Health Management Information System (HMIS) is a Government to Government (G2G) web-based Monitoring Information System that has been put in place by Ministry of Health & Family Welfare (MoHFW), Government of India to monitor the National Health Mission and other Health programmes and provide key inputs for policy formulation and appropriate programme interventions.
HMIS has been utilized in Grading of Health Facilities, identifications of aspirational districts, review of State Programme Implementation Plan (PIPs), etc.
The analytical reports generated through HMIS also provides gap analysis and evidence based course correction.
HMIS was launched in October 2008. Currently, around 2 lakh health facilities (across all States/UTs) are uploading facility wise service delivery data on monthly basis, training data on quarterly basis and infrastructure related data on annual basis on HMIS web portal.
The following comparative table provides a high-level overview of the transformative journey of the HMIS in India.
Uses of Health Management Information System (HMIS)
- Make sense of the data that is collected for various Programmes
- Monitor and evaluate health services and Programmes
- Provide feedback to the health facilities about their own performance individually
- Compare performance of neighboring blocks, regions, states, etc.
- Prepare reports for the block, district, state and country regarding health status and give a clear picture of progress
- Implement corrective measures in the services and Programmes to improve efficiency
- Reveal the areas that are weak and focus on them for intervention and research.
Components of HMIS
- Data Elements
- Recording and Reporting Formats
- Data Compilation
- Data Flow
- Logistic and technology
- Analysis of Data and Indicators
- Feedback
Data Elements
- The basic unit in HMIS is data, and data element is a record of a health event or a health related event. The number and nature of data elements collected at each level will vary. It depends upon the services provided by that level. The Three parts of data element are:-
- Part A. Reproductive and Child Health
- Part B. Health Facility Services
- Part C. Mortality details
Example of data element
- Antenatal care
- Deliveries
- Caesarean Section
- Pregnancy Outcome
- Complicated pregnancies
- MTP
- RTI/STI
- Post natal care
- Family planning
- Child immunization
- Vitamin A
- Childhood diseases
- Other Programmes
- Patient Services
- Lab Test
A significant modern development is the strategic shift from a purely aggregate, facility-based system to a more granular, name-based tracking approach. This transformation is embodied by the Mother and Child Tracking System (MCTS), a web-based portal developed to facilitate the timely delivery of antenatal, postnatal, and immunization services. Unlike the traditional HMIS, which captured service data at an aggregate level, MCTS tracks individual beneficiaries by name and generates a unique 12-digit ID for each pregnant woman, which is then carried forward when she becomes a mother and a separate ID for the child. This capability is further enhanced by the ANMOL (Auxiliary Nurse Midwife Online) application, a tablet-based tool that allows frontline workers to enter data at the source, thus improving data quality and enabling the tracking of individual beneficiaries.
Recording and Reporting Formats
Data is recorded at the health facilities in various registers and data formats. At sub-centre level, an ANM and male health worker maintain a number of registers
Example-
- Eligible Couple Register including Contraception
- Maternal and Child Health Register:
- a) Antenatal, Intra-natal, postnatal
- b) Under-five register:
- i) Immunization
- ii) Growth monitoring
- Births and Deaths Register
- Drug Register
- Equipment Furniture and other accessories Register
- Communicable diseases/ Epidemic Register
- Passive surveillance register for malaria cases
- Register for records pertaining to Janani Suraksha Yojana
- Register for maintenance of accounts including untied funds
- Register for water quality and sanitation
- Minor ailments Register
- Records/registers as per various National Health Programme guidelines
The foundational HMIS relied on a paper-based system of registers and standardized formats for data recording and reporting. At the sub-centre level, an Auxiliary Nurse Midwife (ANM) and male health worker maintained a number of registers, including those for
Eligible Couple, Maternal and Child Health, and Births and Deaths. The data was recorded in these registers as a line list, which was then aggregated and compiled into monthly reporting formats for transmission to higher levels.
The contemporary HMIS has made a decisive leap to a digital reporting model. The HMIS portal and mobile applications like ANMOL enable direct, at-source data entry by health workers using tablets. This digital approach eliminates the need for manual paperwork and reduces human errors in reporting by utilizing features like drop-down options, templates, and standard codes. For example, the ANMOL application allows ANMs to enter data and update records for beneficiaries within their jurisdiction.
However, the HMIS is not a completely paperless system. Research indicates that a hybrid model is currently in place. The National Health Mission has released revised monthly HMIS reporting formats for various healthcare facilities, effective from April 2025. This suggests that while digital data entry is the preferred method, the system maintains a structured set of physical formats. This pragmatic approach likely serves as a safeguard against infrastructure limitations, such as poor internet connectivity in rural areas , ensuring data collection can continue even when digital systems are offline.
