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Background Medical care can fail for various reasons: diseases can remain undetected and their severity misjudged, therapies can be incorrectly dosed or ineffective, and therapies can trigger new conditions or adverse drug reactions (ADR). To manage the complexity of changing patient circumstances, data-driven techniques play an increasingly important role in monitoring patient safety and treatment success. Therefore, clinical prediction models need to consider longitudinal factors (“Prescribing Monitoring”) to ensure clinically meaningful results and avoid misclassification in the dynamic health situation of the individual patient. Methods We have conducted a scoping review (OSF registration: https://doi.org/10.17605/OSF.IO/P93TZ ) on prediction models for ADR to collect potential use cases for Prescribing Monitoring. This review identified 2435 relevant studies in English that were published in MEDLINE or EMBASE. Two reviewers screened the records for inclusion, with a third reviewer making the final decision in the event of discrepancies. In order to derive recommendations on the way towards a Prescribing Monitoring system, the following elements were extracted and interpreted: the prediction models used, selection of candidate predictors, use of longitudinal factors, and model performance. Results A total of 56 studies were included after the screening process. We identified the main areas of current research in ADR prediction, all covering clinically important outcomes. We identified Prescribing Monitoring use cases based on their potential to (i) make individual predictions considering specific patient characteristics, (ii) make longitudinal predictions in a near time frame, and (iii) make dynamic predictions by updating predictions with previous risk predictions and newly available data. As a further aside, we use hyperkalaemia as an example to discuss the framework for developing Prescribing Monitoring in an electronic health record (EHR). Conclusion This scoping review provides an overview of the use of time-varying effects and longitudinal variables in current prediction model research. For application to clinical cases, prediction models should be developed, validated and implemented on this basis, so that time-dependent information can enable continuous monitoring of individual patients.

Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia.

Background The increasing complexity of current drug therapies jeopardizes patient adherence. While individual needs to simplify a medication regimen vary from patient to patient, a straightforward approach to integrate the patients’ perspective into decision making for complexity reduction is still lacking. We therefore aimed to develop an electronic, algorithm-based tool that analyses complexity of drug treatment and supports the assessment and consideration of patient preferences and needs regarding the reduction of complexity of drug treatment. Methods Complexity factors were selected based on literature and expert rating and specified for integration in the automated assessment. Subsequently, distinct key questions were phrased and allocated to each complexity factor to guide conversation with the patient and personalize the results of the automated assessment. Furthermore, each complexity factor was complemented with a potential optimisation measure to facilitate drug treatment (e.g. a patient leaflet). Complexity factors, key questions, and optimisation strategies were technically realized as tablet computer-based application, tested, and adapted iteratively until no further technical or content-related errors occurred. Results In total, 61 complexity factors referring to the dosage form, the dosage scheme, additional instructions, the patient, the product, and the process were considered relevant for inclusion in the tool; 38 of them allowed for automated detection. In total, 52 complexity factors were complemented with at least one key question for preference assessment and at least one optimisation measure. These measures included 29 recommendations for action for the health care provider (e.g. to suggest a dosage aid), 27 training videos, 44 patient leaflets, and 5 algorithms to select and suggest alternative drugs. Conclusions Both the set-up of an algorithm and its technical realisation as computer-based app was successful. The electronic tool covers a wide range of different factors that potentially increase the complexity of drug treatment. For the majority of factors, simple key questions could be phrased to include the patients’ perspective, and, even more important, for each complexity factor, specific measures to mitigate or reduce complexity could be defined.

Background A complex drug treatment might pose a barrier to safe and reliable drug administration for patients. Therefore, a novel tool automatically analyzes structured medication data for factors possibly contributing to complexity and subsequently personalizes the results by evaluating the relevance of each identified factor for the patient by means of key questions. Hence, tailor-made optimization measures can be proposed. Methods In this controlled, prospective, exploratory trial the tool was evaluated with nine general practitioners (GP) in three study groups: In the two intervention groups the tool was applied in a version with (G I_with ) and a version without (G I_without ) integrated key questions for the personalization of the analysis, while the control group (G C ) did not use any tools (routine care). Four to eight weeks after application of the tool, the benefits of the optimization measures to reduce or mitigate complexity of drug treatment were evaluated from the patient perspective. Results A total of 126 patients regularly using more than five drugs could be included for analysis. GP suggested 117 optimization measures in G I_with , 83 in G I_without , and 2 in G C . Patients in G I_with were more likely to rate an optimization measure as helpful than patients in G I_without (IRR: 3.5; 95% CI: 1.2—10.3). Thereby, the number of optimization measures recommended by the GP had no significant influence ( P  = 0.167). Conclusions The study suggests that an automated analysis considering patient perspectives results in more helpful optimization measures than an automated analysis alone – a result which should be further assessed in confirmatory studies. Trial registration The trial was registered retrospectively at the German Clinical Trials register under DRKS-ID DRKS00025257 (17/05/2021).

