IMPROVING QUALITY OF HIV/AIDS AND RELATED DATA QUALITY IN MAYUGE DISTRICT

BY

KATO CLEMENT MUSOLO, B.sc (statistics) & PGD PPM

& KALEGERE JULIET, BA (urban planning) & PGD PPM MEDIUM­TERM FELLOWS

August 2010 Contents DECLARATION ...... v 1.0: INTRODUCTION AND BCKGROUND ...... 1 1.2: Literature review ...... 3 1.3: Statement of the problem...... 7 1.3.0: Statement of the Problem ...... 7 1.3.1:Justification/Rationale...... 9 1.3.2: Conceptual framework...... 10 2.0 General project Objective ...... 12 2.1 Specific Objectives ...... 12 3.0 Methodology ...... 13 3.1 Meet project stakeholders ...... 13 3.2 Training of 10 records Assistants in data collection and management using MOH tools ...... 13 3.3 Photocopy of HMIS monthly reporting forms ...... 14 3.4 On Job Mentoring ...... 14 3.5 Data collection...... 15 3.6 Data validation and verification ...... 15 3.7 Mid­Term Review meeting ...... 16 3.8 Dissemination of project results ...... 17 4.0 Project Outcomes ...... 18 4.1 Lessons learned ...... 21 4.2 Challenges experienced and how they were overcome ...... 22 4.3 Summary ...... 23 4.5 Recommendations...... 24 5.0 Next steps...... 25 References...... 26

ii DECLARATION

I, KATO CLEMENT MUSOLO and KALEGERE JULIET do hereby declare that this end of project report entitled IMPROVING HIV/AIDS AND RELATED DATA QUALITY IN MAYUGE DISTRICT has been prepared and submitted in fulfillment of the requirements of the medium term fellowship programme at Makerere university school of public health and has not been submitted for any academic and non­academic qualifications.

Signed……………………………………………………………Date………………….. Kato Clement Musolo, Medium­Term Fellow Signed …………………………………………………………...Date…………………… Kalegere Juliet, Medium­Term Fellow Signed……………………………………………………………Date…………………… Dr. Isiko K. Paul, Institutional supervisor Signed ……………………………………………………………Date………………….. Ms Evelyn Akello, Academic supervisor

iii FELLOWS’ ROLE IN PROJECT IMPLEMENTATION The fellows played the lead role in project identification, design, implementation and monitoring. The fellows facilitated the project team formation, facilitated during trainings and meetings. The fellows were involved in all project activities as coaches and co­ mentors with other members of the project team and data collectors besides the overall project management. The fellows also disseminated the project results and came up with the end of project report.

iv ACKNOWLEGEMENT

We extend our gratitude to Dr. Isiko Paul Kawanguzi (DHO) our institutional supervisor and Dr. Nabangi Charles for giving us the opportunity to participate in this fellowship and for their continued support and guidance throughout the process.

We would like to express our sincere appreciation to Mayuge District Health Team, health facility in charges and records personnel for their contribution towards the development of this document.

We are equally grateful for the support and encouragement rendered to us by our academic mentor Ms Evelyn Akello towards design, implementation and successful completion of this Project.

We cannot over look the support and technical guidance provided by Mr. Joseph Matovu (MUSPH) during the implementation of the project. He was always there whenever we needed help.

We cannot forget our families, for they stood with us all the time we were executing this project.

Above all we extend our heartfelt gratitude to the Almighty God without whom, this project could not have succeeded.

v ACRONYNMS

AIDS Acquired Immune Deficiency Syndrome ART Antiretroviral Therapy DHMT District health management team DHO District health Officer DHT District Health Team HMIS Health Management Information Systems HSD Health Sub­district RHIS Routine health information systems

vi OPERATIONAL DEFINITIONS

Data validation ­ processes and activities undertaken to ensure data is correct before is entered into a computer or processed for use in planning and decision­making.

