Issue Analysis: A Use-Driven Approach to Data Governance Can Promote the Quality of Routine Health Data in India

1. Actionable Health Data Is Still Hard to Come By

The use of data to inform policies and programs to improve health service delivery and health outcomes is not commensurate with the huge public investments in data collection. This is a problem of design for the symbolism of reporting rather than use for driving responsive health system management. Further, a failure to recognize the importance of engaging users of data at all levels to find out what they need also contributes to this poor data use culture. A study assessed that the overall data collection volume for aggregate data (HMIS data and state-specific data) varies between 3,000 to 8,000 data elements per month.⁷ Further, only 10% of these data elements are used to generate indicators, nearly 20% were inactive (returned no values), and about 50% consistently returned blanks or zeroes.⁷ This clearly shows that the systems collect far more data than what's reported on and an even smaller proportion is fed into meaningful indicators. The zero/blank value elements may point to gaps in a facility's readiness to deliver certain services (e.g., if “zero” stunted children are reported consistently by a given facility in a poor district, it is more likely that the facility doesn't have weighing scales and height measures rather than that there are no stunted children). Currently, when a “zero/blank” is reported for a given service at a health facility, there's no way to tell if it represents a nonutilized service or a nonexistent service. One way to circumvent this could be to make all data fields required when submitting electronic data (so they cannot be left blank) and to add a pop-up list of options for elaboration. The list could include service not offered, no clients, equipment/medicine shortage, inadequate staffing, other reason, so that appropriate programmatic action may be taken. Additionally, if facility staff are entering data on a paper-based form, provide space for recording a short list of options, to make it easier for staff to complete the form and for whoever is eventually entering the form into an electronic database.

Furthermore, for public health data, denominators are crucial.⁷ Although absolute numbers (e.g., number of children immunized) are readily available, it is often difficult to ascertain coverage levels of health services such as the percentage of children immunized fully (immunization coverage) because the denominators remain disparate and vary widely across administrative levels, departments, and health programs. The coverage indicators are vitally important from a health systems performance management perspective, as they reflect the extent to which the people in need receive important health interventions. At times, the absence of standardized denominators creates confusion in setting targets for health facilities due to a lack of a common understanding of how much ground has been covered between different levels or programs.

Additionally, since data quality functions are largely centralized—meaning that there is no standardized approach to checking data at the local levels—the principle of “data quality corrections are best done closest to the source of data collection” is violated.

2. Private Health Care Sector Generates a Lot of Data but Barely Reports Any

The National Sample Survey 2014 estimated that of all episodes of illness, 72% of rural episodes and 79% of urban, were treated in the private sector. The private sector provides about two-thirds of inpatient care and three-fourths of outpatient care treatments in India. It has been estimated that 60% of the total volume of health data is produced by non-state actors.¹³ However, in the absence of regulation and incentives, the reporting is minimal to nonexistent.

3. Citizens' Data Access and Privacy Needs to Be Addressed

Communities and researchers have limited access to routine administrative health data. Lack of data sharing can affect the people's trust in the system as they do not know what services are provided or their quality. This, in turn, has implications for citizens' empowerment and mobilization for better population health because they don't have information on the major causes of illness and death and what services are underutilized, apart from stymieing data used for research to improve the quality of care and health outcomes.

Lack of adequate data regulation and privacy standards while collecting case-based data makes households vulnerable to unsolicited phone calls from call centers to verify a pregnancy and potential data mining for market research, apart from graver implications such as stigma or discrimination against people with certain health conditions. Further, data are only as good as the trust that people have in the confidentiality of the data. Reports of misuse or breach of privacy (e.g., through the media) could impact future response rates, make respondents likely to withhold or falsify information, or withdraw their consent to share the data, thereby impacting the quality of the data. Health data systems can therefore benefit from an inclusive, learning, and iterative data governance style, which allows for decisions to be scrutinized and approaches to evolve, based on periodic inputs from the health workers, patients, and researchers.^14–16

4. Reporting Data Into Multiple Systems Imposes Additional Administrative Burden on Health Workers

The MCTS/RCH portal sends all patient-level data to the national portal. However, most patient-level details, especially those pertaining to maternal and child health services, are only needed by the facility-level staff. The privacy risk and the sheer volume of patient-level data necessitate having a more selective system in which only necessary patient-level data are reported to the national level (e.g., data on outbreaks). Instead of using the current broad-brush approach to collecting data, more careful planning and assessment of what patient-level data are needed at the national level must be done and followed through. Further, parallel data reporting in MCTS/RCH portal and HMIS means that health workers have to enter the data twice.⁷

The government's introduction of and focus on the MCTS/RCH portal undermined the older HMIS by encumbering health workers who needed to focus on reporting into this new system. Furthermore, several other state-specific portals that were created to serve the needs of states and donors exacerbated their workload. While increasing the staff workload, who estimated that 60% of their time was spent on data-related work,⁷ the volume of data collected likely had implications for its quality. Such data of questionable quality does not lend itself to effective use while taking valuable health worker time away from their essential caregiving duties.⁷ According to a study, ANMs spent an average of 6 hours a week for data collection for MCTS in addition to their routine program activities.¹⁷ This issue has been addressed to some extent in the RCH by introducing ANMOL,¹⁸ designed in collaboration with United Nations Children's Fund. Data are collected at the source by health workers. ANMOL is being rolled out in phases across states and helps reduce the burden of manual data entry and travel for ANMs.

