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Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, MalaysiaPharmacy Department, Hospital Sultanah Nur Zahirah, Ministry of Health, Malaysia
Corresponding author. Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800 Gelugor, Penang, Malaysia.
Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, MalaysiaInstitutional Planning and Strategic Center, Universiti Sains Malaysia, Malaysia
To compare patient care multipliers estimated from subjective evaluation against work sampling (WS) techniques in genetic nursing activities.
Methods
An observational WS technique was conducted from November to December 2019 with nine genetic nurses in a tertiary referral center in Malaysia. The WS activity instrument was devised, validated, and pilot tested. All care- and non-care-related activities were sampled at 10-minute intervals within 8 hours of working over 14 days, followed by a subjective evaluation of activities survey over the same period. Bonferroni correction was undertaken for multiple testing with a p value of 0.0025.
Results
The two techniques produced significant differences in genetic nurses’ activities categorization. The WS showed that compared with subjective evaluation, direct care (19.3% vs. 45.0%; p < .001) was estimated to be significantly lower, and indirect care (40.4% vs. 25.6%; p < .001) and unit-related activity (28.5% vs. 16.9%; p < .001) were higher. Both techniques produced a similar proportion of time spent in other non-care activities (12.0%) but differed in genetic meetings and information-gathering activities. While the multipliers for patient face-to-face contact were significantly larger between WS (4.57) and the survey (1.94), the multipliers for patient care time were smaller between WS (1.47) and the survey (1.24), indicating that caution should be taken when multiplying for patient contact time compared to patient care activity to determine the cost of care provision.
Conclusion
A considerable proportion of time spent away from the patient needs to be allocated to patient-related care time. Thus, estimating the paid cost solely based on direct time with patients considerably underestimates the cost per hour of nurses' care. It is recommended to employ ‘patient-related activity’ instead of the ‘face-to-face contact’ multiplier because the former did not significantly differ from the one estimated using WS.
Rare disease (RD) is a debilitating hereditary disease that affects only a small fraction of the population and is extremely difficult to treat. There are between 7000 and 8000 RDs worldwide. Approximately 80% of them are genetic disorders affecting young children, and the remainders are rare cancers, autoimmune diseases, congenital malformations, and the rare manifestation of common diseases. Unfortunately, due to a lack of attention from health care providers (HCPs), health system leaders, and health policy-makers, these patients are at a risk of missing life-saving treatment. The issue stems from gaps in disease knowledge among HCPs, diagnostic difficulties, and high treatment costs [
]. In Malaysia, patients with RD are managed by a tertiary referral genetic unit. Geneticists, paediatricians, genetic counsellors, and nurses are the front-line providers of medical genetics services in the country [
]. In today's economic climate, HCPs, including nurses, must constantly perform at the highest level by re-evaluating their quality of care. As a result, there is considerable interest in measuring nurses' activities and/or workload via a professional judgement or subjective evaluation [
]. However, there have been no works published thus far to the best of the author's knowledge about the work activity of genetic nurses in comparison to geneticists [
Assessment of clinical workload for general and specialty genetic counsellors at an academic medical center: a tool for evaluating genetic counselling practices.
]. Thus, there is an obvious need to comprehend and disseminate information regarding their progressive role as the patient's primary point of contact in this largely unexplored area of RD.
] that nurses engage in while performing clinical activities, patient-related tasks, administrative work, communication, and personal tasks. Generally, these multitasking activities are categorized as either direct or indirect patient care activities, depending on whether they are performed in front of or away from the patient [
]. There are significant disparities in the proportion of time spent by nurses on direct and indirect care activities that have been reported in other areas of practice [
]. In addition, previous studies over the last decade have demonstrated that the workload and the interplay between direct and indirect patient-related activity significantly impact the quality-of-care delivery [
]. Thus, estimating direct contact time alone will underestimate the time required to provide care.
