Dataset Information

Automating the medication regimen complexity index.

ABSTRACT:

Objective

To adapt and automate the medication regimen complexity index (MRCI) within the structure of a commercial medication database in the post-acute home care setting.

Materials and methods

In phase 1, medication data from 89 645 electronic health records were abstracted to line up with the components of the MRCI: dosage form, dosing frequency, and additional administrative directions. A committee reviewed output to assign index weights and determine necessary adaptations. In phase 2 we examined the face validity of the modified MRCI through analysis of automatic tabulations and descriptive statistics.

Results

The mean number of medications per patient record was 7.6 (SD 3.8); mean MRCI score was 16.1 (SD 9.0). The number of medications and MRCI were highly associated, but there was a wide range of MRCI scores for each number of medications. Most patients (55%) were taking only oral medications in tablet/capsule form, although 16% had regimens with three or more medications with different routes/forms. The biggest contributor to the MRCI score was dosing frequency (mean 11.9). Over 36% of patients needed to remember two or more special instructions (eg, take on alternate days, dissolve).

Discussion

Medication complexity can be tabulated through an automated process with some adaptation for local organizational systems. The MRCI provides a more nuanced way of measuring and assessing complexity than a simple medication count.

Conclusions

An automated MRCI may help to identify patients who are at higher risk of adverse events, and could potentially be used in research and clinical decision support to improve medication management and patient outcomes.

SUBMITTER: McDonald MV

PROVIDER: S-EPMC3628060 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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Publications

Automating the medication regimen complexity index.

McDonald Margaret V MV Peng Timothy R TR Sridharan Sridevi S Foust Janice B JB Kogan Polina P Pezzin Liliana E LE Feldman Penny H PH

Journal of the American Medical Informatics Association : JAMIA 20121225 3

<h4>Objective</h4>To adapt and automate the medication regimen complexity index (MRCI) within the structure of a commercial medication database in the post-acute home care setting.<h4>Materials and methods</h4>In phase 1, medication data from 89 645 electronic health records were abstracted to line up with the components of the MRCI: dosage form, dosing frequency, and additional administrative directions. A committee reviewed output to assign index weights and determine necessary adaptations. In ...[more]

PMID: 23268486

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Project description:BACKGROUND: The Medication Regimen Complexity Index (MRCI) is a 65-item instrument that can be used to quantify medication regimen complexity at the patient level, capturing all prescribed and over-the-counter medications. Although the MRCI has been used in several studies, the narrow scope of the initial validation limits application at a population or clinical practice level. PURPOSE: To conduct a MRCI validation pertinent to the desired clinical use to identify patients for medication therapy management interventions. METHODS: An expert panel of clinical pharmacists ranked medication regimen complexity for two samples of cases: a single-disease cohort (diabetes mellitus) and a multiple-disease cohort (diabetes mellitus, hypertension, human immunodeficiency virus infection, geriatric depression). Cases for expert panel review were selected from 400 ambulatory clinic patients, and each case description included data that were available via claims or electronic medical records (EMRs). Construct validity was assessed using patient-level MRCI scores, medication count, and additional patient data. Concordance was evaluated using weighted ? agreement statistic, and correlations were determined using Spearman rank-order correlation coefficient (?) or Kendall ?. RESULTS: Moderate to good concordance between patient-level MRCI scores and expert medication regimen complexity ranking was observed (claims data, consensus ranking: single-disease cohort 0.55, multiple disease cohort 0.63). In contrast, only fair to moderate concordance was observed for medication count (single-disease cohort 0.33, multiple-disease cohort 0.48). Adding more-detailed administration directions from EMR data did not improve concordance. MRCI convergent validity was supported by strong correlations with medication count (all cohorts 0.90) and moderate correlations with morbidity measures (e.g., all cohorts; number of comorbidities 0.46, Chronic Disease Score 0.46). Nonsignificant correlation of MRCI scores with age and gender (all cohorts 0.08 and 0.06, respectively) supported MRCI divergent validity. LIMITATIONS: This study used cross-sectional, retrospective patient data for a small number of patients and clinical pharmacists from only two universities; therefore, results may have limited generalizability. CONCLUSIONS: The patient-level MRCI is a valid tool for assessing medication regimen complexity that can be applied by using data commonly found in claims and EMR databases and could be useful to identify patients who may benefit from medication therapy management.

