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. 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 claims-derived predictive models of cost and hospitalization in non-elderly patients; however, the improvements are minimal for advanced diagnosis-based models.

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.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 (36.3 [37.9], P < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], P < 0.001; 30-day PFR = 75.7 [23.6%], P < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, P < 0.001) or with 30-day PFR (SC = -0.17, P < 0.001) at significant levels. CONCLUSIONS: Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts. DISCLOSURES: No outside funding supported this study. The authors have nothing to disclose. The abstract of this work was presented at INFORMS Healthcare Conference, held on July 27-29, 2019, in Cambridge, MA.

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.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.

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. METHODS: Using online medical consultation records between April 1, 2009 and May 31, 2010 from four ambulatory practices affiliated with a major healthcare provider, we conduct simple descriptive analysis to understand the users of online clinical consults and their usage patterns. Multilevel Logit regression is employed to measure the effect of patient and primary care provider characteristics on the likelihood of eVisit adoption by the patient, and survival analysis and Ordered Logit regression are applied to study eVisit adoption patterns that delineate elements describing early or late adopters. RESULTS: On average, eVisit adopters are younger and predominantly female. Their primary care providers participate in the eVisit service, highlighting the importance of physician's role in encouraging patients to utilize the service. Patients who are familiar with the patient portal are more likely to use the service, as are patients with more complex health issues. Younger and female patients have higher adoption hazard, but gender does not affect the decision of adopting early vs. late. These adopters also access the patient portal more frequently before adoption, indicating that they are potentially more involved in managing their health. The majority of eVisits are submitted during business hours, with female physicians responding faster (from submission to reply), on average. CONCLUSIONS: This study addresses virtualization of primary care delivery via patient portals and online clinical consultations and examines factors that distinguish eVisit adopters from patient portal users. Among many delineating characteristics, it is particularly significant that familiarity with the patient portal service and participation of primary care provider are found to be key elements that motivate patients to become an eVisit user and early/late adopter. These findings can be used by provider organizations to design and implement strategies to improve uptake of online medical consultations to complement traditional office visits. Offering such alternative channels of care delivery may potentially improve access, efficiency and outcomes for both patients and providers alike.