Identification Factors of Poor Quality of Data in the DOTS Program

Bahtera B. D. Purba, Anggi Pramono Siregar, Cristica I. Surbakti, Bunga Rimta Barus

Faculty of Public Health and Faculty Of Pharmacy, Insitut Kesehatan DELI HUSADA

[Bahterabd, bungarimtabarus, christica, anggipramono95]@delihusada.ac.id

Keyword: Tuberculosis, Quality of Data, DOTS Program, Information Systems

Abstract: At Deli serdang, Indonesia, in 2016, 52% of the DOTS data quality was in the bad category and did not

show changes since 2013. This study aims to determine the factor of poor quality of data in the DOTS

program. Participants in this study were DOTS officers from 34 Puskesmas in Deli Serdang and 16

Puskesmas in Serdang Badagai to 50 respondents. The study was conducted by descriptive analytic method

with Cross Sectional approach. The research instrument has been tested for validity and reliability at a

confidence level α = 0.05. Data were analyzed using logistic regression at a confidence level α = 0.05. The

results of the research showed that there was a affect of the behavioral (p = 0,000; p <0.05), organizational

(p = 0.018; p <0.05), and technical determinant (P = 0.006; P> 0, 05) with DOTS data quality in Deli

Serdang. It is suggested to head of the Puskesmas to conduct data management training to DOTS staff of the

health center regularly (minimum 1 time a year.

1 INTRODUCTION

In Deli Serdang Regency, Indonesia, in 2016 52% of

DOTS data (directly observed treatment Short

course) programs were classified as poor (Purba,

2018). Poor quality of data is accompanied by an

increase in TB cases (tuberculosis) from 1156 cases

in 2015 to 1336 in 2016 (Purba, 2018). This increase

has an impact on TB cases of treatment failure and

MDR-TB cases (Akil, 2017).

Data quality is essential in the TB surveillance

system. In many cases, the quality of health data and

information is only considered as a responsibility in

the organizational structure. This situation leads to

the assumption of organizations and individuals that

the data is not useful and biased. Data is only

considered as a document that contains the

achievement of programs that are written quite well

(Hartati, 2016).

The data quality methodology builds on existing

data quality assurance mechanisms. The

methodology and indicators have been developed

and selected on the basis of broad consultation with

international health programs experts from leading

donor and technical assistance agencies. It is

expected that individual health and disease programs

will use the findings of a completed DQ to inform

their respective detailed assessments of data quality

and program-specific information systems. The goal

of the DQ is to contribute to the improvement of the

quality of data used by stakeholder for reviews of

progress and performance – such as annual health

sector reviews, program planning, and monitoring

and evaluation – in order to facilitate decision-

making (Purba, 2017)

These conditions cause a gap between the

existing information and reality. If this data is the

only source of information used for decision

making, a decision which cannot be accounted for

will be produced. The results of research in

Nicaragua illustrate the quality of data in the district

health information system reaching only 33%. Poor

quality of data is also found in health information

systems in Indonesia, which is 43.6%. Hartati's

research results in Loksamawe district found that the

quality of the data in the SP2TP report only reached

27.6%.

According to Lafound, there are three

determinant factors that affect the quality of data in a

health information system that is organizational,

technical and behavioral determinants..

Organizational determinants reflect TB program

information management. Technical determinants

concern technical factors such as finance, human

resources, and technology. Whereas behavioral

determinants concern the skills and motivation of

TB data recording and reporting officers at the

puskesmas.

Purba, B., Siregar, A., Surbakti, C. and Barus, B.

Identiﬁcation Factors of Poor Quality of Data in the DOTS Program.

DOI: 10.5220/0009463400580065

In Proceedings of the International Conference on Health Informatics and Medical Application Technology (ICHIMAT 2019), pages 58-65

ISBN: 978-989-758-460-2

Organizational determinants reflect TB program

information management. Technical determinants

concern technical factors such as finance, human

resources, and technology. While behavioral

determinants concern the skills and motivation of

TB data recording and reporting officers at

puskesmas.

