Factors Affecting the Use of Electronic Logistics Management Information System (eLMIS) Data in Bottom-up Quantification of Health Commodities in Public Health Facilities in Coast Region, Tanzania: A mixed-methods study

DOI: https://doi.org/10.21203/rs.3.rs-2714700/v1

Abstract

Background: Effective supply chain management is an essential component of effective/affordable healthcare services. It ensures accurate quantification and availability of health commodities. Bottom-up quantification has proved the most effective in Tanzania, it relies mostly on electronic logistic management information system (eLMIS). This study aims to explore factors influencing the use of eLMIS data in bottom-up quantification of health commodities in Coast region, Tanzania.

Methods: An explorative cross-sectional study design employing mixed-method approach (qualitative and quantitative for data collectiona and analysis) was conducted. We focused on healthcare workers whose role is to create health commodities report by eLMIS and finally aggregate in bottom-up quantification tools (N=30). Structured interviews and checklist were adopted for qualitative and quantitative data collection, respectively. Data analyses were achieved with help of NVivo 12 and SPSS version 23.

Results: We found that lack of training to healthcare workers on bottom-up quantification was one of the key factors affecting them to use eLMIS data. Furthermore, insufficient infrastructure such as computers, poor ICT skills to using computers and eLMIS software, lack of regular supportive supervision on the use of eLMIS data, and inadequate fund and qualified staff emerged as other major factors that affect the use of eLMIS data in bottom-up quantification of health commodities in public health facilities. Training was shown to be a significant source of knowledge to improve eLMIS system use, and to enhance positive attitude toward the use of eLMIS data (p=0.003).

Conclusion: In summary, the factors unveiled in the present study may affect forecasting which relies on data that comes from eLMIS which tracks the demand for use of commodities over time. This is of particular concern for consistent availability and affordability of health commodities in public health facilities, as inaccurate forecasting may lead to inaccurate quantification of the commodities.

Introduction

Health commodities are items such as medicines, medical devices, and other health-related supplies intended for medical uses. Their availability in health facilities is an essential component of effective and affordable health care services [1]. An effective supply chain management ensures not only their availability at an affordable cost but also their proper control including rational use [2]. Procurement, distribution and inventory management are critical points in the supply chain cycle of health commodities that may largely affect their affordability and availability to the end users or customers [3].

The Medical Stores Department (MSD), an autonomous government agency established by MSD Act No.13 of 1993 is responsible for procurement, storage and distribution of health commodities in the public sector. The MSD receives funds from the government to procure pharmaceuticals while other funders procure products through international procurement agencies and donate products to Tanzania through the MSD. Upon arrival at the central MSD warehouse, commodities flow through to MSD nine zonal warehouses. MSD delivers individually packed facility orders either directly to the facility or to the district, which then is responsible for distribution to lower-level facilities.

In the 1990s, Tanzania employed a ‘push’ system in the supply of health commodities to health facilities. This system faced several challenges including a lack of flexibility in selecting health commodities by the facilities. As a result, shortages or surpluses of items were frequently encountered in health facilities [4]. The ‘pull’ system, famously known as indent/integrated logistics system (ILS) was later introduced, where, each facility determines the types and quantities of medicines needed and places orders with the supply source, MSD [5][6]. The ILS improved both the availability or number of health commodities in the facilities as well as flexibility in selecting them as per facility needs [7]. Despite this achievement, several other issues such as improper, inefficiently, and mismatch between what is ordered from the supply source (central MSD) and what is delivered to the health facility were raised as major challenges of the ILS [4].

In order to streamline the flow of health commodities and information through various levels, the MoH introduced and implemented an electronic Logistic Management Information System (eLMIS). In addition to eLMIS, the MoH established a Logistics Management Unit (LMU) to make sure of the effective use of the eLMIS data for oversight and performance improvements [8]. The use eLMIS by LMU has helped to improve the availability of health commodities at the health facility level. This has been possible by making sure of good forecasting that relies on reliable and accurate data that comes from the logistic management information system (LMIS). LMIS data tracks the demand for use of commodities over time from health facilities [9].

