January 2015

Recommended Citation: Government of Kenya. 2015. Addendum toKenya Health Sector Data Quality Assurance Protocol (2014) Data QualityReview: Guidelines for Conducting Data Quality Reviews at All Levels,Nairobi, Kenya: Ministry of Health, AfyaInfo Project.

These guidelines are aimed at standardizing data quality reviews at all levels and arecreated with assistance from the AfyaInfo project. AfyaInfo is a technical assistanceprogram to support the Government of Kenya to strengthen their health informationsystems. The program is implemented by Abt Associates, Inc. in partnership withTraining Resources Group, ICF International, the University of Oslo, Knowing Inc., theKenya Medical Training College, and the University of Nairobi. It is funded by theUnited States Agency for International Development (USAID), under the AIDSSupport and Technical Assistance Resources (AIDSTAR) Sector II IQC, contractnumber GHH-I-00-07-00064-00 AID-623-TO-11-00005, Kenya Health InformationSystem.

DISCLAIMER:The author’s views expressed in this publication do not necessarily reflect the views of the UnitedStates Agency for International Development or the United States Government.

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Table of Contents

List of Abbreviations ........................................................................................................................................ ii

Foreword........................................................................................................................................................... iii

Acknowledgements ......................................................................................................................................... iv

1. BACKGROUND .....................................................................................................................................5

1.1 Introduction............................................................................................................................................5

1.2 Data Quality Assurance (DQA) Protocol Background ................................................................5

1.3 Definition of Data Quality Review ....................................................................................................6

1.4 Data Quality Reviews: Purposes and Fora ......................................................................................6

1.5 Data Quality Dimensions: Operational Definitions......................................................................7

2. KEY STEPS FOR DATA QUALITY REVIEW PROCESS ................................................................9

2.1 Data Quality Improvement Teams...............................................................................................9

2.2 Data Quality Review Process........................................................................................................9

3. HOW TO IMPLEMENT THE DATA QUALITY REVIEW PROCESS AT DIFFERENT LEVELS

........................................................................................................................................................................... 12

3.1 Community Level .......................................................................................................................... 12

3.2 Facility Level ................................................................................................................................... 18

3.3 Sub County Level/County Level ................................................................................................ 23

3.4 County to Sub County Data Quality Reviews ....................................................................... 29

3.5 National Level ................................................................................................................................ 30

4. OUTCOMES OF DATA QUALITY REVIEWS .................................................................................. 34

ANNEXES ....................................................................................................................................................... 35

Annex 1: Community Health Data Monitoring Tool....................................................................... 36

Annex 2: Community Health Data Summary Report....................................................................... 39

Annex 3: Community Health Data Quality Improvement Plan...................................................... 41

Annex 4: Health Facility Data Monitoring Tool ................................................................................. 42

Annex 5: Health Facility Data Summary Report ................................................................................ 46

Annex 6: Health Facility Data Quality Improvement Plan ............................................................... 49

Annex 7: County/ Sub County Data Assessment Tools .................................................................. 50

Annex 8: Action Plans for Different levels .......................................................................................... 54

List of Tables

Table 1: Operational Definitions of Data Quality Dimensions ..............................................................7

List of Figures

Figure 1: Options For Conducting Data Quality Reviews- The Continuum.................................... 24

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List of Abbreviations

AOP Annual Operating Plan

ARV Antiretroviral

AWP Annual Work Plan

CHC Community Health Committee

CHEW Community Health Extension Worker

CHRIO County Health Records and Information Officer

CHV Community Health Volunteer

CQI Continuous Quality Improvement

SCASCO Sub County AIDS and STI’s Coordinator

DHIS District Health Information System

SCHMT Sub County Health Management Team

SCHRIO Sub County Health Records Information Officer

SCMLT Sub County Medical Laboratory Technician

SCMOH Sub County Medical Officer of Health

SCPHN Sub County Public Health Nurse

DQA Data Quality Assurance

DTLC District Tuberculosis and Leprosy Coordinator

EMR Electronic Medical Record

ERS Economic Recovery Strategy

FBOs Faith Based Organizations

FHMT Facility Health Management Team

HIS Health Information System

HMIS Health Management Information System

HMT Health Management Team

HRIO Health Records and Information Officer

ICT Information and Communications Technology

LLITNs Long-Lasting Insecticide Treated Nets

M & E Monitoring & Evaluation

MFL Master Facility List

MOH Ministry Of Health

RDQA Routine Data Quality Assurance

SOPs Standard Operating Procedures

TB Tuberculosis

TWG Technical Working Group

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Foreword

Data Quality Assurance is a valuable instrument in ensuring that health sector performance is

effectively and objectively monitored in order to appropriate resources efficiently. An

important aspect of data quality assurance is periodic review of data quality at all levels in order

to identify and address the contributing factors to suboptimal data quality. On many occasions

data quality reviews have been reported to have been done; however, these have not had a

standardization mechanisms for the same levels and therefore their outcome cannot be

comparatively analyzed.

This Data Quality Review package is a supplementary document to the Data Quality Assurance

Protocol (2014). It serves to give a general guide in conducting Data Quality Reviews at

different levels. It is expected that all levels will adopt and adapt it to suit their needs in

conducting data reviews forums, and resulting resolutions and actions will be documented and

acted upon in order to streamline data quality.

All stakeholders, faith based organizations, private sector, non-governmental organizations and

partners, training institutions and community are called up to support and collaborate in

ensuring effective utilization of the package.

The package is intended to be a living document that will be updated and used periodically by all

to guide reviews at various levels from the community to the national level in order to

strengthen data and data management processes for evidence based decision making, planning

and policy development.

Dr. Nicholas MuraguriDIRECTOR OF MEDICAL SERVICES

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Acknowledgements

The Ministry of Health wishes to acknowledge all who participated in the development of this

Data Quality Review Package

Special thanks and appreciation go to the Director of Medical Services (DMS), Dr. Nicholas

Muraguri for his leadership, encouragement and continued support during the development of

this protocol. We would also like to acknowledge USAID through the AfyaInfo project for the

financial and technical support.

Our sincere appreciations to all members of staff from MOH who provided critical

contributions and insights on practicality of the processes described. These included program M

& E officers, County and Sub County Health Records and Information Officers and facility staff..

We particularly acknowledge the contributions of the following HIS Unit staff: Dr. Martha

Muthami (MOH), Mr. Jeremiah Mumo (MOH), Ms. Nancy Amayo (MOH), Mr. Francis Gikunda

(MOH), Ms. Margaret Chiseka (MOH), Mr Patrick Warutere (MOH), Ms Esther Kathini

(MOH), Mr Boniface Isindu(MOH), Ms Jedida Obure(MOH), Mr Paul Malus(MOH)i, Ms Gladys

Echesa(MOH), Mr Robert Wathondu(MOH), County Teams- Ms Alice Kimani (Nairobi

County), Mr James Ondiga(Siaya County), Mr Julius Ominde(Kisumu County), Mr. James

Kuya(Busia County) Ms Jacinta Mbinyo (Machakos County), Mr Michael Ahomo (Migori

County) and Rosemary Mboin (Homa Bay County); as well as partners; Mr. Erastus Marugu

(AfyaInfo), Dr. Salome Ngata (AfyaInfo),) Maria Kamau (AfyaInfo), Joseph Warero (MSH),

Charles Kimani (URC), Akaco Ekirapa (PIMA) and Antony Irungu (CHS) and Rachael Muinde

(CHS).

