CoChat
Sign Up
Cited by 25Biomedical Text Mining and OntologiesMachine Learning in HealthcareAdvanced Text Analysis Techniques

Natural language processing-driven state machines to extract social factors from unstructured clinical documentation

Open access
Allen, Katie S, Hood, Dan R, Cummins, Jonathan, Kasturi, Suranga, Mendonca, Eneida A, Vest, Joshua R·2023·JAMIA Open
DOI 10.1093/jamiaopen/ooad024PMC PMC10112959
Open this paper in CoChat — free accountOpen PDF

Abstract

Lay Summary: Social factors, such as an individual’s housing, food, employment, and income situations, affect their overall health and well-being. As a result, data on patients’ social factors aid in clinical decision making, planning by hospital administrators and policy-makers, and enrich research studies with data representative of more factors influencing the life of an individual. Data on social factors can be collected at the time of a healthcare visit through screening questionnaires or are often documented in the clinical text as part of the social narrative. This study examines the use of natural language processing—a machine method to identify certain text within a larger document—to identify housing instability, financial insecurity, and unemployment from within the clinical notes. Using a relatively unsophisticated methodology, this study demonstrates strong performance in identifying these social factors, which will enable stakeholders to utilize these details in support of improved clinical care.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/jamiaopen/ooad024