Data Compilation
Compilation of data happens at three levels:
- First level Compilation is at Block PHC where the Block Data Manager makes the “Block Monthly Consolidated Report” from data obtained from its own PHC as well as other PHCs and Sub-centers.
- Second level Compilation is at District level where the District Data Manager will make the “District Monthly Consolidated Report” after data from all institutions within its limits. Both private and public send their respective reports. This report will be electronically uploaded on the central Web Portal. Where ever State HMIS application is functional one copy of the entire database will be stored in the State HMIS application.
- Third level Compilation will be at State level where monthly, quarterly and annual reports of the state will be prepared. Aggregation will be carried out by accessing all District consolidated reports and all State specific data entry that was done at the State level (quarterly, monthly, annually). ‘State Aggregated Report’ will be uploaded on the Web Portal, and a copy of the same will be available in the State specific HMIS application running on the State server.
The advent of the electronic HMIS (eHMIS) portal has fundamentally automated and centralized this process. Data from districts are now "uploaded" onto a central national web portal, which serves as a "Single Window" for all public health data for the Ministry of Health and Family Welfare. The HMIS portal enables automated aggregation, significantly reducing the manual workload and the potential for errors.
This centralization has fundamentally changed the role of data managers at the block and district levels. They are no longer just manual collators of hard copies; they are now operators of a digital system, responsible for collecting, checking data quality and preparation of the block report. The HMIS reform initiative recognizes that a crucial part of this new role is not just technical proficiency but also a deep understanding of data quality issues and the ability to troubleshoot errors.
Data Flow
Data is transmitted either in line list format, if there is limited number to report, or more commonly in a reporting format, where it is aggregated.
In the initial, paper-based HMIS, the data flow was described as a hierarchical and largely unidirectional movement of hard-copy reports. Reports were filled at the Sub-Centre, sent to the PHC, then consolidated and sent to the Block PHC, and finally transmitted to the District headquarters and beyond. The feedback, while essential, flowed in the opposite direction, from higher to lower levels, often through formal reports and meetings.
The eHMIS system has transformed this linear, physical flow into a dynamic, networked information highway. Data is entered at the facility level and is transmitted electronically to the central server, where it becomes accessible in near real-time to authorized personnel at all levels of the healthcare hierarchy. This bi-directional flow allows for rapid transmission of data upward for reporting and of feedback and directives downward for timely programmatic course correction.
Logistic and technology
- Hardware- Computers are needed at all levels with eHMIS with minimum specifications i.e. intel Pentium, 254 MBRAM, 20 GB hard disk space, explorer 6 and above. Along with computers, its peripherals like printers and UPS and modem will be needed.
- Software
- Health Management Information System (HMIS) Portal
- Mother and Child Health Tracking System - It is prepared for tracking of Pregnant mothers and children. MCTS also has components of work plans for various functions like -
- Registration of pregnant mothers
- ANC service
- Delivery service
- Postnatal care visits
- Child Immunization
- Child Care
- Adolescents
- Family Planning
Today, the HMIS operates within a much more advanced and diverse technological ecosystem. This includes the adoption of cloud-based HMIS, which offers scalability, cost-effectiveness, and remote accessibility, making it an ideal solution for India's varied geographical landscape and resource constraints. There is also a concerted effort to integrate cutting-edge technologies like artificial intelligence (AI) and machine learning to enhance diagnostics, patient monitoring, and predictive modeling, enabling HMIS to move beyond simple reporting to provide advanced analytics. Future HMIS solutions are expected to integrate with telemedicine modules and IoT-enabled devices for real-time patient monitoring.
Analysis of Data and Indicators
The purpose of data collection in HMIS is to facilitate analysis and generate meaningful indicators for planning and management. The foundational document defines an indicator as a measure that converts raw data into useful information for program monitoring and management, citing examples such as the percentage of institutional deliveries and immunization coverage. The analysis was meant to be conducted at every level, from the ANM to the national government, to monitor performance, identify weaknesses, and amend activities.
Data analysis can be done at every stage.
- ANM can analyze the data in her area and use it to bring about changes in her activities. If there are 16 deliveries in an ANM’s area in the past year and if the delivery register shows that, three of them were home deliveries; then institutional delivery in her area is only 81.3%, which should be actually 100%.
- She can go back to those cases and inquire into reasons for home delivery, and try to tackle those reasons either at her own level or have it brought to the attention of the PHC/CHC depending upon the reasons.
