Working papers in
Information Systems
COMPUTER-BASED HEALTH INFORMATION SYSTEMS
- PROJECTS FOR COMPUTERIZATION OR HEALTH
MANAGEMENT?: EMPIRICAL EXPERIENCES FROM
INDIA
Ranjini C Raghavendra and Sundeep Sahay
WP 11/2005
Copyright © with the author(s). The content of this material is to be considered preliminary and are not to
be quoted without the author(s)'s permission.
Information Systems group
University of Oslo
Gaustadalléen 23
P.O.Box 1080 Blindern
N-0316 Oslo
Norway
http://www.ifi.uio.no/~systemarbeid
Raghavendra and Sahay
Copyright © with the author(s). The content of this material is to be considered
preliminary and are not to be quoted without the author(s)'s permission.
Computer-based Health Information Systems - Projects for computerization or
health management?: Empirical experiences from India
Ranjini C. Raghavendra
Sundeep Sahay
Institute for Women’s Studies
Lancaster University
United Kingdom
<c.r.ranjini@lancaster.ac.uk>
Dept. of informatics
University of Oslo
P.O. Box 1080 Blindern
0316 Oslo
Norway
<sundeeps@ifi.uio.no>
+47 2284 0073 (phone)
+47 2285 2401 (fax)
Citation: http://www.ifi.uio.no/forskning/grupper/is/wp/112005.pdf
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1. Introduction
Information and communication technologies (ICTs) in primary healthcare settings offer
a number of opportunities to enhance the efficiency of administration and improve
delivery of healthcare services. Health information systems (HIS), telemedicine, webbased initiatives, Geographic Information Systems (GIS) and development of healthcare
databases (see Bodavala, 2002) are examples of a few ICT-based initiatives currently
ongoing in the primary health sector in India, and also in other developing countries.
Despite the undoubted potential that ICTs provide, obtaining their practical benefits on
the ground is a very complex undertaking, and there are various reports of “total”,
“partial”, “sustainability” and “replication” failures (Heeks et al, 2000). Contributing to
this unrealized potential are a number of complex inter-related issues such as
inadequacies in both computer-based infrastructure (for example, Nhampossa, 2004), and
physical infrastructure of roads and transportation which are required for the transmission
of reports (Mosse and Nielsen, 2004), the persistent presence of legacy systems
embroiled with different institutional interests (Nhampossa, 2004), weak human resource
capacities both in numbers and skills (Chilundo, 2004), heavy workload of health staff
who need to give priority to providing care over administrative tasks like reporting
(Mosse and Sahay, 2003) and a culture of information use which sees periodic reports as
primarily fulfilling the needs of the bureaucracy rather than of using the information to
support action (Quraishy and Gregory, 2005). All these contextual influences make the
challenge of introducing ICTs in the healthcare sector a very difficult one in practice
(Sahay, 2001).
The critical role that HIS can play in public health has been emphasized since the early
1980s in attempts to integrate data collection, processing, reporting, and use of
information to strengthen management at all levels of health services (Lippeveld et al,
2000). HIS in most developing countries have been described by researchers and also
policy documents emerging from international agencies as being grossly inadequate (for
example, WHO, 1987; Lippeveld, Foltz and Mahouri, 1992). Sauerborn and Lippeveld
(2000) argue that this ineffectiveness stems from various reasons including the
irrelevance and poor quality of data being gathered, duplication and waste among parallel
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HIS, lack of timely reporting and feedback, and poor use of information. As a result, what
we typically find is HIS that are “data-led” rather than being “action-led” (Sandiford,
Annett and Cibulskis, 1992), which tends to see data as an end in itself, rather than as a
basis for planning, making and evaluating interventions. Institutionally, HIS in
developing countries are situated in rather centralized structures (Braa et al, 2001, Braa &
Nermukh, 2000, Braa, Heywood & Shung King, 1997) in which local use of information
is not encouraged (Opit, 1987). This has led Sandiford et al (1992) to comment that what
is needed is not necessarily more information but more use of information.
While as a part of various health reform efforts, including HIS, ICTs are being actively
introduced by international agencies and national and local governments, what is often
found is that the focus of such efforts are primarily on the means – computerization –
rather than the ends of what needs to be achieved – strengthening information support for
health management. Introducing ICTs in the development of HIS is not necessarily the
“silver bullet” that solves the efficiency problem of the health services (Sandiford et al,
1992), and over the years research has emphasized that critical issues to be addressed in
the implementation of IS are social and organisational, not solely technical, (Anderson
1997, Walsham 1993).
As Helfenbein et al (1987) have argued changing the way
information is gathered, processed, and used for decision-making implies making
changes in the way an organization operates. They also suggest that, producing and
utilizing information more effectively will affect the behaviour and motivation of all
personnel. The HIS-related challenges are thus socio-technical in nature rather than
merely technical or only social.
We argue that taking a technology deterministic perspective to HIS development implies
a lack of sensitivity to the socio-political-institutional context, and contributes
significantly to the unrealized potential of computerization of HIS in developing
countries. There is an urgent need to shift this focus from computerization itself to the
question of “what is it that we want to achieve through computerization?” in order to
strengthen these HIS-focused reform efforts. Analysis of this question helps to develop an
argument against the adoption of a techno-centric approach to the development of HIS
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and for approaching this effort with a focus on integration and sensitivity to the sociotechnical context. Introducing computers does not in itself lead to better handling and use
of information, and the challenge is to initiate a parallel informational reform process
where information is seen as a resource for action rather than one for fulfilling the needs
of the bureaucracy.
The present chapter analyzes empirically some of these challenges drawing upon a study
of two specific HIS initiatives introduced in primary healthcare settings in Andhra
Pradesh (a state in southern India): The India Health Care (IHC) project and the Family
Health Information Management System (FHIMS). In the following section, we provide a
brief summary of the broad context of the health sector and HIS in India. In section 3, we
describe the research setting broadly followed by a narrative of the HIS projects. In
section 4, which presents the analysis and discussion, we discuss the challenges and
opportunities inherent in shifting the focus of HIS efforts from the computerization itself
to how it can support health management. Finally, some brief conclusions are presented.
