ArticleHealth systems (Basingstoke, England)2018
Using the theory of constraints' processes of ongoing improvement to address the provider appointment scheduling system design problem.
Article in Health systems (Basingstoke, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Towards a solution to the global healthcare crisis: using hierarchical decomposition and theory of constraints (TOC) to address the healthcare supply chain wicked problem.Health systems (Basingstoke, England) · 2025Article
- Achieving rapid and significant results in healthcare services by using the theory of constraints.Health systems (Basingstoke, England) · 2024Article
- Outcomes of managing healthcare services using the Theory of Constraints: A systematic review.Health systems (Basingstoke, England) · 2022Article
- Using the theory of constraints to create a paradigm shift in organisation performance at a large primary care provider practice.Health systems (Basingstoke, England) · 2022Article
- Identifying and Addressing the Underlying Core Problems in Healthcare Environments: An Illustration Using an Emergency Department Game.International journal of environmental research and public health · 2021Article
- Using the theory of constraints' processes of ongoing improvement to address the provider appointment scheduling system execution problem.Health systems (Basingstoke, England) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health care is in crisis today: costs are rising, demand exceeds supply, quality is questioned and patient wait times are excessive while providers and staff are simultaneously overworked and frustrated. No one has a comprehensive system solution to providing more, cheaper, better, and faster health care, even in primary care practices, the first link in the health care supply chain. Additionally, this link like others frequently experiences the combination of complexity, uncertainty, and local optimisation simultaneously to create a chaotic environment. Health care problems have been called ill-structured (also "wicked") and because of their tangled web of stakeholders with different and conflicting objectives defy traditional optimisation research methodologies. Proper design and management of the provider appointment scheduling system (PASS) provides a direction for a win-win health care solution (more, cheaper, better, and faster). Our objective is to provide a generic strawman process for developing a robust PASS for most environments. A theory of constraints thinking processes (TP) analysis was conducted on the academic research using a primary care practice to validate both entity and causality existence. From this integrated analysis, a robust process for designing a PASS resulted. Last, we show that Goldratt's TP provides a logical, rigorous framework for qualitative research and design science.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.