If the organizational, technical and behavioral

determinants are able to improve the quality of the

data, then the three determinants are thought to be

able to improve the quality of the DOTS data

program. If the quality of the DOTS data program

can be improved, then the TB data surveillance

system can be strengthened. If this hypothesis is

tested the factors causing poor quality of data can be

identified.

2 METHOD

This research uses descriptive analytic method with

Cross Sectional approach conducted in Puskesmas in

Deli Serdang and Serdang Bedagai at 50 Puskesmas.

Research sample set by using a minimal sampling

formula with probability of DOTs Program data with

a bad category of 0.23 (p = 0.23).

Figure 1: Establishing key information of

puskesmas.

Information from 50 DOTs officers was

collected with inclusion criteria: work experience ≤

1 year and exclusion criteria: refusing to participate,

and unable. Data was collected using a questionnaire

that was compiled based on external validity, face

validity, construct validity and reliability. The

questionnaire used in this study was first tested by

researchers in populations that have almost the same

characteristics in different places.

Validity and reliability tests were performed on 3

puskesmas in Deli Serdang and 2 puskesmas in

Serdang Badagai. Validity test is done by using Item

Corrected Correlation (ICC) at the real level α =

0.05 and reliability testing using Cronbach's Alpha

method, namely analyzing the reliability of

measuring instruments from one measurement with

the provisions if the value of r Alpha> r table, then

declared reliable.

The data analysis method is carried out in three

stages, namely univariate, bivariate, and multivariate

analysis. Bivariate analysis is used to see the

relationship between the independent variable and

the dependent variable independently with Chi-

square (X2) at the real level α = 0.05. While

multivariate analysis is used to determine the

relationship between independent and dependent

variables together using multiple regression tests at

the real level α = 0.05. A statistical decision is stated

on starting Ho if p <0.05.

The desk review examines data quality across

four dimensions: completeness, internal consistency,

external comparisons and external consistency of

population data. Further, the desk review examines a

core set of tracer indicators selected across program

areas in relation to these dimensions. The desk

review requires monthly or quarterly data by

subnational administrative area for the most recent

reporting year and annual aggregated data for the

selected indicators for the last three reporting years.

This cross-cutting analysis of the recommended

program indicators across quality dimensions

quantifies problems of data completeness, accuracy

and consistency according to individual program

areas but also provides valuable information on the

overall adequacy of health-facility data to support

planning and annual monitoring. WHO recommends

that the desk review component of the DQ be Desk

conducted annually. The desk review compares the

performance of the country information system with

recommended benchmarks for quality, and flags for

further review any subnational administrative units

which fail to attain the benchmark. User-defined

benchmarks can be established at the discretion of

assessment planners (Purba, 2017).

3 RESULTS

This research was conducted in Deli Serdang and

Serdang Bedagai in North Sumatra Province. The

number of samples in this study were 34 puskesmas

at Deli Serdang Regency and were expanded to 16

Puskesmas in Serdang Bedagai to complete the

List of Puskesmas

in two districts

(N=62)

List of key

informants

(information

system officers

Refuse to

participate

Willing to

participate

Identiﬁcation Factors of Poor Quality of Data in the DOTS Program

number of samples in the determined study. Until

this research was completed, there were 3

Puskesmas that received the accreditation status and

8 Puskesmas were preparing for accreditation in

2019.

The characteristics of puskesmas data officers in

this study showed that the age of DOTS data officers

was most often found ≤ 30 years by 76%. Working

period <5 years 56%, education is mostly found with

the category> 12 years of school by 94%,

Participation in data training that has been carried

out in the last 2 years is mostly found in the ≤ 2

times category by 74%. The description of the

characteristics of DOTS data officers in Deliserdang

Regency in 2019 can be seen as in the following

table.

Table 1: The characteristic of DOTS data officer in Deli

Serdang.

Characteristics of DOTS Data

Officers

Age (Years)

< 30 years old

≥ 30 years old

Years of working

< 5 years

≥ 5 years

Level of education

≤ 12 years school

 12 years school

Trainning

≤ 2 time

 2 time

The results of this study also show the

characteristics of health centers in Deli Serdang

district with the characteristics of plenary

Accreditation of only 2%, while accreditation is

good at 8%, and accreditation is sufficient at 24%.

