Table 1 Existence studies in literature.
S. no. | Author (year) | Type of quality characteristic | Description |
|---|---|---|---|
1 | Poloniecki et al.9 | Binary | To detect changes in post-surgery mortality while considering variations in case mix, the study focused on binary quality characteristics monitoring during Phase II, following the Bernoulli distribution |
2 | Steiner et al.10 | Binary | The procedure is exemplified using bivariate outcome data from a series of pediatric surgeries, with methodology adaptable for multivariate normal, binomial, or Poisson responses |
3 | Lovegrove et al.11 | Binary | The refinement of the cumulative sum method offers a comprehensive display of surgical performance over time by accounting for each patient's risk status in assessing cardiac surgery outcomes |
4 | Steiner and Jones12 | Binary | propose an updating EWMA control chart for monitoring risk-adjusted survival times in continuous time, offering ongoing estimates with favorable efficiency compared to other methods |
5 | Cook et al.13 | Binary | Risk-adjusted process control charting procedures for continuous monitoring of intensive care unit outcomes, incorporating risk adjustment based on the Acute Physiology and Chronic Health Evaluation III model, are proposed as quality management tools |
6 | Biswas and Kalbfleisch14 | Continuous | A risk-adjusted CUSUM procedure based on the Cox model for failure time outcomes is proposed and evaluated through simulations and application to transplant facility data from the Scientific Registry of Transplant Recipients |
7 | Sego et al.15 | Continuous | A risk-adjusted survival time CUSUM chart is proposed for monitoring continuous, right-censored variables, showing higher efficiency in detecting mortality odds increases compared to the RA Bernoulli CUSUM chart, especially with low censored observations or small mortality odds increases |
9 | Paynabar et al.16 | Binary | The paper introduces a risk-adjusted control chart for binary surgical outcomes, incorporating surgeon groups as categorical covariates to improve detection performance |
10 | Mohammadian et al.17 | Binary | A novel risk-adjusted geometric control chart for monitoring patient survival post-surgery outperforms binary variable charts in power, demonstrated through simulations and a case study |
12 | Aminnayeri and Sogandi18 | Binary | proposed risk-adjusted Bernoulli cumulative sum control charts utilize dynamic probability control limits, offering robust performance across various shifts, without assumptions about patients' risk distributions or process parameters |
13 | Zhang et al.19 | Binary | apply dynamic probability control limits to risk-adjusted CUSUM charts for multiresponses, showing through simulation that their in-control performance can be tailored for different patient populations, eliminating the need for estimating or monitoring patients' risk distribution |
14 | Sogandi et al.20 | Binary | This study introduces a Bernoulli state-space model with latent risk variables and dynamic probability control limits for monitoring multistage medical processes, showing satisfactory performance in identifying out-of-control stages and addressing corresponding causes |
15 | Asif and Noor-ul-Amin21 | Binary | Proposed an adaptive risk-adjusted EWMA (ARAEWMA) control chart using AFT regression, outperforming traditional methods in detecting shifts in cardiac surgery patient data |
16 | Yeganeh et al.22 | Binary | The paper proposes an ANN-based control chart with heuristic training for monitoring binary surgical outcomes, demonstrating superior performance compared to existing methods based on ARL, along with real-life applications and robustness analysis using the Beta distribution |
