Patchwork Protections: Regulatory Oversight in Assisted Living

Staffing Patterns in Nursing Homes, Assisted Living Communities, and Memory Care Units: Variation Across Shifts

OBJECTIVES

To assess the staffing patterns of direct care nursing staff by shift in nursing homes (NHs), assisted living communities (ALCs), and their corresponding memory care units (MCUs).

DESIGN

Observational study of the 2021 Ohio Biennial Survey of Long-Term Care Facilities and the Payroll-Based Journal data for December 2021.

SETTING AND PARTICIPANTS

NHs (n = 678) and ALCs (n = 542) that reported staffing by shift in Ohio.

METHODS

Resident-to-staff ratios in Ohio were calculated from staffing data. The proportion of daily nursing staff assigned to each shift was based on staffing data from the Biennial Survey for NHs and ALCs. Outcomes were calculated for aides and licensed nursing staff.

RESULTS

Ohio's NHs and ALCs had lower resident-to-staff ratios on the day shift. Lower resident-to-staff ratios mean there were more staff per resident (ie, better staffed). For both types of staff and all shifts, overall resident-to-staff ratios were lower in NHs than ALCs. However, resident-to-staff ratios for ALC MCUs were on par with NH MCUs. This was consistent with ALC and NH MCUs staffing in a more similar manner. Across all settings, the day shift had the most staff members present, while a number of ALCs had no licensed nurses on duty during the overnight shift.

CONCLUSIONS AND IMPLICATIONS

NHs and ALCs have different staffing patterns due to differences in resident needs. However, as ALCs provide for more residents that need NH level of care, ALCs may require additional staffing, especially on the overnight shift where some ALCs have no licensed nurses on duty. The evidence here can be used for decision making about future staffing policies, whether facility-wide policies that encompass MCUs or state-wide policies, so that care delivery aligns with care needs.

Implementing Large-Scale Data-Driven Quality Improvement in Assisted Living

OBJECTIVES

Develop and evaluate the implementation of a proposed model for large-scale data-driven quality improvement in assisted living.

DESIGN

We conducted a mixed-methods evaluation of the implementation of a large-scale data-driven quality improvement collaborative of Wisconsin assisted living communities (ALCs).

SETTING AND PARTICIPANTS

The model has been voluntarily implemented by 810 Wisconsin-licensed ALCs serving >20,000 residents.

METHODS

The model was codesigned iteratively 2009-2012 by a public-private multistakeholder advisory group. Using system usage statistics and project records, we evaluated implementation outcomes: appropriateness, acceptability, adoption, feasibility, fidelity, penetration, and sustainability.

RESULTS

Implementation for ≥1 quarter was feasible for 92% of the 810 ALCs that enrolled. The model has been deemed appropriate and acceptable by public-private stakeholders representing residents, providers, regulators, and payers, and appropriateness for ALCs serving different populations has been iteratively improved through targeted workgroups. The model is currently adopted in Wisconsin by 31% of the 1573 ALCs in provider associations. Among adopters, 88% on average implemented the model with fidelity to key membership rules per quarter. The model achieved demographic and institutional penetration by currently reaching 24% of Wisconsin ALC residents and by leveraging initial grant funding to become integrated in Wisconsin's annual Medicaid budget and being central to Wisconsin's incentive program to managed care organizations. Model implementation for 8 years has been sustained by member enrollment for nearly 4 years on average, with 71% of members enrolled >2 years and sustained early adopters representing 37% that have been enrolled >5 years.

CONCLUSIONS AND IMPLICATIONS

This is the first implementation study of large-scale data-driven quality improvement in assisted living, despite its demonstrated value in other health care sectors. The article proposes a model with core components and implementation strategies drawing on a decade-long public-private collaboration. The implementation study findings establish a promising path and future directions for wider implementation.