2. The Indian health sector context
The goal of health policy in India has been stated as supporting universal and free
primary health care for all (Gupta and Chen, 1996). A primary health centre was
conceived in India as an institutional structure to provide integrated preventive,
promotional, curative and rehabilitative services for the entire rural population. The
public health sector includes an elaborate system of primary health centers (PHCs), subcenters (SCs), community health centers, dispensaries, and hospitals. Each PHC is
expected to provide healthcare for a population of about 30,000, whereas in practical
terms this ranges from twenty to seventy thousand. Paramedical workers (health
assistants) work at the SCs and are assigned theoretically to serve a population of about
5,000, which in practice ranges from three to seven thousand.
The health staff in the PHCs and SCs generate considerable amounts of data about the
services that they render, collate them on a weekly or monthly basis, and report them in
prescribed formats, sometimes duplicating the same report format to different
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departments or programme managers. These informational activities take up a
considerable amount of the health staff’s time. Moreover, frontline health assistants are
mere producers of data rather than its users. While the focus on reporting serves to fulfil
the needs of the bureaucracy and their dependencies on international aid agencies, and
potentially the need for epidemiological analysis, it is rarely ever used to guide local
action at the level at which the data are collected (Sahay, 2001). There are also qualityrelated issues which inhibit the active use of data. For example, data collected at every
level are aggregated and by the time this data reaches the higher levels of the health
system, the specific situations in the peripheral areas are completely masked (Quraishy
and Gregory, 2005). The flow of data is largely uni-directional, from the lower levels to
the higher ones, with very little constructive feedback being given to the local levels to
strengthen their work processes. Instead, often the only form of feedback is in the form of
reprimand, where health staff are pulled up for not meeting their (rather unrealistic)
targets. Mechanisms to facilitate mutual exchange of information are also minimal.
Acknowledging these limitations of the existing HIS, recently, during the launch of a new
programme named the ‘National Rural Health Mission,’ the Prime Minister of India Dr
Manmohan Singh called for reorienting the HIS to support local action (April 12, 2005):
The monitoring systems have to become oriented (sic) outward towards the
community and not upward towards the bureaucracy. For example, so far the
health information we have in our country through the National Family Health
Services Reports are seen at State and Central levels and hardly ever at district
and below district levels. If information is to lead to action, it should be available
and used at the local level. We must reorient the information system to support an
accountability-structure by developing district health reports, state health reports
and so on.
While the challenges to HIS reform are now being recognized at the policy level, the
harder task is to address them on the ground. With this as the backdrop, we now shift to
describing the specific research setting and the particular HIS implementation efforts.
3. Research Settings: Ongoing HIS initiatives in Andhra Pradesh
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The empirical material reported here is derived from a situational analysis of the
implementation of two HIS projects in Andhra Pradesh (AP), a state in southern India
with a population of 75.7 million (www.censusindia.net). The state has about 1,386 PHCs
and 10,568 SCs spread across 22 administrative districts covering an area of about
246,793 sq km. PHCs serve as the hub for implementing the national programmes such as
for population control, and disease control programmes like malaria, tuberculosis, leprosy,
gastroenteritis among others. Mainly, however, the PHCs are the cornerstone of the
maternity and child health programme, now integrated into the Reproductive and Child
Health programme. Over the last few years, HIS projects have been initiated in the
Family Welfare Department in AP to help provide electronic support tools to the health
staff at different levels of the organisation.
These HIS efforts of the state government were initiated in the backdrop of larger egovernance reform policies initiated by the former Chief Minister Chandrababu Naidu
within the framework of “SMART governance” (Simple, Moral, Accountable, Reliable
and Trustworthy). As a component of this ambitious e-governance policy, the chief
minister drew up vision statements for every government department. The government’s
IT vision states: “Andhra Pradesh will leverage Information Technology to attain a
position of leadership and excellence in the information age and to transform itself into a
knowledge society.” The vision document also established ambitious and demanding
goals for the health department to be achieved by the year 2020. These goals were,
among others, to achieve infant and child mortality rates of 10 per 1,000 (live births) and
20 per 1000 respectively, a total fertility rate of 1.5 (average number of children per
woman) and population growth of 0.8 per cent a year. ICTs were seen as important tools
to help achieve these goals.
Towards achieving Vision 2020 and the SMART objectives, the government undertook a
number of IT initiatives, some of which have had direct implications for the health
initiatives under study. One such was the Multipurpose Household Survey (MPHS),
which we now describe below.
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3.1 Towards a computerised health database: Vision and reality
The MPHS was an ambitious project of the government of Andhra Pradesh to build a
massive ‘name-based’ database of 75.7 million citizens, 25 million land records and to
help provide a social security identification number for every citizen of the state. The
survey was originally compiled in 1995. It was later computerised and the data hosted on
a central server for public access. The citizen’s database with about 130 data elements has
been installed in 1125 mandal1 revenue offices. Although this database was created by
the state revenue department for its own purposes, the government passed a directive that
MPHS should be used as a standard database by all government departments and other
agencies in an attempt to prevent multiplicity of databases. In order to comply with this
directive, other departments started to add data from their own databases to the MPHS.
Creating a name-based database with various socio-economic indicators for a population
of 75.7 million is a challenging task. A consultant in the revenue department explained
that this database was compiled in 1995 by conducting a household survey, and
subsequently between 1998 and 2003, two private companies were given contracts to
update this data and also to include other data such as on deaths, births, migration etc.