Institutional Deliveries (%) = No. of delivery in Institution x 100
Total No of Deliveries
(13/16) x 100 = 81.33%
- Medical officer in the PHC and other staff including supervisors can use data from their areas, provide feedback and amend their own activities to be more efficient or effective.
An indicator is a measure which denotes the health status, service delivery or efficiency of operations according to how it is calculated. Indicators helps one to know :
- how healthy a population group is,
- what is the quality of service provided, and
- how efficient an activity is
- finding the vulnerable groups which are affected, place that are most affected as well as time when certain health problems occur.
Modern HMIS has expanded the scope of its indicators to a broader range of public health issues, including hypertension, HIV/AIDS, malaria, and tuberculosis, providing a more comprehensive view of the national health landscape. More importantly, the availability of vast digital datasets has enabled the use of advanced analytics and graphical dashboards. These tools convert complex data into easily understandable visuals, enabling data-driven decisions
Feedback
Feedback is an essential and robust component of a functional HMIS, flowing in the opposite direction of data transmission. The foundational document describes feedback mechanisms as a formal process, primarily through written reports, monthly or quarterly meetings, and annual review meetings. The purpose is to ensure that data collection, aggregation, and analysis are not in vain, but rather inform and guide programmatic improvements.
The digital HMIS has institutionalized this feedback mechanism. Real-time dashboards and automated reports provide timely, evidence-based data for review meetings, allowing managers to conduct gap analysis and evidence-based course correction more efficiently. This streamlines the process, making it more responsive than the old, manual system. The HMIS has the potential to create a constant digital loop of accountability, where performance metrics are transparently available and can be acted upon swiftly.
Despite the technological facilitation, the effectiveness of the feedback loop still hinges on human action. A study on HMIS found that a significant portion of managers at the block and district levels were not consistently holding review meetings, even when the data was available. This indicates that while technology has made the process of providing feedback more efficient, the human element—organizational culture, accountability, and the proactive use of data—remains a critical bottleneck. Technology can facilitate and enable, but it cannot compel leadership to act on the information provided.
Feedback can be given in the form of
- Written reports
- In monthly/quarterly meetings
- In annual review meetings
DATA QUALITY
Data quality is an important factor which determines whether it can be used effectively
for planning and management of services. Quality is measured in three different aspects
of completeness, timeliness and accuracy:
A) Completeness -
For data to be of good quality, it has to be complete. Completion can be seen in two
ways:
1) Facility wise completion: Of the total facilities both private and public existing in an area, what percentage are sending their reports and they are included in the District report?
2) Number of data elements reported among total data elements in a reporting format. The forms have to be assessed for zeros and blanks.
B) Timeliness
For data to be useful, it has to be reported timely. Delayed reports will hinder accurate assessment and action. There is enough time given for the facilities to submit data after the month ends i.e. earliest being 5th of next month or 20th in case of quarterly report.
C) Accuracy
Data should measure what it is supposed to measure and if it does that, then it is said to be accurate. It means that accurate data will be correct and useful. If data is incorrect for any reason, it will lead to false interpretation and actions that might be harmful for population/facility health and service provision.
The HMIS reform initiative recognizes that improving data quality is a central objective. To address errors, the system utilizes validation checks and queries to identify logical inconsistencies. For example, a system check ensures that the number of women discharged within 48 hours of delivery does not exceed the total number of institutional deliveries. The system also uses statistical outlier detection, where values that deviate significantly from the norm are flagged for review and verification.
However, the analysis of HMIS reveals that data quality is not merely a technical problem that can be solved with software alone. It is a systemic and cultural issue. Sources indicate that a significant limitation of the data is the incomplete reporting of private sector data and the poor quality of death reporting. The existence of multiple, parallel data systems also contributes to data duplication and fragmentation, which affects accuracy and completeness. The HMIS reform manual points out that the common assumptions about the causes of poor data quality—such as simple data entry errors or lack of training—are only a small part of a much larger issue. The root of the problem is often systemic, arising from misinterpretation of data elements and logistical challenges like a lack of staff or proper supervision.
The shift to a digital system also introduces new security and governance challenges. The collection of sensitive, name-based data necessitates a robust cybersecurity framework to protect patient privacy. The lack of a functional public policy framework to guide data use and sharing across different systems is a significant weakness. This suggests that the future of HMIS data quality is inextricably linked to the development of a comprehensive legal and policy framework that establishes clear data standards, ensures interoperability, and protects the integrity and privacy of public health information.
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