Indian Journal of Public Health

: 2018  |  Volume : 62  |  Issue : 4  |  Page : 259--264

Assessment of health management information system for monitoring of maternal health in Jaleswar Block of Balasore District, Odisha, India

Ranjit Kumar Dehury1, Suhita Chopra Chatterjee2,  
1 Assistant Professor, School of Management Studies, University of Hyderabad, Hyderabad, Telangana, India
2 Professor, Department of Humanities and Social Sciences, Indian Institute of Technology, Kharagpur, West Bengal, India

Correspondence Address:
Dr. Ranjit Kumar Dehury
School of Management Studies, University of Hyderabad, Hyderabad, Telangana - 500 046


Background: In 2005, the Government of India implemented the National Rural Health Mission for reduction of maternal mortality. One of the major impediments in improving maternal health since then has been a poor management of the Health Management Information System (HMIS) at grass-roots level which could integrate data collection, processing, reporting, and use of information for necessary improvement of health services. Objective: The paper identifies the challenges in generating information for HMIS and its utilization for improvement of maternal health program in the tribal-dominated Jaleswar block in Odisha, India. It also aims to understand the nature and orientation of the HMIS data generated by the government for the year 2013–2014. Methods: The study is a cross-sectional type which used observation and interview methods. Primary data were gathered from health professionals to understand the challenges in generating information for HMIS and its utilization. Next, to understand the nature and orientation of HMIS, data pertaining to tribal block were analyzed. Results: The findings show that there are challenges in generation of quality data, capacity building of workforce, and monitoring of vulnerable tribal population. The discrepancies between HMIS data and field reality display the gap in formulation of policy and its implementation. Conclusion: The study unearths the existing politics of knowledge generation. This shows highly standardized procedures and information gathering by use of dominant biomedical concepts of maternal health with limited inclusion of local birthing conceptions and needs of vulnerable tribal pregnant women.

How to cite this article:
Dehury RK, Chatterjee SC. Assessment of health management information system for monitoring of maternal health in Jaleswar Block of Balasore District, Odisha, India.Indian J Public Health 2018;62:259-264

How to cite this URL:
Dehury RK, Chatterjee SC. Assessment of health management information system for monitoring of maternal health in Jaleswar Block of Balasore District, Odisha, India. Indian J Public Health [serial online] 2018 [cited 2019 Jan 16 ];62:259-264
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The Government of India implemented the National Rural Health Mission (NRHM) in 2005 for reduction of maternal mortality in order to achieve the Millennium Development Goal-5. NRHM mostly focuses on five key issues for the improvement of maternal health which centers on a good communication network, satisfactory and flexible financing, monitoring against a quality standard, providing adequate human resource, and capacity building at all levels of the continuum of care.[1],[2] However, one of the major impediments in improving maternal health has been a poor management of the Health Management Information System (HMIS) at grass-roots level which could integrate data collection, processing, reporting, and use of information for necessary improvement of health services. There are medical reasons for high maternal mortality in India due to nonavailability of obstetricians and skilled birth attendants (SBAs) in rural areas. This could be monitored using HMIS and allocating resources for optimal planning and programming.[3],[4],[5],[6] Developing countries needs to be equipped with systematic data management system for improvement of health programs.[7] In a commentary on maternal health in India, researchers found that some of the indicators monitored using HMIS, i.e., immunization status, institutional delivery, and antenatal care (ANC) have the potential of improving the quality of services.[8]

The training of workforce remains a major challenge for implementation of HMIS. A rapid assessment study in Bihar, India, shows that poor capacity building at different administrative levels seriously affects the program outcomes.[9] In some cases, despite training, challenges pertaining to monitoring and supervision remained substandard for HMIS. However, researchers observe that evidence-based planning by utilizing HMIS would help in effective monitoring and supervision of the NRHM activities.[10] According to some studies, HMIS needs a greater focus on public health perspective rather than clinical interventions.[11]

The quality of data too remains a major challenge for efficient policy-making and improving maternal health. According to a study conducted during 2008–2011 by the National Health Systems Resource Centre (NHSRC) on a sample of 35 districts of India, gaps were found in data collection. Duplication of work also predominates as there were both old and new reporting systems still functional at the same center at any point of time.[12]

To establish HMIS, the state of Odisha, India, has made a significant investment under the NRHM program. The Mother and Child Tracking System (MCTS) was commissioned in the year 2010 specifically to track pregnant women and monitor important statistical indicators on maternal health.[13] Thirty-two district hospitals, 286 community health centers, and 6688 subcenters have auxiliary nurse midwives (ANMs) and data entry operators (DEOs) posted and trained for data generation in Odisha.[1] This paper has two objectives: first, to identify the challenges at ground level in generating information for HMIS in terms of capacity building and application and second, to evaluate the nature and orientation of data recorded in HMIS for effective policy-making for maternal health.