Then, some additional data (such as the number of school-going children, the number of
working children
in the age group 6-14) from another survey called the Human
Development Survey (HDS), which was conducted by the Planning Department, was
integrated with the MPHS data in January 2000. Integrating the data from the HDS into
the MPHS database had inherent problems because the basic unit for the MPHS was the
village and that for the HDS the habitation2. In the HDS, data was collected on total
numbers and not on the details of all individuals in a family. For example, details about a
family included ‘total number of children in the age group of 0-5’, ‘number of illiterates
above 15 years’, ‘number of family members above 65 years’, while in the MPHS data
was collected about every individual member. There were commissions and omissions
because of the migration of people and also because some people were reluctant to give
their details for the survey. There were also quality issues raised about the HDS data as it
1
2
A mandal is an administrative unit
Group of households in a particular community is classified as habitation.
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had been collected by school children. As a result, the database was not accurate and the
quality of data was very poor.
In addition, with regard to our main concern here, the HIS, the PHC as the fundamental
unit for the state health department did not match with the fundamental unit of the
revenue department (the owners of the MPHS), the village. As a result, many of the
health parameters necessary for the health services were not included in the MPHS
database and it had to be updated with these data subsequently.
Around the time that the MPHS was being upgraded, in June 2000, Infodev (The
Information for Development Programme), an agency of the World Bank, granted USD
250,000 funding for the first of the projects under study here, the India Health Care (IHC)
project. The key objectives of the IHC were: to reduce or eliminate the redundant entry of
data prevalent in paper registers, to generate monthly reports automatically for health
assistants, and to make data electronically available for further analysis and compilation
at higher levels of the healthcare system. The project was eventually implemented in 200
sub-centres (spread across 32 health centres). The larger objective of the initiative was to
reduce infant mortality and maternal mortality by improving the quality of antenatal care
and child health through improved information management.
The second project under study here, the FHIMS, was an offshoot of the IHC project.
One district, Nalgonda, out of the 22 districts in the state, was selected to pilot this project.
It took off simultaneously in all 67 PHCs in the district in 2002. The information needs of
this project forced the family welfare department to embark on a more elaborate project
to have information systems not just at the PHCs, but also in the district offices and the
state office. The overall aim of the project was to ‘computerize the activities of the PHCs
as a whole to take care of (a) Family Welfare needs (b) control of communicable diseases
and (c) PHC management’ (CFW, Circular – I : 2003).
As already mentioned, during the initial days of the IHC project, it was decided that
MPHS should form the basis for a name-based health registry and that instead of
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conducting a new survey, the health staff should update the MPHS. The survey data were
crucial for both the projects -- IHC and FHIMS -- because it was to be the foundation of
the name-based IS. This name-based system (for example, who has got malaria) was to
replace the earlier one in which data were collected based on numbers (for example, how
many cases of malaria in a region). In the Family Welfare Department (FWD), the
family/household was the basic unit of analysis, and it was expected to maintain with this
unit of analysis, data on births, marriages, family planning methods, ante-natal and postnatal cases, immunisation of children, deaths, and diseases. It was argued by the
proponents of the name-based system that it was necessary to provide targeted healthcare,
especially in following up ante-natal cases, immunising children over a period of time
and in tracking patients. While the officially stated aim of the FHIMS was to improve
patient-specific care, in practice, as one doctor said, the rationale underlying FHIMS was
to try and “prevent the manipulation of figures by the health staff, since with names it is
easier to track whether or not the right numbers have been filled.”
Apart from these data-related issues, there were also software challenges. A consultant in
the revenue department explained:
Quality problems with the software are because each department hires an agency
on its own to get the necessary fields from the MPHS. That company may not
know the design of the MPHS data completely. They are given a very short time,
say a month or so, to deliver the product. Understanding a huge database and to
know how to do it takes time. Quality suffered because of this.
Another software was developed to download the MPHS and print the MPHS data
habitation-wise, in the form of a ‘household register’. A software engineer of the IT
company that had been involved in the development of the software for the FWD said:
Nobody knows about the accuracy of the data. The survey was initially done for
the revenue department. We did not know the software codes because it was done
many years back. We cannot assure the accuracy of the data.
The development of the FHIMS software was contracted to an IT company located in the
state capital, at a cost of about USD 68,500 and the requirements included 17 modules
(such as family welfare activities, tuberculosis control, leprosy eradication, budget
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monitoring, personnel information etc). The software was developed on an Oracle backend using Visual Basic. Although the project itself was delayed (because the household
survey which was expected to be completed by December 2003 was still ongoing in
March 2004 in many PHCs), the software was expected to be deployed towards the end
of 2003 itself. The health department mobilised about USD 7.3 million for the project
through a consortium of funding agencies including the World Bank to buy about 1500
computers and install it in every PHC, district office and in the state office, and load the
computers with Oracle software (with a license fee of about USD 200 per computer) and
Microsoft software.
The idea of computerization was that the MPHS data would be loaded in the FHIMS
software, and using this health survey data could be entered (based on names). It was
soon realized, however, that the MPHS data was unsuitable because of quality issues and
the fact that the data did not match the needs of the health department. As already
mentioned, this was because the MPHS was conducted by the revenue department and
hence did not have data that were necessary for the health department. For example, data
on family welfare methods (sterilisation/intrauterine devices/condom/oral pills) adopted
by couples did not figure in the MPHS. It was then decided by the state office to have
health assistants conduct a new house-to-house survey to update the data.
This process of collecting data for such a massive population, as can be imagined, was a
non-trivial task and involved multiple challenges. When the health staff took the MPHS
survey books to their respective habitations, they found them afflicted with many errors.
