Time Allocated to Nursing Tasks on Hospital Units Caring for Older Patients

Abstract

Introduction :

Hospitals need objective data about the time allocated to nursing tasks, particularly for older inpatients who often need assistance with activities of daily living (ADLs), such as toileting and mobility.

Methods :

This descriptive time–motion study objectively measured the time registered nurses (RNs) and nursing assistants (NAs) spent on clinical and ADL care and made comparisons by staff type. Research staff completed 277 standardized observation hours on three hospital units caring for older patients.

Results:

Registered nurses and NAs spent 38% and 34% of their time, respectively, on indirect care tasks, with medical record documentation being most common. Both staff types spent an additional 34% of their time on direct care tasks. Medication pass consumed the most RN direct care time, and ADL care consumed the most NA direct care time. Activities of daily living care was observed in fewer than 25% of patient encounters, despite 73%–89% of patients across the three units requiring ADL care assistance. Overall, staff spent less than 10% of their time idle.

Conclusions:

Objective data related to the time allocated to nursing tasks are necessary to inform skill mix adjustments or other staffing strategies to meet older inpatients’ care needs.

Introduction

Higher registered nurse (RN) staffing is associated with a shorter hospital stay and lower rates of adverse outcomes among inpatients including mortality, readmission, hospital acquired infections, and care omissions.^1-4 A national shortage of RNs was exacerbated by the COVID-19 pandemic.⁵ The demand for RNs continues to outpace availability, with an estimated 200,000 position vacancies annually.⁶ Thus, alternative hospital staffing strategies may include adjustments to nursing skill mix through the addition of nursing assistants (NAs) to support RN utilization for clinical tasks specific to their job role and potentially reduce care omissions.⁷ Although, some studies suggest that reducing RNs and increasing NAs can lead to worse patient outcomes.^8,9

Nursing assistants’ scopes of practice vary between hospitals but typically include activities of daily living (ADL) care (e.g., toileting and mobility assistance) and other support tasks. Given few or no NAs, RNs are responsible for these tasks. Survey studies of hospital RNs reveal frequent care omissions, particularly for toileting and mobility assistance. Moreover, nurses attribute care omissions to the staff time required for care, especially among older patients with frequent ADL care needs.^10,11 These studies also characterize the hospital work environment as focused predominately on patients’ complex clinical needs.^10-12 Prior studies to examine the relationship between hospital nursing skill mix and patient outcomes have not evaluated the impact of NA staff on ADL care omissions or the timeliness of care. This knowledge gap is important to address given evidence that early and frequent mobility assistance can reduce hospital length of stay and improve patient outcomes.¹³ In addition, patient attempts to toilet or ambulate contribute to inpatient falls.¹⁴

There are also limited data to quantify RN and NA time spent on inpatient nursing tasks and whether the presence of NAs allows RNs more time for tasks that require their clinical expertise, which may, in turn, improve overall care quality and patient safety. Few studies have used objective time–motion study (TMS) methods to quantify hospital nursing care.^15-18 Although prior studies show that nurses spend most of their time on clinical tasks and documentation, there are sparse data related to the time spent on nonclinical tasks and ADL care. In addition, only one prior hospital TMS study included NAs but did not report types of ADL care.¹⁹

When considering nurse staffing allocations and skill mix for hospital bedside care, objective data are necessary to quantify the tasks and related time for both RNs and NAs. Thus, the aims of this study were to (1) report descriptive TMS data for clinical and nonclinical tasks, inclusive of ADL care; (2) compare the time allocated to different types of tasks between RNs and NAs; and (3) describe the frequency of patient call light requests, which also contributes to staff workload.

Objective data related to the nursing time allocated to various types of tasks can be used to guide nursing staff training, task and/or patient care assignments, and skill mix adjustments to meet older inpatients’ care needs. Importantly, the nursing time allocated to specific tasks may not reflect the optimal time required for those tasks. However, descriptive data about the time allocated are a necessary prerequisite to inform and evaluate improvement interventions that rely on bedside nurses. The primary purpose of this study was to quantify RN and NA time allocated to nursing tasks for older inpatients. These data were used to inform a broader hospital quality improvement initiative related to alternative models of nursing care that considered skills training and skill mix.²⁰

Methods

Setting and Participants

This study included three inpatient units within one U.S. tertiary academic medical center. The units were an acute care for elders (ACE) unit and two surgical stepdown units (cardiac/vascular and orthopedic/spine). Bedside staff are RNs and NAs, and the scope of practice for NAs includes blood draws, vital signs assessment, and ADL care. Table 1 displays unit and patient characteristics. The units had a median daily census of 18–30 patients. The average RN-to-patient ratios were 1:3 to 1:5. Each unit also had an RN manager and clinical shift leader. At least half of RNs (51%–64%) had less than 2 years of experience. The average NA-to-patient ratio was 1:9 or 1:10. Bedside RNs and NAs were included in the TMS data.

