1. Representation of Social History Factors Across Age Groups: A Topic Analysis of Free-Text Social Documentation
Elizabeth A. Lindemann, BS1, Elizabeth S. Chen, PhD2,
Yan Wang, PhD3, Steven J. Skube, MD1, Genevieve B. Melton, MD, PhD1, 3
1Department of Surgery, 3 Institute for Health Informatics,
University of Minnesota, Minneapolis, MN;
2Center for Biomedical Informatics, Brown University, Providence, RI

2. No relationships with commercial interests to disclose
Disclosure
No relationships with commercial interests to disclose

3. Introduction Social Determinants Impact Patient Health
Social determinants of health (SDOH) play an important role in health status and outcomes of patients
40% of deaths caused by modifiable behavior patterns1
SDOH Differ for Pediatric and Adult Populations
SDOH of infant and pediatric patients are influenced by parents and guardians, specifically exposure-based
Topics broaden as individuals reach adolescence and young adulthood
Residence and living conditions prevalent for all ages
Examples of SDOH
1. McGinnis JM, Williams-Russo P, Knickman JR, The Case for More Active Policy Attention to Health Promotion. Health Affairs. 2002:

4. Introduction
Representation and Standardization of SDOH in the Electronic Health Record (EHR)
Increased EHR use provides opportunity to understand:
How SDOH is documented
What is valued by providers
How factors change over lifespan
National Academy of Medicine2 has recognized importance of standardizing SDOH entry for clinical decision-making
Be more explicit: how does standardization help healthcare – tie in discussion
2. Recommended Social and Behavioral Domains and Measures for Electronic Health Records, The National Academy of Medicine [cited /3/2017]. Available from

5. Prior Work
Our group has done foundational work in understanding the current state of SDOH representation in the EHR:
Aldekhyyel et al.3 and Lindemann et al.4 – occupation information in free-text comment fields in an EHR and from multiple clinical note sources
Chen et al.5, Wang et al.6, Winden et al.7, Carter et al.8 – tobacco, alcohol, and drug use within free-text fields of the EHR
Winden et al.9 – creation of social history schema
All multi investigator studies elim et al
3. Aldekhyyel R, Chen ES, Rajamani S, Wang Y, Melton GB. Content and Quality of Free-Text Occupation Documentation in the Electronic Health Record. AMIA Annu Symp Proc. 2016: Lindemann EA, Chen ES, Rajamani S, Manohar N, Wang Y, Melton GB. Representation of Occupation Information in Clinical Texts: An Analysis of Free-Text Clinical Documentation in Multiple Sources. AMIA Jt Summits on Transl Sci Proc In Press. 5. Chen ES, Carter EW, Sarkar IN, Winden TJ, Melton GB. Examining the use, contents, and quality of free-text tobacco use documentation in the Electronic Health Record. AMIA Annu Symp Proc Nov 14;2014: Wang Y, Chen ES, Pakhomov S, Arsoniadis E, Carter EW, Lindemann E, et al. Automated Extraction of Substance Use Information from Clinical Texts. AMIA Annu Symp Proc. 2015;2015: Winden TJ, Chen ES, Wang Y, Sarkar IN, Carter EW, Melton GB. Towards the Standardized Documentation of E-Cigarette Use in the Electronic Health Record for Population Health Surveillance and Research. AMIA Jt Summits Transl Sci Proc Mar 25;2015: Carter EW, Sarkar IN, Melton GB, Chen ES. Representation of Drug Use in Biomedical Standards, Clinical Text, and Research Measures. AMIA Annu Symp Proc. 2015;2015: Winden TJ, Chen ES, Lindemann E, Wang Y, Carter EW, Melton GB. Evaluating living situation, occupation, and hobby/activity information in the electronic health record. AMIA Annu Symp Proc. 2014:139.

6. Study Objective
At a high level, this study sought to provide a first step in understanding the variation in documentation of SDOH in free-text clinical documentation.

