Dataset Information

Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims.

ABSTRACT: Objective:Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications. Materials and Methods:We developed an algorithm linking EHR prescribing data (RxNorm terminology) to claims-based dispensing data (NDC terminology), within sample of adult (19-64) community health center (CHC) patients with diabetes from a network of CHCs across 12 states. We demonstrate an application of the method by calculating dispense rates for a set of commonly prescribed diabetes and cardio-protective medications. To further inform clinical care, we computed adjusted odds ratios of dispense by patient-, encounter-, and clinic-level characteristics. Results:Seventy-six percent of cardio-protective medication prescriptions and 74% of diabetes medications were linked to a dispensing record. Age, income, ethnicity, insurance, assigned primary care provider, comorbidity, time on EHR, and clinic size were significantly associated with odds of dispensing. Discussion:EHR prescriptions and pharmacy dispense data can be linked at the record level across different terminologies. Dispensing rates in this low-income population with diabetes were similar to other populations. Conclusion:Record linkage resulted in the finding that CHC patients with diabetes largely had their chronic disease medications dispensed. Understanding factors associated with dispensing rates highlight barriers and opportunities for optimal disease management.

SUBMITTER: Hoopes M

PROVIDER: S-EPMC6188526 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims.

Hoopes Megan M Angier Heather H Raynor Lewis A LA Suchocki Andrew A Muench John J Marino Miguel M Rivera Pedro P Huguet Nathalie N

Journal of the American Medical Informatics Association : JAMIA 20181001 10

<h4>Objective</h4>Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications.<h4>Ma ...[more]

PMID: 30113681

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Project description:BACKGROUND:Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA1c) screening are lacking. Current screening guidelines are based on simple, categorical rules developed for populations of patients. Although personalized diabetes risk calculators have been created, none are designed to predict current glycemic status using structured data commonly available in electronic health records (EHRs). OBJECTIVE:The goal of this project was to create a mathematical equation for predicting the probability of current elevations in HbA1c (≥5.7%) among patients with no history of hyperglycemia using readily available variables that will allow integration with EHR systems. METHODS:The reduced model was compared head-to-head with calculators created by Baan and Griffin. Ten-fold cross-validation was used to calculate the bias-adjusted prediction accuracy of the new model. Statistical analyses were performed in R version 3.2.5 (The R Foundation for Statistical Computing) using the rms (Regression Modeling Strategies) package. RESULTS:The final model to predict an elevated HbA1c based on 22,635 patient records contained the following variables in order from most to least importance according to their impact on the discriminating accuracy of the model: age, body mass index, random glucose, race, serum non-high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate, and smoking status. The new model achieved a concordance statistic of 0.77 which was statistically significantly better than prior models. The model appeared to be well calibrated according to a plot of the predicted probabilities versus the prevalence of the outcome at different probabilities. CONCLUSIONS:The calculator created for predicting the probability of having an elevated HbA1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA1c screening initiatives.

Project description:BackgroundFinancial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.ObjectiveThe aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records.MethodsTwo electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation.ResultsFor the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97).ConclusionsWe demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

Project description:ImportanceInterventions to improve judicious prescribing of opioid analgesics for acute pain are needed owing to the risks of diversion, misuse, and overdose.ObjectiveTo assess the effect of modifying opioid analgesic prescribing defaults in the electronic health record (EHR) on prescribing and health service use.Design, setting, and participantsA cluster randomized clinical trial with 2 parallel arms was conducted between June 13, 2016, and June 13, 2018, in a large urban health care system comprising 32 primary care and 4 emergency department (ED) sites in the Bronx, New York. Data were analyzed using a difference-in-differences method from 6 months before implementation through 18 months after implementation. Data were analyzed from January 2019 to February 2020.InterventionsA default dispense quantity for new opioid analgesic prescriptions of 10 tablets (intervention) vs no change (control) in the EHR.Main outcomes and measuresThe primary outcome was the quantity of opioid analgesics prescribed with the new default prescription. Secondary outcomes were opioid analgesic reorders and health service use within 30 days after the new prescription. Intention-to-treat analysis was conducted.ResultsOverall, 21 331 patients received a new opioid analgesic prescription from 490 prescribers. Comparing the intervention and control arms, site, prescriber, and patient characteristics were similar. For the new prescription, compared with the control arm, patients in the intervention arm had significantly more prescriptions for 10 tablets or fewer (7.6 percentage points; 95% CI, 6.1-9.2 percentage points), a lower number of tablets prescribed (-2.1 tablets; 95% CI, -3.3 to -0.9 tablets), and lower morphine milligram equivalents (MME) prescribed (-14.6 MME; 95% CI, -22.6 to -6.6 MME). Within 30 days after the new prescription, significant differences remained in the number of tablets prescribed (-2.7 tablets; 95% CI, -4.8 to -0.6 tablets), but not MME (-15.8 MME; 95% CI, -33.8 to 2.2 MME). Within this 30-day period, there were no significant differences between the arms in health service use.Conclusions and relevanceIn this study, implementation of a uniform reduced default dispense quantity of 10 tablets for opioid analgesic prescriptions led to a modest reduction in the quantity prescribed initially, without significantly increasing health service use. However, during 30 days after implementation, the influence on prescribing was mixed. Reducing EHR default dispense quantities for opioid analgesics is a feasible strategy that can be widely disseminated and may modestly reduce prescribing.Trial registrationClinicalTrials.gov Identifier: NCT03003832.

