Maximizing the Use of Social and Behavioural Information from Secondary Care Mental Health Electronic Health Records
Open accessAbstract
Purpose The contribution of social and behavioural factors in the development of mental health conditions and treatment effectiveness is widely supported, yet there are weak population level data sources on social and behavioural determinants of mental health. Enriching these data gaps will be crucial to accelerating precision medicine. Some have suggested the broader use of electronic health records (EHR) as a source of non-clinical determinants, although social and behavioural information are not systematically collected metrics in EHRs, internationally. Objective In this commentary, we highlight the nature and quality of key available structured and unstructured social and behavioural data using a case example of value counts from secondary mental health data available in the UK from the UK Clinical Record Interactive Search (CRIS) database; highlight the methodological challenges in the use of such data; and possible solutions and opportunities involving the use of natural language processing (NLP) of unstructured EHR text. Conclusions Most structured non-clinical data fields within secondary care mental health EHR data have too much missing data for adequate use. The utility of other non-clinical fields reported semi-consistently (e.g., ethnicity and marital status) is entirely dependent on treating them appropriately in analyses, quantifying the many reasons behind missingness in consideration of selection biases. Advancements in NLP offer new opportunities in the exploitation of unstructured text from secondary care EHR data particularly given that clinical notes and attachments are available in large volumes of patients and are more routinely completed by clinicians. Tackling ways to re-use, harmonize, and improve our existing and future secondary care mental health data, leveraging advanced analytics such as NLP is worth the effort in an attempt to fill the data gap on social and behavioural contributors to mental health conditions and will be necessary to fulfill all of the domains needed to inform personalized interventions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Despite the wealth of information contained in medical records, socio-behavioral data are commonly excluded.
“Despite the wealth of information contained in medical records, socio-behavioral data are commonly excluded [22,23].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [23]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Closing the gap in a generation: health equity through action on the social determinants of health
Marmot, M. G., Friel, S., Bell, R., Houweling, T. A. J., & Taylor, S. A. J. (2008) · The Lancet · doi:10.1016/s0140-6736(08)61690-6 · Cited by 8,238 · see citing works
The Inevitable Application of Big Data to Health Care
Murdoch, T. B., & Detsky, A. S. (2013) · JAMA · doi:10.1001/jama.2013.393 · Cited by 1,676 · see citing works
Social determinants of mental health
Allen, J., Balfour, R., Bell, R., & Marmot, M. (2014) · International Review of Psychiatry · doi:10.3109/09540261.2014.928270 · Cited by 1,411 · see citing works
Savova, G. K., Kipper-Schuler, K. C., Hurdle, J. F., & Meystre, S. M. (2008) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1638592 · Cited by 895 · see citing works
WALSH, R. N. (2011) · American Psychologist · doi:10.1037/a0021769 · Cited by 705 · see citing works
Andermann, A. (2018) · Public Health Reviews · doi:10.1186/s40985-018-0094-7 · Cited by 323 · see citing works
Integrating Data On Social Determinants Of Health Into Electronic Health Records
Cantor, M. N., & Thorpe, L. (2018) · Health Affairs · doi:10.1377/hlthaff.2017.1252 · Cited by 308 · see citing works
Moving Electronic Medical Records Upstream
Gottlieb, L. M., Tirozzi, K. J., Manchanda, R., Burns, A. R., & Sandel, M. T. (2015) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2014.07.009 · Cited by 178 · see citing works
Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis
Beaulieu‐Jones, B. K., Lavage, D. R., Snyder, J. W., Moore, J. H., Pendergrass, S. A., & Bauer, C. R. (2018) · JMIR Medical Informatics · doi:10.2196/medinform.8960 · Cited by 169 · see citing works
