Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges
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Aim/Purpose: The primary use of electronic health records (EHRs) is in the care of the individual patient. Secondary research uses employ information in EHRs for purposes beyond that of care of the individual. Secondary research uses may broadly be divided into studies which focus on improving care and treatment of individuals and those which aim to increase knowledge about disease causes, associations and prevalence at a population level. This paper provides a review of studies that have used EHRs to increase knowledge at a population level. It examines the methods used, types of research conducted, difficulties and challenges faced and implications for future research and mental health research in particular.Method: A review was undertaken based on a search for peer-reviewed and recently (i.e. since 2005) published articles with full-text available online.Findings/Results: The studies which have used EHRs to increase knowledge have predominantly involved; (1) data mining to identify biomarkers and gene–disease associations, (2) epidemiological research using linked/merged health records and (3) surveillance, prediction and alerts for diseases/illnesses. The principal methodological challenges identified were data quality, discrepancies/inconsistencies in data and interoperability of EHRs.Conclusions: Despite the challenges faced in secondary usage of EHRs, significant research has been undertaken and researchers have proposed and tested various approaches to address methodological issues. The study methods employed in other fields of medical research can be extrapolated to study issues of significance to mental health using EHRs.
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It would not be possible, without extensive utilization of EHR, to place in practice a system of effective public health surveillance specially to predict, monitor and manage pandemic like COVID-19.
“It would not be possible, without extensive utilization of EHR, to place in practice a system of effective public health surveillance specially to predict, monitor and manage pandemic like COVID-19 [58], [82].”
From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [58]
Cites
Strimbu, K., & Tavel, J. A. (2010) · Current Opinion in HIV and AIDS · doi:10.1097/coh.0b013e32833ed177 · Cited by 2,062 · see citing works
Mining electronic health records: towards better research applications and clinical care
Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680 · see citing works
Häyrinen, K., Saranto, K., & Nykänen, P. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2007.09.001 · Cited by 1,303 · see citing works
PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations
Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., Wang, D., Masys, D. R., Roden, D. M., & Crawford, D. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq126 · Cited by 1,219 · see citing works
Weiskopf, N. G., & Weng, C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000681 · Cited by 1,216 · cited 2× · see citing works
“Weiskopf and Weng (2012) proposed five dimensions of data quality: completeness, correctness, concordance, plausibility and currency.”
Background(2002) · Computers in health care · doi:10.1007/978-1-4613-0069-4 · Cited by 425 · see citing works
Electronic health records: new opportunities for clinical research
Coorevits, P. L., Sundgren, M., Klein, G. O., Bahr, A., Claerhout, B., Daniel, C., Dugas, M., Dupont, D., Schmidt, A., Singleton, P., et al. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12119 · Cited by 350 · see citing works
Yearbook of Medical Informatics
International Medical Informatics Association (2017) · Yearbook of Medical Informatics · doi:10.1055/s-00034612 · Cited by 295 · see citing works
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts
Roque, F. S., Jensen, P. B., Schmock, H., Dalgaard, M., Andreatta, M., Hansen, T. F., Søeby, K., Bredkjær, S., Juul, A., Werge, T. M., et al. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002141 · Cited by 283 · see citing works
BMC Medical Informatics and Decision Making
Pala, I. (2014) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-14-7 · Cited by 252 · see citing works
Perlis, R. H., Iosifescu, D. V., Castro, V. M., Murphy, S. N., Gainer, V. S., Minnier, J., Cai, T., Goryachev, S., Zeng, Q., Gallagher, P. J., et al. (2012) · Psychological Medicine · doi:10.1017/s0033291711000997 · Cited by 193 · see citing works
