Medicine 2.0 for the Emergency Department & Public Health - Beyond the Basics

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Emergency Departments (EDs) are the gateway to healthcare for the majority of Americans. There are over 125 million ED visits a year. One-third of these visits are due to injury, one-eighth to mental …

Emergency Departments (EDs) are the gateway to healthcare for the majority of Americans. There are over 125 million ED visits a year. One-third of these visits are due to injury, one-eighth to mental illness or substance abuse, and over one-third are from patients who are under- or un-insured. ED patients are often the members of society who are the most at-risk for poor health outcomes and also those with the fewest resources to address their acute or chronic medical conditions.

Yet ED patients have tremendous access to technology: recent studies show that despite low socioeconomic status, 95% of ED patients own cellphones, 93% access the internet, and 65% have smartphones. Technology provides potential solutions to perennial barriers to improving the health of the ED population, such as time constraints, access to post-ED care, and fidelity of interventions. And the ED patient population provides potential solutions to many perennial challenges for public health research: accessing difficult-to-reach populations, adequately sampling the most at-risk, and proving real-world efficacy.

This panel discussion will feature emergency physician-researchers, each of whom are currently conducting funded research on technology-based public health interventions. We will discuss the ways in which we have successfully stretched the boundaries of “Medicine 2.0” (interactive computer programs, text messaging, social media) to improve the health of the ED population. We will discuss ways to translate technology’s efficiency, efficacy and fidelity into the hectic ED environment. We will also outline some of the reasons to test mHealth innovations in the ED environment.

The use of mHealth to impact this at-risk population is imperative. Yet it is something that few researchers, computer scientists, pharmaceutical companies, mental health professionals, or hospital administrators have done. By the close of this panel discussion, attendees will have attained critical knowledge about the advantages of, and possibilities for, extending mHealth into the ED setting.

Panelists:
* Esther K. Choo, MD MPH, Assistant Professor of Emergency Medicine at Brown University, has grants from NIDA to develop interactive, computer-based interventions to address partner violence and substance use in the ED.

* Nick Genes, MD PhD is Assistant Professor of Emergency Medicine at Mt Sinai School of Medicine. Dr. Genes has spoken at national conferences on the utility of social media for physicians career development and ED community relations, and is researching physician usage of social media tools, HIT, and patient-support tools.

* Megan L Ranney, MD MPH is Assistant Professor of Emergency Medicine at Brown University, a core researcher in the Injury Prevention Center of Rhode Island Hospital. Her research includes using text-messaging to assess and deliver interventions to adolescent ED patients.

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  • 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. Unique  environment  
  • 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. Unique  paKent  populaKon  
  • 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. Unique  Kme  constraints  
  • 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. Improveexisting ERfunctions?
  • 20. ExisKng  FuncKons   PM Roy, JM Chrétien, SPEED decision support for diagnosis of pulmonary embolism
  • 21. Improve Expand ERexisting 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,  YaleImages courtesy of Federico Vaca, Department of Emergency Medicine, Yale School of
  • 25. (Brief)IntervenKons     Alcohol  &  Youth  Violence  –  Cunningham,  UMichImages 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 ofEM, 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  
  • 54. social  networks  in  disasters  •  disseminate  relief  informaKon,  whereabouts  •  improve  coordinaKon,  situaKonal  awareness  •  spontaneous;  not  led  by  emergency  physicians    
  • 55. EM-­‐led  efforts  •  educaKon,  reminders,  ED  status    •  idenKfy  resources    •  warnings  (and  assessment  of  reach,  efficacy)  
  • 56. biosurveillance  with     online  social  networks  •  Eysenbach  –  infodemiology  (2006)  •  Polgreen  –  Twiuer  &  H1N1  (2011)  •  Sadilek  –  Twiuer  NLP  +  geolocaKon  (2012)  •  MappyHealth  –  realKme  (2012)  
  • 57. 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…  
  • 58. 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  
  • 59. 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  
  • 60. NSDUH 2010 survey, illicit drug usage in past month among Americans 12+
  • 61. %  of  tweets  about  a  drug  that  indicate  usage  
  • 62. characterize  drug  usage  tweets  •  word  choice,  syntax  may  reveal  educaKon,  age  •  social  network  analysis  /  retweet  propensity    may  indicate  reveal  trends  in  usage,  popularity    
  • 63. 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  
  • 64. cocaine
  • 65. marijuana
  • 66. coffee
  • 67. MarijaunaCocaineCoffee
  • 68. 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  
  • 69. next  steps  (for  EM  social  networks)  •  share  data,  coding,  methods  •  collaborate  &  promote  •  assess  readiness,  need,  and  guide  paKents  to   appropriate  resources  for  emergency  care  
  • 70. thank  you!   and  special  thanks  to:           Michael  Chary                                                  Alex  Manini    
  • 71. contact  info  /  further  reading  Nicholas  Genes,  MD,  PhD  nickgenes.com  @nickgenes    references,  links  &  more:  tox.sinaiem.org