Good quality data ­Data fit for use. It is timely, accurate, complete, reliable and valid.

vii EXECUTIVE SUMMARY

This end of project report is submitted to Makerere University School of Public Health in fulfillment of the requirements for fellowship programme. The main objective was improving the quality of HIV/AIDS data by April 2011. The specific objectives are;

• To improve completeness of inpatient reporting from 53% to 85% and outpatient reporting form 84% to 100% by April 2011.

• To improve timeliness of data reporting from the District to Ministry of Health from 75% to 100% by April 2011

• To improve data accuracy by 50% by April 2011

The project was implemented in the whole district with special emphasis on the eight health facilities offering HIV/AIDS services. The main activities conducted sensitization on importance or benefits of quality data, training health workers in data collection and management, data validation, on job coaching and mentoring, provision of reporting forms, advocacy for increased budget allocation, reviewing project progress and dissemination of project results among others.; After implementation of this object; Tüh e quality of data s improved in terms of timeliness from facilities to district as well district to MOH

Tüi meliness of reporting improved from 75% to 80%

Cüo mpleteness of OPD reporting improved from 84% to 92%

Rüa nking of the district in the National league table improved from 61 st in 2010 to 49 th in 2011 and 3 rd in Busoga region during the project implementation period.

Tüh e project results revealed the importance of involvement of management and other stakeholders in improving data quality and the urgent need for prioritization of HMIS activities to improve quality of HIV/AIDS data and health care as a whole.

viii All the activities were implemented at a cost of 5,527,800/= (Five million, five hundred twenty seven thousand and eight hundred shillings only.

ix 1.0: INTRODUCTION AND BCKGROUND Mayuge, carved out of Bunya county of Iganga district in 2000 is the only district surpassed by Kalangala district in terms of area covered by water. 77% of Mayuge is water. The district is located in the Eastern region of Uganda at 120km from Kampala and 48 km from Jinja. Mayuge is bordered by Iganga to the North, Jinja in the West, and Bugiri in the East and Lake Victoria in the South. It shares the shores of Lake Victoria with Mukono, Bugiri and Jinja districts. Population According to the 2010 midyear population projections, Mayuge district has a total population of 430,656 with the sex distribution as shown in the table 3. 51% of the total population is female with 31% of these in the reproductive age group. 5% of the total population is under 1year. The annual population growth rate is estimated to be 3.5% see table below. Table 1: Population of Mayuge district by sex and selected age groups

IND IC AT O R M a le F e m a le T ot a l Tota l p op u la tion 2 1 0 ,7 1 2 2 1 9 ,9 4 4 4 3 0 ,6 5 6 C h ild ren u n d er on e y ea r 1 0 ,5 3 6 2 1 ,5 3 3 3 2 ,0 6 8 C h ild ren 1 ‐4 y ea rs 3 5 ,8 2 1 3 7 ,3 9 0 7 3 ,2 1 2 p op u la tion 5 ‐1 4 y ea rs 6 5 ,3 2 1 1 3 3 ,5 0 3 1 9 8 ,8 2 4 p op u la tion 1 5 ‐4 9 y ea rs 8 2 ,1 7 8 1 6 7 ,9 5 6 2 5 0 ,1 3 4 p op u la tion a b ov e 5 0 y ea rs 1 6 ,8 5 7 3 4 ,4 5 2 5 1 ,3 0 9

Table 1: Source :Mayuge health management information system

Monitoring and evaluation in Mayuge health department Mayuge District Health Department uses Health Management Information System for monitoring and evaluation of health programs at all levels. The HMIS tools are designed by Ministry of Health (MOH) and distributed to districts. Under this system, information flows from health facilities to Health sub districts, districts and finally to the national databank at MOH.

However, like other districts HMIS in Mayuge has faced a number of challenges including; inaccurate data, untimely reporting, incomplete reports, poor records management, low utilization of HMIS data among others. 1 This is mainly because most of the records assistants are not trained in records management and have not been given thorough training in the use of MOH tools. In addition, data collection and management is not given adequate consideration and onsite support supervision particularly on HMIS is not done effectively since it is integrated with other activities. To this effect a project was designed by the M& E fellows with the aim of improving quality of HIV/AIDS and related data in Mayuge District Health Department.

