The document compares health data collected electronically using a mobile application versus traditional paper records in Rwanda. It finds that the electronically collected data more closely matches growth standards from the WHO and has higher accuracy. Specifically, the electronic data shows a better fit to WHO models, is normally distributed unlike the paper data, and has less than half the percentage of data points failing normality tests compared to the paper records. The results suggest mobile data collection can improve data availability, timeliness, and accuracy for community health programs.

1. How does health data collected electronically compare to
the data from the standard paper system?
Suzana Brown 1,2, Patrick McSharry 1,3, Bertin Akim Mpagazi 1
1) Carnegie Mellon University, 2) University of Colorado Boulder, 3) Oxford University
Introduction
Historically, the main form of medical data
collection has been paper records. Storing and
updating paper records has proved challenging
and most developed countries are moving
towards an electronic system [1].
In Rwanda, as in many developing countries,
health data is collected by Community Health
Workers (CHWs), who are volunteers, and focus
mostly on children’s health, vaccination, and
malnutrition. We developed a custom mobile
application for CHWs to collect electronic health
data for monitoring children’s growth and
development, and stored them as electronic
medical records (EMR).
Research question
Does mobile data collection improve data availability
and accuracy?
The considerations are:
Timeliness: electronic reporting is timelier
(instantaneous collection) than paper reporting since the
present paper system is submitted once a month;
Accuracy: we compare the accuracy of the present
paper system with the EMR approach and how both
match the WHO model;
Consistency: by comparing the values that are input
with a model based on previous measurements, it is
possible to ensure self-consistency and detect errors.
Data description
Paper data: we collected data from CHWs books for
tracking children in their village and entered this in the
excel spread sheet. Data contains information on weight
and age for 320 boys and 380 girls.
Electronic data: we collected data using our custom
application run on a smart phone. Data is available for
625 girls and 628 boys.
WHO model: this provides the mean and standard
deviation for children's growth [2]. We sampled from
these normal distributions to generate data sets.
Results
Conclusions
Data Cubic
In community care, EMR could be a bridge from
untrained CHWs to healthcare providers with timely
and relevant data. CHWs use our custom application
to collect data on children's weight, and the
application has a built in error correction because it
issues warning to data outside of normal range of
±2sd.
We compare the data collected using that application
and the paper records with WHO growth charts,
which leads to several conclusions:
1) Electronically collected data shows more
similarities to WHO data than paper data
2) Both Rwandan growth curves, electronic and
paper, are above the WHO model for up to about
one year in age. After that they both fall below the
WHO growth chart.
Polynomial
R2
Power-law
R2
Jarque-Bera
test for
normality
t-test standard
mean equals to
zero
3) Data collected by paper records shows low
goodness of fit, and is not normally distributed.
4) Electronically collected data has a goodness of fit,
measured by R2, which is twice that of the paper
records.
Speculations and future research:
Very young children in Rwanda who breastfeed grow
larger that the world average. When they start eating
regular food they do not get all the nutrients and fall
below world standards.
The next step is getting longitudinal data on
individual children’s growth and testing the predictive
power of the model.
Paper
0.33 0.32
Rejects
P=0.001
P=0.001
50% has p<0.05
Electronic
0.57 0.61
Not reject
P=0.5
P=0.001
30% has p<0.05
WHO
0.89 0.91
Not reject
P=0.99
P= 0.17
5% has p<0.05
0 10 20 30 40 50 60
4 6 8 10 12 14 16
WHO Girls Model
age in months
weight in kg
0 10 20 30 40 50 60
5 10 15 20
WHO Girls
age in months
weight in kg
Age
in months
Paper Girls
Electronic Girls
WHO Girls
WHO Girls Model
Age
in months
Age
in months
Weight in kg Weight in kg Weight in kg Weight in kg
Age
in months
Growth data from children under 5 is often modeled by expressing
weight as a cubic polynomial of age [3]. We achieve equivalent
goodness of fit using a power-law model. Differences between growth
charts has previously been assessed by visual comparison [4].
Our list of questions include:
1) What is the relationship between weight and age for each data set?
(Goodness of fit - R2)
2) Are the distributions of these 3 data sets normal (Jarque-Bera test)
3) Do the standard means equal to zero (t-test) and what percentage
for each month* do not pass t-test with confidence of 0.05.
Literature cited
[1] L. Poissant, J. Pereira, R. Tamblyn, Y. Kawasumi, et al. “The impact of electronic health records on
time efficiency of physicians and nurses: A systematic review”, Journal of the American Medical
Informatics Association, vol. 12, issue 5, pp. 505-516, 2005, DOI: 10.1197/jamia.M1700
[2] http://www.who.int/childgrowth/standards/en/
[3] K. Dewey, J. Peerson, K. Brown, N. Krebs, K. Michaelson, L. Persson, L. Salmenpera, R. Whitehead,
D. Yeung, and WHO Working group on Infant Growth, “Growth of Breast-Fed Infants Deviates from
Current Reference Data: A Pooled Analysis of US, Canadian, and European Data Sets, Pediatrics, vol.
96, no. 3, 1995
[4] M. Onis, C. Garza, A. Onyango, E. Borghi, “Comaprison of the WHO Child Growth Standards and the
CDC 2000 Growth Chars”, The Journal of Nutrition, vol. 137, pp 144-148, 2007.
* We used only months with 10 or more observations.