7. Overview of Approach
A large-scale automated topic analysis representative of Fairview Health Services (FHS) system
Granular analysis of free-text clinical documentation through manual annotation analysis
Comparative analysis

8. Study Setting Fairview Health Services Epic EHR
Integrated healthcare delivery system that serves large metropolitan area and rural parts of greater Minnesota
9 community hospitals across the state of Minnesota
Tertiary medical care center associated with University of Minnesota
Epic EHR
Free-text Social History (SH) Documentation section of the EHR

9. Age Groups Pediatric Age Groups Adult Age Groups Defined age groups:
– Pediatric patients: National Institute for Child Health and Human Development (NICHD)10 – Adult patients: U.S. Census11 guidelines
Pediatric Age Groups
Infant and Toddler
Birth to 24 months of age
Early Childhood
2-5 years
Middle Childhood
6-11 years
Early Adolescence
12-18 years
Standardized age groups…
Adult
Age Groups
Young Adulthood
19-44 years
Middle Adulthood
45-64 years
Older Adulthood
65 years and older
10. Whetzel PL, Noy NF, Shah NH, Alexander PR, Nyulas C, Tudorache T, Musen MA. BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res Jul;39(Web Server issue):W Epub 2011 Jun 14.
11. United States Census Briefs, US Census Bureau, Department of Commerce [cited /7/2017]. Available from

10. Methods: Automated Topic Analysis
Collected data to represent each patient in FHS with SH documentation one time between (n=187,920)
SH Documentation was extracted and pre-processed using an open source biomedical NLP pipeline12
Statements were parsed by Stanford Probabilistic Context-Free Grammars (PCFGs) parser13
Statements collected separately to account for sentences with more than one statement of social history
The R implementation of LDA14 was used to fit the topic models and compute the optimum number of topics
12. BioMedical Information Collection and Understanding System (BioMedICUS) [Internet]. [cited 8 March 2017]. Available from:
13. Klein D, Manning CD. Accurate Unlexicalized Parsing. Proc of the 41st Meeting of the Assoc for Comp Ling. 2003:
14. Gruen B, Hornik K, topicmodels: An R package for fitting topic models. Journal of Statistical Software. 2011;40:1-30.

11. Methods: Manual Analysis
Collected random patient selections of SH documentation between (n=1,400)
Using a previously developed annotation schema9
2 expert raters annotated text at the sentence level for 29 social history types
9. Winden TJ, Chen ES, Lindemann E, Wang Y, Carter EW, Melton GB. Evaluating living situation, occupation, and hobby/activity information in the electronic health record. AMIA Annu Symp Proc. 2014:139.

12. Methods: Example Sentences
Environmental History: The family lives in a 6 year old home in a rural setting.
Residence
Parent Relationship: Married
Marital Status
Lives with her husband and 3 healthy children.
Family
More about sentence level – give example
Add sentences

13. Methods: Comparative Analysis
Automated Topic Analysis
n=187,920
Manual Analysis
n=1,400
Centroid cluster sentences were mapped to annotation schema
Most frequent topics in automated topic analysis and manual analysis were compared
Coverage of SDOH topics in clinical documentation was evaluated

14. Results: Manual Analysis Demographics
Infant and Toddler
Early Childhood
Middle Childhood
Early Adolescence
Young Adulthood
Middle Adulthood
Older Adulthood
Total
Number of Patients
200
1,400
Gender – Male
111
110
116 92 46 58 80
613
Gender – Female 89 90 84
108
154
142
120
787
Total Annotations
1,101
1,126
1,170
1,158
1,929
1,060
791
8,335
* = 0.67, proportion agreement = 0.92

15. Results: Topic Analysis Demographics
Infant and Toddler
Early Childhood
Middle Childhood
Early Adolescence
Young Adulthood
Middle Adulthood
Older Adulthood
Total
Number of Patients
5,383
10,837
31,103
14,120
74,530
44,355
25,592
187,920
Gender – Male
2,781
5,683
6,673
6,599
18,934
15,220
8,979
64,869
Gender – Female
2,602
5,153
6,429
7,520
53,504
29,135
16,612
120,955
More about races

16. Intertopic Distance Map

17. Results: Topic Analysis (1)
Assigned Topic
Instances
Examples 1
SH Other
Seatbelt
Language
Abuse
Helmets
Primary Language Spoken: English
Do you/your family use safety helmets?
Abuse: Current or Past (Physical, Sexual, or Emotional)
Seatbelts used. 2
Residence
Location Names
House
Apartment
NAME lives with her parents and sister in PLACE.
Environmental History: The family lives in a 6 year old home in a rural setting.
Lives with parents and half sister in the upstairs of a house while grandmother and a few cousins live on the first floor of a house. 3
Living Situation Exposure
Smoke exposure
Safe
Guns
NO: Lead, smokers at home, radon, pool/spa, known TB exposure.
Do you feel safe in your home: Yes/No
No guns at home.

