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Effects of multidisciplinary teamwork in non-hospital settings on healthcare and patients with chronic conditions: a systematic review and meta-analysis

BMC Primary Care volume 26, Article number: 110 (2025) Cite this article

Abstract

Background

There is evidence that multidisciplinary teams can improve health outcomes for patients with chronic conditions, enhance the quality and coordination of care, and promote teamwork among staff in hospital settings. However, their effectiveness in non-hospital settings remains unclear. Therefore, we conducted a systematic review and meta-analysis to assess the effects of multidisciplinary teams on patients with chronic conditions, health professionals, and healthcare in non-hospital settings.

Methods

We searched PubMed, Web of Science, Embase, EconLit, OpenGrey, China National Knowledge Infrastructure (CNKI), and WanFang for randomised controlled trials published before March 2025. Narrative syntheses were used to synthesise the characteristics of multidisciplinary teams, interventions, and effects. Data were statistically pooled using both random-effects and fixed-effects meta-analyses to synthesize the outcomes. The methodological quality of the included studies was assessed using Cochrane’s risk of bias tool.

Results

Thirty-nine studies were analyzed, with a total of 8186 participants. Nurses, general practitioners, and specialists were the most common members of the multidisciplinary teams. Staffing models, shared care and role expansion or task shifting are the most common multidisciplinary teamwork interventions. Narrative syntheses revealed improvements in self-management, self-efficiency, satisfaction, health behaviours, and knowledge. A meta-analysis found a significant reduction in hospitalisation days for patients with chronic obstructive pulmonary disease (MD=-0.66, 95% CI -1.05 to -0.26, I2 = 0%) and significant improvement in quality of life for patients with chronic heart failure (MD=-4.63, 95% CI: -8.67 to -0.60, I2 = 0%). There is no consistent evidence of other indicators of this effect.

Conclusions

Multidisciplinary teamwork can improve patient-reported outcomes for patients with chronic conditions in non-hospital settings, but the effects on clinical outcomes, health utilisation, and costs are not evident.

Trial registration

The study protocol was registered with PROSPERO on January 21, 2019, with the registration number CRD42019121109.

Peer Review reports

Introduction

The epidemic of chronic conditions and multimorbidity is a challenging health problem worldwide, as it poses a significant burden for individuals, families, and communities. According to World Health Statistics 2023, four major non-communicable diseases (cardiovascular disease, cancer, chronic respiratory disease, and diabetes) collectively led to the deaths of 33.3 million people in 2019, an increase of 28% compared with 2000 [1]. The overall prevalence of multimorbidity among adults was 37.2% globally [2]. Additionally, the prevalence of other chronic conditions also increased dramatically, such as mental disorders and persistent communicable diseases. Patients with chronic conditions, especially those with multimorbidities, have a higher demand for continuous and coordinated healthcare from different health professionals [3].

Inter-professional collaboration among health professionals is advocated internationally as a means of addressing the increasingly complex health needs of patients with chronic conditions [4]. Multidisciplinary teamwork, a model of inter-professional collaboration, is a dynamic process involving two or more healthcare professionals with complementary backgrounds and skills exercising efforts to provide care for patients [5]. Multidisciplinary teamwork, especially teamwork with health professionals in communities, has been implemented in many countries, such as the Inter-professional Team in the Netherlands, the Integrated Primary Care Team and the Family Health Team in Canada [6]. Interdisciplinary collaboration among healthcare professionals in a multidisciplinary team strengthens actions to prevent, diagnose, treat, and manage patients with chronic conditions and promote their health and well-being in a timely and continuous manner [7]. Theoretically, multidisciplinary teams can provide a more comprehensive understanding of patient needs. Based on this understanding, they can develop more comprehensive and integrated intervention strategies, provide better patient management, and, therefore, improve chronic disease diagnosis and management performance.

The growing practice of multidisciplinary teamwork has drawn attention to its effects on the healthcare and outcomes of patients with chronic conditions. There is a lack of conclusive evidence on the effects of multidisciplinary teamwork on the quality of care, healthcare utilisation and costs, and patient satisfaction in chronic patients in general. Comprehensive geriatric assessment by multidisciplinary teams for community-dwelled older adults increased the chance of living at home, reduced mortality, and improved cognitive function [8]. Community-based, Flexible, Assertive Treatment by multidisciplinary teams could reduce risks of cardiovascular disease [9], and had a positive impact on improving the physical activity level for patients with severe mental disorders [10]. In addition, there is some evidence of multidisciplinary teamwork on better health outcomes for patients with a single chronic condition, such as chronic lower back pain, hypertension, or diabetes or specific population groups, such as older adults [11,12,13,14]. However, there was also some evidence that multidisciplinary teamwork had no significant effect on the quality of life and survival of patients with diabetic foot or led to a longer diagnosis-to-treatment interval in cancer patients [15, 16].