We also thank all those whose names may have been inadvertently left out but who in one way

or another contributed to the development of the package.

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1. BACKGROUND

1.1 Introduction

This is an adjutant document to the Kenya Health Sector Data Quality Assurance (DQA)

Protocol (2014). It seeks to provide guidance to all levels – including Community, Facility,

Sub County/ County and National levels – on how to prepare for and implement Data

Quality Reviews, how to engage relevant stakeholders, and how to plan and execute any

follow-up actions. This document provides processes, tools and templates to guide the

Data Quality Review element of the DQA Protocol.

1.2 Data Quality Assurance (DQA) Protocol Background

High quality information is an important resource for the health sector in planning,

managing, delivering and monitoring high quality, safe, and reliable health care. Data are

the building blocks for information and have been described as “numbers, symbols,

words, and images, graphics that have yet to be organized or analyzed”1. Once data are

collated, analyzed and contextualized, they then become information that can be used.

Good quality information is dependent on good quality data. Within the health sector,

good quality data meets the needs of data users to support service delivery, quality

improvement, performance monitoring, and planning.

The different levels of the health system -- National, County, Sub County, facility, and the

community -- are ultimately responsible for the quality of the data they produce. Each

level should have defined procedures for periodic data quality reviews and should carry

out reviews and make modifications and improvement strategies based on the results of

these reviews in order to maximize the quality of their data.

The DQA Protocol was developed in 2013-2014 by the Ministry of Health Division of

Health Informatics, Monitoring and Evaluation as a tool to facilitate data quality

improvement strategies at all levels of health care. The DQA Protocol provides data

quality standards, roles and responsibilities, and identifies the structures and processes

that organizations should have in place to create a supportive environment for data

quality. As quality health information is dependent on quality data, it is therefore logical

that efforts to improve the information on which decisions are based will start with

1 International Review of Data Quality, April 2011 Health Information and Quality Authority, Dublin

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ensuring that data is collected, processed and analyzed appropriately.

The DQA Protocol outlines a framework to enable the collection, analysis, sharing and

use of good quality data to support the delivery of health services and to monitor

performance. It follows that each level should periodically review these processes and

the supportive framework to ensure that the appropriate corrective actions are

instituted. Among the key data quality assurance strategies identified in the DQA

protocol are;

Periodic data quality audits with an aim to:

o Assess the ability of data management and reporting systems to generate

quality data

o Verification of reported data against source data

Periodic data review meetings with stakeholders to communicate the status of

data quality and solicit for combined efforts

Regular data quality facilitative/supportive supervision by higher levels to data

collection and aggregation sites

Deployment of Data Quality Improvement Teams to champion processes and

structures to support generation of quality data.

1.3 Definition of Data Quality Review

A Data Quality Review is a systematic process of determining and communicating the

strengths and weaknesses of data and data management frameworks. To do this, those

involved in conducting data quality reviews must reference important data quality

dimensions (see section 1.5 below) and standards to determine the status of data quality.

Communication to relevant stakeholders on the status of data quality helps to improve

awareness and appreciation of key data quality issues and to solicit support and action

from all key players and stakeholders to maintain and improve data quality.

1.4 Data Quality Reviews: Purposes and Fora

The overall purpose of Data Quality Reviews is to:

Enhance information sharing

Compare targets for data quality dimensions with actual achievements

(performance on data quality dimensions)

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Provide feedback on data quality audits against set standards

Evaluate existing data management processes, structures and systems

Discuss and prioritize appropriate actions to improve data quality

Mobilize needed resources (internally or externally) to fulfil the agreed data

quality improvement actions.

Data Quality Review fora (meetings, workshops, conferences) should be held periodically

at each appropriate level (national, County/Sub County, facility, and community). The

frequency of these fora may be different depending on the level and identified needs.

This is described in more detail in Sections 2 and 3.

1.5 Data Quality Dimensions: Operational Definitions

Data quality reviews provide a process through which data quality is reviewed against

several standard data quality dimensions. Table 1 below provides the operational

definitions of the data quality dimensions, as outlined in the DQA Protocol, to be

assessed and discussed during the Data Quality Review process.

Table 1: Operational Definitions of Data Quality Dimensions

Data QualityDimension

Operational Definition

Accuracy Accuracy refers to the extent to which the data reflect the

actual/correct information. It defines validity of the data and is achieved

by minimizing errors from recording or interviewer bias and

transcription.

Completeness Completeness means that an information system from which the results

are derived is appropriately inclusive: it represents the complete list of

records (eligible persons, facilities, units) and the fields in each record

are provided appropriately.

Reliability Data are reliable if they are arguably complete and accurate, measure

the intended indicator and are consistent; not subject to

inappropriate alteration over time.

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Precision This means that the data have sufficient detail. For example, an indicator

requires the number of individuals who received HIV counseling &

testing and received their test results, by sex of the individual. In this

case, an information system lacks precision if it is not designed to

record the sex of the individual who received counseling and testing

Timeliness Data are timely when they are up-to-date (current), and when

the information is available on time. Timeliness is affected by:

(a)the rate at which the program’s information system is

updated;

(b) the rate of change of actual program activities; and

(c) when the information is actually used or required.

Integrity Data have integrity when the system used to generate them is protected

from deliberate bias or manipulation for political or personal reasons.

Confidentiality Confidentiality means that clients are assured that their data will

be maintained according to national and/or international standards for

data.

This means that personal data are not disclosed inappropriately, and that

data in hard copy and electronic form are treated with appropriate

levels of security (e.g. kept in locked cabinets and/or in password

protected files).Source: Kenya Health Sector Data Quality Assurance Protocol (2014)

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2. KEY STEPS FOR DATA QUALITY REVIEW PROCESS

This section outlines basic steps for the Data Quality Review process for all levels as

outlined in the Data Quality Assurance Protocol. Section 3 provides additional details on

how these steps are carried out at various levels. Various tools are provided in Annexes

for use at each level.

2.1 Data Quality Improvement Teams

It is expected that every level will constitute a Data Quality Improvement (DQI) Team

who will be charged with the responsibility of data quality assurance activities in their

respective level. They will oversee the implementation of the DQA Protocol, advocacy

for data quality, resource mobilization and monitoring and evaluation of the data quality

assurance and indicators and support supervision.

The specific roles of the DQI teams are to:

Familiarize themselves with the data management process of their respective level

and the also the continuum of data management from the facility/community to

the national level.

Identify specific areas for improvement

Define performance measurements

Develop and oversee the implementation of data quality improvement plans2

Monitor the implementation of the improvement plans

Liaison with other levels in the continuum of data quality management.

The proposed composition of DQI teams is outlined in the next chapter.

2.2 Data Quality Review Process

The following steps are to be applied in order to conduct a comprehensive data quality

review, regardless of level. The steps are further exemplified for the different levels in

the next chapter. The basic Data Quality Review process includes;

2 Data Quality Improvement Plans- these are plans to encompassing the problems identified with dataquality, suggested strategies and activities to address these problems together with resources required,timeframes and assigned responsible persons.

