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Published in: BMC Medical Research Methodology 1/2023

Open Access 01-12-2023 | Research article

Methodological issues of the electronic health records’ use in the context of epidemiological investigations, in light of missing data: a review of the recent literature

Authors: Thomas Tsiampalis, Demosthenes Panagiotakos

Published in: BMC Medical Research Methodology | Issue 1/2023

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Abstract

Background

Electronic health records (EHRs) are widely accepted to enhance the health care quality, patient monitoring, and early prevention of various diseases, even when there is incomplete or missing information in them.

Aim

The present review sought to investigate the impact of EHR implementation on healthcare quality and medical decision in the context of epidemiological investigations, considering missing or incomplete data.

Methods

Google scholar, Medline (via PubMed) and Scopus databases were searched for studies investigating the impact of EHR implementation on healthcare quality and medical decision, as well as for studies investigating the way of dealing with missing data, and their impact on medical decision and the development process of prediction models. Electronic searches were carried out up to 2022.

Results

EHRs were shown that they constitute an increasingly important tool for both physicians, decision makers and patients, which can improve national healthcare systems both for the convenience of patients and doctors, while they improve the quality of health care as well as they can also be used in order to save money. As far as the missing data handling techniques is concerned, several investigators have already tried to propose the best possible methodology, yet there is no wide consensus and acceptance in the scientific community, while there are also crucial gaps which should be addressed.