2 1.2: Literature review

Accurate, timely and accessible health care data play a vital role in the planning, development and maintenance of health services. Quality improvement and the timely dissemination of quality data are essential if the health care authorities wish to maintain health care at an optimal level In the recent past, data quality has become an important issue not only because of its importance in promoting high standards of patient care but also because of its impact on government budgets for maintenance of health services(WHO ,2003).

Sound policy, resource allocation and day­to­day management decisions in the health sector require timely information from routine health information systems (RHIS). In most low­ and middle­income countries, the RHIS is viewed as being inadequate in providing quality data and continuous information that can be used to help improve health system performance. In addition, there is limited evidence on the effectiveness of RHIS strengthening interventions in improving data quality and use(Evaluation of performance of routine information system management PRISM framework: (David R Hotchkiss etal, 2010) The term data quality as presented in the IS­literature is mostly characterized as a multidimensional conception of the properties and conditions of data(Abate et al,1998,Fox et al ,1994,Huh et al 1990,Redman 1996,Wand & Wand 1996).

Quality as defined by Donabedian(1998) consists of the ability to achieve desirable objectives using legitimate means. Quality data represents what was intended or defined by their official source, are objective, unbiased with known standards (Abdelhak et al,1996) . Data quality includes ; • Accuracy and validity of the original source data • Reliability­data are consistent and information generated is understandable. • Completeness­all required data are present • Currency/timeliness­data are recorded at the time of observation among others(WHO ,2003)

3 Whether collecting data to be stored in paper medical/health record in a computer –based electronic patient record for statistics, data must be accurate, reliable and organized in such away that they are understood and health information can be retrieved.

Some causes of poor data are poorly designed data collection forms, inefficient clerical staff, lack of training in interviewing patients and recording details, lack of time caused by pressure of work. Poorly trained and insufficient staff, lack of understanding of the need for accurate data and lack of understanding of the requirements of data collection and data quality by medical officers, nurses and other health professionals are other causes of poor quality data. (WHO, 2003).

Use of technology­oriented data quality improvement efforts are commendable and definitely, a step forward in the right direction, however, technology alone cannot eradicate the root causes of poor quality data because poor quality data is not as much an IT problem but rather a business problem. Other enterprise disciplines must be developed, taught, implemented and enforced to improve data quality in a holistic, cross­ organizational way. Therefore because data quality improvement is a process not an event the following enterprise –wide disciplines be phased in and improved upon over time; • A stronger personal involvement of management • High level leadership for data quality • New incentives • New performance evaluation measures • Data quality enforcement policies • Data quality audits • Additional training for data owners and data stewards about their responsibilities • Data standardization rules ( Larisse Terpeluk Moss etal,2005)

4 “Virtually everything in business today is an differentiated commodity, except how a company manages its information. How you manage your information determines whether you win or lose” –Bill Gates

Research coordinated by the University of Sheffield examined the introduction of HMIS in Uganda and identified the problems experienced in the process which included Many staff being required to perform tasks in information collection for which they had been poorly trained. As a result, using the information gathered proved to be difficult and targets continued to be set at the district and national level, instead of the local level. The report also reveals lack of tools and management systems that could be used to monitor the implementation of HMIS. (http://www.id21.org/health/h1jg4g1.html)

The international journal of public health(WHO ,2005) also cites duplications and inconsistencies between sectors in the collection, reporting, storage and analysis of social economic data and that statistics offices give higher priority to economic data than other social statistics. The report recommends that there is need for a long­term commitment to improve training and career structures of statisticians and information technicians working in the health and other social sectors.(Macfarlane etal, 2005)

According to a WHO data quality assessment report card 2010­2011, the intervention coverage estimates are often poor and likely to lead to incorrect ranking for at least one third of the districts in Uganda. The report further indicates that completeness of district reporting is poor in 9% of the districts and completeness of facility reporting is problematic for one third of the districts in Uganda. It goes further to reveal that accuracy of reporting is only partly adequate with 18% of the district reports zero or missing,7% of the districts having extreme outliers and 9% of the districts having major differences between the annual total and the sum of the monthly reports. Population projections for denominators in 2010/2011 are estimated to be off by more than one third for 22% of districts. Overall 71 of the 112 districts met the quality criteria for inclusion in the ranking tables.

