Sequence Analysis of Long-Term Readmissions among High-Impact Users of Cerebrovascular Patients

Abstract

Objective Understanding the chronological order of the causes of readmissions may help us assess any repeated chain of events among high-impact users, those with high readmission rate. We aim to perform sequence analysis of administrative data to identify distinct sequences of emergency readmissions among the high-impact users. Methods A retrospective cohort of all cerebrovascular patients identified through national administrative data and followed for 4 years. Results Common discriminating subsequences in chronic high-impact users (n = 2863) of ischaemic stroke (n = 34208) were “urological conditions-chest infection,” “chest infection-urological conditions,” “injury-urological conditions,” “chest infection-ambulatory condition,” and “ambulatory condition-chest infection” (p < 0.01). Among TIA patients (n = 20549), common discriminating (p < 0.01) subsequences among chronic high-impact users were “injury-urological conditions,” “urological conditions-chest infection,” “urological conditions-injury,” “ambulatory condition-urological conditions,” and “ambulatory condition-chest infection.” Among the chronic high-impact group of intracranial haemorrhage (n = 2605) common discriminating subsequences (p < 0.01) were “dementia-injury,” “chest infection-dementia,” “dementia-dementia-injury,” “dementia-urine infection,” and “injury-urine infection.” Conclusion. Although common causes of readmission are the same in different subgroups, the high-impact users had a higher proportion of patients with distinct common sequences of multiple readmissions as identified by the sequence analysis. Most of these causes are potentially preventable and can be avoided in the community. Conclusion Although common causes of readmission are the same in different subgroups, the high-impact users had a higher proportion of patients with distinct common sequences of multiple readmissions as identified by the sequence analysis. Most of these causes are potentially preventable and can be avoided in the community.

Analytical methods for identifying sequences of utilization in health data: a scoping review

“…Looking at the entire list of authors of the articles included in each case, several overlaps in authorship were identified. This includes two articles considering Japanese data [ 37 , 41 ], four articles using Italian and French data [ 28 – 31 ], two articles using British data [ 52 , 53 ], four studies using mainly Chinese data [ 9 , 61 , 62 , 66 ], two more articles using Chinese data [ 24 , 25 ] and another five articles using data from various countries [ 10 , 37 , 43 , 49 , 59 ]. The bibliography contains more information about the authors of the studies.…”

“…Further studies use prescription data (11, 22 %) as care events, which can include various events related to medication and treatment. In nearly half of the included records (23, 45 %), visits to medical institutions were analyzed, including consultations in general [ 5 , 45 ] as well as inpatient visits [ 6 , 36 , 46 , 48 , 52 , 53 , 61 ], outpatient consultations [ 6 , 36 , 45 , 48 , 55 , 59 ], hospitalizations [ 30 , 31 , 50 ] and the consultation of specialist physicians [ 5 , 26 , 58 ]. The studies included sometimes considered additional aspects of health care.…”

“…This included statistical techniques applying conditional probability (2) [ 47 , 54 ], a probability based patient pathway analysis (1) [ 40 ], and a presentation of a stochastic optimization scheme to perform group-specific temporal event signatures based on convolutional non-negative matrix factorization (cNMF) with added sparsity regularization (1) [ 59 ]. State sequence analysis, a sequence cluster method originally from the social sciences for the analysis of life histories, was applied in two studies using predefined patient populations [ 52 , 53 ]. An algorithm to extract temporal association rules (TAR) to identify frequent patterns was found in one study [ 29 ].…”

“…Similarity measures and clustering algorithm are provided by TraMineR but can be modified to meet the needs of individual projects [ 78 ]. Some studies (2) conducted a state sequence analysis in TraMineR on grouped patients without explaining the procedure in more detail [ 52 , 53 ]. A similar tool for the identification of sequences and for patient pathway analyses is the Java tool ProM.…”