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I. Define data quality needs and monitoring approach – Define the

indicators, data sources, process and frequency for monitoring data quality

dimensions.

II. Analyze information environment - Gather, compile, and analyze any

relevant information about the contextual environment for the period under

review. For example, was there an outbreak of a certain disease? Were there

ongoing health campaigns? Were data collection tools available as appropriate?

III. Assess data quality - Evaluate data quality based on the quality dimensions.

The assessment results provide a basis for immediate remedial action (see IV).

IV. Assess data quality impact - From findings of step III above, conduct root

cause analysis to determine the causes that may have contributed to these

findings and determine the impact of the resultant quality of data on general

perception of data, decision making and general information use. This step

provides input to establish the data quality case for improvement, to gain

support for improving information quality, and to determine appropriate and

interventions for investments to ensure quality information resources.

V. Develop improvement plans – From the identified causes of poor data

quality, identify and prioritize data quality problems and develop specific

corrective actions for addressing them. This will constitute a Data Quality

Improvement Plan with identified resources, responsible persons and

timeframes. The complexity of the Data Quality Improvement Plan will be

determined by the issues to be addressed and the level of care. (For example,

the facility level may have a simple plan with fewer items as compared to the

county and the national level.)

VI. Correct current data errors where possible e.g. aggregation errors. In some

cases it would be advisable to leave the data as is to avoid introducing bias

and more errors- for example, if the register field have been left blank, it is

advisable not to attempt to fill these fields unless the primary source of

information is available.

VII. Implement and Monitor Data Quality Improvement Plan actions -

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monitor the implementation of the Data Quality Improvement Plans and

maintain improvements by standardizing, documenting, and scaling up

successful improvements.

VIII. Communicate actions and results - document and communicate to

various audiences, including stakeholders at all levels the status of data quality,

identified remedial actions, improvements made, and results of those

improvements.

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3. HOW TO IMPLEMENT THE DATA QUALITY REVIEWPROCESS AT DIFFERENT LEVELS

3.1 Community Level

Data Management at the Community Level

Data at community level is collected by Community Health Volunteers (CHVs) and

handed over to the Community Health Extension Worker (CHEW) who submits the

data to the primary health care facility (dispensary or health center). In cases where

there is no CHEW, the data is submitted directly by the CHVs to the facility. The

current accepted tools used to collect and manage the community data include:

MOH Form 513: Household register –- for recording household information

MOH Form 514: CHVs log book –-for recording key events e.g. referrals, deaths

etc.

MOH Form 515: CHEW summary tool –-for summarizing the data collected from

the CHVs. Data from this tool is directly entered into DHIS by 15th day of every

month.

MOH Form 516: Chalk board for presenting analyzed information to the

community

Description of a Data Quality Review at Community level

Data Quality Review at the community level comprises a process of examining data and

data management processes at the household and community level and communicating

the findings with the relevant stakeholders for support and action.

The focus of review includes the following:

Data quality dimensions including accuracy, completeness, timeliness of

submission, and others. The number of data quality dimensions to be examined

will depend on the time and available resources and the skills to conduct such a

review.

Data management processes including data collection, compilation, analysis,

transmission and dissemination and accompanying operation procedures.

The availability of data collection tools and complexity of using the tools.

The human resource capacity to collect and manage community health data.

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Purpose

The purpose of a community-level data quality review is to:

Review the previous quarter’s data reports

Report on the data collection environment and identify data management and

reporting challenges at the community level

Highlight the key achievements in the relevant quality data dimensions

Identify data quality challenges

Develop solutions and document them in an improvement action plan

Responsible Persons

A community level DQI team is responsible for conducting this process. The DQI team

comprises of the Link Facility3 In-charge, CHEW/Data focal person for the facility- HRIO

or Nurse, and clinical officer who is responsible for data within the link facility.

Periodicity

Data Quality Reviews at the community level will be conducted every quarter. The

suggested months for conducting the reviews are January, April, July and October to

coincide with the Performance Review cycle for the preceding quarter.

I. Preparatory Process

The DQI team will meet at the beginning of every quarter and agree on the quality

aspects to be monitored in the quarter. These aspects will be monitored throughout the

quarter and information entered at the end of every month. Among the issues to be

monitored are;

Dimensions of quality;

o Timeliness of Reporting-

o Completeness/Availability

o Accuracy of data – From CHVs log books/ registers to the summary tools and

from Summary tools to DHIS. For data accuracy 1-2 indicators are selected

for monitoring at each quarter, e.g. number of women referred for ANC,

number of births occurring in the home environment etc.

Other data management structures affecting data quality e.g. availability of CHVs,

3 Every community unit is linked to either a dispensary, health center or a county/ sub county hospital

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Tools, SOPs.

II. Data/ Information Collection for the Purposes of Review

The task of collecting this information is allocated to a select member of the DQI team..

The data/ information collected are recorded in the Community Health Data Monitoring

Tool (See Annex 1).

a) Assess Data Quality Dimensions

Availability of Reports

Each facility has several community units linked to it. The assessment

determines the proportion of community units whose reports are

expected against those that actually reported.

Timeliness of Reporting

Indicate when the reports were received against the pre-agreed

reporting date on the tool.

Data Accuracy

During each quarterly Data Quality Review process, the DQI team

selects 1-2 data indicators based on the services offered at the

community level (e.g., number of women referred for ANC, number

of households with toilets etc.).

The accuracy of the data is ascertained referencing it to other data

sources (such as, household register, facility records for number of

pregnant women referred for ANC etc.) (NB: Some data may be

difficult to ascertain accuracy without requiring significant resources or

effort, the community leaders can provide insights, for example, the chief

can ascertain the number of births or deaths)

Data Completeness

Review the data completeness of the data collection tool for the

select indicators i.e. CHVs log book.

Review summary tools and note any areas of incompleteness

Record the findings on the tool.

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b) Analyze the Data Collection Environment:

The DQI team gathers, compiles, and analyzes information about the

environment of the period under review. This includes determining what

data was to be collected and how frequently, who was collecting the data

(CHVs or CHEWS). Identify the data collection tools used and standard

operating procedures (SOPs) for data collection and transmission. In

addition, assess and determine the skills of the data collectors, and levels

of community awareness, response, and support. This assessment should

include the identification of any weaknesses or challenges that should be

addressed.

c) Determine the Consequences of the Data Quality

For each data quality issue identified, determine how the identified data

challenges may have contributed to any type of decisions made or to

outputs/outcomes. For example, did incomplete or late reporting lead to

the facility not ordering enough Long Lasting Insecticide Treated Nets

(LLITN) for pregnant women in the village? If data collection tools were

missing, how did this affect data collection?

III. Conduct the Stakeholders’ Data Quality Review Meeting

The stakeholder review meetings are held during the first month of a new quarter

and the period to be reviewed is the preceding quarter whose information was

collected throughout the quarter. The DQI team presents their findings in a

Community Health Data Summary Report (template provided in Annex 2). The

DQI team will lead a discussion to identify possible solutions.

a) Objectives of Stakeholder Review Meeting

The primary objectives of the stakeholders’ review are to:

Give feedback on the progress of implementation of action points from

the previous month’s review meeting.

