Validation of an algorithm to prioritize patients for comprehensive medication management in primary care settings
Background Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients’ medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied. Aim Determine the face validity of a claim...
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| Published in | International journal of clinical pharmacy Vol. 46; no. 5; pp. 1232 - 1236 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
01.10.2024
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2210-7703 2210-7711 2210-7711 |
| DOI | 10.1007/s11096-024-01770-6 |
Cover
| Abstract | Background
Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients’ medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied.
Aim
Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM.
Method
We used claims data to construct patient-level markers of “regimen complexity” and “high-risk for adverse effects,” which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic.
Results
Most records identified as ‘very likely’ (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the ‘very unlikely’ group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6,
p
< 0.001).
Conclusion
Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings. |
|---|---|
| AbstractList | Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied.BACKGROUNDComprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied.Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM.AIMDetermine the face validity of a claims-based algorithm to prioritize patients who likely need CMM.We used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic.METHODWe used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic.Most records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001).RESULTSMost records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001).Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings.CONCLUSIONClaims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings. Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied. Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM. We used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic. Most records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001). Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings. Background Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients’ medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied. Aim Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM. Method We used claims data to construct patient-level markers of “regimen complexity” and “high-risk for adverse effects,” which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic. Results Most records identified as ‘very likely’ (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the ‘very unlikely’ group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001). Conclusion Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings. Background Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied. Aim Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM. Method We used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic. Results Most records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001). Conclusion Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings. BackgroundComprehensive medication management (CMM) programs optimize the effectiveness and safety of patients’ medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied.AimDetermine the face validity of a claims-based algorithm to prioritize patients who likely need CMM.MethodWe used claims data to construct patient-level markers of “regimen complexity” and “high-risk for adverse effects,” which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic.ResultsMost records identified as ‘very likely’ (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the ‘very unlikely’ group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001).ConclusionClaims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings. Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied. Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM. We used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic. Most records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001). Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings. |
| Audience | Academic |
| Author | Shermock, Kenneth M. Bishop, Martin A. Pandya, Chintan J. Kitchen, Christopher Brown, Dannielle Chang, Hsien-Yen Weiner, Jonathan P. Gudzune, Kimberly A. |
| Author_xml | – sequence: 1 givenname: Martin A. surname: Bishop fullname: Bishop, Martin A. organization: Department of Pharmacy, The Johns Hopkins Hospital – sequence: 2 givenname: Hsien-Yen surname: Chang fullname: Chang, Hsien-Yen organization: Johnson and Johnson Innovative Medicine – sequence: 3 givenname: Christopher surname: Kitchen fullname: Kitchen, Christopher organization: Center for Population Health Information Technology, Johns Hopkins University – sequence: 4 givenname: Chintan J. surname: Pandya fullname: Pandya, Chintan J. organization: Center for Population Health Information Technology, Johns Hopkins University, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health – sequence: 5 givenname: Dannielle surname: Brown fullname: Brown, Dannielle organization: Department of Pharmacy, The Johns Hopkins Hospital – sequence: 6 givenname: Jonathan P. surname: Weiner fullname: Weiner, Jonathan P. organization: Center for Population Health Information Technology, Johns Hopkins University, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health – sequence: 7 givenname: Kenneth M. orcidid: 0009-0008-0908-6169 surname: Shermock fullname: Shermock, Kenneth M. email: ken@jhmi.edu organization: Department of Pharmacy, The Johns Hopkins Hospital, Center for Drug Safety and Effectiveness, Johns Hopkins University, Department of Medicine, Johns Hopkins University School of Medicine – sequence: 8 givenname: Kimberly A. surname: Gudzune fullname: Gudzune, Kimberly A. organization: Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Department of Medicine, Johns Hopkins University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39042353$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.amjmed.2020.12.008 10.1016/j.sapharm.2017.05.006 10.2146/ajhp161061 10.1111/jgs.15767 10.1002/jac5.1181 10.1016/j.sapharm.2022.04.006 10.1345/aph.1D479 10.1007/s11096-023-01561-5 10.1097/00005650-199105000-00006 10.1093/ageing/afu145 10.1331/JAPhA.2012.10145 10.1016/j.sapharm.2020.01.001 10.1007/s11096-023-01648-z 10.1177/1060028013510898 10.1111/jgs.12504 10.3390/pharmacy11010037 10.3122/jabfm.2015.03.140303 |
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Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients’ medication regimens, but CMM may be... Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized.... Background Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be... BackgroundComprehensive medication management (CMM) programs optimize the effectiveness and safety of patients’ medication regimens, but CMM may be... |
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