The availability of health commodities at the health facility are thus linked directly with LMIS data that are fed in eLMIS before they are submitted to central MSD [10]. Unfortunately, in most cases LMIS remains predominantly paper-based resulting into incomplete data, poor data quality, and large lag times between data entry and availability for use. This is because most facilities in the country are still struggling with poor network connectivity and lack of electricity, leading to ineffective eLMIS utilization [11]. As a result, an unreliable estimate of health commodities sent to central MSD was a major weakeness of this approach. An unreliable estimate of the approach could be a major reason for poor quantification of the commodities, which was pointed out by the MoH in 2017.

The ministry then recommended a bottom-up quantification, where health facilities become the source of demand forecast for essential health commodities [12]. this was adopted countrywide and came into operationalization in 2018 (1). In bottom-up quantification, a health facility serves as a tool in streamlining the procedure and process in the forecast [10]. Here, quantification relies not only on eLMIS data, but also on the collaboration and coordination of various stakeholders, including MoH officers, supply chain personnel, and service delivery staff. Without coordination among these stakeholders, forecast and supply plans are likely to be inaccurate and with limited impact [9]. Therefore, the use of digital eLMIS data is one of the crucial factors in executing an effective bottom-up quantification.

Tanzania, through MoH and its partners, recognize the potential of digital data systems in transforming healthcare delivery. This is achieved by enabling information access and use to support healthcare operations, management, and decision making. However, since the introduction of bottom-up quantification in 2018, there is not much research has been conducted to assess factors affecting the use of eLMIS data in bottom-up quantification of health commodities.

Methods

Survey Administration

A cross-sectional explorative study design using both qualitative and quantitative methods for primary and secondary data collection, respectively, was employed. Coast region (circled in red in Fig. 1) is one of the administrative regions in Tanzania and it was targeted because of availability relatively higher proportion of staff who had been trained on the use of eLMIS data in bottom-up quantification at all levels of regional public health facilities. Further, healthcare workers whose primary job is to create health commodities report by using eLMIS and finally aggregate in bottom-up quantification tools were the study participants. Purposeful sampling method was used to obtain both the health facilities and healthcare workers from the respective facilities.

All healthcare workers who were the in-charges of doing bottom-up quantification in the Coast region were eligible to participate in the study. They were invited through a word of mouth or email from the investigator or data collection assistants. Those who rejected or did not reply to the emailthey were excluded from this study. The survey was conducted by employing semi-structured interviews between April and July 2021 in the Coast region. Interviews were conducted physically in Kiswahili language, whereby each interview lasted between 30 to 45 min (with an average length of 40 min).

Survey Content

The study focused on healthcare workers dealing with creating quarterly health commodities report by using eLMIS data and aggregate in bottom-up quantification. A qualitative data collection tool was developed, this involved assessing the factors affecting the use of eLMIS data in bottom-up quantification of health commodities.

For the quantitative part, an observational checklist of questions on the factors presented in the qualitative part was posed to participants.

Sample Size And Selection

The estimated sample size for qualitative study was predetermined as recommended by Morse (2000). Where, at least 6 to 35 participants for phenomenological studies the number of interviews per informant was important [13]. Therefore, this study included 1 regional hospital, 5 district hospitals from each district, and 5 health centers one from each district, and one dispensary from each district. Hence at the regional and district levels each health facility provided three participants while at health centers and dispensaries only one participant was included in the study [14].

For quantitative part in both qualitative and quantitative study design, Krejcie and Morgan table was used to determine the minimum sample size for health facilities at a confidence interval of 95% and 0.05 of marginal error [15]. The Table 1 summarizes, whereby the sample size was comprised of 31 health workers from different levels of health facilities [14].

Table 1

Sample of key informants and their distribution

Target population

Health facilities

Sample size

 

Tumbi RR§

Mkuranga DC*

Kisarawe DC*

Kibiti DC*

Chalinze DC*

Kibaha DC*

 

Healthcare Workers

3

5

6

5

6

5

30

Total

3

5

6

5

6

5

30

§ RR - regional referral hospital
* DC - district council representing number of participants from the district hospital, one health center and one dispensary in that district

Data Analysis

Data analysis was conducted parallel to data collection to ensure availability of data that respond to study questions. The study used both quantitative and qualitative data analysis methods.