Discuss the findings of the previous quarter.

Agree on an action plan/action points for the next quarter with assigned

responsibilities and timelines.

Identify the resources (personnel, funds, tools) required to implement the

action plan/action points. The resources may be drawn from the facility,

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community, county leadership or from partners.

Communicate to the community leaders and solicit support for the identified

needs to enhance facilitation of community activities and data collection.

The duration of the meeting should range between 2-3 hours to avoid exhausting

members of the community and CHVs and CHEWs who are mostly volunteers. The

venue of the review would be either within the facility or a rented venue in the

neighborhood.

b) Participants:

The DQI team, Facility in-charge and relevant facility staff (nurses, clinical officers,

etc.), public health officer, Sub county management staff, Community Health

Extension Workers (CHEWs), Community Health Workers (CHVs) and community

representatives (e.g. Chiefs and opinion leaders) where possible.

VI. Develop Data Quality Improvement Plan

During the meeting, the DQI team will identify the causes of poor data at the community

level and develop a list of remedial actions to address these problems. These will be

noted down in a Data Quality Improvement Plan and the DQI team will assign

relevant responsible persons, identify resources required, and set timelines for

completion of action points. Action points could include items such as having opinion

leaders create awareness on need for community to volunteer information through

barazas or announcements, or could include working with the CHVs, the CHEW, or the

facility to address the identified gaps in data quality. A template for Community Health

Data Quality Improvement Plan attached in Annex 3.

V. Communicate actions and results

The Data Quality Improvement Plan should include action points related to

communicating issues through the appropriate channels to relevant stakeholder groups.

For example, the chief may encourage the community to disclose information on

deliveries, deaths, or specific illnesses. The CHV may communicate to the facility through

the CHEW or Facility In-Charge on the need to avail tools, and give feedback on their

reported data.

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VI. Monitor Data Quality Improvement Plan

The Facility in-Charge or a delegated member of the DQI team will monitor the

implementation of the remedial actions. Challenges and bottlenecks should be reported

to the data quality improvement team regularly to allow for redress. The monitoring

results will be discussed during the following quarter’s review meeting.

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3.2 Facility Level

Data Management at the Facility Level

Data at the facility level are collected by various staff depending on their functions. The

clinicians, -doctors, clinical officers or nurses record history and findings in the patient

cards or files. Key data elements are transcribed from cards and files into registers. The

entries in the register are further summarized and recorded into the

summary/aggregation forms. The examples of registers and summaries in the facility are;

1. MOH Form 405: Antenatal (ANC) Register

2. MOH Form 512: Family Planning (FP) Register

3. MOH Form 511: Child Welfare Clinic (CWC) Register

4. MOH form 301: Inpatient (IP) Register

5. MOH Form 240: Laboratory (LAB) Register

6. MOH Form 204B: Outpatient Register Over 5 Years

7. MOH Form 333: Maternity (MAT) Register

8. MOH Form 204A: Outpatient Register Under 5 Years

9. MOH Form 510: Immunization Register

10. MOH Form 406: Post-Natal Care (PNC) Register

11. MOH Form 209: Radiology (X-Ray) Register

12. MOH Form 514: Community Health Workers Log Book

13. MOH Form 513: Household Register

14. MOH Form 268: Diagnostic Index Card

Summaries/Aggregation forms

1. MOH Form 711: Integrated for RH, HIV/AIDS, Malaria, TB AND Child

Nutrition

2. MOH Form 705A: Outpatient Under 5 Years

3. MOH Form 705B: Outpatient Over 5 Years

4. MOH Form 710: Immunization

5. MOH Form 515: CHEW Summary

The summaries are sent to the sub county office by the 5th day of each month for entry

into the DHIS. For larger facilities, the summaries are entered directly into the DHIS2

while the hard copies are sent to the Sub county office for filling.

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Description:

Data Quality Review at the facility level is a process of examining the data management

processes within the facilities as well as examining the data collected against the data

quality dimensions.

The focus of review is;

Data quality dimensions among them accuracy, completeness, timeliness of

submission of reports from the various departments as well submission to the

Sub county office. The number of dimensions to be examined will depend on the

time and available resources and the skills to conduct such a review. The

examination of the various data quality dimensions can be done on different

months on a rotational basis.

Data management processes including data collection, compilation, analysis,

transmission and dissemination and accompanying operation procedures.

The availability of data collection tools and complexity of using the tools.

The human resource capacity to collect and manage health facility data.

The infrastructure in place to support data collection, compilation, analysis,

transmissions and dissemination e.g. computers, EMRs, access to DHIS etc.

Purpose:

The purpose of data quality review at the facility level is to

Review previous month’s reports with regard to data quality dimensions

Report on data collection environment for the period under review

Identify areas of improvement.

Develop collective solutions to identified data management and reporting

challenges at the facility. Develop a data quality improvement plan based on the discussions.

Provide feedback to other staff on data quality issues requiring address.

Responsible Persons: The Facility In charge and Data Quality Improvement Team

comprised of the HRIO, Head of Department and Programmes, clinicians and other staff.

For small facilities, the teams can be comprised of 2-3 key persons in the facility.

Periodicity and timing; Facility-level Data Quality Reviews should be conducted

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monthly.

All the data for the preceding month is summarized and reported to the Sub

County by 5th of every Month.

Data assessment of the preceding month’s data shall be done during the 2nd week

of every month.

The data quality review meeting of the preceding month’s data shall be done

during the 3rd week of every month.

I. Preparatory Process

The team will at the beginning of every month select the 2-3 indicators on which to base

the review. The indicators will be selected randomly by the DQI team and may involve

any department or programme within the facility. Among the issues to be assessed are

data completeness, accuracy from the cards/files/registers/ summary tools and where

possible the DHIS. Timeliness for the submission of reports from the departments to the

central data collection points and submission of reports to the sub county office will also

be assessed. In addition, the data management processes within the facility will be

assessed.

II. Collect Data/Information for Review Purposes

The DQI team collects information on;

a) Assessment of Data Quality Dimensions

Timeliness of Reporting

Indicate when the reports were received from the service points against

the pre-agreed reporting date. Record this on the Facility Data Quality

Monitoring Tool in Annex 4

Data Accuracy

The DQI team manually cross-check the data summaries submitted to

the sub county against the registers and source documents including

patient files and cards.

This verification will inform the facility management of the accuracy of

recording and reporting at each service point.

Data Completeness

Review the availability of data from all service areas/departments.

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Review the availability of source documents.

Cross-check data summaries with source documents-registers, cards,

etc. to ensure that all of the expected variables have been entered.

Check the data fields in the source documents to ensure that all the

expected data elements have been entered- e.g. is age, sex, etc.

recorded?

b) Data Management Environment. The DQI team gathers, compiles, and

analyzes information about the data management environment of the period

under review. This includes determining who was handling data at different

points, skills and training needs, existence of tools and SOPs, etc.

c) Determine the Consequences of the Data Quality

For each data quality issue identified, the DQI examines the impact the

data quality may have had on any decisions made. For example, did

inaccurate data lead to a pharmacy order that did not match actual

demand? Did lack of tools affect quality of data? Did lack of a staff assigned

to handle data affect timeliness of reports? This assessment will help to

identify areas requiring improvements and investments.