Table 1.

Unit and Patient Characteristics and TMS Observational Data Collection Summary for Three Hospital Units

Unit and patient characteristics	ACE Mean (SD)	Cardiac/vascular stepdown Mean (SD)	Orthopedic/spine stepdown Mean (SD)
Unit size and staffing levels
Total number of beds	22	34	20
Average daily census	19 (±1)	30 (±2)	18 (±2)
Average RN to patient ratioa	1:5	1:3	1:5
Average NA to patient ratioa	1:10	1:9	1:10
Percent of RNs with < 2 years of experience	64%	51%	56%
Unit-level patient characteristics
Percent aged 65 years or older	85.4%	50.4%	37.5%
Average length of stay (days)	6.1 (±0.98)	5.1 (±0.93)	5.6 (±0.58)
Percent discharged to postacute care facility	31.7 (±6.2)	8.3 (±1.9)	23.1 (±3.3)
Percent in isolationb	12.9 (±8.8)	8.5 (±5.4)	4.8 (±5.9)
Percent with urinary deviceb	16.9 (±9.3)	10.4 (±5.3)	11.6 (±8.2)
Percent with cognitive impairmentc	36.6 (±13.5)	8.6 (±5.1)	14.1 (±8.4)
Percent requiring ambulation and/or toileting assistance	89.2 (±10.1)	73.2 (±10.2)	88.3 (±7.9)
Observational data collection
Total hours observed	145.62	73.75	57.82
Total observation sessions	89	52	37
Registered nurses	58 (65%)	34 (65%)	21 (57%)
Nursing assistants	31 (35%)	18 (35%)	16 (43%)
Total patients receiving care	438	191	164

ACE, acute care for elders; NA, nursing assistant; RN, registered nurse; TMS, time–motion study.

^aStaffing ratios are based on the average daily census.

^bIsolation included contact, droplet, and/or airborne precautions. Urinary device included catheters.

^cCognitive impairment: Medical record documentation of a dementia diagnosis and/or nurse documented orientation score ≤ 2 (for person, place, time, and situation).

TMS Data Collection Tool

Time–motion study data were collected using a standardized protocol by trained observers via a computerized software program used in prior studies.^21-23 The software has an adaptable, customizable user interface. A trained research observer using a touch screen laptop selected predefined tasks in real time, which automatically logged a timestamp (minutes and seconds). The software generated the total and percentage of time spent on each task per observation session.

Operational definitions for each task were based on prior studies.^15,19,24 Pilot observations, in conjunction with unit clinical team input, were used to refine the task list. In total, 45 tasks were organized into 9 task categories that encompassed direct clinical care, ADL care, communication (with patients/families or other staff), and indirect care (Table 2). Predefined subtasks within each category (Table 2) provide more granular data relative to previous TMS studies.^15,16 Interrater reliability was conducted among research observers before formal data collection until intraclass correlation coefficients (ICC) reached 0.80 for each task category.

Table 2.