Cites

8 of 8 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

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

Flagged first, then by citations

Research works printed but not linked: 55

  1. [1]Vrijheid M. The exposome: a new paradigm to study the impact of environment on health. Thorax 2014; 69 (9): 876– 8. 24906490
  2. [3]Hatef E, Ma X, Rouhizadeh M, Singh G, Weiner JP, Kharrazi H. Assessing the impact of social needs and social determinants of health on health care utilization: using patient- and community-level data. Popul Health Manag 2021; 24 (2): 222– 30. 32598228 PMC8349715
  3. [4]Kreuter MW, Thompson T, McQueen A, Garg R. Addressing social needs in health care settings: evidence, challenges, and opportunities for public health. Annu Rev Public Health 2021; 42 (1): 329– 44. 33326298 PMC8240195
  4. [6]Hatef E, Rouhizadeh M, Tia I, et al Assessing the availability of data on social and behavioral determinants in structured and unstructured electronic health records: a retrospective analysis of a multilevel health care system. JMIR Med Inform 2019; 7 (3): e13802. 31376277 PMC6696855
  5. [7]Feller DJ, Bear Don't Walk Iv OJ, Zucker J, Yin MT, Gordon P, Elhadad N. Detecting social and behavioral determinants of health with structured and free-text clinical data. Appl Clin Inform 2020; 11 (1): 172– 81. 32131117 PMC7056402
  6. [8]Weed LL. Medical records that guide and teach. N Engl J Med 1968; 278 (12): 652– 7. 5637250
  7. [9]Zander LI. Recording family and social history. J R Coll Gen Pract 1977; 27 (182): 518– 20. 616839 PMC2158499
  8. [10]Podder V, Lew V, Ghassemzadeh S. SOAP notes. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2022: 1–5. http://www.ncbi.nlm.nih.gov/books/NBK482263/. Accessed July 1, 2022. 29489268
  9. [11]Chen ES, Manaktala S, Sarkar IN, Melton GB. A multi-site content analysis of social history information in clinical notes. AMIA Annu Symp Proc 2011; 2011: 227– 36. 22195074 PMC3243209
  10. [12]Patra BG, Sharma MM, Vekaria V, et al Extracting social determinants of health from electronic health records using natural language processing: a systematic review. J Am Med Inform Assoc 2021; 28 (12): 2716– 27. 34613399 PMC8633615
  11. [14]Reading Turchioe M, Volodarskiy A, Pathak J, Wright DN, Tcheng JE, Slotwiner D. Systematic review of current natural language processing methods and applications in cardiology. Heart 2022; 108 (12): 909– 16. 34711662 PMC9046466
  12. [17]Walsh C, Elhadad N. Modeling clinical context: rediscovering the social history and evaluating language from the clinic to the wards. AMIA Jt Summits Transl Sci Proc 2014; 2014: 224– 31. 25717417 PMC4333691
  13. [18]Kotay A, Huang JL, Jordan WB, Korin E. Exploring family and social context through the electronic health record: physicians’ experiences. Fam Syst Health 2016; 34 (2): 92– 103. 27149050
  14. [19]Gundlapalli AV, Carter ME, Palmer M, et al Using natural language processing on the free text of clinical documents to screen for evidence of homelessness among US veterans. AMIA Annu Symp Proc 2013; 2013: 537– 46. 24551356 PMC3900197
  15. [20]Bejan CA, Angiolillo J, Conway D, et al Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records. J Am Med Inform Assoc 2018; 25 (1): 61– 71. 29016793 PMC6080810
  16. [21]Zech J, Husk G, Moore T, Kuperman GJ, Shapiro JS. Identifying homelessness using health information exchange data. J Am Med Inform Assoc 2015; 22 (3): 682– 7. 25670759 PMC11737380
  17. [22]Hatef E, Singh Deol G, Rouhizadeh M, et al Measuring the value of a practical text mining approach to identify patients with housing issues in the free-text notes in electronic health record: findings of a retrospective cohort study. Front Public Health 2021; 9: 697501. 34513783 PMC8429931
  18. [23]Hatef E, Rouhizadeh M, Nau C, et al Development and assessment of a natural language processing model to identify residential instability in electronic health records’ unstructured data: a comparison of 3 integrated healthcare delivery systems. JAMIA Open 2022; 5 (1): ooac006. 35224458 PMC8867582
  19. [24]Frederick TJ, Chwalek M, Hughes J, Karabanow J, Kidd S. How stable is stable? Defining and measuring housing stability: defining and measuring housing stability. J Community Psychol 2014; 42 (8): 964– 79.
  20. [25]Perron BE, Victor BG, Bushman G, et al Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning. Child Abuse Negl 2019; 98: 104180. 31521909
  21. [26]Stemerman R, Arguello J, Brice J, Krishnamurthy A, Houston M, Kitzmiller R. Identification of social determinants of health using multi-label classification of electronic health record clinical notes. JAMIA Open 2021; 4 (3): ooaa069. 34514351 PMC8423426
  22. [27]Lybarger K, Ostendorf M, Yetisgen M. Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction. J Biomed Inform 2021; 113: 103631. 33290878 PMC7856628
  23. [28]Gordon DD, Patel I, Pellegrini AM, Perlis RH. Prevalence and nature of financial considerations documented in narrative clinical records in intensive care units. JAMA Netw Open 2018; 1 (7): e184178. 30646344 PMC6324587
  24. [29]Han S, Zhang RF, Shi L, et al Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing. J Biomed Inform 2022; 127: 103984. 35007754
  25. [30]Rouillard CJ, Nasser MA, Hu H, Roblin DW. Evaluation of a natural language processing approach to identify social determinants of health in electronic health records in a diverse community cohort. Med Care 2022; 60 (3): 248– 55. 34984989
  26. [31]Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc 2011; 18 (5): 544– 51. 21846786 PMC3168328
  27. [33]National Alliance to End Homelessness. State of Homelessness: 2022 Edition. 2022. https://endhomelessness.org/homelessness-in-america/homelessness-statistics/state-of-homelessness/. Accessed February 13, 2023.
  28. [34]Vera L, Reed KK, Rose E, et al Prevalence of housing insecurity in survivors of traumatic injury. Am Surg 2022; 88 (9): 2274– 9. 35608389
  29. [35]D’Alessandro D, Appolloni L. Housing and health: an overview. Ann Ig 2020; 32(5 Supple 1): 17– 26. 33146364
  30. [36]Stringhini S, Sabia S, Shipley M, et al Association of socioeconomic position with health behaviors and mortality. JAMA 2010; 303 (12): 1159– 66. 20332401 PMC2918905
  31. [37]Braveman P, Gottlieb L. The social determinants of health: it’s time to consider the causes of the causes. Public Health Rep 2014; 129 (Suppl 2): 19– 31. PMC3863696 24385661
