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Similar to Medicine 2.0 for the Emergency Department & Public Health - Beyond the Basics
Medicine 2.0 for the Emergency Department & Public Health - Beyond the Basics
- 1. Medicine 2.0, Emergency Departments, & Public Health: Beyond the basics Esther Choo MD MPH, Alpert Medical School, Brown Univ. Nick Genes MD PhD, Mt. Sinai School of Medicine Megan Ranney MD MPH, Alpert Medical School, Brown Univ.
- 2. Goals of this didacKc 1. Why the Emergency Department seNng is ripe for innovaKon 2. What is currently being done in the ED seNng: -‐ Computer -‐ Mobile phone -‐ Social media 3. How we could disrupt the status quo…
- 3. But before we get started….
- 4.
- 5. Unique environment 136.1 million visits/year
- 6. Unique environment 40% increase in visits in last 10 years
- 7. Unique environment 50% of admissions come from the ED
- 8.
- 9. Unique paKent populaKon 55% say “my problem was too serious to go anywhere else”
- 10. Unique paKent populaKon 95% of ED paKents use cellphones, 92% use computers
- 11.
- 12. Unique Kme constraints EPs average 3+ paKents/hour
- 13. Unique Kme constraints Median LOS = 154 minutes
- 14. Unique definiKon of “privacy”
- 15. Unique definiKon of “privacy” 36% overhear others’ conversaKons
- 16. Unique set of care providers
- 17. References • Babcock Irvin C, Wyer PC, Gerson LW. PrevenKve care in the emergency department, Part II: Clinical prevenKve services-‐-‐an emergency medicine evidence-‐based review. Acad Emerg Med 2000 7(9):1042-‐54. • NHAMCS, “2009 Emergency Department Visits: Summary Tables “, May 2012 • Centers for Disease Control and PrevenKon’s NaKonal Center for Health StaKsKcs: “Emergency Room Use Among Adults Aged 18–64: Early Release of EsKmates From the NaKonal Health Interview Survey, January–June 2011,” May 2012 • Ranney ML, Choo EK, et al “Emergency Department PaKents’ Preferences for Technology-‐ Based Behavioral IntervenKons.” Ann Emerg Med 2012 60(2):218-‐227 • Choo EK, Ranney ML, et al “A systemaKc review of emergency department technology-‐based behavioral health intervenKons” Acad Emerg Med 2012 19(3):318-‐28 • Moskop JC et al. “From Hippocrates to HIPAA: Privacy and ConfidenKality in Emergency Medicine Part II: Challenges in the Emergency Department.” Ann Emerg Med 2005 45(1): 60-‐67 • US General AccounKng Office, “'Hospital Emergency Departments: Crowded CondiKons Vary among Hospitals and CommuniKes”, March 28, 2003
- 18. InteracKve Computer Programs in the ED Esther K. Choo, MD MPH Department of Emergency Medicine Warren Alpert Medical School Brown University
- 19.
- 20. ExisKng FuncKons PM Roy, JM Chrétien, SPEED decision support for diagnosis of pulmonary embolism
- 21. Improve Expand ER existing ER functions? functions?
- 22. Expanding FuncKons What is an “emergency”?
- 23. Screening Partner Violence – Houry, Emory Images courtesy of Deb Houry, Department of Emergency Medicine, Emory School of Medicine
- 24. (Brief) IntervenKons Alcohol – Vaca, Yale Images courtesy of Federico Vaca, Department of Emergency Medicine, Yale School of
- 25. (Brief)IntervenKons Alcohol & Youth Violence – Cunningham, UMich Images courtesy of Rebecca Cunningham, Department of Emergency Medicine, U Mich
- 26. (Brief)IntervenKons Alcohol & Youth Violence – Cunningham, UMich
- 27. Referrals DARSSA – Boudreaux, U Mass Images courtesy of Ed Boudreaux, Department of Emergency Medicine, U Mass
- 28. Referrals DARSSA – Boudreaux, U Mass
- 29. Referrals DARSSA – Boudreaux, U Mass
- 30. Future DirecKons? [Need graphic]
- 31. References Boudreaux, ED et al, 2009. The Dynamic Assessment and Referral System for Substance Abuse (DARSSA): development, funcKonality, and end-‐user saKsfacKon. Drug and alcohol dependence 99, 37-‐46. Cunningham, RM et al, 2012. Brief MoKvaKonal Interviewing IntervenKon for Peer Violence and Alcohol Use in Teens: One-‐Year Follow-‐up. Pediatrics 129, 1083-‐90. Houry, D et al, 2008. Does screening in the emergency department hurt or help vicKms of inKmate partner violence? Annals of emergency medicine 51, 433-‐42, 442.e1-‐7. Rhodes, K.V., Lauderdale, D.S., He, T., Howes, D.S., Levinson, W., 2002. Between me and the computer: increased detecKon of inKmate partner violence using a computer quesKonnaire. Annals of emergency medicine 40, 476-‐84. Roy, PM et al, 2009. A computerized handheld decision-‐support system to improve pulmonary embolism diagnosis: a randomized trial. Ann Intern Med.151, 677-‐86. Vaca, FE et al, 2011. Six-‐month follow-‐up of computerized alcohol screening, brief intervenKon, and referral to treatment in the emergency department. Substance abuse : official publicaKon of the AssociaKon for Medical EducaKon and Research in Substance Abuse 32, 144-‐52.