The findings above are summarized in Health Facility Data Quality Report

(see Annex 5).

III. Conduct Stakeholders’ Data Quality Review Meeting - Once all of the

information has been collected, the data quality review meeting is held with all the

relevant staff and team members to discuss the findings. The duration of the

meeting will range from 2-3 hours.

a) Objectives

The objectives of the meeting are to:

Give feedback on the progress of implementation of action points

from the previous month’s review meeting

Discuss the findings of the current month

Develop an action plan with action points, assigned

responsibilities, and timelines

Identify the resources (personnel, funds, support from the

County/Sub county, etc.) required to implement the action

22

plan/action points

b) Participants: DQI Team, Facility in Charge, relevant facility staff (heads of

departments and units), Health Records and Information Officers (HRIOs), and

partners working to support the facility. Member(s) of the County Health

Management Team (CHMT) and Sub County Health Management Team

(SCHMT), as well as other partners will be invited periodically and on an as-

needed basis.

IV. Develop Data Quality Improvement Plan – During the Data Quality Review

meeting, the team also identifies the causes of poor data quality and corrective

measures to be taken. The team puts the corrective actions into a rolling action

plan, assigns responsible persons, identifies resources, and sets timelines. A

template for an action plan is attached in Annex 6.

V. Communicate actions and results – After the Data Quality Review meeting,

the Facility In Charge or other departmental In Charges will communicate the

findings of the preceding month to all staff in the facility. The staff will be called to

collective action while specific actions will be assigned to the specific relevant

staff. Staff will be encouraged to report any bottlenecks and give ideas for

improvement.

VI. Monitor the Data Quality Improvement Plan – The Facility In Charge, in

collaboration with the data quality improvement team, will develop a mechanism

to monitor the implementation of the action plan and improvement of data

quality/ data quality management systems. Bottlenecks to implementation (e.g.

resources) should be identified early and communicated to the Facility in Charge.

The monitoring results will be discussed during the following month’s review

meeting.

23

3.3 Sub County Level/County Level

Data Management at the Sub County/ County Level

Summarized data in the summary/aggregation forms from the facilities are received at the

Sub County in hard copy. The data should be received by 5th day of every month. The

data is entered into the DHIS2 by the Sub County HRIO. The hard copies are filed at the

sub county level. At the County level, usually no data is entered but rather the County is

able to see all the data entered into DHIS2 by the sub counties.

Description of Data Quality Review

At the sub county and county levels there are three opportunities for data quality

review: informal, semiformal and formal data quality reviews (Figure1) and the counties

should endeavor to adopt all of them as they answer to different needs.

The informal data quality review takes place during monthly submissions of reports.

Facility monthly reports are submitted to the sub county HRIO office not later than the

5th of the following month, per existing practices. Each sub county shall set aside a day on

which all facilities will submit their reports, during which the SCHRIO and assistants

analyze the individual reports by data quality elements for completeness and apparent

inconsistencies. Any quality gaps shall be highlighted immediately and communicated to

the facility for correction. This can be done either though mail, telephone or in person.

This feedback may be given before 15th of every month before the data is entered into

the DHIS.

The semiformal review should be done either by the SCHRIO or any other member

of the SCHMT who is responsible for data (e.g. Sub County AID and STI Coordinating

Officer (SCASCO), Reproductive Health Coordinators, TB coordinators). These reviews

can be periodic or ad hoc depending on the need. In this case, the SCHMT officer(s)

visits a facility to discuss any data quality issues, identify the causative factors, and develop

remedial actions. The issues can be identified during the informal data review at the end

of the month or may come as a report through other channels. Depending on the issue,

the SCHMT officer(s) may hold meetings with the facility’s Data Quality Improvement

Team or may also decide to hold the meetings with the persons doing the actual handling

of data.

24

The formal County/ Sub County data review forum will be a full-day meeting held

on a quarterly basis. The details for this formal review are outlined below. The County

DQI team may give feedback to the participants of the review on service indicators that

are of priority to the County/Sub County. The forum will also give facilities an

opportunity to share challenges related to data quality and service provision, share their

best practices, and develop solutions at the sub county level to address identified gaps.

Other updates such as supervision reports and changes in policy as regard to data can

also be shared during these formal data reviews.

Figure 1: Options for Conducting Data Quality Reviews at the County and

Sub County Levels - The Continuum

Informal

Monthly during the

reports submissionby the facilities

Should be done on

or before 5th ofevery month

Immediate feedback

is given to thefacility

Immediate action is

required from thefacility

Counties may give

before 15th of every

month before the

data is entered into

the DHIS.

Semi- Formal

Periodic or ad hoc

Comes as result ofan identified need

Mostly for

programme-specific data

Immediate

feedback is givento the facility

Immediate action

may be requiredor action plan with

timelines,

resources andresponsible

persons developed

Formal

Held on quarterly

basis Prior preparation

by the County/ Sub

County is done todetermine the

status of key

dimensions ofquality for the

quarter under

review Formal meeting /

forum with facility

reps and otherstakeholders

Should take a

minimum of oneday

Concrete action

plans are developedwith resources to

address the actions

identified andtimelines set

Feedback on past

25

improvement

efforts given

A selected topic isgiven special

emphasis during

each of the reviewmeetings

Formal County/ Sub County Data Review

Purpose:

The purpose of the review at this level is to;

Review previous quarter’s reports

Review the prevailing circumstances in data management (staff, tools, SOPs)

Identify data quality challenges and causes of poor data

Identify areas for improvement and develop solutions Develop a data quality improvement plan based on the discussion

Mobilize resources to address the identified actions

Provide feedback to other stakeholders, facilities, partners, community, and

county leadership.

Responsible Persons: County Director of Health, DQI team comprised of County/

Sub County Health Records Officer/ County/ Sub County programme coordinators,

health facilities in charges and partners where possible.

Periodicity: The formal data quality review at this level shall be conducted quarterly.

These are conducted in January, April, July and October in order to synchronize with the

performance review cycle.

I. Preparation

Each quarter, the DQI team meets to decide on the quality aspects/indicators to assess in

quarter under review. Depending on the availability of resources the team may conduct a

mini DQA or assess the data quality dimensions e.g. completeness, timeliness as well as

data management structures.

26

II. Data/Information collection for Review purposes

The DQI team oversees the collection of data/information which is collected

using the tool an adapted RDQA tool (Annex 7)

a) Assess Data Quality Dimensions

Timeliness of Reporting

All facilities are expected to report the Sub County by 5th of

every month

All data from the facilities is expected to have been entered into

the DHIS by 15th of very month

The data quality improvement team will report on the timeliness

of reporting for various facilities and also the various data sets

using the Sub County/County Data Monitoring tool in Annex.