Cost and cost-effectiveness (CE) intervention is designed to achieve the maximum possible health care delivery through the optimum use of limited resources. However, despite the wealth of literature available in this field [
], there is a dearth of discussion of how ‘hidden’ or indirect patient-related activities should be handled and how they ultimately affect costs. The cost of care accounts for a sizeable amount of HCPs' time. Previously, the problem was addressed with the hidden-to-observed cost ratio [
] focuses more on cost-effective care, less attention is placed on how to account for this overhead that was needed for the provision of care-related activity [
]. Thus, this study extends and generalizes the concept of an adjustment factor or multiplier (e.g., the ratio of direct to indirect time spent on care activity) as a metric to allow for the higher costs associated with providing patient-related care activity than the direct care cost [
]. The given estimate is then converted to a ratio of direct to indirect time spent with patients or performing patient-related activities. When calculating the total time required to deliver patient care, the ratio's components are added together and multiplied by the total direct contact time and unit cost component. This time allocation more accurately reflects actual care activities, resulting in a more equitable valuation of the resources required to hire an HCP. These approaches have become the incumbent method for obtaining information on the proportion of direct and indirect time spent on different locations (e.g., patient, clinic) or activities due to practical and cost reasons. This is the approach employed by the Personal Social Services Research Unit (PSSRU) [
], which have been used in health care service research. For instance, WS is widely used as an experimental and practical technique for measuring work in nursing and non-nursing research [
]. However, it was not used to estimate the multiplier because it required more time and effort than the previous methods. Compared to TMS, WS measures activity frequencies that can easily be proportioned to determine the percentage of total time spent and the ratio of direct to indirect time [
]. Moreover, WS is less prone to personal and recall bias than the typical methods, e.g., focus groups, surveys, and verbal reports, which rely heavily on subjective judgement. The WS method not only provides an estimate of the amount of time spent in each activity but also accounts for actual work patterns in circumscribed working areas [
]. To our knowledge, no prior studies have explicitly compared the generation of multipliers for nurses’ paid care hours unit cost between WS and the survey.
Therefore, it is imperative to investigate nursing care time allocation, as the provision of medical genetics management has shifted dramatically over the past two decades. Clearly, additional evidence is required for the conference, continuous medical education, and general administrative tasks that we assumed to be overheads on patient activity to accurately reflect the actual cost of care provision [
The costs of training a nurse practitioner in primary care: the importance of allowing for the cost of education and training when making decisions about changing the professional-mix.
The purpose of this study was to compare the proportions of time spent on each activity in the total activity using two methods: WS and the survey. The following goals are of interest: (1) estimate the percentage of time required for patient-related care activities; (2) calculate the multipliers for human resource cost estimations in future economic evaluation of RDs.
Methods
Study design
The study was conducted in two stages. First, an observational WS technique was used to describe the work activities of genetic nurses for two weeks within an eight-hour working period, immediately followed by a subjective evaluation of activities over two weeks. Nurses were unaware that the subjective evaluation technique would subsequently be employed.
Setting and participants
The observations were taken at a tertiary referral genetic centre at a government hospital in Malaysia between November and December 2019. The centre is overseen by a head nurse and 11 registered nurses who work during office hours (from 8.00 am to 5.00 pm). The centre provides care to approximately 8000 patients with RD and receives approximately 600 new referrals per year. A total of nine registered nurses were recruited. The nurses were purposively sampled with job scopes focusing on patient care rather than managerial roles.
Sample size and power
The study was designed to capture the least often occurring activity for indirect care activity (ICA) in obtaining a representative number of observations needed to allow for acceptable statistical inferences to be made in line with earlier studies [
Distribuição do tempo das enfermeiras: identificação e análise em Unidade Médico-Cirúrgica = Nurses’ time distribution: identification and analysis in a medical-surgical unit.
Rev Esc Enferm USP.2009; 43 (Internet) ([cited 2021 Dec 14]; Available from:) (Portuguese, English): 833-837
]. First, the preliminary sample estimates are obtained from the trial runs. Then, the minimum number of observations for each activity category is determined (Table 1). For instance, ICA was expected to occur 43.0% of the time, and the minimum number of observations needed was 600, so we have 95% confidence that the activity is within the ±5% level of accuracy (i.e., the minimum acceptable error, d, was set to 0.04 as presented in Table 1). To determine the sample period, we have τ = time interval between observations (10 minutes), p = minimum probability of occurrence for an activity (0.001), ηʄ = number of nurses per shift, and 480 = 8 h × 60 minutes; hence, the minimum sample period corresponded to 11 days. With observations every 10 minutes, the maximum working time is 8 hours per day, and the number of nurses who can be observed simultaneously is 2; the daily data points required are 28 (e.g., 600 observations ÷ 11 days ÷ 2 nurses).
