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The association between multi-disciplinary staffing levels and mortality in acute hospitals: a systematic review



Health systems worldwide are faced with the challenge of adequately staffing their hospital services. Much of the current research and subsequent policy has been focusing on nurse staffing and minimum ratios to ensure quality and safety of patient care. Nonetheless, nurses are not the only profession who interact with patients, and, therefore, not the only professional group who has the potential to influence the outcomes of patients while in hospital. We aimed to synthesise the evidence on the relationship between multi-disciplinary staffing levels in hospital including nursing, medical and allied health professionals and the risk of death.


Systematic review. We searched Embase, Medline, CINAHL, and the Cochrane Library for quantitative or mixed methods studies with a quantitative component exploring the association between multi-disciplinary hospital staffing levels and mortality.


We included 12 studies. Hospitals with more physicians and registered nurses had lower mortality rates. Higher levels of nursing assistants were associated with higher patient mortality. Only two studies included other health professionals, providing scant evidence about their effect.


Pathways for allied health professionals such as physiotherapists, occupational therapists, dietitians, pharmacists, to impact safety and other patient outcomes are plausible and should be explored in future studies.

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Having enough healthcare workers with the right skills is essential for maintaining patient safety and quality of care. Nonetheless, several health systems face critical shortages of staff either due to short supply or economic constraints, or both [1-3]. Despite absolute staff numbers increasing in many countries [4], staff workload has also increased, in part due to increase in patient volumes, ageing populations with more complex health conditions, meaning that the healthcare staff shortages persists.

The evidence that adequate staffing levels are important for good patient outcomes is extensive, but it has focused primarily on nursing. Several reviews have concluded that when patients are exposed to higher levels of registered nursing staff, the risk of dying while in hospital or soon after discharge is lower [5-8]. Despite the predominance of observational evidence, careful analysis supports a conclusion that a causal relationship is both plausible and likely [5, 6]. This has led a number of countries to introduce policies that mandate safe staffing ratios for nursing hospital services [9-12], but such policies have not extended to other healthcare professional groups.

Nonetheless, the healthcare workforce is made up of many different professional groups. Of all the healthcare professional groups, patients are most exposed to nursing staff when in hospital [13], but nurses are not the only professionals who interact with patients and staffing levels of other staff groups are also likely to influence the quality and safety of care. The focus purely on nurse staffing is thus a problem as there is potential for bias in effect estimates. If studies do not account for other occupational groups, an observed association between nurse staffing and patient mortality could be partly or wholly due to an effect of other occupational groups [14].

Evidence that could drive policy around other staffing groups, including pharmacists, physiotherapists, occupational therapists, dietitians, speech therapists, and podiatrists is sparse [15, 16]. Although there is more research on patient outcomes and physician staffing [17], we are not aware of any comprehensive systematic review synthesising evidence around the impact of staffing levels across multi-disciplinary teams. Therefore, the aim of this systematic review was to synthesise the evidence on the relationship between nurse and other occupational groups staffing levels and the risk of patients dying after being admitted to hospital.


Eligibility criteria

We included quantitative or mixed methods studies with a quantitative component exploring the association between multi-disciplinary hospital staffing levels and mortality. We considered only studies that explored multivariable associations for more than one staffing group simultaneously and which included or adjusted for nurse staffing levels, as the causal influence of nurse staffing is well supported and so omission of this as a variable from other studies is likely to be a critical source of bias. We excluded studies that reported on one staffing group only, including studies exclusively exploring the mix of workers or substitutions within a single occupational group, for example a study considering only registered nurses and nursing assistants, or physicians and physician assistants would not be included. Due to the absence of previous reviews on the topic and due to limited knowledge around the depth and breadth of this body of evidence, no publication date restrictions were applied.

Studies that reported on all-cause or disease-specific mortality (or survival) in hospital or within 30 days of admission were included. Studies conducted in hospitals providing acute care were eligible for inclusion. We excluded studies conducted in the community, long-term or mental health facilities and studies that were only reported as conference abstracts.

Study selection and data extraction

We performed the search in November 2021, following the registered systematic review protocol (PROSPERO registration CRD42020219869). We used Embase subject headings (Emtree) and Medical Subject Headings (MeSH) terms with additional free text keywords to search Embase, Medline, CINAHL, and the Cochrane Library. We hand-searched for additional articles by checking reference lists of included articles. While our search overall included three main facets “staffing groups and levels”, “hospital setting”, and “mortality” combined with the Boolean operator “and”, the exact search terms varied according to each database specific search functions. The full search strategy is available as Additional file 1: File S1.

One reviewer de-duplicated and assessed titles and abstracts for eligibility. Full text was obtained for all relevant studies and for those where there was uncertainty on eligibility. These were assessed independently by two reviewers. Manuscripts with uncertain eligibility after full text review were discussed with all co-authors to reach a consensus.