Qualitative Data Analysis

Using a short questionnaire, key demographic information was collected, followed by a semi-structured interview. Prompts were used as appropriate to gain depth of understanding. Digital voice recorders recorded the data for transcription to provide readable texts. The original text, in Swahili, was then translated into English for analysis. Then the transcripts were read and re-read for several times to establish good understanding on the factor affecting use of eLMIS data in bottom-up quantification. In coding process, the researcher ensured that each objective was categorized as main themes and the responses aligned under it. Harmonization of responses was ensured to generate over aching theme under each objective for report writing. Responses that were not relevant to questions ware isolated in separate standalone theme and were named “other themes”. The researcher categorized the questions into main theme and the responses related to each question. The coding process of transcript was conducted with comprehensive coding and final themes and sub themes were established. Any disagreement and contradiction on understanding the codes was resolved by reading the transcript to make sure that each theme is supported by relevant quotes. All these were checked though the COREQ list. The research team made sure that sampling saturation had been reached with no recurrent themes occurred [16]. This happened after approximately 5 to 6 interviews per health facility (Table 1). Finally, the team set main themes and subthemes as response to the specific research question and objectives. Thematic data analysis was conducted with help of NVivo 12.

Quantitative Data Analysis

Data was coded and entered into a Statistical Package for Social Science (SPSS version 23) which were used for analyzing data. The descriptive statistics was be used to summarize continuous variables and categorical variables. Frequencies and proportions were used to present the data and chi-square test describe association of the independent variable to supplement the qualitative data.

Results

Socio-demographic Characteristics of the Respondents

Interviews were conducted to a total of 30 healthcare workers. Table 2 summarizes the socio-demographics of respondents.

Table 2

Socio-Demographic features of respondents (N = 30)

Variable

Frequency (N)

Percentage (%)

Gender

   

Males

16

53.3

Females

14

46.7

Age (years old)

   

25–35

9

30.0

36 − 34

15

50.0

35–55

6

20.0

Role (Profession)

   

Pharmacist

13

43.3

Pharmaceutical Technician

3

10.0

Laboratory Technologist

6

20.0

Physician/physician associate

5

16.7

Nurse

3

10.0

Level of education

   

Master’s degree

3

10.0

Bachelor’s degree

11

36.7

Diploma

14

46.7

Certificate

2

6.7

Years of Experience

   

1–5

9

30.0

6–10

10

33.3

11–15

6

20.0

16–20

5

16.7

Findings from Primary and Secondary Data

Three main themes were generated in relation to the factors affecting the use of eLMIS data in bottom-up quantification in health facilities in Coast region Tanzania, and these include; individual behavior, technological issues, and organizational issues.

Individual Behavior in Relation to the Use of eLMIS data in Bottom-up Quantification

We found that five main issues emerged with respect to individual behavior on the use of eLMIS data.

i. Lack of training in bottom-up quantification

Most participants revealed that they have attended several trainings, however, the focus of most trainings were basically on the use eLMIS system and not on bottom-up quantification.Further, it was revealed that district pharmacists are the ones who have access on the quantification tools. They are responsible for sharing it to the facility’s in-charges whose job is to create health commodities report by using eLMIS and finally aggregate in bottom-up quantification.

I have never attended training related to eLMIS and bottom-up quantification, I am not competent enough to get the right estimate of the next period.” (Participant 8)

“They take time because I didn’t attend training so I have to calculate manually then filling one after another to the quantification tools. This takes time to analyze how much you have consumed last year, and how much will be required the next year. So, it takes us time to prepare.” (Participant 13)

ii. Knowledge gap on the use of eLMIS

Even though most respondents declared attending training on the use of eLMIS, they revealed that it was not on the bottom-up quantification. This creates a gap on the aggregating data in the bottom-up quantification tools. It was realized that most of the respondents were not able to give proper definition of bottom-up quantification.

An application that helps to procure health commodities at my facility. In other words, our R&R (report and requisition) is done twice a year, after every six months, and it tells about the estimate of next year’s consumption.” (Participant 11)

“We usually use the average monthly expenditure. What is your expenditure per month and then multiply it 12 times in a year” (Participant 12)

iii. Attitude towards the use of eLMIS data in bottom-up quantification

In the present study most respondents expressed a positive attitude. They feel that the use of eLMIS data for bottom-up quantification is good, has importance and everyone should be involved in use of data in different activities like inventory management. Further, respondents mention that they use this data for planning and budgeting activities, they get reporting rate, they get R&R report, that is used for decision making during therapeutic committee.