Conclusions

Through the present thorough investigation, the importance of the EHRs’ implementation in clinical practice was established, while at the same time the gap of knowledge regarding the missing data handling techniques was also pointed out.
Literature
1.
go back to reference Katehakis DG. Electronic medical record implementation challenges for the national health system in Greece. Int J Reliable Quality E-Healthcare (IJRQEH). 2018;7(1):16–30.CrossRef Katehakis DG. Electronic medical record implementation challenges for the national health system in Greece. Int J Reliable Quality E-Healthcare (IJRQEH). 2018;7(1):16–30.CrossRef
4.
go back to reference Watson R. EU sets out plans to digitise health records across member states. 2022.CrossRef Watson R. EU sets out plans to digitise health records across member states. 2022.CrossRef
5.
go back to reference Institute of Medicine. Key Capabilities of Electronic Health Record. Washington, DC: National Academy Press; 2003. Institute of Medicine. Key Capabilities of Electronic Health Record. Washington, DC: National Academy Press; 2003.
7.
go back to reference Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;14410:742–52.CrossRef Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;14410:742–52.CrossRef
8.
go back to reference Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;16312:1409–16.CrossRef Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;16312:1409–16.CrossRef
9.
go back to reference Hossain ME, Khan A, Moni MA, Uddin S. Use of electronic health data for disease prediction: A comprehensive literature review. IEEE/ACM Trans Comput Biol Bioinf. 2019;18(2):745–58.CrossRef Hossain ME, Khan A, Moni MA, Uddin S. Use of electronic health data for disease prediction: A comprehensive literature review. IEEE/ACM Trans Comput Biol Bioinf. 2019;18(2):745–58.CrossRef
10.
go back to reference Casey JA, Pollak J, Glymour MM, Mayeda ER, Hirsch AG, Schwartz BS. Measures of SES for electronic health record-based research. Am J Prev Med. 2018;54(3):430–9.PubMedCrossRef Casey JA, Pollak J, Glymour MM, Mayeda ER, Hirsch AG, Schwartz BS. Measures of SES for electronic health record-based research. Am J Prev Med. 2018;54(3):430–9.PubMedCrossRef
11.
go back to reference Gianfrancesco MA, Goldstein ND. A narrative review on the validity of electronic health record-based research in epidemiology. BMC Med Res Methodol. 2021;21(1):1–10.CrossRef Gianfrancesco MA, Goldstein ND. A narrative review on the validity of electronic health record-based research in epidemiology. BMC Med Res Methodol. 2021;21(1):1–10.CrossRef
12.
go back to reference Goldstein BA, Bhavsar NA, Phelan M, Pencina MJ. Controlling for informed presence bias due to the number of health encounters in an electronic health record. Am J Epidemiol. 2016;184(11):847–55. ISO 690.PubMedPubMedCentralCrossRef Goldstein BA, Bhavsar NA, Phelan M, Pencina MJ. Controlling for informed presence bias due to the number of health encounters in an electronic health record. Am J Epidemiol. 2016;184(11):847–55. ISO 690.PubMedPubMedCentralCrossRef
13.
go back to reference Nelson A. Unequal treatment: confronting racial and ethnic disparities in health care. J Natl Med Assoc. 2002;94(8):666.PubMedPubMedCentral Nelson A. Unequal treatment: confronting racial and ethnic disparities in health care. J Natl Med Assoc. 2002;94(8):666.PubMedPubMedCentral
14.
go back to reference Polubriaginof F C, Ryan P, Salmasian H, Shapiro AW, Perotte A, Safford MM, ... Vawdrey DK. Challenges with quality of race and ethnicity data in observational databases. J Am Med Informatics Assoc. 2019;26(8–9):730–736. Polubriaginof F C, Ryan P, Salmasian H, Shapiro AW, Perotte A, Safford MM, ... Vawdrey DK. Challenges with quality of race and ethnicity data in observational databases. J Am Med Informatics Assoc. 2019;26(8–9):730–736.
15.
go back to reference Larkins NG, Craig JC, Teixeira-Pinto A. A guide to missing data for the pediatric nephrologist. Pediatr Nephrol. 2019;34(2):223–31.PubMedCrossRef Larkins NG, Craig JC, Teixeira-Pinto A. A guide to missing data for the pediatric nephrologist. Pediatr Nephrol. 2019;34(2):223–31.PubMedCrossRef
17.
go back to reference Bell ML, Kenward MG, Fairclough DL, Horton NJ. Differential dropout and bias in randomised controlled trials: when it matters and when it may not. BMJ. 2013;346:e8668. ISO 690.PubMedPubMedCentralCrossRef Bell ML, Kenward MG, Fairclough DL, Horton NJ. Differential dropout and bias in randomised controlled trials: when it matters and when it may not. BMJ. 2013;346:e8668. ISO 690.PubMedPubMedCentralCrossRef