Regarding the settings of multidisciplinary teamwork, current evidence from systematic reviews has shown that multidisciplinary teamwork in hospitals always improves the quality and coordination of care, shortens hospitalisation days, and improves job satisfaction and teamwork for healthcare staff [17, 18]. However, evidence about the effect of multidisciplinary teamwork in non-hospital settings (primary care, community, or family settings) is vague. Pooled quantified effects of multidisciplinary teamwork conducted in non-hospital settings were limited because of the substantial heterogeneity between studies [19]. Additionally, as the existing studies focus on developed countries, there is extremely limited evidence for policymakers in low-and middle-income countries [20]. Therefore, existing evidence is inconclusive to inform decision-makers in all countries of the potential effects of moving towards multidisciplinary teamwork on chronic patients and health care in non-hospital settings.

This systematic review aimed to provide evidence on the effects of multidisciplinary teamwork on healthcare and the outcomes of patients with chronic conditions in non-hospital settings. The current study can contribute to providing up-to-date evidence for the effects of multidisciplinary teams on patients with chronic diseases and effective interventions provided by teams in developed and developing countries, especially in community and family settings.

Methods

We conducted a systematic review with narrative synthesis and meta-analysis, qualitatively extracting the characteristics and effects of interventions in multidisciplinary teams and quantitatively assessing the effects of these interventions on patients, health professionals, and healthcare. Owing to the heterogeneity in the interventions, a subgroup meta-analysis was conducted according to chronic conditions. Our review was registered with PROSPERO (CRD42019121109). It was reported accordingly with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline [21].

Search strategy and inclusion criteria

Data source and search strategy

We searched PubMed, Embase, Web of Science, EconLit, OpenGrey, CNKI and WanFang for studies published before March 2025. Additionally, we manually searched the reference lists of all included studies and review articles for additional references. The terms multidisciplinary team, chronic conditions, and effects were combined using the same search strategy for each database. The complete search strategies for all databases are provided in Supplementary Table S1 to S7.

Inclusion criteria and exclusion criteria

Studies were included in the review if they met all the following criteria: (a) the patients were diagnosed with one or more chronic conditions, (b) the intervention was provided by multidisciplinary teams comprising members from multiple disciplines, (c) the study settings were not in hospitals, (d) the study design was randomised controlled trials (RCTs), and (e) the study measured the effects from the perspectives of patients or healthcare providers.

Studies were excluded if they met any of the following criteria: (a) the study was published in a language other than English or Chinese, (b) the full text was not available, (c) the study was a sub-study derived from a larger study with identical inclusion criteria and intervention measures, (d) healthcare for both the intervention group and the control group was provided by a multidisciplinary team.

To be included in the meta-analysis, studies must simultaneously meet the following three criteria: (a) focus on the same disease, (b) evaluate the same outcome measures, and (c) have available data.

Screening and extraction of data

Screening of studies

Two researchers (YS and HL) independently scanned the titles and abstracts of all identified studies in EndNote X9 software. They excluded those that were not relevant to the effectiveness of multidisciplinary teams for chronic conditions in non-hospital settings. The full text of all remaining studies was assessed for eligibility. If there was a disagreement, a third researcher (XW) was consulted. The identified studies were screened between July and September 2023, and updated in March 2025. The screening process and results are reported in Fig. 1. A consensus was reached among the three researchers of the included studies.

Fig. 1
figure 1

Flow-chart of study selection according to PRISMA

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Data extraction

Two researchers extracted the data from the included studies in a dependent manner in a predetermined standardised spreadsheet form. The spreadsheet mainly included three parts: (a) characteristics of the studies, location, publication year, target population/patients (b) characteristics of the multidisciplinary teams and interventions conducted in the teams, and (c) effects of interventions, such as patients’ clinical outcomes, health utilisation, and costs.

Quality assessment

Two researchers (HL and XW) independently assessed the risk of bias in the included studies using the quality criteria for randomised controlled trials (Cochrane Effective Practice and Organization of Care Review Group) [22]. The criteria included concealment of allocation, follow-up of professionals, follow-up of patients or episodes of care, blinded assessment of primary outcomes, baseline measurements, reliable primary outcome measures, and protection against contamination. Disagreements regarding quality ratings were resolved through discussions. No studies were excluded because of the risk of bias.

Data synthesis and analysis

Narrative syntheses were used to synthesise the characteristics, interventions, and effects of the multidisciplinary teams. The characteristics of multidisciplinary teams reflect the composition of the teams’ disciplines. Two researchers (YS and XW) categorised the interventions in multidisciplinary teams of included studies using the Cochrane Effective Practice and Organization of Care (EPOC) taxonomy of health system interventions [23]. The EPOC taxonomy covers four topics, and interventions in included studies fit into the category of Delivery Arrangement, especially the subcategory Coordination of Care and Management of Care Processes. The effects of the intervention were categorised into health outcomes, utilisation of health services, and costs.