5 Research has also shown that the Causes of data inaccuracy are double counting, counting ineligible patients, poor record keeping, incorrect data compilation procedures, and staff rotation and lack of teamwork. Other problems identified are with reporting data in a timely manner. However systems for management of HIV service data and reporting QI indicators at the health facilities are partially developed, providing data which could be effectively used for improvement purposes, although significant gaps have remained for improvement. To overcome these gaps, it is recommended that more staff be trained on the essential components of data management and that a clear set of formal and written guidelines for data management be issued to all facilities. (Semakula R etal, 2011)

6 1.3: Statement of the problem

1.3.0: Statement of the Problem Mayuge district health department had a problem of poor quality data from health facilities. The data was inaccurate, inconsistent, incomplete and untimely. Data quality report card­Uganda 2010­2011 reveals that 18% of the districts in Uganda had more than 33% discrepancy between aggregated monthly totals sent to MOH and the annual report submitted for 2008­2009. Double counting had been identified as one of the causes of the inaccuracies. The validity of self­assessment data in a Ugandan quality improvement program­June 2011 report indicates that 22 out of 30 health facilities assessed over reported on the ART adherence indicator while two­thirds over reported on the TB assessment indicator. Over reporting was caused by extracting data from patient daily attendance registers or drug consumption logs which contain multiple records of a single patient depending on the number of clinic visits each month (Richard semakula et al, June 2011).

According to MOH annual statistical abstract, in FY 2008/09 timely reporting for outpatient dropped from 79% to 78% at national level. This might have been contributed to by a number of factors like formation of new districts, recruitment of new staff in the districts, and changing of information systems like change to electronic reporting system and formats both at the centre and in the districts, as well as lack of reporting HMIS tools. Mayuge district was cited as one of the districts with facility completeness below 80% in the same period (2008/2009). (Data quality report card­Uganda, 2010­2011).

On the other hand, timeliness of inpatient reporting for the same period was 55% while completeness was 67%(Uganda, Ministry of Health Statistical Abstract 2008/09, December 2009, Kampala).

In Mayuge district in financial year 2009/10, approximately 53% of the health facilities submitted their inpatient returns while outpatient returns submitted averaged 84%. The ideal would be 100% completeness to ensure that analysis made on this data and

7 decisions made thereafter are a reflection of the true picture on the ground or in the community. Incomplete and untimely reporting has majorly been due to absence of reporting forms and lack of adequate skills in collecting and management of data by nurses, clinicians and the records staff. Despite all this, the District has been able to submit 75% of its reports on time to the ministry of health.

Data accuracy stands at approximately 43% for health IV’s and 0% for Health centre III’s (source: HMIS­four health facilities were sampled out of 8 providing HIV/AIDS services. 2 health centre IV’s and 2 Health centre III. HCT data was then audited and the number of correct entries counted and divided by the total number of HCT entries in the HMIS form 105) In addition health sub­district staffs did not track timeliness of reporting from the facilities therefore it was difficult to determine performance of each facility in terms of timeliness. This was as a result of lack of motivation, inability to use the tracking tools and inadequate monitoring by their supervisors. There was also under/over reporting, reporting of non­existent diseases e.g. Guinea worm, incomplete filling of forms, reports not being signed by facility/HSD in charges, use of tools that were not appropriate at some levels was also common i.e facility report forms were used to report by health sub­districts at times. The district had continually tried to provide the right forms but it appeared there was just a knowledge gap that needed to be addressed. (see graph for completeness of HMIS returns for 2009/2010)

Figure 1

8 1.3.1:Justification/Rationale When the problems highlighted in the problem statement are addressed; The ranking of the district in the national league table improved from 61 st position in 2010 to 45 th in 2011 and 1 st in Busoga region in the 3 rd quarter of FY 2010/2011 1. Demand for data increased, since our data was now more reliable and valid.

