Presentation on theme: "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."— Presentation transcript:
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 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 Academic & Research Advisors Shaun Grannis, MD, MS Malaz Boustani, MD, MPH Dennis Watson, PhD Paul Halverson, DrPh
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 FDA’s 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 mHealthmobile health technology (for healthcare or public health) IT information technology HIV human immunodeficiency virus CTRcounseling, testing, and referral FTCFederal Trade Commission FDAFederal Food and Drug Administration NISTNational Institute of Standards and Technology PHINPublic Health Information Network RHIERegional 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 when, where, how, and by whomHas 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. That’s 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 TraditionalNon-Traditional (Community Based 0.47%0.82% Test Positivity Rates by setting (CDC, 2012)
DEVELOPMENT CONSIDERATIONS Dimensions of mHealth Technologies focused on HIV CTR
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 patient- specific screening, counseling and preventive recommendations from well- known and established authorities [Appendix B]. Mobile apps that use patient characteristics such as age, sex, and behavioral risk factors to provide patient- specific screening, counseling and preventive recommendations from well- known and established authorities [Appendix B]. Mobile apps that enable, during an encounter, a health care provider to access their patient’s personal health record (health information) that is either hosted on a web-based or other platform [Added March 12, 2014]. Mobile apps that enable, during an encounter, a health care provider to access their patient’s 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 eHealth Exchange Community/Regional HIE Local EHR
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
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., 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 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 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 was not compromised.” (Onono, et. al., 2011) Study ExampleOnono 2011King 2013 Paper Based$10,313$13,883 Electronic Based$6,471$10,320 Savings$3,842$3,563
A NEW PROPOSED PROCESS Designed from a Use-Case Analysis “It has to be compatible with our reporting system….If it’s just another exercise in collecting data, it doesn’t do us any good. It has to be compatible.”
Community Health Worker (CHW) Collects Client’s Information on pen and paper. CHW reenters info into 3 rd party web- based portal 3 rd party manages data Bi-annual hard upload to CDC CHW collects info via Mobile Tablet Encrypted data transmitted to 3 rd party data management company Data sent to CDC Current Process Proposed Process
Security Community Health Worker (CHW) Collects Client’s Information on pen and paper. CHW reenters info into 3 rd party web- based portal 3 rd party manages data Bi-annual hard upload to CDC Current Process Double data entry Data Accuracy Cost CHW collects info via Mobile Tablet Encrypted data transmitted to 3 rd party data management company Data sent to CDC Proposed Process NIST Compliant Lower cost Data validity increases Saved man power Diminishing Resources Over Time
Comparison 3 rd 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 resource- constrained 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:Aim 1: Develop mobile data collection system Aim 2:Aim 2: Pilot test (mixed-methods) Aim 3: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 Tell—Mobile 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 Message–Based 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 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 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), 159-178.