And we strengthen health information systems so countries can make better decisions and sustain good health outcomes over time. The information provided on this web site is not official U. Majority of facilities over reported services while under reporting diseases.
Peer Review reports. The Health Management Information System HMIS is a system whereby health data are recorded, stored, retrieved and processed to improve decision-making [ 1 , 2 , 3 , 4 ]. HMIS is one of the six core building blocks of the health system and provides data needed for other components service delivery, health workforce, access to essential medicines, financing, and leadership [ 3 ].
Data delivered through HMIS come from service delivery reports and administrative records kept as part of routine transactions at health facilities and management offices. Data must be collected, processed and transformed, communicated, and used to improve decisions toward improved health outcomes [ 3 , 5 ]. High quality data are needed to enable safe and reliable healthcare delivery [ 6 ] and health facility data are critical inputs to monitor performances [ 7 ].
Though different organizations consider different dimensions of data quality, the World Health Organization WHO states that the dimensions of data quality are accuracy, validity, reliability, completeness, legibility, timeliness, accessibility, usefulness and confidentiality [ 5 ]. But in practice, no health data from any source can be considered perfect. All data are subject to a number of limitations related to data quality such as missing values, bias, measurement error, and human errors in data entry and computation [ 8 ] and factors associated with these errors are categorized in to technical, behavioral, and organizational factors [ 9 ].
Ethiopia has a three tier health system: primary, secondary and tertiary. Primary health care unit comprises health posts, health centers and primary hospitals. Health centers and health posts are networked by the linkage in which one health center is responsible for supporting approximately five health posts.
Secondary level includes general hospitals while tertiary level includes teaching and referral specialized hospitals. Ethiopia has been implementing HMIS at all levels of the health system to ensure information use for evidence-based health planning and decision-making [ 10 ] with reforms focusing on rationalizing and standardizing the system and information use mechanisms [ 11 ]. All levels of health facilities use standard registers and individual cards to record and standard formats to report data.
These registers and reporting formats are designed considering services provided at each levels of health facilities and are distributed by federal ministry of health. Except very few hospitals that use computerized data system, all service delivery points use printed materials for recording. Regarding reporting, health posts report to cluster supporting supervising health centers or primary hospitals which then report to district health office.
General and teaching hospitals report to zones where they are located. Some health centers and all hospitals use computer for data entry and analysis.
Facilities using computers enter data and submit softcopy while those facilities without computer submit hardcopy to district health office. HMIS reports submitted to district by hardcopy or softcopy are digitalized and shared by higher levels through web system. Except health posts where any of two health extension workers can compile reports, all organizations have person in charge of HMIS activities. From census, its population was estimated to be 19,, in It is the most diverse region in the country in terms of language, culture and ethnic background.
Administratively, the region is divided into 14 zones, 1 city administration and 4 special woredas. Zones are divided in to woreda and town administrations. Woreda equivalent to district is administrative structure in zone with approximate population of , while special woreda is a woreda that is directly accountable to the region not included in zone. In , there were 57 hospitals of all type, health centers and health posts reporting data through routine HMIS. This study was stand-alone survey, was not linked to community data verification was done only at facility level , and used both quantitative and qualitative methods.
Public health facilities reporting data to government system through the routine HMIS for more than a year were included in the study. Distribution of sample size to facility type considered health center to hospital ratio and pairing health center HC with health post HP.
Sample size was allocated to zones and special woredas proportionally considering existing number of functional facilities. Multi-stage sampling was used to select HCs and HPs while hospitals were selected using simple random sampling. At first level, woredas were selected from zones and at second stage, health facilities were selected from woredas using simple random sampling.
In this way, 25 woredas in addition to determined sample size were included. The overall facilities included were 65 HCs, 65 HPs, 8 hospitals and 25 woreda health offices giving a total of facilities. At facility level, data accuracy was assessed by comparing source documents and reports while at woreda health office level, accuracy of data entry was assessed by comparing reports from facilities and report sent to higher level through HMIS over the same period. We considered data of the most recent completed quarter in Ethiopian fiscal year November , December and January But administrative reports were not selected because of unavailability of source document to verify.
Most administrative reports, for example number of villages free from open defecation, can preferably be verified at community level. Hence, excluded from verification. Total malaria includes both confirmed and clinically treated malaria cases.
BSc holder nurses and health officers reviewed documents. Module 2. Desk review of data quality. Module 3. Data verification and system assessment.
Geneva: World Health Organization; Ginsberg SD, editor. Accuracy and quality of immunization information systems in forty-one low income countries. Tropical Medecine and International Health. The immunization data quality audit: verifying the quality and consistency of immunization monitoring systems. Assessment of health facility data quality: Data quality report card. Cambodia; Global Health: Science and Practice. View Article Google Scholar Comparing individual-level clinical data from antenatal records with routine health information systems indicators for antenatal care in the West Bank: A cross-sectional study.
Agyepong I, editor. Republic of Rwanda. Data Quality Assessment Procedures Manual. Ministry of Health; Toward utilization of data for program management and evaluation: quality assessment of five years of health management information system data in Rwanda. Global Health Action. Journal of Community Health. International Journal of Public Health Research. Three success factors for continual improvement in healthcare: an analysis of the reports of improvement team members.
BMJ Quality and Safety. Quality of care for pregnant women and newborns-the WHO vision. Impact of a district-wide health center strengthening intervention on healthcare utilization in rural Rwanda: Use of interrupted time series analysis. Rwanda Demographic and Health Survey — Journal of Global Health. Tackling the hard problems: implementation experience and lessons learned in newborn health from African Health Initiative.
