ORIGINAL ARTICLE
Open Access

Optimizing the Health Management Information System in Uttar Pradesh, India: Implementation Insights and Key Learnings

Ankita Meghani, Anand B. Tripathi, Huzaifa Bilal, Shivam Gupta, Ravi Prakash, Vasanthakumar Namasivayam, James Blanchard, Shajy Isac, Pankaj Kumar and B.M. Ramesh
Global Health: Science and Practice August 2022, 10(4):e2100632; https://doi.org/10.9745/GHSP-D-21-00632

Figures & Tables

Figures

  • FIGURE 1
    FIGURE 1

    Mapping of Activities Conducted by UP-TSU, in Collaboration With the GOUP to Strengthen the Inputs and Processes to Enhance Overall Quality and Use of HMIS/UP-HMIS Data in Uttar Pradesh

    Abbreviations: HMIS, health management information system; M&E, monitoring and evaluation; PS/MD-NHM, Principal Secretary/Mission Director- National Health Mission; UP-HMIS, Uttar Pradesh Health Management Information System.

  • FIGURE 2
    FIGURE 2

    Timeline of the Implementation of Major Activities and Policy Guidelines to Strengthen the Performance of UP-HMIS

    Abbreviations: GOUP, Government of Uttar Pradesh; HMIS, health management information system; M&E, monitoring and evaluation; UP, Uttar Pradesh; UP-HMIS, Uttar Pradesh Health Management Information System.

    aPolicy guidelines released by the GOUP.

  • Figure

    District program manager in Etah district, with support of the monitoring and evaluation specialist, facilitates a district review meeting using the Uttar Pradesh Health Dashboard. © 2022 Shekhar Dutt Sharma, Department of Medical Health and Family Welfare, Uttar Pradesh

  • FIGURE 3
    FIGURE 3

    Process of Enhancing the Use of Data for Decision Making During Program Review Meetings at the District Level

    Abbreviation: UP-HMIS, Uttar Pradesh Health Management Information System.

  • FIGURE 4
    FIGURE 4

    Percentage of All Facilities in Uttar Pradesh Reporting Dataa,b in the UP-HMIS and HMIS Portals, September 2014–March 2021

    Abbreviations: HMIS, health management information system; UP-HMIS, Uttar Pradesh Health Management Information System.

    aOn-time reporting defined as “% of facilities which have uploaded data on portal as on 30th of the month.” This indicator was tracked for HMIS portal (before April 2017) and UP-HMIS portal (after April 2017) from across the 75 districts in the state.

    bTotal reporting defined as “% of facilities which have ever reported for the specified month on the portal.” Prior to 2017, the “total report” refers to total reports of HMIS formats. Following 2017, the “total report” refers to the UP-HMIS format, which include both HMIS and new UP-HMIS data elements.

  • FIGURE 5
    FIGURE 5

    Percentage of Total Facilities With Greater Than 80% Non-Blank Data Element (Data Completeness)a,b

    Abbreviation: UP-HMIS, Uttar Pradesh Health Management Information System.

    aSource: UP-HMIS data quality analysis.

    bCompleteness data after March 2020 is not comparable because HMIS data began being captured using a mobile/tablet application at the source in phased manner across districts (as opposed to being entered on paper and then reentered on the web-based UP-HMIS portal).

Tables

  • TABLE 1.

    Barriers Affecting the Availability, Quality, and Use of India National HMIS Administrative Data, and Subsequent Activities and Policies Addressing the Barriers,a 2020

    Barriers Affecting Existing Availability, Use, and Quality of National HMIS DataMajor Activities Implemented During Landscape Analysis to Understand and Address BarriersGOUP Policy Actions Taken to Address Barriers
    Data availability

    • Untimely or no reporting of data from both public and private facilities; many were absent from HMIS

    • Duplication of data and reporting across different manual paper-based forms (several data elements included in existing routine data sources were considered irrelevant or duplicative)

    • Data elements that program managers found useful in daily decision making were not listed in the existing national HMIS or paper-based reports

    • Mapped all public and private health facilities in UP

    • Reviewed and reduced duplication of data elements across 80 reporting forms

    • Revised reporting forms to include relevant data for decision making

    • Developed a definition guide for each data element

    • Conducted auxiliary nurse midwives orientation workshops to increase familiarity with forms and data elements

    • Established state data portal allowing easy data download and analysis

    • Outlined reporting timelines for all the health facilities

    • Required private health facilities to report data to UP-HMIS

    • Required updated UP-HMIS formats to be printed and made available to all facilities

    Data quality

    • Processes for data quality review were absent, poorly implemented, and inadequate

    • A routine platform to review the quality of routine data missing from block, district, and state levels

    • Manual paper-based data collection reduced the timeliness, completeness, and accuracy of reporting

    • Conducted training sessions for data-related managers at the block and district levels on how to implement data validation checks

    • Required the establishment of data validation committee meetings at the block, district, and state levels

    • Replaced manual paper-based reporting with digital UP-HMIS reporting across all districts

    Data use

    • Difficulty downloading data from the national HMIS to conduct analysis

    • No uniform framework for review meeting and data use

    • Review mostly focused on logistics issues rather than using data to address utilization/coverage issues

    • Complexity of data made analysis difficult

    • Lack of human resources (both in number and skill) to analyze the data for use

    • Developed UP Health Dashboard and shared login with all block, district, and state level program managers

    • Shared monthly district-level rankings with districts

    • Disseminated guidelines, including a framework on how to use data to address program barriers, develop action plans, and conduct review meetings

    • Directed all programs to review data based on UP-HMIS and the UP Health Dashboard to promote a culture of data-informed decision making

    • Abbreviations: GOUP, Government of Uttar Pradesh; HMIS, health management information system; UP, Uttar Pradesh; UP-HMIS, Uttar Pradesh Health Management Information System.