 Materials and Methods

Design and settings

The study is a cross-sectional type which used observation and interview methods for deriving various themes such as challenges in data collection, infrastructure and capacity building, tools for data gathering, grass-roots level surveillance, understanding cultural complexity, and utilization of data at the local level. Further, the HMIS and MCTS data have been critically analyzed and the discrepant information were emphasized.

The study was conducted in one of the vulnerable administrative blocks – Jaleswar, which is part of an otherwise high-performing district of Balasore within the state of Odisha.[14] Jaleswar is inhabited largely by poor tribal population and lacks basic infrastructure and community amenities. It is also marked by seasonal inaccessibility on account of its riverine geographical location and is characterized by intermittent floods which interrupt communication, transportation, and other networks, especially during the monsoon season.[15] Further, Jaleswar is one among the 46 blocks of Odisha having the Modified Area Development Approach where special programs for improvement of the tribal community are monitored by Tribal Affairs Ministry.[16] Many of the health centers of Jaleswar are declared as “difficult” by NHSRC. The health centers studied have also high concentration of tribal population.[17]

For study purpose, tribal-dominated pockets have been selected based on census 2011. The panchayats (consisting of 3–5 villages) having >50% of the tribal population were included for the study. Primary data were collected by observing and interviewing different types of health workers (accredited social health activists [ASHAs], lady health volunteers [LHVs], and DEOs). The secondary assessment was done by analyzing MCTS and HMIS data from the Government of India portal for the year 2013–2014 in the light of field observation.

Two types of secondary information – MCTS and HMIS data from the Government of India portal for the year 2013–2014 for Jaleswar block – were critically appraised and the discrepant information were highlighted. The data were scrutinized for its correctness, relevance, and orientation for policy-making both at grass-roots and government level. Interview with field staff was done in their respective workplaces by the researcher to unearth the field reality. For this purpose, 10 ASHAs, 2 LHVs, and 5 DEOs were interviewed who were working in the tribal area of Jaleswar. These health workers are grass-roots level workers helping in the promotion of maternal and child health activities. The respondents were representatives for providing the managerial and implementation-related issues on maternal health care. They were considered purposively to provide the detailed accounts of HMIS implantation at health centers and their catchment areas. Besides these, health administrators and community members were also consulted for understanding of maternal issues.

Secondary data have been analyzed to understand the effectiveness of government policy in the improvement of maternal health at the local level. In secondary data analysis, factors such as ANC and Janani Suraksha Yojana (JSY) registrations, anemia, and hypertension among pregnant women, home deliveries, institutional deliveries, and cesarean section delivery as reported in the HMIS have been examined in the light of field data. Primary data were collected from the field during the period of June 2014–March 2015. Ethical clearance was taken from the institutional review board.

Data analysis

Primary data were collected from health professionals to understand the challenges in generating information for HMIS and its utilization. Based on primary data – interviews of ASHAs, DEOs, LHVs, and administrators (block and district level), the section on challenges in data has been described. Five themes have been identified such as inadequate infrastructure and capacity building, inadequate tools for data gathering, weak grass-roots level surveillance, poor understanding of cultural complexity, and underutilization of data. To understand the nature and orientation of HMIS, data pertaining to tribal block were analyzed.


Challenges in data generation

The objective of MCTS is to track each pregnant woman from the registration of pregnancy to postnatal care. However, field work shows that various factors impede the full utilization of the tracking system for evidence-based decision-making. The challenges in data collection are explained in the following sections.

Inadequate infrastructure and capacity building

The factors responsible for poor functioning of HMIS are slow internet connectivity, incomplete data collection by grass-roots health workers – ASHAs and ANMs, and inefficient block level monitoring. Although DEOs and allied field workers have been trained for timely and accurate entry of data into MCTS and HMIS, but in many primary health centers (new), DEOs also work as accountants leaving little time for data entry. Further, refresher training is not provided regularly to them for upgradation of their knowledge and skills. There are little education and training to grass-roots workers like ASHAs and ANMs for generating data in manual formats to be fed into MCTS and HMIS.

