Presented at Society for Gynecologic Surgery (SGS) Annual Meeting 2017. Collaborative Project. Mayo Clinic and W2O Group

1. ©2017 MFMER | slide-1
Listening to the HysterSisters®:
Online Conversations about Hysterectomy Recovery
Arpit Davé, M.D. | Mayo Clinic Arizona
Andy Boothe, MSSTC | W2O Group
Jeff Byrne | W2O Group
Helene Brashear, Ph.D. | W2O Group
Yash Gad, Ph.D. | W2O Group
Johnny Yi, M.D. | Mayo Clinic Arizona
@mayogynecology
@w2ogroup

2. ©2017 MFMER | slide-2
Disclosures
W2O Group employs AB, JB, HB and YG.
W2O Group lists Intuitive Surgical, Inc. as a client.
No funding from Intuitive Surgical, Inc. was obtained for this study.
Mayo Clinic authors have no disclosures.

3. ©2017 MFMER | slide-3
Background
Patient
Driven
Care
Meaningless
Use
CAHPSPROM

4. ©2017 MFMER | slide-4
“A ‘social advocacy’ health education website
dedicated to medical and emotional issues
surrounding the hysterectomy experience and
gynecologic-related conditions and illnesses […]”
Accessed February 24, 2017 from: http://www.hystersisters.com/vb2/view_aboutus.html

5. ©2017 MFMER | slide-5
Objective
• Primary objective:
• Utilize semantic analysis to identify common
patient concerns regarding hysterectomy
recovery
• Secondary objective:
• Identify differences in patient engagement
about hysterectomy recovery based on
hysterectomy type

6. ©2017 MFMER | slide-6
Methods

7. ©2017 MFMER | slide-7
Methods – Initial Comparison
• Three-word cluster frequencies between subject
headings and content replies

8. ©2017 MFMER | slide-8
Results
Subjects
• Symptom (60%)
• hysterectomy back pain
• hysterectomy swelly belly
• hysterectomy feel blue
• Question (20%)
• hysterectomy how long
• hysterectomy anyone else
• Other (20%)
• hysterectomy steri strip
• hysterectomy back work
• hysterectomy vaginal cuff
Replies
• Sympathy/Empathy
• call your doctor
• feel better soon
• does get better
• Advice
• take stool soften
• take pain med
• listen your body

9. ©2017 MFMER | slide-9
Methods – Secondary Comparison
• Creation of a symptom keyword taxonomy
• Iterative process using keywords identified
from initial comparison
• Symptom mentions by hysterectomy route
• Overall frequency
• Time survival

10. ©2017 MFMER | slide-10

11. ©2017 MFMER | slide-11
Methods
• Population limits
• Users provided hysterectomy type and date
• From 1/1/2005 to 9/30/2016
• Only threads from first 12 weeks after
hysterectomy
• Selected only subject heading from first post
in each thread

12. ©2017 MFMER | slide-12
33,311
Hysterectomies
• 80,704 threads
• 42.4% have ≥1 symptom

13. ©2017 MFMER | slide-13

14. ©2017 MFMER | slide-14

15. ©2017 MFMER | slide-15
Abdominal
(n=13,306)
Vaginal
(n=10,589)
Laparoscopic
(n=9,416)
TAH
(total abdominal)
TVH
(total vaginal)
TLH
(total laparoscopic)
SAH
(supracervical abdominal)
LAVH
(laparoscopically-assisted
vaginal)
LSH
(laparoscopic supracervical)
TAH/SAH
(either, not specified)
DVH
(Da Vinci)
SILS/LESS
(single-incision laparoscopy)

16. ©2017 MFMER | slide-16

17. ©2017 MFMER | slide-17
Symptom Mention Frequency
Vaginal Laparoscopic
Pain 0.98% 0.21%
Bleeding -1.46% 3.03%**
Hormones & Emotions 0.28% -0.49%
Digestion 0.41% 1.55%
Sleep & Fatigue -1.69%* -2.22%**
Urinary 2.29%*** -0.50%
Intimacy 2.38%*** -0.25%
Swelling -3.04%*** -2.94%**
* p<0.05 | ** p<0.005 | *** p<0.005

18. ©2017 MFMER | slide-18
Mention Survival Interquartiles (in Days)
Vaginal Laparoscopic
Pain -4.0*** -7.0***
Bleeding -2.0*** -1.6**
Hormones & Emotions -5.6*** -5.6***
Digestion -2.3*** -4.0***
Sleep & Fatigue -3.6** -6.3***
Urinary -4.6*** -7.6***
Intimacy 1 -0.3
Swelling -4.0** -7.0***
* p<0.05 | ** p<0.005 | *** p<0.005

19. ©2017 MFMER | slide-19
Symptom Mention Survival Curves
Pain Digestion
Days Days

20. ©2017 MFMER | slide-20
Conversational Perspectives
• Engagement needs change over recovery
• Most activity in first three weeks
• Peaks at 1, 3, and 6 weeks
• Engagement extends past 6 weeks
• Engagement needs differ by hysterectomy route
• Bleeding after laparoscopy procedures
• Urination after vaginal procedures

21. ©2017 MFMER | slide-21
Limitations
• Taxonomy is subjective & incomplete
• Motivations for posting not addressed
• SYMPTOM MENTION ≠ COMPLAINT
• Patients driven to post by a need to engage
• Expectation vs. experience mismatch
• Fill knowledge gaps
• Important subgroups not addressed
• Ovary status
• Prolapse/incontinence

22. ©2017 MFMER | slide-22
Conclusions
• Unique approach to understanding
hysterectomy recovery
• Largest self-reported hysterectomy data set
• Patient-driven information
• Procedure-specific, actionable information

23. ©2017 MFMER | slide-23
Future Directions
• Motivations
• Translating patient experience data into practice
• Web portals
• “Just-in-time” information delivery
• Patient (and provider) satisfaction
• Collaborative projects
• Data mining
• Intellectual property

24. ©2017 MFMER | slide-24
Acknowledgements
for the leeway and enthusiasm
to say YES to this
cooperative project.