While as many as 66% have not been accredited.

This health center classification also consists of 78%

non-inpatient health center and 22% in-patient

health center.

The quality of data produced by the DOTs

information system of the Puskesmas program in

Deli Serdang is classified as poor where the lowest

is found in use of information category by 68%,

followed by the data completeness category by 36%,

data confidentiality by 34%, data validity by 24%,

and on time data delivery in 12%. The use of

information from data generated in decision making

at the puskesmas level is also still very low. Where

only found by 4% with good categories, the rest are

in the category of not good enough and poor quality.

Figure 2: Distribution of data quality system information

program DOTs.

The relationship between organizational

determinant with the quality of DOTS data shows

that the quality of DOTS data at the puskesmas

program is mostly found in the organizational

determinant category of less 21 (84%) of 25 DOTS

officers, higher than the good organizational

determinant 13 (52.0%) of 25 DOTS officers and

found to be statistically significant with p = 0.01 (p

<0.05).

Technical factors in the DOTS information

system of the puskesmas program are one of the

elements that influences the quality of the data. The

results of this study indicate that the quality of

DOTS data at the puskesmas program is mostly

found in the category of technical determinants of

less than 25 (78.1%) of 32 DOTS officers, higher

than the technical determinant of either 9 (50.0%) of

18 DOTS officers. Statistical analysis showed that

there was a significant relationship between

technical factors and the quality of DOTS data at the

puskesmas program with p = 0.04 (p <0.05).

Behavioral determinants show the least quality

data found in the category of behavioral

determinants of less 20 (80%) of 25 DOTS officers,

higher than the determinant of good behavior 14

(56%) of 25 DOTS officers. Statistical analysis

showed that there were significant differences in the

quality of determinant data of less behavior with

100

120

Percentation

Poor Moderate

Good Convidentaly

ICHIMAT 2019 - International Conference on Health Informatics and Medical Application Technology

good behavior (p = 0.06; p <0.05). This means that

there is a relationship between behavioral

determinants and the quality of DOTS data at the

puskesmas program in Deli Serdang.

Table 2: Organizational, technical, and behavior

determinants with data quality program DOTs.

Category

Data Quality

value

Less

Well

Organizationa

l Determinant

Less

Well

Teknikal

Determinant

Less

Well

Behavior

Determinant

Less

Well

84.0

52.0

78.1

50.0

80.0

56.0

16.0

48.0

21.9

50.0

20.0

44.0

0.018

0.006

0.000

4 DISCUSSION

The relationship of organizational determinants with

the quality of DOTS data in Deli Serdang shows the

consistency of the results of this study compared to

previous studies. Although not many research results

have shown the same relationship in the field of

information systems, but the results of this study are

consistent with measure evaluation research which

also finds an organizational determinant relationship

with data quality (Ahanhanzo, 2018).

DOTS workers work within the organization of

puskesmas where this organization influences them

through the organization's rules, values and

practices. The DOTS program in puskesmas

organizations is a health service system and

managed by government organizations.

Organizational factors such as lack of human and

financial resources, low management support, lack

of supervision and leadership affect data quality

(Kim, 2018).

Organizational determinants are important to

influence performance and define this category

because all factors are related to organizational

structure, resources, procedures, support services,

and culture to develop, manage and improve

information systems processes and performance.

Information systems enhance evidence-based

decision making, manage knowledge and create

transparency and good governance without changing

the organizational hierarchy. Lippeveld shows that

information systems need to follow the

communication channels that exist in the

organizational hierarchy (Lippeveld, 2019). Kim in

the socio-technical system of information systems

emphasizes the measurement of organizational

processes of human and technological interactions

that lead to the quality of services and products

(Hartzema, 2016).

Likewise, Lind stated 'Every system is designed

to achieve the results achieved', which shows that

performance is a characteristic of the system

(Lafound, 2017). Thus, the DOTS information

system framework emphasizes that all system

components and actors, leaders and workers, are

responsible for improving DOTS data quality

performance. Leadership roles are seen as role

models and facilitate work processes (Adomou,

2017).