Observation Task Categories and Subtasks With ICCs

Task categories		Tasks	Description of tasks
Clinical care tasks	Direct care   ICC = 0.987 (0.972–0.994)	Wound carea	Cleansing or putting ointment on a wound, burn, or cut; placing or removing bandages, dressings, or pressure wound protective stickers
		Medication passa	Administering medications via oral, subcutaneous, transdermal, sublingual, nasal, intramuscular intravenous routes; attesting medications
		IV line carea	Inserting or removing IV; connecting or replacing IV bags; turning on/off IV machines
		Assessments and/or vitals	Administering physical or verbal assessments; checking vitals including blood pressure, temperature, blood sugar, and body weight
		Lab specimen collection	Collecting lab specimens including blood, urine, and contents of drains; assisting with lumbar punctures
		Treatments and/or proceduresa	Blood transfusions; feeding tube care (e.g., programming, priming, insertion, removal, adjustment, and changing bags); pain intervention management including cold compression devices, placing/removing or adjusting braces/casts; administration of breathing treatments including spirometry
Activities of daily living	Toileting   ICC = 0.830 (0.670–0.917)	Toileting assistance	Transfer assistance to and from toilet, assistance with cleansing of the patient at toilet.
		Bed pan/urinal use	Positioning of patient to use bed pan or urinal, assistance with cleansing of the patient
		Bedside commode	Transfer assistance to and from bedside commode, assistance with cleansing of the patient on bedside commode
	Incontinence care   ICC = 0.926 (0.849–0.965)	Changing linens	Changing soiled linens due to an incontinence episode, changing soiled bed pads, supplying new bed pads
		Changing undergarments	Changing soiled briefs, pads, underwear, cleansing of the patient, repositioning patient for changing
		Disposing of soiled items	Disposing of soiled linens, incontinence pads
		Catheter care	Inserting, removing, or adjusting a catheter or other urinary device. Emptying catheter or urinary device container
	Personal hygiene   ICC = 0.960 (0.917–0.981)	Shower/tub bath	Transfer assistance to and from shower/tub, assistance with washing patient with a washcloth or sponge, drying patient off, undressing/redressing patient
		Bed bath	Washing patient with washcloth or sponge in bed, drying patient off, undressing/redressing patient
		Dressing	Dressing in hospital or street clothing including clothing, socks, and shoes (separately from bathing episode)
		Grooming	Vanity-related care including hair brushing and styling, shaving, makeup application, nail care
		Oral care	Brushing teeth, flossing, applying dentures
	Nutrition and hydration   ICC = 0.978 (0.954–0.990)	Food/fluid delivery/set-up	Taking food and fluid orders, delivering food and fluid, setting up meal tray, food or fluid preparation (e.g., placing straws in drinks, cutting up food)
		Mealtime assistance	Physical assistance to feed patient or verbal cues to prompt patient to eat during regularly scheduled meals (breakfast, lunch, dinner)
		Snacks	Delivery of snacks between meals and/or physical assistance to feed snack to patient, or verbal cues to prompt patient to eat snack
		Hydration	Delivery of fluids between meals and/or physical assistance to help patient drink, or verbal cues to prompt patient to drink
	Mobility   ICC = 0.967 (0.930–0.984)	Repositioning in bed/chair	Physical repositioning of the patient including turning, moving up or down in bed, use of repositioning blanket to move patient, use of electronic bed controls including moving the bed up/down and raising/lowering head or footrest
		Lying to sitting	Positioning patient from either lying down to sitting up or sitting up to lying down
		Lying/sitting to standing	Positioning patient from lying to standing, standing to lying, sitting to standing, and standing to sitting
		Bed to chair transfer	Transfer from hospital bed to bedside or in-room chair
		Chair to bed transfer	Transfer from hospital room chair to bed, transfer from wheelchair or other chair to bed
		Walking assistance	Assistance walking with patient, including assistance helping the patient out of bed/chair and back to bed/chair, demonstration and/or assistance with mobility devices/aides (e.g., walkers and canes)
	Communicationb   ICC = 0.968 (0.933–0.985)	Other staff conversation	Conversation with other hospital staff members including other nurses, care partners, physicians, physical therapists, occupational therapists, other clinicians
		Patient conversation	Conversation with patient about care matters/hospital and/or discharge care plans, personal conversations, care coordination and planning, and verbal education
		Family conversation	Conversation with patients’ family members about hospital and/or discharge care plans/care coordination, personal matters, and verbal education
		Patient and family conversation	Conversation with patients and their family members together about hospital and/or discharge care plans/care coordination, personal matters, and verbal education
		Unit phone use	Use of unit provided cellular device or unit landline phones
Additional routine tasks	Indirect care tasks   ICC = 0.975 (0.947–0.988)	Staff in transit	Staff in transit within the unit (e.g., nurses’ station to patient room(s) or between patient rooms)
		Gathering supplies	Staff in transit to gather supplies including medication, storage drawers, supply room, food/drink items
		Documentationb	Electronic or physical charting of patient information using the unit issued phone, electronic health record, or on paper
		Observe patient	Supervising a patient either inside or outside their room using visual or auditory methods including conducting visual periodic checks in the patient’s room and/or standing outside the patient’s room and listening before entering
		Clinician hygiene	Putting on personal protective equipment, washing hands, or sanitizing hands before and after entering a patient’s room
	Miscellaneous   ICC = 0.923 (0.842–0.963)	Other care task	Miscellaneous other care-related tasks including adjusting or checking oxygen masks, tubes, drains, turning on/off bed alarms, or putting on patient personal protective equipment, checking medication fridges to ensure proper temperature
		Other noncare assistance	Noncare-related tasks including changing unsoiled bed linens, changing the tv channel, or general housekeeping in hospital rooms
		Cleaning equipment	Cleaning and sanitizing patient care equipment including vitals machine, computers, scales, glucometers
		Personal break	Staff taking a personal break including use of personal phone, eating snacks, drinking water, restroom break, and time spent in break room (i.e., meal breaks)
		Idle	Staff is inactive, not engaged in any other task

