The effect of medication related clinical decision support at the time of physician order entry.

Background In advanced clinical decision support systems, patient characteristics and laboratory values are included in the algorithms that generate alerts. These alerts have a higher specificity than basic medication surveillance alerts. The alerts of advanced clinical decision support systems can be shown directly to the prescriber during order entry, without the risk of generating an overload of irrelevant alerts. We implemented five advanced algorithms that are shown directly to the prescriber. These algorithms are for gastrointestinal prophylaxis, folic or folinic acid prescribed with orally or subcutaneously administered methotrexate, vitamin D prescribed with bisphosphonates, hyponatremia and measuring plasma levels for vancomycin and gentamicin. Objective We evaluated the effect of the implementation of the algorithms. Setting We performed prospective intervention studies with a historical group for comparison in both inpatients and outpatients at a teaching hospital in the Netherlands. Methods We compared the time period after implementation of the algorithm with the time period before implementation, using data from the hospital information system Epic. Difference in guideline adherence were analyzed using Chi square tests. Main outcome measure The outcome measures were the number of alerts, the acceptance rate of the advice in the alert, and for the algorithm measuring plasma levels for vancomycin and gentamicin the time to the correct dose. Results For all algorithms, the implementation resulted in a significant increase in guideline adherence, varying from 11 to 36%. The acceptance rate varied from 14% for hyponatremia to 90% for methotrexate. For gastrointestinal prophylaxis the acceptance rate was 4.4% for basic drug-drug interaction alerts when no gastrointestinal prophylaxis was prescribed and increased to 44.7% after implementation of the advanced algorithm. This algorithm substantially decreased the number of alerts from 812 before implementation to 217 after implementation. After implementation of the algorithm for measuring plasma levels for vancomycin and gentamicin, the proportion of patients receiving the correct dose after 48 h increased from 73 to 84% (p = 0.03). Conclusion Implementation of advanced algorithms that take patient characteristics into account and are shown directly to the physician during order entry, result in an increased guideline adherence.

Software de contenido farmacocinético para la monitorización terapéutica de fármacos: Protocolo de revisión exploratoria / Pharmacokinetic software for therapeutic drug monitoring: A scoping review protoco

Objetivo: Los nomogramas, ecuaciones y software de contenido farmacocinético se consideran las principales herramientas disponibles para la monitorización farmacocinética clínica. Debido a su gran aplicabilidad en numerososgrupos de fármacos, el empleo de software se encuentra ampliamente extendido en la práctica clínica. Generalmente, el objetivo principal de los estudiosque incluyen el uso de estos software no es la descripción de los mismos, porlo que la información disponible es escasa y, además, no se dispone de unarevisión que aúne toda la información disponible referente a este tipo de softwareEl objetivo de este estudio será sintetizar la evidencia disponible sobre losdistintos software de aplicación en la monitorización farmacocinética parafacilitar a los usuarios su identificación, evaluación y selección.Método: Se realizará una revisión exploratoria de la literatura cuyo protocolo se describe en este artículo, de acuerdo con las recomendacionesPRISMA para la elaboración de revisiones exploratorias y publicaciónde protocolos. Se realizará una búsqueda bibliográfica en las bases dedatos Medline, Embase, OpenAire y Bielefeld Academic Search Engine.Se incluirán en el estudio aquellos software detectados de los que se disponga de la siguiente información: nombre del software, desarrollador/comercializador, tipo de análisis farmacocinético y fármacos incluidos.Resultados: En este estudio se espera realizar una síntesis de lascaracterísticas más relevantes en la práctica clínica de los software decontenido farmacocinético disponibles en el mercado. Se realizará una síntesis narrativa crítica de las fuentes de información utilizadas. Además,se llevará a cabo, si es posible, una comparación de los mismos parafacilitar la evaluación y selección por parte de los usuarios. (AU)