Inadequate tools for data gathering

The MCTS format through which data are collected from the field by ASHAs and ANMs was designed to find location, identification, and health provider details of the pregnant women. This format excludes sensitive information about tribal communities necessary for program implementation. For instance, a grouping of information according to only three social categories (Scheduled Caste, Scheduled Tribe, and others) is available in the manual data collection format. Details about the specific tribal subgroups are overlooked causing problems in providing culture-specific interventions, while the MCTS manual data collection format has provision for tracking beneficiaries by telephone numbers, but during fieldwork, telephone connectivity was found to be erratic, and many times, the beneficiaries did not have access to a telephone. Again, although there is provision of information about provider details for delivery, there is no mention of the referral center in case of complicated delivery.

Weak grass-roots level surveillance

To convey important information about beneficiaries who are in advanced stage of pregnancy, the MCTS has provision for automated mobile notifications to the administrators. Failure to avail services by the beneficiaries is expected to generate a notification from HMIS to the administrators for needful action. However, due to slow internet connectivity, data are often not entered by DEO resulting in a breakdown of the automated notification.

Further, seasonal migration of pregnant women for agricultural labor makes it difficult for community health workers to track them for immunization and ANC. During rainy season, pregnant women in Jaleswar migrate from their village to the nearby district as laborers in a Brick Kiln. The MCTS and HMIS are not designed to capture this seasonal migration.

Poor understanding of cultural complexity

The details of data in tribal areas are complex and do not conform to standard biomedical categories. During fieldwork, it is found that the manual format for data collection, though capable of generating rich information, is difficult to administer among illiterate tribal women. Even major components of the format such as identification of the beneficiary, residential location, name of health provider, and date of pregnancy-related information for ANC and postnatal care are unknown to many of the tribal respondents. There is also limited scope in the HMIS to record various tribal-specific practices relating to birthing practices, preferences for place of delivery, diet regimen, and rituals during pregnancy.

Underutilization of data

Fieldwork shows that there is little emphasis on utilization of HMIS data at ground level for decision-making. According to senior functionaries of the district, the tool (software) currently used in MCTS is not conducive at ground level because the data collection overlooks local realities which help in tailoring the maternal health program to community needs. For example, important data regarding abortions among women having undergone ANC are not recorded in MCTS. Since it does not include local information, the district administrators are unlikely to use this system for taking local policy decisions. Despite provision of data filtration and analysis in the system, the data manager manually compiles data at district level to report to higher officials.

Nature and orientation of data: Interpretations and discrepancies

The nature and orientation of data are examined by careful interpretation of the HMIS data for Jaleswar and by highlighting discrepant information in the light of field observations. There is also an attempt to interpret and “decode” meaning of the raw information, otherwise missed in the government analysis of HMIS data, and delineate the reasons for the particular appearance of data in the government portal. The following sections extract some important information presented in the HMIS portal pertaining.

Antenatal care and Janani Suraksha Yojana registrations: Poor targeted attainments

Early registration for ANC of pregnant women is an important feature for betterment of reproductive and child health in a community. However, the data pertaining to Jaleswar reveals poor attainment of targeted registration for ANC and universal coverage. HMIS indicates that only about half of the pregnant women (46.4%) were registered in the first trimester for ANC services.[18] This may be interpreted to mean that the target of at least three mandatory ANCs is not achieved reflecting poor compliance for maternal health care. The data show that about one-tenth of the women do not complete three ANCs although these are mandatory for monitoring the pregnancy. To complicate matters, there is a lack of information about the demographic characteristics of the pregnant women not completing ANC which is needed for necessary interventions by health workers.[18]

Anemia and hypertension among pregnant women: Discrepant information

Anemia and hypertension are among the risk factors during pregnancy which need reporting by the HMIS system. Anemia is a major concern among tribal women but HMIS reports “zero” figure and thereby grossly misinterprets the reality.[18] Contrary to HMIS reporting, field interview with public health professionals suggests that there is a high level prevalence of anemia (almost 50% of the pregnant women suffer from anemia in Jaleswar block). According to professional opinion during fieldwork, 10% of pregnant women suffer from severe anemia (hemoglobin <7). Despite high prevalence of anemia, HMIS indicates that almost one-fifth of women (21.4% Jaleswar) are not provided IFA supplements to maintain desired Hemoglobin levels during pregnancy.[18] Discrepant reporting of such important data regarding anemia and hypertension undermine government's efforts to detect such cases and target specific interventions. With respect to hypertension, field observation shows that one-third of the pregnant women suffer from hypertensive disorders which is significantly higher than the reported HMIS data.

Home deliveries: Underreporting of facts or cultural insensitivity?

The tribal community under study has a preference for home deliveries. However, HMIS data have considerable underreporting of such information (only 10% reported).[18] This is against the field impression where considerable home deliveries were actually observed. Therefore, it appears that underreporting of such cases in Jaleswar has taken place which poses a serious problem for maternal health care. The involvement of SBAs in home deliveries is also reported to be low. HMIS portal has a “zero” in the corresponding column related to “percentage of SBA attended home deliveries against total reported home deliveries for Jaleswar.”[18]

Institutional deliveries: Lack of inclusive data?