- 32. Mobile phones, public health, & Emergency Medicine Where are we now? Where are we going? Megan L. Ranney MD MPH Dept of EM, Warren Alpert Medical School, Brown University @meganranney
- 33. GeNng paKents into the emergency department
- 34. Providing paKents with appropriate care * Screenshots courtesy of Tim Green, EMT (SMART-ICE) and Neal Sikka MD (Dept of EM, George Washington Univ)
- 35. Engaging paKents with the pursuit of health – part I
- 36. Engaging paKents with the pursuit of health – part II
- 37. Engaging paKents with the pursuit of health – part II
- 38. Automated SMS Dialog with PaKents arer ED Discharge • Measure oral antibiotic use • Tailored queries • Improve adherence to prescription • Personalized feedback • Measure alcohol consumption • Reduce binge drinking Patient Server Phone • Assessment responses • Scales, free text • Measure symptoms after mild traumatic brain injury (concussion) • Measure risky sexual encounters • Improve self-care to reduce post-concussion syndrome • Improve safe sex practices
- 39. Automated SMS Dialog with PaKents arer ED Discharge • Measure oral antibiotic use • Tailored queries • Improve adherence to prescription • Personalized feedback • Measure alcohol consumption • Reduce binge drinking Patient Server Phone • Assessment responses • Scales, free text • Measure symptoms after mild traumatic brain injury (concussion) • Measure risky sexual encounters • Improve self-care to reduce post-concussion syndrome • Improve safe sex practices
- 40. Automated SMS Dialog with PaKents arer ED Discharge: AnKbioKcs
- 41. Automated SMS Dialog with PaKents arer ED Discharge • Measure oral antibiotic use • Tailored queries • Improve adherence to prescription • Personalized feedback • Measure alcohol consumption • Reduce binge drinking Patient Server Phone • Assessment responses • Scales, free text • Measure symptoms after mild traumatic brain injury (concussion) • Measure risky sexual encounters • Improve self-care to reduce post-concussion syndrome • Improve safe sex practices
- 42. Automated SMS Dialog with PaKents arer ED Discharge: Concussion
- 43. Automated SMS Dialog with PaKents arer ED Discharge • Measure oral antibiotic use • Tailored queries • Improve adherence to prescription • Personalized feedback • Measure alcohol consumption • Reduce binge drinking Patient Server Phone • Assessment responses • Scales, free text • Measure symptoms after mild traumatic brain injury (concussion) • Measure risky sexual encounters • Improve self-care to reduce post-concussion syndrome • Improve safe sex practices
- 44. Automated SMS Dialog with PaKents arer ED Discharge: Binge Drinking
- 45. Automated SMS Dialog with PaKents arer ED Discharge • Measure oral antibiotic use • Tailored queries • Improve adherence to prescription • Personalized feedback • Measure alcohol consumption • Reduce binge drinking Patient Server Phone • Assessment responses • Scales, free text • Measure symptoms after mild traumatic brain injury (concussion) • Measure risky sexual encounters • Improve self-care to reduce post-concussion syndrome • Improve safe sex practices
- 46. Automated SMS Dialog with PaKents arer ED Discharge: Risky Sex 100 90 80 Vaginal sex episodes with a condom, % 70 60 100% 50 1%-‐99% 40 None No Vaginal Sex 30 20 10 0 Control IntervenKon Control IntervenKon Baseline 3-‐month
- 47. iHeal * Screenshots courtesy of Ed Boyer MD PhD, Dept of EM, Univ of Mass-Worcester
- 48. What is the wave of the future?