18. Results: Topic Analysis (2)
Assigned Topic
Instances
Examples 4
Family
Parents
Brother
Sister
Lives at home with mother, grandmother, aunt and 2 older half-siblings.
Lives with biological mother and maternal half sister. 5
Marital Status
Relationship
Married
Single
Parent Relationship: Married
Divorced-almost.
Lives with husband and 2 dogs. 6
Occupation
Work
Part-time/Full-time
Company Names
Patient did some part time work as a cashier for COMPANY in the past.
Mom is a homemaker.
Father is an engineer at COMPANY. 7
Living Situation
Lives with
Boyfriend/Significant Other (S.O.)
Siblings/Parents
Lives in PLACE with S.O. NAME and son, NAME.
NAME lives at home with his mother.
Lives with her husband and 3 healthy children.

19. Results: Topic Analysis (3)
Assigned Topic
Instances
Examples 8
Diet
Calcium
Food
Calcium intake: eats cheese and drinks a lot of milk.
Age solids introduced – 4 months, table food.
Balanced diet: Yes 9
Alcohol Use
Alcohol
Socially
Drinks
Alcohol use is < 1 alcoholic drinks per week
Describes intermittent problems with alcohol in terms of excessive drinking in the past.
Alcohol use is rare. 10
Tobacco Use
Cigarettes
Smoking
Exposure
They live in a house with no smoke exposure.
Grandmother smokes outside.
The patient has 20 yr hx of intermittent pipe and cigar use.

20. Results: Social History Other

21. Results: Diet

22. Results: Family

23. Breadth of SDOH representation across age groups
Discussion
Breadth of SDOH representation across age groups
Large areas of overlap between analyses
Manual – more granularity
Manual topics confirmed by automated topic analysis
25 of 29 topics represented
High volume of SH Other information for adults demonstrates changes in how social history is documented for pediatric and adult patients
Potential needs

24. Current representation of SDOH in clinical text
Discussion
Current representation of SDOH in clinical text
Context for how SDOH changes with age and how providers currently document in free-text documentation
Standardization would provide more robust documentation for clinical decision-making

25. Limitations – Gender and Demographics
Discussion
Limitations – Gender and Demographics
Representative of FHS population, but may not be representative of other areas or systems
Pediatric population is comprised of slightly more male than female patients
As patients enter adulthood, dramatic shift occurs towards more female patients
This population primarily identifies as ‘White’

26. Conclusion
Demonstrated changes in SDOH topics as individuals age
Breadth and depth of SDOH topics that impact health status
Serves as a basis for further NLP techniques and more robust tools
Further work to standardize SDOH topics are entered
More robust documentation and representation of social, behavioral, and environmental factors in the EHR

27. Acknowledgments
SFHERE (Social and Family History Extraction, Representation and Evaluation) Team
University of Minnesota/ Fairview Health Services
Genevieve Melton-Meaux, MD, PhD, FACMI
Sripriya Rajamani, MBBS, PhD, MPH
Elizabeth Lindemann, BS
Ranyah Aldekhyyel, PhD (c)
Yan Wang, PhD
Tamara Winden, PhD, MBA
Lindsay Bork, MBA
Serguei Pakhomov, PhD
Brown University
Elizabeth Chen, PhD, FACMI
Neil Sarkar, PhD, MLIS, FACMI
Ashley Lee, MS
Isabel Restrepo, PhD
Vivekanand Sharma, PhD
Paul Stey, PhD
University of Vermont/ University of Vermont Medical Center
Paul T. Rosenau, MD, MS
This work was supported in part by NIH/NLM grant R01LM and University of Minnesota Clinical and Translational Science NIH grant 8UL1TR

28. Elizabeth Lindemann: linde527@umn.edu
Thank you!
Contact Information
Elizabeth Lindemann:
Project Site