[논문]Critical Success Factors for the Adoption of Health Management Information Systems in Public Hospitals in Zimbabwe

Caleb Manjeese; Indira Padayachee

doi:10.1633/jistap.2023.11.2.6

Critical Success Factors for the Adoption of Health Management Information Systems in Public Hospitals in Zimbabwe 원문보기

Journal of information science theory and practice : JISTaP, v.11 no.2, 2023년, pp.82 - 103

Caleb Manjeese (School of Management, IT and Governance, University of KwaZulu-Natal) , Indira Padayachee (School of Management, IT and Governance, University of KwaZulu-Natal)

Abstract ▼ AI-Helper

The Zimbabwean healthcare sector faces huge challenges due to increased demands for improved services for a growing number of patients with fewer resources. The use of information and communications technologies, prevalent in many industries, but lacking in Zimbabwean healthcare, could increase productivity and innovation. The adoption of health management information systems (HMISs) can lead to improved patient safety and high-level patient care. These technologies can change delivery methods to be more patient focused by utilising integrated models and allowing for a continuum of care across healthcare providers. However, implementation of these technologies in the health care sector remains low. The purpose of this study is to demonstrate the advantages to be attained by using HMISs in healthcare delivery and to ascertain the factors that influence the uptake of such systems in the public healthcare sector. A conceptual model, extending the technology, organization, and environment framework by means of other adoption models, underpins the study of adoption behavior. A mixed method methodology was used to conduct the study. For the quantitative approach, questionnaires were used to allow for regression analysis. For the qualitative approach, thematic analysis was used to analyse interview data. The results showed that the critical success factors (namely, relative advantage, availability, complexity, compatibility, trialability, observability, management support, information and communication technology expertise, communication processes, government regulation, infrastructure support, organizational readiness, industry and competitive support, external support, perceived ease of use, perceived usefulness, attitude, and intention to use) influenced adoption of HMISs in public hospitals in Zimbabwe.

주제어

표/그림 (13)

그림 Fig. 1. Relationships between adoption models. TRA, theory of reasoned action; DOI, diffusion of innovation; TOE, technology, organization, and environment; TAM, technology acceptance model; TPB, theory of planned behavior.
표 Table 1. Conceptual framework constructs
그림 Fig. 2. Research model and hypotheses: H1, Environmental factors influence technological decision making on adoption; H2, Technological factors influence technological decision making on adoption; H3, Organizational factors influence technological decision making on adoption; H4, Individual factors influence technological decision making on adoption; H5, Technological decision making on adoption influences adoption. ICT, information and communication technology.
그림 Fig. 3. Convergence model of mixed method data analysis for the study. QUAN, quantitative; QUAL, qualitative. Adapted from Creswell, 2014. Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.
표 Table 2. Participants profile for critical success factors questionnaire
표 Table 3. Summary of participant profile
표 Table 4. Summary of themes, subthemes, and codes
표 Table 4. Continued
표 Table 5. Factor analysis results
표 Table 6. Hypotheses results
표 Table 7. Themes, codes, and RQ
그림 Fig. 4. Final health management information system adoption model. ICT, information and communication technology.
표 Table 8. Main factor level test results

AI 본문요약
AI-Helper

가설 설정

H1
H2
H3
H4
Hypotheses 1-3 are direct derivatives of the original TOE model with additional constructs from DOI; hypothesis 4 is an addition, adapted from the mixture of TOE and technology acceptance theories (TRA, TPB, TAM, TAM2), and H5 is from the TOE model.