18.
go back to reference Little RJ, Rubin DB. The analysis of social science data with missing values. Sociol Methods Res. 1989;18(2–3):292–326.CrossRef Little RJ, Rubin DB. The analysis of social science data with missing values. Sociol Methods Res. 1989;18(2–3):292–326.CrossRef
19.
go back to reference Tsiampalis T, Panagiotakos DB. Missing-data analysis: socio-demographic, clinical and lifestyle determinants of low response rate on self-reported psychological and nutrition related multi-item instruments in the context of the ATTICA epidemiological study. BMC Med Res Methodol. 2020;20:1–13.CrossRef Tsiampalis T, Panagiotakos DB. Missing-data analysis: socio-demographic, clinical and lifestyle determinants of low response rate on self-reported psychological and nutrition related multi-item instruments in the context of the ATTICA epidemiological study. BMC Med Res Methodol. 2020;20:1–13.CrossRef
20.
go back to reference Tsiampalis T, Vassou C, Psaltopoulou T, Panagiotakos DB. Socio-Demographic, clinical and lifestyle determinants of low response rate on a self-reported psychological multi-item instrument assessing the adults’ hostility and its direction: ATTICA Epidemiological Study (2002–2012). Int J Stat Med Res. 2021;10:1–9.CrossRef Tsiampalis T, Vassou C, Psaltopoulou T, Panagiotakos DB. Socio-Demographic, clinical and lifestyle determinants of low response rate on a self-reported psychological multi-item instrument assessing the adults’ hostility and its direction: ATTICA Epidemiological Study (2002–2012). Int J Stat Med Res. 2021;10:1–9.CrossRef
21.
go back to reference Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 2021;88:105906. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 2021;88:105906.
22.
go back to reference Vuppalapati J, Kedari S, Vuppalapati R, Vuppalapati C, Ilapakurti A. The Role of Selfies in Creating the Next Generation Computer Vision Infused Outpatient Data Driven Electronic Health Records (EHR). In: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. 2019. p. 2458–2466. Vuppalapati J, Kedari S, Vuppalapati R, Vuppalapati C, Ilapakurti A. The Role of Selfies in Creating the Next Generation Computer Vision Infused Outpatient Data Driven Electronic Health Records (EHR). In: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. 2019. p. 2458–2466.
23.
go back to reference Bar-Dayan Y, Saed H, Boaz M, Misch Y, Shahar T, Husiascky I, Blumenfeld O. Using electronic health records to save money. J Am Med Inform Assoc. 2013;20:e17-20.PubMedPubMedCentralCrossRef Bar-Dayan Y, Saed H, Boaz M, Misch Y, Shahar T, Husiascky I, Blumenfeld O. Using electronic health records to save money. J Am Med Inform Assoc. 2013;20:e17-20.PubMedPubMedCentralCrossRef
24.
go back to reference Lardon J, Asfari H, Souvignet J, Trombert-Paviot B, Bousquet C. Improvement of diagnosis coding by analysing EHR and using rule engine: application to the chronic kidney disease. Stud Health Technol Inform. 2015;210:120–4.PubMed Lardon J, Asfari H, Souvignet J, Trombert-Paviot B, Bousquet C. Improvement of diagnosis coding by analysing EHR and using rule engine: application to the chronic kidney disease. Stud Health Technol Inform. 2015;210:120–4.PubMed
25.
go back to reference Garnica O, Gómez D, Ramos V, Hidalgo JI, Ruiz-Giardín JM. Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers. EPMA J. 2021;2:365–81.CrossRef Garnica O, Gómez D, Ramos V, Hidalgo JI, Ruiz-Giardín JM. Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers. EPMA J. 2021;2:365–81.CrossRef
26.
go back to reference Zaballa O, Pérez A, Gómez Inhiesto E, Acaiturri Ayesta T, Lozano JA. Identifying common treatments from electronic health records with missing information. An application to breast cancer. PloS one. 2020;15(12):e0244004. Zaballa O, Pérez A, Gómez Inhiesto E, Acaiturri Ayesta T, Lozano JA. Identifying common treatments from electronic health records with missing information. An application to breast cancer. PloS one. 2020;15(12):e0244004.
27.
go back to reference King J, Patel V, Jamoom EW, Furukawa MF. Clinical Benefits of Electronic Health Record Use: National Findings. Health Serv Res. 2014;49:392–404.PubMedCrossRef King J, Patel V, Jamoom EW, Furukawa MF. Clinical Benefits of Electronic Health Record Use: National Findings. Health Serv Res. 2014;49:392–404.PubMedCrossRef
28.
