American Journal of Managed Care最新文献

Objectives: To use publicly available price transparency data files to establish empirical regularities about hospital-insurer contracting.

Study design: Retrospective analysis of 10 price transparency data files from HCA Healthcare.

Methods: Cross-sectional qualitative analysis of 524 hospital-insurer contracts across 10 hospitals.

Results: We ascertain 4 empirical regularities in these files. First, hospitals contract with many payers, ranging from 35 to 82 across the hospitals in the sample. Second, contract structure varies significantly within and across hospitals: Of the 524 contracts in our sample, the median contract contained 9 contract elements, whereas the mean contract contained 1285 contract elements. Third, most of the contracts in our sample contained multiple contracting methodologies (eg, both fixed fee and percentage of charges). Fourth, these contracts indicated substantial variation for the same service within and across hospitals, validating findings from analyses based on claims data and hospital price transparency files.

Conclusions: Hospital-insurer contracts dictate the flow and structure of a significant portion of total health care expenditure in the US. Increased attention by both researchers and policy makers would lead to a greater understanding of this vital-yet understudied-element of the market for hospital services.

Objectives: Transitions of care are pivotal, vulnerable times as patients are discharged from the hospital. Telephonic care coordination is standard care, but labor intensive. We implemented a patient postdischarge digital engagement (PDDE) program to scale coordination. We hypothesized that PDDE could reduce readmissions for low-risk patients and supplement care coordination for medium- and high-risk patients.

Study design: Pragmatic, stepped-wedge cluster randomization trial with 5 implementation waves based upon primary care clinic region.

Methods: All inpatient hospital discharges between March 2020 and November 2020 were stratified by readmission risk. Low-risk patients were offered access to PDDE, and moderate-risk and high-risk patients were offered access to PDDE and care coordination. Readmission was defined as an unplanned inpatient admission within 30 days from discharge. An intention-to-treat primary analysis was conducted using mixed-effects logistic regression clustering for wave; a treatment-on-the-treated analysis was also conducted to assess the impact among program users.

Results: A total of 5490 patient discharges were examined (2735 control; 2755 intervention); 1949 patients were high risk, 2032 were medium risk, and 1509 were low risk. PDDE intervention did not significantly affect readmission among low-risk (95% CI, -0.23 to 0.90; P  = .23), medium-risk (95% CI, -0.14 to 0.60; P  = .21), and high-risk (95% CI, -0.32 to 0.64; P  = .48) groups after adjustment for time and patient factors. In a treatment-on-the-treated analysis, among patients who activated the PDDE program, readmission was also similar among the low-, medium-, and high-risk cohorts.

Conclusions: Our study expanded resource-limited care coordination by offering low-risk patients a service they were unable to receive previously while having no impact on readmission. PDDE efficiently provided additional touch points between patients and providers.

Objectives: Health inequities are frequently driven by social determinants of health (SDOH) and structural determinants of health. Our pilot sought to test the feasibility of screening for health literacy (HL) and perceived health care discrimination (PHD) through a live telephonic-facilitated survey experience with managed care patients.

Study design: Cross-sectional study.

Methods: Newly enrolled Medicare Advantage patients were screened for self-reported PHD, HL, and multiple SDOH using validated screening tools. Response rates for both HL and PHD screens were analyzed. A χ2 test for association between response to PHD screen and patient race was conducted. A weighted logistic regression model was used to understand how HL is associated with SDOH and demographic factors (age, gender, race/ethnicity, and income).

Results: HL and PHD screening questions have different levels of feasibility. Administering the HL screen did not present a challenge, and patients felt comfortable responding to it. On the other hand, the PHD question had a lower response rate among patients, and some concierge advocates felt uncomfortable asking patients the question. Based on the self-reported HL data collected, low/limited HL is associated with patients who were Black, were low income, reported loneliness or isolation, or reported food insecurity. It is important to note that the study's findings are limited by the small sample size and that study results do not imply causality.

Conclusions: It is feasible to collect self-reported HL data through a live telephonic format at the time of patient enrollment into a health plan. Health plans can leverage such screenings to better understand patient barriers for health equity-oriented interventions.

Objectives: We examined the association between electronic health information sharing and repeat imaging in readmissions among older adults with and without Alzheimer disease (AD).

Study design: Cohort study using national Medicare data.

Methods: Among Medicare beneficiaries with 30-day readmissions in 2018, we examined repeat imaging on the same body system during the readmission. This was evaluated between fragmented and nonfragmented (same-hospital) readmissions and across categories of electronic information sharing via health information exchanges (HIEs) in fragmented readmissions: admission and readmission hospitals share the same HIE, admission and readmission hospitals participate in different HIEs, one or both do not participate in HIE, or HIE data missing. This relationship was evaluated using unadjusted and adjusted logistic regression.