- 49. Future thoughts… • The use of gamificaKon and advanced smartphone plasorms • The role of the “quanKfied self” in the ED seNng • Proving that this is not only feasible and acceptable – but also effecKve & scalable
- 50. Contact InformaKon • Ed Boyer MD PhD, Dept of EM, University of Massachuseus-‐Worcester Edward.Boyer@umassmemorial.org • Megan Ranney MD MPH, Dept of EM, Brown University megan_ranney@brown.edu • Neal Sikka, MD, Dept of EM, George Washington University nsikka@mfa.gwu.edu • Brian Suffoleuo, MD MS, Dept of EM, University of Piusburgh suxp@upmc.edu
- 51. References • Boyer EW, Fletcher R, Fay RJ, Smelson D, Ziedonis D, Picard RW. Preliminary efforts directed toward the detecKon of craving of illicit substances: the iHeal project. J Med Toxicol. 2012 Mar;8(1):5-‐9. • Dolan PL. “Emergency departments turn to texKng waiKng Kmes.” American Medical News 2011 Aug 22: hup://www.ama-‐assn.org/amednews/2011/08/22/bisb0822.htm • Kim MJ, Park JM, Je SM, You JS, Park YS, Chung HS, Chung SP, Lee HS. Effects of a short text message reminder system on emergency department length of stay. Int J Med Inform. 2012 May;81(5):296-‐302 • Ranney ML, Choo EK, Wang Y, Baum A, Clark MA, Mello MJ. Emergency department paKents' preferences for technology-‐based behavioral intervenKons. Ann Emerg Med. 2012 Aug;60(2):218-‐227.e48. • Sikka N, Pirri M, Carlin KN, Strauss R, Rahimi F, Pines J. The Use of Mobile Phone Cameras in Guiding Treatment Decisions for LaceraKon Care. Telemed J E Health. 2012 Jul 23. • Sikka N, Carlin KN, Pines J, Pirri M, Strauss R, Rahimi F. The use of mobile phones for acute wound care: aNtudes and opinions of emergency department paKents. J Health Commun. 2012;17 Suppl 1:37-‐42 • Suffoleuo B, Calabria J, Ross A, Callaway C, Yealy DM. A mobile phone text message program to measure oral anKbioKc use and provide feedback on adherence to paKents discharged from the emergency department. Acad Emerg Med. 2012 Aug;19(8):949-‐58. • Suffoleuo B, Callaway C, Kristan J, Kraemer K, Clark DB. Text-‐message-‐based drinking assessments and brief intervenKons for young adults discharged from the emergency department. Alcohol Clin Exp Res. 2012 Mar;36(3):552-‐60. • hup://ice4safety.com/ • hup://www.emergencysms.org.uk/
- 52. Social Networks in Emergency Medicine Nicholas Genes, MD, PhD Mount Sinai School of Medicine @nickgenes
- 53. EM, as a specialty, in social networks • great content for educaKon, discussion • less opportunity for doctor/paKent interacKon • sKll, online social networks have potenKal to improve emergency preparedness & response
- 56. social networks in disasters • disseminate relief informaKon, whereabouts • improve coordinaKon, situaKonal awareness • spontaneous; not led by emergency physicians
- 60. EM-‐led efforts • educaKon, reminders, ED status • idenKfy resources • warnings (and assessment of reach, efficacy)
- 62. biosurveillance with online social networks • Eysenbach – infodemiology (2006) • Polgreen – Twiuer & H1N1 (2011) • Sadilek – Twiuer NLP + geolocaKon (2012) • MappyHealth – realKme (2012)
- 66. Twiuer research for emergency med • content relevant for ED preparaKon, training • a public API for searching recent tweets • established linguisKc analysis methods • then one day…
- 68. is there a need for Twiuer tox? • NSDUH survey data is comprehensive but old • DAWN, poison control funding declining • separaKng real trends from media hype means beuer trained, prepared EDs
- 69. determining drug usage in tweets • develop corpora of tweets for different drugs • classify tweets, determine inter-‐rater agreement • use NLP to train AI, review with human oversight • characterize, correlate with NSDUH survey data
- 73. NSDUH 2010 survey,illicit drug usage in past month among Americans 12+
- 74. % of tweets about a drug that indicate usage
- 75. characterize drug usage tweets • word choice, syntax may reveal educaKon, age • social network analysis / retweet propensity may indicate reveal trends in usage, popularity
- 76. tweets indicaKng drug usage Flesch-‐Kincaid level lexical diversity • heroin = 3.18 • heroin = 0.909 • cocaine = 5.2 • cocaine = 0.702 • marijuana = 7.03 • marijuana = 0.541 • coffee = 8.28, beer =6.5 • coffee = 0.633, beer = 0.582
- 77.
- 78.
- 79.
- 80.
- 81. next steps (for Twiuer toxicology) • Expand corpora with algorithmic processing • correlate tweets with NSDUH data (locaKon?) • idenKfy trends in drug use / toxicosurveillance • follow users’ tweets over Kme to assess risk, recidivism, resource uKlizaKon
- 82. next steps (for EM social networks) • share data, coding, methods • collaborate & promote • assess readiness, need, and guide paKents to appropriate resources for emergency care
- 83. thank you! and special thanks to: Michael Chary Alex Manini
- 84. contact info / further reading Nicholas Genes, MD, PhD nickgenes.com @nickgenes references, links & more: tox.sinaiem.org