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1.
J Am Pharm Assoc (2003) ; 63(5): 1566-1573.e1, 2023.
Article in English | MEDLINE | ID: mdl-37399927

ABSTRACT

BACKGROUND: Home health care delivery is projected to increase. Intravenous immunoglobulin (IVIG) therapy has high potential to move from the outpatient hospital (OPH) setting to home delivery. OBJECTIVE: This study examined the relationship between home and OPH IVIG infusions and health care utilization. METHODS: We used a retrospective cohort study design and the Humana Research Database to identify patients with 1 or more medical or pharmacy claims for an IVIG infusion agent from January 1, 2017, to December 31, 2018. Eligible patients were enrolled in a Medicare Advantage Prescription Drug (MAPD) or commercial health plan, with at least 12 months of continuous enrollment before and after their first infusion (i.e., index date) received in the home or OPH setting. We measured the odds of experiencing an inpatient (IP) stay or emergency department (ED) visit, adjusted for baseline differences in age, sex, race, region, population density, low-income, and dual eligibility status, MAPD or commercial health plan, plan type, treatment-naïve status, home health use, RxRisk-V comorbidity burden score, and indications for IVIG use. RESULTS: A total of 208 and 1079 patients received IVIG infusions in the home and OPH setting, respectively. The odds for an IP stay (odds ratio [OR] 0.56 [95% CI 0.38-0.82]) and ED visit (OR 0.62 [95% CI 0.41-0.93]) were significantly lower in patients who received IVIG infusion in the home than patients receiving infusion in the OPH setting. CONCLUSIONS: Our findings suggest there may be value to increasing referrals for IVIG home infusion. Decreased health care utilization provides value to the system in cost savings and to patients and families owing to less disruption and improved clinical outcomes. Further study can help inform health policy designed to maximize the benefits of IVIG home infusion while minimizing potential risks.


Subject(s)
Immunoglobulins, Intravenous , Outpatients , Aged , Humans , United States , Immunoglobulins, Intravenous/therapeutic use , Retrospective Studies , Medicare , Delivery of Health Care , Patient Acceptance of Health Care , Hospitals
2.
Appl Clin Inform ; 10(5): 927-934, 2019 10.
Article in English | MEDLINE | ID: mdl-31801174

ABSTRACT

OBJECTIVE: This study aimed to determine the effects of reducing the number of drug-drug interaction (DDI) alerts in an order entry system. METHODS: Retrospective pre-post analysis at an urban medical center of the rates of medication alerts and alert acceptance during a 5-month period before and 5-month period after the threshold for firing DDI alerts was changed from "intermediate" to "severe." To ensure that we could determine varying response to each alert type, we took an in-depth look at orders generating single alerts. RESULTS: Before the intervention, 241,915 medication orders were placed, of which 25.6% generated one or more medication alerts; 5.3% of the alerts were accepted. During the postintervention period, 245,757 medication orders were placed of which 16.0% generated one or more medication alerts, a 37.5% relative decrease in alert rate (95% confidence interval [CI]: -38.4 to -36.8%), but only a 9.6% absolute decrease (95% CI: -9.4 to -9.9%). 7.4% of orders generating alerts were accepted postintervention, a 39.6% relative increase in acceptance rate (95% CI: 33.2-47.2%), but only a 2.1% absolute increase (95% CI: 1.8-2.4%). When only orders generating a single medication alert were considered, there was a 69.1% relative decrease in the number of orders generating DDI alerts, and an 85.7% relative increase in the acceptance rate (95% CI: 58.6-126.2%), though only a 1.8% absolute increase (95% CI: 1.3-2.3%). CONCLUSION: Eliminating intermediate severity DDI alerts resulted in a statistically significant decrease in alert burden and increase in the rate of medication alert acceptance, but alert acceptance remained low overall.


Subject(s)
Medical Order Entry Systems/statistics & numerical data , Drug Interactions , Female , Humans , Male , Medication Errors/statistics & numerical data , Middle Aged
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