PurposeTo describe the prevalence of complexity factors in the medication regimens of community-dwelling patients with more than five drugs and to evaluate the relevance of these factors for individual patients.MethodsData were derived from the HIOPP-6 trial, a controlled study conducted in 9 general practices which evaluated an electronic tool to detect and reduce complexity of drug treatment. The prevalence of complexity factors was based on the results of the automated analysis of 139 patients’ medication data. The relevance assessment was based on the patients’ rating of each factor in an interview (48 patients included for analysis).ResultsA median of 5 (range 0–21) complexity factors per medication regimen were detected and at least one factor was observed in 131 of 139 patients. Almost half of these patients found no complexity factor in their medication regimen relevant.ConclusionIn most medication regimens, complexity factors could be identified automatically, yet less than 15% of factors were indeed relevant for patients as judged by themselves. When assessing complexity of medication regimens, one should especially consider factors that are both particularly frequent and often challenging for patients, such as use of inhalers or tablet splitting.Trial registrationThe HIOPP-6 trial was registered retrospectively on May 17, 2021, in the German Clinical Trials register under DRKS-ID DRKS00025257.

Purpose: Medical decision-making in older adults with multiple chronic conditions and polypharmacy should include the individual patient's treatment preferences. We developed and pilot-tested an electronic instrument (PolyPref) to elicit patient preferences in geriatric polypharmacy. Patients and Methods: PolyPref follows a two-stage direct approach to preference assessment. Stage 1 generates an individual preselection of relevant health outcomes and medication regimen characteristics, followed by stage 2, in which their importance is assessed using the Q-sort methodology. The feasibility of the instrument was tested in adults aged [greater than or equal to]70 years with [greater than or equal to]2 chronic conditions and regular intake of [greater than or equal to]5 medicines. After the assessment with PolyPref, the patients rated the tool with regard to its comprehensibility and usability and assessed the accuracy of the personal result. Evaluators rated the patients' understanding of the task. Results: Eighteen short-term health outcomes, 3 long-term health outcomes, and 8 medication regimen characteristics were included in the instrument. The final population for the pilot study comprised 15 inpatients at a clinic for geriatric rehabilitation with a mean age of 80.6 ([+ or -] 6.0) years, a median score of 28 (range 25-30) points on the Mini-Mental State Examination, and a mean of 11.6 ([+ or -] 3.6) regularly taken medicines. Feedback by the patients and the evaluators revealed ratings in favor of understanding and comprehensibility of 86.7% to 100%. The majority of the patients stated that their final result summarized the most important aspects of their pharmacotherapy (93.3%) and that its ranking order reflected their personal opinion (100%). Preference assessment took an average of 35 ([+ or -] 8.5) min, with the instrument being handled by the evaluator in 14 of the 15 participants. Conclusion: Preference assessment with PolyPref was feasible in older adults with multiple chronic conditions and polypharmacy, offering a new strategy for the standardized evaluation of patient priorities in geriatric pharmacotherapy. Keywords: geriatric pharmacotherapy, medication priorities, multimorbidity, multiple chronic conditions, patient-centered, preference assessment

ObjectivesIn 2017, an in-house best-practice process for medication documentation was developed and implemented to meet the new German legal requirements concerning the management of patient discharge from the hospital. Because this law regulates the common steps of good discharge practices (eg, specification of discharge mediation documentation), we used its implementation to assess the impact of such a measure on the quality of medication documentation and related workflows in clinical routine.MethodsBy observing workflows and interviewing the affected employees, we analysed the medication workflow processes from admission to discharge of seven representative departments of a large university hospital before and early after implementation of a newly defined best-practice process. To investigate the implementation impact, following measures were determined overall and for five key process steps: quality of medication documentation as measured by predefined criteria, the adherence to the best-practice process (range 0%–100%), workload and potential shifts in responsibilities.ResultsAlready early after implementation, all departments met the legal requirements and the quality of the medication documentation increased from low to high quality in most departments. Mean adherence to the best-practice process was 77% (range 60%–100%) with strictest adherence of 100% in one department. Thereby, the number of process steps and hence, likely also the workload increased in all departments. New tasks were mainly performed by physicians and in one department by pharmacists.ConclusionsThe new lawful best-practice process led to a higher quality in medication documentation at the cost of a higher workload for physicians, potentially limiting time for other care tasks. Therefore, it could be important to define areas of the medication documentation process in which physicians could be supported by other professions or new tools facilitating accurate medication documentation as the basis of continuity of care.

Purpose Electronic clinical decision support systems (CDSS) require drug information that can be processed by computers. The goal of this project was to determine and evaluate a compilation of variables that comprehensively capture the information contained in the summary of product characteristic (SmPC) and unequivocally describe the drug, its dosage options, and clinical pharmacokinetics. Methods An expert panel defined and structured a set of variables and drafted a guideline to extract and enter information on dosage and clinical pharmacokinetics from textual SmPCs as published by the European Medicines Agency (EMA). The set of variables was iteratively revised and evaluated by data extraction and variable allocation of roughly 7% of all centrally approved drugs. Results The information contained in the SmPC was allocated to three information clusters consisting of 260 variables. The cluster “drug characterization” specifies the nature of the drug. The cluster “dosage” provides information on approved drug dosages and defines corresponding specific conditions. The cluster “clinical pharmacokinetics” includes pharmacokinetic parameters of relevance for dosing in clinical practice. A first evaluation demonstrated that, despite the complexity of the current free text SmPCs, dosage and pharmacokinetic information can be reliably extracted from the SmPCs and comprehensively described by a limited set of variables. Conclusion By proposing a compilation of variables well describing drug dosage and clinical pharmacokinetics, the project represents a step forward towards the development of a comprehensive database system serving as information source for sophisticated CDSS.