Table 1Activity Categories’ Frequency and Sampling required.
% Category (no of observations)
DCA
ICA
URA
OA
Preliminary trial 1
14.5 (17)
47.0 (55)
28.2 (33)
10.3 (12)
Preliminary trial 2
17.5 (32)
38.8 (71)
21.3 (39)
22.4 (41)
Estimated proportion
16.0 (25)
42.9 (63)
24.8 (36)
16.3 (27)
Minimum observations needed, n
350
600
400
350
Limit of error
0.04
0.04
0.04
0.04
Note. n = number of data points; DCA = direct care activity; ICA = indirect care activity; URA = unit-related activity; OA = ‘others’ activity.
A literature review identifies the list of potential nurses' activities attributable to care delivery. Then, interviews with practising nurses and leaders were conducted to elicit the normal work activities. These lists were contrasted and grouped. Then, a single list of activities was discussed for inclusion or exclusion from the final WS instrument. We adopted a categorization of work activity from the established sources [
Distribuição do tempo das enfermeiras: identificação e análise em Unidade Médico-Cirúrgica = Nurses’ time distribution: identification and analysis in a medical-surgical unit.
Rev Esc Enferm USP.2009; 43 (Internet) ([cited 2021 Dec 14]; Available from:) (Portuguese, English): 833-837
]: (1) direct care activity, DCA (e.g., care activity requires patient's interaction or is performed directly with patient and family); (2) indirect care activity, ICA (e.g., care activity that is performed from a distance of patient and family but benefitting them); (3) unit-related activity, URA (non-care activity pertaining to the normal maintenance of the unit and its organization); (4) personal activity, PA (including rest periods and personal requirements unrelated to the professional task). Alternatively, we classified PA, idle time, and breaks as ‘other’ activities (OA).
Additionally, the activity descriptions are developed and refined during the testing period by the author. Next, the author and nurse manager validated the instrument's content by standardizing the terminology and comparing the listed activities to local clinical practices [
]. This process informed the refinement of techniques and methods for the trial runs. The runs help the author become familiar with the criteria and activity descriptions that will be used to identify the dominant activity occurring concurrently during the observations [
]. The emphasis was to document what had taken place and not to define which activity had the greater priority. Following this procedure, the final data collection form organized 26 nurse activities (Figure 1) that were mutually exclusive, defined, and precoded into four categories of work: DCA, ICA, URA, and OA. The list of work activity descriptions with inclusion and exclusion criteria is available for each of the tasks in the online appendix.
Figure 1Genetic Nurse Activities and Their Categorization identified from the Study. The normal activities of genetic nurses obtained from the work sampling. The insert shows the mean proportions for the category. The error bar represents the standard error. Note. CME = continuous medical education.
A pilot WS study was initially undertaken with seven nurses for three working days at random. The trial runs and study observations were made from 8.00 am to 4.30 pm and from 8.00 am to 5.00 pm, respectively, at 10-min fixed intervals by the first author. The interval set time was considered less demanding than the randomly distributed time sampling and a standard method in health services research [
]. The pilot study provided an opportunity to test the data collection tool and gain insight into the following observation techniques. The observer moved to an exact spot from eight observation locations at random and positioned within earshot of the nurses prior to observing the activity while remaining as unobtrusive as possible. Then, two observations were made at each time interval instantaneously. The observed activities were logged on a tabular data collection form using a unique numbering assigned to each task adopted from a previous study [
]. The observer documented all unforeseeable events that occurred during the period.
Following that, the same author provided the nurses with similar activity descriptions and forms to evaluate and reflect on over the same period. Then, they were required to give estimates on the amount of time they spent on the specific activities and rated their confidence for those estimates via a survey form.
Data analysis
First, data from WS were aggregated by individual activity and grouped into categories. Then, the descriptive statistics were used to compute the average proportion of time for each activity and category. All values are reported as the mean and standard error of the mean. Second, the summarized data are contrasted with the estimates we obtained from the survey. The survey data are presented as the mean and standard deviation (SD) of the mean. The Bonferroni correction was undertaken for multiple testing and adopted a p value of 0.0028 for the significance test [
]. All data were entered into Microsoft Excel 2013 (Microsoft Corporation, Redmond, Wa) and then transferred electronically to R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria) for data wrangling and statistical analyses.