Screening for Social Determinants of Health: The Known and Unknown
Davidson, K. W., & McGinn, T. (2019) · JAMA · doi:10.1001/jama.2019.10915 · Cited by 106 · see citing works
Walsh, A. J., Kormilitzin, A., Hinds, C., Sexton, V., Brain, O., Keshav, S., Uhlig, H. H., Geddes, J. R., Goodwin, G. M., Peters, M., et al. (2018) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjy184 · Cited by 77 · see citing works
Self-citationMedišauskaitė, A., & Kamau, C. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027362 · Cited by 69 · see citing works
Downs, J. M., Ford, T. J., Stewart, R. J., Epstein, S., Shetty, H., Little, R., Jewell, A., Broadbent, M. T. M., Deighton, J., Mostafa, T., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-024355 · Cited by 67 · see citing works
Natural language processing for structuring clinical text data on depression using UK-CRIS
Vaci, N., Liu, Q., Kormilitzin, A., Crescenzo, F. D., Kurtulmuş, A., Harvey, J., O’Dell, B., Innocent, S. H. S., Tomlinson, A., Cipriani, A., et al. (2020) · Evidence-Based Mental Health · doi:10.1136/ebmental-2019-300134 · Cited by 64 · see citing works
Self-citationRiley, W. T., Nilsen, W., Manolio, T. A., Masys, D. R., & Lauer, M. S. (2015) · Translational Behavioral Medicine · doi:10.1007/s13142-015-0320-5 · Cited by 58 · see citing works
Petersen, I., Welch, C. A., Nazareth, I., Walters, K. R., Marston, L., Morris, R. W., Carpenter, J. R., Morris, T. P., & Pham, T. M. (2019) · Clinical Epidemiology · doi:10.2147/clep.s191437 · Cited by 55 · see citing works
Tomlinson, A., Furukawa, T. A., Efthimiou, O., Salanti, G., De Crescenzo, F., Singh, I., & Cipriani, A. (2020) · Evidence Based Mental Health · doi:10.1136/ebmental-2019-300118 · Cited by 54 · see citing works
Self-citationSenior, M., Burghart, M., Yu, R., Kormilitzin, A., Liu, Q., Vaci, N., Nevado‐Holgado, A. J., Pandit, S. A., Zlodre, J., & Fazel, S. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00268 · Cited by 47 · see citing works
Self-citationMorrill, J., Kormilitzin, A., Nevado‐Holgado, A. J., Swaminathan, S., Howison, S., & Lyons, T. (2019) · Computing in cardiology · doi:10.22489/cinc.2019.014 · Cited by 44 · see citing works
Self-citationThe True Colours Remote Symptom Monitoring System: A Decade of Evolution
Goodday, S., Atkinson, L. Z., Goodwin, G. M., Saunders, K. E. A., South, M., Mackay, C. E., Denis, M., Hinds, C., Attenburrow, M.-J., Davies, J., et al. (2019) · Journal of Medical Internet Research · doi:10.2196/15188 · Cited by 43 · see citing works
Self-citationHollister, B. M., & Bonham, V. L. (2018) · The AMA Journal of Ethic · doi:10.1001/amajethics.2018.873 · Cited by 31 · see citing works
Morrill, J., Kormilitzin, A., Nevado‐Holgado, A. J., Swaminathan, S., Howison, S., & Lyons, T. (2019) · Computing in cardiology · doi:10.23919/cinc49843.2019.9005805 · Cited by 22 · see citing works
Self-citationKormilitzin, A., Walsh, A. J., Matini, L., Kantschuster, R., Lepetyukh, M., Wilson, J., Brain, O., Palmer, R. L., Ambrose, T., Satsangi, J., et al. (2020) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjz203.412 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
3 of the 26 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- M.K.A. Hofer, P. Goldberg, A. Nevado-Holgado, Few-shot Learning for Named Entity Recognition in Medical Text. arXiv. 2018;preprint arXiv:1811.05468. · arXiv
- A. Kormilitzin, N. Vaci, Q. Liu, A. Nevado-Holgado. Med7: a transferable clinical natural language processing model for electronic health records, 2020. arXiv pre- print arXiv:2003.01271. · arXiv
- J. Devlin, M.W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirec- tional transformers for language understanding, 2018. arXiv preprint arXiv:1810. 04805.
- A.B. Kormilitzin, K.E.A. Saunders, P.J. Harrison, J.R. Geddes, T.J. Lyons, Application of the signature method to pattern recognition in the CEQUEL clinical trial. arXiv preprint arXiv:160602074. 2016.
- A. Kormilitzin, K.E. Saunders, P.J. Harrison, J.R. Geddes, T. Lyons, Detecting early signs of depressive and manic episodes in patients with bipolar disorder using thesignature-based model. arXiv preprint arXiv:170801206, 2017.