We used a standardised data extraction form, developed a priori in Excel. Two reviewers independently extracted data on publication (authors, title, and year and country of publication), study characteristics (design, data collection period, data sources, number of hospitals/units/patients included), measures of staffing levels (staff groups and definitions), outcomes including how they were measured, methodology (level of aggregation, type of data analyses), and findings (estimates with precision measures).

Risk of bias assessment

We adapted the risk of bias assessment tool developed for studies of the association between healthcare staffing and outcomes [18]. This was based on the framework for assessment of quantitative studies reporting correlations and associations in the National Institute for Health Care Excellence (NICE) guidance for reviews in Public Health guidance [19]. The tool assesses the study’s internal and external validity separately. For each criterion, a rating of strong was assigned when the method adopted was likely to minimise bias, a rating of moderate where items lacked clarity or the methods did not address all likely sources of potential bias, or rating of weak where significant sources of bias might arise. A blank checklist is attached as supplementary material (Additional file 1: File S2). Two reviewers independently assessed all manuscripts included in the review for risk of bias. There was a percentage agreement of 92% and the Cohen’s kappa was 0.58, indicating moderate agreement, with 100% agreement reached after the moderation process. Disagreements were discussed with all co-authors until a consensus was obtained.


We performed a narrative synthesis of the evidence as we were unable to conduct a formal meta-analysis due to the lack of studies using similar measures of staffing that could be grouped, and due to the different combinations of staffing groups included in the individual studies. Where studies presented results for more than one statistical model, we reported relationships from the most complete model (i.e. adjusted for the largest number of occupational groups).


We found 4222 abstracts, of which 3681 were screened after removal of 541 duplicates. We identified 312 potentially relevant studies were reviewed in full for eligibility, of which 12 met the inclusion criteria. Reasons for exclusion are listed in the PRISMA flowchart (Fig. 1).

Fig. 1
figure 1

PRISMA flow diagram. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. For more information, visit:

Study characteristics

All studies’ characteristics are reported in Table 1. Studies were published between 1999 and 2020 and included data from USA (5 studies), UK (2), South Korea (2), and one each from Denmark, France, and the Netherlands. Only one study was single-centred [20], with others including data from between four [21] and 3763 hospitals [22]. Ten studies were cross-sectional [20, 22-30] and two were cohort studies [21, 31].

Table 1 Summary of included studies' characteristics

Patient sample sizes varied, ranging from 1864 [29] to 23,879,998 [22]. Studies with smaller samples focused on specific patient populations, e.g. patients who had a gastrectomy [29], or patients from ICU settings only [21, 31], whereas the larger studies included less specific populations of general medical and/or surgical patients.

All studies used bed-to-staff or staff-to-bed ratios to measure staffing levels, apart from two studies which reported staff-to-patient ratios [20, 21], and one study which reported the number of Full Time Equivalent staff employed per 100 adjusted admissions [28]. The majority of studies (n = 10) reported on all-cause mortality as the primary outcome, while two restricted on mortality after specific procedures (i.e. post-percutaneous coronary intervention [26] and post-gastrectomy [29]). All estimates from the multivariable models are reported in Table 2.

Table 2 Estimates from multivariable models

Risk of bias assessment

All risk of bias assessments are reported in Table 3. Four studies were classified with strong internal validity [25, 26, 30], and eight with moderate internal validity [20-24, 27-29]. Studies classified as stronger from an internal validity perspective were longitudinal, meaning that bias due to simultaneity was less likely to occur. All studies were ranked as strong in terms of reliability and completeness of outcome measurement because patient mortality was derived from administrative systems which are less prone to bias than, for example, surveys where outcomes are reported by individual respondents. Studies scored strongly in the confounding and methods domain when, in addition to robust risk-adjustment of patient mortality, they were able to take into account clustering of responses in units and hospitals, or at least one of the two [20, 24, 25, 30]. Confidence intervals, where reported, were generally narrow in absolute terms although absolute effects tended to be small and so proportionate changes in effects could still be large. Ten studies had strong external validity because of the large number of hospitals included giving the studies high power and representativeness in a defined administrative area [20, 22-25, 27-31], while two had moderate external validity [21, 26].

Table 3 Risk of bias assessment

Nurse staffing levels

There was a statistically significant association (p < 0.05) between higher levels of registered nurse staffing and lower mortality rates in seven studies out of 12 [20-24, 27, 30]. The effect sizes were typically small and were difficult to compare because of the varying staffing measures (see Table 2). For example, an increase of 1 registered nurse hour per patient day reduced odds of death by less than 1% based on the reported beta coefficient of − 0.008 [20]. An additional nurse per bed reduced the absolute death rate by 0.26 [22]. An additional RN per 100 beds reduced the odds of death by 1% [24]. Odds of death were reduced by 10% when there were ≥ 1.359 registered nurses per bed compared to between 0 and 0.75 registered nurses per bed [27]. In ICU settings, an additional registered nurse per bed reduced the odds of death by 8% [30]. An increase of one in the bed-to-nurse ratio was associated with a 3.7% higher mortality rate [23]). A larger effect was observed in the longitudinal study by Neuraz et al. [21] in an ICU setting, where having more than 2.5 patients per registered nurses was associated with an almost fourfold increase in the risk of mortality (risk ratio = 3.5) compared to having less than 1 patient per registered nurse.

