Inability of Primary Care Providers to Predict Medication Fulfillment of New Prescriptions

Background/Aims: Physician prediction of patient medication adherence to chronic therapy is unreliable, but the accuracy of physician predictions is largely unstudied for new prescriptions. Our aim was to determine if provider perception of the likelihood a patient will pick up a medication is an accurate predictor of primary medication nonadherence. Methods: We conducted a prospective cohort study as part of a randomized clinical trial. Providers at 24 primary care and family medicine Geisinger clinics were asked to complete a “best practice alert” (BPA) within the electronic health record when placing an order for a new antihypertensive, antidiabetic, antihyperlipidemic or antiasthmatic medication. The BPA asked: “In your opinion, how likely is it that this patient will pick up this medication?” The provider could select from a 5-level Likert item with responses ranging from “very unlikely” to “very likely.” Provider response was correlated to the principle outcome variable, medication first fill after 14 days as identified from the records of the pharmacy to which the prescription was transmitted. Results: A total of 4,822 patients over 11 months were included, and 4,532 (94%) patients filled their prescription within 14 days. Providers answered the BPA 89% of the time. Among respondents, most felt their patients would be likely or very likely to pick up their new medication (90.6% vs. 86.8% of providers chose likely or very likely among adherent and nonadherent groups, respectively). Only 10 (3.9%) of new medication orders not filled (nonadherent) versus 110 (2.7%) filled (adherent) were suspected by providers to be unlikely or very unlikely to be picked up, resulting in only an 8.3% positive predictive value for primary medication nonadherence. Discussion: Our study suggests that physicians overwhelmingly believe their patients are likely/very likely to pick up their first prescription. A physician’s intuition about a patient’s likelihood of filling a new medication does not reliably identify patients who do not fill new prescriptions for chronic medications. Our study’s ability to assess prediction accurately is limited by the unusually high first-fill rate of patients in this trial. Our findings are congruent with other reports assessing physician perceptions of patient adherence to chronic medications

Longitudinal Evaluation of Chronic Rhinosinusitis Symptoms in a Population-Based Sample

Background: Chronic rhinosinusitis (CRS) is a prevalent and disabling condition of the nose and sinuses. The natural history of CRS symptoms in a general population sample has not been previously studied. Objective: In a general population–based sample from Pennsylvania, we used 2 questionnaires mailed 6 months apart to estimate the prevalence of, and identify predictors for, stability or change in symptoms over time. Methods: We mailed the baseline and 6-month follow-up questionnaires to 23,700 primary care patients and 7,801 baseline responders, respectively. We categorized nasal and sinus symptoms using European Position Paper on Rhinosinusitis (EPOS) epidemiologic criteria. We defined 6 symptom groups over time on the basis of the presence of CRS symptoms at baseline and follow-up. We performed multivariable survey logistic regression controlling for confounding variables comparing persistent versus nonpersistent, recurrent versus stable past, and incident versus never. Results: There were 4,966 responders at follow-up: 558 had persistent symptoms, 190 recurrent symptoms, and 83 new symptoms meeting EPOS criteria for CRS. The prevalence of persistent symptoms was 4.8% (95% CI, 3.8-5.8), whereas the annual cumulative incidence of new symptoms was 1.9% and of recurrent symptoms was 3.2%. More severe symptoms at baseline were associated with persistence, whereas minor symptoms, allergies, and multiple treatments were associated with the development of new symptoms. Conclusions: Less than half with nasal and sinus symptoms meeting CRS EPOS criteria in our general, regional population had symptom persistence over time, with symptom profiles at baseline and age of onset being strongly associated with stability of symptoms

Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network

Abstract The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations

Lessons learned from the eMERGE Network: balancing genomics in discovery and practice

The Electronic Medical Records and Genomics (eMERGE) Network, established in 2007, is a consortium of academic and integrated health systems conducting discovery and implementation research in translational genomics. Here, we outline the history of the network, highlight major impacts and lessons learned, and present the tools and resources developed for large-scale genomic analyses and translation into a clinical setting. The network developed methods to extract phenotypes from the electronic medical record to perform genome-wide and phenome-wide association studies. Recruited cohorts were clinically sequenced off a custom panel for targeted sequencing of variants and monogenic disease risks and returned to participants to investigate the impact of return of genomic results. After generating a 105,000 participant-imputed genome-wide association study (GWAS) dataset for discovery, the network enrolled and sequenced 24,998 participants. Integration of these results into the medical record and the effects of results on participants provided key lessons to the field. These learned lessons inform genetic research in diverse populations and provide insights into the clinical impact of return and implementation of genomic medicine using the electronic medical record. The lessons produced by the eMERGE Network can be utilized by other consortia as translational genomic medicine research evolves