{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:01:33Z","timestamp":1790683293290,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2014,6,20]],"date-time":"2014-06-20T00:00:00Z","timestamp":1403222400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/"}],"funder":[{"name":"ALSAM Foundation Skaggs Scholars Program"},{"name":"University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences"},{"name":"University of California San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences"}],"content-domain":{"domain":["accpjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacotherapy"],"published-print":{"date-parts":[[2014,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      The Medication Regimen Complexity Index (\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      ) is a 65\u2010item instrument that can be used to quantify medication regimen complexity at the patient level, capturing all prescribed and over\u2010the\u2010counter medications. Although the\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      has been used in several studies, the narrow scope of the initial validation limits application at a population or clinical practice level.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      To conduct a\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      validation pertinent to the desired clinical use to identify patients for medication therapy management interventions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      An expert panel of clinical pharmacists ranked medication regimen complexity for two samples of cases: a single\u2010disease cohort (diabetes mellitus) and a multiple\u2010disease 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 (\n                      <jats:styled-content style=\"fixed-case\">EMRs<\/jats:styled-content>\n                      ). Construct validity was assessed using patient\u2010level\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      scores, medication count, and additional patient data. Concordance was evaluated using weighted \u03ba agreement statistic, and correlations were determined using Spearman rank\u2010order correlation coefficient (\u03c1) or Kendall \u03c4.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Moderate to good concordance between patient\u2010level\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      scores and expert medication regimen complexity ranking was observed (claims data, consensus ranking: single\u2010disease cohort\u00a00.55, multiple disease cohort\u00a00.63). In contrast, only fair to moderate concordance was observed for medication count (single\u2010disease cohort\u00a00.33, multiple\u2010disease cohort\u00a00.48). Adding more\u2010detailed administration directions from\n                      <jats:styled-content style=\"fixed-case\">EMR<\/jats:styled-content>\n                      data did not improve concordance.\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      convergent validity was supported by strong correlations with medication count (all cohorts\u00a00.90) and moderate correlations with morbidity measures (e.g., all cohorts; number of comorbidities\u00a00.46, Chronic Disease Score\u00a00.46). Nonsignificant correlation of\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      scores with age and gender (all cohorts\u00a00.08 and 0.06, respectively) supported\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      divergent validity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Limitations<\/jats:title>\n                    <jats:p>This study used cross\u2010sectional, retrospective patient data for a small number of patients and clinical pharmacists from only two universities; therefore, results may have limited generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      The patient\u2010level\n                      <jats:styled-content style=\"fixed-case\">MRCI<\/jats:styled-content>\n                      is a valid tool for assessing medication regimen complexity that can be applied by using data commonly found in claims and\n                      <jats:styled-content style=\"fixed-case\">EMR<\/jats:styled-content>\n                      databases and could be useful to identify patients who may benefit from medication therapy management.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/phar.1452","type":"journal-article","created":{"date-parts":[[2014,6,20]],"date-time":"2014-06-20T01:43:16Z","timestamp":1403228596000},"page":"826-835","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Validation of a Patient\u2010Level Medication Regimen Complexity Index as a Possible Tool to Identify Patients for Medication Therapy Management Intervention"],"prefix":"10.1002","volume":"34","author":[{"given":"Jan D.","family":"Hirsch","sequence":"first","affiliation":[{"name":"Skaggs School of Pharmacy and Pharmaceutical Sciences University of California, San Diego La Jolla California"},{"name":"Veterans Affairs of San Diego Healthcare System San Diego California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelli R.","family":"Metz","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy Skaggs School of Pharmacy and Pharmaceutical Sciences University of Colorado Anschutz Medical Campus Aurora Colorado"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick W","family":"Hosokawa","sequence":"additional","affiliation":[{"name":"Colorado Health Outcomes Program School of Medicine University of Colorado Anschutz Medical Campus Aurora Colorado"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne M.","family":"Libby","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy Skaggs School of Pharmacy and Pharmaceutical Sciences University of Colorado Anschutz Medical Campus Aurora Colorado"},{"name":"Colorado Health Outcomes Program School of Medicine University of Colorado Anschutz Medical Campus Aurora Colorado"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2014,6,20]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"Medication Therapy Management in Pharmacy Practice.Core Elements of an MTM Service Model. 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