Aligning mHealth with U.S. National Health IT Initiatives
Aligning mHealth with U.S. National Health IT Initiatives for HIV Counseling in Non-Clinical Settings *Macey L. Henderson, JD, Adam C. Knotts, MBA & Martin C. Were, MD, MS Acknowledgements Academic & Research Advisors Shaun Grannis, MD, MS Malaz Boustani, MD, MPH Dennis Watson, PhD Paul Halverson, DrPh Mobilify Technology Michael Dowden, MBA Andrew Roden, BS Kigho Emenike, MPH Marc Lane, JD, MBA Tarik Rabie, MPH
Research Assistants Braden Paschall Kate Creager Kaila Dunnick Tyler Bowles LaQuita Sparks Christopher Huff Christian Zimmerman Learning Objectives Learning Objective 1: Describe emerging role of mHealth in HIV counseling, testing, and referral (CTR) within non-clinical setting, as a demonstration of broad use of mobile technologies for public health across multiple conditions.
Learning Objective 2: Explore national health IT standards and their application for mHealth CTR tools that serve public health needs. Learning Objective 3: Discuss the implications of FDAs final guidance on mobile applications, and current certification guidelines for health information technologies on mHealth applications for public health, using mHealth applications for HIV CTR as a reference use-case. Commonly Used Acronyms mHealth IT HIV CTR FTC
FDA NIST PHIN RHIE mobile health technology (for healthcare or public health) information technology human immunodeficiency virus counseling, testing, and referral Federal Trade Commission Federal Food and Drug Administration National Institute of Standards and Technology Public Health Information Network Regional Health Information Exchange MOBILE APPS FOR IMPROVED HIV CTR IMPACT
(photo credit: ROBYN BECK/AFP/Getty Images) mHealth The use of mobile information and health IT for improving population health outcomes: - health promotion - illness prevention - health care delivery - information systems - workforce and training Has the potential to shift the paradigm on when, where, how, and by whom health services are provided and accessed mHealth and HIV HIV prevention, care, and treatment
mHealth tools support HIV priorities including linkage to care, retention in care, and adherence to ART treatment Short messaging services (SMS) can be used for appointment and medication reminders Offers an opportunity to expand health care services in areas with
limited resources mHealth and HIV Mobile phones have the potential to induce a paradigm shift in resource-limited settings by encouraging patients to stay connected to health care providers Will improve low-cost, highly engaging, and ubiquitous STD/HIV prevention and treatment support interventions Just in one of our programs, we did 1200 tests last year. Thats 1200 pieces of paper that have to be entered into the system.
Instead of doing that, if we were doing it on an iPad or whatever, and that went into the system, think of the steps we could save. So those hours and dollars could be spent doing more testing or more outreach, saving the agency money, you know, going into something else. Enhanced Possibilities With a Mobile Data Collection System in Non-traditional Settings Improved surveillance and reporting Improved reach and public health impact Test Positivity Rates by setting (CDC, 2012)
Traditional Non-Traditional (Community Based 0.47% 0.82% Dimensions of mHealth Technologies focused on HIV CTR DEVELOPMENT CONSIDERATIONS FDA Regulations of Mobile Apps
MAY meet the definition of medical device but for which FDA intends to exercise enforcement discretion. May be intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease. Even though these mobile apps MAY meet the definition of medical device, FDA intends to exercise enforcement discretion for these mobile apps because they pose lower risk to the
public. Mobile apps that use patient characteristics such as age, sex, and behavioral risk factors to provide patientspecific screening, counseling and preventive recommendations from wellknown and established authorities [Appendix B]. Mobile apps that enable, during an encounter, a health care provider to access their patients personal health record (health information) that is either hosted on a web-based or other platform [Added March 12, 2014]. National Health IT Standards Common standards and implementation specifications recommended for electronic data exchange within
