This study was designed to cross-validate a composite measure of the pain scales CHEOPS (Children's Hospital of Eastern Ontario Pain Scale), OPS (Objective Pain Scale, simplified for parent use by replacing blood pressure measurement with observation of body language or posture), TPPPS (Toddler Preschool Postoperative Pain Scale) and FLACC (Face, Legs, Activity, Cry, Consolability) in 167 Thai children aged 1-5.5 yr. The pain scales were translated and tested for content, construct and concurrent validity, including inter-rater and intra-rater reliabilities. Discriminative validity in immediate and persistent pain for the age groups < or =3 and >3 yr were also studied. The children's behaviour was videotaped before and after surgery, before analgesia had been given in the post-anaesthesia care unit (PACU), and on the ward. Four observers then rated pain behaviour from rearranged videotapes. The decision to treat pain was based on routine practice and was made by a researcher unaware of the rating procedure. All tools had acceptable content validity and excellent inter-rater and intra-rater reliabilities (intraclass correlation >0.9 and >0.8 respectively). Construct validity was determined by the ability to differentiate the group with no pain before surgery and a high pain level after surgery, before analgesia (P<0.001). The positive correlations among all scales in the PACU and on the ward (r=0.621-0.827, P<0.0001) supported concurrent validity. Use of the kappa statistic indicated that CHEOPS yielded the best agreement with the routine decision to treat pain. The younger and older age groups both yielded very good agreement in the PACU but only moderate agreement on the ward. On the basis of data from this study, we recommend CHEOPS as a valid, reliable and practical tool.

Background The typical hospital Length of Stay (LOS) distribution is known to be right-skewed, to vary considerably across Diagnosis Related Groups (DRGs), and to contain markedly high values, in significant proportions. These very long stays are often considered outliers, and thin-tailed statistical distributions are assumed. However, resource consumption and planning occur at the level of medical specialty departments covering multiple DRGs, and when considered at this decision-making scale, extreme LOS values represent a significant component of the distribution of LOS (the right tail) that determines many of its statistical properties. Objective To build actionable statistical models of LOS for resource planning at the level of healthcare units. Methods Through a study of 46, 364 electronic health records over four medical specialty departments (Pediatrics, Obstetrics/Gynecology, Surgery, and Rehabilitation Medicine) in the largest hospital in Thailand (Siriraj Hospital in Bangkok), we show that the distribution of LOS exhibits a tail behavior that is consistent with a subexponential distribution. We analyze some empirical properties of such a distribution that are of relevance to cost and resource planning, notably the concentration of resource consumption among a minority of admissions/patients, an increasing residual LOS, where the longer a patient has been admitted, the longer they would be expected to remain admitted, and a slow convergence of the Law of Large Numbers, making empirical estimates of moments (e.g. mean, variance) unreliable. Results We propose a novel Beta-Geometric model that shows a good fit with observed data and reproduces these empirical properties of LOS. Finally, we use our findings to make practical recommendations regarding the pricing and management of LOS.

The typical hospital Length of Stay (LOS) distribution is known to be right-skewed, to considerably vary across Diagnosis Related Groups (DRG), and to contain markedly high values, in significant proportions. These very long stays are often considered outliers, and thin-tailed statistical distributions are assumed. Moreover, modeling is typically performed by Diagnosis Related Group (DRG) and is consequently based on small empirical samples, thus justifying the previous assumption. However, resource consumption and planning occur at the level of medical specialty departments covering multiple DRG, and when considered at this decision-making scale, extreme LOS values represent a significant component of the distribution of LOS (the right tail) that determines many of its statistical properties. Through a study of 46,364 electronic health records over four medical specialty departments (Pediatrics, Obstetrics/Gynecology, Surgery, and Rehabilitation Medicine) in the largest hospital in Thailand (Siriraj Hospital in Bangkok), we show that the distribution of LOS exhibits a tail behavior that is consistent with a subexponential distribution. We analyze some empirical properties of such a distribution that are of relevance to cost and resource planning, notably the concentration of resource consumption among a minority of admissions/patients, an increasing residual LOS, where the longer a patient has been admitted, the longer they would, counter-intuitively, be expected to remain admitted, and a slow convergence of the Law of Large Numbers, making empirical estimates of moments (e.g. mean, variance) unreliable. Consequently, we propose a novel Beta-Geometric model that shows a good fit with observed data and reproduces these empirical properties of LOS. Finally, we use our findings to make practical recommendations regarding the pricing and management of LOS.