Ethical considerations
Ethical approval was granted by the Medical and Research Ethics Committee of the Ministry of Health, Malaysia (NMRR-19-610-46077). This study poses no risk to patients and nurses, and the observations did not disrupt the patient's visit or nurse's routine care. The participants volunteered to participate in the study.
Results
Demographic characteristics
The participants’ age ranged from 28 to 35 years, with a median age of approximately 30 years [IQR = 3.51]. The majority were female (77.8%, n = 7), and most had obtained a 3-year diploma in nursing and were staff nurses (75.5%, n = 6). In addition, the vast majority (75.5%, n = 6) had between 5 and 10 years of clinical experience. The nurses qualified as nurses as early as 2001 (the latest in 2016).
Work sampling
A total of 2,333 observations were recorded (including 300 trial run observations). The minimum daily observations required were not achieved at three periods for category OA due to one nurse being on emergency leave and meeting the week after. No significant changes to the WS instrument were made except for one activity, i.e., ‘hygiene/cleaning’ was excluded from the finalized instrument because it was not observed during trials. There are 26 types of activity descriptions that have been identified; of these, the 7, 9, 6, and 4 activities are categorized into DCA, ICA, URA, and OA, respectively (see Table 2 for list of activities).
Table 2Genetic Nurses’ Time Distribution during Clinical Operation Hours.
Note. ‘0’ = zero; Admin. = administration; Dr = doctor; Max = maximum; Meds. = medication; n = number of data points; Obs. = observations; Pt. = patient; Treat. = treatment.
Nurses in our study spent 19.3% of their clinical operational time on direct patient contact performing DCA. Most of the activities were spent away from the patient (80.7%); 40.4% were on ICA, 28.5% on URA, and 11.8% on OA. Together, DCA (19.3%) and ICA (40.4%) equate to 59.7% of patient-related care activities. On the other hand, time spent on non-patient care activities accounted for 40.3% of the activities, with 28.5% and 11.8% on URA and OA, respectively. The complete time distribution of the activities is presented in Table 2 below.
Overall category analysis indicates that nurses spend the most time on ICA (40.4%), followed by URA (28.5%), DCA (19.3%), and OA (11.8%) (see insert in Figure 1). The three activities for ICA with the most significant frequency were ‘daily patient progress documentation’ (19.2%), ‘professional communication’ (18.0%), and ‘attending genetic meetings’ (16.6%). These three combined equal 53.8% of ICA (508 observations). In the URA category, ‘clerical work’ dominated with 36.1%, followed by ‘patient transit’ and ‘education’ at 16.1% and 16.0%, respectively. These three combined equal 68.2% of URA (448 observations). In the DCA category, the three activities with the highest frequency of observed activities were ‘assisting the doctor during review’ (31.1%), ‘patient/family interaction’ (26.5%), and ‘patient assessment’ (12.8%). These combined activities equal 70.4% of DCA (319 activities). Conversely, in OA, nurses spend more than half of the time in ‘idling’ (56.8%) activity alone, more than in ‘personal’ (18.2%) and ‘pantry’ (14.3%) combined. These three combined equal almost 90.0% of OA (251 observations) (see Table 2).
Overall activity analysis shows that nurses spend most of their time on ‘clerical work’ (10.2%), ‘patient progress documentation’ (8.1%), ‘professional communication’ (7.3%), ‘idle time’ (6.7%), ‘assisting doctor during review’ (5.9%), and ‘genetic meetings’ (5.8%). In contrast, the lowest proportion of time was spent on ‘mobilizing patient’ (0.8%), ‘gathering information’ (0.9%), ‘medication administration’ (1.2%), and ‘using the restroom’ (1.3%). From Table 2, we can see that, although ‘education,’ ‘administrative meetings,’ ‘genetic meetings,’ and ‘mobilization of patient’ activities were not observable (higher number of zero observations) daily, the activities had a significant impact on the overall proportion of the time spent. Collectively, URA (22.6%) had the highest percentage of zero observations, followed by ICA (15.1%), DCA (14.3%), and OA (10.7%).