meaningful use guidelines The concept of meaningful use rested on the '5 pillars' of health outcomes policy priorities, namely: Improving quality, safety, efficiency, and reducing health disparities Engage patients and families in their health Improve care coordination Improve population and public health Ensure adequate privacy and security protection for personal health information Infrastructure and Workforce National initiative to increase the capacity of public health agencies to electronically exchange data and information across organizations and jurisdictions Promotes the use of standards
Defines functional and technical requirements for public health information exchange www.healthit.gov SUPPORTING EVIDENCE FOR DEVELOPMENT OF NEW MOBILE DATA COLLECTION SYSTEM King et al. (2013) King, J. D., Buolamwini, J., Cromwell, E. A., Panfel, A., Teferi, T., Zerihun, M., & Emerson, P. M. (2013). A novel electronic data collection system for large-scale surveys of neglected tropical diseases. PloS one, 8(9), e74570. Studied the collection of data using mobile technology. Results: Gained 265 person-days using mobile technology as seen in Table 1.1
Able to collect more data in less time 12% decrease in data entry error pertaining to blank field in census record (age, sex, availability) Cost of equipment was similar between both methods, though continual use of mobile equipment suggested increased savings overtime Gave instant results and obviated the need for double-data entry and cross-correcting, thus reducing errors Electronic data collection using an Android-based technology was suitable for a large-scale health survey, saved time, provided more accurate geo-coordinates, and was preferred by recorders over standard paper-based questionnaires. Onono, M. A., Carraher, N., Cohen, R. C., Bukusi, E. A., & Turan, J. M. (2011). Use of personal digital assistants for data collection in a multi-site AIDS stigma study in rural south Nyanza, Kenya. African health sciences, 11(3). Describes the development, cost effectiveness, and implementation in a PDA Based electronic system to collect, verify, and manage data
from a multi-site study on HIV/AIDS. PDA programmed for collecting and screening eligibility study data and responses to structured interviews on HIV/AIDS stigma. Successes included: 1. Capacity building of interviewers (workforce development) 2. Low cost of implementation 3. Quick turnaround time of data entry with high reliability 4. Convenience Advantages of Paper to Mobile Transition Double data entry 265 person-days were gained Final data set available one month sooner
Accuracy Electronic: 1.8% error rate (n=38,652) Paper: 2.3% error rate (n=33,800) King et al. (2013) Cost & Security Study Example Onono 2011 King 2013 Paper Based $10,313
$13,883 Electronic Based $6,471 $10,320 Savings $3,842 $3,563 One device was stolen The stolen PDA was not recovered, but because data on the SD card were encrypted and the PDA password protected, participant privacy
(Onono, et. al., 2011) was not compromised. It has to be compatible with our reporting system.If its just another exercise in collecting data, it doesnt do us any good. It has to be compatible. Designed from a Use-Case Analysis A NEW PROPOSED PROCESS Current Process Community Health Worker (CHW)
Collects Clients Information on pen and paper. CHW reenters info into 3rd party webbased portal 3rd party manages data Bi-annual hard upload to CDC Proposed Process
CHW collects info via Mobile Tablet Encrypted data transmitted to 3rd party data management company Data sent to CDC Current Process Community Health Worker (CHW) Collects Clients
Information on pen and paper. CHW reenters info into 3rd party webbased portal Security 3rd party manages data Bi-annual hard upload to CDC Cost
Double data entry Data Accuracy Proposed Process CHW collects info via Mobile Tablet NIST Compliant Diminishing Resources Over Time Encrypted data transmitted to 3rd party data management company Saved man power
Data validity increases Data sent to CDC Lower cost Comparison 3rd party web-based portal cost to CDC and community organizations Security concerns No audit trail for paper, unable