Temporal pattern discovery in longitudinal electronic patient records
Norén, G. N., Hopstadius, J., Bate, A., Star, K., & Edwards, I. R. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0152-3 · Cited by 170 · see citing works
Chapter 13: Mining Electronic Health Records in the Genomics Era
Denny, J. C. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002823 · Cited by 165 · see citing works
Health Services Research and Data Linkages: Issues, Methods, and Directions for the Future
Bradley, C. J., Penberthy, L., Devers, K. J., & Holden, D. J. (2010) · Health Services Research · doi:10.1111/j.1475-6773.2010.01142.x · Cited by 162 · see citing works
Engels, E. A., Pfeiffer, R. M., Ricker, W., Wheeler, W., Parsons, R., & Warren, J. L. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr146 · Cited by 144 · see citing works
Zhao, D., & Weng, C. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.05.004 · Cited by 135 · see citing works
Byrd, R. J., Steinhubl, S. R., Sun, J., Ebadollahi, S., & Stewart, W. F. (2013) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2012.12.005 · Cited by 130 · see citing works
Kurreeman, F. A. S., Liao, K. P., Chibnik, L. B., Hickey, B., Stahl, E. A., Gainer, V. S., Li, G., Bry, L., Mahan, S. P., Ardlie, K., et al. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.12.007 · Cited by 120 · see citing works
Chanlekha, H., Kawazoe, A., & Collier, N. (2010) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-10-1 · Cited by 110 · see citing works
Morley, K. I., Wallace, J., Denaxas, S., Hunter, R. J., Patel, R. S., Perel, P., Shah, A. D., Timmis, A., SCHILLING, R. J., & Hemingway, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110900 · Cited by 94 · see citing works
Clinical Data Mining: a Review
Cohen, G., Depeursinge, A., Müller, H., Meyer, R., Geissbuhler, A., & Iavindrasana, J. (2009) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1638651 · Cited by 91 · see citing works
Schwartz, B. S., Stewart, W. F., Godby, S., Pollak, J., DeWalle, J., Larson, S. L., Mercer, D. G., & Glass, T. A. (2011) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2011.06.038 · Cited by 89 · see citing works
Data-driven approach for creating synthetic electronic medical records
Buczak, A. L., Babin, S. M., & Moniz, L. J. (2010) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-10-59 · Cited by 84 · see citing works
Xu, H., Jiang, M., Oetjens, M. T., Bowton, E., Ramirez, A. H., Jeff, J. M., Basford, M., Pulley, J. M., Cowan, J. D., Wang, X., et al. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000208 · Cited by 83 · see citing works
Bias Associated with Mining Electronic Health Records
Hripcsak, G., Knirsch, C., Zhou, L., Wilcox, A., & Melton, G. (2011) · Journal of Biomedical Discovery and Collaboration · doi:10.5210/disco.v6i0.3581 · Cited by 83 · see citing works
Weiss, J. C., Natarajan, S., Peissig, P. L., McCarty, C. A., & Page, D. (2012) · AI Magazine · doi:10.1609/aimag.v33i4.2438 · Cited by 81 · see citing works
Denny, J. C., Choma, N. N., Peterson, J. F., Miller, R. A., Bastarache, L. A., Li, M., & Peterson, N. B. (2011) · Medical Decision Making · doi:10.1177/0272989x11400418 · Cited by 79 · see citing works
Morgan, V. A., Croft, M. L., Valuri, G. M., Zubrick, S. R., Bower, C. I., McNeil, T. F., & Jablensky, A. V. (2012) · The British Journal of Psychiatry · doi:10.1192/bjp.bp.111.093070 · Cited by 79 · see citing works
Lamb, S. (2018) · Johns Hopkins University Press eBooks · doi:10.1353/book.35552 · Cited by 75 · see citing works
Pathologist of the mind: Adolf Meyer and the origins of American psychiatry
(2015) · Choice Reviews Online · doi:10.5860/choice.189239 · Cited by 64 · see citing works
Integrating Clinical Practice and Public Health Surveillance Using Electronic Medical Record Systems
Klompas, M., McVetta, J., Lazarus, R. M., Eggleston, E., Haney, G. A., Kruskal, B. A., Yih, W. K., Daly, P., Oppedisano, P., Beagan, B. M., et al. (2012) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2012.04.005 · Cited by 62 · see citing works
Atreja, A., Gordon, S. M., Pollock, D. A., Olmsted, R. N., & Brennan, P. J. (2008) · American Journal of Infection Control · doi:10.1016/j.ajic.2008.01.002 · Cited by 57 · see citing works
Monitoring the prevalence of chronic conditions: which data should we use?