Information system performance is indicated by

improving data quality and the continuous use of

information. Data quality is further described in four

dimensions: relevance, completeness, timelines, and

accuracy. Relevance is assessed by comparing data

collected against management information needs.

Completeness is measured not only with respect to

all data elements filled out but also the proportion of

puskesmas reports in an administrative area.

Timeliness is assessed as a merging of reports within

a specified time period. Accuracy is measured by

comparing field data and Puskesmas reports, and

between Puskesmas reports and data based on

administrative regions (Hanefeld, 2018).

The debate that often arises lately concerns the

quality and quality of the data used in decision

making. The right decision in the determination of

health interventions will affect the outcome and

performance of the health system (Links, 2018).

The challenges facing our country lately are

about good governance, transparency, and

accountability which are indicators of development,

and their impact is strengthening evidence-based

decision-making and information systems that

demand quality data. Thus, health information

systems require an increase in the quality and use of

appropriate information in policy making (Ho,

2018).

In the effort to develop the National Health

Information System to support decentralization in

the health sector at local and regional levels, various

weaknesses were still encountered. Among them is

the integration of SIK which is still not good. Each

unit tends to collect as much data as possible using

each reporting method and format. As a result, the

lowest units such as puskesmas which must record

Identiﬁcation Factors of Poor Quality of Data in the DOTS Program

data and report it become much burdened. The

negative impact of these activities is the inaccuracy

of data, the slow delivery of data reports, incomplete

data and the integrity of puskesmas data so that the

quality of data and information produced is poor

(Hartzema, 2016).

According to Purba (2018) the performance of

information systems depends on the process of

gathering information that is influenced by technical,

behavioral, and organizational factors. Behavioral

factors have a direct influence on information

systems processes and performance. Technical and

organizational factors influence the information

system process and performance directly or

indirectly through behavioral factors.

Behavior in health information systems is related

to the competencies, motivations, knowledge,

experiences, values and attitudes of individuals

involved in the system that affect the information

system process and directly affect SP2TP

performance (Leveled, 2018).

Purba Research (2018) in Medan City found that

workload and job responsibilities are related to the

performance of the Puskesmas SP2TP coordinator as

seen from the completeness of data and on time

delivery. Hartati (2016) found a positive and

significant relationship between personal factors and

the performance of health workers in the

implementation of Keluarga Sadar Gizi program in

Sukoharjo Regency. According to Mangkunegara in

Bienchet (2017) employee performance will be high

if motivation is high and supported by high ability.

Work motivation is one of the personal factors that

influences performance achievement (work

performance). Based on McClelland's (1961)

research, it was concluded that there was a positive

relationship between success motivation and

achievement.

In addition to work motivation, other behavioral

factors that affect performance are work abilities

(knowledge and skills). With adequate education for

the position and skilled in doing daily work, then it

will be easier to achieve the expected performance.

Therefore, employees need to be placed in jobs that

match their expertise (Purba, 2018).

The sector of employment was associated with

the quality of data in our study. This phenomenon

may be explained in this context by the fact that the

private sector in Benin is not significantly involved

in the RHIS. Support activities such as training and

supervision are, therefore, mostly dedicated to the

public sector. Moreover, our findings confirm this

difference of resources for the RHIS with the

difference observed in our sample regarding

supervision and availability of material resources for

RHIS. Moreover, it is also interesting to note that

despite the fact that the health workers had

mentioned unsuitable RHIS training, training and

retraining during the previous twelve months could

have a positive impact on the quality of data.

Improving the quality of training will produce

improved results, particularly by tailoring it

according to the following three components: the

trainer, the health worker being trained and the tool

being taught.