Although most analyses assumed a linear effect, those that categorised staffing levels across more than two categories found that Higher registered nurse staffing categories were associated with lower mortality and vice versa [21, 26, 27] although non-linearity was not formally assessed. There was evidence that estimated nurse staffing effects were lower in multivariable models controlling for other staff groups than in models including nurse staffing only. For example in Griffiths et al.’s study of English NHS hospitals, a reduction in the mean registered nurse workload from 10 or more patients to 6 or fewer was associated with a 20% reduction in the risk of death in the single staff group model which reduced to 11% in the model including medical staffing levels [25]. Five studies did not find statistically significant associations between registered nurse staffing levels and mortality, although in all cases point estimates were in the direction of a beneficial effect from higher levels of registered nurse staffing [25, 26, 28, 29, 31]. No studies found that hospitals with more registered nurses had higher mortality rates.

Six studies included nursing assistant staffing levels, with one finding a beneficial effect from higher staffing levels (odds ratio from β coefficient for hours per patient day (HPPD) = 0.99) [20], and two finding that higher nursing assistant staffing levels were associated with higher patient mortality risk (with a 0.4% absolute risk increase for each assistant per occupied bed [22] and occupied beds per nursing assistant OR = 0.93 [25]). The three remaining studies did not report statistically significant associations, but estimates, where available, pointed to higher staffing levels being associated with higher mortality [29, 30].

Physician staffing levels

Eleven studies reported associations with physician staffing levels. Of these, seven found that higher levels of physician staffing were statistically significantly associated with lower hospital mortality rates, after adjusting for nurse staffing levels [20-22, 25, 27, 30, 31]. Effect sizes tended to be small, apart from Neuraz et al., where the risk of mortality doubled when having more than 14 patients per physician compared to having less than 8 patients per physician [21]. When adding one physician per bed, effect sizes were odds ratio = 0.99 [20, 22] and having more than 1.359 physicians per bed compared to between 0 and 0.75 physicians per bed was associated with a 10% reduction in the likelihood of a patient dying [27]. When adding one bed per physician, the likelihood of patients dying increased by 8% [25] and 16% [31]. Estimates from other studies were also small and not statistically significant but all were in the direction of a protective effect from having more physicians per bed [23, 24, 30]. In one instance, claims of no associations meant that analyses were not reported [26]. One study compared different physician grades (i.e. intensivists vs consultants), but none of these staff groups were associated with mortality [30]. One study included physician assistants, and, while estimates indicated that higher staffing levels were associated with lower mortality, these were not statistically significant [22].

Other staff groups

Only two studies reported on staff groups other than medical and nursing staff (Table 1). Robertson and colleagues, analysing data from 1791 US hospitals over 3 years (1989–1991), considered (in addition to nurses, nursing assistants, and physicians) respiratory therapists; physical therapists; pharmacists; occupational therapists; laboratory staff; dietitians; medical technologists; administrative staff; and social workers. They found that higher levels of staff employed per 100 adjusted admissions were significantly associated with lower mortality rates from chronic obstructive pulmonary disease (COPD) for respiratory therapists (odds ratio from β coefficient = 0.53), respiratory therapy technicians (odds ratio from β coefficient = 0.22), and laboratory staff (odds ratio from β coefficient = 0.68). Associations for other staff groups were not statistically significant [28].

Bond et al. analysed 1992 data from 3763 US hospitals and included (in addition to nurses, nursing assistants and physicians, and physician assistants) respiratory therapists; physical therapists; respiratory therapy technicians; radiographers and radiologic technologists; pharmacists; occupational therapists; dietitians; radiation therapists; nuclear medicine technologists; medical technologists; administrative staff; and social workers. Of these, they found statistically significant associations between more pharmacists per bed (OR from β coefficient = 0.97) and medical technologist staff per bed (β coefficient = 0.99) and lower mortality rates, while hospitals with more administrative staff per bed had higher hospital mortality (β coefficient = 0.006). Associations for other staff groups were not statistically significant [22].