Objective: Nomograms, equations and pharmacokinetic software areconsidered the main tools available for therapeutic drug monitoring. Dueto its great applicability to various groups of drugs, the use of software iswidely extended in clinical practice. The main goals of the studies usingthis type of software do not normally include the description of its features,therefore, the information about its characteristic is scarce. Moreover, noreview of the literature has been published that brings together all theinformation available about these software. The present study aimed tosynthesize the available evidence regarding software applied to therapeutic drug monitoring to facilitate its identification, evaluation and selectionby users.Method: This article describe a scoping review protocol, developedfollowing the PRISMA-P and PRISMA-ScR guidelines. An electronic literature search was performed in MEDLINE, EMBASE, OpenAire and BASE(Bielefeld Academic Search Engine) databases. Only those software forwhich the following information was available were included: name of thesoftware, developer/marketer, type of pharmacokinetic analysis allowed,and drugs included in the analysis.Results: In this study we will synthesized the most relevant characteristics for the clinical practice of the pharmacokinetic software available.A critical appraisal of the sources if information will be included. Also,if it is possible, a comparison of the available tools will be carried out in order to facilitate the evaluation and selection of pharmacokineticsoftware. (AU)

Effectiveness of a complex intervention on Prioritising Multimedication in Multimorbidity (PRIMUM) in primary care: results of a pragmatic cluster randomised controlled trial.

OBJECTIVES: Investigate the effectiveness of a complex intervention aimed at improving the appropriateness of medication in older patients with multimorbidity in general practice. DESIGN: Pragmatic, cluster randomised controlled trial with general practice as unit of randomisation. SETTING: 72 general practices in Hesse, Germany. PARTICIPANTS: 505 randomly sampled, cognitively intact patients (≥60 years, ≥3 chronic conditions under pharmacological treatment, ≥5 long-term drug prescriptions with systemic effects); 465 patients and 71 practices completed the study. INTERVENTIONS: Intervention group (IG): The healthcare assistant conducted a checklist-based interview with patients on medication-related problems and reconciled their medications. Assisted by a computerised decision support system, the general practitioner optimised medication, discussed it with patients and adjusted it accordingly. The control group (CG) continued with usual care. OUTCOME MEASURES: The primary outcome was a modified Medication Appropriateness Index (MAI, excluding item 10 on cost-effectiveness), assessed in blinded medication reviews and calculated as the difference between baseline and after 6 months; secondary outcomes after 6 and 9 months' follow-up: quality of life, functioning, medication adherence, and so on. RESULTS: At baseline, a high proportion of patients had appropriate to mildly inappropriate prescriptions (MAI 0-5 points: n=350 patients). Randomisation revealed balanced groups (IG: 36 practices/252 patients; CG: 36/253). Intervention had no significant effect on primary outcome: mean MAI sum scores decreased by 0.3 points in IG and 0.8 points in CG, resulting in a non-significant adjusted mean difference of 0.7 (95% CI -0.2 to 1.6) points in favour of CG. Secondary outcomes showed non-significant changes (quality of life slightly improved in IG but continued to decline in CG) or remained stable (functioning, medication adherence). CONCLUSIONS: The intervention had no significant effects. Many patients already received appropriate prescriptions and enjoyed good quality of life and functional status. We can therefore conclude that in our study, there was not enough scope for improvement. TRIAL REGISTRATION NUMBER: ISRCTN99526053. NCT01171339; Results.

KALIS - An eHealth System for Biomedical Risk Analysis of Drugs.

BACKGROUND: In Germany, adverse drug reactions and events cause hospitalizations, which lead to numerous thousands of deaths and several million Euros in additional health costs annually. OBJECTIVES: Approximately one in two deaths could be avoided by an appropriate system for risk analysis of drugs. METHODS: The integration and storage of several data sources from life sciences are an ongoing need to address various questions with respect to drug therapy. A software architecture for data integration was implemented in order to build up a new data warehouse named KALIS-DWH, which includes pharmacological, biomolecular and patient-related data. RESULTS: Based on this comprehensive KALIS-DWH, an eHealth system named KALIS for biomedical risk analysis of drugs was implemented. The task-specific modules of KALIS offer efficient algorithms for analyzing medication and supporting decision-making in drug therapy. CONCLUSION: KALIS is meant to be a web-based information system for health professionals and researchers. KALIS provides comprehensive knowledge and modules for risk analysis of drugs, which can contribute to minimizing prescribing errors.

Warfarin Guide: Co-Design of a Mobile Computer-Assisted Anticoagulant Therapy.

Patients with some types of cardiovascular disease are prescribed anticoagulation therapy with Warfarin in order to control the ability of blood clotting. This work presents a co-designed mobile application, called Warfarin Guide, for a computer-assisted anticoagulant therapy. The application addresses the challenges that unexperienced patients may find when having to remember to regularly check their INR values and make temporary adjustments for INR value fluctuations that are not easy to interpret without direct medical advice.