제안 방법

The interview data showed that the following factors influenced HMIS adoption in public hospitals in Zimbabwe: the organizational factor constructs of communication processes, management support, organizational readiness, and ICT expertise; the technological factor constructs of technology availability, relative advantage, compatibility, complexity, trialability, and observability; the environmental factor constructs of external support, technology infrastructure support and government regulation; and the individual factor constructs PEOU, PU, attitude, and intention. Because of the discrepancy between the results of the interviews and the questionnaires, a third instrument was designed, including constructs that had been validated in either the interviews or the questionnaire. The set of the constructs were validated as shown in the HMIS model in Fig.
Where divergent results were obtained in quantitative and qualitative findings, the researcher considered the potential complementarity and completeness of the findings. Confirmation of findings was addressed in a later phase of the study by use of a third set of data collected to evaluate the model developed after the first two sets of data collection and analysis. The approach is depicted in Fig.
Validity can be ensured in two ways, namely internal and external. Internal validity ensures the obtained results are legitimate, and for this study it was ensured by the way that the respondents were selected (health workers), and the way that the data was handled, i.e., recorded and analysed.
The evaluation questionnaire was made up of closed and open-ended questions. The questionnaire was made up of six sections, which were designed for evaluating the fit of the various factors in the model.
The goal of this research study is to ascertain the CSFs that drive the adoption of an HMIS in Zimbabwe. The factors from the technology, organization, and environment (TOE) theory are used with additional constructs from other organizational and individual theories, such as the diffusion of innovation (DOI) theory, theory of reasoned behavior (TRB), theory of reasoned action (TRA), and the technology acceptance model (TAM), in order to construct a modified theoretical framework for explaining the CSFs influencing the adoption of HMISs at the organizational level.
The focus of this research is therefore to test the relationships among the constructs of HMIS adoption in the context of technology, organization, environment, and individual characteristics. In the investigation of technological, organizational, environmental, and individual factors affecting the adoption of HMIS by health institutions, 21 constructs were used in order to extend the existing knowledge base of technology adoption.
The evaluation questionnaire was made up of closed and open-ended questions. The questionnaire was made up of six sections, which were designed for evaluating the fit of the various factors in the model. The first section was designed to test for the general agreement of the participants with the model’s adequacy, completeness, and applicability.
The constructs retained after the Cronbach’s test were the ones deemed to be internally consistent. They were subjected to correlational and regression testing in a bid to come up with the existing relationships between the constructs and the DV as well as to ascertain the predictors of HMIS adoption. Those with a Cronbach’s value of less than 0.

대상 데이터

The total number of distributed questionnaires was 360 as per the calculated sample size distributed to 3 participating institutions, and each had an allocation of 120 questionnaires. At the end of the data collection period, 122 questionnaires were deemed usable from those received. Usable questionnaires are those that were correctly filled in and with missing information, if any, deemed to be insignificant to distorting the results.
The participants included 36 hospital staff and four academic experts (employed in the academic field and with an interest in adoption studies as shown by their publications). The researcher contacted the participants and sought their consent.
The interviews were held at times convenient to the participants and at their places of work so as not to inconvenience their work schedules and to minimise costs for the researcher. The researcher interviewed two participants from one hospital and four participants from each of the other two hospitals. All interviewees agreed to accommodate follow-up questions by e-mail or telephone if clarity on their responses was needed.
Each institution was allocated the same number of questionnaires. The sample for the study comprised 360 participants.
Purposive sampling was used for the CSF interviews. Those interviewed were senior personnel in the medical fraternity and a small number of participants from administration or service departments, namely, the medical superintendent, sisters-in-charge, accountants, human resource administrators, and IT personnel. The total number of participants interviewed was 10.

데이터처리

These techniques help researchers to draw conclusions and find trends in data about a population, helping them to study the connections between variables in a sample and to make generalisations and predictions about the relationships between the variables within the entire population. The inferential statistics used for this study included factor analysis, correlation analysis, and regression analysis.
The quantitative part of the questionnaire was analysed using the Wilcoxon signed ranks test (non-parametric equivalent to the one-sample t-test), in which the median is tested against the central score of ‘3’ to determine significance of agreement or disagreement.

이론/모형

The open response questions in the model confirmation questionnaire were analysed using the thematic analysis technique using R-QDA software.

