Factors affecting quality management at the Thai national institute of health

Chansatitporn, Natkamol; Pobkeeree, Vallerut; Nongkhai, Surachart Na; Sangkijporn, Somchai

doi:10.1108/ijhcqa-05-2018-0107

Cited by 6 publications

(7 citation statements)

References 59 publications

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“…This negatively affects the commitment and value given to data by the healthcare providers. This finding is also consistent with studies from the Southern Nations, Nationalities, and People's Region (SNNPR) of Ethiopia ( 20 , 31 ), Iran ( 28 ), Thailand ( 33 ), Canada ( 1 ), and Kenya ( 30 ). Health facility managers and other responsible bodies usually do not use the collected data owing to reliability and accuracy problems.…”

Section: Discussionsupporting

confidence: 90%

Barriers to healthcare data quality and recommendations in public health facilities in Dire Dawa city administration, eastern Ethiopia: a qualitative study

Tolera,

Firdisa,

Roba

et al. 2024

Front. Digit. Health

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BackgroundMaintaining good quality of healthcare data at various levels is a critical challenge in developing countries. The barriers to healthcare data quality remain largely unexplored in eastern Ethiopia.ObjectiveThis study aimed to assess the barriers to quality of healthcare data in urban public health facilities in the Dire Dawa city administration from 7 April to 7 May 2019.MethodsAn institutional-based qualitative exploratory approach was used among 17 purposefully selected key informants. In-depth interviews were inductively coded using the ATLAS.ti 7.5.4 version software. Inductive analysis was used by semantically analyzing the explicit content of the data to determine our themes.ResultsSeveral key themes and subthemes with different barriers, some of which are mutually non-exclusive, were identified. These include: Organizational Barriers: Lack of an adequate health management information system and data clerk staff, poor management commitment, lack of post-training follow-up, work overload, frequent duty rotation, lack of incentives for good performers, lack of targeted feedback, and poor culture of information use. Behavioral/Individual Barriers: Gaps in the skill of managers and health professionals, lack of adequate awareness of each indicator and its definitions, inadequate educational competence, lack of feeling of ownership, poor commitment, lack of daily tallying, and lack of value for data. Technical Barriers: Lack of a standard form, diverse and too many data entry formats, manual data collection, shortage of supplies, failure to repair system break down in a timely manner, interruption in electricity and network, delay in digitizing health information systems, lack of post-training follow-up, and inadequate supervision. External Barriers: Poor collaboration between stakeholders, dependence on the software program of non-governmental organizations, and very hot weather conditions.ConclusionDiverse and complex barriers to maintenance of data quality were identified. Developing standardized health management information system implementation plans, providing advanced supervisory-level training, supportive supervision, and site-level mentorship may be very effective in identifying and resolving bottleneck data quality issues. Healthcare managers should understand the imperative of data quality and accept responsibility for its improvement and maintenance. Interventions targeted only at supplies will not fully overcome limitations to data quality. Motivation of staff and recognition of best performance can motivate others and can create cooperation among staff.

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Section: Discussionsupporting

confidence: 90%

Barriers to healthcare data quality and recommendations in public health facilities in Dire Dawa city administration, eastern Ethiopia: a qualitative study

Tolera,

Firdisa,

Roba

et al. 2024

Front. Digit. Health

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“…Self-leadership mainly leads to self-direction and control for a person who sets his/her own expectations. Chansatitporn et al (2019) noted that self-leadership can be found in staff members of any level who have leadership attributes within an organization and can facilitate organizational learning (Yukl, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Leadership and quality management measurement models: an empirical study

