Cluster and sporadic cases of herbaspirillum species infections in patients with cancer.

BACKGROUND: Herbaspirillum species are gram-negative Betaproteobacteria that inhabit the rhizosphere. We investigated a potential cluster of hospital-based Herbaspirillum species infections. METHODS: Cases were defined as Herbaspirillum species isolated from a patient in our comprehensive cancer center between 1 January 2006 and 15 October 2013. Case finding was performed by reviewing isolates initially identified as Burkholderia cepacia susceptible to all antibiotics tested, and 16S ribosomal DNA sequencing of available isolates to confirm their identity. Pulsed-field gel electrophoresis (PFGE) was performed to test genetic relatedness. Facility observations, infection prevention assessments, and environmental sampling were performed to investigate potential sources of Herbaspirillum species. RESULTS: Eight cases of Herbaspirillum species were identified. Isolates from the first 5 clustered cases were initially misidentified as B. cepacia, and available isolates from 4 of these cases were indistinguishable. The 3 subsequent cases were identified by prospective surveillance and had different PFGE patterns. All but 1 case-patient had bloodstream infections, and 6 presented with sepsis. Underlying diagnoses included solid tumors (3), leukemia (3), lymphoma (1), and aplastic anemia (1). Herbaspirillum species infections were hospital-onset in 5 patients and community-onset in 3. All symptomatic patients were treated with intravenous antibiotics, and their infections resolved. No environmental source or common mechanism of acquisition was identified. CONCLUSIONS: This is the first report of a hospital-based cluster of Herbaspirillum species infections. Herbaspirillum species are capable of causing bacteremia and sepsis in immunocompromised patients. Herbaspirillum species can be misidentified as Burkholderia cepacia by commercially available microbial identification systems.

Impact of complex care management on spending and utilization for high-need, high-cost Medicaid patients.

OBJECTIVES: Complex care management programs have emerged as a promising model to better care for high-need, high-cost patients. Despite their widespread use, relatively little is known about the impact of these programs in Medicaid populations. This study evaluated the impact of a complex care management program on spending and utilization for high-need, high-cost Medicaid patients. STUDY DESIGN: Randomized quality improvement trial conducted at CareMore Health in Memphis, Tennessee. A total of 253 high-need, high-cost Medicaid patients were randomized in a 1:2 ratio to complex care management or usual care. METHODS: Intention-to-treat analysis compared regression-adjusted rates of spending and utilization between patients randomized to the complex care program (n = 71) and those randomized to usual care (n = 127) over the 12 months following randomization. Primary outcomes included total medical expenditures (TME) and inpatient (IP) days. Secondary outcomes included IP admission, emergency department (ED) visits, care center visits, and specialist visits. RESULTS: Compared with patients randomized to usual care, patients randomized to complex care management had lower TME (adjusted difference, -$7732 per member per year [PMPY]; 95% CI, -$14,914 to -$550; P = .036), fewer IP bed days (adjusted difference, -3.46 PMPY; 95% CI, -4.03 to -2.89; P <.001), fewer IP admissions (adjusted difference, -0.32 PMPY; 95% CI, -0.54 to -0.11; P = .014), and fewer specialist visits (adjusted difference, -1.35 PMPY; 95% CI, -1.98 to -0.73; P <.001). There was no significant impact on care center or ED visits. CONCLUSIONS: Carefully designed and targeted complex care management programs may be an effective approach to caring for high-need, high-cost Medicaid patients.

Identifying Patients Experiencing Homelessness in an Electronic Health Record and Assessing Qualification for Medical Respite: A Five-Year Retrospective Review.

Our team developed a transitional care and medical respite program for people experiencing homelessness and designed a retrospective chart review study to more fully understand the unique needs of this population. Using four independent techniques, we identified individuals (N=1,656) who were experiencing homelessness during at least one hospital encounter (emergency department and/or in-patient admission) in a teaching hospital in the Southeastern United States over a five-year period. Data were manually abstracted from a random sample of patients to determine which patient encounters would or would not have qualified for medical respite if it had been available at the time. This article reports the methods used to identify people experiencing homelessness in the electronic health record, the data abstraction process, the cohort description, and the primary reasons patients would not have qualified for the medical respite program.

Mobile health devices: will patients actually use them?

Although mobile health (mHealth) devices offer a unique opportunity to capture patient health data remotely, it is unclear whether patients will consistently use multiple devices simultaneously and/or if chronic disease affects adherence. Three healthy and three chronically ill participants were recruited to provide data on 11 health indicators via four devices and a diet app. The healthy participants averaged overall weekly use of 76%, compared to 16% for those with chronic illnesses. Device adherence declined across all participants during the study. Patients with chronic illnesses, with arguably the most to benefit from advanced (or increased) monitoring, may be less likely to adopt and use these devices compared to healthy individuals. Results suggest device fatigue may be a significant problem. Use of mobile technologies may have the potential to transform care delivery across populations and within individuals over time. However, devices may need to be tailored to meet the specific patient needs.