Cited by
Sorted by citations
Patra, B. G., Sharma, M. M., Vekaria, V., Adekkanattu, P., Patterson, O. V., Glicksberg, B., Lepow, L. A., Ryu, E., Biernacka, J. M., Furmanchuk, A., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab170 · Cited by 248 · see citing works
Han, S., Zhang, R. F., Shi, L., Richie, R., Liu, H., Tseng, A., Quan, W., Ryan, N., Brent, D., & Tsui, F. R. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103984 · Cited by 139 · see citing works
Data privacy in healthcare: Global challenges and solutions
Conduah, A. K., Ofoe, S. H., & Siaw-Marfo, D. (2025) · Digital Health · doi:10.1177/20552076251343959 · Cited by 142 · see citing works
Liu, Q., Vaci, N., Koychev, I. G., Kormilitzin, A., Li, Z., Cipriani, A., & Nevado‐Holgado, A. J. (2022) · BMC Medicine · doi:10.1186/s12916-022-02250-2 · Cited by 42 · see citing works
Self-citationHarnessing the potential of machine learning and artificial intelligence for dementia research
Ranson, J. M., Bucholc, M., Lyall, D. M., Newby, D., Winchester, L. M., Oxtoby, N. P., Veldsman, M., Rittman, T., Marzi, S. J., Skene, N., et al. (2023) · Brain Informatics · doi:10.1186/s40708-022-00183-3 · Cited by 39 · see citing works
Self-citationLi, Z., Kormilitzin, A., Fernandes, M., Vaci, N., Liu, Q., Newby, D., Goodday, S., Smith, T. D., Nevado‐Holgado, A. J., & Winchester, L. M. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104704 · Cited by 26 · see citing works
Self-citationRichie, R., Ruiz, V. M., Han, S., Shi, L., & Tsui, F. (. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad046 · Cited by 25 · see citing works
Costa, T., Menzat, B., Engelthaler, T., Fell, B., Franarin, T., Roque, G., Wei, Y., Zhang, X., & McAllister-Williams, R. H. (2022) · Journal of Psychopharmacology · doi:10.1177/02698811221090628 · Cited by 22 · see citing works
Hindiyeh, N. A., Riskin, D. J., Alexander, K., Cady, R. K., & Kymes, S. M. (2022) · The Journal of Headache and Pain · doi:10.1186/s10194-022-01493-x · Cited by 15 · see citing works
Noaeen, M., Amini, S., Bhasker, S., Ghezelsefli, Z., Ahmed, A., Jafarinezhad, O., & Abad, Z. S. H. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc40787.2023.10340232 · Cited by 7 · see citing works
An efficient representation of chronological events in medical texts
Kormilitzin, A., Vaci, N., Liu, Q., Ni, H., Nenadić, G., & Nevado‐Holgado, A. J. (2020) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/2020.louhi-1.11 · Cited by 8 · see citing works
Self-citationA Pilot Study Using Natural Language Processing to Explore Textual Electronic Mental Healthcare Data
Delanerolle, G., Bouchareb, Y., Shetty, S., Cavalini, H. F. S., & Phiri, P. (2025) · Informatics · doi:10.3390/informatics12010028 · Cited by 8 · see citing works
Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors
Yen, C., Epling, J. W., Rockwell, M., & Vaughn-Cooke, M. (2026) · Diagnostics · doi:10.3390/diagnostics16020347 · Cited by 6 · see citing works
Clustering digital mental health perceptions using transformer-based models
Ibitoye, A., Oladimeji, O. O., & Afe, O. F. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100262 · Cited by 6 · see citing works
Stressful life events in electronic health records: a scoping review
Scherbakov, D., Mollalo, A., & Lenert, L. A. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae023 · Cited by 6 · see citing works
Yan, L., & Albarrak, A. S. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104196 · Cited by 6 · see citing works
Digital Transformation in Measuring Social Determinants of Health
Liu, C., Wang, W. Y. C., & Khan, G. F. (2023) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.328530 · Cited by 6 · see citing works
Abulibdeh, R., Tu, K., & Sejdić, E. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127928 · Cited by 4 · see citing works
Kariotis, T. C., Prictor, M., Gray, K. M., & Chang, S. (2025) · JMIR Medical Informatics · doi:10.2196/64162 · Cited by 4 · see citing works
