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Technology: The Bridge to Access to Care Mary R Haack, PhD, RN, FAAN Professor University of Maryland School of Nursing Baltimore, Maryland.

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Presentation on theme: "Technology: The Bridge to Access to Care Mary R Haack, PhD, RN, FAAN Professor University of Maryland School of Nursing Baltimore, Maryland."— Presentation transcript:

1 Technology: The Bridge to Access to Care Mary R Haack, PhD, RN, FAAN Professor University of Maryland School of Nursing Baltimore, Maryland

2 Myths about Underserved Patients  Patients will barter or sell phones or computers  Patients will not respond truthfully  Patients lack technological competence

3 Fear  “I will lose my job”  “Technology undermines clinical relationships”

4 Effectiveness of Technology and Behavioral Health  Two decades of research  30 studies show the participants are more likely to respond truthfully to telephone surveys  Daily monitoring improves likelihood of early identification of relapse and reduces the length of the relapse

5 Two Pilot Studies  Cell phones to monitor medication adherence among homeless  Computers to provide counseling to court-involved clients

6 Baltimore City  42, 560 people have opioid use disorders  50% suffer from co-occurring psychiatric disorders

7 Study 1: Cell Phones to Monitor Medication Adherence  Study Partners University of Maryland School of Nursing Health Care for the Homeless Baltimore

8 University of Maryland School of Nursing  Founded in 1889  Social Justice Mission  Ranked seventh in the US  1,600 students  Psychiatric Mental Health (PMH) Graduate Faculty lead the project  PMH faculty have a clinical faculty practice at HCH

9 Healthcare for the Homeless  FQHC in Baltimore City  Offers Medical, Psychiatric & Addiction Services in one site  Serves the Homeless Adults of Baltimore City  HCH served 6,574 individuals in 2008  Provides education & advocacy to reduce the incidence & burden of homelessness


11 Goal of Research  Explore the feasibility of using cell phones to monitor medication adherence among homeless patients  Increase access to concurrent psychotropic medication and substance abuse treatment for patients with co-occurring disorders  Assess use of technology for data collection for a larger study

12 Cell Phones  Provided to 10 patients meeting inclusion criteria  Patients given free unlimited phone service for 45 days  Computer sent an automated call to the participant at 10 AM every day  Patients responded to questions by pressing cell phone keys  Computer called missed patients again in the afternoon

13 Questions Asked Daily  Salutation  Since last call, did you take your medication as prescribed? Yes: press 1 No: press 9  Are you having any difficulty or side effects from your medication? Yes: press 1 No: press 9  Exit comments

14 Inclusion Criteria  Ages 21-64  Diagnosis of Substance Use Disorder  Co-morbid Axis 1 DSM-IV-TR diagnosis  Homelessness, based on clinical interview  Prescribed a psychotropic medication  Willing to receive telephone contact  Able to demonstrate ability to use phone

15 Exclusion Criteria  Recent history of violence  Active psychosis or acute crisis  Unable to follow directions

16 Characteristics of the Subjects Characteristics of 10 homeless subjectsMean / Percent Standard deviation Percent male80%0.16 Percent black80%0.16 Average age46.908.80 Modified Mini Screen for Mental Illness12.603.17 Center for Epidemiological Studies Depression Scale 29.7010.95 Lifetime years of cocaine use10.959.45 Lifetime years of heroin use6.207.90 Life time years of alcohol use at more than 3 times/week 23.609.43

17 Demographics/Co-Occurring Disorders AgeGenderDiagnosisSubstance 46Female Bipolar Mixed, Severe, Psychotic BehaviorPoly 43MaleMDD, Psychotic FeaturesETOH 57Female MDD Recurrent, Severe, Psychotic FeaturesETOH 49MaleBipolar I Opioid dep, on Suboxone 45MaleSchizoaffectiveCocaine & ETOH 49FemaleBipolar D/OCrack, ETOH, & Heroin 46MaleMDD, Psychotic FeaturesETOH & Cocaine 24MaleBipolarOxycontin & ETOH 49MaleBipolar MixedETOH 53MaleSchizoaffectiveETOH & Cocaine

