Implementation of a closed-loop medication reconciliation process for ambulatory oncology patients at Winchester District Memorial Hospital

2019 ◽  
Vol 33 (2) ◽  
pp. 85-89
Author(s):  
Emily Mulligan ◽  
LCdr Randy Tuff ◽  
Joanne Leclair ◽  
Jacqueline Mcmillan ◽  
Brian Devin ◽  
...  
Keyword(s):  
Medication Reconciliation ◽  
Closed Loop ◽  
Adverse Drug Events ◽  
Medication Management ◽  
Behavioural Change ◽  
Healthcare Providers ◽  
Open Loop ◽  
Memorial Hospital ◽  
Post Intervention ◽  
Ambulatory Oncology

Medication Reconciliation (MedRec) is a proven method of optimizing pharmacotherapy and decreasing incidence of Adverse Drug Events (ADEs); however, consistent and correct execution is often a challenge in the setting of outpatient oncology. Ambulatory chemotherapy patients are particularly susceptible to polypharmacy and ADEs and their medication management is often complicated due to gaps in communication between an increased volume of non-co-located, multidisciplinary, healthcare providers. Acknowledging these challenges, Winchester District Memorial Hospital (WDMH) led an initiative to create an ambulatory chemotherapy MedRec process using behavioural change approaches. Prior to the intervention, ambulatory chemotherapy MedRec at WDMH was conducted informally via an “open-loop” process. Through an iterative quality improvement process which involved understanding and communicating failure points in the transmission of patients’ medication information directly with the frontline medical staff, a practical and sustainable “closed-loop” system evolved, which improved rates to 97.8% overall completion post-intervention.

Improving medication reconciliation in ambulatory cancer care.

2020 ◽  
Vol 38 (29_suppl) ◽  
pp. 224-224
Author(s):  
Carissa Milley-Daigle ◽  
Celina Dara ◽  
Genevieve Bouchard-Fortier ◽  
Anet Julius ◽  
Vishal Kukreti ◽  
...  
Keyword(s):  
Medication Reconciliation ◽  
Adverse Drug Events ◽  
Medication Management ◽  
Median Number ◽  
Medication History ◽  
Time Motion ◽  
Motion Study ◽  
Study Results ◽  
Ambulatory Oncology ◽  
Medication Documentation

224 Background: Adverse drug events are common in ambulatory oncology where care spans multiple providers and medication documentation is often poor. We undertook a QI project with the aim of having 30% of patients have a best possible medication history (BPMH) or medication reconciliation (MedRec) documented within 30 days of starting systemic therapy. Methods: An Electronic Medical record-Integrated Tool (EMITT) was developed to facilitate documentation. 2 Plan-Do-Study-Act (PDSA) cycles have been completed to date; PDSA 1 consisted of piloting EMITT in 3 clinics run by physician champions. PDSA 2 which consisted of expanding pharmacy support and addition of a 4th clinic was impacted by care changes related to COVID. The proportion of patients with BPMH/MedRec documented in EMITT was calculated monthly for each period (PDSA 1, PDSA 2 pre-COVID and PDSA 2 post-COVID). The balancing measure of time to complete an entry was evaluated through a time motion study. Results: Between 9/9/2019 and 31/5/2020, 9.4% (233/2488) of patients had BPMH/MedRec completed; Table shows proportion of patients by month. BPMH and MedRec were most frequently performed by pharmacists followed by pharmacy students and nurses. On average, it took 5.5 minutes to complete an entry (n = 10; median number of medications per patient = 12.3). Conclusions: BPMH was documented more often than MedRec. While some usage was sustained, the changes to care as a result of COVID-19 negatively impacted ambulatory medication reconciliation. Future PDSA cycles will involve engaging patients in MedRec and extending EMITT to all ambulatory cancer clinics where medication management is a major component of care. [Table: see text]

Evaluation of Adverse Drug Events and Medication Discrepancies in Transitions of Care Between Hospital Discharge and Primary Care Follow-Up

2014 ◽  
Vol 29 (2) ◽  
pp. 132-137 ◽  
Author(s):  
Becky L. Armor ◽  
Avery J. Wight ◽  
Sandra M. Carter
Keyword(s):  
Primary Care ◽  
Hospital Discharge ◽  
Medication Reconciliation ◽  
Adverse Drug Events ◽  
Medication Management ◽  
Transitions Of Care ◽  
Therapeutic Monitoring ◽  
Medication List ◽  
Medication Discrepancies

Approximately two-thirds of adverse events posthospital discharge are due to medication-related problems. Medication reconciliation is a strategy to reduce medication errors and improve patient safety. Objective: To evaluate adverse drug events (ADEs), potential ADEs (pADEs), and medication discrepancies occurring between hospital discharge and primary care follow-up in an academic family medicine clinic. Adult patients recently discharged from the hospital were seen by a pharmacist for medication reconciliation between September 1, 2011, and November 30, 2012. The pharmacist identified medication discrepancies and pADEs or ADEs from a best possible medication history obtained from the electronic medical record (EMR) and hospital medication list. In 43 study participants, an average of 2.9 ADEs or pADEs was identified ( N = 124). The most common ADEs/pADEs identified were nonadherence/underuse (18%), untreated medical problems (15%), and lack of therapeutic monitoring (13%). An average of 3.9 medication discrepancies per participant was identified (N = 171), with 81% of participants experiencing at least 1 discrepancy. The absence of a complete and accurate medication list at hospital discharge is a barrier to comprehensive medication management. Strategies to improve medication management during care transitions are needed in primary care.