Subjective evaluation
The nurses had estimated that they spent 45.0% of their time on DCA [SD = 14.4], 25.6% (SD = 8.21) on ICA, 16.8% (SD = 5.94) on URA, and 12.5% (SD = 7.07) on OA. The nurses were 67% (SD = 13.35) confident with their estimates (on a 0–100 scale). Regarding specific care activities, the nurses approximated a minimum of 30–60 minutes and a maximum of 60–90 minutes weekly for ‘gathering information.’ In contrast, ‘genetic meetings’ had twice that weekly time spent, ranging from 60 to 120 minutes to a maximum of 120 to 210 minutes. We divided these activity estimates (minutes per week) by the total minutes per week (2,400 minutes) to obtain the expected proportion of time for the survey method (see Table 3). The chi-square tests showed that the given estimates differed significantly from the WS data for DCA, ICA, and URA, with p < .001 except for OA, for which p = .578 indicated no significance. Additionally, the two techniques showed no significant difference in the proportion of time spent on ‘genetic meetings’ (5.8% vs. 7.3%, p = .578), whereas ‘gathering information’ was significant (0.9% vs. 3.4%, p < .001).
Table 3Comparison Frequency of Activities Derived from Work Sampling and Subjective Evaluation.
Category/activity
Observed (%)
Expected (%)
d.f.
χ2
p
Modified Bonferroni adjusted alpha
Adjusted significance
WS
SEV
Direct
453 (19.3)
1,049 (45.0)
1
616.94
<.001∗∗∗
<.001 ∗∗∗
No
Indirect
944 (40.4)
596 (25.6)
1
594.55
<.001∗∗∗
<.001 ∗∗∗
No
Genetic meetings
157 (5.8)
170 (7.3)
1
1.12
.289
.578
No
Gather information
20 (0.9)
79 (3.4)
1
45.92
<.001∗∗∗
<.001 ∗∗∗
No
Unit
656 (28.5)
394 (16.9)
1
209.06
<.001∗∗∗
<.001 ∗∗∗
No
Others
280 (11.8)
291 (12.5)
1
0.53
.467
.578
No
Note. SEV = subjective evaluation; WS = work sampling; d.f. = degree of freedom; ∗∗∗p < .001.
We have identified that the ‘others’ activity can be removed from the proportion of time used for patient care. Therefore, this overhead should not be included in the ratio and multiplier. Our results can be interpreted likewise (Table 4). Since we have a complete breakdown of time use, time spent on OA is classified outside of these care and non-care activities that did not generate outputs to the patient care delivery. The procedure for estimating multipliers to allocate time is illustrated using the three steps below [
Step 1. To calculate loading, we require a multiple of patient care-providing hours that reflect the relationship between care (C) and non-care (NC) activities and the total number of hours worked (T). The basic equation for this multiple (q) is represented as follows:
Table 4 shows the breakdown of time spent delivering patient care at 59.7%. Thus the proportion of NC time is:
Step 2. The multiplier, q, is then calculated as follows:
Step 3. However, we identified the need to include the information above to ensure that OA is not allocated to patient care delivery. To calculate the time spent on patient care, we must subtract the time spent on ‘others’:
Therefore, when estimating the cost per hour of a nurse in the genetic clinic, cost = 1.47 × ‘cost per hour.’ In comparison, the multiplier calculated from the survey data was 1.24. Similarly, other multipliers, e.g., patient face-to-face contact, can be approximated as 4.57 and 1.94 for WS and the survey, respectively (calculation not shown).
Discussion
This study explicitly compares two techniques for estimating unit cost multipliers of nurses in medical genetics. The WS and the survey indicated that nurses spend more than half of their time working away from patients, at 80.7% and 55.0%, respectively. The nurses spent half (40.4%) and less than half (25.6%) of that away time on patient care activities in WS and the survey, respectively. In addition, the nurses reported that they spent more time on DCA (45.0%) than on ICA (25.6%) in the survey. However, we discovered from WS that the nurses spent significantly less time on DCA (19.3%) than on ICA (40.4%). This finding is consistent with nurses who work in the ICU [
], all of which indicate that HCPs tend to overestimate the time spent on the direct patient-related activity. While surveys are beneficial in providing insights into work patterns, their accuracy might be impacted by recall error and desirability bias [
We demonstrated that OA was excluded from the multiplier, with idle (6.7%) and personal (2.2%) activity accounting for the majority (75.0%) of the category. Studies have shown that PA varied widely among nurses in surgical wards (2.5%), home nursing (5.1%), medical–surgical clinics (12–18.0%), neurorehabilitation units (19.0%), ICUs (24.0%), critical care units, and EDs (42.0%) [
Distribuição do tempo das enfermeiras: identificação e análise em Unidade Médico-Cirúrgica = Nurses’ time distribution: identification and analysis in a medical-surgical unit.