Field study shows that procedures for collection of data regarding institutional delivery in the block do not follow government protocols which encourage integrated approach to include private and government sector hospitals. About 68.8% of the beneficiaries in Jaleswar are discharged from the hospital within 48 h of care as per HMIS.[18] However, an early discharge (<48 h after delivery of the baby) violates JSY protocol for discharge planning. Maintaining this protocol is vital for postnatal delivery care. Field observations show that due to lack of hospital beds, discharges are made early, thereby seriously affecting postnatal care.

Nearly one-third (34.1%) of the women in Jaleswar do not avail institutional care as reported by HMIS.[18] HMIS fails to provide information about population characteristics of those who do not seek institutional delivery.

Cesarean section delivery: Low reporting, a serious public health concern

Although cesarean section deliveries are conducted as a life-saving measure, data pertaining to it are not well maintained in the HMIS in Jaleswar. This is clear from the fact that (a) HMIS portal does not present any data (i.e., vacant cell exists) on cesarean section deliveries conducted in private health-care institutions although field observations show a high prevalence. The HMIS data in government spread sheet under C-section delivery have vacant cell which does not provide any meaningful correspondence with the reality. Field observation and interview with LHV and block program manager confirm the presence of high prevalence of cesarean section delivery.[18] (b) HMIS portal also reports low percentage (10.8%) of cesarean sections at public facilities which is contrary to the high percentage of cesarean section deliveries conducted at District Head Quarter Hospital.[18]


Although the Government of Odisha has newly introduced HMIS, a critical analysis brings out considerable disparity between government assessment and reporting of maternal health program and field observations. Underreporting and discrepant reporting have taken place occasionally which have not been verified by HMIS, thereby affecting the quality of services. A study in Assam, India, also reports that the HMIS staffs find difficulty in entering, uploading, analysis, and management of HMIS which result in its poor quality.[19] Moreover, lack of segregation of data by social group characteristics impedes the chance of tailoring JSY program specifically to the need of tribal population. Information relating to the culture and beliefs of the tribal community is underreported or not reported at all in the system, thus affecting its application.

The challenges in MCTS and HMIS are also mostly related to the poor capacity of grass-roots level workers, lack of training of DEOs, and allied health workers. Poor capacity building is also found to be a challenge in Himachal Pradesh during implementation of HMIS in a pilot basis.[20] There are also issues relating to the quality of data, accuracy, and timely generation of information for the MCTS – a feature reflected by other studies as well.[8],[10] Since, HMIS is unable to reflect the needs and characteristics of local populace, more micro-level studies are suggested by researchers.[21] The study confirms the need to adapt specific e-health applications according to the tribal societies and cultures. Panjamapirom and Musa rightly point out that HMIS is neither information and communication technology nor artifact due to its dynamic nature of applicability.[22] Most HMIS applications would normally need to be modified to fit local contexts. Serious challenges in data management and information gathering are found in India at the early phase of implementation of informatics in health care.[23],[24] Despite these limitations, there are some indicators monitored using HMIS, i.e., immunization status, institutional delivery, and ANC which have the potential for improving the functioning of the maternal health program.[8] A study in Ballabgarh, Haryana, confirms that the HMIS is effective, efficient, and save resources for the health-care organization.[25]

Admittedly, HMIS portal is a template to capture mainly quantitative data, and information related to health needs, and services of the people of whole nation which is used in policy framing and monitoring of the existing program. However, it needs to be modified for local use and monitoring of the health services in challenging areas like tribal pockets.

The study shows that although there is a scope for using HMIS and MCTS for better monitoring of maternal health program under the ongoing NRHM, the full potential is yet to be exploited in Jaleswar. [Table 1] details the challenges and remedial measures for improvement of HMIS and MCTS in Jaleswar.{Table 1}


The study unearths the existing politics of knowledge generation. This shows highly standardized procedures and information gathering by use of dominant biomedical concepts of maternal health with limited inclusion of local birthing conceptions and needs of vulnerable tribal pregnant women.


We would like to thank University Grants Commission, New Delhi (India), for providing UGC-SRF fellowship to Dr. Ranjit Kumar Dehury at Indian Institute of Technology Kharagpur. Grant No: UGC – REF. NO. 1995 (NET-DEC. 2009)/27 AUG 2010. The authors also acknowledge the anonymous reviewers for their scholarly criticism.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


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