Data Accuracy

In each quarter, it is expected that the County/ Sub County data

quality improvement team conducts a mini Data Quality Audit on

two or three indicators for a small sample of facilities. The

facilities selected should include a mix of public, FBOs/NGOs, and

private facilities. The data to be assessed will not be

communicated to the facility until the day of the exercise

Accuracy of data will be determined through manually cross

checking the DHIS data against summary reports, facility registers,

and source documents including patients’ files and cards

The county data quality improvement team will prepare a brief

report on data accuracy during the quarterly data review meeting

Data Completeness

Review the availability of data from all facilities

Compare the services offered by the facilities as indicated in the

Master Facility List (MFL) with the facilities that are actually

reporting on the given services

27

Identify the non-reporting facilities

Assess the completeness of reported data sets for the reporting facilities

Integrity of data

The assessment will be conducted randomly or targeted to any

suspected cases of data manipulation4.

A report on the integrity of data will be compiled

Data Confidentiality

Problems with data confidentiality will also be reported during the

review. When the CHMT/SCHMT/ data quality improvement teams

visits facilities, they should randomly check on the storage of patient

cards and files, examine how exposed registers are at service points,

and inquire from the in charges, staff about cases of unauthorized

data disclosure. Any instances of inadequate data confidentiality

should be recorded and discussed during the quarterly review (and

should be addressed immediately on the spot with facility staff as they

are identified).

b) Data/ Information collection environment

Before the quarterly review, the Sub County/County Data quality improvement

team gathers, compiles, and analyzes the data collection environment for the

period under review. This includes determining the availability of SOPs, tools,

human resources, skills and training needs. The team is expected to have

gathered data on the quality dimensions of various data sets for review during

the forum.

c) Determine the Consequences of the Data Quality

For each data quality issue identified, determine how the identified data challenges

may have contributed to the any type of decisions made or to outputs/outcomes.

For example, did incomplete or late reporting lead to some facilities not ordering

or receiving vaccines, leading to stock outs? What improvements in data quality

are required to facilitate data-based decision-making at these levels? Are there

4 There have been incidences where the denominators are interfered with so as to give favorable results

especially on reporting rates.

28

adequate data management staff? This assessment helps to identify the areas

requiring improvements and investments.

III. Conduct Stakeholders’ Data Review Meeting:

The data review forum at the Sub county/ county level should take a minimum of one

day.

a) Objectives

The objectives of the forum are to:

Give feedback to the stakeholders on the progress of implementation of

action points from the previous quarter’s review meeting

To discuss the findings of the current quarter

Identify challenges and causes of poor data quality and develop solutions

Develop an action plan with action points to address the identified

challenges with assigned responsibilities, resources and timelines.

b) Participants: County Executive Committee (CEC) Member for Health,

members of the CHMT/SCHMT, Facility In Charges and relevant facility staff

(heads of departments and units, HRIOs), representatives from faith-based

organizations (FBOs), non-governmental organizations (NGOs), private health

facilities, the community, and partners (donors and implementing partners),

other sectors (e.g. water, agriculture, education) where necessary.

IV. Develop Data Quality Action Plan – One of the deliverables of the meeting

is an action plan with timelines, resources, and assigned responsibilities. The

action plan is cascaded to the facilities and feeds into the facilities’ action plans. A

template for the action plan is attached in Annex 8.

V. Communicate actions and results – A summary of the data review meeting

will be communicated to the relevant players including the facilities, stakeholders,

community and Sub County and County leadership. This shall be done either

through memos, meetings, emails or any other relevant media. This is to solicit

for support and a build a spirit of collaboration. Stakeholders at all levels will be

encouraged to report any bottlenecks and give ideas for improvement.

VI. Monitor Data Quality Action Plan – The DQI team monitor the

implementation of the action plan and improvement of data quality and data

29

management systems. Bottlenecks to implementation (such as limited financial or

human resources) should be identified early and communicated to the Sub

County/County leadership. The monitoring results will be discussed during the

next quarter’s review meeting.

3.4 County to Sub County Data Quality Reviews

A 2014 country-wide Data Quality Audit5found that only 37% of data in the Data

Aggregation Form summary sheets matched with data in the DHIS2. Based on this

finding, it is imperative that counties begin to review the data entered into DHIS2 against

the data in the (hard copy) summary sheets. This can be completed through County-to-

sub county Data Quality Reviews. The Counties will conduct special reviews on DHIS2

data in collaboration with sub counties and partners. This will be done quarterly or on an

ad hoc basis where the CHMT will request for summaries from the SCHMT and

compare the data entered into the DHIS with the data reported on the summary sheets.

Immediate feedback will be given by the County DQI team to the relevant officers and

actions for improvement identified, documented, and monitored.

5 DQA Report 2014, Ministry of Health, Nairobi, Kenya

30

3.5 National Level

Data Management at the National Level

Most health service delivery data is collected at the facility level and entered into the

DHIS2 at the Sub county level. It is then accessible at the National level. The national

level may conduct more detailed analysis and give feedback to the lower levels for

reporting and planning purposes.

Description of Data Quality Review

The health sector’s national level mandate is primarily policy development, provision of

updated and relevant guidelines, and capacity building. As such, the data quality reviews at

the national level will focus largely on determining whether data correctly respond to the

different health sector indicators is being collected, on streamlining data collection tools,

and also providing general feedback to a wide variety of stakeholders on the status of

data quality for the country. To do so, in addition to reviewing the quality dimensions

reviewed by the county and sub county levels, national level data quality reviews will also

focus on data quality dimensions of data precision and reliability as well as the other

quality dimensions.

National level data quality reviews should be conducted twice biannually. However,

programmatic data reviews (i.e. programme specific data e.g. HIV/AIDS, Malaria etc.) can

be held periodically based on need or as per programmatic plans.

Purpose:

The purpose of data quality review at the national level is to;

Review the quality of data collected by the different data collection mechanisms

Determine the suitability of the data in meeting the country policy and planning

needs and reporting obligations

Assess the suitability of the data collection tools and existing data management

structures in the continuum of data collection, aggregation, transmission, analysis,

dissemination and use.

Objectives

The primary objectives of the review are:

31

Review the data in general against the data quality dimensions with special

emphasis on reliability and precision.

Examine the data collection tools to determine their suitability in collecting the

required data.

Examine the existing data management systems for suitability, enhancement,

integration and linkages and recommend appropriate actions.

Identify key data quality management challenges and recommend the appropriate

actions.

Develop an action plan based on the discussion.

Responsible Persons: Division of Health Informatics and Monitoring/Evaluation/

National level data quality improvement team comprised of DivHIM/E, programmes and

partners.

Periodicity: Data Quality Reviews should be conducted biannually. A data quality

review meeting will focus on issues identified during the course of the review period that

have a bearing on data quality, including data collection tools, indicator definitions, skills,

data collection systems and other resources.

I. Preparation

The DQI team will hold meetings to decide the quality aspects to be reviewed. These

include:

Data quality dimensions with special emphasis on data reliability and precision.

Data management structures, systems and resources

Data collection tools

Status of data use

Data quality reviews will be held in March and September.

II. Data/ Information Collection for the Purposes of Review

The DQI team oversees the collection of the following data aspects.

a) Data quality

Determine the general status of reporting rates, timeliness, accuracy,

completeness, integrity, and confidentiality. This will be complied from

32

the DHIS reports and data quality review reports submitted from the

counties and periodic data quality audits.