Medication reconciliation in ambulatory oncology new patient clinics

2020 ◽  
pp. 107815522096424
Author(s):  
Roxanne Dobish ◽  
Carol Baumgarten ◽  
Frances Folkman ◽  
Carole R Chambers
Keyword(s):  
Patient Safety ◽  
Medication Reconciliation ◽  
Medication Management ◽  
Clinic Visit ◽  
Healthcare Staff ◽  
Safe Medication ◽  
Multiple Factors ◽  
Patient Surveys ◽  
Alternative Approach ◽  
Ambulatory Oncology

Medication Reconciliation (MedRec) is an essential part of safe medication management and plays a key role in ensuring patient safety. A variety of methods and a number of different healthcare disciplines can be involved in the MedRec process and the timing and location of conducting MedRec can vary. In an effort to streamline the process in ambulatory oncology new patient clinics, a pilot of an alternative approach was undertaken whereby pharmacists with advanced prescribing privileges completed MedRec with patients prior to their clinic visit. Evaluation of the pilot was completed through the collection of various metrics, a pharmacist focus group, healthcare staff and patient surveys. Overall the evaluation indicated that there are multiple factors to consider regarding the timing and method of MedRec completion. The various phases of the pilot demonstrated that flexibility to the process is key and ongoing efforts are required at reducing duplication.

On closed-loop equilibrium strategies for mean-field stochastic linear quadratic problems

2020 ◽  
Vol 26 ◽  
pp. 41
Author(s):  
Tianxiao Wang
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Optimal Control Problems ◽  
Mean Field ◽  
Open Loop ◽  
Time Inconsistency ◽  
Control Problems ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Equilibrium Strategies

This article is concerned with linear quadratic optimal control problems of mean-field stochastic differential equations (MF-SDE) with deterministic coefficients. To treat the time inconsistency of the optimal control problems, linear closed-loop equilibrium strategies are introduced and characterized by variational approach. Our developed methodology drops the delicate convergence procedures in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. When the MF-SDE reduces to SDE, our Riccati system coincides with the analogue in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. However, these two systems are in general different from each other due to the conditional mean-field terms in the MF-SDE. Eventually, the comparisons with pre-committed optimal strategies, open-loop equilibrium strategies are given in details.

Comparison of Dc-Dc SEPIC, CUK and Flyback Converters Based LED Drivers

2020 ◽  
pp. 99-107
Author(s):  
Erdal Sehirli
Keyword(s):  
Integrated Circuit ◽  
Closed Loop ◽  
Input Voltage ◽  
Open Loop ◽  
Current Sensor ◽  
Switching Frequency ◽  
Led Driver ◽  
Advantages And Disadvantages ◽  
Led Drivers ◽  
Conduction Mode

This paper presents the comparison of LED driver topologies that include SEPIC, CUK and FLYBACK DC-DC converters. Both topologies are designed for 8W power and operated in discontinuous conduction mode (DCM) with 88 kHz switching frequency. Furthermore, inductors of SEPIC and CUK converters are wounded as coupled. Applications are realized by using SG3524 integrated circuit for open loop and PIC16F877 microcontroller for closed loop. Besides, ACS712 current sensor used to limit maximum LED current for closed loop applications. Finally, SEPIC, CUK and FLYBACK DC-DC LED drivers are compared with respect to LED current, LED voltage, input voltage and current. Also, advantages and disadvantages of all topologies are concluded.

scholarly journals Preventable adverse drug events causing hospitalisation: identifying root causes and developing a surveillance and learning system at an urban community hospital, a cross-sectional observational study

2021 ◽  
Vol 10 (1) ◽  
pp. e001161
Author(s):  
Jane de Lemos ◽  
Peter Loewen ◽  
Cheryl Nagle ◽  
Robert McKenzie ◽  
Yong Dong You ◽  
...  
Keyword(s):  
Observational Study ◽  
Adverse Drug Events ◽  
Healthcare Providers ◽  
Medication Use ◽  
Learning System ◽  
Chronic Obstructive ◽  
Action Plans ◽  
Medical Conditions ◽  
Cross Sectional ◽  
Medication Changes

ObjectivesTo identify root causes of preventable adverse drug events (pADEs) contributing to hospital admission; to develop key messages which identify actions patients/families and healthcare providers can take to prevent common pADEs found; to develop a surveillance learning system for the community.MethodsCross-sectional observational study; 120 patients and families, 61 associated healthcare providers were interviewed then root cause analysis was performed to develop key learning messages and an electronic reporting tool was designed. Most common pADE-related medical conditions and their root causes and most common pADE root causes of entire cohort are reported.ResultsMost common pADE-related medical conditions: chronic obstructive pulmonary disease/asthma (13.3%), bleeding (12.5%), hypotension (12%), heart failure (10%), acute kidney injury (5%) and pneumonia (5%). Most common root causes were: providers not confirming that the patient/family understands information given (29.2%), can identify how a medication helps them/have their concerns addressed (16.7%), can identify if a medication is working (14.1%) or causing a side effect (23.3%); can enact medication changes (7.5%); absence of a sick day management plan (12.5%), and other action plans to help patients respond to changes in their clinical status (10.8%); providers not assessing medication use and monitoring competency (19.2%). Ten key learning messages were developed and a pADE surveillance learning system was implemented.ConclusionsTo prevent pADEs, providers need to confirm that patients/families understand information given, how a medication helps them, how to recognise and respond to side effects, how to enact medication changes and follow action plans; providers should assess patient’s/families’ medication use and monitoring competency.

scholarly journals Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

2021 ◽  
Vol 13 (15) ◽  
pp. 2868
Author(s):  
Yonglin Tian ◽  
Xiao Wang ◽  
Yu Shen ◽  
Zhongzheng Guo ◽  
Zilei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Closed Loop ◽  
Real Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Training Model ◽  
Labor Cost ◽  
Open Loop ◽  
Training Dataset ◽  
Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

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