2. The budget allocation for the health department increased especially from the funds from the locally raised revenue.

.

9 1.3.2: Conceptual framework

Actions taken outcomes

• Data validation • Improved ranking and cleaning in the national league table Problem • Training of health workers Poor quality of • Increased demand HIV/AIDS and and records for data for related data assistants in planning purposes collection and Figure 2 management • Increase in district budget allocation • On job by ministry of mentoring and finance, planning coaching and economic development • Sensitization on the • Appropriate importance of interventions are made quality data

• Review • Better performance of organizational image HMIS.

• Support supervision

• Provision of data reporting forms

The conceptual framework in figure 2 above shows the linkages between the problem, actions that were taken to address the problem and outcomes. Data validation and cleaning was done, through conducting completeness checks, reasonableness checks, limit checks, review of the data to identify outliers, documenting and checking of suspect records. This l helped identify root causes of the errors and focus on preventing those errors from re­occurring. Training of health workers on job and support supervision went along way into improving completeness, accuracy, timeliness as well as reliability and validity of HIV/AIDS related data collected from the facilities.

Sensitization on the importance of data quality was aimed at improving attitudes of health workers towards data collection and management.

10 Reviewing HMIS performance helped identify gaps and thus come up with appropriate interventions that improved and informed decision making as well as motivation of staff. ,After project implementation there was improved ranking of the district in the National league Table increased demand for data for planning purposes, increase in budget allocation for the health department particularly from the locally raised revenue in the district.

11 2.0 General project Objective Improving quality of HIV/AIDS and related data in Mayuge District Health Department.

2.1 Specific Objectives • To improve completeness of inpatient reporting from 53% to 85% and outpatient reporting from 84% to 100% by April 2011. • To improve timeliness of data reporting from the District to Ministry of Health from 75% to 100% by April 2011 • To improve data accuracy by 50% by April 2011

12 3.0 Methodology

3.1 Meet project stakeholders A project stakeholders’ meeting was held at the District health office on 15 th Feb 2011 and was officiated by the DHO­Dr. Isiko K Paul. The fellows made a presentation and clarified on the roles to be played by the different stakeholders on the project. The DHO urged all stakeholders to embrace the project and to work as a team towards the success of the project. The objective of the meeting was to buy support from all stakeholders and ensuring that relevant stakeholders are brought on board before the actual start of the project. It was also aimed at explaining to the stakeholders the relevancy of the project and its justification for implementation in the district.

3.2 Training of 10 records Assistants in data collection and management using MOH tools

In order to achieve the data quality improvement project objectives, the records assistants being key stakeholders in the Health management information system had to be trained in data management. The activity was aimed at; • Providing the necessary skills to records assistants in the use of HMIS reporting tools (Outpatient and inpatient) reporting forms,

• To mentor records assistants on data extraction from the registers, including outpatient and inpatients registers.

• To remind records assistants of their roles and responsibilities as far as facility data and records are concerned.

The fellows and HMIS focal person organized two­day training. All records assistants in the district were contacted through the HMIS focal person and the Biostatistician to come and attend this training. The HMIS focal person, together with the M and E fellows, then facilitated the training. The participants were taken through the reasons for collecting accurate data, practical sessions of how to extract data from the different registers and

13 transferring it to the HMIS reporting forms. They were also made to know what a complete report looks like and skills on data verification and validation. They were also reminded of the HMIS reporting schedule.