Many health assistants complained that names were missing or inaccurate, ages of family
members were wrong or sometimes data on entire habitations were missing. Sometimes
there would be a Muslim name for the head of the household and the family members
would have Hindu names (a quite unlikely possibility!). In another instance, the same
name (of a head of the family) was in all the families in a particular village. Also, since
house numbers were not arranged in an order in many villages, the health assistants had
to rely on the names of people to locate their families. Some of the health assistants said
they were ordered to conduct this survey at their cost and time. However, higher officials
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disagreed and said doing such surveys is part of their job. The health assistants would go
in the evenings after work to conduct the survey, and had to be accompanied by their
husbands because they had to travel after dark. Also, as the list of names in the forms
(printed from the FHIMS database) given to them did not match with how people were
physically located in the habitations, they found it easier to buy new books and write the
names by pen. However, in the absence of any financial support, the health staff had to
buy these books at their own cost. Also, because of time constraints, and the fact that they
had to enter whether a person had (or had not) a list of about nine diseases and particulars
of physical disability (six types) in code numbers, the assistants would typically simply
enter “no” for all. A project coordinator of FHIMS explained some of these difficulties
experienced by the health assistants while conducting the survey during the pilot phase of
the project as follows:
The other big difficulty which our staff faced is they were given registers… per
habitation, per village like that. Each book runs into, depending on the size of the
habitation, anywhere between 500-600 pages to any bulk. Some habitations are
really big which have 10,000 population. So we have got those many pages bound
into a register. Our health assistants have to go to the field with these registers
not knowing in which page the family which they have to survey is there. Our
books did not have any index. Neither were families printed in a serial order as to
how they are (physically) placed nor in an alphabetical order. So the health
assistant practically had to turn all the 600 pages or 10,000 pages every time to
locate a family. And she couldn’t just tear all the pages and arrange it one after
the other…. She knows perfectly well which house comes next to which… That
was also not possible because in one sheet we had two to three families. In
replication phase we have made sure we get only one family per page and we now
have an index for every bound book… in an alphabetical order.
After the house-to-house survey, the next stage of the project was to enter the data into
the computerised database, which was a time-consuming and arduous task given the
already heavy workload and time constraints of the health assistants. The data entry into
the household database which would then be imported to FHIMS was outsourced to a
private party. This private agency further recruited Data Processing Operators (data
operators) (at a salary of about less than USD 100 per month) to do the data entry.
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During the pilot phase of the FHIMS project involving data entry, there were many
omissions because the work of the data operators was not well monitored. While the
health assistants were instructed to be present with their registers when data were being
entered by the data operators and oversee the process, the health assistants would simply
leave their registers with the data operators and go away to fulfil other duties. The data
operators, for their part, found it difficult to make out the handwriting of the health
assistants and the variety of informal symbols they had made in the registers. A retired
district officer, who was appointed a consultant for the project, explained why this lack of
oversight of the data entry process occurred as follows:
On many occasions health assistants were not present when the data entry was
done. If the health assistant sat with the data operator with her register, that
would slow down the data operator because his work would be constantly doublechecked. On the other hand, work would proceed faster when the health assistant
was not present because the data operator would worry less about whether he
was entering the records correctly or not. Remember his payment was based on
the number of records he entered per day. So naturally, his interest was not
whether it was right or wrong, his interest was to enter as many records as
possible in a day and collect his money. The health assistants were not able to sit
with the data operators, oversee and clarify, because she had so many other
priorities. Moreover, that sort of accountability and sense of responsibility was
not there among most of the field staff. So, in effect, the data entry was not done in
a systematic manner.
As the project expanded, data entry was moved to the respective PHCs. Now, a new set
of problems caused delays in the process. Multiple and unattended software bugs,
software incompatibility between the household survey module and the FHIMS database
were some of the problems encountered. The result was that certain data -- for instance,
data on antenatal women not resident in the habitation or data on pregnancies leading to
abortion or medical termination -- could not be entered into the database. In some PHCs,
the household survey module could not be linked to FHIMS database leading to
discrepancies in population figures that were entered and what was actually displayed in
FHIMS. In fact, although it was claimed that all the data had been entered, close
examination revealed later that data entry was done only for the Family Welfare module
and even then for only a portion of it.
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3.2 The India Health Care project
The IHC project was started as a pilot in three health centres in Nalgonda district where
health assistants were given Personal Digital Assistants (PDAs), and another health
centre was selected to pilot the same application on a desktop computer. Subsequently,
PDAs, which are mobile computing devices, were given to about 200 women health
assistants. Typically, health assistants spend a considerable amount of time collecting,
collating and reporting health data to different officials. This paper work is time
consuming, cumbersome and historic data is difficult to manage, retrieve and use. The
stated objectives of the project were to reduce manual paper work and eliminate
redundant data entry and thereby free-up their time for health care delivery. The
application was also expected to facilitate the generation of schedules with information
pertaining to immunization and antenatal services for the health assistants, which would
help them to geographically identify antenatal cases to be visited and children to be
immunized. The system was also supposed to help the health assistant in tracking the
history of the patients and enable her to take preventive measures such as treating highrisk pregnant women or give timely vaccinations to children. The schedules also
highlighted pending and those cases which had been missed by her. This was supposed to
help the health assistant to prioritise her work and attend to cases which needed
immediate attention. The PDAs were expected to eventually replace her paper registers.
Thus the use of these mobile computing devices was projected as providing ample
opportunities for assisting health staff in their routine work. At the PHC level, it was
expected that the data from PDAs would regularly be transferred and uploaded onto
desktop computers located at the PHCs and support the routine HIS.