Although in our study organizational factors such

as the availability of resources, supervision,

financial incentives and the perceived complexity of

the technical factors were not associated with the

quality of the data, from a statistical point of view,

the results of the focus group illustrate their role in

data quality. This phenomenon is confirmed by the

positive relationship found with the training. In the

framework of RHIS performance, these factors were

more directly linked with behavioral factors

nevertheless, some authors have shown a direct

positive link between financial incentives and

performance. Although this positive relationship

between financial incentive and performance is still

under investigation, with the experience of results-

based financing in many developing countries,

including Benin, it would be interesting to look at

the issue in greater depth. We could accomplish this

goal by incorporating, as a contractual performance

result, an indicator for the quality of the data

produced by the health center. In light of comments

made by health workers in the focus groups, the

format of the reporting form (design, number of

items to fill, etc.) may need to be addressed. Shorter

forms with pertinent items from the health workers’

point of view should certainly improve data quality.

The main issue would be involving the health

workers in the design process because the choice of

indicators and thus form items is dependent on

national and partner priorities (Purba, 2019).

It would have been worthwhile to perform

modelling with adjustments, but taking into account

the very low staff numbers in some categories of our

sample, we were unable to accomplish this; this is

one of the main study limitations. The comparisons

made with other studies take into account the

methods used in those studies because differences in

methods could justify the differences in the findings.

The analysis methodology we used together with

LQAS sampling and the survival analysis is

worthwhile, but it has its limitations, particularly in

this study, where we had low staff numbers for

certain categories, which made analyses with

ICHIMAT 2019 - International Conference on Health Informatics and Medical Application Technology

adjustment impossible. The simplicity of the LQAS

methodology offers the opportunity to reproduce the

work with more flexible criteria for evaluating data

quality. Moreover, by working with a larger sample,

more in-depth analyses could be conducted.

This study identified some factors associated

with the quality of the RHIS data. The type of

factors identified, such as those linked with the

human resources as work engagement, self-

perceived efficacy, and organizational factors show

that the strategies for data quality improvement must

focus on human resources, perhaps more than other

resources. Indeed, in our context of limited

resources, the first steps taken to improve the

performance of RHIS should focus on investments

in material and financial resources. Moreover, in a

practical way, for example at the operational level,

the choice of the staff delegated to data collection

could take into account the relationship we found

between data quality and the responsibility of the

health worker (Purba, 2019). The results of the

analysis in this study indicate that there is a

relationship between technical determinants and the

quality of DOTS data at the Puskesmas Deli

Serdang. Akil in his research also found a

relationship of technical determinants with data

quality in improving information system

performance (Ahanhanzo, 2018).

Technical determinants are important factors

related to specific knowledge and technology to

develop, manage and improve the processes and

performance of health information systems. These

factors refer to the development of indicators;

designing data collection forms and preparing

manual procedures such as information technology

types such as updated software for data processing

and analysis.

Weak technical determinants in the DOTS

program in Deli Serdang Regency are caused by the

low availability and capability of information

technology both by the Puskesmas and DOTS

program organizations. According to Lafound

computerized technology and the use of

communication tools when gathering information

will be the way to develop information systems.

(Lafound, 2018). Thus, users of information

technology in DOTS organizations can effectively

increase the knowledge, skills of information

technology in DOTS organizations in the future.

In circumstances or regions with inadequate

resources, the use of low technology in managing

information systems can still achieve acceptable

levels of performance. Data quality is very

dependent on the type of technology used. Computer

technology and the internet now makes the system

of analysis, sending, and the percentage of data easy

to do. However, this must be complemented by the

competence of users of the system to produce good

quality data. If indicators are irrelevant, data

collection forms will be very difficult to fill out, and

if computer software is not easily applied it will

affect the level of accuracy and motivation of

implementing health information systems (Khan,

2018).

When the software cannot process data correctly

and on time, the resulting analysis does not provide

meaningful conclusions in decision making, it will

affect the use of information. Therefore, technical

determinants can affect performance directly or

indirectly through behavioral factors (Adejumo,

2017). The results of this study indicate there is a

behavioral relationship with the quality of DOTS

data in Deli Serdang Regency. The relationship of

behavior as one of the determinants of the quality of

health information data has also been recognized by

Ahanhanzo (Adejumo, 2017). The same thing was

also found in the Measure evaluation in his study in

Guanea about health facilities (Lippeveld, 2018).