This is the first literature review to synthesise evidence of associations between patient mortality and multi-disciplinary hospital staffing. Having more physicians and registered nurses was associated with lower mortality, and higher levels of nursing assistants were associated with higher patient mortality. Only two studies reported associations with other staffing groups, finding statistically significant associations between higher pharmacists and medical technologists staffing and lower mortality in one study and higher laboratory staff, respiratory therapists and respiratory therapy technicians and lower mortality from COPD in another. While data in these studies are drawn from thousands of hospitals, the data are now over 30 years old, and the roles and responsibilities of staff groups are likely to have changed substantially since then, so the extent to which these findings generalise to current contexts is questionable.

For all staff groups, beneficial effects for patients potentially extend far beyond reducing the risk of death. Occupational groups such as physiotherapists, nutritionists, and occupational therapists play an important role in hospitals in providing early mobilisation and/or adequate nutrition, and improving functional ability and activities of daily living [32, 33] although the limited evidence hampers any conclusion.

The finding that physician staffing levels were associated with patient risk of death is not surprising, as physicians, are, in general, the main decision-makers when it comes to patients’ care pathways and treatments, and the relationship we found is plausibly causal. Nurse staffing levels and physician staffing levels tend to be strongly correlated [34] and so it is possible that associations between nurse staffing and mortality in studies that omit physician staffing are partly attributable to medical staffing levels. Nonetheless, nurse staffing levels were associated with mortality after controlling for physicians in most studies and so the possibility that there is no independent nurse staffing effect can be discounted. The finding that having higher levels of nursing assistants was associated with higher mortality in most studies mirrors that of studies focusing on nursing only [35]. The reasons for an adverse effect from additional nursing support staff are complex, but suggested mechanisms include excessive substitution of assistants for registered nurses and insufficient registered nurses to properly supervise assistants [36].

Most studies used data from large patient samples from multiple hospitals across several years, but analyses were often cross-sectional, and associations measured at the hospital level, whereby staffing over one year was averaged and related to the average mortality rate for that same year. This level of aggregation and analysis means that estimates could still be biased by endogeneity, in particular the simultaneity bias [37] whereby hospitals with more acutely ill patients, who also have higher mortality risk, may have higher staffing levels to meet patient demand. Although risk adjustment makes this an unlikely explanation of results, estimates of effect could still be attenuated. Aggregating staffing levels in the form of bed-to-staff employed or employed staff-to-bed at the hospital level also masks considerable variation between units and from day to day, which again would tend to attenuate estimated adverse effects from staffing variation.

In recent years, the evidence base around nurse staffing levels has advanced substantially thanks to longitudinal studies analysing routinely collected data, which allow exploration of associations at the ward level or even at the patient level [6]. Nonetheless, the availability and quality of such data for other staff groups is currently unknown. Future studies using data extracted from nursing rosters should simultaneously explore the availability of roster data of other staff groups. Such studies have the potential to enhance the quality of the evidence base to guide policy-makers and those in charge of planning the health workforce nationally and locally.


We produced an extensive search strategy, but it is possible that we did not capture all studies due to the complexity of the topic and the vast number of existing healthcare professional figures. Nonetheless, it is unlikely that we would have missed a sufficient number of recent studies to change our conclusions.


The association between higher nurse staffing levels and reduced mortality stands also when controlling for other staff groups, highlighting that the research and policy endeavour around nurse staffing is justified and necessary. Nonetheless, physicians’ staffing levels are also associated with patients’ risk of death, although the evidence is sparse and, while professional bodies globally produced standards and guidelines, no policy directly addresses how to appropriately staff services with physicians. The picture for other staff groups becomes even blurrier, as the evidence for other staffing groups is both scant and unclear, although the pathways for such staffing groups to impact patient outcomes are plausible and should be further explored in future studies, possibly including other outcomes in addition to mortality. The role of occupational groups such as physiotherapists, occupational therapists, dietitians, pharmacists, and other clinical staff should not be discounted based on absence of evidence of an effect on patient mortality. Future research and policy should strive to address this gap to ensure safe staffing is achieved for all professional groups in hospital.

Availability of data and materials

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This study/project is funded by the NIHR [Health and Social Care Delivery Research (NIHR128056)] and the NIHR Applied Research Collaboration Wessex. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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CDO contributed to data extraction and risk of bias assessment, and drafted the manuscript; BR wrote the review protocol, conducted the searches, extracted data; CS contributed to data extraction and risk of bias assessment; LT contributed to data extraction and risk of bias assessment; JB contributed to data extraction and risk of bias assessment; CB contributed to data extraction and risk of bias assessment; PG conceptualised the review. All authors read and approved the final manuscript.

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Correspondence to Chiara Dall’Ora.

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Supplementary Information

Additional file 1: File S1.

Search terms. File S2. Risk of bias assessment checklist, adapted from Griffiths et al. [18].

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Dall’Ora, C., Rubbo, B., Saville, C. et al. The association between multi-disciplinary staffing levels and mortality in acute hospitals: a systematic review. Hum Resour Health 21, 30 (2023).

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