However, in HMIS, there's redundancy in recording data because the ANMs first enter data in physical registers, which are then digitized by the data entry operators at the primary health centers. This step also introduces time lags in data collecting and reporting and increases the risk of data errors, especially if there's discordance between the terms used in the register columns and the software fields of the health data systems. Lack of HMIS data entry training for ANMs and the expectation that health workers enter data in the physical registers alone poses additional concerns about the data quality.¹³ Poor translation of medical terms, such as “eclampsia” and “hypothermia” in the local language, results in frequent misinterpretation and poor data quality.¹³ This is vastly different from the experience in some other developing countries, which have emphasized the need for greater participatory engagement with health workers and community members in designing the health information system before implementation to ensure sustainability.^4,19

1. Make Data Collection About Use and Actionability

It is vital that the systems collect data that either contribute to indicators or support health workers in critical daily tasks. The guiding principle should be to reduce the health care workers' burden while strengthening health systems. The decisions about which data elements to capture should begin with what is needed to support the daily activities of the frontline health workers, see what can be repurposed for computing aggregate indicators, and only then, consider additional data elements for collection. The digital tracking and support systems should be user-centered and emphasize the principle of "collect once, use for many purposes"—so that data collected for service delivery can also be used for accountability (i.e., to calculate aggregate indicators required for reporting and monitoring provider, stock, and system performance).

The national health program should specify the information that it needs for public health management and policy and how often and allow state health departments to deliver this information the best way they can. Reducing the volume of data would make it easier for states to validate data and ensure data quality. Further, data may be fragmented across the ministry, state, and agency silos. A lack of data standards for reporting and interoperability between data systems can limit the ability to synthesize information across multiple data sources to fully understand programmatic issues.²⁹ An open dialogue between producers and consumers of data at both the national and subnational levels on the various data elements, duplications, and gaps, can help harmonize data sharing and use and improve data quality.

2. Enable Health Workers to Enter Accurate Data Directly in HMIS

To eliminate the issues of duplicative work, time lag between collection and reporting, discordance between paper and digital records, and poor translation, the health workers should be supported to enter data directly and accurately at the point of care. Apart from organizing data entry training on HMIS, every effort should be made to include clear translations of medical terms in the local language.

3. Make Decentralization a Priority

The main purpose of information technology systems in the states and districts should be for decentralized management at that level. The electronic health records should typically be maintained at the facility level and only aggregate data stored in the cloud. No level should receive routine data for more than 2 levels below (which implies no patient data should go above block or district, other than what's necessary from a high-level policy standpoint, such as data on outbreaks). This will keep the systems decentralized and ensure that the center sees only what it needs for policy or programmatic action. Mature public health systems may provide patient-specific data from each facility with considerable granularity whereas less mature systems can give aggregate numbers from a block or district level. Decentralization addresses multiple challenges at once: helping maintain citizens' data privacy and focusing on what's actionable at each level of the health system while obviating the need for multiple systems.

4. Embed True Interoperability

Lack of interoperability precludes a unified view of multiple data sources to comprehensively understand programmatic issues and use this to monitor and improve health systems performance.²⁹ Despite government policies on the use of open-source software for all public systems, the Application Program Interface (API) access for the national HMIS (which is built on a proprietary platform) is not currently available to the states. For instance, in 2012, the National Data Sharing and Accessibility Policy was announced to strengthen sharing of information across ministries and systems to promote evidence-based decision making, while discouraging data duplications.³⁰ In 2015, the government issued its open API policy, which recognizes the need for an interoperable data ecosystem, applications, and processes to make the right information available to the right user at the right times.³¹ So, it might be worthwhile for the government to do a stock-taking exercise to assess whether the code is available on public repositories and if open API access is provided.

5. Consider Legacy Systems and Transition

While introducing new systems, it's imperative to be deliberate and judicious. A new system may not always fix existing problems and can create new problems because it may not be appropriate for all technical areas nor be interoperable with other systems. So, while new systems are being proposed to replace all others (e.g., the Integrated Health Information Portal [IHIP], which began as a system for IDSP is now looking to include 25 other programs), it may not be feasible to incorporate the needs of different health programs into 1 software. Further, while introducing new systems, the legacy data systems landscape should be considered to enable a smooth transition. For example, the efforts of the National Vector Borne Diseases Control Program to develop a malaria surveillance system were put on hold as the IHIP was supposed to address it, but as the operationalization of IHIP has been delayed,⁷ this has resulted in an avoidable data gap.

Finally, the World Health Organization has developed standard modules for TB, malaria, Expanded Program for Immunization, HIV, and mortality reporting on the open-source District Health Information Software-2 (DHIS2) platform and has invited countries to adapt these. So, there's also the question of whether India should seek to work on these globally designed standards as opposed to having new systems developed by information technology vendors who may have a limited understanding of public health. The focus should likely be on data integration and interoperability instead of software integration.

6. Seek to Engage Private Health Care Providers to Gather Data

As most providers are not included under the central government's new hospital insurance scheme, they need to be connected to portals to provide the required aggregate information. Further, they may not be willing to provide patient-specific information and may have to be engaged in dialogue to better understand their concerns and assure them on anonymizing the collected data.

7. Define Institutional Mechanisms for Using Data for Research

Separate mechanisms and authorities must be created to use data from central repositories for research to verify the aims of third parties wishing to access data so that data are only used appropriately for research. Research use must not be conflated with the use of personal health identifiers in individual patient care. This is important to prevent data mining for market research as opposed to academic research to keep the sanctity of the purpose for which the individuals trusted providers with the data.

8. Make Aggregate Data Widely Available

Data collected by routine health information systems, government programs, and large-scale surveys for policy purposes, as well as the data collected by health and biomedical research institutions using public funds, should be made publicly available for researchers and advocates, while ensuring that it is not used for market research. Making data widely available builds the citizens' trust in public data and enables responsive governance through generating high-quality data.