The results of this study are also supported by

studies in the United States that find information

system performance that includes data quality and

information usage depends on the motivation of

information system officers in addition to other

factors that influence it (Teklegiorgis, 2016).

According to Akil, behavior directly affects the

process and data quality. Limited skills and

knowledge about data management are the main

factors of the low quality of data and information

usage (Adejumo, 2017; Ahanhanzo, 2018).

Behavior as a determinant of health information

systems concerns self-confidence, motivation,

knowledge, attitudes and competencies of DOTS

officers in a series of health information systems.

Behavior is directly related to a person's feelings

towards work and work results.

According to Akil, behavior influences whether

or not work will be done (Ahanhanzo, 2018).

One of the main internal factors that influence

DOTS officer behavior is motivation. Motivation is

defined as a psychological process that provides

direct strength and leads to the perseverance of

intentional behavior. Attitudes, knowledge, skills

and abilities are also important internal factors in

behavior. Without the ability a person will not be

able to do the work given. Attitudes consisting of

beliefs, feelings and the tendency of a person's

behavior, influence behavior indirectly through

intention or purpose (Ahanhanzo, 2018).

Identiﬁcation Factors of Poor Quality of Data in the DOTS Program

Poor organizational behavior by DOTS officers

in Deli Serdang shows poor data performance and

quality. This is caused by the low motivation,

knowledge, and competence of DOTS organizations

in handling data. Utilization of data in decision

making is not used properly by Puskesmas leaders.

The information system paradigm among officers is

an obstacle in improving the quality of information.

This will certainly relate to a system of transparency,

accountability and decision-making choices for

better performance.

Knowledge of the HMIS (Health Management

Information System) concepts was found to be

associated with better quality of HMIS data and yet

this was not true of training on HMIS. This shows

that, understanding the basic HMIS concepts might

not be related to the basic training on HMIS. It has

been reported that often training is not the problem;

instead it is probably a manifestation of

unwillingness to fill in the forms and lack of

commitment and accountability of the poorly

supervised health workers. Supervision, regardless

of the reported duration, had been shown in this

study to have no relationship with improved data

completion (Purba, 2018).

This raises some doubts on the quality of

supervision provided by Council Health

Management Teams (CHMT) to the health facility

workers. Except for monitoring the number of visits

made by members of CHMT to the facilities, there is

no mechanism for measuring and monitoring quality

of supervision at health facilities as well as at district

level. Thus the onus of ensuring that supervision is

done effectively, is left to the supervisor. This study

has shown that the presence of HMIS focal person

facilitates data processing hence improving the

quality of data. However, this study did not go

further into assessing the merits and demerits of

having an HMIS focal person. Given that team

approach is an integral part of Primary Health Care

(PHC) strategy delegation of HMIS duties to one

person might undermine integration of the system

into other programs. Accountability as measured

through queries made by district officials on data

received from health facilities revealed an

association with better quality of data, however, this

was not the case with query on delay of reporting.

This might indicate that Municipal health workers

are keen to receive reports irrespective of their

quality in order to satisfy the needs of higher

authorities. It has been reported that data is often

collected in order to meet bureaucratic obligations

rather than performance monitoring (Purba, 2018).

5 CONCLUSIONS

The quality of the DOTs data program at the

Puskesmas Deli Serdang is still poor and has not

been used as material in making strategic decisions.

The results of this study conclude that there is a

relationship between behavioral determinants,

organizational determinants, and technical

determinants with the quality of DOTs data in the

Puskesmas Deli Serdang.

6 SUGGESTION

It is recommended to the Deli Serdang District

Health Office to conduct data management training

to DOT information system management officers

routinely (at least once a year) in an effort to

improve data quality, information system

performance, and TB surveillance performance.

ACKNOWLEDGMENT

This research was supported by Institut Kesehatan

Delihusada Delitua, Institut Kesehatan Medistra

Lubuk Pakam, Sembiring Hospitel Foundation, and

Grand Med Hospital Foundation, Indonesia.

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