Figure 3: Records assistants in a practical session on extracting data from registers on reporting forms

3.3 Photocopy of HMIS monthly reporting forms HMIS reporting forms 105 outpatient reporting form and 108­Inpatient reporting form were photocopied and supplied to the different health facilities since the lack of reporting forms had been identified as one of the obstacles to timely and complete facility reporting. Each health facility was given reporting forms enough for six months and three copies for each month.

3.4 On Job Mentoring The objective of this activity was to equip health workers with adequate skills to manage data.

14 Every month a team from the district health office would move to health facilities to mentor and coach health workers on data management issues. On reaching the facilities, the team would look at the registers, and the reports previously submitted to the district /health sub­district to identify gaps, and thereafter sit with the health workers to address these gaps.This exercise required a lot of time as many issues were discovered at the facilities that needed taking immediate corrective action. Some health workers even confessed lacking the skills to extract data from registers on the reporting forms.

3.5 Data collection Data collection is a routine activity for the department of health done to ensure that accurate data for planning, decision making and performance measurement is available in time. During the implementation of the data quality improvement project, every month records assistants at HSD, district or HC IIIs would be facilitated to move to health facilities to collect the monthly reports .The biostatistician/M and E fellow would make phone calls to facility in­charges to ask them whether the reports are ready for collection. On collection of the reports, the records assistants had to check for completeness of the forms to ensure all fields were filled. In the event that a report was incomplete, the records assistant would work with the facility staff to complete the missing fields. This made it possible to have complete reports of individual health facilities before submission to the next level.

3.6 Data validation and verification Data verification or validation is an exercise done to ensure that data is accurate before it is processed for use or entered into a database. Data verification is done to prevent errors that would occur when data is say copied from a register onto a reporting form. During the data verification exercises, the fellows would use a number of methods. These included visual checks where the fellows would check for errors by looking through the data in the different registers and health facility reports. This is somewhat similar to proof reading.

15 The fellows also employed a method similar to double keying in. The ideal would have been double keying in of the data by two different data entrants but because the was no software to be used for this, the two fellows had to enter say a facility report at different times and then compare the outputs. The fellows also could extract data from the same data source/register at different times, compare the reports each generated and lastly compared with the health facility reports for that period. This was done mostly to suspicious (records suspected to be with errors) records. Other methods included range checks to ensure that is within specified range, presence checks to ensure important data such as facility name, month of report exist and type checks to ensure data is of the right type for examples, texts and numbers.

3.7 Mid‐Term Review meeting A mid­term project review meeting was held to assess the progress made in implementing the project activities. The participants included project team members (DHT), M & E fellows­lead implementers, Health facility staff including records assistants.

Figure 3:A fellow presents at a mid­term project review meeting The fellows made a presentation on the activities implemented, achievements, challenges faced during implementation and reasons for that had led to the successful implementation of the project activities. One of the key areas that needed taking corrective action was handling data validation since it was costly to implement yet compared to the budget attached to it yet a good

16 sample of health facilities had to be taken to ensure validity of the findings. It was at this meeting that participants suggested that validation exercises continue with support from the district to cover up other costs.

3.8 Dissemination of project results The fellows at Imperial Royale hotel Kampala presented the results of the HIV/AIDS and related data quality improvement project on 7 th October 2011.

17 4.0 Project Outcomes A number of achievements have been made. The project team has been able to identify reasons for not and or late reporting by some facilities and these include; • Absenteeism from work by many records assistants • limited skills on how to extract data from the registers • Heavy workload especially at HC II level where in most cases one person operates a facility.

Secondly, health workers have been equipped with data management skills to collect data and this has increased on the number of facilities reporting and reduced incompleteness of reports. • Registered improvement in inpatient reporting

Figure:4: M & E fellow gives feedback to a nursing officer after a data verification exercise • There has also been significant improvement in ranking of the district in the national league table. In the FY 2009/2010 Mayuge was ranked 61 st with a score of 4 out possible 5 on the HMIS indicators of timeliness and completeness while in FY 2010/2011 the district was ranked 49 th moving up 12 places in the table having scored a score of 4.2 out of the possible 5.