  32. [38]Burgard SA, Seefeldt KS, Zelner S. Housing instability and health: findings from the Michigan Recession and Recovery Study. Soc Sci Med 2012; 75 (12): 2215– 24. 22981839
  33. [39]Sinclair RR, Cheung JH. Money matters: recommendations for financial stress research in occupational health psychology. Stress Health 2016; 32 (3): 181– 93. 27400815
  34. [41]Dooley D. Unemployment, underemployment, and mental health: conceptualizing employment status as a continuum. Am J Community Psychol 2003; 32 (1–2): 9– 20. 14570431
  35. [42]McDonald CJ, Overhage JM, Barnes M, et al; INPC Management Committee. The Indiana network for patient care: a working local health information infrastructure. Health Affairs 2005; 24 (5): 1214– 20. 16162565
  36. [43]Schwartz BS, Stewart WF, Godby S, et al Body mass index and the built and social environments in children and adolescents using electronic health records. Am J Prev Med 2011; 41 (4): e17–28. 21961475
  37. [47]Karttunen L, Chanod JP, Grefenstette G, Schille A. Regular expressions for language engineering. Nat Lang Eng 1996; 2 (4): 305– 28.
  38. [48]Sai Prashanthi G, Deva A, Vadapalli R, Das AV. Automated categorization of systemic disease and duration from electronic medical record system data using finite-state machine modeling: prospective validation study. JMIR Form Res 2020; 4 (12): e24490. 33331823 PMC7775202
  39. [49]Weiner M, Dexter PR, Heithoff K, et al Identifying and characterizing a chronic cough cohort through electronic health records. Chest 2021; 159 (6): 2346– 55. 33345951
  40. [50]Duke J, Chase M, Poznanski-Ring N, Martin J, Fuhr R, Chatterjee A. Natural language processing to improve identification of peripheral arterial disease in electronic health data. J Am Coll Cardiol 2016; 67 (13): 2280.
  41. [52]Weerahandi HM, Horwitz LI, Blecker SB. Diabetes phenotyping using the electronic health record. J Gen Intern Med 2020; 35 (12): 3716– 8. 32948954 PMC7728952
  42. [53]Wong A, Plasek JM, Montecalvo SP, Zhou L. Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacotherapy 2018; 38 (8): 822– 41. 29884988
  43. [54]Feller DJ, Zucker J, Srikishan B, et al Towards the inference of social and behavioral determinants of sexual health: development of a gold-standard corpus with semi-supervised learning. Appl Clin Inform 2020; 11 (01): 172– 81. PMC6371339 30815082
  44. [56]Chang KW, Prabhakaran V, Ordonez V. Bias and fairness in natural language processing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China: Association for Computational Linguistics; 2019.
  45. [57]Institute of Medicine (U.S.), editor. Capturing Social and Behavioral Domains in Electronic Health Records. Phase 1. Washington, DC: The National Academies Press; 2014: 123p. 24757748
  46. [60]Berg M. Practices of reading and writing: the constitutive role of the patient record in medical work. Sociol Health Illness 1996; 18 (4): 499– 524.
  47. [61]Bansler J, Havn E, Mønsted T, Schmidt K, Svendsen JH. Physicians’ progress notes. In: Bertelsen OW, Ciolfi L, Grasso MA, Papadopoulos GA, eds. ECSCW 2013: Proceedings of the 13th European Conference on Computer Supported Cooperative Work, 21–25 September 2013, Paphos, Cyprus. London: Springer, London; 2013: 123– 42. http://link.springer.com/10.1007/978-1-4471-5346-7_7. Accessed August 31, 2022. · DOI
  48. [62]Weiner SJ, Wang S, Kelly B, Sharma G, Schwartz A. How accurate is the medical record? A comparison of the physician’s note with a concealed audio recording in unannounced standardized patient encounters. J Am Med Inform Assoc 2020; 27 (5): 770– 5. 32330258 PMC7647276
  49. [63]Bardsley M, Billings J, Dixon J, Georghiou T, Lewis GH, Steventon A. Predicting who will use intensive social care: case finding tools based on linked health and social care data. Age Ageing 2011; 40 (2): 265– 70. 21252036
  50. [64]Nijhawan AE, Clark C, Kaplan R, Moore B, Halm EA, Amarasingham R. An electronic medical record-based model to predict 30-day risk of readmission and death among HIV-infected inpatients. J Acquir Immune Defic Syndr 2012; 61 (3): 349– 58. 23095935
  51. [65]Hao S, Jin B, Shin AY, et al Risk prediction of emergency department revisit 30 days post discharge: a prospective study. PLoS One 2014; 9 (11): e112944. 25393305 PMC4231082
  52. [66]Vest JR, Ben-Assuli O. Prediction of emergency department revisits using area-level social determinants of health measures and health information exchange information. Int J Med Inform 2019; 129: 205– 10. 31445257
  53. [67]Vest JR, Menachemi N, Grannis SJ, et al Impact of risk stratification on referrals and uptake of wraparound services that address social determinants: a stepped wedged trial. Am J Prev Med 2019; 56 (4): e125–33– e133. 30772150
  54. [68]Gottlieb LM, Tirozzi KJ, Manchanda R, Burns AR, Sandel MT. Moving electronic medical records upstream: incorporating social determinants of health. Am J Prev Med 2015; 48 (2): 215– 8. 25217095
  55. [69]Bazemore AW, Cottrell EK, Gold R, et al “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Inform Assoc 2016; 23 (2): 407– 12. 26174867 PMC11740537

Cited by

Sorted by citations

Authors
Allen, K. S., Hood, D. R., Cummins, J., Kasturi, S., Mendonca, E. A., & Vest, J. R.
Affiliations
  • Regenstrief Institute (US)
  • Center for Biomedical Informatics, Regenstrief Institute, Inc. , Indianapolis, Indiana, USA
  • Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA
Corresponding author
Allen, Katie S
ORCID
Venue
JAMIA Open
Publisher
Oxford University Press (OUP)
Published
2023-04-06
Volume
6
Issue
2
Pages
ooad024-ooad024
ISSN
2574-2531 (ISSN-L)
DOI
10.1093/jamiaopen/ooad024
PMC
PMC10112959
OpenAlex
W4366332256
Cited by
25
References
8
Citations by year
2026: 1 · 2025: 10 · 2024: 10 · 2023: 4
FWCI
2.31
Type
journal article
Language
en
Open access
Licence
Locations
  • JAMIA Open (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion, cc-by-nc)
Funders
Indiana University Addictions Grand Challenge
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
Keywords
social determinants of health, clinical documentation, natural language processing, rule-based NLP, state machines, housing insecurity, financial hardship, unemployment, clinical text, n-gram features

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