Iterative Development and Evaluation of a Pharmacogenomic-Guided Clinical Decision Support System for Warfarin Dosing.

OBJECTIVE: Pharmacogenomic-guided dosing has the potential to improve patient outcomes but its implementation has been met with clinical challenges. Our objective was to develop and evaluate a clinical decision support system (CDSS) for pharmacogenomic-guided warfarin dosing designed for physicians and pharmacists. METHODS: Twelve physicians and pharmacists completed 6 prescribing tasks using simulated patient scenarios in two iterations (development and validation phases) of a newly developed pharmacogenomic-driven CDSS prototype. For each scenario, usability was measured via efficiency, recorded as time to task completion, and participants' perceived satisfaction which were compared using Kruskal-Wallis and Mann Whitney U tests, respectively. Debrief interviews were conducted and qualitatively analyzed. Usability findings from the first (i.e. development) iteration were incorporated into the CDSS design for the second (i.e. validation) iteration. RESULTS: During the CDSS validation iteration, participants took more time to complete tasks with a median (IQR) of 183 (124-247) seconds versus 101 (73.5-197) seconds in the development iteration (p=0.01). This increase in time on task was due to the increase in time spent in the CDSS corresponding to several design changes. Efficiency differences that were observed between pharmacists and physicians in the development iteration were eliminated in the validation iteration. The increased use of the CDSS corresponded to a greater acceptance of CDSS recommended doses in the validation iteration (4% in the first iteration vs. 37.5% in the second iteration, p<0.001). Overall satisfaction did not change statistically between the iterations but the qualitative analysis revealed greater trust in the second prototype. CONCLUSIONS: A pharmacogenomic-guided CDSS has been developed using warfarin as the test drug. The final CDSS prototype was trusted by prescribers and significantly increased the time using the tool and acceptance of the recommended doses. This study is an important step toward incorporating pharmacogenomics into CDSS design for clinical testing.

Effectiveness of a computerized decision support system for anticoagulation management in hemodialysis patients: A before-after study.

Introduction The risk-benefit profile for warfarin anticoagulation in hemodialysis (HD) patients differs compared with the non-HD population. HD patients are at increased risk of both thromboembolism and bleeding related to anticoagulation therapy. In addition, anticoagulation control may be more difficult to achieve in the HD population due to frequent comorbidities, subclinical Vitamin K deficiency, altered pharmacokinetics due to uremia and the concurrent use of multiple medications. While computerized decision support systems (CDSS) to assist with anticoagulation management are safe and effective in the non-HD population, they have not been well studied in HD outpatients. Methods A before-after study compared anticoagulation control for HD outpatients receiving warfarin at a tertiary medical center in Canada during two time periods: an initial period of nephrologist-led anticoagulation management and a second period after implementation of a pharmacist-led, CDSS-assisted anticoagulation management strategy. Findings Forty-two patients were included. Following implementation of the CDSS-assisted strategy, there was no significant change in median therapeutic time-in-range (3.7% difference (IQR, -9.5% to 20.6%); P = 0.247). Median change in INR testing frequency was 1.2 (IQR, 0.1-2.2; P = 0.0001) fewer tests per patient per month, which equates to approximately 15 fewer tests per patient per year. Adverse events were similar. Discussion Implementing a CDSS-assisted strategy for anticoagulation management in HD outpatients is effective. Doing so may lead to modest cost savings related to less frequent INR testing.

Provider variation in responses to warnings: do the same providers run stop signs repeatedly?