• Completeness of OPD reporting improved from 84% to 92% in FY 2010/2011. Particularly during the project implementation period(Q3) FY 2010/2011 the district was ranked 45 th with a score of 10 out 10 on the HMIS completeness indicator out of 112 districts and became number 1 in the Busoga region.

18 Figure 5: comparison of completeness of reporting between implementation period(Jan­May )2011 and the same period in 2010

Figure 6: The graph shows timeliness of inpatient reporting for (Jan­May)2011­project period in comparison with Jan­May 2010

19 From the graph, its evident that there was also significant improvement in timeliness of inpatient report as compared to the period before the project.

Figure 7: working as a team to improve quality of data at Kigandalo HC IV • Working together with the health workers has improved on the relationship between the district health team and the health facility staff who now look at them as being supportive rather than inspectors. • Timeliness has also improved compared to how it was before the beginning of the project and many health workers are realizing the importance of data after seeing how much resources are being invested in it. Some of the facilities reached are Buwaiswa HC III, Mayuge HC III,Buwaaya HC II, Busuyi HC II, Namusenwa HCII, Nkombe HC II,Bukaleba HC II, Buyemba HC II, Butte HC II ,Ntinkalu HC II,Bwiwula HC II. This activity is still ongoing. • We were able to develop a timeliness tracking tool at distirct/Sub­district and a data entry screen for this tool in Epi info .(see attachments)

4.1 Lessons learned • During and after implementation of the project, we learnt that good quality data is a responsibility of all stakeholders from health managers to nurses and records assistants. In the past, it was believed that data management was work for records

20 assistants and whenever there were issues to do with quality of HMIS data, the blame would be put on records assistants.

• We also learnt that additional minimum resources towards data management and health management information systems could lead to significant improvements in data management and consequently improved services delivery. Resources in form of data management tools, funds for on job training, support supervision and human resources are very critical and therefore need to be availed in time • We further learnt that good performance is a very big staff motivator. After improvement in ranking of the district, everyone in the department felt his or her efforts were not wasted as indicators showed improvement and started discussing how to further improve. • As M &E fellows, we learnt that change is gradual and providing training and on­ site are paramount in causing a positive change. We realized that there is serous need to train and provide on job support to all cadres in the health sector in order to improve the quality of data. During the on job coaching and mentoring sessions, the facilitators used to spend a lot of time explaining issues that looked rather obvious. • Involving district health teams (DHT and DHMT) in monitoring ensures sustainability of project results. Since quality improvement is not a one off time activity, in order to ensure continuity of quality improvement, management support and buy in is very critical. This will ensure availability of required resources needed to accomplish improvement tasks. The project was implemented by the DHT as the project team so each member of the DHT had to present a few issues on this project in monthly DHT meetings enabling taking of corrective action where necessary.

21 Figure 8:DHO makes remarks at review meeting­stake holder involvement­critical

4.2 Challenges experienced and how they were overcome • There is few health staff that can mentor lower level staff on data so the few staff who are available had to move to facilities more times than planned.

• One of the notable challenges was the absence of health workers at the facilities. Sometimes we could meet facilities closed and the health workers reported to be in a workshop especially at health centre IIs where they are few health staff. • On Job coaching requires a lot of time since there are many issues /gaps that need to be addressed. At some health facilities we could spend five hours making it difficult to cover at least three facilities as earlier planned • Data validation exercises required more time and resources more than it had been budgeted for. This meant that a few health facilities had to be reached during project implementation and so the results on data accuracy could not be generalized to the entire district because of not having a representative sample. It was agreed that the district funds this activity to completion for better accuracy results.