Assess data precision and reliability for select key programmes (especially

those whose data are key in monitoring the performance of the sector).

Asses the general understanding of the indicator definitions among those

collecting, aggregating and using data. These can be done through

interviews and assessments.

Examine data collection tools for availability, standardization, and usability

in collecting the data.

Examine the existing data management processes and systems and their

ability to produce quality data (denominators for calculating the rates, in

built formulas etc.)

I. Data/information environment

Engaging expertise with technical knowledge is crucial in isolating data dimensions

shortcomings- timeliness, completeness, accuracy etc. with probable contributing

factors to the findings associated with each dimension. For example, unclear

indicators definition may lead to inaccuracy or incompleteness of data. Special

emphasis will be given to the reliability and precision dimensions as these may not

be routinely assessed in the lower levels. Participation of different programs is

crucial in providing guidance in these dimensions and shedding light on their exact

data needs. In addition assessment of the suitability and usability of the data

collection tools should be done beforehand to inform future revisions and short

term measures. The national DQI team should also analyze general data

management structures, processes, and systems to determine areas of weakness

as well as areas with good performance.

II. Data quality impact

For each data quality issue identified, determine how the identified data challenges

may have contributed to any type of decisions made or to ability to utilize

information effectively. This assessment will be conducted through interviews

with key stakeholders. For example, if data collection systems are non-

communicating, how has this affected monitoring of key health sector indicators?

33

III. Conduct Stakeholders’ Data Quality Review Meeting

a) Objectives

The objective of the meeting will be to

To give feedback on the status of the performance of the various data quality

dimensions

To identify the systematic factors contributing to poor data quality

Develop action points/plan with assigned responsibilities and timelines.

Identify the resources required (personnel, funds) and support from

stakeholders and solicit collective responsibility.

The data quality review meeting will be held to present:

A general report on the timeliness of reporting, accuracy of data based on

DQAs (programmatic or comprehensive); data completeness including

reporting rates for all the facilities in the MFL, data integrity, and

confidentiality.

A report on data precision and reliability for select key programmes

especially those whose data are key in monitoring the performance of the

sector.

Indicator definitions and their relationship to data quality (denominators,

formulas, etc.)

Data collection tools (availability, standardization, usability)

Data management processes and systems and their ability to produce quality

data

b. Participants: MoH leadership, Counties leadership, programmes, partners

(donors and implementing partners), FBOs/NGOs, private facilities, training

institutions, representatives from the technology sector, and other relevant

government institutions

IV. Develop Data Quality Improvement Plans – One of the key deliverables of

the national data quality review will be an aim to generate consensus on various

issues contributing to poor data quality. This will lead to the identification of action

points which can then be incorporated in the wider sector plans (e.g. DiVHIM/E

plans, Health Sector Plans) or short-term remedial actions e.g. Data Quality

Improvement plan.

34

V. Communicate actions and results – The DQI team will communicate resolutions

of the data quality review meeting to the relevant stakeholders through various

mechanisms such as memoranda, directives, assignments, etc.

VI. Monitor the Data Quality Improvement Plans – Proper monitoring and

evaluation mechanisms will be instituted at various levels to monitor progress and

evaluate the results of implementing the Data Quality Improvement Plan. These can

either be stand alone or integrated within the existing health sector monitoring and

evaluation structures and mechanisms.

4. OUTCOMES OF DATA QUALITY REVIEWS

It is expected that carrying out periodically at all level will progressively improve the data

quality which will in turn improve data use which again leads to improved quality.

Engaging stakeholders in dialogue is also expected to foster collaboration and consolidate

support and resources to improve data quality. Engagement of stakeholders also ensures

that the needs are articulated and refined so that the data management systems only

collect data that is of relevance to the stakeholders.

35

ANNEXES

Annex 1: Community Health Data Monitoring Tool

Link Facility-Name: MFL Code:

Month: Quarter: Year:

Indicator Name for Review:

Name of Community Unit MCUL Code Presence of CHVReport

received

ReportReceived by2nd of the

monthReport

Complete

%(Estimate)

DataAccurate/Source VsSummary

Tool

% (Estimate)Data Accurate

Summary tool VsDHIS2

Yes NoYes No Yes No Yes No % % % %

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

a) How many reports should there have been from all the Community Units? [A]

b)How many reports are there? [B]

C)Calculate % Available Reports [B/A]

d)How many reports were received on time? [C]

e)Calculate % On time Reports [C/A]

f)

How many reports were complete? (i.e., complete means that the report contained all the required fields). [D]

g)Calculate % Complete Reports [D/A]

Qualitative Findings

Tools-

Primary Tools-Were tools available to every CHV?

What proportion of CHVs did not have the Primary Tools?

Explain the reasons for missing tools where applicable

Summary Tools- Were the summary tools available in the facility? How often were the summary tools not available?Explain the reason for unavailability.

Any other comments on the tools

Human Capacity

Are the CHVs trained on data collection? If so , what is the proportion of those trained?

Do the CHVs understand what they are supposed to do? Explain (Information gathered from the CHVs)

Do they have SOPs? What proportion of CHVs has SOPs? Are they using them? (Information gathered from the CHVs)

How do the CHVs feel about the data collection work? Explain (Information gathered from the CHVs)

Any other comments?

Consequences of Data Quality Issues

Outline any issues that may have arisen due to problems surrounding quality; for example, low reporting due to lack of tools etc.

Annex 2: Community Health Data Summary Report

Item Performance

% Community units with CHVs

% Reports Available

% Reports Complete

% Reports Accurate

Qualitative findings

Tools

Human Capacity/skills

Any Other finding

Consequences of Data Quality

Issues

Link Facility-Name: MFL Code:

Month: Quarter: Year:

Indicator Name for Review:

Status of Implementation of

Improvement activities identified

last quarter

Activity 1:

Activity 2:

Activity 3:

Activity 4

Annex 3: Community Health Data Quality Improvement Plan

Link Facility-Name MFL Code

Month Quarter Year

Activity Responsibility Timeframe Resources Required

Activity 1

Activity 2

Activity 3

Activity 4

Activity 5

Activity 6

Activity 7

Annex 4: Health Facility Data Monitoring Tool

Annex 4: Health facility Data Monitoring ToolFacility-Name MFL Code

Month QuarterYear

Indicator under review

Department/ Programme Reportreceived

ReportReceived at

the facility by2nd of the

monthReport

Complete

% (Estimate)Data

Accurate/Source VsSummary

Tool

% (Estimate)Data

AccurateSummary

tool Vs DHIS

AggregateReports

submitted by5

thto Sub

County Office

Yes No Yes No Yes No % % % % Yes No

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

a)How many reports should there have been from all the Departments/Programmes[A]

b)How many reports are there? [B]

C)

Calculate % Available Reports [B/A]

d)How many reports were received on time? [C]

e)Calculate % On time Reports [C/A]

f)

How many reports were complete? (i.e., complete means that the report contained all the required fields). [D]

g)Calculate % Complete Reports [D/A]

