A Model to Determine Staffing Resources for Dementia Care Sandra F. Simmons, PhD Associate Professor of Medicine Vanderbilt University, Center for Quality Aging VA Geriatric Research Education & Clinical Center (GRECC)
Objectives • To describe a methodology to determine staffing needs • To describe the application of this methodology to the nursing home care setting • To describe the potential application of this methodology to dementia care in assisted-living
Dementia Care in Assisted Living • Assisted-Living as an alternative care setting for those with dementia: - 72% of ALFs offer dementia care services - 22% of ALFs have distinct dementia care units/neighborhoods
- 42% of ALF residents have a dementia diagnosis - 74% of ALF residents require assistance with 1 or more ADLs
Dementia Care in Assisted Living • ALF residents have become comparable to nursing home residents in risk for: -
Functional decline Falls Polypharmacy Healthcare utilization (e.g. hospitalization and ER visits)
• Thus, concerns about care quality and resident safety are similar in the absence of comparable federal regulations and oversight
Dementia Care in Assisted Living • ALFs licensed and regulated at the state-level with variability between states • Variability within states related to ALF resident populations • Currently no objective system to determine ALF staffing based on residents’ care needs or the acuity level of the ALF population • The Center for Excellence in Assisted Living recently recommended an acuitybased system to determine staffing needs
Dementia Care in Assisted Living • Most common approach “Flexible Staffing” (40 states) wherein ALFs are supposed to staff a “sufficient” number to “meet residents’ needs” • 19 states specify minimum standards for direct care staff • Only 7 states specify minimum staffing standards for dementia care, and most are related to RN/Manager-level staff • Why is the level of direct care staff important for dementia care?
Importance of Direct Care Staff for Dementia Care • Assistance with ADLs is labor-intensive IF provided in a way that: - Maximizes independence (e.g. verbal cueing instead of physical assistance) - Enhances choice (e.g., when to get out of bed, what to wear, timing of meals)
- Ensures resident safety (e.g., adequate supervision, frequent checks)
Importance of Direct Care Staff for Dementia Care • Studies in the nursing home setting have revealed that nurse aides often: - Provide excessive physical assistance - Few to no offers of choice - Frequently miss or delay care episodes • Research studies using standardized observations have shown that: - Physical assistance versus verbal cueing for eating, dressing - Changing soiled garments versus toileting frequently/when needed - Missed/delayed care for those who require 2 staff members for assistance
Staffing Levels based on National Data • Nursing Homes: Average total staffing (licensed nurses + aides) = 4.1 HPRD (Average for Aides = 2.4, range 1.1 to 4.1) • Assisted-Living: Average total staffing = 2.8 HPRD (Average for Aides = 2.3, range 1.2 - 3.9)
• Dementia Care in Assisted-Living: Average total staffing = 3.6 HPRD (Average for Aides = 3.1, range unknown)
Discrete Event Simulation (DES) Methodology • National Academy of Medicine recommends Discrete Event Simulation (DES) as a method to improve the quality and efficiency of healthcare delivery • DES applied to hospitals, operating rooms, emergency rooms and out-patient clinics
• DES originally applied to the nursing home setting (CMS, 2001) and recently updated (JAMDA, 2016) using national data related to residents’ care needs and staffing levels Reference: Schnelle et al. Determining nurse aide staffing requirements to provide care based on resident workload: A Discrete Event Simulation model. Journal of the American Medical Directors Association, 2016;17(11):970977.
Discrete Event Simulation (DES) Methodology • Systems-engineering approach to design and manage complex work environments • Efficiently and safely explore “what if” changes in the work environment: - Variability (e.g., work flow, time to provide care, interruptions) - Relationship between different aspects of care (e.g., order/timing of tasks) • Identify management policies (e.g., staffing) likely to improve healthcare delivery • Goal of DES is NOT to comprehensively model every aspect of care and all possible care scenarios, rather to model the primary “drivers” and most common scenarios
Discrete Event Simulation (DES) Methodology • Identify key aspects of care delivery • Number of people who need care • Amount of staff time required per person per episode of care and variability • Other factors (staff schedules, order of tasks, defined time periods) Example: • Assistance with Eating: 40% of resident population • Average Staff Time: 20 minutes per person per meal (range 10 to 30) • Each meal served within a defined 2-hour window, 3 meals per day
Application of DES to Nursing Home Care • Resident data (ADL care needs) for 13,533 nursing homes • Seven ‘workload’ categories from ‘light’ to ‘heavy’ (each representing a different combination of ADL care needs) captured 98% of residents
• ADL Care Needs included in the nursing home model: - Getting in/out of bed and Dressing/Grooming (morning and evening) - Incontinence care (toileting and changing) - Feeding assistance (scheduled meals) - Mobility assistance (or Range-of-Motion exercise) - Bathing twice per week - Repositioning
Application of DES to Nursing Home Care • Range of workload scores for the 13,533 homes: 5th percentile (‘low workload’ score, or greater proportion in ‘light’ category) 25th percentile 50th percentile 75th percentile 95th percentiles (‘high workload’ score, or greater proportion in ‘heavy/heaviest’ categories)
• Range of staffing levels (1.6 – 4.0 nurse aide HRPD) at 0.2 hour increments simulated for each workload percentile (5th, 25th, 50th, 75th, 95th) • Goal was to simulate full range of resident acuity and staffing combinations
Application of DES to Nursing Home Care • Model yields an estimate of the percentage of care omissions. • ‘Percentage of omitted care minutes’ = total number of care minutes provided by staff based on the DES model / total number of care minutes required for all scheduled care in a day. • Example: Model predicted one missed incontinence care episode scheduled for a resident at 10AM (estimated time = 7 minutes). If all other scheduled care for that resident/day was provided and summed to 120 total minutes, the missed care minutes would be 7 / 127, or 5.5% for this resident.
