Applying the Medications at Transitions and Clinical Handoffs Toolkit in a Rural Primary Care Clinic

Jarrett, Traci; Cochran, Jill; Baus, Adam

doi:10.1097/ncq.0000000000000454

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…This reduces the quality of life of HCBS users; it also increases overall health care spending. Several interventions are known to improve discharge care and transitional care including Re-Engineered Discharge (RED) and Project Better Outcomes for Older Adults through Safer Transitions (BOOST) (Jarrett et al, 2020; Josephson, 2016; Mansukhani et al, 2015; Mitchell et al, 2017). For instance, Project BOOST is an intervention that aims to improve transitional care.…”

Section: Discussionmentioning

confidence: 99%

Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods

2022

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We compare multiple machine learning algorithms and develop models to predict future hospitalization among Home- and Community-Based Services (HCBS) Users. Furthermore, we calculate feature importance, the score of input variables based on their importance to predict the outcome, to identify the most relevant variables to predict hospitalization. We use the 2012 national Medicaid Analytic eXtract data and Medicare Provider Analysis and Review data. Predicting any hospitalization, Random Forest appears to be the most robust approach, though XGBoost achieved similar predictive performance. While the importance of features varies by algorithm, chronic conditions, previous hospitalizations, as well as use of services for ambulance, personal care, and durable medical equipment were generally found to be important predictors of hospitalization. Utilizing prediction models to identify those who are prone to hospitalization could be useful in developing early interventions to improve outcomes among HCBS users.

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Section: Discussionmentioning

confidence: 99%

Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods

2022

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“…Subtle errors do not pose a danger to the patient and are possible to be discovered only in a post-factum analysis. The basis for this division was the AHQR Medications at Transitions and Clinical Handoffs (MATCH) Toolkit for Medication Reconciliation, 11 which distinguishes nine categories of medication errors due to possible effects on the patient. Due to the small sample size and the need for statistical analysis, the researchers decided to reduce the nine categories to three.…”

Section: Methodsmentioning

confidence: 99%

Analysis of 10 years of medical errors investigated by the Poznan University Forensic Medicine Department

et al. 2023

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At the turn of 2005 and 2006, Polish emergency medical service underwent profound changes. Emergency medical service teams (EMS) were divided into P-type, which included only paramedics, and S-type, which consisted of paramedics and a physician (Statue from 8th September 2006 about State Medical Rescue). This study aimed to determine which elements of the medical rescue system in Poland most often occurred, what kind of errors they were, and how often they resulted in a patient's death. This article analyzed 147 opinions issued by the Forensic Medicine Department at Poznań University of Medical Sciences in 2005-2015 that concerned medical errors in emergency medicine commissioned by judicial authorities from all over Poland. The study excluded opinions on the areas covered by the jurisdiction of the Appellate Prosecutor's Office in Poznań and the District Prosecutor's Office in Ostrów Wlkp., which is about 85% of the country's territory. The results of the analyses identified the Hospital Emergency Departments as the place where the most significant number of errors of all types had been committed, which were not particularly surprising. The results concerning mistakes made by the EMS teams seemed much more significant. They showed that errors in the P-type ambulances (i.e., without a doctor in the rescue team) had occurred much more often, including those resulting in the patient's death. It can therefore be concluded that the higher level of preparation of the emergency medical team members translates into a higher level of patient safety. However, some scientific reports indicate that this does not have to be the case. The most significant limitation of this study lies in the source of the analyzed data. Opinions of the Department of Forensic Medicine in Poznań in cases concerning errors in emergency medicine reflect only a tiny percentage of medical errors.

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Applying the Medications at Transitions and Clinical Handoffs Toolkit in a Rural Primary Care Clinic

Cited by 2 publications

References 16 publications

Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods

Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods

Analysis of 10 years of medical errors investigated by the Poznan University Forensic Medicine Department

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