Summary/Aggregate Form SummaryAvailable

SummaryCompiled by4

thof the

MonthSummaryComplete

% (Estimate)DataAccurate/Summary/Sourcedocument

% (Estimate)DataAccurateSummarytool Vs DHIS

AggregateReportssubmitted by5

thto Sub

County Office

Aggregateentered intoDHIS by 10thwhereapplicable

Yes No Yes No % % % % Yes No Yes No

1

2

3

4

5

6

7

8

9

10

a)How many aggregates/summaries are expected? [E]

b)How many are there? [F]

C)Calculate % Available Summaries/ Aggregation forms [F/E]

d)How many reports were compiled on time? [G]

e)Calculate % On time Summaries/Aggregates [G/F]

f)How many Summaries were complete? (i.e., complete means that the Summary contained all the required fields). [H]

g)Calculate % Complete Reports [H/F]

I)How many have been submitted to the County office by 4

thof the month [I]

j)Calculate % on time submission[I/F]

k)How many have been entered into the DHIS2 where applicable by 10

thof the Month[J]

l)Calculate % on time entry into DHIS submission[J/F]

Qualitative Findings

Tools-

Primary Tools-Were tools available to every data collection point? What proportion of data collection points did nothave tools? Explain the reasons for unavailability of tools

Summary Tools- Were the summary tools available? What proportions of summary tools were unavailable? Explainthe reasons for unavailability

Any other comments on the tools

Human Capacity

Are the health workers trained on data collection? If so how many?

Do the CHVs understand what they are supposed to do? Explain

Do they have SOPs? Are they using them?

Are they motivated to work? Explain

Any other comments on the tools

Consequences of Data Quality Issues

Outline any issues that may have arisen due to problems surrounding quality; for example, low reporting due to lack of tools etc.

Annex 5: Health Facility Data Summary Report

Item Performance

% Reports Available

% Reports Timely

% Reports Complete

% Reports Accurate

% Summaries Available

% Summaries Complete

% Summaries Accurate

% Summaries Compiled on time

% Summaries Submitted on time

% Reports entered Into DHIS2 on

time

Qualitative findings

Tools

Human Capacity/skills

Facility-Name MFL Code

Month QuarterYear

Indicator under review

Any Other finding

Consequences of Data Quality Issues

Status of Implementation of

Improvement activities identified last

quarter

Activity 1:

Activity 2:

Activity 3:

Activity 4

Annex 6: Health Facility Data Quality Improvement Plan

Facility-Name MFL Code

Month Quarter Year

Activity Responsibility Timeframe Resources

Activity 1

Activity 2

Activity 3

Activity 4

Activity 5

Activity 6

Activity 7

Annex 7: County/ Sub County Data Assessment Tools

(Adopted from RDQA tools)

Part 1: Data Verifications

A - Recounting reported Results:

Recount results from the periodic reports sent from service sites to the Sub County and compare to the value reported by the Sub County.

Explain discrepancies (if any).

B - Reporting Performance:

Review availability, completeness, and timeliness of reports from all Service Delivery Sites. How many reports should there have been from all

Sites? How many are there? Were they received on time? Are they complete?

No. Indicators Response5. How many reports should there have been from all service sites? [A]

6. How many reports are there? [B]

7. Calculate % Available Reports [B/A]

8. Check the dates on the reports received. How many reports were received on time? (i.e., received bythe due date). [C]

9. Calculate % On time Reports [C/A]

10. How many reports were complete? (i.e., complete means that the report contained all the required

No Indicators Response1. Re-aggregate the numbers from the reports received from all Service Delivery Points. What is the re-

aggregated number? [A]

2. What aggregated result was contained in the summary report prepared by the Sub County (and submittedto the next reporting level)? [B]

3. Calculate the ratio of recounted to reported numbers. [A/B]

4. What are the reasons for the discrepancy (if any) observed (i.e., data entry errors, arithmetic errors,missing source documents, other)? If unknown, state “unknown”.

indicator data). [D]

11. Calculate % Complete Reports [D/A]

Part 2. Systems Assessment

I - M&E Structure, Functions and Capabilities

No Indicators Response1. There are designated staffs responsible for reviewing the quality of data (i.e., accuracy, completeness and

timeliness) received from sub-reporting levels (e.g., service points).2. There are designated staff responsible for reviewing aggregated numbers prior to submission to the

next level (e.g., to the central M&E Unit).3. All relevant staff have received training on the data management processes and tools.

II- Indicator Definitions and Reporting Guidelines

The M&E Unit has provided written guidelines to each sub-reporting level on …

No Indicators Response

4 ,,, what they are supposed to report on.

5 … how (e.g., in what specific format) reports are to be submitted.

6 … to whom the reports should be submitted.

7 … when the reports are due.

III- Data- collection and Reporting Forms / Tools

No Indicator Response

8Clear instructions have been provided by the M&E Unit on how to complete

the data collection and reporting forms/tools.

9The M&E Unit has identified standard reporting forms/tools to be used by all

reporting levels

10….The standard forms/tools are consistently used by the Service Delivery

Site.

11All source documents and reporting forms relevant for measuring the

indicator(s) are available for auditing purposes (including dated print-outs incase of computerized system).

IV- Data Management Processes

No. Indicator Response

12Feedback is systematically provided to all service points on the quality of their

reporting (i.e., accuracy, completeness and timeliness).

13If applicable, there are quality controls in place for when data from paper-

based forms are entered into a computer (e.g., double entry, post-data entryverification, etc).

14If applicable, there is a written back-up procedure for when data entry or

data processing is computerized.

15If yes, the latest date of back-up is appropriate given the frequency of update

of the computerized system (e.g., back-ups are weekly or monthly).

16Relevant personal data are maintained according to national or international

confidentiality guidelines.

17

The recording and reporting system avoids double counting people withinand across Service Delivery Points (e.g., a person receiving the same servicetwice in a reporting period, a person registered as receiving the same service intwo different locations, etc).

18The reporting system enables the identification and recording of a "drop out",

a person "lost to follow-up" and a person who died.

19There is a written procedure to address late, incomplete, inaccurate and

missing reports; including following-up with service points on data quality issues.

20If data discrepancies have been uncovered in reports from service points, the

Intermediate Aggregation Levels (e.g., Sub county or Counties) havedocumented how these inconsistencies have been resolved.

V - Links with HIS Reporting System

21When applicable, the data are reported through a single channel of the HISreporting system.(DHIS2)

22When available, the relevant HIS tools are used for data-collection andreporting.

23The system records information about where the service is delivered (i.e.County, Sub County, ward, etc.)

24 ….if yes, place names are recorded using standardized naming conventions.

Part 3: Recommendations for the Service Site

Based on the findings of the systems’ review and data verification at the service site, please describe any challenges to data quality identified and

recommended strengthening measures, with an estimate of the length of time the improvement measure could take. These will be discussed

with the Program.

No. Identified Weaknesses Description of Action Point Responsible(s) Time Line

1

2

3

4

Annex 8: Action Plans for Different levels

Planning Unit (National, County, SubCounty, Facility/ CommunityContact PersonTelephone NumberProgram Area:Relevant Indicators:Review Date:

Findings Recommended Action Responsible

Person

Timeline Resources

Required

55