18 Results: Percent of Subjects Reached per Day

19 Results  93% daily response rate  When reached, 100% self reported medication adherence  Patients reported increase structure  Felt cared for by having daily calls  Calls were medication reminders  Increased contact with families  Staff witnessed positive change in subject clinical presentation

20 Conclusion  Cell phones were not lost, bartered or sold  Cell phones can monitor adherence  Participation rate was high

21 Study 2: Online Counseling with Computers in the Home  Explore the feasibility of placing computers in the home to improve access to substance abuse counseling Vulnerable populations Underserved areas Court involved

22 Research Partners  Essex County Superior Court Juvenile Court in Newark New Jersey  Rutgers College of Nursing  Alexandria VA Probation Office  Others

23 Essex County New Jersey  Essex County estimated 42,516 people in need of SUD treatment  Heroin, cocaine and other illicit drugs  Newark 4000 child abuse and neglect cases per year  80 to 90% involve substance abuse  Children placed in foster care  Parents court ordered to treatment

24 Adoption and Safe Families Act Federal Law  Fate of these families must be decided in 12 to 18 months  Parents must meet requirements for reunification: substance abuse treatment and parenting skills training  If unable to meet requirements, court terminates the parental rights and child is eligible for adoption

25 Substance Abuse Treatment in Newark  6 to 8 weeks waiting list  Detox  Outpatient counseling 3 X week

26 Method of Recruitment  Information was distributed to case managers, halfway houses, and the Court  Clients interviewed in person  Consent required Patient consent Others living with patient

27 Inclusion Criteria  14 years or older  Substance abuse problem  Members of the household agreed to share the computer and phone line  Read and type at high school level  Signed release of information for the Court or health care provider for evaluation purposes only

28 Inclusion criteria  Willing to participate in online counseling for 15 minutes a day  Willing to do bi-weekly urine tests  Willing to have face to face visit with counselor as needed

29 Study Design  Participants randomly assigned to experimental or control group  Experimental and control group participants received an Internet- ready computer with 1 year access to the Internet

30 Study Design  Experimental group received online counseling; control group did not  Both groups received Internet service for 12 months  Both groups were encouraged to attend self help groups and face to face treatment

31 Online Counseling Protocol  Online motivational counseling Daily triggers: scripted email message broadcasted to all participants in the same stage of recovery every day Prompt: Email message ended with question that served as a prompt to engage the participant in dialogue Freeform dialogue: Addiction counselor maintained daily conversation  Counselors helped patients through stages of change  In person or on phone meetings when necessary

32 Example Emails  Initial Email: “Tell me how is your life?” Patients report various problems More problems reported over time  Later email: Why do you think you are having so many difficulties? Patients blame others Patients attribute events to luck Eventually they mention drug use  Summary Email: You told me …  Counselor changes patient’s stage of change from denial to pre-contemplation

33 Characteristics of Study Population Number of cases10301722 Number of experimental cases515910 Referral sourceIndian Reservation clinic Halfway house & family court Probation agency Substance abuse & mental health clinic Percent White0%13%6%18% Percent Black0%83%88%59% Percent Hispanic0%3%6%14% Percent Indian100%0% 9% Percent male40%10%71%50% Average years of education (st. dev.) 12.0 (.9) 11.9 (1.9) 12.6 (2.1) 12.6 (2.2) Percent days worked in last 30 days 26%12%39%48% Percent on probation30%20%100%27% Percent with medication for psychological problems 10%20%12%32%

34 Results: Daily Probability of Drug Use ExperimentalControl Drug Use +Urine test8.95%25.35% Self-reported use in last 30 days 6.94%7.01% Self-reported Alcohol Use2.36%1.75% Note: Observed differences are not statistically significant.

