Continuous-time Markov models for geriatric patient behaviour. (English) Zbl 0910.92018
The authors propose a continuous-time Markov model for description of the movements of a cohort of patients entering the system at time \(t=0\). Patients are initially admitted into acute care \((S_{1})\), from where they may: (1) be released and therefore reenter the community of released patients \((S_{3})\); (2) die \((S_{4})\); or (3) they may be considered to be unable to look after themselves and therefor be moved into long-stay care \((S_{2})\) where they will eventually die \((S_{4})\). Patients who have been released back into the community \((S_{3})\) may be readmitted to acute care \((S_{1})\) or die \((S_{4})\).
The authors obtain the transition probability matrix and calculate the expected number of patients in a state \((S_{i}),\;i=1,\ldots,4\), and variances of these numbers. This model is fitted to data from St George’s Hospital, London, using the method of maximum likelihood.
The authors obtain the transition probability matrix and calculate the expected number of patients in a state \((S_{i}),\;i=1,\ldots,4\), and variances of these numbers. This model is fitted to data from St George’s Hospital, London, using the method of maximum likelihood.
Reviewer: A.D.Borisenko (Kyïv)
MSC:
92C50 | Medical applications (general) |
62P10 | Applications of statistics to biology and medical sciences; meta analysis |
60J20 | Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) |