ICCs were calculated to yield an overall (upper and lower) ICC per task category. ICC, intraclass correlation coefficients; IV, intravenous; RN, registered nurse.

^aDelineates tasks that can only be completed by a RN.

^bCommunication directly related to providing care such as verbal cues to take medication as well as point of care documentation (e.g., documenting medication administration time) were included in the care task. Communication and documentation tasks are activities independent of hands-on care provision.

Data Collection Procedures

From April 2021 through June 2023, observations of bedside RNs and NAs were conducted on weekdays between 5 a.m. and 10 p.m., which mostly represented dayshift (7 a.m.–7 p.m.). Observations were scheduled to achieve an even distribution across days of the week and time of day within this range. Study personnel attempted to observe all scheduled staff with a patient assignment. Each observation session lasted a maximum of 2 hours to minimize observer fatigue. Neither staff nor patient names were recorded during observations, and data were aggregated to protect confidentiality. All study procedures were approved by the Institutional Review Board (#191017).

Unit and patient characteristic data were collected on the same days as observations. A standardized form was used to abstract the following data from the electronic medical record for each patient on the unit per observation day: census (number of patients on the unit), presence of isolation precautions (any type), and presence of a urinary device (i.e., catheter or urostomy bag). In addition, presence of cognitive impairment was defined as documentation of a dementia diagnosis and/or RN assessment of orientation to person, place, time, and situation as a score of 2 or less. Orientation was documented by unit RNs for all patients per shift, and the most recently documented orientation score was abstracted for each patient. Unit RNs also documented the level of assistance each patient required for ambulation. Hospital administrative data for patient length of stay and discharge disposition was extracted for a 6-month period during the same timeframe as observations. In addition, the hospital call light system data (“Responder 5”) were retrieved for the same 6-month period. The call light system data showed the frequency of call light requests per hour across all patients on each unit.

Data Analysis

To establish interobserver reliability, ICCs were calculated to yield an overall, upper and lower ICC per task category. Unit-level patient characteristics were summarized as means and standard deviations. For the TMS data, the proportion of unit staff time spent on different tasks was summarized using descriptive statistics at the staff observation level. The proportion of time spent on each type of task and task category was compared between RNs and NAs using Analysis of Variance (ANOVAs) and independent sample t-tests. The ADL task categories were combined for these comparisons due to low frequencies. A Pareto chart was created based on RN time allocations across task categories due to more RN staff per unit, and differences in staff time allocations. The total time for each task was summarized per patient per episode, and descriptive analyses were conducted for patient care episode time. Medians and interquartile ranges were calculated due to abnormal distributions for task time data. Task time data comparisons also were conducted between patients with versus without documentation of cognitive impairment and patients in isolation versus those not in isolation using independent samples t-tests.

Results

Unit-Level Patient Characteristics

Table 1 shows unit-level patient characteristics. The ACE unit had the highest proportion of patients aged 65 or older. Patients’ average length of stay was approximately 5–6 days, with 8%–32% of patients discharged to a postacute care facility. The percentage of patients on isolation precautions ranged from 5% to 13%, and 10%–17% had a urinary device. The percentage of patients with cognitive impairment ranged from 9% to 37%, and most patients (73%–89%) were rated by RNs as requiring assistance with ambulation (i.e., toileting and mobility). The ACE unit had patients with a longer length of stay and a higher proportion with cognitive impairment and those discharging to a postacute care facility.

