Role of Pharmacy Analytics in Creating a Data-Driven Culture for Frontline Management.

Purpose: The purpose of this article is to offer key recommendations based on the authors' experiences for utilizing pharmacy analytics to support moving beyond standard-of-practice operational metrics towards high impact reporting to drive day-to-day decisions for frontline leaders. Summary: There is a continuous and vast amount of data generated through all facets of a health system's daily operations, yet many data elements go unused and fail to contribute to value creation and increased performance at an organizational level. It is critical, therefore, for departments of pharmacy to identify and implement effective strategies to leverage data through robust business analytics and reporting, ensuring managers at every level are provided the information they need to support data-driven decisions and meaningful interventions in the day-to-day operations of the organization. At the authors' institution, development and growth of a dedicated Pharmacy Analytics (PA) team has been instrumental to the pharmacy department for generating value and proactively supporting a business intelligence strategy that focuses on a data-driven management culture. Key recommendations to leverage pharmacy analytics are provided within four overarching themes: building transparency, leveraging synergy, optimizing actionability, and prioritizing partnerships. Conclusion: Through creation of a data-driven management culture, the authors provide recommendations for leveraging pharmacy analytics to reduce costs and impact outcomes across a range of hospital pharmacy operations.

The Impact of Self-Monitoring Blood Glucose Adherence On Glycemic Goal Attainment in an Indigent Population, With Pharmacy Assistance.

Self-monitoring of bood glucose alone is not a good predictor of HbA1c goal attainment. Health plans might benefit from formulary restrictions to provide more cost-effective care, without negatively impacting glycemic control. And by using targeted inteventions, healthcare providers could help maximize SMBG's clinical benefit for patients who receive test strips. Self-monitoring of blood glucose (SMBG) can be an important tool in diabetes treatment, both for patient self-management and for guiding clinicians regarding medication adjustments. Evidence supports the association of SMBG with clinical outcomes in patients with type-1 diabetes mellitus (T1DM) although it is mixed for patients with type-2 diabetes mellitus (T2DM). The cost of SMBG comprises a substantial portion of the total cost for patients with diabetes, and test strips are one of the main expenditures of the University of North Carolina Medical Center Pharmacy Assistance Program (PAP), which provides medication coverage, including test strips, to indigent patients who have no pharmacy insurance. The objective of this study is to evaluate the utility of SMBG based on the impact of test-strip adherence on glycemic goal attainment in an indigent population that is provided with low-copay test strips. This retrospective cohort study included patients with T1DM or T2DM who were enrolled in PAP in 2016 and who received a prescription for test strips during the 90 days prior to hemoglobin A1c (HbA1c) measurement. Adherence was defined as the proportion of days covered (PDC) > 0.8. Of the 498 patients encountered, 20% of the adherent group (n = 245) and 25% of the nonadherent group (n = 253) had a goal of HbA1c < 7% (P = 0.24). There were no differences in mean HbA1c between the groups, except in the multiple daily injections (MDI) of the insulin subgroup (8.9% vs. 9.6%, P = 0.009). The adherent group was 80% less likely to have a diabetes-related hospitalization (odds ratio [OR], 0.2; 95% CI, 0.04-0.92). The total test-strip cost to PAP was more than $200,000. In conclusion, in an indigent population, adherence to SMBG does not correlate with glycemic goal attainment and imposes a substantial cost burden on the healthcare system.

Factors influencing the acceptance of referrals for clinical pharmacist managed disease states in primary care.

OBJECTIVE: Clinical pharmacists use population health methods to generate chronic disease management referrals for patients with uncontrolled chronic conditions. The purpose of this study was to compare primary care providers' (PCPs) referral responses for 4 pharmacist-managed indications and to identify provider and patient characteristics that are predictive of PCP response. DESIGN: Retrospective cohort study. SETTING: This study occurred in an academic internal medicine clinic. PARTICIPANTS: Clinical pharmacy referrals generated through a population health approach between 2012 and 2016 for hypertension, chronic pain, depression, and benzodiazepine management were included. MAIN OUTCOME MEASURES: Proportion of referrals accepted, left pending, or rejected and influencing provider and patient characteristics. RESULTS: Of 1769 referrals generated, PCPs accepted 869 (49%), left pending 300 (17%), and rejected 600 (34%). Compared with referrals for hypertension, benzodiazepine management, and depression, chronic pain referrals had the lowest likelihood of rejection (odds ratio [OR] 0.31; 95% CI 0.19-0.49). Depression referrals had an equal likelihood of being accepted or rejected (OR 1.04; 95% CI 0.66-1.64). Provider characteristics were not significantly associated with referral response, but residents were more likely to accept referrals. Patient characteristics associated with lower referral rejection included black race (OR 0.39; 95% CI 0.18-0.87), higher systolic blood pressure (OR 0.98; 95% CI 0.97-0.99), and missed visits (OR 0.24; 95% CI 0.07-0.81). CONCLUSION: The majority of referrals for clinical pharmacists in primary care settings were responded to, varying mostly between acceptance and rejection. There was variability in referral acceptance across indications, and some patient characteristics were associated with increased referral acceptance.

Voriconazole and posaconazole therapeutic drug monitoring: a retrospective study.

BACKGROUND: Therapeutic drug monitoring (TDM) aims to minimize the clinical impact of posaconazole and voriconazole pharmacokinetic variability. However, its benefits on clinical outcomes are still being defined. Additionally, TDM data are limited for posaconazole IV and delayed-release tablet formulations among specific patient populations, including critically ill. The aim of this study was to determine the percentage of therapeutic posaconazole and voriconazole drug levels across all formulations in a real-world clinical setting and elucidate factors affecting attainment of target concentrations. METHODS: This study was a retrospective cohort study conducted at the University of Colorado Hospital between September 2006 and June 2015 that evaluated patients who received posaconazole or voriconazole TDM as part of routine care. RESULTS: Voriconazole (n = 250) and posaconazole (n = 100) levels were analyzed from 151 patients. Of these, 54% of voriconazole and 69% of posaconazole levels were therapeutic. For posaconazole, 14/38 (37%), 28/29 (97%) and 27/33 (82%) levels were therapeutic for the oral suspension, IV, and delayed-release tablet, respectively. Intravenous and delayed-release tablet posaconazole were 20 fold (p < 0.01) and sevenfold (p = 0.002) more likely than the oral suspension to achieve a therapeutic level. Subsequent levels were more likely to be therapeutic after dose adjustments (OR 3.31; 95% CI 1.3-8.6; p = 0.02), regardless of timing of initial non-therapeutic level. In a multivariable logistic regression analysis, no characteristics were independently predictive of therapeutic voriconazole levels and only absence of H2RA/PPI use was independently predictive of therapeutic posaconazole levels. There was no correlation between survival and therapeutic drug levels for either voriconazole (p = 0.67) or posaconazole (p = 0.50). CONCLUSIONS: A high percentage of drug levels did not achieve TDM targets for voriconazole and posaconazole oral suspension, supporting the need for routine TDM for those formulations. The utility of TDM for the IV and delayed-release tablet formulations of posaconazole is less apparent.