Global Health Data Methods_

1. What is a HMIS?
A health management information system (HMIS) collects, stores, analyses, and
evaluates health-related data from health facility to district, regional and national
administrative levels. It provides analytical reports and visualisations that facilitate
decision making at all these levels. HMIS are also referred to as routine health
information systems.
A HMIS derives much of its information from patient-provider interactions in health
facilities. Hospitals, health centres, and community outreach services provide health
care across preventive, promotive, medical and surgical, rehabilitation, and palliative
care interventions.
The HMIS also collects data from beyond government-run facilities including from non-
pro t, for-pro t, faith-based facilities and from service delivery sites such as prisons,
schools, workplaces and communities.
• Facilities collect data – which are integral to the services they provide – to ensure
good management of patients.
• Health managers aggregate and report the data to higher administrative levels, for
example district, regional and national levels.
• When aggregated, the data provide information for epidemiological surveillance and
for monitoring health services performance in terms of access, coverage, quality, and
equity at all levels of the health system.
• The information generated show the range and volume of services delivered to the
population, including: prevention such as immunisation, antenatal, delivery and
postnatal care; treatment of acute conditions such as malaria, diarrhoea, and upper
respiratory tract infections; chronic conditions such as HIV, tuberculosis, high blood
pressure; and management of surgery and trauma.
• In addition to generating information on interventions – admissions, treatments
administered and health outcomes – a HMIS also produces data on the availability of
services, infrastructure, equipment and supplies needed to deliver such interventions.
The HMIS provides information for local planning. It also contributes to country level
monitoring and evaluation, research, policy and planning and generates indicators
about outputs, outcomes and impact.
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Why a HMIS?
A HMIS has di erent and sometimes con icting functions: operational; informational;
and decision-making. The operational and decision-making functions are essential to
ensure proper management of health services for patient/clients. The broader utility of
the HMIS is to complement data from other HIS sources to build an evidence base for
health sector performance assessment and strategic planning.
The main users of HMIS are managers and care providers at district level and below.
Executive managers, public policymakers and researchers can also use HMIS data for
governance and research.
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Main sources of HMIS data
HMISs are complex, re ecting the multifaceted and heterogeneous nature of health care
provision and management. They draw on: individual patient records; family record
cards; admissions and discharge registers; ward registers and tally sheets; community
level records, infrastructure and resource records, records of health interventions
delivered in communities, and periodic assessments of health facility infrastructure and
resources. We classify these record systems as:
Individual patient record systems
The majority of data that a HMIS collects at health facilities derives from individual
records of patient-provider interactions that include for example: patient identi cation,
clinical diagnoses, results of laboratory and diagnostic tests; prescriptions; preventive,
promotive, curative and rehabilitative interventions delivered; and payments made.
Managers report summary indicators based on a subset of the data to the next level of
the health system which compiles them to produce indicators across facilities.
Most low-and middle-income countries continue to use paper-based systems for
individual records. But hospitals increasingly use electronic medical records. Electronic
records require advanced technology and networking skills, sophisticated management
processes and maintenance that are often not available at remote facilities in low-
resource settings.
Facility-based registry systems
Facility-based registers include admission and discharge registers and ward registers.
Some registers list and follow particular individuals requiring ongoing management over
a period for time, such as antenatal or immunization care registers, or registers of
chronic diseases such as cancer. Each register maintains the minimum information
necessary to follow-up the patients.
Regular review of registers enables the health team to identify patients who must be
actively pursued to assure compliance with treatment interventions, such as completion
of immunization, full treatment of tuberculosis, compliance with anti-retroviral
regimens, or regular monitoring and control of blood pressure.
Patient registries are useful for monitoring the quality of health services and for
capturing treatment interventions. In addition to data that identify individuals, these
registries include diagnosis on admission and discharge, results of laboratory tests and
treatments. If a patient died in hospital, the registry also provides cause of death
assigned according to the standards of the International Classi cation of Diseases.
Community level record systems
HMISs integrate data from community-based workers who provide health promotion
and disease prevention activities. These providers may:
• Work for the health system, for example the Health Extension Workers in Ethiopia or
community health workers in Kenya, or
• Work informally as community-based providers, for instance volunteers serving
people living with HIV who may or may not be associated with the health system.
