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

Ma, Xiaomeng; Jung, Changmi; Chang, Hsien Yen; Richards, Thomas M.; Kharrazi, Hadi

doi:10.18553/jmcp.2020.26.7.860

Cited by 9 publications

(15 citation statements)

References 59 publications

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“…MRCI measures the complexity of medication management for patients, containing major characteristics of a prescription. [6][7][8] MRCI relies on EHR data and consists of 3 weighted elements of dosage form, dosing frequency, and additional drug administration instructions. 4,6,10 MPR, a common measure of medication adherence using claims data, is generated from drug dispensing records.…”

Section: Risk Adjustersmentioning

confidence: 99%

“…[6][7][8] MRCI relies on EHR data and consists of 3 weighted elements of dosage form, dosing frequency, and additional drug administration instructions. 4,6,10 MPR, a common measure of medication adherence using claims data, is generated from drug dispensing records. 5,6 MPR indicates the proportion of times when patients with an adequate amount of medication follow their refill schedule.…”

Section: Risk Adjustersmentioning

confidence: 99%

“…7,8 For example, patients having a high MRCI (ie, high complexity of medication regimen) or low MPR/PFR (ie, low [re-]filling rates) are prone to develop conditions leading to high health care costs and utilization rates. [11][12][13] Previous studies have identified the variation and correlation among MRCI, 14,15 MPR, 16,17 and PFR 6,7 in different morbidity groups. However, constructing such medication adherence profiles requires individually linked EHR and insurance claims data, thus only a few studies have compared the impact of MRCI, MPR, or PFR on predictive models of health care utilization.…”

Section: Introductionmentioning

confidence: 99%

“…6 MRCI, MPR, and PFR are calculated using structured patient-level data, [4][5][6] have been validated in previous studies as measures of medication complexity and adherence, and have been shown to represent modestly different aspects of medication adherence. [6][7][8] Several clinical and administrative data sources can be used to measure medication adherence. Traditionally, administrative claims data (eg, pharmacy claims) have been used to extract medication dispenses and refills (eg, calculating MPR).…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7][8] The wide adoption and increasing maturation of health information systems 9 have led electronic health records (EHRs) to become an alternative source of prescription data for medication adherence measurement (eg, calculating MRCI). 6,10 Despite the availability of medication data sources on a population level, certain measures of medication adherence require more than 1 data source (eg, PFR needs both EHR and claims data [6][7][8] ), hence making such measures more appropriate for subpopulations that have all required data sources available simultaneously. 11 Risk stratification enables population health programs to allocate available resources to high-risk and high-need patient populations.…”

Section: Introductionmentioning

confidence: 99%

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

Kharrazi

Chang

et al. 2021

Population Health Management

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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.

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Section: Risk Adjustersmentioning

confidence: 99%

Section: Risk Adjustersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kharrazi

Chang

et al. 2021

Population Health Management

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Consequences of Nonadherence and Correlates of Adherence

Rapoff¹,

Duncan²,

Karlson³

2023

Issues in Clinical Child Psychology

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A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models

Wang

Landers

Adams³

et al. 2022

Journal of the American Medical Informatics Association

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Objective Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model’s potential to introduce bias. Materials and Methods Our methods include developing a bias evaluation checklist, a scoping literature review to identify 30-day hospital readmission prediction models, and assessing the selected models using the checklist. Results We selected 4 models for evaluation: LACE, HOSPITAL, Johns Hopkins ACG, and HATRIX. Our assessment identified critical ways in which these algorithms can perpetuate health care inequalities. We found that LACE and HOSPITAL have the greatest potential for introducing bias, Johns Hopkins ACG has the most areas of uncertainty, and HATRIX has the fewest causes for concern. Discussion Our approach gives model developers and health care providers a practical and systematic method for evaluating bias in predictive models. Traditional bias identification methods do not elucidate sources of bias and are thus insufficient for mitigation efforts. With our checklist, bias can be addressed and eliminated before a model is fully developed or deployed. Conclusion The potential for algorithms to perpetuate biased outcomes is not isolated to readmission prediction models; rather, we believe our results have implications for predictive models across health care. We offer a systematic method for evaluating potential bias with sufficient flexibility to be utilized across models and applications.

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

Cited by 9 publications

References 59 publications

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

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

Consequences of Nonadherence and Correlates of Adherence

A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models

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