Medication Regimen Complexity and Hospital Readmission for an Adverse Drug Event

Willson, Megan N.; Greer, Christopher L.; Weeks, Douglas L.

doi:10.1177/1060028013510898

Cited by 81 publications

(91 citation statements)

References 26 publications

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“…Previous studies that seeked to exploit EHR information with a similar postulate have mainly been developing score-based techniques. For instance the works found in [33,34,35,5] also assume that the complexity of the patient's medication regimen is a good indicator of the complexity of the patient's condition. The existence of correlations between MRCI at admission and occurence of complications is empirically demonstrated in the work of Lepelley et al [5].…”

Section: Related Workmentioning

confidence: 99%

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission

et al. 2018

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We show how the analysis of very large amounts of drug prescription data make it possible to detect, on the day of hospital admission, patients at risk of developing complications during their hospital stay. We explore, for the first time, to which extent volume and variety of big prescription data help in constructing predictive models for the automatic detection of at-risk profiles.Our methodology is designed to validate our claims that: (1) drug prescription data on the day of admission contain rich information about the patient's situation and perspectives of evolution, and (2) the various perspectives of big medical data (such as veracity, volume, variety) help in extracting this information. We build binary classification models to identify at-risk patient profiles. We use a distributed architecture to ensure scalability of model construction with large volumes of medical records and clinical data.We report on practical experiments with real data of millions of patients and hundreds of hospitals. We demonstrate how the fine-grained analysis of such big data can improve the detection of at-risk patients, making it possible to construct more accurate predictive models that significantly benefit from volume and variety, while satisfying important criteria to be deployed in hospitals.

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Section: Related Workmentioning

confidence: 99%

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission

et al. 2018

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“…need to take medications with food) also contribute to complexity. Medication regimen complexity has been associated with poor medication adherence [2,3], hospital readmission [4][5][6], adverse drug events [5], poor quality of life [7] and hospital discharge directly to an aged care facility [8]. However, it is unclear to what extent regimen complexity may confer a greater risk of adverse events over and above that associated with number of medications.…”

Section: Introductionmentioning

confidence: 98%

Factors associated with medication regimen complexity in older people: a cross-sectional population-based study

Wimmer

Johnell

Fastbom

et al. 2015

Eur J Clin Pharmacol

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Older people with probable difficulties managing complex regimens, including those with impaired dexterity and living in institutional settings, had the most complex medication regimens even after adjusting for receipt of help to sort medications. The strong correlation between regimen complexity and number of medications suggests that clinicians could use a person's number of medications to target interventions to reduce complexity.

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“…Several studies have suggested medication parameters such as use of high-risk medication and polypharmacy (use of five or more medications) that may increase the risk of rehospitalization. 17) Given that elderly patients often take multiple medications and are more susceptible to adverse drug events, it is particularly important to ensure that a complete and accurate list of medications is obtained at discharge. 18,19) Age-related pharmacodynamic and pharmacokinetic changes increase the susceptibility of elderly patients to adverse reactions from medications.…”

Section: Discussionmentioning

confidence: 99%

Medication-Related Factors Affecting Discharge to Home

Hashimoto

Matsuzaki

Kawahara

et al. 2014

Biological & Pharmaceutical Bulletin

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To assess the reasons for barriers to home discharge by determining whether they were predicted by medication, clinical variables, and patient characteristics, the retrospective cohort study of 282 patients discharged from Kanazawa Red Cross Hospital in Kanazawa, Japan from January 2011 to December 2012 was performed. The percentage of patients discharged was 67.4%. By multivariate logistic analysis, significant differences in home discharge destination were determined by six factors: the duration of hospitalization before discharge (odds ratio (OR) 0.993; 95% 95% confidence interval (CI) 0.988-0.999), the presence of excretion assistance (OR 0.115; 95% CI 0.043-0.308), individual payment of medical expense (OR 0.344; 95% CI 0.146-0.811), the degree of independent living for the demented elderly (OR4.570; 95% CI 1.969-10.604), presence of the primary caregiver (OR 8.638; 95% CI 3.121-23.906), and admission to a hospital from home (OR 5.483; 95% CI 2.589-11.613). This study suggests that necessity of excretion assistance, long duration of hospitalization, and high individual payment of medical expense were barriers to home discharge. In contrast, three factors i.e., admission to a hospital form home, low degree of independent living for the demented elderly, and presence of the primary caregiver, favored home discharge. The relation between a patient's status (cognitive status and incontinence) and a caregiver has an important effect on the home discharge. However, medication characteristics appeared to have little effect on recuperation destination.

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Medication Regimen Complexity and Hospital Readmission for an Adverse Drug Event

Abstract: Complex medication regimens at hospital admission are predictive of rehospitalizations for ADEs. This finding suggests that medication regimen complexity be considered as a target for interventions to decrease the risk for readmission.

Cited by 81 publications

References 26 publications

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission

Factors associated with medication regimen complexity in older people: a cross-sectional population-based study

Medication-Related Factors Affecting Discharge to Home

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