“We use eLMIS data for the report of the reagent and prepare a budget, we do research to find out which kit is available and which is not available.” (Participant 2)

“We use to prepare reports of medicines, medical device and reagents, meaning that it enable us to know how much we need to order.” (Participant 3)

iv. Attitude toward the eLMIS software

Respondents explain that the system was good because it minimized error and it simplifies work and also it gives them assurance on availability of data. It helps the facility to determine their need accurately and it gives report like R&R to central medical stores department (MSD), report on commodities to send to Ministry of health (MoH) and available data for quantification of health commodities as respondent says

“In short it is a good system, that gives us the data of consumption and how to estimate for the next year, it is the same as in the consumption data of reagents that we enter on the lab system” (Participant 11)

“What impresses me the most is the fact that technology is good….you can use the data you have to know how much the commodities will cost, and you are sure that data.” (Participant 1)

“It is good because it provides accurate and quality information because it starts from where the end user at the facility to the highest level Ministry of health.” (Participant 10)

Table 3 summarizes secondary data in relation to individual behaviours to the use of eLMIS data in bottom-up quantification. In general, based on their professional roles, the majority of pharmacist and pharmaceutical technician, who were mostly found at district and regional hospital, received training on the use of eLMIS data in bottom-up quantification, 61.5% and 66.7%, respectively, while other roles did not (p < 0.001). A vast majority them were aware and had a positive attitude towards the use of eLMIS system and its data, which was significant as compared with other professional roles (p < 0.007). Contrally, other roles such as physicians/physician associates and nurses, majority of whom managed health commodities at low-level health facilities (health centres and dispensaries), were never trained on the use of eLMIS data and bottom-up quantification.

Table 3

Proportion of health workers who manage health commodities

 

Pharmacist

Pharm tech

Lab Tech

Physician/ Associates

Nurse

p-Value

Formal training

61.5%

0.0%

50.0%

0.0%

0.0%

< 0.001

On-the-job Training

38.5%

66.7%

16.7%

0.0%

0.0%

< 0.001

Never been Trained

0.0%

33.3%

33.3%

100.0%

100.0%

< 0.001

eLMIS report match with ledger

76.9%

66.7%

50.0%

60.0%

33.3%

0.640

Ability to use the system

84.6%

66.7%

50.0%

0.0%

33.3%

0.007

Use personal computers

53.8%

100.0%

33.3%

60.0%

33.3%

0.482

Use data for planning and budgeting

100.0%

33.3%

83.3%

100.0%

100.0%

0.024

Technological Issues Related to the Use of eLMIS Data in Bottom-up Quantification

Four main issues emerged with respect to technological issues on the use of eLMIS data.

i. Insufficient infrastructure for bottom-up quantification

It was found that infrastructure are available in health facilities, however, they are not sufficient. All participants relied on the available infrastructure to support them access the information required for bottom-up quantification. Most respondents reported using their personal computers, and yet they have to buy internet packages out of their pockets. This is especially true during critical steps for bottom-up quantification, including; during process of preparing report and sending the request to medical stores department (MSD), and during the process of aggregating eLMIS data in bottom-up quantification.

“We use existing ledgers in laboratories and patient register. In the laboratory ledgers, most of the reagents and machines are missing and or lacks specifications. Products should be added but once you enter the eLMIS laboratory system. however, some reagents are missing in the system and quantification tools. I think the system needs to be updated for improvement.” (Participant 2)

“Our offices should be equipped with enough computers. The facility has very few computers in place, so access is not easy. Also, there is no compensation for air time, so I have to use my money to buy internent packages” (Participant 8)

Further, we found that the eLMIS system and quantification tools lacks some crucial information, making it insufficient.

“The eLMIS system is slow and the internet is not good here at our facility, so we use own expenses to ensure that we send the report” (Participant 1)

ii. Limited ICT literacy among healthcare workers

Most participants in the present study showed a limited literacy in ICT, especially when it comes to operating basic computer functions. Despite their limited ICT skills, participants tended to accept being assigned to the task of bottom-up quantification using computers. Further, many participants expressed discontent with the technology.