TMS Observational Data: Defined Task Categories

Table 2 shows the TMS task categories, subtasks, and task descriptions with ICCs for each category. Study personnel completed 277 observation hours across 178 sessions, with each session lasting 1.5–2 hours (Table 1). Most observations were of RNs (63%) compared to NAs (37%), which reflects the higher number of RNs on each unit. Overall, staff were observed providing care to 793 patients. Observations were mostly of dayshift staff (96%), which was intentional to capture ADL care. A higher proportion of observations were conducted on the ACE unit (50%) due to a focus on time to provide care for older patients.

Task Category Comparison Between RNs and Nas

Figure 1 shows the proportion of observed staff time spent on each task category for RNs and NAs across all units. The line indicates the cumulative time spent by RNs on each task category. Registered nurses spent significantly more time engaged in direct care activities relative to NAs (26% ± 13.9% vs. 13% ± 12.6%, respectively, p < .001), which reflects subtasks within this category assigned to RNs (i.e., medication pass, wound and intravenous care). Consistent with their assigned duties, NAs spent significantly more time providing ADL care (21% ± 16.1% vs. 8% ± 7.6%, p < .001). Nursing assistants’ scope of practice also included blood draws and vital signs assessments, but NAs completed only 27.1% of observed blood draws and 48.7% of vital signs assessments, with the remainder completed by RNs.

Figure 1.

The Pareto bar graph displays the percentage of time RNs and NAs engaged in different care tasks. The line reflects the cumulative time spent by RNs. ADL care includes assistance with mobility, incontinence, toileting, nutrition, and personal hygiene tasks. ADL, Activities of daily living; NAs, nursing assistants; RNs, registered nurses.

Registered nurses also spent significantly more time engaged in indirect care tasks (38% ± 13.5% vs. 34% ± 12.4%, p = .04), namely documentation, whereas NAs spent more time on miscellaneous noncare tasks (7% ± 6.6% vs. 4% ± 4.2%, p = .005). Both staff types engaged in communication tasks (with staff, patients and/or families) for comparable proportions of time (18% ± 10.2% vs. 16% ± 9.9% for RNs and NAs, respectively). Finally, both staff types spent less than 10% of their time, on average, idle or taking required breaks (6% ± 10.5% for RNs vs. 9% ± 11.5% for NAs).

TMS Observational Data: Time to Complete Tasks

The task and subtask category frequency and time data are shown in Table 3 by unit and overall. The sample size for each subtask reflects the total number of episodes across all staff observations and patient encounters. Table 3 shows median Interquartile Range (IQR) time values per patient encounter. The most frequently observed task categories were indirect care (99%) and communication (88%) followed by direct care (65%). Conversely, ADL care occurred in fewer than 25% of observed patient encounters.

Table 3.