    • ↵aSource: Authors’ analysis.

  • TABLE 2.

    Reduction in the Number of Data Elements Collected in India National HMIS and UP-HMIS Formatsa by Facility Types, 2014–2019

    HMIS Data ElementsUP-HMIS Data Elements
    SectionHealth Domains and Subdomains20142017b20172019c
    AHuman resources001020
    BTraining1 03916
    CAvailability of RMNCH+Ad drugs, supplies, and equipment as per 5X5 matrix02711332
    DPerformance indicator     
     D.1Hospital and laboratory services40751926
     D.2Maternal/newborn health42545943
     D.3Maternal complication528448
     D.4Newborn complication013624
     D.5Child immunization385100
     D.5Child health9142912
     D.6Family planning1825280
     D.7Adolescent and reproductive health060c0
     D.8JSSKe and grievance redressal001616
     D.9NVBDCP and RNTCP31500
    EDeath detailsLine listing4100
    FProcess indicator and ASHA grievance redressal00435
    GHome-based newborn care001919
    HVillage health and nutrition days/community process00 216
    INational program (blindness)600 0
    JOther (Janani Suraksha Yojana and urban health) 0 00 3
    Total162311608250

    • Abbreviations: ASHA, accredited social health activist; GOUP, Government of Uttar Pradesh; HMIS, health management information system; NVBDCP, National Vector Borne Disease Control Programme; RMNCH+A, reproductive, maternal, neonatal, child, and adolescent health; RNTCP, Revised National TB Control Programme; UP, Uttar Pradesh; UP-HMIS, Uttar Pradesh Health Management Information System; UP-TSU, Uttar Pradesh Technical Support Unit.

    • ↵aSource: UP-TSU analyses of HMIS and UP-HMIS.

    • ↵bIn 2017, the Government of India updated the national HMIS facility-wise formats as per feedback received from the states.

    • ↵cA data rationalization activity of UP-HMIS elements was conducted during June–September 2019 to remove several elements not used for decision making and to add new programs at the state and national levels. In UP-HMIS, there are rows with zero data elements because these data are captured in HMIS and then integrated in UPHMIS portal for data review. The UP-HMIS numbers are additional data elements beyond what is found in the HMIS.

    • ↵dRMNCH+A standards.

    • ↵eJanani Shishu Suraksha Karyakaram (JSSK) covers the delivery costs, including for cesarean delivery, for all pregnant women delivering in public health facilities.

  • TABLE 3.

    Participants Trained on the UP-HMIS, 2017–2018a

    Eligible ParticipantsTotal Participants Targeted, No.Training Attendees, No. (%)
    StateProgram managers, directors, assistant research officers, monitoring & evaluation specialists7365 (89)
    DivisionAdditional directors4848 (93)
    DistrictChief medical officers, chief medical superintendent, assistant chief medical officers, assistant research officers, district program managers, HMIS operator225233 (104)
    BlockBlock program managers, block assistant research officers, HMIS operator2,6162,431(93)
    SubcenterAuxiliary nurse midwives, nurse supervisors18,028b15,738 (87)
    Total20,99018,515 (88)

    • Abbreviations: HMIS, health management information system; UP-HMIS, Uttar Pradesh Health Management Information System.

    • ↵aSource: Participant attendance sheet.

    • ↵bOnly 1 auxiliary nurse midwife (ANM) per subcenter was targeted for the training (if more than 1 ANM was on staff). For all other positions, everyone was invited to participate in the trainings.

  • TABLE 4.

    Improvement in the Data Accuracy of MNCH Data Elements Between Rounds 1 and 2 Observed During Data Audits Conducted by the UP-TSU Across 130 Facilities in 25 High-Priority Districtsa

    Facility TypeNumber of MNCH DataElements AuditedNumber of Facilities Audited for Data AccuracyData Elements Matched With Source (Round 1,October 2017)Data Elements Matched With Source (Round 2,January 2018)Mean Difference (Round 2–Round 1)P Value
    Mean, %SDMean, %SD
    District hospitals98264732722825.007
    Block-level community health center and block primary health care97585236702917.001
    Community health center97205137663315.178
    Primary health center97173338693936.002
    Subcenter9795340703717.142
    Total1304936703121.000

    • Abbreviations: MNCH, maternal, neonatal, and child health; SD, standard deviation; UP-TSU, Uttar Pradesh Technical Support Unit.

    • a Source: Based on the analysis of Maternal and Newborn Complication Data Audit conducted from Round 1 (October 2017) to Round 2 (January 2018) by the UP-TSU. The 2 rounds of data quality assessment (data audit) for MNCH data elements were conducted by UP-TSU independently. During the audits, selected data elements were matched between reported data and source register data for accuracy.

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Optimizing the Health Management Information System in Uttar Pradesh, India: Implementation Insights and Key Learnings
Ankita Meghani, Anand B. Tripathi, Huzaifa Bilal, Shivam Gupta, Ravi Prakash, Vasanthakumar Namasivayam, James Blanchard, Shajy Isac, Pankaj Kumar, B.M. Ramesh
Global Health: Science and Practice Aug 2022, 10 (4) e2100632; DOI: 10.9745/GHSP-D-21-00632
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