Standards for improving quality of maternal and newborn care in health facilities. Rwanda Ministry of Health. Fourth Health Sector Strategic Plan. Kigali, Rwanda: Republic of Rwanda; Rwanda Ministry of Health; R Core Team. Bazzano AN, editor. Assessing the quality of routine data for the prevention of mother-to-child transmission of HIV: An analytical observational study in two health districts with high HIV prevalence in South Africa.
International Journal of Medical Informatics. Quality of antenatal care service provision in health facilities across sub-Saharan Africa: Evidence from nationally representative health facility assessments.
The quality-coverage gap in antenatal care: toward better measurement of effective coverage. PLoS Medicine. Reports from sub Saharan Africa indicate that vital health decisions, in this context, are made based on crude estimates of disease and treatment burdens [ 2 , 3 ]. Findings from this region indicate that the problem of under reporting is huge and is linked to lack of knowledge and practice among the health workers characterized by insufficient analysis skills, training and lack of initiative for using information [ 4 — 6 ].
In Tanzania the first version of the health management information system was launched in and the second in [ 7 ]. The first version was entirely in English and it was soon realized upon testing that the users had limited commands in this language and was therefore technically changed to Kiswahili, the national language.
The latest version involves manual data entry into 12 HMIS booklets. The system covers all health programs and health care services, and requires all health facilities, regardless of ownership, to use this system and report to the district health authority on quarterly basis. The overall goal of this system is to optimize the performance of health services at all levels of administration through the timely provision of necessary and sufficient information needed by the health managers to monitor, evaluate and plan their activities [ 7 , 8 ].
Its success requires a system that is integrated, decentralized, functional and reliable [ 9 ]. The conception of this study was based on the concerns about the poor quality data and inadequate integration of the HMIS despite a number of changes it has undergone since inception and the need to bridge the gaps in the on-going changes in health sector.
In an attempt to strengthen the health services to meet national and international commitments, the government of Tanzania has developed the Primary Health Service Development Program PHSDP whose main goal is to accelerate provision of quality primary health care services to all by This program is implemented by increasing intakes of trainees for health care, reviewing and standardizing curricula for all medical and paramedical cadres to competence-based [ 10 ].
This article explores the gaps and factors for change in HMIS in Tanzania and presents a detailed account on how they could be best bridged in the ongoing changes in country's health sector. It attempts to link the required changes in the HMIS and the evidence-based John Kotter's eight-step process for implementing successful changes in any organizations [ 11 ].
Tanzania is divided into 25 administrative regions that are subdivided into districts that are further subdivided into divisions, wards and villages. Administratively, the ministry of health is the main coordinating body for health information in the country, the regional level is responsible for coordinating activities in the districts and the districts are responsible for coordinating services delivery activities at the health facility levels.
The 12 HMIS booklets in Tanzania include the guidelines, summary from the other books , village profile, inventory ledger for equipment, drugs and supplies , outpatient services, antenatal services, postnatal services, family planning services, communicable diseases, HMIS report book, dental services and delivery services. These booklets consist of forms and registers, where the registers are pre-set frameworks for data processing. In an attempt to map the gaps and factors for change in the country's HMIS a cross-sectional descriptive study was conducted between January and February in Kilombero, one of the most rural districts in Tanzania.
Kilombero district is in the southeastern part of the country about km from Morogoro, the headquarters of the region and km from Dar es Salaam, the largest business city in the Tanzania. The district has a total area of 14, km 2 , a population of , people [ 12 ] and 44 health facilities. Among these health facilities are 2 hospitals both owned by non-governmental institutions, 4 health centres all owned by the government and 38 dispensaries of which only 15 are owned by the government.
The health facilities were as far as km from the district headquarters and are expected to provide all primary health care services, refer complicated cases and complete the relevant booklets.
A stratified random sampling technique was used to obtain one hospital, one health centre and 9 dispensaries. Of these facilities, 5 were governmental and 6 non-governmental. The study team aimed to interview at least 5 health care providers including those in-charge of the health facility available on the day of study visit. However, the team managed to interview 43 care providers because many of the facilities had less than five health care providers. At the hospital a list of care providers was obtained from the administration and the following departments were included: outpatient, reproductive and child health RCH clinic and labour ward where at least 2 health workers were interviewed from each department.
Data collection was carried out by the author and 4 research assistants. A semi-structured questionnaire was used to interview health care providers and facility administrators to assess their level of knowledge, attitudes and practices concerning HMIS and factors for change Additional file 1.
The parameters recorded in booklet 6 antenatal services register included: 1 booking visit: date, registration number, name, age, gravidity, gestation age, height, danger signs; 2 re-attendance visits: presence of anaemia, oedema, proteinuria, lie of the foetus, vaginal bleeding, syphilis test, date of TT vaccine for the index pregnancy, last childbirth year, live or died and referral information.
The parameters for book 7 underfive services register were: date, registration number, date of birth, weight, date for BCG, DPT, polio, measles vaccinations and vitamin A, mother's information name and TT vaccination status ; and those for book 12 delivery services register were: date, registration number, name of the mother, age, gravidity, parity, date of admission, date of delivery, mode of delivery, birth before arrival BBA , complication of labour, status at birth live birth or stillbirth , condition of the mother at discharge and name of the health provider.
The parameters which were mostly not recorded were documented. In the cases of frequent incomplete records, the research team inquired about reasons for the incompleteness. The research team also interviewed the district HMIS coordinator for the factors that affected health information system.
The permission to carry out this study was obtained from district medical authority and verbal consent was obtained from the interviewees. A total of 43 health care providers from 11 health facilities were interviewed.
0コメント