Myers, J. A. C., Stafford, T., Koychev, I. G., Perneczky, R., Bandmann, O., & Vaci, N. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70149 · Cited by 3 · see citing works
Self-citationPrabhugouda, A., & Asra, S. (2025) · Sensing and Imaging · doi:10.1007/s11220-025-00589-3 · Cited by 3 · see citing works
Harnessing digital health data for suicide prevention and care: A rapid review
Bennett‐Poynter, L., Kundurthi, S., Besa, R., Joyce, D. W., Kormilitzin, A., Shen, N., Sunwoo, J., Szkudlarek, P., Sequiera, L., & Sikstrom, L. (2025) · Digital Health · doi:10.1177/20552076241308615 · Cited by 3 · see citing works
Self-citationHummel, L., Lorenz-Artz, K. C. A. G., Bierbooms, J. J. P. A., & Bongers, I. M. B. (2025) · JMIR Medical Informatics · doi:10.2196/74240 · Cited by 3 · see citing works
Swirsky, E. S., Boyd, A. D., Gu, C., Burke, L., Doorenbos, A. Z., Ezenwa, M. O., Knisely, M. R., Leigh, J. W., Li, H., Mandernach, M. W., et al. (2023) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2023.101218 · Cited by 2 · see citing works
Abulibdeh, R., Tu, K., & Sejdić, E. (2025) · Journal of Primary Care & Community Health · doi:10.1177/21501319251404193 · Cited by 2 · see citing works
Kariotis, T. C., Prictor, M., Gray, K. M., & Chang, S. (2025) · SSM - Health Systems · doi:10.1016/j.ssmhs.2025.100118 · Cited by 1 · see citing works
Kormilitzin, A., Joyce, D. W., Tsiachristas, A., Borschmann, R., Kapur, N., & Geulayov, G. (2026) · JMIR Mental Health · doi:10.2196/87586 · Cited by 1 · see citing works
Self-citationThe Emerging Role of AI in Dementia Research and Healthcare
Ranson, J. M., Bucholc, M., Lyall, D. M., Newby, D., Winchester, L. M., Oxtoby, N. P., Veldsman, M., Rittman, T., Marzi, S. J., Skene, N., et al. (2022) · Brain informatics and health · doi:10.1007/978-981-19-5272-2_4 · Cited by 1 · see citing works
Self-citationDigital Biomarkers and Passive Digital Indicators of Generalized Anxiety Disorder
Heinz, M. V., Price, G. D., Song, S. H., Bhattacharya, S., & Jacobson, N. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-10698-9_2 · Cited by 1 · see citing works
Delanerolle, G., Shetty, S., Cavalini, H. F. S., & Phiri, P. (2022) · doi:10.2196/preprints.37897
Hummel, L. M., Lorenz-Artz, K., Bierbooms, J. J. P. A., & Bongers, I. M. B. (2025) · doi:10.2196/preprints.74240
Kormilitzin, A., Joyce, D. W., Tsiachristas, A., Borschmann, R., Kapur, N., & Geulayov, G. (2025) · medRxiv · doi:10.1101/2025.10.27.25338892
Self-citationKormilitzin, A., Joyce, D. W., Tsiachristas, A., Borschmann, R., Kapur, N., & Geulayov, G. (2025) · doi:10.2196/preprints.87586
Self-citationLiu, Q., Vaci, N., Koychev, I. G., Kormilitzin, A., Li, Z., Cipriani, A., & Nevado‐Holgado, A. J. (2022) · Figshare · doi:10.6084/m9.figshare.19101743
Self-citation
- Authors
- Goodday, S. M., Kormilitzin, A., Vaci, N., Liu, Q., Cipriani, A., Smith, T., & Nevado-Holgado, A.
- Affiliations
- University of Oxford (GB)
- Oxford Health NHS Foundation Trust (GB)
- Corresponding author
- Goodday, S.M.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 107
- Pages
- 103429
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W3019725509
- Cited by
- 34
- References
- 26
- Citations by year
- 2026: 2 · 2025: 14 · 2024: 1 · 2023: 6 · 2022: 9 · 2021: 1 · 2020: 1
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Explore Bristol Research (publishedVersion, other-oa)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Funders
- University of Oxford
- 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, natural language processing, clinical text, missing data, selection bias, data harmonization, precision medicine, ethnicity, marital status
- MeSH terms
- Humans, Mental Health, Natural Language Processing, Databases, Factual, Electronic Health Records, Secondary Care, Secondary Health Care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
