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Comparison of Pharmacy Database Methods for Determining Prevalent Chronic Medication Use.

ABSTRACT: BACKGROUND:Pharmacy dispensing data are frequently used to identify prevalent medication use as a predictor or covariate in observational research studies. Although several methods have been proposed for using pharmacy dispensing data to identify prevalent medication use, little is known about their comparative performance. OBJECTIVES:The authors sought to compare the performance of different methods for identifying prevalent outpatient medication use. RESEARCH DESIGN:Outpatient pharmacy fill data were compared with medication reconciliation notes denoting prevalent outpatient medication use at the time of hospital admission for a random sample of 207 patients drawn from a national cohort of patients admitted to Veterans Affairs hospitals. Using reconciliation notes as the criterion standard, we determined the test characteristics of 12 pharmacy database algorithms for determining prevalent use of 11 classes of cardiovascular and diabetes medications. RESULTS:The best-performing algorithms included a 180-day fixed look-back period approach (sensitivity, 93%; specificity, 97%; and positive predictive value, 89%) and a medication-on-hand approach with a grace period of 60 days (sensitivity, 91%; specificity, 97%; and positive predictive value, 91%). Algorithms that have been commonly used in previous studies, such as defining prevalent medications to include any medications filled in the prior year or only medications filled in the prior 30 days, performed less well. Algorithm performance was less accurate among patients recently receiving hospital or nursing facility care. CONCLUSION:Pharmacy database algorithms that balance recentness of medication fills with grace periods performed better than more simplistic approaches and should be considered for future studies which examine prevalent chronic medication use.

SUBMITTER: Anderson TS

PROVIDER: S-EPMC6742560 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Comparison of Pharmacy Database Methods for Determining Prevalent Chronic Medication Use.

Anderson Timothy S TS Jing Bocheng B Wray Charlie M CM Ngo Sarah S Xu Edison E Fung Kathy K Steinman Michael A MA

Medical care 20191001 10

<h4>Background</h4>Pharmacy dispensing data are frequently used to identify prevalent medication use as a predictor or covariate in observational research studies. Although several methods have been proposed for using pharmacy dispensing data to identify prevalent medication use, little is known about their comparative performance.<h4>Objectives</h4>The authors sought to compare the performance of different methods for identifying prevalent outpatient medication use.<h4>Research design</h4>Outpa ...[more]

PMID: 31464843

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Project description:BackgroundThis study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.MethodsData from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.ResultMachine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.ConclusionThis study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

Project description:BackgroundMedication reconciliation reduces potential medication discrepancies and adverse drug events. The role of pharmacy technicians in obtaining best possible medication histories (BPMHs) and performing reconciliation at the admission and transfer interfaces of care for pediatric patients has not been described.ObjectivesTo compare the completeness and accuracy of BPMHs and reconciliation conducted by a pharmacy technician (pilot study) and by nurses and/or pharmacists (baseline). The severity of identified unintentional discrepancies was rated to determine their clinical importance.MethodsThis prospective cohort comparison study involved patients up to 18 years of age admitted to and/or transferred between the Cardiology ward and the Cardiac Critical Care Unit of a pediatric tertiary care teaching hospital. A pharmacy resident conducted two 3-week audits: the first to assess the completeness and accuracy of BPMHs and reconciliation performed by nurses and/or pharmacists and the second to assess the completeness and accuracy of BPMHs and reconciliation performed by a pharmacy technician.ResultsThe total number of patients was 38 in the baseline phase and 46 in the pilot period. There were no statistically significant differences between the baseline and pilot audits in terms of completion of BPMH (82% [28/34] versus 78% [21/27], p = 0.75) or completion of reconciliation (70% [23/33] versus 75% [15/20], p = 0.76) within 24 h of admission. Completeness of transfer reconciliation was significantly higher during the pilot study than at baseline (91% [31/34] versus 61% [11/18], p = 0.022). No significant differences between the baseline and pilot audits were found in the proportions of patients with at least one BPMH discrepancy (38% [13/34] versus 22% [6/27], p = 0.27), at least one unintentional discrepancy upon admission (21% [7/33] versus 10% [2/20], p = 0.46), or at least one unintentional discrepancy at the transfer interface (6% [1/18] versus 3% [1/34], p = 0.58). None of the 16 unintentional discrepancies were rated as causing severe patient discomfort or clinical deterioration.ConclusionsA trained pharmacy technician can perform admission and transfer medication reconciliation for pediatric patients with completeness and accuracy comparable to those of nurses and pharmacists. Future studies should explore the sustainability and cost-effectiveness of this practice model.

Project description:BackgroundTo optimize medication use in older adults, the Centers for Medicare & Medicaid Services (CMS) launched Medication Therapy Management (MTM) services as part of Medicare Part D policy; however, strategies for achieving high quality MTM outcomes are not well understood.ObjectiveThe objective of this study was to generate hypotheses for strategies contributing to community pharmacies' high performance on policy-relevant MTM quality measures.MethodsThis mixed-methods comparative case study was guided by the Positive Deviance approach and Chronic Care Model. The study population consisted of pharmacy staff employed by a Midwestern division of a national supermarket-community pharmacy chain. Data consisted of demographics and qualitative data from semi-structured interviews. Qualitative and quantitative data were analyzed deductively and inductively or using descriptive statistics, respectively. MTM quality measures used to evaluate participant pharmacies' MTM performance mirrored select 2017 Medicare Part D Plans' Star Rating measures.ResultsThirteen of 18 selected case pharmacies (72.2%) participated in this study, of which 5 were categorized as high performers, 4 moderate performers, and 4 low performers. Eleven pharmacists, 11 technicians, and 3 student interns participated in interviews. Eight strategies were hypothesized as contributing to MTM performance: Strong pharmacy staff-provider relationships and trust, Inability to address patients' social determinants of health (negatively contributing), Technician involvement in MTM, Providing comprehensive medication reviews in person vs. phone alone, Placing high priority on MTM, Using available clinical information systems to identify eligible patients, Technicians using clinical information systems to collect/document information for pharmacists, Faxing prescribers adherence medication therapy problems (MTPs) and calling on indication MTPs.ConclusionsEight strategies were hypothesized as contributing to community pharmacies' performance on MTM quality measures. Findings from this work can inform MTM practice and Medicare Part D MTM policy changes to positively influence patient outcomes. Future research should test hypotheses in a larger representative sample of pharmacies.

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Comparison of Pharmacy Database Methods for Determining Prevalent Chronic Medication Use.

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Comparison of Pharmacy Database Methods for Determining Prevalent Chronic Medication Use.

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