RESUMEN

OBJECTIVE: To assess whether a two-phase intervention was associated with improvements in antibiotic prescribing among nonhospitalized children with community-acquired pneumonia. STUDY DESIGN: In a large health care organization, a first intervention phase was implemented in September 2020 directed at antibiotic choice and duration for children 2 months through 17 years of age with pneumonia. Activities included clinician education and implementation of a pneumonia-specific order set in the electronic health record. In October 2021, a second phase comprised additional education and order set revisions. A narrow spectrum antibiotic (eg, amoxicillin) was recommended in most circumstances. Electronic health record data were used to identify pneumonia cases and antibiotics ordered. Using interrupted time series analyses, antibiotic choice and duration after phase one (September 2020-September 2021) and after phase two (October 2021-October 2022) were compared with a preintervention prepandemic period (January 2016-early March 2020). RESULTS: Overall, 3570 cases of community-acquired pneumonia were identified: 3246 cases preintervention, 98 post-phase one, and 226 post-phase two. The proportion receiving narrow spectrum monotherapy increased from 40.6% preintervention to 68.4% post-phase one to 69.0% post-phase two (P < .001). For children with an initial narrow spectrum antibiotic, duration decreased from preintervention (mean duration 9.9 days, SD 0.5 days) to post-phase one (mean 8.2, SD 1.9) to post-phase two (mean 6.8, SD 2.3) periods (P < .001). CONCLUSIONS: A two-phase intervention with educational sessions combined with clinical decision support was associated with sustained improvements in antibiotic choice and duration among children with community-acquired pneumonia.

Asunto(s)

RESUMEN

BACKGROUND: Collaborations between clinical/operational leaders and researchers are advocated to develop "learning health systems," but few practical examples are reported. OBJECTIVES: To describe collaborative efforts to reduce missed appointments through an interactive voice response and text message (IVR-T) intervention, and to develop and validate a prediction model to identify individuals at high risk of missing appointments. RESEARCH SUBJECTS AND DESIGN: Random assignment of 8804 adults with primary care appointments to a single IVR-T reminder or no reminder at an index clinic (IC) and 7497 at a replication clinic (RC) in an integrated health system in Denver, CO. MEASURES: Proportion of missed appointments; demographic, clinical, and appointment-specific predictors of missed appointments. RESULTS: Patients receiving IVR-T had a lower rate of missed appointments than those receiving no reminder at the IC (6.5% vs. 7.5%, relative risk=0.85, 95% confidence interval, 0.72-1.00) and RC (8.2% vs. 10.5%, relative risk=0.76, 95% confidence interval, 0.65-0.89). A 10-variable prediction model for missed appointments demonstrated excellent discrimination (C-statistic 0.90 at IC, 0.89 at RC) and calibration (P=0.99 for Osius and McCullagh tests). Patients in the 3 lowest-risk quartiles missed 0.4% and 0.4% of appointments at the IC and RC, respectively, whereas patients in the highest-risk quartile missed 24.1% and 28.9% of appointments, respectively. CONCLUSIONS: A single IVR-T call reduced missed appointments, whereas a locally validated prediction model accurately identified patients at high risk of missing appointments. These rigorous studies promoted dissemination of the intervention and prompted additional research questions from operational leaders.

Asunto(s)

Asunto(s)

RESUMEN

BACKGROUND: The Centers for Medicare and Medicaid Services provide significant incentives to health plans that score well on Medicare STAR metrics for cardiovascular disease risk factor medication adherence. Information on modifiable health system-level predictors of adherence can help clinicians and health plans develop strategies for improving Medicare STAR scores, and potentially improve cardiovascular disease outcomes. OBJECTIVE: To examine the association of Medicare STAR adherence metrics with system-level factors. RESEARCH DESIGN: A cross-sectional study. SUBJECTS: A total of 129,040 diabetes patients aged 65 years and above in 2010 from 3 Kaiser Permanente regions. MEASURES: Adherence to antihypertensive, antihyperlipidemic, and oral antihyperglycemic medications in 2010, defined by Medicare STAR as the proportion of days covered ≥ 80%. RESULTS: After controlling for individual-level factors, the strongest predictor of achieving STAR-defined medication adherence was a mean prescribed medication days' supply of > 90 days (RR=1.61 for antihypertensives, oral antihyperglycemics, and statins; all P < 0.001). Using mail order pharmacy to fill medications > 50% of the time was independently associated with better adherence with these medications (RR = 1.07, 1.06, 1.07; P < 0.001); mail order use had an increased positive association among black and Hispanic patients. Medication copayments ≤ $10 for 30 days' supply (RR = 1.02, 1.02, 1.02; P < 0.01) and annual individual out-of-pocket maximums ≤ $2000 (RR = 1.02, 1.01, 1.02; P < 0.01) were also significantly associated with higher adherence for all 3 therapeutic groupings. CONCLUSIONS: Greater medication days' supply and mail order pharmacy use, and lower copayments and out-of-pocket maximums, are associated with better Medicare STAR adherence. Initiatives to improve adherence should focus on modifiable health system-level barriers to obtaining evidence-based medications.

Asunto(s)

RESUMEN

BACKGROUND: Administratively derived morbidity measures are often used in observational studies as predictors of outcomes. These typically reflect a limited time period before an index event; some outcomes may be affected by rate of morbidity change over longer preindex periods. OBJECTIVES: The aim of the study was to develop statistical models representing the trajectory of individual morbidity over time and to evaluate the performance of trajectory versus other summary morbidity measures in predicting a range of health outcomes. METHODS: From a retrospective cohort study of integrated health system members aged 65 years or older with 3 or more common chronic medical conditions, we used available diagnoses for up to 10 years to examine associations between variations of the Charlson Comorbidity Index (CCI, Quan adaptation) and health outcomes. A linear mixed effects model was used to estimate the trajectory of individual CCI over time; estimated parameters describing individual trajectories were used as predictors for health outcomes. Other variations of CCI were: a "snapshot" measure, a cumulative measure, and actual baseline and rate of change. Models were developed in an initial cohort for whom we had survey data, and verified in a larger cohort. RESULTS: Among 961 surveyed members and 13,163 members of a secondary cohort, cumulative and snapshot measures provided best fit and predictive ability for utilization outcomes. Incorporating trajectory resulted in a slightly better model for self-reported health status. CONCLUSIONS: Modeling longitudinal morbidity trajectories did not add substantially to the association between morbidity and utilization or mortality. Standard snapshot morbidity measures likely sufficiently capture multimorbidity in assessing these outcomes.

Asunto(s)