Median Time (in Minutes) to Complete Tasks per Patient Episode

Task category	ACE unit n, median [IQR]		Cardiac and vascular stepdown n, median [IQR]		Orthopedic/spine n, median [IQR]		Overall
Direct care tasks	260	4.72 [2.73, 9.14]	145	4.78 [2.57, 8.12]	116	4.80 [2.50, 8.09]	4.72 [2.65, 8.52]
Medication pass	117	5.38 [2.83, 9.03]	62	4.52 [2.80, 8.80]	70	3.78 [2.61, 6.47]
Vitals/assessments	170	3.20 [2.33, 4.42]	112	2.89 [1.49, 4.53]	61	2.12 [1.53, 3.91]
IV care	42	1.40 [0.91, 3.06]	24	1.09 [0.53, 5.32]	14	2.02 [0.55, 4.13]
Lab specimen collection	27	4.83 [0.78, 8.60]	19	2.07 [0.95, 4.57]	24	2.00 [1.16, 3.11]
Toileting tasks	63	2.97 [1.55, 5.37]	41	0.77 [0.39, 3.98]	27	1.68 [0.63, 4.28]	1.93 [0.63, 4.95]a
Bed pan/urinal use	15	2.35 [0.50, 6.13]	25	0.43 [0.29, 0.85]	15	0.97 [0.48, 1.82]
Toileting and bedside commode	48	3.13 [1.68, 5.28]	18	2.89 [0.68, 5.04]	13	2.70 [1.81, 5.90]
Incontinence care tasks	81	4.00 [1.49, 7.68]	25	1.97 [1.12, 3.72]	10	2.93 [1.20, 8.17]	3.02 [1.35, 6.83]a
Catheter care	34	1.99 [1.20, 3.55]	12	1.44 [1.07, 2.03]	6	2.23 [1.20, 7.81]
Changing linens and undergarments	59	5.05 [1.53, 7.83]	13	2.78 [1.40, 6.41]	5	5.18 [1.86, 7.69]
Personal hygiene	34	2.23 [1.12, 6.30]	13	2.42 [0.56, 10.75]	15	3.88 [1.05, 10.93]	2.48 [1.04, 8.33]
Nutrition and hydration	125	0.85 [0.43, 2.03]	32	0.37 [0.20, 0.73]	34	0.29 [0.18, 0.70]	0.60 [0.27, 1.58]
Mobility	118	1.39 [0.62, 2.37]	30	1.30 [0.43, 3.37]	37	1.60 [0.88, 3.56]	1.43 [0.64, 2.80]
Communication	372	1.65 [0.63, 3.97]	173	2.25 [0.89, 6.35]	154	2.24 [0.76, 5.21]	1.88 [0.70, 4.75]
Staff conversation and unit phone	338	1.39 [0.48, 3.46]	156	1.94 [0.81, 5.84]	133	1.78 [0.80, 4.23]
All patient and family conversations	187	0.65 [0.25, 1.22]	109	0.58 [0.25, 1.46]	113	0.60 [0.25, 1.45]
Indirect care tasks	436	3.59 [1.25, 9.52]	191	5.37 [2.17, 10.50]	163	5.92 [2.33, 13.32]	4.23 [1.75, 10.45]a
Documentation	292	2.58 [0.82, 8.19]	149	3.52 [1.35, 7.28]	124	3.72 [1.54, 8.52]
Clinician hygiene	384	0.32 [0.13, 0.70]	152	0.26 [0.12, 0.67]	134	0.21 [0.08, 0.37]
Staff transit	390	0.55 [0.28, 1.12]	173	0.70 [0.35, 1.43]	151	0.58 [0.27, 1.23]
Gathering supplies	261	1.70 [0.83, 3.19]	132	1.85 [0.99, 3.22]	129	2.13 [1.17, 4.05]
Observe patient	25	0.90 [0.29, 1.60]	20	0.22 [0.09, 0.67]	20	0.29 [0.15, 0.60]
Miscellaneous	258	1.47 [0.55, 3.78]	133	2.10 [0.57, 5.64]	112	1.27 [0.66, 4.85]	1.50 [0.58, 4.40]a
Other noncare assistance	209	0.92 [0.45, 1.98]	100	0.58 [0.26, 1.97]	90	0.82 [0.35, 1.79]
Personal break	51	3.18 [0.80, 9.05]	67	2.57 [0.53, 7.80]	48	1.24 [0.27, 5.92]

Sample size (N) for each task category is not a sum of all subtasks within that category. For example, if an RN passed medications to five different patients, this counted as five patient encounters for medication pass. However, if an RN provided medications and assessed vital signs for the same patient, this counted as one patient encounter for the “Direct Care Task” category, but each subtask counted separately (one episode of medication pass and one episode of vitals assessment).

^aOne-way ANOVA significant at p < .05

Per patient encounter, the task categories with the longest duration were direct care (median = 4.72 minutes per patient/episode) and indirect care (median = 4.23 per patient/episode). The median time spent on indirect care tasks was comparable to the time spent on direct care tasks, meaning staff spent as much time in transit, gathering supplies, observing patients and completing documentation as they did in direct clinical care provision to patients. Overall, the indirect care time was primarily influenced by medical record documentation and gathering supplies. Patient observation episodes reflected visual checks to ensure patient safety, most often due to behavioral disturbance associated with cognitive impairment that increased the risk for falls. Medication pass, vital signs assessment, and lab specimen collection were the most time-consuming direct care tasks. The cumulative impact of frequent direct and indirect care tasks across all patients assigned to one staff person throughout a 12-hour shift resulted in little time spent idle or on required breaks (Figure 1). For example, if an RN was assigned five patients and each patient required four medication passes, the time spent only on medication pass averaged 91.6 minutes (range 54.7–170.7 minutes per RN). Within the ADL task categories, toileting and mobility assistance episodes reflect frequent use of bedpans and urinals, rather than a commode, and repositioning in bed or transfer from bed to chair, rather than walking assistance.