go back to reference Huang Z, Lu Y, Dong W. Utilizing electronic health records to predict multi-type major adverse cardiovascular events after acute coronary syndrome. Knowl Inf Syst. 2019;60(3):1725–52.CrossRef Huang Z, Lu Y, Dong W. Utilizing electronic health records to predict multi-type major adverse cardiovascular events after acute coronary syndrome. Knowl Inf Syst. 2019;60(3):1725–52.CrossRef
29.
go back to reference Linder JA, Rigotti NA, Schneider LI, Kelley JH, Brawarsky P, Haas JS. An electronic health record–based intervention to improve tobacco treatment in primary care: a cluster-randomized controlled trial. Arch Intern Med. 2009;169(8):781–7.PubMedPubMedCentralCrossRef Linder JA, Rigotti NA, Schneider LI, Kelley JH, Brawarsky P, Haas JS. An electronic health record–based intervention to improve tobacco treatment in primary care: a cluster-randomized controlled trial. Arch Intern Med. 2009;169(8):781–7.PubMedPubMedCentralCrossRef
30.
go back to reference Goldstein BA, Navar AM, Pencina MJ, Ioannidis J. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017;24(1):198–208.PubMedCrossRef Goldstein BA, Navar AM, Pencina MJ, Ioannidis J. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017;24(1):198–208.PubMedCrossRef
31.
go back to reference Bloomfield GS, Hogan JW, Keter A, Holland TL, Sang E, Kimaiyo S, Velazquez EJ. Blood pressure level impacts risk of death among HIV seropositive adults in Kenya: a retrospective analysis of electronic health records. BMC Infect Dis. 2014;14(1):1–10.CrossRef Bloomfield GS, Hogan JW, Keter A, Holland TL, Sang E, Kimaiyo S, Velazquez EJ. Blood pressure level impacts risk of death among HIV seropositive adults in Kenya: a retrospective analysis of electronic health records. BMC Infect Dis. 2014;14(1):1–10.CrossRef
32.
go back to reference Martín-Merino E, Calderón-Larrañaga A, Hawley S, Poblador-Plou B, Llorente-García A, Petersen I, Prieto-Alhambra D. The impact of different strategies to handle missing data on both precision and bias in a drug safety study: a multidatabase multinational population-based cohort study. Clin Epidemiol. 2018;10:643.PubMedPubMedCentralCrossRef Martín-Merino E, Calderón-Larrañaga A, Hawley S, Poblador-Plou B, Llorente-García A, Petersen I, Prieto-Alhambra D. The impact of different strategies to handle missing data on both precision and bias in a drug safety study: a multidatabase multinational population-based cohort study. Clin Epidemiol. 2018;10:643.PubMedPubMedCentralCrossRef
33.
go back to reference Dalton A, Bottle A, Soljak M, Okoro C, Majeed A, Millett C. The comparison of cardiovascular risk scores using two methods of substituting missing risk factor data in patient medical records. J Innov Health Inform. 2011;19(4):225–32.CrossRef Dalton A, Bottle A, Soljak M, Okoro C, Majeed A, Millett C. The comparison of cardiovascular risk scores using two methods of substituting missing risk factor data in patient medical records. J Innov Health Inform. 2011;19(4):225–32.CrossRef
35.
go back to reference Xu D, Hu PJ, Huang TS, Fang X, Hsu CC. A deep learning-based, unsupervised method to impute missing values in electronic health records for improved patient management. J Biomed Inform. 2020;111: 103576.PubMedCrossRef Xu D, Hu PJ, Huang TS, Fang X, Hsu CC. A deep learning-based, unsupervised method to impute missing values in electronic health records for improved patient management. J Biomed Inform. 2020;111: 103576.PubMedCrossRef
36.
go back to reference Hwang U, Choi S, Lee HB, Yoon S. Adversarial training for disease prediction from electronic health records with missing data. arXiv preprint arXiv:1711.04126. 2017. Hwang U, Choi S, Lee HB, Yoon S. Adversarial training for disease prediction from electronic health records with missing data. arXiv preprint arXiv:1711.04126. 2017.
37.
go back to reference Wang F, Zhou J, Hu J. DensityTransfer: a data driven approach for imputing electronic health records. In 2014 22nd International Conference on Pattern Recognition. IEEE. 2014. p.2763–68. Wang F, Zhou J, Hu J. DensityTransfer: a data driven approach for imputing electronic health records. In 2014 22nd International Conference on Pattern Recognition. IEEE. 2014. p.2763–68.
39.
go back to reference Winslow EH, Nestor VA, Davidoff SK, et al. Legibility and completeness of physicians’ handwritten medication orders. Heart Lung. 1997;26(2):158–64.PubMedCrossRef Winslow EH, Nestor VA, Davidoff SK, et al. Legibility and completeness of physicians’ handwritten medication orders. Heart Lung. 1997;26(2):158–64.PubMedCrossRef
40.
41.
