Hospitalization Pattern, Inpatient Service Utilization and Quality of Care in Patients With Alcohol Use Disorder: A Sequence Analysis of Discharge Medical Records

Han, Xueyan; Jiang, Feng; Zhou, Huixuan; Needleman, Jack; Guo, Moning; Yin, Chen; Liu, Yuanli; Tang, Yi-Lang

doi:10.1093/alcalc/agz081

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…We also did not have information on the community care availability and intensity [26] so we were unable to examine their effects on the risk of readmission or incorporate these factors in the risk-adjustment models. We also did not include patient readmissions due to physical illnesses so the psychiatric readmission rate estimated in this study may be lower than the all-cause readmission rates among psychiatric inpatients [51].…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis

et al. 2020

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Background: Psychiatric readmissions negatively impact patients and their families while increasing healthcare costs. This study aimed at investigating factors associated with psychiatric readmissions within 30 days and 1 year of the index admissions and exploring the possibilities of monitoring and improving psychiatric care quality in China. Methods: Data on index admission, subsequent admission(s), clinical and hospital-related factors were extracted in the inpatient medical record database covering 10 secondary and tertiary psychiatric hospitals in Beijing, China. Logistic regressions were used to examine the associations between 30-day and 1-year readmissions plus frequent readmissions (≥3 times/year), and clinical variables as well as hospital characteristics. Results: The 30-day and 1-year psychiatric readmission rates were 16.69% (1289/7724) and 33.79% (2492/7374) respectively. 746/2492 patients (29.34%) were readmitted 3 times or more within a year (frequent readmissions). Factors significantly associated with the risk of both 30-day and 1-year readmission were residing in an urban area, having medical comorbidities, previous psychiatric admission(s), length of stay > 60 days in the index admission and being treated in tertiary hospitals (p < 0.001). Male patients were more likely to have frequent readmissions (OR 1.30, 95%CI 1.04-1.64). Receiving electroconvulsive therapy (ECT) was significantly associated with a lower risk of 30-day readmission (OR 0.72, 95%CI 0.56-0.91) and frequent readmissions (OR 0.60, 95%CI 0.40-0.91). Conclusion: More than 30% of the psychiatric inpatients were readmitted within 1 year. Urban residents, those with medical comorbidities and previous psychiatric admission(s) or a longer length of stay were more likely to be readmitted, and men are more likely to be frequently readmitted. ECT treatment may reduce the likelihood of 30day readmission and frequent admissions. Targeted interventions should be designed and piloted to effectively monitor and reduce psychiatric readmissions.

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Section: Strengths and Limitationsmentioning

confidence: 99%

Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis

et al. 2020

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“…Classification according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision Chapter Description of Chapter according to ICD-10 classification N (%) Ref. 1 Certain infectious and parasitic diseases 2 (4 %) [ 40 , 50 ] 2 Neoplasms 8 (16 %) [ 7 , 20 , 28 , 32 , 37 , 41 , 44 , 46 ] 4 Endocrine, nutritional and metabolic diseases 10 (20 %) [ 22 , 23 , 29 , 30 , 47 , 49 , 55 , 57 , 59 , 60 ] 5 Mental, Behavioral and Neurodevelopmental disorders 2 (4 %) [ 27 , 35 ] 6 Diseases of the nervous system 2 (4 %) [ 6 , 31 ] 9 Diseases of the circulatory system 10 (20 %) [ 8 , 10 , 24 , 26 , 38 ...…”

Section: Resultsmentioning

confidence: 99%

“…Unfortunately, not all studies explicitly stated which measure of similarity they applied. The use of Optimal Matching to calculate the similarity or dissimilarity of the sequences was documented in five studies [ 5 , 6 , 27 , 35 , 42 ]. The idea of Optimal Matching is to calculate fictional costs pairwise between sequences, i.e.…”

Section: Resultsmentioning

confidence: 99%

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

Flothow,

Novelli,

Sundmacher

2023

BMC Med Res Methodol

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Background Healthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of patient movement. This can help to evaluate treatment processes and outcomes, and in turn improve the quality of care. This scoping review provides an overview of the algorithms and methods that have been used to identify care pathways from healthcare utilization data. Method This review was conducted according to the methodology of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews (PRISMA-ScR) Checklist. The PubMed, Web of Science, Scopus, and EconLit databases were searched and studies published in English between 2000 and 2021 considered. The search strategy used keywords divided into three categories: the method of data analysis, the requirement profile for the data, and the intended presentation of results. Criteria for inclusion were that health data were analyzed, the methodology used was described and that the chronology of care events was considered. In a two-stage review process, records were reviewed by two researchers independently for inclusion. Results were synthesized narratively. Results The literature search yielded 2,865 entries; 51 studies met the inclusion criteria. Health data from different countries ($$n=12$$ n = 12 ) and of different types of disease ($$n=26$$ n = 26 ) were analyzed with respect to different care events. Applied methods can be divided into those identifying subsequences of care and those describing full care trajectories. Variants of pattern mining or Markov models were mostly used to extract subsequences, with clustering often applied to find care trajectories. Statistical algorithms such as rule mining, probability-based machine learning algorithms or a combination of methods were also applied. Clustering methods were sometimes used for data preparation or result compression. Further characteristics of the included studies are presented. Conclusion Various data mining methods are already being applied to gain insight from health data. The great heterogeneity of the methods used shows the need for a scoping review. We performed a narrative review and found that clustering methods currently dominate the literature for identifying complete care trajectories, while variants of pattern mining dominate for identifying subsequences of limited length.

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“…The second most common objective of the studies (N = 74; 21.5%) aimed to develop AI for medical data representation including describing clinical pathways and taking into consideration time domain of medical data. These studies used sophisticated mathematical methods applied to longitudinal and heterogenous data ( Zhang et al, 2018 ) to describe sequence of clinical events ( Esteban et al, 2015 ) or inpatient services ( Han et al, 2020 ), or to model disease progression ( Powell et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review

et al. 2022

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Introduction: The ongoing collection of large medical data has created conditions for application of artificial intelligence (AI) in research. This scoping review aimed to identify major areas of interest of AI applied to health care administrative data.Methods: The search was performed in seven databases: Medline, Embase, CINAHL, Web of science, IEEE, ICM digital library, and Compendex. We included articles published between January 2001 and March 2021, that described research with AI applied to medical diagnostics, pharmacotherapy, and health outcomes data. We screened the full text content and used natural language processing to automatically extract health areas of interest, principal AI methods, and names of medications.Results: Out of 14,864 articles, 343 were included. We determined ten areas of interest, the most common being health diagnostic or treatment outcome prediction (32%); representation of medical data, clinical pathways, and data temporality (i.e., transformation of raw medical data into compact and analysis-friendly format) (22%); and adverse drug effects, drug-drug interactions, and medication cascades (15%). Less attention has been devoted to areas such as health effects of polypharmacy (1%); and reinforcement learning (1%). The most common AI methods were decision trees, cluster analysis, random forests, and support vector machines. Most frequently mentioned medications included insulin, metformin, vitamins, acetaminophen, and heparin.Conclusions: The scoping review revealed the potential of AI application to health-related studies. However, several areas of interest in pharmacoepidemiology are sparsely reported, and the lack of details in studies related to pharmacotherapy suggests that AI could be used more optimally in pharmacoepidemiologic research.

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Hospitalization Pattern, Inpatient Service Utilization and Quality of Care in Patients With Alcohol Use Disorder: A Sequence Analysis of Discharge Medical Records

Cited by 8 publications

References 23 publications

Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis

Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis

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

Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review

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