The time spent on all tasks shown in Table 3 was compared between patients with versus without cognitive impairment. Results showed that staff spent significantly more time per patient episode when providing ADL care to those with cognitive impairment for the following tasks: bedpan and urinal use (median = 6.68 [IQR: 2.85, 9.66] vs. 0.61 [IQR: 0.37, 2.13]; t = −3.256, p < .01), incontinence care (median = 5.41 [IQR: 1.47, 8.99] vs. 2.78 [IQR: 1.53, 6.38]; t = −2.11, p < .05), and mealtime assistance (median = 11.05 [IQR: 2.31, 15.27] vs. 0.92 [0.43, 2.13]; t = −3.32, p < .01). The time spent on all other tasks (Table 3) did not differ for cognitively impaired patients. For patients in isolation, the only observed difference was the time spent on clinician hygiene (median = 1.40 [IQR: 0.83, 2.25] vs. 0.25 [IQR: 0.12, 0.53]; t = −8.03, p < .001), which reflects the use of personal protective equipment. In addition, observer notations indicated that staff often completed multiple care tasks within one care delivery episode for patients in isolation.

Call Light Requests

Figure 2 shows the average frequency of call light requests by hour of the day and overall (24-hour mean) for each unit. Call lights reflect unscheduled requests that require a response from staff; thus, call lights may contribute to interruptions in scheduled care routines and total workload for both RNs and NAs. The average number of call light requests per hour was 6.1 (±1.6) for the 22-bed ACE unit, 9.1 (±1.8) for the 34-bed cardiac/vascular stepdown unit, and 7.0 (±1.8) for the 20-bed orthopedic/spine stepdown unit. The overall pattern of call light frequencies was similar across units, with peak call light frequencies occurring at 8 a.m., 1 p.m., and 6 p.m., which corresponded to scheduled meal service and shift changes.

Figure 2.

The control chart graph shows the call light frequency by hour and the 24-hour mean frequency for each unit.

Limitations

This study included three units within one academic medical center, which may limit generalizability. Data collection was deidentified to include all staff and patients to increase generalizability. However, this approach limited the data related to patient characteristics (e.g., admission diagnoses or other measures of illness severity) that could influence the nursing time allocated to observed tasks. In addition, other known nursing tasks not captured in this study include transport of patients off the unit, rapid response events, and staff continuing education and training activities. Other important aspects of the bedside RN job role not reflected in the time data include management and supervision of novice nurses and/or NAs and critical thinking necessary to synthesize assessment results to inform treatment plans. The variability in the time data likely reflects both between-patient and staff variability, given that over half the RNs had less than 2 years of experience. Level of experience data were not available for NAs but may have contributed to RNs completing tasks within NAs’ scope of practice. Future studies should examine whether experience level influences how nursing staff manage their patient and task assignment. Seasonal variations in hospital admissions also may have contributed to variability between patients and units, although the 2-year study duration should minimize this type of variation. Observations occurred during weekdays and primarily dayshift hours; thus, results could differ during nightshift or weekends. In addition, this study did not compare patient care plans to observed care delivery to identify patient-specific care omissions. Tasks with lower observed frequencies, such as ADL care, could have occurred at other times. Finally, this study reports the nursing time allocated to different types of tasks, but these allocations may not reflect optimal time requirements to achieve certain clinical and/or patient safety outcomes (e.g., a reduction in inpatient falls).

Discussion

This hospital TMS study reports objective data for the time RNs and NAs spent providing care to older inpatients. Results are comparable to prior studies showing that RNs spend a significant portion (25%–30%) of their time completing documentation and other indirect care tasks.^15,18 When engaged in direct care, medication administration has previously been cited as one of the most frequent and time-consuming RN tasks, and it was the direct care task that required the most RN time in this study. Most prior studies did not include NAs or otherwise report the hospital staff time spent on specific ADL care tasks. In this study, ADL care was observed infrequently, despite prevalent ADL care needs across all three units. These findings align with prior studies wherein hospital nurses report frequent ADL care omissions, particularly toileting and mobility assistance, and a prioritization of clinical care tasks.^4,12 Other contributing factors to infrequent ADL care in this study may include insufficient NA staff to support this care on units wherein the majority of patients required assistance and lack of a consistent schedule for ADL care. In contrast to other scheduled clinical care tasks (e.g., medication pass), ADL care is more reliant on patient requests. Notably, staff spent significantly more time when providing ADL care to cognitively impaired patients. This finding suggests that hospital units caring for patients with cognitive impairment may need NA, or other support staff, to help provide this care.