“Computer itself is a class (that one needs to attend to understand its operation). Therefore, it is difficult to use the eLMIS system if you don’t know how to operate a computer. This is because quantification work needs one to make charts by using excel sheets and pivot tables. All of which are very difficult and requires a high level of understanding.” (Participant 2)

Moreover, healthcare workers declared that they need more training or refresher course on the use of the ICT system so that they could improve their capacity on the use of the eLMIS system. We found that at the lower health facilities (especially dispensaries), the exposure to using the eLMIS system was low. At worst cases, some facilities lacks even one computer to carry-out basic activities, as one of the respondent said;

“The eLMIS system is not difficult for me! However, for the bottom-up quantification I still face some difficulties and at times I fail to continue.” (Participant 14)

iii. Knowledge gap on process of aggregating eLMIS data in bottom-up quantification tool of health commodities

Most of the participants reported feeling the gap in their knowledge on the process of aggregating eLMIS data in bottom-up quantification. Furthermore, they explained on the complexity of aggregating the eLMIS data into bottom-up quantification tools that it is something that needs training and also the tools should be available at all level of health facility. One participant stated:

“The current bottom-up quantification system is up to date and very modern. I use register and ledgers, and I have to sit down and start calculating manually because I am not able to use modern system.” (Participant 12)

“I am not able to run bottom-up quantification process because I have no experience with it. However, my district pharmacist always helps me to do it” (Participant 11)

iv. Time-consuming activities of data entry to the eLMIS system

The vast majority of participants in this study expressed a self-reluctant behaviour to using eLMIS data in bottom-up quantification. Most claimed that data entry to eLMIS system is very difficult, cumbersome and takes a lot of time. They claimed that there are a lot of data to enter, a lot of paper work and yet they have other work to do. Additionally, the respondents explained the situation of workers in the health facilities to be worse as they have to do their assigned duties and fill in the HMIS tools for every client. One participant said;

“For better improvement the template for bottom-up quantification should come according to the level of health facility, number of staff should be increased because we do work beyond our capacity for example the facility is supposed to have 36–55 numbers of health workers but the present number of staff are 9,and tools should be available.” (Participant 9)

In the quantitative secondary data, we found that 90.9% of the participants had never been trained and they had difficulties in using ICT (computers and softwares) compared to 81.1% of those who had attended training. This difference was statistically significant (p = 0.003) (Fig. 2).

Furthermore, our findings revealed that 76.7% of participants used their personal computers and internet packages to do bottom-up quantification, and 63.3% still used paper-based report form. The summary is provided in Table 4.

Table 4

The proportion on availability of infrastructures

Variables

Frequency (N)

Percentage (%)

Own computer

   

Personal

23

76.7

Facility

7

23.3

Use paper-based report form

   

Yes

19

63.3

No

11

36.7

Organizational Issue Related to the Use of eLMIS Data in Bottom-up Quantification of Health Commodities

We found that four main issues emerged with respect to organizational behavior on the use of eLMIS data.

i. Inadequate supportive supervision

The study participants, particularly those from the district level, had a feeling that the council does not have capacity to carry out adequate supportive supervision at their health facilities as stipulated in the national guidelines. Ideally, supportive supervision has to be conducted at least quarterly (4 times in a year), however, it is almost always done less often than that. Furhter, most of the participants said supervision done inntheir facilities concentrated mostly on health commodities audit and not for use of eLMIS data.

“Supportive supervision is not done sufficiently. When they come for a visit, they are more concerned with the health commodities audit (availability) and not on the use of eLMIS data for bottom-up quantification.” (Participant 10)

ii. Inadequate funds

All the participants declared that the number of computers in their facilities are inadequate. The vast majority of participants stated that there are only two to three desktop computers available for the whole facility. This forces most of them to use their own computers and internet packages to do their tasks. We found that the health facilities do not have adequate and reliable sources of funds to support their day-to-day activities. Morever, they revealed that their health facilities’ budget are very constrained and they cannot allocate funds to procure computers and internent packages which would enable the process of entering eLMIS data as well aggregating data to bottom-up quantification tools.

