A model for measuring effectiveness of quality management practices in health care
Bibliographic record
Abstract
Purpose Health care is an example of an organization where the needs of potential clients are much greater than the capabilities of the service delivery system. The implementation of any medical procedure, as well as the provision of any service, just like the manufacturing of any product, can be decomposed into a series of tasks. The purpose of this paper is to propose a model for measuring the effectiveness of quality assurance tasks in health-care delivery processes. Design/methodology/approach The authors analyze a system of factors that affect the implementation of tasks in a process. In their considerations, they have focused on four areas of science that describe conditions that are related to the implementation of tasks: Scheduling as a methodology for allocating resources to perform tasks; Capacity planning as a methodology for assigning values to given resources expressed by the number of tasks that can be executed with the resources; Queueing theory, used as a methodology for describing phenomena in which not all planned tasks are performed within the prescribed specification limits; and Quality management, as a methodology to ensure appropriate conditions for completing tasks (CCTs), where CCT is a representation of parameters of casual relationship between variables. Findings The authors show that the effectiveness of executing any scheduled tasks in the process is determined by the difference between the capacity of resources allocated (at a given time interval) and the number of tasks planned to be carried out at that time. The CCT conditions determine the level of capacity of the fixed amount of resources. It is shown that their deviation from the reference CCT specification may cause the nominally correct amount of resources be either too small (causing queue formation and longer wait time in hospitals) or too large to contribute to the waste in the system by creating idle capacity. Practical implications The scope of application of the model is wide. It covers tasks performed with different degrees of uncertainties regarding the capacity of resources. It applies in all areas of health care where unlike manufacturing, the services delivered and the tasks performed in the health-care delivery system are seldom identical. Every patient is treated differently than the one waiting next in line. The workloads are pre-arranged in the order they are needed and completed in accordance with the FI-FO (first in-first out) principle. The model presented in this paper makes it possible to better understand the mechanism of effectiveness and efficiency improvement and the role of humans as a specific carrier of capacity. Originality/value As most of the health-care organizations are still stuck in the soft side of quality assurance, there has been little research conducted to test the applicability of well-known productions/operation management methodologies and theories benefitting health-care systems. The formulation of a reference point of CCT in this study is to serve as a stabilizing control point with the same connotation as that of a central reference line in the statistical process control chart. The correct capacity planning is needed to determine with a high degree of probability of success in implementation of all tasks to assure quality all the time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".