However, the use of the PDAs was largely unsuccessful for a variety of reasons. For
example, the memory supplied was insufficient (16MB). There were problems with
charging the batteries in the rural areas. Many health assistants said the device was slow,
and since using the device while on the job was time-consuming, a few health assistants
would do the data entry later, in the bus on their way back from work or at home. One
health assistant said she would ask her children to do the data entry at home. Those health
assistants with eye-sight problems complained that they found it difficult to read small
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letters on the monochrome black and white screen, and said that a colour screen would
have been better. Also, while standing with the PDAs in the sun, it made it difficult for
the health assistants to see the screen clearly. Also, since they had to deal with long lines
of people waiting to get their attention, they found it troublesome to spend time trying to
enter data into the PDAs. Added to the poor quality of the MPHS and the database, there
were technical problems during uploading of data into the database. The PDAs were
perceived to be costly, and the health assistants were scared that they might lose them and
they would have to pay for it from their pockets. Due to these and other problems, the
PDA use has now literally stopped. A health worker shared her experience:
The PDAs which were supplied were low in memory. When we started using
PDAs it was taking a lot of time to search the name, or open the screen or save
the entry. When we had a long queue of patients, mothers holding their small
children waiting for us to give the children immunization shots, if we were
operating a PDA which was slow, the patients would be frustrated. If I don’t
attend to them, they will go away. So, very soon I realised this PDA was not much
of use when I was on the job. So I resorted back to writing in my old book. What I
did was I would go home and then again enter all the day’s entry into the PDA.
To tell you frankly, though it has helped me in getting schedules, it has also
increased my work load.
A doctor working at a health centre also shared a similar opinion on the reasons for non
use of the devices:
The health assistants are reluctant to use PDAs because the operating system is
such that if the power is discharged or if a health assistant forgets to recharge it,
all the data will vanish. She has to come to the PHC and upload the data from the
desktop to the PDA. In this division, the reason for reluctance of health assistants
to use computers is that we got the PDAs first and desktops later. So whenever
data vanished, the software engineers would take the PDAs to Hyderabad (the
state capital) and would feed the data there into the PDA. The second problem
was memory. Third problem is when we said the memory is not sufficient, they
changed the software so only the target population is there in the PDA. There are
many software problems because of this. The health assistants who are really
interested even today are not able to use them because some records are not
captured properly.
Another doctor who was involved in training and supervising the project said both
technical and non-technical factors contributed to the failure of the PDA project:
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The processing capacity of the PDA was slow. The intention of giving PDAs was
that the health worker would give immunisation and immediately enter that data
in the field. But that did not happen in practice. People won’t wait. So they wrote
manually in their registers. Once the immunisation session was completed, the
health workers would have to again enter the data, come to the health centre and
then upload the data (into the desktop). Only after that could they get the schedule.
In that schedule few names would be missed because some records would be
rejected. So due to all these issues health assistants were not able to use the PDAs
comfortably. If these technical problems could have been solved, the percentage
(of health assistants using the PDAs) would have been about 70-75%, but not
100%, because you cannot make everyone use that device. Also, battery has to be
replaced after every two years or so. All these issues were not taken into
consideration.
The reason to do a pilot is to try out a technology on a small scale in order to identify
problems and resolve them before making a large investment. However, about 200 PDAs
were brought by the IT company all at once. The consultant of the IHC project said:
The irony of it is we started the project in three PHCs initially as a pilot. So they
could have procured the PDAs only for those 3 PHCs. When we raised this
problem of memory they could have procured higher memory PDAs later. After
having gained experience during this pilot phase they could have bought the rest
of the PDAs. Anybody would do that. But the software company said they have
bought all the PDAs at once. For half of the district, it was 200+ PDAs.
Poor quality of the survey database, recurring software problems, insufficient memory
capacity, absence of timely and on-going technical support and maintenance gradually
led to non-use of these devices on the job. The health assistants who were trained and
were enthusiastic initially gradually lost interest and reverted back to their earlier manual
system of recording health data. The project was neither sustainable nor was it scalable
because of the costs involved. Out of 200, only about 9 health assistants were using PDAs
in March 2004 during a field visit by one of the authors. During the visit the health staff
took out the PDAs from locked cupboards and showed the researcher blank PDAs which
had not been used for months. In some other clinics we saw the children of the health
assistants playing with these devices.
The aim of the project was to provide support tools that would allow health assistants to
reduce time spent doing paper work. However in practice, it actually increased their work.
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The second aim was to increase the accuracy of the data flowing up from the health
assistants; however, this could not be achieved because the quality of multipurpose
survey data itself was poor. The third aim was to provide a means for getting healthcare
data at the village level into electronic form and generate reports. This was too ambitious
to achieve because shifting from a manual to an electronic form was difficult for reasons
cited above. Finally, the project aimed to provide health assistants with information that
allowed them to provide more targeted and effective services to the people. Health
assistants need not just support tools, but also support to use those tools. In the absence of
this, they were not able to use these devices effectively. As the consultant said, “it works
only if the supporting staff trouble-shoot problems promptly and in a reasonable period.
But this did not happen, so gradually the health assistants lost interest and developed a
negative attitude towards computers.” So when plans to expand the computerisation
project to the whole state were being drawn it was decided not to opt for PDAs because
of these problems, and also because it was considered too expensive.
3.3 Family Health Information Management System
The FHIMS project was conceived based on the experience of the IHC project. While the
IHC project covered only one aspect of the work of the health assistants (family welfare
services), it was decided by the department that a comprehensive HIS on desktop was
necessary, and that it should include the different activities of PHCs including family
welfare services, various health services (disease control for example) and administrative
aspects (such as budgets and logistics support). The department in its official circular
(CFW, 2003) outlined the key objectives of this project as follows:
 Name-based follow up of family welfare services: antenatal services and
immunization, early identification and timely referral of high risk antenatal
cases;
 To improve the full immunization rates and thereby contribute to the reduction
of infant mortality rates and maternal mortality rates;
 To facilitate the health assistants to easily get the schedule of services to be
rendered in each habitation during a month. That is, to provide schedules on
pregnant women and children to be visited by the health assistants in their
respective sub-centres;
 To reduce the burden of manual record keeping by the field staff and all the
higher levels;
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 Contain the spread of communicable diseases and blindness by tracking
incidence of diseases;
 Improve functioning of PHCs by facilitating effective service delivery;
 Streamlining inventory and infrastructure management at the health centres; and,
 Managing career and training issues of field personnel.
The project was conceived over the following phases: Pilot at Nalgonda district (Dec
2001-Nov 2002) and state-wide replication (June 2003-ongoing). We discuss these two
phases followed by a summary of the identified implementation challenges.