go back to reference Chen P, Tanasijevic MJ, Schoenenberger RA, et al. A computer-based intervention for improving the appropriateness of antiepileptic drug level monitoring. Am J Clin Pathol. 2003;119(3):432–8.PubMedCrossRef Chen P, Tanasijevic MJ, Schoenenberger RA, et al. A computer-based intervention for improving the appropriateness of antiepileptic drug level monitoring. Am J Clin Pathol. 2003;119(3):432–8.PubMedCrossRef
42.
go back to reference Tierney WM, Miller ME, Overhage JM, McDonald CJ. Physician inpatient order writing on microcomputer workstations Effects on resource utilization. JAMA. 1993;269(3):379–83.PubMedCrossRef Tierney WM, Miller ME, Overhage JM, McDonald CJ. Physician inpatient order writing on microcomputer workstations Effects on resource utilization. JAMA. 1993;269(3):379–83.PubMedCrossRef
43.
go back to reference Agrawal A. Return on investment analysis for a computer-based patient record in the outpatient clinic setting. J Assoc Acad Minor Phys. 2002;13(3):61–5.PubMed Agrawal A. Return on investment analysis for a computer-based patient record in the outpatient clinic setting. J Assoc Acad Minor Phys. 2002;13(3):61–5.PubMed
44.
go back to reference Aspden P. Patient Safety Achieving a New Standard for Care. Washington, D.C: National Academies Press; 2004. Aspden P. Patient Safety Achieving a New Standard for Care. Washington, D.C: National Academies Press; 2004.
45.
go back to reference Cifuentes M, Davis M, Fernald D, Gunn R, Dickinson P, Cohen DJ. Electronic health record challenges, workarounds, and solutions observed in practices integrating behavioral health and primary care. J Am Board Family Med. 2015;28(Suppl 1):S63–72.CrossRef Cifuentes M, Davis M, Fernald D, Gunn R, Dickinson P, Cohen DJ. Electronic health record challenges, workarounds, and solutions observed in practices integrating behavioral health and primary care. J Am Board Family Med. 2015;28(Suppl 1):S63–72.CrossRef
46.
go back to reference Atreja A, Gordon SM, Pollock DA, Olmsted RN, Brennan PJ, Healthcare Infection Control Practices Advisory Committee. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control. Am J Infect Control. 2008;36(3):S37-46.PubMedPubMedCentralCrossRef Atreja A, Gordon SM, Pollock DA, Olmsted RN, Brennan PJ, Healthcare Infection Control Practices Advisory Committee. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control. Am J Infect Control. 2008;36(3):S37-46.PubMedPubMedCentralCrossRef
47.
go back to reference Kukafka R, Ancker JS, Chan C, et al. Redesigning electronic health record systems to support public health. J Biomed Inform. 2007;40(4):398–409.PubMedCrossRef Kukafka R, Ancker JS, Chan C, et al. Redesigning electronic health record systems to support public health. J Biomed Inform. 2007;40(4):398–409.PubMedCrossRef
48.
go back to reference Madden JM, Lakoma MD, Rusinak D, Lu CY, Soumerai SB. Missing clinical and behavioral health data in a large electronic health record (EHR) system. J Am Med Inform Assoc. 2016;23(6):1143–9.PubMedPubMedCentralCrossRef Madden JM, Lakoma MD, Rusinak D, Lu CY, Soumerai SB. Missing clinical and behavioral health data in a large electronic health record (EHR) system. J Am Med Inform Assoc. 2016;23(6):1143–9.PubMedPubMedCentralCrossRef
49.
50.
go back to reference Kotseva K, Wood D, De Bacquer D, De Backer G, Rydén L, Jennings C, ... EUROASPIRE Investigators. EUROASPIRE IV: A European Society of Cardiology survey on the lifestyle, risk factor and therapeutic management of coronary patients from 24 European countries. Eur J Prev Cardiol. 2016;23(6):636–648. Kotseva K, Wood D, De Bacquer D, De Backer G, Rydén L, Jennings C, ... EUROASPIRE Investigators. EUROASPIRE IV: A European Society of Cardiology survey on the lifestyle, risk factor and therapeutic management of coronary patients from 24 European countries. Eur J Prev Cardiol. 2016;23(6):636–648.
52.
go back to reference Austin PC, White IR, Lee DS, van Buuren S. Missing data in clinical research: a tutorial on multiple imputation. Can J Cardiol. 2021;37(9):1322–31.PubMedCrossRef Austin PC, White IR, Lee DS, van Buuren S. Missing data in clinical research: a tutorial on multiple imputation. Can J Cardiol. 2021;37(9):1322–31.PubMedCrossRef
53.
go back to reference Beaulieu-Jones BK, Lavage DR, Snyder JW, Moore JH, Pendergrass SA, Bauer CR. Characterizing and managing missing structured data in electronic health records: data analysis. JMIR Med Inform. 2018;6(1):e11.PubMedPubMedCentralCrossRef Beaulieu-Jones BK, Lavage DR, Snyder JW, Moore JH, Pendergrass SA, Bauer CR. Characterizing and managing missing structured data in electronic health records: data analysis. JMIR Med Inform. 2018;6(1):e11.PubMedPubMedCentralCrossRef