Project description:BackgroundIdentification of people with HIV from electronic health record (EHR) data is an essential first step in the study of important HIV outcomes, such as risk assessment. This task has been historically performed via manual chart review, but the increased availability of large clinical data sets has led to the emergence of phenotyping algorithms to automate this process. Existing algorithms for identifying people with HIV rely on a combination of International Classification of Disease codes and laboratory tests or closely mimic clinical testing guidelines for HIV diagnosis. However, we found that existing algorithms in the literature missed a significant proportion of people with HIV in our data.ObjectiveThe aim of this study is to develop and evaluate HIV-Phen, an updated criteria-based HIV phenotyping algorithm.MethodsWe developed an algorithm using HIV-specific laboratory tests and medications and compared it with previously published algorithms in national and local data sets to identify cohorts of people with HIV. Cohort demographics were compared with those reported in the national and local surveillance data. Chart reviews were performed on a subsample of patients from the local database to calculate the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the algorithm.ResultsOur new algorithm identified substantially more people with HIV in both national (up to an 85.75% increase) and local (up to an 83.20% increase) EHR databases than the previously published algorithms. The demographic characteristics of people with HIV identified using our algorithm were similar to those reported in national and local HIV surveillance data. Our algorithm demonstrated improved sensitivity over existing algorithms (98% vs 56%-92%) while maintaining a similar overall accuracy (96% vs 80%-96%).ConclusionsWe developed and evaluated an updated criteria-based phenotyping algorithm for identifying people with HIV in EHR data that demonstrates improved sensitivity over existing algorithms.

Project description:Electronic health record (EHR) or medical claims-based algorithms (i.e., operational definitions) can be used to define safety outcomes using real-world data. However, existing tools do not allow researchers and decision-makers to adequately appraise whether a particular algorithm is fit for purpose (FFP) to support regulatory decisions on drug safety surveillance. Our objective was to develop a tool to enable regulatory decision-makers and other stakeholders to appraise whether a given algorithm is FFP for a specific decision context. We drafted a set of 77 generic items informed by regulatory guidance documents, existing instruments, and publications. The outcome of ischemic stroke served as an exemplar to inform the development of draft items. The items were designed to be outcome independent. We conducted a three-round online Delphi panel to develop and refine the tool and achieve consensus on items (> 70% agreement) among panel participants composed of regulators, researchers from pharmaceutical organizations, academic clinicians, methodologists, pharmacoepidemiologists, and cardiologists. We conducted a qualitative analysis of panel responses. Five pairs of reviewers independently evaluated two ischemic stroke algorithm validation studies to test its application. We developed a user guide, with explanation and elaboration for each item, guidance on essential and additional elements for user responses, and an illustrative example of a complete assessment. Furthermore, we conducted a 2-h online stakeholder panel of 16 participants from regulatory agencies, academic institutions, and industry. We solicited input on key factors for an FFP assessment, their general reaction to the Algorithm CErtaInty Tool (ACE-IT), limitations of the tool, and its potential use. The expert panel reviewed and made changes to the initial list of 77 items. The panel achieved consensus on 38 items, and the final version of the ACE-IT includes 34 items after removal of duplicate items. Applying the tool to two ischemic stroke algorithms demonstrated challenges in its application and identified shared concepts addressed by more than one item. The ACE-IT was viewed positively by the majority of stakeholders. They identified that the tool could serve as an educational resource as well as an information-sharing platform. The time required to complete the assessment was identified as an important limitation. We consolidated items with shared concepts and added a preliminary screen section and a summary assessment box based on their input. The final version of the ACE-IT is a 34-item tool for assessing whether algorithm validation studies on safety outcomes are FFP. It comprises the domains of internal validity (24 items), external validity (seven items), and ethical conduct and reporting of the validation study (three items). The internal validity domain includes sections on objectives, data sources, population, outcomes, design and setting, statistical methods, reference standard, accuracy, and strengths and limitations. The external validity domain includes items that assess the generalizability to a proposed target study. The domain on ethics and transparency includes items on ethical conduct and reporting of the validation study. The ACE-IT supports a structured, transparent, and flexible approach for decision-makers to appraise whether electronic health record or medical claims-based algorithms for safety outcomes are FFP for a specific decision context. Reliability and validity testing using a larger sample of participants in other therapeutic areas and further modifications to reduce the time needed to complete the assessment are needed to fully evaluate its utility for regulatory decision-making.

Dataset Information

Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims.

Publications

Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims.

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