Chansatitporn

Pobkeeree

2019

IJHCQA

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Purpose The purpose of this paper is to explore, confirm and verify leadership with regards to quality management measurement models. This research focused on identifying individual staff members’ leadership attributes at the Thai National Institute of Health in relation to quality management. Design/methodology/approach The research instrument used in this study was a modified questionnaire on self-leadership and quality management that was distributed to the institute’s staff. Leadership and quality management construct variables were observed and measured through staff perceptions, attitudes, practices and existing facts at the institute. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to examine the data. Findings The questionnaire had a 65 percent response rate. EFA revealed six factors from 27 questionnaire items and CFA was used to confirm the measurement models that were fitted to the data. The leadership attributes of staff members at the institute were statistically associated to and impacted on quality management by SEM analysis. Research limitations/implications In-depth understanding of leadership and quality management could be done through a longitudinal study because the two factors would change over time. Even though this model is not a longitudinal study, it could help the institute facilitate and manage quality in practice through leadership. Originality/value A cross-sectional study is used to examine the effect of leadership on quality management through factor analysis and SEM, which provided empirical evidence for future research. Leadership and quality management measurement models have statistically proven to be appropriately, technically and theoretically correct by design for observing variables used in the leadership measurement model that affects quality management.

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“…Most of the operations management papers in healthcare settings have presented context specific empirical studies. Evidence related to the above directions are visible in the studies described in the research papers such as Alaloola and Albedaiwi (2008), Mustaffa and Potter (2009), Ferreira and Teixeira (2010), Walston et al (2010), Robinson et al (2012), Akpieyi et al (2015) and Chansatitporn et al (2019). These papers highlight the issues in healthcare management and institutions located in different regions.…”

Section: Introductionmentioning

confidence: 95%

Analysis of the operational risk factors in public hospitals in an Indian state

Chalavadi

Sridharan

Kumar

et al. 2019

IJHCQA

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Purpose Risk management in the healthcare sector is a highly relevant sub-domain and a crucial research area from the humanitarian perspective. The purpose of this paper is to focus on the managerial/supply chain risk factors experienced by the government hospitals in an Indian state. The present paper analyzes the inter-relationships among the significant risk factors and ranks those risk factors based on their criticality. Design/methodology/approach The current research focuses on 125 public hospitals in an Indian state. Questionnaire-based survey and personal interviews were conducted in the healthcare sector among the inpatients and hospital staff to identify the significant risk factors. An integrated DEMATEL–ISM–PROMETHEE method is adopted to analyze the impact potential and dependence behavior of the risk factors. Findings The analysis asserts the absence of critical risk factors that have a direct impact on patient safety in the present healthcare system under investigation. However, the results illustrate the remarkable impact potential attributed to the risk factor, namely, staff shortage in inducing other risk factors such as employee attitudinal issues, employee health issues and absenteeism altogether resulting in community mistrust/misbeliefs. Maintenance mismanagement, monsoon time epidemics, physical infrastructure limitations are also found to be significant risk factors that compromise patient satisfaction levels. Practical implications Multiple options are illustrated to mitigate significant risk factors and operational constraints experienced by public hospitals in the state. The study warrants urgent attention from government officials to fill staff vacancies and to improve the infrastructural facilities to match with the increasing demand from the society. Furthermore, this research recommends the hospital authorities to start conducting induction and training programs for the hospital employees to instill the fundamental code of conduct while working in hectic, challenging and even in conditions with limited resources. Originality/value Only limited papers are visible that address the identification and mitigation of risk factors associated with hospitals. The present paper proposes a novel DEMATEL–ISM–PROMETHEE integrated approach to map the inter-relationships among the significant risk factors and to rank those risk factors based on their criticality. Furthermore, the present study discloses the unique setting of the public healthcare system in a developing nation.

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Factors affecting quality management at the Thai national institute of health

Cited by 6 publications

References 59 publications

Barriers to healthcare data quality and recommendations in public health facilities in Dire Dawa city administration, eastern Ethiopia: a qualitative study

Barriers to healthcare data quality and recommendations in public health facilities in Dire Dawa city administration, eastern Ethiopia: a qualitative study

Leadership and quality management measurement models: an empirical study

Analysis of the operational risk factors in public hospitals in an Indian state

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