성능/효과

Section E was designed to cover individual factors and Section F was designed to cover general hospital operations. A 5-point Likert scale was used with 1 denoting strongly disagree and 5 denoting strongly agree to rate the questions in the main sections of the questionnaire.
Although random sampling was used, the information can be deemed to be reliable to localisation of the research to a particular area where the respondents were typical or representative of the whole population. A high level of validity (internal and external) was achieved through strict protocol execution and study design.
Reliability and validity were ensured by monitoring the parameters of the instruments to maintain the same properties. A pilot test was used to ensure reliability by giving the instrument to different respondents in different health institutions to check the consistency of the results prior to the main data collection exercise. Validity can be ensured in two ways, namely internal and external.
23 health staff per 1,000 population in Zimbabwe. According to the WHO, density=population/workers, which in this study gives an estimate of 1,829 health workers comprising the target population for the province.
All the codes and themes answer all four research questions as well as the fifth research question (“How can the CSFs be integrated into an adoption model for HMIS in public health institutions in Zimbabwe?”), helping us to come up with a model that explains the factors at play in the organization’s adoption of technology, as shown in the Fig
Several conclusions can be drawn from the results presented, and these pertain to the research objectives. Although the sample from which data was gathered is small, especially for qualitative data, the researcher believes that the results provide meaningful insights that could be generalised to adoption experiences of HMIS in public health institutions in Zimbabwe.
Because of the discrepancy between the CSF questionnaire and CSF interview results and in order to achieve completeness, complementarity, and triangulation, another instrument was designed and administered. The results of the two methods of correlation and thematic analyses are presented in the two sections below.
ESP, external support; GR, government regulation, PRES, industry and competitive pressure; TA, availability; RA, relative advantage 9; CBT, compatibility; OB, observability; OR, organisational readiness; SI, support and skills; PU, perceived usefulness; PEOU, perceived ease of use; SN, subjective norm; F, adoption.
From the quantitative results, only three factors within the technological, organizational, and environmental context proved to be significant and to have an influence in the adoption of HMIS by public hospitals in Zimbabwe.
Hypotheses 1-3 are direct derivatives of the original TOE model with additional constructs from DOI; hypothesis 4 is an addition, adapted from the mixture of TOE and technology acceptance theories (TRA, TPB, TAM, TAM2), and H5 is from the TOE model.
The focus of this research is therefore to test the relationships among the constructs of HMIS adoption in the context of technology, organization, environment, and individual characteristics. In the investigation of technological, organizational, environmental, and individual factors affecting the adoption of HMIS by health institutions, 21 constructs were used in order to extend the existing knowledge base of technology adoption.
No potential conflict of interest relevant to this article was reported.
Purposive sampling was the choice of the researchers in selecting respondents for the questionnaire designed to evaluate the HMIS adoption model, as the researcher sought participants who had managerial and ICT insights.
Reliable composite measures were computed for the main factor levels, namely organization, technology, environment, and individual, to measure the significance of the main factors for adoption of HMIS. The results are given in Table 8.
The constructs retained after the Cronbach’s test were the ones deemed to be internally consistent
The results of the CSF questionnaire proved only three factors as significant for the adoption of HMIS, namely, the environmental factor construct of industry and competitive pressure, the technological factor construct of relative advantage, and the organizational factor construct of management support and available skills. The interview data showed that the following factors influenced HMIS adoption in public hospitals in Zimbabwe: the organizational factor constructs of communication processes, management support, organizational readiness, and ICT expertise; the technological factor constructs of technology availability, relative advantage, compatibility, complexity, trialability, and observability; the environmental factor constructs of external support, technology infrastructure support and government regulation; and the individual factor constructs PEOU, PU, attitude, and intention. Because of the discrepancy between the results of the interviews and the questionnaires, a third instrument was designed, including constructs that had been validated in either the interviews or the questionnaire.
The model was derived from the combination of TOE, DOI, TRA, TPB, TAM, and other adoption models and was tested for relevance and confirmation in public health provision settings. The model and hypotheses are shown in Fig.
2. The more agreement that current technology gives the organization relative advantage, the higher the adoption of HMIS technologies, p=0.031.
3. The more agreement that there is management support and available skills, the higher the adoption of HMIS technologies, p=0.003.
1. The more agreement there is that industry and competitive pressure (external pressure) is good for the organization, the higher the adoption of HMIS technologies, p=0.014.
The results also show that there is significant agreement that technological factors are important for adoption, as shown by the mean scores and the Wilcoxon signed ranks test (relative advantage M=4.55, p<0.005; trialability M=4.29, p<0.005; observability M=4.16, p<0.005 & M=3.84, p<0.005; availability M=3.77, p<005 & M=4.06, p<005; compatibility M=4.29, p<0.005 & M=4.26, p<005; and technology complexity M=4.13, p<0.005 & M=3.94, p<0.005).
The results of the CSF questionnaire proved only three factors as significant for the adoption of HMIS, namely, the environmental factor construct of industry and competitive pressure, the technological factor construct of relative advantage, and the organizational factor construct of management support and available skills. The interview data showed that the following factors influenced HMIS adoption in public hospitals in Zimbabwe: the organizational factor constructs of communication processes, management support, organizational readiness, and ICT expertise; the technological factor constructs of technology availability, relative advantage, compatibility, complexity, trialability, and observability; the environmental factor constructs of external support, technology infrastructure support and government regulation; and the individual factor constructs PEOU, PU, attitude, and intention.
The results show that the participants agreed that the organizational (M=4.45, p<0.005), environmental (M=4.21, p<0.005), technological (M=4.09, p<0.005), and individual (M=4.12, p<0.005) factors are important for adoption of HMIS.
The results showed that environmental (external support, support infrastructure, government support, market scope, industry and competitive pressure), technological (relative advantage, trialability, technology availability, observability, compatibility, complexity), organizational (management support, size, communication processes, organizational readiness, ICT expertise) and individual factors (PEOU, PU, attitude, intention to use) do have an influence on the adoption of HMIS in Zimbabwe, as shown in Table 7.
The tests conducted proved that there is significant agreement that organizational factors are important for adoption of HMISs (communication processes M=4.52, p<0.0005; management support M=4.35, p<0.005 & M=4.29, p<0.005; organizational readiness M=4.48, p<0.005 & M=4.39; and ICT expertise M=4.65, p<0.005).
The total number of distributed questionnaires was 360 as per the calculated sample size distributed to 3 participating institutions, and each had an allocation of 120 questionnaires. At the end of the data collection period, 122 questionnaires were deemed usable from those received.
Managers of adoption should also assess all possible factors that can affect the success of the adoption process and should not concentrate on technological, organizational, and environmental factors only but should, in addition, assess the behaviorbehavioral factors of the individuals within the organization. The traditional approach of separating organizational from individual adoption and treating them as primary and secondary should be discontinued to allow the success rate of technological adoption to increase and, more importantly, to enable the impact of adopted technologies for the health sector to result in improved organizational processes and ultimately high-quality patient care.
Those who indicated their willingness were sent the questionnaire together with the informed consent form by electronic means. Thirty-one (31) questionnaires were returned in total and were usable, of which 28 were from hospital employees while three were from academic experts.
The total number of participants interviewed was 10. This number was deemed sufficient and representative as there are generally fewer management and administrative staff members in relation to the entire staff complement at the hospital.