Results: Overall, 14.3% of beneficiaries experienced repeat imaging during their readmission. Compared with nonfragmented readmissions, fragmented readmissions were associated with 5% higher odds of repeat imaging on the same body system in older adults without AD. This was not mitigated by the presence of electronic information sharing: Fragmented readmissions to hospitals that shared an HIE had 6% higher odds of repeat imaging (adjusted OR, 1.06; 95% CI, 1.00-1.13). There was no difference seen in the odds of repeat imaging for older adults with AD.

Conclusions: Despite substantial investment, HIEs as currently deployed and used are not associated with decreased odds of repeat imaging in readmissions.

Objectives: The near-universal access to Medicare coverage at age 65 years improves access to care. However, little is known about whether Medicare eligibility promotes the diagnosis of chronic diseases. We examined the effects of Medicare eligibility at age 65 years on the diagnosis of chronic conditions.

Study design: Using data from the 2007-2019 Medical Expenditure Panel Survey, we employed a regression discontinuity design.

Methods: Our sample includes 43,620 individuals aged 59 to 71 years. Our primary outcomes were diagnoses of 19 chronic conditions. Using a regression discontinuity design, we exploited the discontinuity in eligibility for Medicare at age 65 years and compared individuals just before and after age 65 years.

Results: Medicare eligibility at age 65 years led to significant increases in having any coverage or Medicare coverage: 8.8 percentage points (95% CI, 8.4-9.2) and 78.1 percentage points (95% CI, 74.9-81.4), respectively. However, there were no or small changes in the diagnosis of chronic conditions at age 65 years. Specifically, there were no significant changes in the diagnoses of 17 chronic conditions, and the changes were minor in magnitude. Significant changes were observed only in the diagnosis of stroke and cancer, at -0.6 percentage points (95% CI, -1.0 to -0.2) and -1.7 percentage points (95% CI, -2.8 to -0.6), respectively.

Conclusions: Our findings suggest that Medicare coverage did not necessarily lead to increased diagnosis of chronic conditions. Further research is necessary to explore the underlying mechanisms behind this observation.

Objectives: This study determined whether naturally occurring but significantly different outpatient follow-up frequencies are associated with clinical outcomes and service waiting times.

Study design: Longitudinal retrospective study.

Methods: This study was conducted in an outpatient setting. Participants consisted of 340 patients with major depressive disorder who were randomly assigned to 4 psychiatrists and were followed at a variable frequency defined by the clinician. Patients were assessed at baseline and at every visit with structured interviews and self-reported questionnaires. These groups were also compared according to their baseline characteristics, treatment, and appointment frequencies. Little's law was used to estimate the impact of modifying the appointment frequencies on the service waiting time.

Results: The demographic variables, prescriptions, and depression severity at intake of patients across the 4 groups were similar. The mean times between appointments of the 4 groups were significantly different (87.0, 46.9, 67.9, and 61.5 days, respectively; P  < .001), but these differences in outpatient follow-up frequency were not associated with clinical outcomes (eg, mean last Quick Inventory of Depressive Symptomatology Self-Report score, 10.5, 10.0, 11.9, and 9.7; P  = .25). However, different outpatient follow-up frequencies had an estimated impact on waiting times for access to care; using Little's law, it was observed that the waiting list would be eliminated by reducing by 23.9% the follow-up frequencies of the 3 psychiatrists with the highest frequencies.

Conclusions: Although variations in appointment frequencies do not appear to have a major impact on clinical outcomes, they could be managed to achieve significant improvements in the accessibility of the clinic.

Objectives: Counseling and education on Medicare coverage options are available through the federal State Health Insurance Assistance Program (SHIP), but little is known about the population that SHIP reaches.

Study design: Cross-sectional study.

Methods: Using a novel data source on SHIP counseling site locations, we characterized the availability of in-person SHIP counseling by zip code tabulation area (ZCTA) and used linear regression and t tests to evaluate whether SHIP counseling sites are disproportionately located in higher-income communities.

Results: Our sample included 1511 SHIP counseling sites. More than half (63%) of the localities in our sample have a SHIP site within the ZCTA or county. Twenty-four percent do not have a SHIP site within the county but have one in an adjacent county. The remaining 13% do not have a nearby SHIP site. There is a disproportionate number of individuals eligible for Medicare in localities without a SHIP site. Moreover, the population living in areas without in-person SHIP sites is more likely to have low income and fewer years of education than the population living in areas with a SHIP site.

Conclusions: These results suggest that there are areas where in-person SHIP service expansion or other additional navigation support may be warranted.