“Finance is a problem! Sometimes I have to use my personal internet package to finish the work, and the next day when I come I find some more stuff to enter into the system that needs money.” (Participant 15)

iii. Inadequate qualified staff

Most participants, with managerial positions, declared to have many inadequate-skilled staff that are handling commodities at low-level health facilities, especially dispensaries. Further, they declared that the present staff have a heavy workload of providing clinical services and still they have to do quantification of health commodities. One participant said;

“If it is possible for the government to employ more pharmacists, who are primarily responsible for these stuff (quantification of health commodities), otherwise we have a lot of work to do as in-charge of facility.” (Participant 7)

Other participants argued that to know use of data is important and there must be qualified staff for that from higher-level health facilities to district level, as one respondent said;

“Government should increase number of qualified staff because we do work beyond our capacity. For example, our level of facility is supposed to have at least 36–55 healthcare workers, however, currently we are only 9 of us! Yet, quantification tools should be available and filled in time as required.” (Participant 13)

iv. Adequate Supportive supervision and feedback report

All the participants tended to accept that the top management share technical and operational information with practitioners during supervisory visits for the purpose of improving quality of data used and to insist the use of eLMIS system. We were notified that supervision was done at least once in a quarter, and during the visits feedback was provided. Respondents viewed it to be beneficial as it improves their work by knowing what they have done best and worst for rectification. One participant said;

Supportive supervision is good. They visit us from district level (members of council health management team (CHMT) and regional pharmacist) came up to visit us with data from the system and then compare it with those on hardcopy. If there was a problem they would show us, identify the gaps that exist and then find a solution” (Participant 6)

Also, we found that most of participants who were trained on the eLMIS system had a culture of data use. They explained that culture of data use has been changing since we have started realizing the value in using data for planning and budgeting.

“When they come for supervision, members from council health management team (CHMT), regional health management team (RHMT) and ministry of health, would observe if there are any shortcomings or if one has done good (they would congratulate you). At the end of the day, after the service they would come and sit down with you to make an action plan, in order to have a good service.” (Participant 14)

Quantitatively, 70% of facilities had feedback report after supervision, 90.0% had culture of using data on planning and budgeting, and 63.3% had their report matching with ledger balance (Table 5).

Table 5

Summary of observed organizational related issue

Variable

Frequency (N)

Percentage (%)

Facility with feedback report after supervision

   

Yes

21

70.0

No

9

30.0

Facility with records on planning and budgeting

   

Yes

27

90.0

No

3

10.0

Facility with bottom-up quantification guideline

   

Yes

13

43.3

No

17

56.7

eLMIS report match with ledger balance

   

Yes

19

63.3

No

11

36.7

Discussion

The vast majority of participants in the present study had limited training on the use of eLMIS data in bottom-up quantification of health commodities. Even though most participants tended to have a positive attitude towards the use of eLMIS data to bottom-up quantification of health commodities, they had different views on factors affecting their ICT skills on the use of eLMIS data in bottom-up quantification. It was seen that limited or inadequate training is a major factor affecting the use of eLMIS data, for instance, only few pharmaceutical personnel (38.5%) who are primarily involved in handling medicine and medical devices, and about 50% of the laboratory technicians responsible for handling lab equipment and reagent had attended trainings on bottom-up quantification. The findings are in line with the findings from another study done on bridging the gaps in health management information system (HMIS) [17].

Furthermore, it was noted that laboratory technicians and pharmaceutical personnel at regional and district hospitals are responsible for not only making sure that all health commodities are delivered to lower-level health facilities - health centres and dispensaries, but also on the high management focusing on building capacity to healthcare workers in those facilities on the use of eLMIS data. On the other hand, most of the pharmaceutical technicians (66.7%) who are working at lower-level facilities got on-the-job training during supervision by high management on eLMIS system, but no one had attended bottom-up quantification training. This may have contributed significantly to healthcare providers not using the eLMIS data they collect to make decisions. This, again, is in line with another study in Ethiopia that revealed that an effective use of electronic data that leads to a reliable availability of health commodities was associated with frequent trainings to the employees [18]. And other studies suggested that training with data management curriculum should be highly invested as it has impact on knowledge, skills, culture and efficiency of data management, it will also reduce costs for on-the-job training and supervision [19] [20].

Regarding the knowledge gap and attitude towards the use of eLMIS system, our findings revealed that some participants couldn’t remember how to aggregate the eLMIS data to bottom-up quantification, even though they attended some trainings. Further, a positive attitude tended to influence healthcare workers in facilities to use eLMIS data for various activities such as budgeting and planning. Similar results were were observed in a study done on assessment on user satisfaction of public supply chain user with eLMIS system [21].

