Medical Care最新文献

Objectives: Home health care serves millions of Americans who are "Aging in Place," including the rapidly growing population of Medicare Advantage (MA) enrollees. This study systematically reviewed extant evidence illustrating home health care (HHC) services to MA enrollees.

Methods: A comprehensive literature search was conducted in 6 electronic databases to identify eligible studies, which resulted in 386 articles. Following 2 rounds of screening, 30 eligible articles were identified. Each study was also assessed independently for study quality using a validated quality assessment checklist.

Results: Of the 30 studies, nearly half (n=13) were recently published between January 1, 2017 - January 6, 2022. Among various issues related to HHC to MA enrollees examined, which were often compared with Traditional Medicare (TM) enrollees, the 2 most studied issues were HHC use rate (including access) and care dosage/intensity. Inconsistencies were common in findings across reviewed studies, with slight variations in the level of inconsistency by studied outcomes. Several critical issues, such as heterogeneity of MA plans, influence of MA-specific features, and program response to policy and quality improvement initiatives, were only examined by 1 or 2 studies. The depth and scope of scientific investigation were also limited by the scale and details available in MA data in addition to other methodological limits.

Conclusions: Wild variations and conflicting findings on HHC to MA beneficiaries exist across studies. More research with rigorous designs and robust MA encounter data is warranted to determine home health care for MA enrollees and the relevant outcomes.

Background: Involving patients in the health-care delivery innovation has many benefits. Open social innovation (OSI) presents a fitting lens to examine and advance patient engagement in innovation. OSI offers a participatory approach to innovation, in which diverse groups of participants collaboratively generate ideas and scale solutions on complex social challenges.

Purpose: This study: (1) describes a pilot application of OSI, in which individuals serving on a hospital's patients and family advisory councils (PFACs) were invited to participate in an innovation contest; and (2) explores the extent to which patients' beliefs about their role in innovation relate to their participation in the contest.

Methodology/approach: We conducted an innovation contest that invited PFAC members to share ideas that would improve patient experiences and then vote on and select the ideas that they wanted to see move forward. We measured patients' beliefs about their role in innovation in a survey before the contest.

Results: Twenty individuals submitted 27 ideas. Patients who expressed preference for more involvement in innovation were more likely to participate.

Conclusions: Using OSI may help expand patient engagement in innovation, particularly among those who want to be more involved but do not feel authorized to voice ideas in traditional advisory committees.

Practical implications: OSI spurred collaboration among patients, clinicians, quality improvement staff, hospital administrators, and other stakeholders in idea generation, elaboration, and implementation. More experimentation and research are needed to understand how OSI can be leveraged to capture patients' voice and incorporate them in care delivery innovation.

Electronic medical record (EMR) data present many opportunities for population health research. The use of EMR data for population risk models can be impeded by the high proportion of missingness in key patient variables. Common approaches like complete case analysis and multiple imputation may not be appropriate for some population health initiatives that require a single, complete analytic data set. In this study, we demonstrate a sequential hot-deck imputation (HDI) procedure to address missingness in a set of cardiometabolic measures in an EMR data set. We assessed the performance of sequential HDI within the individual variables and a commonly used composite risk score. A data set of cardiometabolic measures based on EMR data from 2 large urban hospitals was used to create a benchmark data set with simulated missingness. Sequential HDI was applied, and the resulting data were used to calculate atherosclerotic cardiovascular disease risk scores. The performance of the imputation approach was assessed using a set of metrics to evaluate the distribution and validity of the imputed data. Of the 567,841 patients, 65% had at least 1 missing cardiometabolic measure. Sequential HDI resulted in the distribution of variables and risk scores that reflected those in the simulated data while retaining correlation. When stratified by age and sex, risk scores were plausible and captured patterns expected in the general population. The use of sequential HDI was shown to be a suitable approach to multivariate missingness in EMR data. Sequential HDI could benefit population health research by providing a straightforward, computationally nonintensive approach to missing EMR data that results in a single analytic data set.

Background: Using federal funds from the 2009 Health Information Technology for Economic and Clinical Health Act, the Centers for Medicare and Medicaid Services funded the 2011-2021 Medicaid electronic health record (EHR) incentive programs throughout the country.

Objective: Identify the market factors associated with Meaningful Use (MU) of EHRs after primary care providers (PCPs) enrolled in the Florida-EHR incentives program through Adopting, Improving, or Upgrading (AIU) an EHR technology.

Research design: Retrospective cohort study using 2011-2018 program records for 8464 Medicaid providers.

Main outcome: MU achievement after first-year incentives.

Independent variables: The resource dependence theory and the information uncertainty perspective were used to generate key-independent variables, including the county's rurality, educational attainment, poverty, health maintenance organization penetration, and number of PCPs per capita.

Analytical approach: All the county rates were converted into 3 dichotomous measures corresponding to high, medium, and low terciles. Descriptive and bivariate statistics were calculated. A generalized hierarchical linear model was used because MU data were clustered at the county level (level 2) and measured at the practice level (level 1).

Results: Overall, 41.9% of Florida Medicaid providers achieved MU after receiving first-year incentives. Rurality was positively associated with MU ( P <0.001). Significant differences in MU achievements were obtained when we compared the "high" terciles with the "low" terciles for poverty rates ( P =0.002), health maintenance organization penetration rates ( P =0.02), and number of PCPs per capita ( P =0.01). These relationships were negative.

Conclusions: Policy makers and health care managers should not ignore the contribution of market factors in EHR adoption.