Comparing the Predictive Effects of Patient Medication Adherence Indices in Electronic Health Record and Claims-Based Risk Stratification Models.

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.

Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims.

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.

The impact of Medicare part D prescription drug benefit program on generic drug prescription: A study in long-term care facilities.

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.

Virtualized healthcare delivery: understanding users and their usage patterns of online medical consultations.

Background: Virtualization of healthcare delivery via patient portals has facilitated the increasing interest in online medical consultations due to its benefits such as improved convenience and flexibility, lower cost, and time savings. Despite this growing interest, adoption by both consumers and providers has been slow, and little is known about users and their usage and adoption patterns.

Objective: To learn characteristics of online healthcare consumers and understand their patterns of adoption and usage of online clinical consultation services (or eVisits delivered via the portal) such as adoption time for portal users, whether adoption hazard changes over time, and what factors influence patients to become early/late adopters.