25. ©2017 MFMER | slide-25
References
1. Wright JD, Herzog TJ, Tsui J, et al. Nationwide trends in the performance of inpatient hysterectomy in the United
States. Obstet Gynecol. 2013;122(2 Pt 1):233-241. doi:10.1097/AOG.0b013e318299a6cf.
2. Chapron C, Dubuisson JB, Ansquer Y, Fernandez B. Total hysterectomy for benign pathologies. Laparoscopic
surgery does not seem to increase the risk of complications. J Gynecol Obs Biol Reprod. 1998;27(1):55-61.
3. Chapron C, Querleu D, Bruhat MA, et al. Surgical complications of diagnostic and operative gynaecological
laparoscopy: A series of 29,966 cases. Hum Reprod. 1998;13(4):867-872. doi:10.1093/humrep/13.4.867.
4. Herling SF, Møller AM, Palle C, Thomsen T. Health-related quality of life after robotic-assisted laparoscopic
hysterectomy for women with endometrial cancer - A prospective cohort study. Gynecol Oncol. 2016;140(1):107-
113. doi:10.1016/j.ygyno.2015.10.024.
5. Alas, P. A, Sajadi, B. K, Goldman, T. H, Anger, T. J. The Rapidly Increasing Usefulness of Social Media in
Urogynecology. Female Pelvic Med Reconstr …. 2013;19(4):210-213. doi:10.1097/SPV.
6. Schwartz DA, Hui X, Velopulos CG, et al. Does relative value unit–based compensation shortchange the acute
care surgeon? J Trauma Acute Care Surg. 2014;76(1). doi:10.1097/TA.0b013e3182ab1ae3.
7. Centers for Disease Control and Prevention NC for HS. National Ambulatory Medical Care Survey: 2013 State
and National Summary Tables.; 2013.
8. Rubenstein EL. “Things my doctor never told me”: Bridging information gaps in an online community. Proc ASIST
Annu Meet. 2012;49(1). doi:10.1002/meet.14504901126.

26. ©2017 MFMER | slide-26
References
9. Huh J. Clinical Questions in Online Health Communities: The Case of “See your doctor” Threads. In: CSCW :
Proceedings of the Conference on Computer-Supported Cooperative Work. Conference on Computer-Supported
Cooperative Work. Vol 2015. CSCW ’15. New York, NY, USA: ACM; 2015:1488-1499. doi:10.1145/2675133.2675259.
10. Fox S, Duggan M. Health Online 2013.; 2013.
11. Amante JD, Hogan PT, Pagoto LS, et al. Access to Care and Use of the Internet to Search for Health Information:
Results From the US National Health Interview Survey. J Med Internet Res. 2015;17(4):e106. doi:10.2196/jmir.4126.
12. HysterSisters: Woman-to-Woman Hysterectomy Support. HysterSisters, Inc. http://www.hystersisters.com/.
Published 2016. Accessed June 20, 2012.
13. Bunde M, Suls J, Martin R, et al. Hystersisters online: Social support and social comparison among hysterectomy
patients on the internet. Ann Behav Med. 2006;31(3):271. doi:10.1207/s15324796abm3103_9.
14. Jurafsky D, Martin JH. Speech and Language Processing (2Nd Edition). Upper Saddle River, NJ, USA: Prentice-
Hall, Inc.; 2009.
15. Rayson P, Garside R. Comparing Corpora Using Frequency Profiling. In: Proceedings of the Workshop on
Comparing Corpora. CompareCorpora ’00. Stroudsburg, PA, USA: Association for Computational Linguistics; 2000:1-6.
16. Dunning T. Accurate Methods for the Statistics of Surprise and Coincidence. Comput Linguist. 1993;19(1):61-74.
17. Rich J, Neely G, Paniello R, et al. A Practical Guide To Understanding Kaplan-Meier Curves. J Otolaryngolg Head
Neck Surg. 2014;143(3):331-336. doi:10.1016/j.otohns.2010.05.007.A.

27. ©2017 MFMER | slide-27
References
18. Yun T-J, Jeong HY, Hill TD, et al. Using SMS to provide continuous assessment and improve health outcomes
for children with asthma. In: Proceedings of the 2nd ACM SIGHIT Symposium on International Health Informatics -
IHI ’12. IHI ’12. New York, NY, USA: ACM; 2012:621. doi:10.1145/2110363.2110432.
19. Yun TJ, Arriaga RI. A text message a day keeps the pulmonologist away. In: Proceedings of CHI 2013. CHI ’13.
New York, NY, USA: ACM; 2013:1769-1778. doi:10.1145/2470654.2466233.
20. Ng SM. Improving patient outcomes with technology and social media in paediatric diabetes. BMJ Qual Improv
Reports. 2015;4(1). doi:10.1136/bmjquality.u209396.w3846.