Rev Esc Enferm USP.2009; 43 (Internet) ([cited 2021 Dec 14]; Available from:) (Portuguese, English): 833-837
]. Our nurses’ personal time was lower due to further subdividing OA into smaller activities to help us identify the activity unrelated to services, patient care, and professional development. Additionally, some authors classified education and training as personal tasks [
], contrary to our approach, which categorized them as an overhead of patient care. Furthermore, variations also existed in the major categorization of the activities. In an Australian study [
], medication preparation was defined as DCA for ICU nurses, whereas we categorized it as ICA. These variations in definitions and categorization are well accepted to accommodate the particular objective of a study [
]. Thus, the specific definition and categorization of the activities should be further inspected whenever a multiplier is to be estimated from the published study.
There are significant disparities in the proportions of time spent by nurses on direct and indirect care activities that have been reported in other areas of practice [
Assessment of clinical workload for general and specialty genetic counsellors at an academic medical center: a tool for evaluating genetic counselling practices.
]. Moreover, the considerable time spent on indirect patient-related activity distinguishes genetic management from many other disciplines. These include gathering disease information, case conferencing, and continuing education of HCPs [
Assessment of clinical workload for general and specialty genetic counsellors at an academic medical center: a tool for evaluating genetic counselling practices.
]. Thus, estimating direct contact time alone will underestimate the time required to provide care.
The study highlighted ‘genetic meetings’ as the activity that significantly contributed to the provision of patient care. Specialists led the meeting, and nurses took turns attending to discuss patient treatment. This is crucial because most patient cases are complicated and require collective input from team members to make pragmatic treatment and care management decisions. These are the most prominent features of genetic nurses compared to nurses in other fields. It is important to note that documentation activities classified as ‘clerical’ and ‘patient progress documentation’ are the most time-consuming tasks for nurses. The timely documentation was congruent with medical-surgical units, acute care, and nursing home nurses [
]. The required documentation is not only necessary for compliance with local health-related policies and regulations but also the maintenance of care quality.
Many studies consistently report that nurses can perform multiple activities simultaneously [
]. Overall, ‘professional communications’ ranks third according to our study and recent findings of neurorehabilitation and medical-surgical unit nurses [
]. Moreover, communication with patients represents the second largest proportion of the nurses' direct care time, contrary to a previous study that indicated nurses devoted little time to interact with their patients [
]. The finding demonstrates that two modes of communication are essential for maintaining the organization's effectiveness and ensuring the delivery of quality genetic care.
Limitations
Our study has some limitations. First, it was conducted in a single genetic clinic. Then, the observation period happened to be two weeks full of unit briefings, audits, and renovation activities to meet the end of the current year. This may partly explain why activities such as ‘education’ were recorded at a slightly higher percentage (4.0%) than for nurses in the rehabilitation unit (<1.0%) [
] also use a significant amount of preclinic and postclinic time to research, read, and prepare cases for patients. We have limited evidence of how this might impact nurses' total care activities. This may limit the generalizability of the findings.
Second, the differences arising from the two techniques could also be attributed to the observer not practicing in nursing and potentially having a different interpretation of the activity definitions. However, we believe the impact was minimal because we rectified any discrepancies with the nurse leaders during the trial runs. In addition, we conducted a short communication with the nurses at gaps between time intervals to resolve any discrepancies immediately. Moreover, we have tested both techniques to differ significantly, prompting the methods themselves to influence the results.
The observer was not permitted to enter observation rooms during patient assessment in certain circumstances. In this instance (unobserved activity), we made a reasonable assumption based on the information provided to the observer about the tasks the nurses were performing. Additionally, we left the observation unrecorded when participants failed to disclose their planned destination (e.g., outside the clinic) to the observer. Occasionally, participants engage in an activity that lasts longer than the activity being recorded. Nonetheless, the pattern identified in this study was obtained from a primary referral centre in Malaysia, and these limitations were found to be minimal.