Appropriately, NAs spent a significantly greater proportion of time on ADL care tasks relative to RNs. However, RNs still spent time on ADL care and other miscellaneous noncare tasks. In addition, despite vital signs assessments and blood draws also being part of NAs’ scope of practice, RNs provided approximately one-half to two-thirds of these observed patient care encounters. These findings suggest that RNs are completing tasks that otherwise could be completed by NAs, or other types of support staff. There may have been unmeasured factors, such as NA experience level and/or the clinical complexity of individual patients, that influenced whether an NA versus an RN completed these tasks. Nurse aide staffing ratios also may have been insufficient to support timely care delivery without RN help. In addition, the frequency of call lights indicates staff often must respond to unscheduled care requests, which may affect scheduled direct care routines, task assignment and overall workload. The combined frequency of direct and indirect care tasks and call light requests across all patients assigned to each staff member resulted in both RNs and NAs spending little time idle or on required breaks.

Future studies should address how RN and NA allocations and skill mix adjustments affect patient safety and care quality (e.g., falls, care omissions) as well as staff job satisfaction and nurse retention rates. The prevalence of nurses with fewer than 2 years of experience in this study is likely common in many hospitals given the national shortage of nurses.²⁵ Thus, the ability of hospitals to allocate often limited nursing resources thoughtfully may have a broader impact on the overall quality of the hospital nursing workforce and inpatient care.

Conclusions

Objective data about the nursing time allocated to different types of tasks can inform the type of support staff that may be most beneficial on hospital units wherein a large proportion of patients are cognitively impaired and/or require ADL care assistance. The results of this study suggest that NA support for direct care tasks within their full scope of practice combined with indirect care could benefit the workload of RNs and the timeliness of patient care. The significant amount of nursing time spent on indirect care tasks also suggests that efforts to improve workflow or task efficiency might benefit patients by increasing the time nurses allocate to direct patient care.^26,27

Implications

Older inpatients often require ADL assistance in addition to clinical care needs. Skill mix adjustments and other nurse staffing strategies to improve staff workload and hospital patient care quality should be informed by objective data related to the amount of time nurses allocate to different types of tasks, inclusive of ADL care. The descriptive time data reported in this study can be used to develop hospital staffing models to reduce the likelihood of clinical and ADL care omissions. Future efforts to improve nursing staff allocations or care efficiency in the hospital setting also should evaluate the impact on patient care quality and related patient safety outcomes.

Biographies

Emily Hollingsworth, MSW, is a research Programs Manager within the Vanderbilt Department of Medicine, Division of Geriatrics, Center for Quality Aging, Nashville, Tennessee. She is trained as a Social Worker and has nearly 15 years of experience coordinating and managing clinical research studies focused on older adults in long-term care, postacute and acute care settings.

Jason Slagle, PhD, is a Research Professor within the Vanderbilt Department of Anesthesiology and the Center for Research and Innovation in Systems Safety (CRISS), Nashville, Tennessee. He is trained as an industrial-organizational psychologist, with research expertise in time–motion studies to quantify clinical care routines in the acute care setting.

Lucy Wilson, earned a Master of Science degree in Gerontology and served as a clinical research coordinator within the Vanderbilt Department of Medicine, Division of Geriatrics, Center for Quality Aging, Nashville, Tennessee. She was trained in time–motion study data collection procedures.

John Schnelle, PhD, is a Professor of Medicine within the Vanderbilt Department of Medicine, Division of Geriatrics, Center for Quality Aging, Nashville, Tennessee. He is a leader in clinical intervention trial research and an expert on long-term care quality and staffing resources.

Jennifer Kim, DNP, GNP-BC, FNAP, FAANP, FAAN, is a certified gerontological nurse practitioner (GNP), professor within the Vanderbilt School of Nursing and an affiliated faculty member in the Center for Quality Aging, Nashville, Tennessee. She has applied clinical experience in long-term care and postacute care settings. She also has clinical intervention research experience focused on improving care quality for older adults in postacute and long-term care settings.

Sandra F. Simmons, PhD, is a Professor of Medicine and Director of the Center for Quality Aging within the Vanderbilt Department of Medicine, Division of Geriatrics, Nashville, Tennessee. She has over 25 years of experience conducting research in aging related to care quality and staffing resources and served as the Principal Investigator for this hospital time–motion study.