OBJECTIVE: Variation in the use of tests and treatments has been demonstrated to be substantial between providers and geographic regions. This study assessed variation between outpatient providers in overriding electronic prescribing warnings. METHODS: Responses to warnings were prospectively logged. Random effects models were used to calculate provider-to-provider variation in the rates for the decisions to override warnings in 6 different clinical domains: medication allergies, drug-drug interactions, duplicate drugs, renal recommendations, age-based recommendations, and formulary substitutions. RESULTS: A total of 157 482 responses were logged. Differences between 1717 providers accounted for 11% of the overall variability in override rates, so that while the average override rate was 45.2%, individual provider rates had a wide range with a 95% confidence interval (CI) (13.7%-76.7% ). The highest variations between providers were observed in the categories age-based (25.4% of total variability; average override rate 70.2% [95% CI, 29.1%-100% ]) and renal recommendations (24.2%; average 70% [95% CI, 29.5%-100% ]), and provider responses within these 2 categories were most often clinically inappropriate according to prior work. Among providers who received at least 10 age-based recommendations, 64 of 238 (27%) overrode ≥ 90% of the warnings and 13 of 238 (5%) overrode all of them. Of those who received at least 10 renal recommendations, 36 of 92 (39%) overrode ≥ 90% of the alerts and 9 of 92 (10%) overrode all of them. CONCLUSIONS: The decision to override prescribing warnings shows variation between providers, and the magnitude of variation differs among the clinical domains of the warnings; more variation was observed in areas with more inappropriate overrides.

Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs.

BACKGROUND: Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging. OBJECTIVE: We sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record. METHODS: We first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problem-medication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision. RESULTS: The resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%. CONCLUSIONS: We validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across healthcare settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes.

Making pharmacogenomic-based prescribing alerts more effective: A scenario-based pilot study with physicians.

To facilitate personalized drug dosing (PDD), this pilot study explored the communication effectiveness and clinical impact of using a prototype clinical decision support (CDS) system embedded in an electronic health record (EHR) to deliver pharmacogenomic (PGx) information to physicians. We employed a conceptual framework and measurement model to access the impact of physician characteristics (previous experience, awareness, relative advantage, perceived usefulness), technology characteristics (methods of implementation-semi-active/active, actionability-low/high) and a task characteristic (drug prescribed) on communication effectiveness (usefulness, confidence in prescribing decision), and clinical impact (uptake, prescribing intent, change in drug dosing). Physicians performed prescribing tasks using five simulated clinical case scenarios, presented in random order within the prototype PGx-CDS system. Twenty-two physicians completed the study. The proportion of physicians that saw a relative advantage to using PGx-CDS was 83% at the start and 94% at the conclusion of our study. Physicians used semi-active alerts 74-88% of the time. There was no association between previous experience with, awareness of, and belief in a relative advantage of using PGx-CDS and improved uptake. The proportion of physicians reporting confidence in their prescribing decisions decreased significantly after using the prototype PGx-CDS system (p=0.02). Despite decreases in confidence, physicians perceived a relative advantage to using PGx-CDS, viewed semi-active alerts on most occasions, and more frequently changed doses toward doses supported by published evidence. Specifically, sixty-five percent of physicians reduced their dosing, significantly for capecitabine (p=0.002) and mercaptopurine/thioguanine (p=0.03). These findings suggest a need to improve our prototype such that PGx CDS content is more useful and delivered in a way that improves physician's confidence in their prescribing decisions. The greatest increases in communication effectiveness and clinical impact of PGx-CDS are likely to be realized through continued focus on content, content delivery, and tailoring to physician characteristics.

Improvement of anticoagulant treatment using a dynamic decision support algorithm: a Danish Cohort study.

INTRODUCTION: Warfarin is the most widely prescribed vitamin K antagonist and in the United States and Europe more than 10 million people are currently in long-term oral anticoagulant treatment. This study aims to retrospectively validate a dynamic statistical model providing dosage suggestions to patients in warfarin treatment. MATERIALS AND METHODS: The model was validated on a cohort of 553 patients with a mean TTR of 83%. Patients in the cohort were self-monitoring and managed by a highly specialised anticoagulation clinic. The predictive model essentially consists of three parts handling INR history, warfarin dosage and biological noise, which allows for prediction of future INR values and optimal warfarin dose to stay on INR target. Further, the model is based on parameters initially being set to population values and gradually individualised during monitoring of patients. PRIMARY OUTCOME: Time in therapeutic range was used as surrogate quality measure of the treatment, and model-suggested dosage of warfarin was used to assess the accuracy of the model performance. RESULTS: The accuracy of the model predictions measured as median absolute error was 0.53 mg/day (interquartile range from 0.25 to 1.0). The model performance was evaluated by the difference between observed and predicted warfarin intake in the preceding week of an INR measurement. In more than 70% of the cases where INR measurements were outside the therapeutic range, the model suggested a more reasonable dose than the observed intake. CONCLUSION: Applying the proposed dosing algorithm can potentially further increase the time in INR target range beyond 83%.