Application of DES to Nursing Home Care • The percentage of missed care minutes can be calculated per resident and per facility to reflect the total resident population. Thus, a 50% rate of omitted care across all residents in a facility would mean that half of all scheduled care minutes were not provided.
• Assumptions about work efficiency and productivity can be easily modified to determine the potential effect on care omissions or other quality metrics (e.g., timeliness of care delivery, or waiting times).
Application of DES to Nursing Home Care
Application of DES to Nursing Home Care
Application of DES to Nursing Home Care • For example, if part-time dining assistants were added to scheduled meals (high workload times), the percentage of care omissions was reduced by 14% in some nursing homes.
• The core DES model can be adapted to reflect the work characteristics of any facility and simple experiments can be conducted to determine how best to staff and schedule care in a facility based on those characteristics: - Overlapping staff schedules - Part-time staff during peak periods (morning care, meals) - Scheduling of staff breaks, Expanded meal delivery times
Application of DES to Dementia Care in ALFs • ALF population likely to have a greater proportion of ‘independent’ residents • Time to provide ADL care for those who require staff assistance comparable • Staffing requirements may be lower relative to nursing homes
• Different configuration of staff in ALFs (e.g., cross-trained personnel) • Propose study to examine staffing requirements for ALF dementia care
Dementia Care in ALFs: Time-and-Motion Study Type of Work Task during Day Shift for 10 Direct Care Staff Members
Average Time Range Percent of Total Time Per Staff in Minutes in Member per Shift Per Staff Minutes
ADL Care Morning Care (dressing, grooming)
84
0-139
28%
Afternoon Care (incontinence, exercise)
10
0-19
3%
Meal Assistance
33
0-101
11%
Other Tasks
ADL Care Total: 42%
Resident - Activities
8
0-58
3%
Travel to Resident Rooms/Other Areas
7
0-29
2%
Documentation of Care Delivery
46
0-13
15%
Random Tasks (e.g., housekeeping)
22
0-103
7%
Staff Breaks (un)scheduled
65
13-180
22%
Other (e.g., staff meetings)
27
1-45
9%
Dementia Care in ALFs: Time-and-Motion Study • 74% required staff assistance with morning/evening ADL care • Morning/Evening ADL care could consist of: Getting in/out of bed Dressing, Grooming (hair, make-up, shaving, oral care) Incontinence care (changing and/or toileting) Bathing (typically not done daily)
Dementia Care in ALFs: Time-and-Motion Study • Average Time for ADL Care = 20 minutes per resident (without bathing) Range = 9 to 39 minutes per resident • Average Time for ADL Care = 35 minutes per resident (with bathing) Range = 17 to 61 minutes per resident Time to provide care comparable to nursing homes, BUT unlike nursing homes: • None of the residents required 2 staff for assistance • None actively resisted care though level of cooperation was variable
Dementia Care in ALFs: Study Aims 1. Quantify the staff time requirements to provide ADL care to ALF residents and determine how cognitive impairment and behavioral disturbance impact staff time.
2. Build a DES model and experiment with different work scenarios to improve care (i.e., lower rate of care omissions and/or waiting times). 3. Develop a web-based, user-friendly product for ALFs to determine safe staffing levels. Pilot-test feasibility and get feedback from providers. Proposal to Agency for Healthcare Research & Quality currently under review
Questions and Contact Information Sandra Simmons, PhD Vanderbilt Center for Quality Aging www.VanderbiltCQA.org Sandra.Simmons@Vanderbilt.edu O: 615-343-6729 If interested in pilot-testing our web-based product (pending funding) and providing feedback, contact us!
Questions? Argentum 1650 King Street, Suite 602 Alexandria, VA 22314
(703) 894-1805