Are there decision support tools that might strengthen the health system for perinatal care in South African district hospitals? A review of the literature

Abstract

BackgroundSouth Africa has a high burden of perinatal deaths in spite of the availability of evidence-based interventions. The majority of preventable perinatal deaths occur in district hospitals and are mainly related to the functioning of the health system. Particularly, leadership in district hospitals needs to be strengthened in order to decrease the burden of perinatal mortality. Decision-making is a key function of leaders, however leaders in district hospitals are not supported to make evidence-based decisions. The aim of this research was to identify health system decision support tools that can be applied at district hospital level to strengthen decision-making in the health system for perinatal care in South Africa.MethodsA structured approach, the systematic quantitative literature review method, was conducted to find published articles that reported on decision support tools to strengthen decision-making in a health system for perinatal, maternal, neonatal and child health. Articles published in English between 2003 and 2017 were sought through the following search engines: Google Scholar, EBSCOhost and Science Direct. Furthermore, the electronic databases searched were: Academic Search Complete, Health Source – Consumer Edition, Health Source – Nursing/Academic Edition and MEDLINE.ResultsThe search yielded 6366 articles of which 43 met the inclusion criteria for review. Four decision support tools identified in the articles that met the inclusion criteria were the Lives Saved Tool, Maternal and Neonatal Directed Assessment of Technology model, OneHealth Tool, and Discrete Event Simulation. The analysis reflected that none of the identified decision support tools could be adopted at district hospital level to strengthen decision-making in the health system for perinatal care in South Africa.ConclusionThere is a need to either adapt an existing decision support tool or to develop a tool that will support decision-making at district hospital level towards strengthening the health system for perinatal care in South Africa.

Is Universal Health Coverage Affordable? Estimated Costs and Fiscal Space Analysis for the Ethiopian Essential Health Services Package

“…48,49 Furthermore, since OHT can only help to calculate point estimates (i.e., OHT does not provide confidence interval or probabilistic sensitivity analysis results), only one-way and multi-way sensitivity analysis results were obtained by varying the inputs values for key parameters. [49][50][51] Third, such types of sector-wide analysis of cost are a complicated and data-hungry process. Therefore, we employed a mixed costing approach (i.e., combining ingredient-based bottom-up and program-based summary costing approaches) in that some of the inputs were based on retrospective expenditure information from reports, budgetary information, or expert judgment for the whole program.…”

Preceptorship in nursing-midwifery: a training-intervention in health work

“…Change processes related to professional practice itself were experienced resulting from the need to maintain a closer relationship with management and other team professionals. Thus, commitments, confrontations and mediations in the field of ethical-aesthetic-political values were revealed, which enhanced training with intervention similar to the devices that support decision-making in the care organization process (10,24) .…”

Implementing electronic decision-support tools to strengthen healthcare network data-driven decision-making

“…To our knowledge, few examples of decision-support tools have been implemented at scale to strengthen data-driven decision-making throughout a health network in LMICs [4,12]. The use of decision-support tools, together with decision-making mechanisms, with clear response times, roles and responsibilities for each organizational level (central, district and health facilities), empowered the MOH in Chiapas to improve rapidly and achieve indicator targets in a short timeframe.…”

“…Examples include electronic scorecards, dashboards, and electronic medical records with automated skip-patterns. Although several publications highlight the need for evidence-based decision making, there are only few examples of interventions to strengthen datadriven decision making [4,9,12]. While technology is important, its success relies on the effective implementation of decision-making mechanisms in which stakeholders have clear response times, roles, and responsibilities, and data is linked to actionable information.…”

“…While technology is important, its success relies on the effective implementation of decision-making mechanisms in which stakeholders have clear response times, roles, and responsibilities, and data is linked to actionable information. Most publications on decision-support tools in LMICs describe improvements in strategic planning or clinical decision-making, examples decision-support tools to improve decision-making in healthcare networks are limited [12].…”