Advantages
There were activities that we recorded at a distance from the participant to reduce the Hawthorne effect. Previous reports suggested that the effect was likely to affect time engaged in personal time, meal breaks, and idle time [
]. However, we did not find any significant difference in the OA category between the two techniques. Moreover, while the previous study showed that the HCP tends to give estimations in favour of PRA [
], we found that the estimated proportion of PRA was lower for WS than that of the survey, suggesting that there was no significant Hawthorne effect in this study.
Whilst surveys are relatively less expensive and take less time to administer, they lack the degree of detail required in assessing the overhead. In this instance, surveys may further reduce the representativeness of the multipliers. It is worth mentioning that WS is incomparable to the ‘gold standard’ TMS study [
]. However, the level of information obtained in this study was sufficient for the purpose of describing the working pattern. Despite its inferiority to TMS, WS enables a more objective description of time distribution while potentially avoiding the recall error and personal bias associated with surveys or self-reports. This suggests that WS appears to be a straightforward and cost-effective technique for estimating the multiplier.
Implications and recommendations
The two techniques produced high variability in the overall categorization of nurses' activities except for OA. While the multipliers for patient face-to-face contact were significantly larger between WS (4.57) and the survey (1.94), the multipliers for patient care time were smaller between WS (1.47) and the survey (1.24). Consequently, when comparing the true cost of patient care delivery between WS (1.47 × $20/h = $29.40) and the survey (1.24 × $20/h = $24.80), the gap in the true cost of patient face-to-face contact was 2.5 times greater between WS (4.57 × $20/h = $91.40) and the survey (1.94 × $20 = $38.80). Thus, estimating the paid cost solely based on direct time with patients considerably underestimates the cost per hour of nurses’ care delivery. Additionally, caution should be taken when multiplying the patient contact time by the patient-related activity to determine the cost of care provision, as demonstrated in this study.
The study demonstrates the importance of adequately allocating overhead to ensure that the activities unrelated to patient care (e.g., PA) are not included as overhead on patient care costs, particularly when evaluating human resource expenses. However, numerous CE studies have ignored this critical component entirely from the analyses [
]. One possible explanation for this is the complexity and difficulty of quantifying overhead. Cost estimation is a fundamental component of economic analysis. Despite the wealth of literature available in this field [
], there is a dearth of discussion of how ‘hidden’ or indirect patient-related activities should be handled and how they ultimately affect costs. This study sheds light on quantifying the actual cost of nursing care intervention for resource prioritization, as highlighted by previous CE studies in nursing [
]. The differences in cost and unit cost estimates can alter the final CE conclusion of whether a new intervention is effective. While the sensitivity analysis [
] can be used to account for the uncertainty around the estimates, it is crucial to have a realistic range of parameter values (e.g., a multiplier) rather than being utterly arbitrary during the model simulation [
A critical first step is to raise awareness to bridge gaps in knowledge and attitudes regarding the role of economic analysis in minimizing harm to the patient by recognizing the ‘value’ of nursing interventions for a cost-effective care strategy. This emphasizes the theoretical basis for characterizing genetic nursing care workloads and work patterns in estimating the actual cost of care delivery [
] when there is no detailed breakdown of time; for instance, limited information is available from standard workload measurements (e.g., patient turnover) for non-care-related activity [
]. This necessitates the use of alternative (e.g., WS) or combined-with-standard methods to reconcile the actual pattern of care provision, provided that the findings and sensitivity of the results are explicitly stated as demonstrated by this study.
The overarching process proves that the multiplier is beneficial in converting other forms of unit cost into terms that are also important compared to the cost per hour, e.g., cost per bed. Alternatively, the term ‘patient-related activity’ found in previous studies can be used in reference to patient care delivery, as in our study, for instance, when estimating geneticist [
Assessment of clinical workload for general and specialty genetic counsellors at an academic medical center: a tool for evaluating genetic counselling practices.
] multipliers from published sources. We recommend employing ‘patient-related activity’ instead of the ‘face-to-face contact’ multiplier because the former did not significantly differ from the one estimated using WS in this study. The actual cost will include care overheads in the cost per hour of care delivery.