54.
go back to reference Buntin MB, Jain SH, Blumenthal D. Health information technology: laying the infrastructure for national health reform. Health Aff (Millwood). 2010;296:1214–9.CrossRef Buntin MB, Jain SH, Blumenthal D. Health information technology: laying the infrastructure for national health reform. Health Aff (Millwood). 2010;296:1214–9.CrossRef
55.
go back to reference Gopalakrishna G, Mustafa RA, Davenport C, Scholten RJ, Hyde C, Brozek J, Schünemann HJ, Bossuyt PM, Leeflang MM, Langendam MW. Applying Grading of Recommendations Assessment, Development and Evaluation (GRADE) to diagnostic tests was challenging but doable. J Clin Epidemiol. 2014;67(7):760–8.PubMedCrossRef Gopalakrishna G, Mustafa RA, Davenport C, Scholten RJ, Hyde C, Brozek J, Schünemann HJ, Bossuyt PM, Leeflang MM, Langendam MW. Applying Grading of Recommendations Assessment, Development and Evaluation (GRADE) to diagnostic tests was challenging but doable. J Clin Epidemiol. 2014;67(7):760–8.PubMedCrossRef
56.
go back to reference Hu Z, Melton GB, Arsoniadis EG, Wang Y, Kwaan MR, Simon GJ. Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. J Biomed Inform. 2017;68:112–20.PubMedPubMedCentralCrossRef Hu Z, Melton GB, Arsoniadis EG, Wang Y, Kwaan MR, Simon GJ. Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. J Biomed Inform. 2017;68:112–20.PubMedPubMedCentralCrossRef
57.
go back to reference Institute of Medicine. Key Capabilities of Electronic Health Record. Washington, DC: National Academy Press; 2003. Institute of Medicine. Key Capabilities of Electronic Health Record. Washington, DC: National Academy Press; 2003.
58.
go back to reference Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001. Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001.
59.
go back to reference Nijman SW, Groenhof TK, Hoogland J, Bots ML, Brandjes M, Jacobs JJ, ... Debray TP. Real-time imputation of missing predictor values improved the application of prediction models in daily practice. J Clin Epidemiol. 2021;134:22-34. Nijman SW, Groenhof TK, Hoogland J, Bots ML, Brandjes M, Jacobs JJ, ... Debray TP. Real-time imputation of missing predictor values improved the application of prediction models in daily practice. J Clin Epidemiol. 2021;134:22-34.
60.
go back to reference Li J, Yan XS, Chaudhary D, Avula V, Mudiganti S, Husby H, Shahjouei S, Afshar A, Stewart WF, Yeasin M, Zand R, Abedi V. Imputation of missing values for electronic health record laboratory data. NPJ digital medicine. 2021;4(1):147.PubMedPubMedCentralCrossRef Li J, Yan XS, Chaudhary D, Avula V, Mudiganti S, Husby H, Shahjouei S, Afshar A, Stewart WF, Yeasin M, Zand R, Abedi V. Imputation of missing values for electronic health record laboratory data. NPJ digital medicine. 2021;4(1):147.PubMedPubMedCentralCrossRef
61.
go back to reference Liu L, Li H, Hu Z, Shi H, Wang Z, Tang J, Zhang M. Learning hierarchical representations of electronic health records for clinical outcome prediction. In AMIA Annual Symposium Proceedings. Am Med Inform Assoc. 2019;2019:597. Liu L, Li H, Hu Z, Shi H, Wang Z, Tang J, Zhang M. Learning hierarchical representations of electronic health records for clinical outcome prediction. In AMIA Annual Symposium Proceedings. Am Med Inform Assoc. 2019;2019:597.
62.
go back to reference Pedersen AB, Mikkelsen EM, Cronin-Fenton D, et al. Missing data and multiple imputation in clinical epidemiological research. Clin Epidemiol. 2017;9:157–66.PubMedPubMedCentralCrossRef Pedersen AB, Mikkelsen EM, Cronin-Fenton D, et al. Missing data and multiple imputation in clinical epidemiological research. Clin Epidemiol. 2017;9:157–66.PubMedPubMedCentralCrossRef
63.
go back to reference Zhang X, Xiao J, Gong Y, Yu N, Zhang W, Jang S, Gu F. Handling the missing data problem in electronic health records for cancer prediction. In 2020 Spring Simulation Conference (SpringSim). IEEE. 2020. p. 1–9. Zhang X, Xiao J, Gong Y, Yu N, Zhang W, Jang S, Gu F. Handling the missing data problem in electronic health records for cancer prediction. In 2020 Spring Simulation Conference (SpringSim). IEEE. 2020. p. 1–9.
Metadata
Title
Methodological issues of the electronic health records’ use in the context of epidemiological investigations, in light of missing data: a review of the recent literature
Authors
Thomas Tsiampalis
Demosthenes Panagiotakos
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2023
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-023-02004-5

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