Prioritization of Patients for Comprehensive Medication Review By a Clinical Pharmacist in Family Medicine

Background: Patients with higher clinical pharmacy priority (CP2) scores are more likely to receive a medication recommendation from a pharmacist to resolve an identified medication related problem (MRP) in a family medicine clinic.Objective: The objective of this study was to compare a modified CP2 score to the original CP2 score in an adult medicine residency clinic and assess whether a modified CP2 score more appropriately captured patients in need of recommendations from a clinical pharmacist.Methods: A convenience sample of patients scheduled for visits in an adult medicine clinic had a comprehensive medication review (CMR) performed. All patients had the original CP2 score and modified CP2 score calculated retrospectively. Both versions of the CP2 scores were compared using Pearson correlation. The relationship between a recommendation from a pharmacist and each CP2 score was evaluated using Pearson correlation and t test.Results: In total, 148 patients who received a CMR by a clinical pharmacist were included in the retrospective chart review. Overall, there was a strong positive correlation between the CP2 score and the modified CP2 score (correlation coefficient (r) 0.968, P < 0.001); thus, as the CP2 scores increase, the modified CP2 score increases.Patients with higher CP2 and modified CP2 scores were more likely to receive a recommendation from a pharmacist (P < 0.001 and P < 0.001, respectively). There was a moderate correlation between both scores and number of recommendations made (CP2: r = 0.436 [P < 0.001], modified CP2: r = 0.456 [P < 0.001]) which did not differ significantly. Conclusions:The CP2 score and the modified CP2 score were both associated with pharmacist recommendations in an adult medicine clinic. Refinement of the CP2 score did not better capture patients in an adult medicine residency clinic with a recommendation from a clinical pharmacist. K E Y W O R D Shealth care systems, health services research, medication therapy management, pharmacists, pharmacy service, hospital

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confidence: 99%

Evaluation of clinical pharmacy priority scoring methods in an adult medicine residency clinic

Krasniak

Szymaniak

Conn

et al. 2019

“…[3][4][5][6][7][8][9][10][11] These clinical pharmacists commonly provide education and clinical care in family medicine residency programs. 12 The CP2 Score is calculated using patient-specific data available from the electronic health record (EHR) including chronic diseases, uncontrolled chronic disease, number of medications, and age. 11 The Clinical Pharmacy Priority (CP2) Score was developed and clinically validated to target patients who are most likely to have medication-related problems within a small, off-campus University of Colorado family medicine clinic.…”

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confidence: 99%

“…The expert panel was provided with the list of elements included in the CP2 score as a starting point, 12 along with a description of the issues identified with the application of the CP2 Score in the family medicine residency program at Colorado. These clinical pharmacists were faculty members at each respective School of Pharmacy, and ranged in academic rank from Assistant Professor to Professor.…”

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confidence: 99%

“…The Clinical Pharmacy Priority (CP2) Score was developed and clinically validated to target patients who are most likely to have medication-related problems within a small, off-campus University of Colorado family medicine clinic. 12 The CP2 Score is calculated using patient-specific data available from the electronic health record (EHR) including chronic diseases, uncontrolled chronic disease, number of medications, and age. The score ranges from 0 (low) to 21 (high).…”

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confidence: 99%

“…These clinical pharmacists were faculty members at each respective School of Pharmacy, and ranged in academic rank from Assistant Professor to Professor. The expert panel was provided with the list of elements included in the CP2 score as a starting point, 12 along with a description of the issues identified with the application of the CP2 Score in the family medicine residency program at Colorado. These clinical pharmacist faculty members individually developed a list of patient-specific elements that they thought to be important for identifying patients who would benefit from comprehensive medication management by a clinical pharmacist.…”

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confidence: 99%

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Identifying patients likely to be referred for a clinical pharmacist intervention in a family medicine residency‐training clinic

Griend

Saseen

Sullivan

et al. 2018

Introduction Little is known about patient populations within family medicine residency‐training clinics that should be targeted for clinical pharmacy interventions. The objective of this study was to identify patient characteristics associated with receiving an intervention by a clinical pharmacist. Methods This retrospective cohort study included 4870 patients aged 40 to 89 years with at least one office visit at the University of Colorado AF Williams Family Medicine clinic from August 1, 2014 to July 31, 2015. Electronic health record data were used to examine associations between 23 previously identified patient‐specific elements and receipt of a clinical pharmacist intervention. Presence of a clinical pharmacist consultation note in the patient's record in the past 12 months was evidence of an intervention. Multivariable logistic regression was used to estimate adjusted odds ratios. Results Four percent (n = 182) of patients received at least one clinical pharmacist intervention. Multivariable logistic regression indicated that ≥10 active medications, A1c ≥ 9%, hypertension, depression/anxiety/panic disorder, asthma, indication for anticoagulant therapy, hospitalization/emergency department visit in the last 30 days, age ≥ 65, and Medicaid/uninsured increased the likelihood of receiving a clinical pharmacist intervention, while non‐English speaking significantly decreased the likelihood of receiving a clinical pharmacist intervention. Patients with 7 or more elements were seven times more likely to have a clinical pharmacist intervention than patients with 6 or fewer items (odds ratio = 7.0, 95% confidence interval = 5.2‐9.5). Conclusions Patients with more elements were more likely to have received a clinical pharmacist intervention. The accumulation of these elements could be used for targeted population‐based clinical pharmacist activities in residency training clinics.

Validation and evaluation of a prior‐to‐admission medication list discrepancy risk‐scoring tool

Fleenor,

McCluggage,

Schell

et al. 2023

IntroductionMedication reconciliation is vital in preventing medication errors during transitions of care. Implementation of effective medication reconciliation, however, remains a challenge for healthcare systems due to cost and resource constraints. The objective of this study was to evaluate a risk‐scoring tool for identifying patients at high risk for medication discrepancies and, therefore, prioritize pharmacy intervention with obtaining an admission medication history.MethodsSingle‐center validation study at an academic medical center including adult patients with a medication history note written by a pharmacy staff member. The primary outcome was the number of changes made to the prior‐to‐admission (PTA) medication list by pharmacy staff. Secondary outcomes included changes in risk score after medication history was completed, the number of changes based on disease state and medication class, and the number of clinically relevant changes for a randomized subgroup of patients.ResultsThe study included 12 122 patient encounters. The median PTA risk score was 29 (low risk), and 41% of patients were aged 40–65 years. The median number of discrepancies was higher for the high‐risk group (6, interquartile range [IQR] 3–11) compared to the low‐risk group (3, IQR 1–7), p < 0.001. For the primary outcome, Pearson's correlation coefficient was 0.37, indicating a weak correlation between the PTA risk score and the number of changes to the medication list. The linear regression also indicated a weak correlation (R2 = 0.14). Parkinsonism, diabetes mellitus, chronic obstructive pulmonary disease, and cystic fibrosis were associated with a higher median count of medication discrepancies.ConclusionsThis study demonstrated that this PTA medication risk‐scoring tool could help identify patients at high risk of medication discrepancies, but the score did not correlate well with the number of medication list changes. Therefore, subgroup analyses and further studies are needed to determine the optimal medication list risk‐scoring tools.