The Routine Health Information System in Punjab Province, Pakistan – Exploring the Potential for Integrating Health Information Systems for Family Planning Data

Author(s): Mustafa M

Year: 2018

Abstract:

Background: Globally, a health management information system (HMIS) includes both routine and non-routine health data. A routine health information system (RHIS) generates data at regular intervals (no longer than a year) that have been collected at the public and private health facilities and institutions, as well as at community-level healthcare posts and clinics (MEASURE Evaluation, 2017). In developed countries, the RHIS exists in its true essence having both a facility-based and a community-based health information system (CHIS), yet the situation is different in developing countries, such as Pakistan. In Punjab, Pakistan, the HMIS is fragmented as there are more than 20 different HMISs, which use dedicated vertical channels. Among these, three systems gather and transmit information related to family planning (FP)/reproductive health: the District Health Information System (DHIS), the Lady Health Workers-Management Information System (LHW–MIS), and the Contraceptive Logistic Management Information System (cLMIS) which is combined with the Population Welfare Management Program-Management Information System (PWMP–MIS). In addition, nongovernmental organizations (NGOs) have their own HMIS, and there are separate HMISs for countless private hospitals and clinics. Gaps exist in the current RHIS, specifically about reproductive health data from different sources, whether public, private, community or facility-based. These data are not integrated and consolidated into the national HMIS and therefore are not used for decision making.

Objectives: The objective of the study was to review the RHIS in Punjab province of Pakistan and explore the potential for integrating community-level data into the national HMIS, particularly FP data, collected by public or private, for-profit, and not-for-profit organizations.

Methods: The study used both primary and secondary data. Primary data were collected through key informant interviews (KIIs), identified purposively and through snowball sampling technique. Secondary data were gathered through document review including reports, articles, and statistical data.

Findings: Community-based FP data are not fully integrated with RHIS. Some effort has been made to integrate FP data through Contraceptive Performance Report by the Pakistan Bureau of Statistics and the cLMIS, which is an integrated system where data from the DHIS, LHW–MIS, Population Welfare Department (PWD), and influential NGOs are presented and compiled in one form. There is potential for organizing CHIS with RHIS, yet structural barriers exist. For example, there is potential for integration between LHW–MIS and DHIS as they come under the province’s Department of Health (DoH), but it is difficult to integrate data between the DoH and PWD, as PWD has a separate administration and ministry. Nevertheless, though the cLMIS has provided a platform for including data from all public and private entities, several NGOs and public departments do not regularly report their data. In addition, there are several data quality issues in the RHIS which should be addressed before integration occurs, such as: fake entries; incomplete information; dissatisfaction about numbers and types of FP indicators; inaccurate data; duplication of data and services; overreporting; poor feedback mechanisms; and the way reports are consolidated. These issues must be tackled along with integration of CHIS into the RHIS.

Recommendations: To facilitate integration of CHIS with RHIS, the study suggests several recommendations. These include shifting the paradigm from an individual-level healthcare approach to a family-centered approach; promoting a culture and system of inter-organizational information sharing; sensitizing decision-makers about the benefits of interlinking the community-level data streams with RHIS; strengthening the computerized national identity card (CNIC)-based data entry; developing a single dashboard with core FP indicators; and expanding FP indicators beyond commodity-based indicators to psychosocial and behavioral indicators to understand the uptake, switching, and dropping of modern FP methods.