AMPATH (http://ampathkenya.com) is the one of the largest HIV
treatment programs in sub-Saharan Africa and is Kenya's most
comprehensive initiative to combat the virus. The program's catchment
area has over 2 million people and provides care to more than 130,000
HIV-positive patients across 55 urban and rural clinics. To provide
care at this scale, AMPATH has invested in tools like OpenMRS
(http://openmrs.org) (an open source medical record system) and Open
Data Kit, to help improve the efficiency and impact of their health
Home-Based Counseling and Testing with ODK Collect
AMPATH has an extensive home-based and counseling program where
community health workers (CHWs) go house to house to identify and
enroll persons in need of care (i.e., pregnant women not in antenatal
care, orphaned children, persons at high risk for tuberculosis
infection). The workers need mobile data collection to document
socio-economic data (including GPS location of household) and to
implement the counseling and testing protocol.
Before using ODK, AMPATH used Palm TX devices running Pendragon Forms.
GPS information was collected using eTrex devices. Problems with this
approach were outlined by Rajput et al. in their Evaluation of an
Android-based mHealth System for Population Surveillance in Developing
(AMIA 2011) paper.
They write, "First, although costs were significantly lower than
paper-based data collection methods the costs were still substantial.
Second, the data collected could not be directly integrated into the
electronic medical record system [OpenMRS] which was already in use at
the AMPATH clinics -- integration required dedicated time by several
experienced data managers. Third, the cable connection between the PDA
and GPS devices was not always reliable, and GPS information had to
occasionally be entered manually into the PDA devices. Fourth, the use
of the proprietary Pendragon Forms Software on the PDA devices limited
flexibility to incorporate some functionality into the data collection
software -- some of these functionality included advanced barcode
scanning and check-digit algorithms."
In late 2009, the we provided an early version of ODK Collect to
AMPATH (on an HTC Dream, the first Android device). We spent a week
with them in Kenya piloting the system, and based on the available
functionality, AMPATH decided to switch to Open Data Kit. Below is a
of one of the CHWs scanning a patient barcode with the phone.
In early 2010, AMPATH finalized the HCT form, made minor changes to
the user interface, acquired Android devices, and started to scale up.
It is important to note that ODK's core developers were not involved
at this stage.
Rajput et al. evaluated the ODK implementation at AMPATH a few months
after and showed that "Users of the system felt it was easy to use,
and facilitated their home visits. It is more cost effective than
pen-and-paper alternatives. Additionally, electronic data collection
facilitated earlier reporting. We have implemented a viable solution
at scale for collecting electronic data during household visits."
More importantly, that study also found that for the 63,000 persons
encountered, "the direct capture of electronic records greatly
facilitated the expeditious performance of initial analyses and
reports prior to the conclusion of the three year HCT program. Our
work has highlighted ... most notably that only 28% of persons we are
identifying as infected with HIV are presenting for follow-up care."
AMPATH has acted on this data by launching programs to improve
It has been almost two years since AMPATH started using ODK Collect.
We recently touched base with the HCT program to see how the use of
technology has scaled. Since early 2010, ODK has been used by a few
hundred CHWs in over 650,000 patient encounters! The HCT program has a
greater than 98% rate of acceptance into the homes it visits, and with
the help of technology, has been able to lower mother-to-child
transmission of HIV/AIDS to lower than 3%.
Mobile Clinical Decision Support with ODK Clinic
We have continued our collaboration with AMPATH, and this year, we
focused on tools for clinicians. We have created a new version of ODK
Clinic (http://code.google.com/p/opendatakit/wiki/ODKClinic), an
Android-based application that downloads patient data like
demographics, disease history, lab results, and recent medications
from OpenMRS (and the Clinical Summary Module
re-designed the entire application from our early v1 release and added
features to enable correction of serious mistakes in the patient
record. We also added decision support so clinicians receive
patient-specific reminders when the system notices that sub-standard
care is being offered. All this in near real time and at the point of
care -- a major improvement over AMPATH's existing paper-based
If, for example, the system notices a scheduled lab test is overdue or
a patient's health indicators have dropped dramatically, a reminder is
inserted into the patient's record and displayed to the clinician. ODK
Clinic can also enforce compliance with the reminders and can help
compliance by wirelessly printing lab order test requisitions complete
with all necessary patient data. We detailed our findings designing
the system in our Design of a Phone-Based Clinical Decision Support
System for Resource-Limited Settings
(ICTD 2012) paper. Below is a video demo
(http://www.youtube.com/watch?v=skV25YchXlE) of the system.
Since that paper was written, the system has been used with about
7,500 patients at two adult HIV clinics. We are still evaluating the
results of that deployment in a controlled trial, but very early
results seem to show that clinicians using ODK Clinic deliver a higher
standard of care. Moreover, clinicians enjoy using the system! As a
few told us, "[We] can't see a patient without this phone."
We believe that technology only magnifies existing human intent and
capacity (http://www.kentarotoyama.org/research) and so, sustainable
improvements require organizations who are dedicated to the
communities they serve. With HCT and within HIV clinics, ODK and
OpenMRS have helped make health care providers more efficient and
through that efficiency, magnified their impact. We believe this is
one of the reasons why this work was featured as one of the mHealth
Alliance and Rockefeller Foundation's Top 11 in 2011 Innovators
Our work at AMPATH has been in deep collaboration with many people. We
want to thank Martin Were, Nyoman Ribeka, Sam Mbugua, Zeshan Rajput
for their help. We also thank all our colleagues at OpenMRS and
Regenstrief (http://www.regenstrief.org) for their support, and Abbott
Fund (http://www.abbottfund.org) and Google (http://google.org) for
funding much of this work. Finally, we thank the CHWs and clinicians
for their participation in our research. It is their hard work that is
making a difference in the lives of the underserved.