Developing Healthcare Process Models: An Example from the Care Home System

2018 ◽  
pp. 475-480
Author(s):  
Rosemary Lim
PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260051
Author(s):  
Glenna Nightingale ◽  
Megan Laxton ◽  
Janine B. Illian

Objectives To model the risk of COVID-19 mortality in British care homes conditional on the community level risk. Methods A two stage modeling process (“doubly latent”) which includes a Besag York Mollie model (BYM) and a Log Gaussian Cox Process. The BYM is adopted so as to estimate the community level risks. These are incorporated in the Log Gaussian Cox Process to estimate the impact of these risks on that in care homes. Results For an increase in the risk at the community level, the number of COVID-19 related deaths in the associated care home would be increased by exp (0.833), 2. This is based on a simulated dataset. In the context of COVID-19 related deaths, this study has illustrated the estimation of the risk to care homes in the presence of background community risk. This approach will be useful in facilitating the identification of the most vulnerable care homes and in predicting risk to new care homes. Conclusions The modeling of two latent processes have been shown to be successfully facilitated by the use of the BYM and Log Gaussian Cox Process Models. Community COVID-19 risks impact on that of the care homes embedded in these communities.


2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.


2018 ◽  
Vol 115 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Brian M. Monroe ◽  
Bryan L. Koenig ◽  
Kum Seong Wan ◽  
Tei Laine ◽  
Swati Gupta ◽  
...  

1997 ◽  
Vol 17 (03) ◽  
pp. 166-169
Author(s):  
Judith O’Brien ◽  
Wendy Klittich ◽  
J. Jaime Caro

SummaryDespite evidence from 6 major clinical trials that warfarin effectively prevents strokes in atrial fibrillation, clinicians and health care managers may remain reluctant to support anticoagulant prophylaxis because of its perceived costs. Yet, doing nothing also has a price. To assess this, we carried out a pharmacoe-conomic analysis of warfarin use in atrial fibrillation. The course of the disease, including the occurrence of cerebral and systemic emboli, intracranial and other major bleeding events, was modeled and a meta-analysis of the clinical trials and other relevant literature was carried out to estimate the required probabilities with and without warfarin use. The cost of managing each event, including acute and subsequent care, home care equipment and MD costs, was derived by estimating the cost per resource unit, the proportion consuming each resource and the volume of use. Unit costs and volumes of use were determined from established US government databases, all charges were adjusted using cost-to-charge ratios, and a 3% discount rate was applied to costs incurred beyond the first year. The proportions of patients consuming each resource were estimated by fitting a joint distribution to the clinical trial data, stroke outcome data from a recent Swedish study and aggregate ICD-9 specific, Massachusetts discharge data. If nothing is done, 3.2% more patients will suffer serious emboli annually and the expected annual cost of managing a patient will increase by DM 2,544 (1996 German Marks), from DM 4,366 to DM 6,910. Extensive multiway sensitivity analyses revealed that the higher price of doing nothing persists except for very extreme combinations of inputs unsupported by literature or clinical standards. The price of doing nothing is thus so high, both in health and economic terms, that cost-consciousness as well as clinical considerations mandate warfarin prophylaxis in atrial fibrillation.


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