후속연구

The adoption of HMISs in Zimbabwe would provide horizontal and vertical linkages of health systems such that patient data can be shared across all health systems with the aim of providing improved healthcare to patients. It should encompass all the aspects of healthcare, such as patient data storage, management of healthcare, and allocation of resources.
The goal of this research study is to ascertain the CSFs that drive the adoption of an HMIS in Zimbabwe. The factors from the technology, organization, and environment (TOE) theory are used with additional constructs from other organizational and individual theories, such as the diffusion of innovation (DOI) theory, theory of reasoned behavior (TRB), theory of reasoned action (TRA), and the technology acceptance model (TAM), in order to construct a modified theoretical framework for explaining the CSFs influencing the adoption of HMISs at the organizational level.
The main research aim of this study was to find the technological, organizational, environmental, and individual factors that are critical for the successful adoption of HMISs in Zimbabwean healthcare institutions. Appropriate research constructs validated in prior studies were borrowed from the literature and used for questionnaire development.
Hospitals are recommended to implement the HMIS technology as it allows for improved service delivery. The use of HMIS will allow for streamlined resource allocation, effective management and monitoring, and consequently its use will allow for improved services to the patients. Resources are a challenge and hospitals can come up with fundraising initiatives such as billing the patients towards the implementation of the HMIS, so as to improve service delivery through technology.

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표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

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