22 Figure 9: Fellows on a HCT data verification exercise at Baitambogwe HC III

4.3 Summary • M & E has gained some popularity in the department and this will go a long way in improving service delivery. There is been great improvement in data management both at the district and facility level and records staff ensure that data is submitted on time. This has also improved performance nationally as reflected in the national league table. However since this a continuous activity, the district with continuous support from the fellows is determined to work towards continuous improvement in data quality • As fellows, we have learned how to design, implement and monitor a project. Having achieved all this, we can proudly say that the project objectives were achieved. 4.4 Conclusions • Data quality improvement is a continuous and gradual process that requires commitment of all stakes holders including top management and political leadership. • Improvement of health services delivery require quality data for planning and decision making and therefore there is serious and urgent need for districts and the government to invest more in training health managers and planners in monitoring and evaluation .

23 4.5 Recommendations • The district should facilitate CME’s at facility level to address data problems and these should be budgeted for in the facilities’ work plans. • The district should urgently fill the vacant posts at health facilities to reduce on the workload especially at centre IIs in order to improve service delivery. • Mak­SPH should give us an opportunity to host a long term fellow to help us streamline our system. • The health staffing structure for health workers at district be reviewed to provide for recruitment of statistical assistants at HC IV’s and HC III to manage data at those levels. • Data management be taught as a core unit at institutions for health proffessionals

5.0 Next steps

The districts has received computers from different implementing partners and a re now working towards developing electronic databases at HSD level. To ensure that this takes off, there will be training for records assistants in basic computer skills. This will be a rather slow process since many of them do not have an computer knowledge at all.

24 References 1. WHO(2003) Improving data quality; a guide for developing countries 2. Health care improvement project (2011) The validity of self assessment data in Ugandan quality improvement program 3. WHO(2011) Assessment of health facility data quality in Uganda 4. BMC health services research(2010) Evaluation of performance of routine information management PRISM framework 5. Larisse Terpeluk Moss(2005) How to improve data quality 6. WHO(2005) The international journal of public health; improvements to data systems for the health sector 7. Uganda ,ministry of health abstract,(2009)

25 7.0 Appendices

Table:2 Access database for completeness and timeliness of reporting

HMIStimeliness MONTH HEALTH DATE OF STATUS OF OFFICERR RECIEVED COMMENTSBYSUB NAMEOFHSD OF YEAR FACILITY REPORT REPORT EPORTING BY MITINGOFFICER REPORT BUNYA WEST BULUBA OCTOBER 11/10/2 2010 COMPLETE basalirwa kato late reporting still a HOSPITA 010 problem L BUNYA WEST BAITAM OCTOBER 11/5/20 2010 INCOMPLE Basalirwa KATO late reporting BOGWE 10 TE HC III BUNYA WEST BUTTE OCTOBER 11/12/2 2010 INCOMPLE basarirwa kato late reporting HC II 010 TE BUNYA WEST NAMUSE OCTOBER 11/8/20 2010 INCOMPLE Basalirwa Kato late reporting

26 HMIStimeliness MONTH HEALTH DATE OF STATUS OF OFFICERR RECIEVED COMMENTSBYSUB NAMEOFHSD OF YEAR FACILITY REPORT REPORT EPORTING BY MITINGOFFICER REPORT NWA HC 10 TE II BUNYA WEST WABULU OCTOBER 11/3/20 2010 INCOMPLE Basalirwa Kato late reporting NGU HC 10 TE III BUNYA WEST MAGAM OCTOBER 11/8/20 2010 INCOMPLE Basalirwa Kato late reporting AGA 10 TE BARRAC KS HC II BUNYA WEST NTINKAL OCTOBER 11/9/20 2010 INCOMPLE Basalirwa kato late reporting U HC II 10 TE BUNYA WEST BUSUYI OCTOBER 11/12/2 2010 INCOMPLE Basarirwa kato late reporting HC II 010 TE BUNYA WEST BUFULU OCTOBER 11/9/20 2010 COMPLETE basarirwa Kato late reporting BI HC II 10 BUNYA WEST NKOMBE OCTOBER 11/5/20 2010 INCOMPLE Basarirwa kato late reporting HC II 10 TE

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