3.3.1 The pilot phase of FHIMS at Nalgonda district
The pilot phase of the FHIMS project was started in 67 PHCs in Dec 2001 about a year
after the IHC project in the same district. A software engineer who had been involved
throughout the design and development of IHC and also the FHIMS explained that
elaborate consultations were held with health officials at different levels, including health
assistants, during the design of the software. However, when we spoke to various doctors
in another district, they typically said that the process had been largely top down with
limited involvement of PHC level doctors.
After the software was installed in all the PHCs, the department appointed data
processing operators (data operators) on a temporary basis for a period of nine months to
do the data entry and to train the staff during this transition period. Although data
operators initiated the data entry process, they however did not seem to have the capacity
to take on the role of change agents, and to motivate the health staff. Also, although the
district administration had instructed every PHC to identify a competent person as a
system administrator who could act as a leader at each PHC and be trained by the data
operators from the beginning, this did not happen. Also, the role of systems
administrators was not clearly defined, and adequate guidelines were not given to them
on how to manage their regular work (for example, as lab assistants) with these additional
responsibilities, and no incentives were provided to concurrently carry on both this task
and as that of a system administrator. The system administrators also complained that
training provided was inadequate and focused primarily on computer awareness and
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hardly anything on the FHIMS and nothing on health management itself. A coordinator
for FHIMS who was involved in conducting training sessions confirmed this exclusion:
Initially all the cadres were trained in all the modules. That was not really useful.
For example, a health assistant need not know about the budget module, vehicle
or personnel module. If she knows about family welfare module and 1 or 2
national health programmes that should be enough. Similarly, a pharmacist need
not know about budget or family welfare modules. If he knows about his stores,
inside out, it is more than enough. It won’t take more than 2 days. He will be
more than happy if he is thoroughly trained for 2 days on his module. But they
were called for 5 days and were taught all the modules. By the time they came to
their module they had probably lost interest.
The doctors said they were not fully involved in the project from the beginning and were
unaware about the aims of the project. A FHIMS coordinator, who is also a doctor said:
From the beginning the doctors did not have ownership of the data… since the
time the survey started. Training on updating the registers was given only to the
health assistants and the doctors did not know what the training was about. Had
the doctors at the PHCs been told about the registers, all about the project, given
guidelines as to how a health assistant has to go about her work, this is where you
as a doctor have to guide them… then things would have been different. However,
the doctor was bypassed. The data entry was done at a central point (not at
PHCs). The doctor lost track there again. That’s how they drifted slowly. They
could have probably fallen back on track when the software was installed but that
didn’t happen due to many reasons. Hence the doctors did not own the project.
Online data transfer took place initially for sometime but stopped later. The coordinator
of FHIMS explained why:
Online data transfer was going on in 50 % of the PHCs for sometime. But the
thing is the district administration is still taking only the manual reports. It is not
taking the reports generated from FHIMS because all the data is not being
entered into FHIMS. So whatever report you get from that (software) is not
complete. The formats in which we report to districts like Form A, Form B, Form
C, I, II, III are not there in FHIMS. We cannot generate all that from FHIMS. The
only thing we can generate from FHIMS is Form B. But that had a few errors.
And even if you could correct 1 or 2 errors with a pen, you need the other data.
And where will the other data come from, only if you enter the data….
In March 2003, the authors visited 12 PHCs, 1 Community Health Centre and the district
office. The health staff complained that many software and system related problems were
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identified but not addressed. In many PHCs, the health staff did not know the passwords,
did not know how to burn CDs and save the data. At the time of our visits a majority of
the health staff were not using the computerised system, were not entering and updating
the heath data regularly and hence were not generating computerised schedules or reports.
The projects in most PHCs had stopped when the data operators left after their temporary
tenure.
3.3.2 State-wide expansion of FHIMS
State-wide replication of the project involved extension of the Nalgonda project to all the
other 21 districts and 1,319 PHCs. The expansion phase of the project which started in
2003 involved computerisation of all the PHCs, District Medical and Health Offices
(DM&HO) and the Director of Health/Commissioner of Family Welfare offices at the
State level (Circular I, CFW, 2003: 1). The replication involved the conduct of a
household survey (to replace the MPHS data as was done in Nalgonda), the installation of
computers and the FHIMS, entering survey data, training the health staff and initiating
other activities (such as identifying private agencies for printing MPHS books, team
building) necessary for starting the project.
Based on the experience of the pilot, many changes were made during the replication
phase. Survey registers were printed with an index and in alphabetical order. A team of
16 people were trained in each district, who in turn trained the health staff at the district
level on how to conduct the household survey. Doctors were involved and system
administrators were identified. Video-conferencing facilities were used regularly for the
Commissioner to coordinate and give instructions to health officials in the district, and to
obtain status reports on the data entry process. A number of circulars were sent to every
PHC regularly giving detailed instructions at different stages of implementation.
The computers were introduced in September 2003 in the PHCs and the initial plan was
to start the project by December 2003. In July 2005, one of the authors (SS) and his team
conducted a situational analysis of the status of implementation of FHIMS in 84 PHCs in
another district (called Chittoor). This evaluation report, which was provided to the
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Collector of the district, the DMHO, and also the state level authorities, identified a
number of ongoing problems in the implementation of FHIMS contributing to it for being
far behind schedule. The evaluation identified that all the PHCs had reported software
bugs of varying levels of complexity. Although household data has been entered, service
data remained grossly incomplete. None of the PHCs were generating any of the reports
that were needed to be sent to the district. This was because except for one report, other
reports could not be generated through the FHIMS. Although in a few cases, schedules
for health assistants were being printed; the demand for these schedules did not come
from the health assistants themselves but had been printed by the data operators (before
they had left) and given to the health assistants. The study also revealed that on an
average only 3 or 4 people per PHC had received training on computer awareness,
through a 5 day training programme. No formal training was given to the PHC staff on
FHIMS. In some cases, the data operators had provided some basic training to the system
administrators. In more than 50 % of the PHCs, none of the staff knew the username and
password to enter the FHIMS modules after the departure of data operators.
