. https://doi.org/10.1016/j.jbi.2007.07.001
Kahn MG, Callahan TJ, Barnard J, Bauck AE, Brown J, Davidson BN, et al. A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. EGEMS (Wash DC). 2016;4(1):1244. https://doi.org/10.13063/2327-9214.1244
Shortreed SM, Cook AJ, Coley RY, Bobb JF, Nelson JC. Challenges and opportunities for using big health care data to advance medical science and public health. Am J Epidemiol. 2019;188(5):851–861. https://doi.org/10.1093/aje/kwy292
Mon DT. Setting the right expectations for the EHR standard. J AHIMA. 2004;75(3):52–54.
Johnson SG, Pruinelli L, Hoff A, Kumar V, Simon GJ, Steinbach M, et al. A framework for visualizing data quality for predictive models and clinical quality measures. AMIA Jt Summits Transl Sci Proc. 2019;2019:630–638.
Lee K, Weiskopf N, Pathak J. A framework for data quality assessment in clinical research datasets. AMIA Annu Symp Proc. 2017;2017:1080–1089.
Kapsner LA, Kampf MO, Seuchter SA, Kamdje‐Wabo G, Gradinger T, Ganslandt T, et al. Moving towards an EHR data quality framework: the MIRACUM approach. Stud Health Technol Inform. 2019;267:247–253. https://doi.org/10.3233/shti190834
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:S21–S29. https://doi.org/10.1097/MLR.0b013e318257dd67
Kahn MG, Brown JS, Chun AT, Davidson BN, Meeker D, Ryan PB, et al. Transparent reporting of data quality in distributed data networks. EGEMS (Wash DC). 2015;3(1):1052. https://doi.org/10.13063/2327-9214.1052
Weiskopf NG, Bakken S, Hripcsak G, Weng C. A data quality assessment guideline for electronic health record data reuse. EGEMS (Wash DC). 2017;5(1):14. https://doi.org/10.5334/egems.218
Zozus MN, Green BB, Kahn MG, Richesson R, Rusincovitch SA, Simon GE, et al. Using data quality for healthcare systems data used in clinical research (Version 1.0). NIH Health Care Systems Research Collaboratory. 2014.
Lewis AE, Weiskopf N, Abrams ZB, et al. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inform Assoc. 2023;30(10):1730–1740. https://doi.org/10.1093/jamia/ocad120
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(1):144–151. https://doi.org/10.1136/amiajnl-2011-000681
Feder SL. Data quality in electronic health records research: quality domains and assessment methods. West J Nurs Res. 2018;40(5):753–766. https://doi.org/10.1177/0193945916689084
Callahan T, Barnard J, Helmkamp L, Maertens J, Kahn M. Reporting data quality assessment results: identifying individual and organizational barriers and solutions. EGEMS (Wash DC). 2017;5(1):16. https://doi.org/10.5334/egems.214
White JM, Brandon RG, Mullins JM, Simmons KL, Kottek AM, Mertz EA. Tracking oral health in a standardized, evidence‐based, prevention‐focused dental care system. J Public Health Dent. 2020;80(Suppl 2):S35–s43. https://doi.org/10.1111/jphd.12413
Cheng J, Chaffee BW, Cheng NF, Gansky SA, Featherstone JD. Understanding treatment effect mechanisms of the CAMBRA randomized trial in reducing caries increment. J Dent Res. 2015;94(1):44–51. https://doi.org/10.1177/0022034514555365
Terry AL, Stewart M, Cejic S, et al. A basic model for assessing primary health care electronic medical record data quality. BMC Med Inform Decis Mak. 2019;19(1):30. https://doi.org/10.1186/s12911-019-0740-0
Dziadkowiec O, Callahan T, Ozkaynak M, Reeder B, Welton J. Using a data quality framework to clean data extracted from the electronic health record: a case study. EGEMS (Wash DC). 2016;4(1):1201. https://doi.org/10.13063/2327-9214.1201
Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev. 2010;67(5):503–527. https://doi.org/10.1177/1077558709359007
Lee SJ, Grobe JE, Tiro JA. Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals. J Am Med Inform Assoc. 2016;23(3):627–634. https://doi.org/10.1093/jamia/ocv156
Li Y, Sperrin M, Martin GP, Ashcroft DM, van Staa TP. Examining the impact of data quality and completeness of electronic health records on predictions of patients' risks of cardiovascular disease. Int J Med Inform (Shannon, Ireland). 2020;133:104033. https://doi.org/10.1016/j.ijmedinf.2019.104033
Goulet JL, Erdos J, Kancir S, Levin FL, Wright SM, Daniels SM, et al. Measuring performance directly using the veterans health administration electronic medical record: a comparison with external peer review. Med Care. 2007;45(1):73–79. https://doi.org/10.1097/01.mlr.0000244510.09001.e5