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BACKGROUND: Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs.OBJECTIVES: To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims.METHODS: We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman's coefficient (SC) after adjusting for age and sex. RESULTS: The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.

Multiple indices are available to measure medication adherence behaviors. Medication adherence measures, however, have rarely been extracted from electronic health records (EHRs) for population-level risk predictions. This study assessed the value of medication adherence indices in improving predictive models of cost and hospitalization. This study included a 2-year retrospective cohort of patients younger than age 65 years with linked EHR and insurance claims data. Three medication adherence measures were calculated: medication regimen complexity index (MRCI), medication possession ratio (MPR), and prescription fill rate (PFR). The authors examined the effects of adding these measures to 3 predictive models of utilization: a demographics model, a conventional model (Charlson index), and an advanced diagnosis-based model. Models were trained using EHR and claims data. The study population had an overall MRCI, MPR, and PFR of 14.6 -17.8, .624 -.310, and .810 -.270, respectively. Adding MRCI and MPR to the demographic and the morbidity models using claims data improved forecasting of next-year hospitalization substantially (eg, AUC of the demographic model increased from .605 to .656 using MRCI). Nonetheless, such boosting effects were attenuated for the advanced diagnosis-based models. Although EHR models performed inferior to claims models, adding adherence indices improved EHR model performances at a larger scale (eg, adding MRCI increased AUC by 4.4% for the Charlson model using EHR data compared to 3.8% using claims). This study shows that medication adherence measures can modestly improve EHR-and claimsderived predictive models of cost and hospitalization in non-elderly patients; however, the improvements are minimal for advanced diagnosis-based models.

To examine whether the Medicare Part D program had an impact on the generic drug prescription rate among residents in long-term care facilities. We analyzed prescription data for 3 drug classes (atypical antipsychotic, proton pump inhibitor, and statin) obtained from a regional online pharmacy serving long-term care centers in Pennsylvania from January 2004 to December 2007. Difference-in-difference is used as a primary analysis method, and different regression methods (probit and multinomial) are used to accommodate different types of outcome measures. Contrary to expectations, the Part D program did not have a statistically significant impact on the generic prescription rate in the long-term care setting during the study period. Only the statin class showed a dramatic increase in generic drug prescriptions, mainly due to the loss of patent protection for one of the most popular brand-name drugs in the class. The complex dynamics of the prescription drug market, particularly the availability of generic versions of popular prescription medications, had a bigger role in increasing the prescription rate of generic drugs than the Part D program. This warrants the need to relax prescription medicines’ patent policies and for further study on the impact of such policies.

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nline education has gained popularity in business schools in recent years as technology advances and student schedules demand more flexibility. The trend has grown exponentially since the start of the COVID-19 pandemic and is now the norm for students, instructors, and parents. By now, most of us know how to navigate Zoom or Google Meet as well as various online connecting tools such as Microsoft Teams and Discord.The principle of distance learning has been around for many decades, with one of the first examples being the use of radio to deliver courses to students. In 1937, schools in Chicago delivered classes via radio broadcasting for a couple of weeks during the polio epidemic. 1 Regardless of the effectiveness and structure, the remote learning format has existed for a longer time than most would readily know. With advanced technology and greater access to and speed of the Internet, the capability and perception of distance learning has evolved over time. Group learning and individualized lessons became available as the use of the Internet melded into our daily lives. The sophistication of remote learning has developed from a oneway mass broadcasting to an interactive environment,