Inadequate infrastructure for collecting and keeping electronic data in health facilities led to delay in data aggregation and reporting. Inadequacy of data collection tools have been a problem for decades. Ministry of health in collaboration with stakeholders need to put up plan on how each facility could secure the tools. This was also found in another study where districts had shortage of data processing tools [22]. Moreover, poor ICT skills including computer use might influence poor forecasting and accurate data and reporting. Several studies showed that knowledge and skills on ICT including computer use is dependent or proportional to the availability of health commodities in health facilities [23].

Among three major factors affecting the use of eLMIS data in bottom-up quantification was organizational issues. Here, we found that apart from majority of the facilities not being supervised timely, we also noted that supervision was regarded as a way of fixing identified mistakes on the use of eLMIS data. This however is not the right approach, rather, the right one should involve providing guidance and learning as well as mutual understanding on the importance of eLMIS data use. Moreover, only few health facilities have been supervised, however, the supervision was in terms of auditing the use and availability of health commodities, and not in termas of use of eLMIS data in bottom-up quantification. Similar findings have been reported in another study that led to the inappropriate data use [24].

Healthcare workers also reported having many other duties assigned to them apart from data collection for eLMIS. And these are such as clinicals or lab testing, this made it difficult for them to effectively use eLMIS data at the health facilities (both at high- and low-level facilities). The findings of our study are in line with those reported in nother study where researchers found that staff faced difficulties in doing multi tasks because of being overwhelmed. They stated an instance where one healthcare worker who engaged in data collection issues for quantification and at the same time they attend to many other facility activities [25][26].

Conclusion

This study examined the factors affecting the use of eLMIS data in bottom-up quantification of health commodities in health facilities. It has shown that lack of training, insufficient or lack of infrastructure, poor ICT skills to using computers and eLMIS software as well as the lack of regular supervision on the use of eLMIS data are the major factors affecting the use of eLMIS data in bottom-up quantification in public health facilities in Tanzania. Furthermore, the funds for setting required infrastructures for bottom-up quantification were unavailable in most health facilities. These factors may affect forecasting which relies on reliable and accurate data that comes from eLMIS data which tracks the demand for use of commodities over time. This is of particular concern for consistent availability and affordability of health commodities in most public health facilities, since inaccurate data leads to inaccurate quantification.

Declarations

Acknowledgement

We would like to send our sincere gratitude to all health facilities’ officers in-charge in Coast region who participated fully in this study and Professor Phares Mujinja from the School of Public Health and Social Sciences at MUHAS for his technical support in this work.

Authors’ Contributions

All authors had full access to the study data and they all took responsibility for the integrity of the data. All authors read and approved the final manuscript. Study conceptualization and design: Nambua Manase and Godeliver A.B. Kagashe. Collection of both quantitative and qualitative data: Nambua Manase. Analysis and or interpretation of the data: Nambua Manase analyzed the data and first drafted the results then Godeliver A.B. Kagashe and Eulambius Mathias cross-checked and drafted the final version of the results. Drafting of the manuscript: Nambua Manase and Rogers Mwakalukwa. Critical revision of the manuscript for important intellectual content: Nambua Manase, Godeliver A.B. Kagashe, Eulambius Mathias, and Rogers Mwakalukwa. All authors read and approved the final manuscript. Administrative, technical, or material support: Godeliver A.B. Kagashe. 

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was greatly supported by the Tanzanian Ministry of Health (MoH). The funder, MoH, did not play any role in the design or conduct of the study, the collection, interpretation, or analysis of the data or reporting of the study findings. That is, views expressed are those of the author(s) and not those of the MoH. 

Availability of data and materials

The data that support the findings will be made available for non-proft scientific research purposes upon reasonable request to the corresponding author. Consent for data sharing with other researchers has been obtained from the participants. Interested researchers will need to provide us with their name, affiliation, and the goal of the research project for which they want to use the data. 

Ethics approval and consent to participate 

Ethical approval was obtained from the Muhimbili University of Health and Allied Sciences (MUHAS-REC-04-2021-554). We confirm that all methods were carried out in accordance with relevant guidelines and regulations, and that informed consent was obtained from all participants during the main data collection. All data used in this study are aggregated secondary data with no personal identification information. The confidentiality of data was maintained anonymously.

Consent for publication

Not applicable.

Competing interests 

The author(s) declared no potential conflict of interest with respect to the research, authorship and/or publication of this article.

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