35 Unexpected results  Family members reported to the counselor when the participant was not available  Family members wanted to know how they could become a participant  Counselors were enthusiastic about the online counseling format  Relapses were shorter when counselors had daily contact

36 Conclusions  Cell phones and computers are a feasible as a means of increasing access to care  Further research is needed to understand and maximize the potential of cell phones and computers for improving what we already do.

37 Acknowledgements  Farrokh Alemi, PhD – collaborator in both studies  Cell Phone study funded by University of Maryland RIF Program  Computer study funded by RWJF

38 References Alemi F, Haack MR, Nemes S, Aughburns R, Sinkule J, Neuhauser D. Therapeutic emails. Substance Abuse Treatment, Prevention, and Policy 2007, 2:7 Tate DF, Wing RR, Winett RA. Using Internet technology to deliver a behavioral weight loss program. JAMA. 2001, 285:1172–1177. Tate DF, Jackvony EH, Wing RR. Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. JAMA. 2003, 289(14):1833-1836. Squires DD, Hester RK. Using technical innovations in clinical practice: the Drinker's Check-Up software program. J Clin Psychol. 2004, 60(2):159-169. Ruggiero KJ, Resnick HS, Acierno R, Coffey SF, Carpenter MJ, Ruscio AM, Stephens RS, Kilpatrick DG, Stasiewicz PR, Roffman RA, Bucuvalas M, Galea S. Internet-based intervention for mental health and substance use problems in disaster-affected populations: a pilot feasibility study. Behav Ther. 2006, 37(2):190-205. Epub 2006 Mar 27. Japuntich SJ, Zehner ME, Smith SS, Jorenby DE, Valdez JA, Fiore MC, Baker TB, Gustafson DH. Smoking cessation via the internet: a randomized clinical trial of an internet intervention as adjuvant treatment in a smoking cessation intervention. Nicotine Tob Res. 2006, 8 Suppl 1: S59-67. Linke S, Murray E, Butler C, Wallace P. Internet-based interactive health intervention for the promotion of sensible drinking: patterns of use and potential impact on members of the general public. J Med Internet Res. 2007, 8;9(2): e10. Ondersma SJ, Chase SK, Svikis DS, Schuster CR. Computer-based brief motivational intervention for perinatal drug use. J Subst Abuse Treat. 2005, 28(4): 305- 12. Hester RK, Delaney HD. Behavioral Self-Control Program for Windows: results of a controlled clinical trial. J Consult Clin Psychol. 1997, 65(4):686-93. Handmaker NS, Hester RK, Delaney HD. Videotaped training in alcohol counseling for obstetric care practitioners: a randomized controlled trial. Obstet Gynecol. 1999, 93(2):213-8. Kivlahan DR, Marlatt GA, Fromme K, Coppel DB, Williams E. Secondary prevention with college drinkers: evaluation of an alcohol skills training program. J Consult Clin Psychol. 1990, 58(6):805-10. Collins SE, Carey KB, Sliwinski MJ. Mailed personalized normative feedback as a brief intervention for at-risk college drinkers. J Stud Alcohol. 2002, 63(5):559- 567. Alemi F, Stephens RC, Javalghi RG, Dyches H, Butts J, Ghadiri A. A randomized trial of a telecommunications network for pregnant women who use cocaine. Medical Care 1996, 34 (10 Supplement):OS10-OS20. Alemi F, Stephens RC, Mosavel M, Ghadiri A, Krishnaswamy J, Thakkar H. Electronic Self Help and Support Groups: A Voice Bulletin Board. Medical Care 1996, 34(10 Suppl):OS32-OS44. Mosavel M. The use of a telephone-based communication tool by low-income substance abusers. Journal of Health Communications. 2005, 10(5):451-63. Burda, C, Haack, M, Duarte, A, Alemi, F. (In press) Medication Adherence among Homeless Patients: A Pilot Study of Cell Phone Effectiveness. Journal of the American Academy of Nurse Practitioners.

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