The information these providers collect at the point of service is essential for community
programme management and decision-making on budget, policies and human
resources. Community health workers use data to follow their clients and manage their
care, especially for interventions that require longitudinal follow-up and community-
facility linkages.
It is important to link community level data to facility-based information systems to
avoid double counting of health events. Data collection tools require literacy and
numeracy skills. Health facility sta should support and supervise community health
workers to ensure properly delegated clinical services, for example to help nd clients or
patients who are lost to follow-up.
Health facility assessments (HFAs)
Alongside the routine collection of data as a by-product of patient management and
facility administration, a HMIS also includes periodic collection of information from
health facilities that is not included in routine reports. HFAs generate information on:
facility infrastructure, equipment and commodities; human resources; readiness to
deliver speci c interventions (such as tuberculosis management); and service utilization.
HFAs are an e cient way to collect information on facility availability and distribution.
They can identify where change is needed to strengthen the health system.
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Users and uses of HMIS data
HMIS units at each level of the health system manage data to inform activities at that
level and below, and to report a required subset of information to the next highest
administrative level. For example, a hospital runs its own information system, which
includes management of patients and of the commodities and supplies needed to run
the hospital.
Managers at higher administrative levels require health care facilities, through the HMIS,
to send regular reports, for example cases of noti able conditions, numbers of
vaccinations administered, antenatal care visits, facility deliveries, and patients seen by
diagnosis. The district level manages and coordinates reports from facilities as well as
from di erent programmes. A HIV/AIDS programme, for example, will collate
information from facilities on coverage of interventions for prevention of mother-to-
child transmission of HIV (PMTCT), and uptake and continuity of antiretroviral
treatments.
Routine facility data produce information on outcomes and impact. For indicators such
as tuberculosis treatment outcomes, coverage of interventions for PMTCT, and uptake
and continuity of antiretroviral treatments, facilities are the sole source of data. The
HMIS tracks delivery of clinical treatment for conditions, such as diabetes, hypertension,
and cancers, that require long-term follow-up and monitoring of treatment compliance
and health outcomes. This is important both from the individual patient perspective, but
also for the management of services for these diseases and for programme planning
and evaluation. For example, Afghanistan uses a Balanced Scorecard framework to
measure the performance of reproductive maternal and child health programs using
HMIS data.
While data managers organize collection and management of data, it is often the user
who collects the data, for example a physician or nurse who completes the patient
records. Managers and users must work closely together to obtain the information users
require.
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Limitations and challenges
HMIS data in low- and middle-income countries are beset by problems of quality so that
end-users do not always trusted them or consider them t for purpose. Data quality
limitations include missing values, measurement errors, and mistakes in data entry and
computation. The perception that routine reports from health facilities and districts are
often late, incomplete and inaccurate undermines credibility and hampers their use.
Completeness of data
Data from a HMIS are, by de nition, limited to those who attend health facilities or use
related community-based services. When hard-to-reach groups – such as
undocumented migrants, ethnic minorities, the very poor – have di culty accessing
health care, it is important to assess the extent of exclusion and how this will a ect the
completeness and reliability of HMIS data.
In many low- and middle-income countries, large proportions of the population have
severely restricted access to health services because of geographic, economic, and
sociocultural barriers. In such settings, facility-based data are not likely to represent the
whole population in any given catchment area and the resulting data and indicators will
be incomplete and biased.
Where access to care is limited, countries estimate population denominators by
extrapolating from the most recent census. Inevitably, such estimates become less
reliable over time. This is a particular problem at sub-national levels. It is not unusual to
see coverage estimates for indicators such as immunization exceeding 120 per cent for
some districts while in others coverage is at unlikely low levels (below 80 per cent). While
this may, in part be due to inaccuracies in the numerators (numbers of immunised
children) studies have found that it is more often a problem of over-estimation or under-
estimation of the target population.
Methods to determine the adequacy of the population data used in evaluating the
performance of health indicators, such as coverage involve assessment of the internal
validity of the HMIS data, such as completeness of reporting from facilities and districts,
as well as comparisons with external sources such as household surveys. For example,
in Liberia used Lot Quality Assurance Sampling (LQAS) to compare intervention coverage
rates obtained from HMIS data with those obtained through a health outcome survey.