Project description:Background The likelihood of elderly patients with heart failure (HF) being readmitted to the hospital is higher if they have a higher medication regimen complexity index (MRCI) compared to those with a lower MRCI. The objective of this study was to investigate whether there is a correlation between the MRCI score and the frequency of hospital readmissions (30-day, 90-day, and 1-year) among elderly patients with HF. Methods In this single-center retrospective cohort study, MRCI scores were calculated using a well-established tool. Patients were categorized into high (≥ 15) or low (< 15) MRCI score groups. The primary outcome examined the association between MRCI scores and 30-day hospital readmission rates. Secondary outcomes included the relationships between MRCI scores and 90-day readmission, one-year readmission, and mortality rates. Multivariate logistic regression was employed to assess the 30- and 90-day readmission rates, while Kaplan-Meier analysis was utilized to plot mortality. Results A total of 150 patients were included. The mean MRCI score for all patients was 33.43. 90% of patients had a high score. There was no link between a high MCRI score and a high 30-day readmission rate (OR 1.02; 95% CI 0.99–1.05; p < 0.13). A high MCRI score was associated with an initial significant increase in the 90-day readmission rate (odd ratio, 1.03; 95% CI, 1.00-1.07; p < 0.022), but not after adjusting for independent factors (odd ratio, 0.99; 95% CI, 0.95–1.03; p < 0.487). There was no significant difference between high and low MRCI scores in their one-year readmission rate. Conclusion The study’s results indicate that there is no correlation between a higher MRCI score and the rates of hospital readmission or mortality among elderly patients with HF. Therefore, it can be concluded that the medication regimen complexity index does not appear to be a significant predictor of hospital readmission or mortality in this population. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-023-04062-2.

Project description:IntroductionType 2 diabetes mellitus (T2DM) and comorbid conditions require patients to take complex medication regimens. Greater regimen complexity has been associated with poorer T2DM management; however, the relationship between overall regimen complexity and glycemic control is unclear.ObjectivesOur objectives were: (1) to examine associations between regimen complexity (with the Medication Regimen Complexity Index [MRCI]) and glycemic control (A1C), and (2) to compare overall MRCI with other measures of regimen complexity (overall and diabetes-specific medication count) and diabetes-specific MRCI.MethodsThis was a secondary data analysis of cross-sectional data from a parent trial. Participants were patients with T2DM taking at least 3 chronic medications followed in safety net clinics in the Chicago area. The MRCI measures complexity based on dosing frequency, route of administration, and special instructions for prescribed medications. MRCI scores were created for overall regimens and diabetes-specific medications. Sociodemographics and outpatient visit utilization were included in models as covariates. Linear regression was used to examine the associations between variables of interest and hemoglobin A1C.ResultsParticipants (N = 432) had a mean age of 56.9 years, most were female (66.0%), and Hispanic or Latino (73.3%). Regimen complexity was high based on overall medications (mean = 6.6 medications, SD: 3.09) and MRCI (mean = 21.4, SD: 11.3). Higher diabetes-specific MRCI was associated with higher A1C in bivariate and multivariable models. In multivariable models, overall MRCI greater than 14, fewer outpatient health care visits, male gender, and absence of health insurance were independently associated with higher A1C. The variance in A1C explained by MRCI was higher compared to medication count for overall and diabetes-specific regimen complexity.ConclusionsMore complex regimens are associated with worse A1C and measuring complexity with MRCI may have advantages. Deprescribing, increasing insurance coverage, and promoting engagement in health care may improve A1C among underserved populations with complex regimens.

Dataset Information

Automating the medication regimen complexity index.

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

Automating the medication regimen complexity index.

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