In summary, various factors have been identified that have impeded the implementation
of the HIS initiatives discussed. These complexities are socio-technical in nature. While
some of these problems are expected, given the extreme complexity of the projects, some
we believe, can be addressed by providing increased care and attention to the sociotechnical issues. In Table 1 below, we summarize some of these key challenges, and in
the following section discuss some opportunities in trying to address them.
Table 1 Summary of the implementation challenges
* Dependency on MPHS which was developed by other departments for building a
database
* Improving data collection approaches
* Providing adequate training not just in using computers to generate regular reports
but also
in interpreting data.
* Developing a culture of using information to support local action
* Providing onsite training (at PHCs) for the health staff on a regular basis
* Adopting an iterative process to improve technical features of the software
* Getting health staff to start doing data entry rather than data processing operators
* Initiating collective participation to facilitate ownership of the systems
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* Motivating health staff to change existing work practices
* Encouraging system administrators and doctors to take on the role of change agents.
* Building capacity of health managers for evidence-based decision making
* Providing timely hardware and software support
* Addressing infrastructural issues
* Switching-over to online reporting
* Sustaining the project overtime by involving officials at the state, district and local
levels
4. Discussion
One of the key recommendations of WHO (2005) in Health and the Millennium
Development Goals is to improve the information basis on which health management
decisions are made. The report notes: “making available information concerning the
location, functioning, and performance of health services should also improve
transparency and accountability in the management of the health sector (p 79)”. Accurate
information can support better management and enhance efficiency of health care
delivery. HIS can contribute to human development by supporting health management.
Jacucci, Shaw and Braa (2005) argue that the overall sustainability of a standards-based
HIS is dependent on the quality of data, which is dependent on the skilful use of data at
the level of collection. In order for this to happen, they emphasise that ‘local
sustainability’ of the HIS is required. It is important to make information of relevance not
just for the managers but also at the local level. Training staff at the PHCs on topics such
as use of information at local levels, on the significance of health indicators, validation,
analysis and interpretation of health data that they collect is essential to achieve this.
However, implementation of HIS is not just matter of introducing technology, but is very
much dependent on the social, organisational and process issues that need to be addressed
thoroughly and simultaneously. Too often, however, technology alone is perceived as a
necessary input to improve existing conditions. In contrast, we consider it important to
understand socio-political-organisational context during introduction of IS. Several
authors have emphasised the need to understand and analyse information systems from a
socio-technical perspective (Kling & Lamb, 2000); Walsham (1993); Coakes, Willis and
Lloyd-Jones (2000). For example, Kling and Scacchi (Kling and Scacchi, 1982; Kling
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1987) utilize web models to take into account the social relations between the set of
participants concerned with the IS, the infrastructure available for its support and the
previous history within the organisation of commitments made in developing and
operating related computer-based technologies. Walsham (1993) provides an analytical
framework for understanding the mutually shaping linkages that exist between content,
social context and social process of IS. Over the years several authors studying IS in
developing countries have widened the scope of focus to include tracing the roots of the
behaviours encountered in the economic, cultural, and political domains of the social
context ( e.g. Madon, 1993; Jarvenpaa and Leidner, 1998; Avgerou and Walsham, 2000;
Sahay, 2005; Walsham, 2001). Avgerou and Madon (2004) argue “for broadening of the
perspective of situated research to make explicit the contextual origins of the meanings,
emotional dispositions, and competencies actors bring to bear on the interactions that
constitute these processes” (p 176).
Even though a participatory approach was stated to have been adopted to a certain extent
during design and development of the FHIMS, this did not necessarily evolve into a
partnership and later lead to end-users owning the new system. The question that this
raises is the need to adopt a complementary approach to building and strengthening
capacities of health staff at different levels to facilitate ownership of new systems. To
make this happen adequate and timely support -- technical, organisational, infrastructural
and training -- is essential. Creation of adequate support systems is important for the
sustainability of the project. The other important aspect is to train the trainers or change
agents to develop an ability to motivate the staff. Their constant presence can potentially
facilitate the change process. In a recent review on the use of information technology in
primary health care in developing countries (Tomasi, Facchini & Maia, 2004), the authors
note that a substantial number of articles reviewed, stress the need for continued
motivation and training for all team members as an important requisite for the success of
any initiative. Involving all the health staff and strengthening their capacities is a slow
and gradual process, but crucial for collective ownership of the project.
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From the case studies presented above it can be understood that the challenge is to shift
the focus from technology per se to health information management in order to strengthen
the informational basis of health management. Some of the opportunities to address these
challenges are now discussed.
4.1 Opportunities for addressing the challenges
In order to address some of these challenges identified above, the State Department has
recently endorsed a project called Integrated Health Information Management System
(IHIMS) through a Memorandum of Understanding (MoU) with HISP India3, (a not-forprofit society set up by University of Oslo). Health Information Systems Programme
(HISP) is an ongoing, large-scale action research initiative that operates as a global
network across a number of developing countries. HISP was started in India in December
2000. The objective of integrating the two projects is to utilise individual capabilities of
the FHIMS and HISP and develop synergies to contribute to the overall health
information management in the state. The District Health Information Software (DHIS),
is a not for profit customised open source software developed by HISP for use at the
district levels of the health system with a focus on providing improved analytical tools.
Unlike the FHIMS, which deals with name based data as its input, DHIS focuses on the
analysis of facility level (aggregated) data. HISP India with its focus on health
management and building capacity at the local level to use these systems effectively has
initiated the following efforts:
 With the FHIMS as foundation, DHIS provides analytical tools for further data
analysis and report generation.
 Web access to all routine data and health indicators up to the level of sub-centres.