Conclusion
The observational data reflect current genetic nurses’ work practices. The ratio of time on patient care activities to time spent on non-care activities is 1:0.47. This means that for every hour nurses spend on care activity, e.g., assisting doctors, an additional 28 minutes are spent on non-care activity, e.g., clerical time, to deliver appropriate patient care. Hence, every hour spent with a patient requires 1.47 paid hours. We will utilize the adjusted total paid hours per nurse attributable to patient care delivery in a future CE model. This ensures that the total cost approximately reflects our current local practices.
Funding
This work was supported by the Universiti Sains Malaysia's Research University Grant (1001/PFARMASI/8011119).
Conflict of interest
The authors declared no conflict of interest.
Acknowledgments
The authors thank all participants for their generous participation in the project. The authors also thank Hospital Kuala Lumpur for providing the facilities for this study. Lastly, the authors would like to thank the Director General of Health Malaysia for his permission to publish the article.
Appendix
Additional file: The definitions of normal work activitites performed by the nurses with inclusion and exclusion examples. Activitites are categorized into four categories of work: direct and indirect care, unit-related and 'others' activity. Codes listed were those used during data collection.
Tabled
1
Code
Activity
Activity definition
Inclusions and exclusions
Direct Care: Performed in the presence of the patient and/or family
1.
Registration and new admission
This involves taking registration at the counter.
Includes: Telephone registration, clearing patient appointment routine. New and returning patients at the main counter. Excludes: Rewriting/re-entering electronic medical record (EMR) in computer
2.
Patient assessment
This involves nursing assessing patient overall health status. Additionally, readings taken from medical devices
Includes: Vital signs, objective and subjective findings, measurement, weighing. Obtaining temperature. Excludes: Other clinical procedures specified at Activity 4.
3.
Medication administration
Administration of medication during clinic hours
Includes: ERT and non-ERT injections such as premedications. Excludes: Activity 2 and 4
4.
Clinical procedures
This involves all medical procedures conducted in the treatment room.
Includes: Branula insertion, poking, equipment attaches to patient, blood and urine samples, emergency procedures Excludes: Procedure conducted in the doctor's room will be classified under Activity 6. Performing patient assessments prespecified under Activity 2.
5.
Patient mobility
This involves moving and directing patient to another location within the clinic.
This involves any clinical and administrative procedures done in the presence of the doctor.
Includes: Conducted in the assessment and treatment room. Filling out other forms. Excludes: Filing conducted outside prespecified rooms
7.
Patient interactions
Spend time communicating and addressing the needs of the patient and family physically.
Includes: Child's handling, instruction or counselling, and conversation with patient or family. Excludes: Over the phone patient contact is classified under Activity 15. Moving and directing patients is under Activity 5.
Indirect Care: Performed away from the patient, but specifically on the patient's behalf
8.
Professional communications
This involves asking, reporting patient results and other required information for patient care.
Includes: Reporting results of patient to other colleagues within the clinics. Seeking consultations, specialist, other hospital staff. Excludes: Activity 14
9.
Room and equipment setup, cleaning
This involves preparatory time before and after seeing the patient.
Includes: Treatment and assessment room. Gathering supplies. Preparing equipment. Excludes: Medication-related is classified under Activity 3
10.
Medication tasks
Indent, send Rx, and collect medication at the pharmacy. Prepare IV administration and dilute.
Includes: Present the prescription to the pharmacy and wait for the medication to be dispensed. Prepare and check medication.
11.
Progress and discharge notes documentation
This involves working on making discharge summaries and progress notes on the observation day.
Includes: Yellow files only Excludes: Long overdue files management. This will be classified under Activity 20.
12.
Data entry and retrieval
This is specific to the activity conducted using the computer.
Includes: White computer. Entry into the SMS system Excludes: Black computer
13.
Gathering information from phones, computers
This includes information searches conducted during patient assessment.
Assessment of clinical workload for general and specialty genetic counsellors at an academic medical center: a tool for evaluating genetic counselling practices.
The costs of training a nurse practitioner in primary care: the importance of allowing for the cost of education and training when making decisions about changing the professional-mix.
Distribuição do tempo das enfermeiras: identificação e análise em Unidade Médico-Cirúrgica = Nurses’ time distribution: identification and analysis in a medical-surgical unit.
Rev Esc Enferm USP.2009; 43 (Internet) ([cited 2021 Dec 14]; Available from:) (Portuguese, English): 833-837
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