Introduction Discussion Results Mobile Health Clinics are transportable health care units that deliver high-value community based health care to underserved populations that may otherwise be hard to reach. To serve these communities more efficiently, mobile health clinics have looked toward advances in information technology (IT; mobile communication devices, portable or desktop computers, electronic health records) to expand their services. Little is known about the utilization of IT in mobile health clinics and the views of clinicians practicing in these settings about the value of IT. To address this knowledge gap we surveyed clinicians and leaders from mobile health clinics in the United States.. Methods Study Design We performed a descriptive cross-sectional analysis of mobile health clinics from a sample of mobile health providers attending the Sixth Annual Mobile Health Clinics Forum. Approximately 225 surveys were distributed; 43% (N=96) were completed. The survey was derived from the Massachusetts Survey of Physicians and Computer Technology and modified for mobile practitioners. The survey measured characteristics of mobile health clinics, providers perceptions of information technology on health care, its ability in caring for patients, providers experience with information technology, and clinician demographics. We were primarily interested in clinic level questions related to funding sources, clinic affiliations, and services delivered among paper users (clinics who use only paper records to care for patients) and clinics that use IT (clinics that employ electronic medical records, mobile communication devices or computers to care for patients). Our secondary analysis focused on the providers perception on the effect of information technology between paper and IT users. Statistical Analysis We used chi-squared or Fishers Exact test to evaluate baseline differences between paper and IT users as well as differences in the perceived benefits (effects) of information technology. Odds ratios were calculated using exact logistic regression.. For our anlysis we used SAS 9.2 Cary, N.C. Conclusion: Mobile Clinics that utilize information technology have a significantly higher patient volume than those primarily using paper Larger academically or hospital affiliated mobile clinics may have more prevalent use of information technology relatively to independent clinics Clinics that use paper records appear less positive about the possible benefits of information technology Limitations: Sample limited to participants attending a national mobile health conference Limited response rate Large heterogeneity of IT systems among users Overall small sample size Implications: Although interest in information technology is evident independently run mobile health clinics with less institutional support are less likely to utilize these systems future studies should focus on barriers among smaller independently operated clinics. Funding Dr. Fleurant was supported by a Ruth L Kirchstein National Research Service Award Grant #T32 HP12706 The Use of Health Information Technology among Mobile Health Clinics in the United States in 2010 Marshall Fleurant MD MPH 1, Caterina Hill MPH 2,3, Jennifer Bennett 2,3, Steven R. Simon MD MPH 3,4,5, Nancy Oriol MD 2,3, LeRoi Hicks MD MPH 6. Boston University School of Medicine, Boston Medical Center, Boston, MA 1 ; Harvard Family Van, Boston, MA 2 ; Harvard Medical School, Boston, MA 3, VA Boston Healthcare System, Boston, MA 4 Division of General Internal Medicine, Brigham and Womens Hospital, Boston, MA 5 ; University of Massachusetts School of Medicine, Worcester, MA 6 Mobile Van Characteristics (N=87) Paper Records N(%) N=38 Information Technology N(%) N=49 P- Value a Primary Affiliation N(%) Independent Academic/Hospital Community Health Center Other Affiliation Missing/Dont Know 8 (21) 3 (8) 7 (18) 20 (53) 0 (0) 4 (8) 9 (18) 5 (10) 30 (61) 1 (2)0.15 Funding (N%) State/Federal Companies Private/ Insurance Philanthropy Other Missing/ Dont Know 6 (16) 3 (8) 14 (37) 13 (34) 2 (5) 9 (18) 6 (12) 19 (39) 13 (27) 2 (4)0.9 Services (N%) Primary Care Only Primary Care + b No Primary Care Missing/ Dont Know 8 (21) 12 (32) 18 (47) 0 (0) 4 (8) 20 (41) 24 (49) 1 (2)0.3 Volume of Patients (Median) c Region (N%) Northeast South Midwest West Missing/ Dont Know 3 (8) 20 (53) 10 (26) 5 (13) 0 (0) 11 (23) 19 (39) 9 (18) 1 (2)0.2) Variable (N=87)PerceptionPaper Users N=38 IT users N=49 Odds Ratio a Efficiency of Providing Health care Very Positive Not Very Positive N/A 24(63) 13(34) 1 (3) 41(84) 8(16) 0 (0)0.4 (0.1 – 0.9) Quality of Health CareVery Positive Not Very Positive N/A 22(58) 16(42) (73) 13(27) (0.2 – 1.2) Interactions within the health care team Very Positive Not Very Positive N/A 21(55) 17(45) (76) 12(24) ( 0.2 – 1.0) WorkflowVery Positive Not Very Positive N/A 22(58) 16(42) (69) 15 (31) (0.3 – 1.5) Provider CommunicationVery Positive Not Very Positive N/A 26 (68) 11 (29) 1 (3) 35(71) 14(29) 0 (0)0.7 (0.4 – 1.5) Patient PrivacyVery Positive Not Very Positive N/A 28 (74) 10 (26) (80) 10 (20) (0.6 – 1.4) Provider Access to up to date knowledge Very Positive Not Very Positive N/A 28 (74) 10 (26) (80) 10 (20) (0.3 – 1.6) Medication ErrorsVery Positive Not Very Positive N/A 19 (50) (59) 20 (41) (0.3 – 1.6) Controlling Costs of CareVery Positive Not Very Positive N/A 19 (50) (55) 21 (43) 1 (2)1.0 (0.5 – 2.0) Table A: Relationships between Paper and IT Using Mobile Vans a P-values calculated using chi-squared or Fishers exact test b Primary Care + = deliver additional services (Dental, Mammography, Mental Health,Vaccinations, Cancer Screening, Health Education) c P-value calculated using Wilcoxin –Rank Sum Test Table B: Effect of Information Technology on Quality of Care: Paper vs IT Users a P-values calculated exact logistic regression