 Spatial analysis of data up to sub-centre using Geographical Information Systems
(GIS).
 By increasing the scope of GIS and web access there are opportunities for providing
necessary information support to healthcare managers and other higher officials.
 HISP places about 80% of its effort on training and capacity building and the rest on
the technical aspects.
3
For more on HISP activities in India see Sahay, 2005; Braa, Monteiro and Sahay, 2004
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With its focus on name based data, FHIMS is seen to be suitable for generating subcentre based reports and schedules to help the health staff identify which households to
visit and where to put their efforts. The household data is then aggregated and imported
into the DHIS by facilities (sub centres), and then the DHIS can process this raw data and
provide analysis reports immediately that are useful for health management.
The
integration is thus aimed at converting some of the raw data (household based) into useful
information through the use of analytical tools, and improving the representation of the
data using GIS generated maps. These maps are useful for district and state level officials
as well as politicians who are interested in issues of resource allocation (such as opening
a new facility or buying an ambulance).
HISP
Integration Bridge
FHIMS
Household level data for various
services such as immunization, disease
statistics, etc.
This program will take the export files from the
FHIMS, reformat the file into notepad text file,
and then import it to DHIS
DHIS generates the following:
Routine monthly data for SC, PHC, and district,
PHC profiles
Target and indicators
Population (census)
Maps (Village boundaries, population, roads,
location of health centre etc.)
Output reports
Routine monthly reports to be sent from PHC to district;
Routine monthly reports to be sent from district to state;
Analysis reports of performance of respective targets and
indicators for the facilities;
Local level reports for health assistants (for example schedules);
Reports for tracking disease (for example tuberculosis);
Reports for tracking particular services ( for example
immunization);
All reports can be GIS enabled and web enabled .
Figure 1 The FHIM-HISP Integration
There are also ongoing attempts to develop a web-based interface to the reports
(especially at the state level where the connectivity is available), so as to allow the
officials to get a better overview of the health status. In the figure below, we provide a
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conceptual schema of the integration as is being implemented currently through the
IHIMS. Another aspect of the HISP which is being leveraged through the MoU is their
strength in capacity building and training. Unlike the FHIMS project, which has focused
primarily on issues of using the computers, HISP focuses largely on the use of
information. The health staff are now being trained in generating reports, analysing data,
doing spatial analysis of data using GIS and accessing data online. In doing so HISP is
trying to develop capacity of the health assistants and other staff at PHC and district level
to engage in the local use of information. This effort is part of a larger and longer term
process of developing an information culture where data is not just seen as a mindless
effort to fulfil the needs of an uncaring bureaucracy, but a resource which can strengthen
the everyday local work. Emphasizing the need for data analysis, interpretation and
presentation, Sandiford et al (1992) argue that “the ability to analyse and interpret data is
indispensable but insufficient to overcome the inertia of the status quo to ensure that the
information is translated into decisions and action…. The need to tailor the presentation
of information to the intended audience must be recognised.” (p1085). The DHIS has
capabilities for improved analysis, ability to illustrate comparisons and present data in the
form of graphs, charts and maps. Similarly, GIS provides particular analytical tools such
as by enabling mapping of health status and diseases, programme planning, displaying
health care coverage, planning health infrastructure and maintenance, displaying
performance indicators amongst others (Sauerborn & Karam, 2000).
However, as research has pointed out, integrating systems is no easy task as it involves
aligning social, technical and political linkages. Integration involves political, cultural,
organisational and social processes in sharing commonalities, fostering coalitions and
building synergies (Hanseth, Ciborra and Braa, 2001; Chilundo, 2004). Chilundo (2004)
argues that “integration involves a heterogeneous network comprised of people (e.g.
health workers, managers, planners, donors, etc), artefacts (e.g. forms, software, etc.),
socio-political structures and representation of diseases (e.g. health indicators), which are
socially constructed involving the use of information systems” (p ii). The health officials
in Andhra Pradesh have now recognised the value of integration of the two approaches
and the two information systems. This partnership provides opportunities to address
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many of the challenges outlined earlier and in supporting health staff during the process
of change.
5. Conclusion
The case studies presented earlier provide evidence of the importance of adopting a sociotechnical and integrated approach to the development of HIS. Too often the introduction of ICTs
is equated with development as an end in itself, rather than as a means for supporting health
management which can in practice support development. These projects end up as mere
computerisation projects and unsuccessful ones at that, rather than effective health management
initiatives. Introduction of computers, development and implementation of HIS is in itself not
sufficient. Rather the focus should be also on how to develop and improve the informational base
whereby information comes to be seen as a resource at all levels--local, district, state and
national—rather than just for bureaucracy.
There is typically an over-emphasis on technical aspects during implementation of HIS and a
relative neglect in addressing problems of information support that is necessary for better health
management. The challenge is to move away from this pattern and to adopt a more integrated,
holistic and comprehensive approach in reforming informational and organisational practices.
Introducing a HIS, particularly a name-based system, without addressing issues related to
information generation, management and use (analysis, interpretation and evidence-based
decision making) does not solve existing problems of poor quality of data, or the manipulation of
figures, because health staff working in the field may have their own logic, stakes and priorities
which might not align with the goals of the promoters. That is, introducing information
technology/systems does not in itself necessarily lead to better information practices or better
health management. The challenge is not just to have adequate information, but better use of it
and a greater ability to act on it.
A grand vision to have a unified system (one-for-all purposes), as in the case of MPHS and
FHIMS may be attractive but it brings with it numerous problems and is difficult to put
effectively in practice. Instead, a more realistic approach should attempt to recognise what exists,
what works well and at what costs new systems are being built. While the name-based approach
of the FHIMS is useful at SCs and PHCs to track patients and provide targeted health care, the
HISP has capabilities to provide analytical tools for better health management and for building
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capacity of the health staff. Initiating synergies between the two and adopting an integrated
approach offers opportunities to develop more sustainable health information systems.
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