to identify data breach Paper process not secure increased risk to HIPAA violations Double data entry Strain on already resourceconstrained organizations I see less likelihood of HIPAA violations with electronic forms, because with the paper forms, currently, they take it back and input it and it might not be the same person taking it back and putting it in. So you have multiple people touching the forms. Implementation Study
Overview Aim 1: Develop mobile data collection system Aim 2: Pilot test (mixed-methods) Aim 3: Develop an implementation strategy for scalability Aim 1: Develop Mobile Data Collection System 1) Design basic system components 2) Develop protocols and procedures Methods: Literature reviews Observations Key informant interviews 3) Alpha Test user-interface
Aim 2: Pilot Testing: Deployment to Community Based Organizations 1) Determine appropriate baseline data Recommendation: 1 month field observations, or analysis of 2) Beta Test (small sample size (n=10) collect quantitative and qualitative data Technical and workforce components 3) Develop a logic model to describe the process and to inform implementation strategy Aim 3:Develop an Implementation Strategy for Scalability 1) When developing the implementation
strategy, fidelity will be a key issue 2) Developing a fidelity tool Could be a checklist, or a scale? References Muessig, K. E., Pike, E. C., LeGrand, S., & Hightow-Weidman, L. B. (2013). Mobile phone applications for the care and prevention of HIV and other sexually transmitted diseases: a review. Journal of medical Internet research, 15(1). Heslop, L., Weeding, S., Dawson, L., Fisher, J., & Howard, A. (2010). Implementation issues for mobile-wireless infrastructure and mobile health care computing devices for a hospital ward setting. Journal of medical systems, 34, 509-518. Veniegas, R. C., Kao, U. H., & Rosales, R. (2009). Adapting HIV prevention evidence-based interventions in practice settings: an interview study. Implementation Science 4(1), 76. Maiorana, A., Steward, W. T., Koester, K. A., Pearson, C., Shade, S. B., Chakravarty, D., & Myers, J. (2012). Trust, confidentiality, and the acceptability of sharing HIV-related patient data: lessons learned from a mixed methods study about Health Information Exchanges. Implementation Science, 7(1), 34. Littman-Quinn, R., Mibenge, C., Antwi, C., Chandra, A., & Kovarik, C. L. (2013). Implementation of m-health applications in Botswana: telemedicine and education on mobile devices in a low resource setting. Journal of telemedicine and telecare, 19(2), 120-125.
Leon, N., Lewin, S., & Mathews, C. (2013). Implementing a provider-initiated testing and counselling (PITC) intervention in Cape town, South Africa: a process evaluation using the normalisation process model. Implementation Science, 8(1), 97. Lester, R. T., Mills, E. J., Kariri, A., Ritvo, P., Chung, M., Jack, W., & Plummer, F. A. (2009). The HAART cell phone adherence trial (WelTel Kenya1): a randomized controlled trial protocol. Trials, 10(1), 87. Lester, R. T. (2013). Ask, Don't TellMobile Phones to Improve HIV Care. New England Journal of Medicine, 369(19), 1867-1868. Furberg, R. D., Uhrig, J. D., Bann, C. M., Lewis, M. A., Harris, J. L., Williams, P., & Kuhns, L. (2012). Technical Implementation of a Multi-Component, Text MessageBased Intervention for Persons Living with HIV. JMIR Research Protocols, 1(2). Hardy, H., Kumar, V., Doros, G., Farmer, E., Drainoni, M. L., Rybin, D., & Skolnik, P. R. (2011). Randomized controlled trial of a personalized cellular phone reminder system to enhance adherence to antiretroviral therapy. AIDS patient care and STDs, 25(3), 153-161. Lester, R. T., Ritvo, P., Mills, E. J., Kariri, A., Karanja, S., Chung, M. H., ... & Plummer, F. A. (2010). Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. The Lancet, 376(9755), 1838-1845. CDC. (2010). Vital Signs: HIV Testing in the U.S. , from http://www.cdc.gov/nchhstp/newsroom/docs/Vital-Signs-Fact-Sheet.pdf CDC. (2011). Estimates of New HIV Infections in the United States, 2006-2009, from http://www.cdc.gov/nchhstp/newsroom/docs/HIV-Infections-2006-2009.pdf CDC. (2012). Healthy People 2020 Leading Health Indicators: Objective HIV-13: Proportion of Persons Living with HIV Who Know Their Serostatus. CDC. (2013). Assessment of 2010 CDC-funded Health Department HIV Testing Spending and Outcomes. Seffah, A., Donyaee, M., Kline, R. B., & Padda, H. K. (2006). Usability measurement and metrics: A consolidated model. Software Quality Journal, 14(2), 159178.
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