Medication Reconciliation

Neuromyelitis optica (NMO, Devic's syndrome), long considered a clinical variant of multiple sclerosis, is now regarded as a distinct disease entity. Major progress has been made in the diagnosis and treatment of NMO since aquaporin-4 antibodies (AQP4-Ab; also termed NMO-IgG) were first described in 2004. In this review, the Neuromyelitis Optica Study Group (NEMOS) summarizes recently obtained knowledge on NMO and highlights new developments in its diagnosis and treatment, based on current guidelines, the published literature and expert discussion at regular NEMOS meetings. Testing of AQP4-Ab is essential and is the most important test in the diagnostic work-up of suspected NMO, and helps to distinguish NMO from other autoimmune diseases. Furthermore, AQP4-Ab testing has expanded our knowledge of the clinical presentation of NMO spectrum disorders (NMOSD). In addition, imaging techniques, particularly magnetic resonance imaging of the brain and spinal cord, are obligatory in the diagnostic workup. It is important to note that brain lesions in NMO and NMOSD are not uncommon, do not rule out the diagnosis, and show characteristic patterns. Other imaging modalities such as optical coherence tomography are proposed as useful tools in the assessment of retinal damage. Therapy of NMO should be initiated early. Azathioprine and rituximab are suggested as first-line treatments, the latter being increasingly regarded as an established therapy with long-term efficacy and an acceptable safety profile in NMO patients. Other immunosuppressive drugs, such as methotrexate, mycophenolate mofetil and mitoxantrone, are recommended as second-line treatments. Promising new therapies are emerging in the form of anti-IL6 receptor, anti-complement or anti-AQP4-Ab biologicals.

BackgroundMedication reconciliation at admission, transfer and discharge has been designated as a required hospital practice to reduce adverse drug events. However, implementation challenges have resulted in poor hospital adherence. The aim of this study was to assess the processes required to carry out medication reconciliation: the health professionals involved, the tasks and time devoted to medication reconciliation in general hospital settings.MethodsA time-and-motion study design was used. Using a systematic sample of patients admitted and discharged from geriatric, medical and surgical units in two academic centers, health professionals involved in medication reconciliation were observed and timed. Descriptive statistics were used to summarize the number of professionals involved, tasks performed, and mean time devoted.ResultsUp to 3 professionals from 2 disciplines (medicine and pharmacy) were involved in the medication reconciliation process. Geriatric reconciliations took the most time to complete at admission (mean: 92.2 minutes (SD = 44.3)) and discharge (mean: 29.0 minutes (SD = 23.8)), followed by internal medicine at admission (mean: 46.2 minutes (SD = 21.1)) and 19.4 (SD = 11.7) minutes at discharge) and general surgery minutes at discharge (mean: 9.9 minutes (SD = 18.2)). Considerable differences in order, type and number of tasks performed were noted between and within units. Tasks independent of direct patient interaction took more than twice the time required to complete than tasks requiring patient interaction.ConclusionLack of coordination, specialized training and agreement on the roles and responsibilities of professionals are among the most probable reasons for work-flow inefficiencies, possibly variability in quality, and time required for the current medication reconciliation process. A better understanding of the admission processes in general surgery is required. Standardization and use of electronic tools could improve efficiency and hospital adherence.

BackgroundHospital readmission occurs often and is difficult to predict. Polypharmacy has been identified as a potential risk factor for hospital readmission. However, the overall impact of the number of discharge medications on hospital readmission is still undefined.MethodsTo determine whether the number of discharge medications is predictive of thirty-day readmission using a retrospective cohort study design performed at Barnes-Jewish Hospital from January 15, 2013 to May 9, 2013. The primary outcome assessed was thirty-day hospital readmission. We also assessed potential predictors of thirty-day readmission to include the number of discharge medications.ResultsThe final cohort had 5507 patients of which 1147 (20.8 %) were readmitted within thirty days of their hospital discharge date. The number of discharge medications was significantly greater for patients having a thirty-day readmission compared to those without a thirty-day readmission (7.2 ± 4.1 medications [7.0 medications (4.0 medications, 10.0 medications)] versus 6.0 ± 3.9 medications [6.0 medications (3.0 medications, 9.0 medications)]; P ConclusionsThe number of discharge medications is associated with the prevalence of thirty-day hospital readmission. A risk score, that includes the number of discharge medications, accurately predicts patients at risk for thirty-day readmission. Our findings suggest that relatively simple and accessible parameters can identify patients at high risk for hospital readmission potentially distinguishing such individuals for interventions to minimize readmissions.

BackgroundMedication reconciliation (MedRec) has the potential to improve patient safety by enhancing the continuity of medication information across settings. MedRec involves 3 core tasks: the creation of a best possible medication history, the identification of medication discrepancies among medication lists, and the resolution of medication discrepancies. While artificial intelligence (AI) has the potential to improve MedRec, existing reviews have not identified the ways in which researchers have used AI to facilitate MedRec tasks and their constituent subtasks or the level of automation achieved.ObjectiveThis scoping review aimed to map how previous research has applied AI to MedRec tasks and subtasks and assess the extent of automation achieved.MethodsWe searched MEDLINE, Embase, Web of Science, IEEE Xplore, and Compendex in June 2024 for studies that used AI to support a MedRec task or subtask, excluding entirely rule-based tools or studies focused on other aspects of medication management. After screening 2345 unique records, we conducted backward citation searching of studies included at the full-text stage, identifying an additional 795 unique records. We used a 4-stage model of human information processing as a structural lens to guide our considerations of automation, mapping the core tasks of MedRec onto this model.ResultsA total of 94 studies met the inclusion criteria. All studies addressed subtasks related to the creation of a best possible medication history. Only 2.1% (n=2) of the studies also addressed the identification of discrepancies. Thus, the highest stage of automated information processing achieved was information analysis, although most studies (92/94, 97.9%) only automated information acquisition steps. Most studies (67/94, 71.3%) used free-text clinical notes from the electronic health record, although a significant proportion (21/94, 22.3%) used images of pills or images of other medication-related items. Studies using text-based data used a variety of machine learning methods (eg, recurrent neural networks, conditional random fields, support vector machines, and transformers), whereas those that leveraged images typically used convolutional neural networks. Most studies (61/94, 64.9%) used publicly available data from benchmarking datasets (eg, n2c2 2022) and were strictly model development studies, with only 1.1% (n=1) being usability studies.ConclusionsThis is the first review to consider the role of AI in the automation of MedRec tasks, offering a basis for prioritizing future development efforts. Current applications of AI to automate MedRec tasks are preliminary, with most work focusing on the extraction of medication information and limited to proof-of-concept model development. Future work should consider addressing infrastructural barriers to the AI-based automation of MedRec tasks (eg, data incompleteness in sources of medication information) and exploring approaches to automate discrepancy resolution. Beyond developing models, there is also a need to implement them in tools and evaluate them in real-world contexts.