Association of a housing based individual socioeconomic status measure with diabetic control in primary care practices

Self-scheduling Medical Visits in a Multispecialty, Multisite Medical Practice: Complexity, Challenges, and Successes

Background

Self-scheduling of medical visits is becoming more common but the complexity of applying multiple requirements for self-scheduling has hampered implementation. Mayo Clinic implemented self-scheduling in 2019 and has been increasing its portfolio of self-schedulable visits since then. Our aim was to show measures quantifying the complexity associated with medical visit scheduling and to describe how opportunities and challenges of scheduling complexity apply in self-scheduling.

Methods

We examined scheduled visits from January 1, 2022, through August 24, 2023. For seven visit categories, we counted all unique visit types that were scheduled, for both staff-scheduled and self-scheduled. We examined counts of self-scheduled visit types to identify those with highest uptake during the study period.

Results

There were 9555 unique visit types associated with 20.8 M (million) completed visits. Self-scheduled visit types accounted for 4.0% (838,592/20,769,699) of the completed total visits. Of seven visit categories, self-scheduled established patient visits, testing visits, and procedure visits accounted for 93.5% (784,375/838,592) of all self-scheduled visits. Established patient visits in primary care (10 visit types) accounted for 273,007 (32.6%) of all self-scheduled visits. Testing visits (blood and urine testing, 2 visit types) accounted for 183,870 (21.9%) of all self-scheduled visits. Procedure visits for screening mammograms, bone mineral density, and immunizations (8 visit types) accounted for 147,358 (17.6%) of all self-scheduled visits.

Conclusion

Large numbers of unique visit types comprise a major challenge for self-scheduling. Some visit types are more suitable for self-scheduling. Guideline-based procedure visits such as screening mammograms, bone mineral density exams, and immunizations are examples of visits that have high volumes and can be standardized for self-scheduling. Established patient visits and laboratory testing visits also can be standardized for self-scheduling. Despite the successes, there remain thousands of specific visit types that may need some staff-scheduler intervention to properly schedule.

The Role of Individual-Level Socioeconomic Status on Nursing Home Placement Accounting for Neighborhood Characteristics

OBJECTIVE

Independent living is desirable for many older adults. Although several factors such as physical and cognitive functions are important predictors for nursing home placement (NHP), it is also reported that socioeconomic status (SES) affects the risk of NHP. In this study, we aimed to examine whether an individual-level measure of SES is associated with the risk of NHP after accounting for neighborhood characteristics.

DESIGN

A population-based study (Olmsted County, Minnesota, USA).

SETTING AND PARTICIPANTS

Older adults (age 65+ years) with no prior history of NHP.

METHODS

Electronic health records (EHR) were used to identify individuals with any NHP between April 1, 2012 (baseline date) and April 30, 2019. Association between the (HOUsing-based index of SocioEconomic Status (HOUSES) index, an individual-level SES measure based on housing characteristics of current residence, and risk of NHP was tested using random effects Cox proportional hazard model adjusting for area deprivation index (ADI), an aggregated SES measure that captures neighborhood characteristics, and other pertinent confounders such as age and chronic disease burden.

RESULTS

Among 15,031 older adults, 3341 (22.2%) experienced NHP during follow-up period (median: 7.1 years). At baseline date, median age was 73 years old with 55% female persons, 91% non-Hispanic Whites, and median number of chronic conditions of 4. Accounting for pertinent confounders, the HOUSES index was strongly associated with risk of NHP (hazard ratio 1.89; 95% confidence interval 1.66‒2.15 for comparing the lowest vs highest quartiles), which was not influenced by further accounting for ADI.

CONCLUSIONS AND IMPLICATIONS

This study demonstrates that an individual-level SES measure capturing current individual-specific socioeconomic circumstances plays a significant role for predicting NHP independent of neighborhood characteristics where they reside. This study suggests that older adults who are at higher risk of NHP can be identified by utilizing the HOUSES index and potential individual-level intervention strategies can be applied to reduce the risk for those with higher risk.

Miller N, L. Pecina J, D. Decker M, I. Wi C, Garrison GM. Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure

Background

While socioeconomic status has been linked to hospital readmissions for several conditions, reliable measures of individual socioeconomic status are often not available. HOUSES, a new measure of individual socioeconomic status based upon objective public data about one's housing unit, is inversely associated with overall hospitalization rate but it has not been studied with respect to readmissions.

Purpose

To determine if patients in the lowest HOUSES quartile are more likely to be readmitted within 30 days (short-term) and 180 days (long-term).

Methods

A retrospective cohort study of 11 993 patients having 21 633 admissions was conducted using generalized linear mixed-effects models.

Results

HOUSES quartile did not show any significant association with early readmission. However, when compared to the lowest HOUSES quartile, the second quartile (OR = 0.90, 95%CI 0.83-0.98) and the third quartile (OR = 0.91, 95%CI 0.83-0.99) were associated with lower odds of late readmission while the highest quartile (OR = 0.91, 95%CI 0.82-1.01) was not statistically different.

Conclusion

HOUSES was associated with late readmission, but not early readmission. This may be because early readmissions are influenced by medical conditions and hospital care while late readmissions are influenced by ambulatory care and home-based factors. Since HOUSES relies on public county assessor data, it is generally available and may be used to focus interventions on those at highest risk for late readmission.