Orueta, J. F., Nuño-Solínis, R., Mateos, M., Vergara, I., Gonzalo Grandes, O., & Esnaola, S. (2012) · BMC Health Services Research · doi:10.1186/1472-6963-12-365 · Cited by 56 · see citing works
Roth, C., Foraker, R. E., Payne, P., & Embí, P. J. (2014) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-14-36 · Cited by 46 · see citing works
Pathak, J., Kiefer, R. C., Bielinski, S. J., & Chute, C. G. (2012) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-3-10 · Cited by 37 · see citing works
Electronic health records: high-quality electronic data for higher-quality clinical research
Weiner, M. G., Lyman, J. A., Murphy, S. N., & Weiner, M. (2007) · Journal of Innovation in Health Informatics · doi:10.14236/jhi.v15i2.650 · Cited by 30 · see citing works
Niemi, K., Geary, S. M., Quinn, B., et al. (2009) · American Journal of Health-System Pharmacy · doi:10.2146/ajhp080143 · Cited by 25 · see citing works
Liljeqvist, G. T., Staff, M. P., Puech, M., et al. (2011) · BMC Public Health · doi:10.1186/1471-2458-11-435 · Cited by 23 · see citing works
Morgan, V. A., Valuri, G. M., Croft, M., Griffith, J. A., Shah, S., Young, D. J., & Jablensky, A. V. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq167 · Cited by 22 · see citing works
Tolar, M., & Balka, E. (2012) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2012.01.003 · Cited by 18 · see citing works
Sequist, T. D., Morong, S. M., Marston, A., Keohane, C. A., Cook, E. F., Orav, E. J., & Lee, T. H.-Y. (2011) · Journal of General Internal Medicine · doi:10.1007/s11606-011-1911-6 · Cited by 16 · see citing works
Secondary uses of clinical data in primary care
Teasdale, S., Bates, D. W., Kmetik, K. S., Suzewits, J., & Bainbridge, M. (2007) · Journal of Innovation in Health Informatics · doi:10.14236/jhi.v15i3.654 · Cited by 15 · see citing works
Detecting adverse drug events through data mining
Glasgow, J., & Kaboli, P. J. (2010) · American Journal of Health-System Pharmacy · doi:10.2146/ajhp090115 · Cited by 11 · see citing works
Clinical data mining and research in the allergy office
Dalan, D. (2010) · Current Opinion in Allergy and Clinical Immunology · doi:10.1097/aci.0b013e328337bce6 · Cited by 10 · see citing works
Pediatric Electronic Health Records and Research
Spooner, S. A., & Kirkendall, E. S. (2012) · Translational bioinformatics · doi:10.1007/978-94-007-5149-1_1 · Cited by 1 · see citing works
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Secondary Use of Electronic Health Record: Opportunities and Challenges
Shah, S. M., & Khan, R. A. (2020) · IEEE Access · doi:10.1109/access.2020.3011099 · Cited by 158 · see citing works
Indicators of mental disorders in UK Biobank—A comparison of approaches
Davis, K. A. S., Cullen, B., Adams, M., Brailean, A., Breen, G., Coleman, J. R. I., Dregan, A., Gaspar, H. A., Hübel, C., Lee, W., et al. (2019) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1796 · Cited by 111 · see citing works
- Authors
- Spiranovic, C., Matthews, A., Scanlan, J., & Kirkby, K. C.
- Affiliations
- University of Tasmania (AU)
- Corresponding author
- Spiranovic, Caroline
- Venue
- Advances in Mental Health
- Publisher
- Informa UK Limited
- Published
- 2015-07-21
- Volume
- 14
- Issue
- 1
- Pages
- 14-25
- ISSN
- 1446-7984 (ISSN-L)
- 1838-7357
- 1837-4905
- OpenAlex
- W1441192051
- Cited by
- 37
- References
- 45
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 6 · 2022: 7 · 2021: 1 · 2020: 5 · 2019: 3 · 2018: 4 · 2016: 4
- FWCI
- 5.15
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- cc-by
- Locations
- Advances in Mental Health (publishedVersion)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- electronic health records, secondary research, mental illness, data mining, biomarkers, gene-disease association, linked health records, disease surveillance, data quality, interoperability, mental health research
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