Quality of data sources
HMIS managers need to undertake regular quality assessments of the relative strengths
and weaknesses of the data sources. Adjustments of reported data to take into account
incomplete reporting and missing values can help increase con dence in and utility of
facility-based data. But such adjustments must be based on scienti c methods and
made transparent to users.
The Data Quality Review (DQR) Toolkit supports a comprehensive review of HMIS data
quality, mainly from facilities, and consists of three components:
• A guide for conducting monthly reviews of data quality with immediate checks and
feedback so that errors can be identi ed and corrected as they occur.
• An annual independent assessment of core indicators to identify gaps and errors in
reporting and assess the plausibility of reported trend data.
• Periodic in-depth programme-speci c reviews timed to feed into programme
planning.
The reviews focus on a limited set of tracer indicators covering maternal health,
immunization, HIV, tuberculosis and malaria. But countries can include other tracer
indicators if needed. Data quality metrics include completeness, timeliness, consistency
and accuracy.
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Innovation and transformation
For years, health facilities and community health workers have collected data using
paper forms or logbooks. This involves laborious and time-consuming data aggregation
and compilation, transcription errors, inadequate analysis and visualization, di culty in
data sharing, and poor data storage and retrieval.
To address such challenges, HMISs now use information and communication technology
for data collection, aggregation, reporting, storage, and analysis, visualization and
dissemination. This revolution has yet to reach all countries and all levels of the health
system. But information and communication technology can improve routine, facility-
based and administrative data collection, management and use for policy and planning.
Although information and communication technology facilitates HMIS functionality,
health managers need to select hardware and software appropriate to their country’s
infrastructure, capacity, and resource availability.
Electronic HMIS
Sophisticated and powerful data management applications are available for facilities to
use to manage their data. For example,
• The District Health Information System (DHIS2) developed by the University of Oslo.
DHIS2 permits data capture on multiple xed and mobile devices. Because the system
allows users to enter data o ine, it can be used in locations with poor connectivity.
DHIS2 Academies facilitate sharing of experiences. They also strengthen national and
regional capacities to successfully set up, design and maintain DHIS2 systems.
• The iHRIS software is an application in support of human resources data
management.
• The eLMIS supports logistics and commodities data management. In addition to data
entry, data aggregation and storage functions, these applications mostly have a
decision support module that can produce routine or ad hoc reports, as well as
tailored data visualization products called data dashboards.
Decision support tools
Electronic data management facilitates production of summary analyses and
visualisations that are readily understandable by non-technical users, or decision
support tools, for example:
• Comparison is a useful analytical method. Comparisons may be: spatial (by health
facility; district or province); temporal (trends by week, month or year); indicator-
speci c (between inputs and outputs); or benchmarked (expected versus achieved
results). Comparisons can identify areas or groups that are disadvantaged or failing to
achieve expected benchmarks and requiring remedial interventions. Whereas cross-
country comparisons of key indicators can be of interest, national decision makers
often prefer to limit external comparisons to countries at similar levels of
development.
• The four Ts: Trends (progress made), Trajectories (whether the direction of change is
positive or negative), Triggers (minimum or maximum acceptable levels at which
action needs to be taken) and Targets (indicator levels to be achieved).
• Data dashboards with summary tables, graphs, and other visualizations can illustrate
such analyses, showing progress towards goals and identify issues for health
programmes to address.
• Geographic Information Systems (GIS) are powerful tools to analyse, organize, and
present spatial data in maps.
RHINoVision is an example of an electronic Decision Support Tool. It was developed
under the MEASURE Evaluation Project as an electronic dashboard that allows further
analysis of HMIS data.
Data architecture to link systems
• A National Health Data Dictionary (NHDD) provides a common language for health
policymakers, managers and care providers to communicate and exchange health
information in a standard manner. The NHDD develops metadata to harmonise data
de nitions of commonly used data and indicators. It facilitates mapping of de nitions
to international standards, such as the International Classi cation of Diseases or the
Systemized Nomenclature for Medicine (SNOMED-CT).
The NHDD requires a sound governance mechanism involving health, statistics and
other relevant entities. For example, in Australia, the National Health Information
Standards and Statistics Committee oversees development of health metadata
standards. A NHDD can be hosted on a software platform, ideally open software
solutions such as the Open Concept Lab.
• A data warehouse is a centralized data storage system that facilitates integration of
data into one, usually virtual, location, linking the data from all data sources via
information exchange protocols. This makes it possible to bring together data across
health facilities at di erent levels, including from patient records and human resource
management systems.
Highly developed warehouses incorporate data from sources other than the HMIS,
such household surveys or the census. If each individual has a unique identi er,
then the system can link data on the same individual across di erent systems,
such as health care, medical insurance and social security.
Developing a data warehouse is a major technological and analytical undertaking.
It requires skills of health analysts, statisticians, computer technicians, and data
scientists. Once established, a warehouse can bene t patients, providers, health
facilities and the entire health system.
Notwithstanding the potential of these digital innovations, in many settings facility-
based data collection and transfer are predominantly paper-based. The architecture
should be designed to evolve and be relevant across locations and levels of the health
system, ready to become more granular and comprehensive with time.
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Creating a culture for using HMIS data
Many countries issue annual reports based on HMIS data. But too often decision-makers
do not use the information to improve health system performance. Poor use of
information is not only due to technical issues, but also results from organizational and
behavioural barriers. Hierarchically organized health systems can leave managers at
lower levels powerless to use the data. Health professionals, while generally well-
prepared for diagnosis and problem identi cation, are not trained for this type of
problem solving. The question is how to build a culture of information use.
The Routine Health Information Network (RHINO) was created in 2001 under the USAID-
funded MEASURE Evaluation Project with support by WHO, the World Bank, and John
Snow, Inc.. The more than 1,000 RHINO members represent developing country
governments, donor agencies, technical groups, and private voluntary organizations.
The shared purpose of all these organizations is the e ective collection and use of HMIS
generated information especially at the district level and below. RHINO as a broader
worldwide advocacy and knowledge management organization promotes behavioral
change for better use of information in decision making.
MEASURE Evaluation developed the Performance of Routine Information System
Management (PRISM) for assessing the reliability and timeliness of routine health
information systems in making evidence-based decisions. It has published a set of tools
on its website.
The private sector uses human-centered design (HCD) for product and technology
development to better understand users’ needs and involve them early in the design of
solutions. HCD is a collaborative problem-solving approach that provides broadly
applicable methods for developing in-depth understanding of human behaviour. HCD
could be applied to establish a culture of using health information, together with other
interventions such as: role modeling by senior managers to promote use of data at the
district level and below; incentive-based systems to promote use of information such as
performance-based nancing schemes; allocation of resources based on HMIS indicator
results; and use of information as criteria for annual performance appraisals.
There is need for comprehensive capacity building interventions at the individual,
organizational as well as system level. Critical focus areas in capacity building are data
management and data quality assurance systems. Technical partners are providing
support for capacity development through an on-line curriculum for routine health
information set-up by MEASURE Evaluation in 2017.
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Contents
• What is a HMIS?
• Why a HMIS?
• Main sources of HMIS data
• Users and uses of HMIS data
• Innovation and transformation
• Creating a culture for using HMIS data
Source chapter
The complete chapter on which we based
this page:
Lippeveld T., Azim T., Boone D., Dwivedi V.,
Edwards M., AbouZahr C. (2019) Health
Management Information Systems:
Backbone of the Health System. In:
Macfarlane S., AbouZahr C. (eds) The
Palgrave Handbook of Global Health Data
Methods for Policy and Practice. Palgrave
Macmillan, London.
Additional resources
USAID, MEASURE Evaluation. Guidelines
for data management standards in
routine health Information systems 2015
World Health Organization. Health facility
and community data tool kit.
Aqil, A., Lippeveld, T., and Hozumi, D.
PRISM Framework: A paradigm shift for
designing, Strengthening and evaluating
routine health information systems.
Belay H, Lippeveld T. Inventory of PRISM
framework and tools: application of PRISM
tools and interventions for strengthening
routine health information system
performance.
Lippeveld T. Routine health facility and
community information systems: Creating
an information use culture.
The Routine Health Information Network
(RHINO) promotes behavioral change for
better use of information in decision
making.
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