If two or more studies targeted the same chronic conditions and reported the same outcomes with extractable data, a meta-analysis was conducted to quantitatively assess the effect. We recorded the mean difference (MD) or standardised mean difference (SMD) with a 95% CI for continuous outcomes. We used Ch2 and I2 to assess the statistical heterogeneity. If the heterogeneity exceeded 50%, calculations were performed using a random-effects model in the RevMan software, whereas the fixed-effects model was utilised if it was below 50%. The results are reported as pooled effects and forest plots. If the data were allowed, a funnel plot was used to show publication bias.

Role of the funding source

The funding source had no role in the design, execution, analyses, interpretation of the data, or decision to submit results.

Results

Search results

A total of 4208 records were pooled, of which 775 (18.42%) were duplicates. The titles and abstracts of 3433 (81.58%) non-duplicate records were screened, of which 3172 (75.38%) were excluded, and 261 (6.20%) remained. Of the 261 potentially eligible records, 39 (0.93%), with a total of 8186 participants, were included in our study. Figure 1 shows the search and selection of studies based on PRISMA flow-charts.

Study characteristics

Table 1 presents the characteristics of the included 39 RCT studies. More than 70% of the studies were published between 2004 and 2020 (n = 30, 76.92%). More than two-third of the studies were conducted in Asia and North America (n = 28, 71.79%). Regarding settings of practice, most studies were conducted in community settings (n = 24, 63.10%), followed by the clinic settings (n = 8, 20.51%). The mean age of the participants in 30 studies (76.92%) was over 60 years. 27 of the 39 studies targeted patients with one chronic disease, including coronary heart disease (n = 5, 12.82%), chronic heart failure and chronic kidney disease (n = 4, 10.26%). Eight studies reported comorbidity of patients.

Table 1 Description of included studies
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Risk of bias assessment

The quality assessment of the included studies is shown in Fig. 2. None of the studies met all of the quality criteria for low risk. All studies were rated as having high risk or unclear risk in at least one domain. We judged 27 studies to have unclear risk of bias [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50]. Twelve studies had a high risk of bias, and the most common biases were attrition biases(n = 9, 23.08%), contamination (n = 2, 5.13%) and selection biases (n = 1, 2.56%) [51,52,53,54,55,56,57,58,59,60,61,62].

Fig. 2
figure 2

Risk of bias assessment

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Characteristics of multidisciplinary teams and interventions

As shown in Table 2, there were two to eight healthcare professionals among the multidisciplinary teams. Nurses were the most common member (n = 33, 84.62%), followed by general practitioners (n = 27, 69.23%), specialists (n = 25, 64.10%), and therapists (n = 15, 38.46%). Therapists are health professionals, such as physiotherapists, occupational therapists, speech therapists, and related specialists who deliver diagnostic, therapeutic and rehabilitation care. Equipped with advanced expertise in specialized clinical domains, they collaborate within interdisciplinary healthcare teams to optimize patient outcomes. Sixteen studies reported special roles, such as care manager or coordinator, in the multidisciplinary teams. The coordinators were mainly nurses (n = 7, 43.75%). According to EPOC taxonomy of interventions, interventions in the studies mainly focused on the category of Delivery Arrangements (as shown in Table 2). All studies included teams and coordination of care amongst different providers. Additionally, staffing models (n = 32, 82.05%) were the most common intervention of the studies, followed by share care (n = 26, 66.67%), role expansion or task shifting (n = 18, 46.15%), outreach services (n = 17, 43.59%), and self-management (n = 17, 43.59%). The duration of interventions ranged from 7 days to 24 months, and the follow-up lasted for 3 months to 60 months.

Table 2 Characteristics of multidisciplinary teams and interventions
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Effects of interventions in multidisciplinary teams

As shown in Table 3, although targeted at different diseases, the included studies reported the following three outcomes: (a) health outcomes (n = 37, 94.87%), (b) utilization of health services (n = 17, 43.59%), and (c) costs (n = 8, 20.51%).

Table 3 Characteristics of outcomes
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  1. (a)

    Health outcomes

Thirty-seven studies reported health outcomes. We then divided the health outcomes into patient clinical or mental health outcomes and patient-reported outcome measure categories based on whether the outcomes were self-reported.

  1. a)

    Patient clinical or mental health outcomes

Twenty-one studies reported clinical outcomes, including mortality, adverse event rate, glycaemic status, blood pressure, triglycerides, serum creatinine (SCr), estimated glomerular filtration rate (eGFR), and depression scores. Six studies [25,26,27, 31, 43, 60] reported mortality rates. Two studies [26, 27] reported that patient mortality was reduced in the intervention group, whereas four studies [25, 31, 43, 60] reported no significant intergroup differences. Two studies [29, 35] reported adverse event rates, and only one study [35] found the incidence in the intervention group was lower than that in the control group.

Nine studies [29, 32, 40, 41, 44, 45, 51, 56, 59] reported glycaemic. Six studies [29, 32, 41, 44, 51, 59] found that the HbA1c reduction in the intervention group was greater than that in the control group, and three studies [40, 45, 56] showed no significant differences between the two groups. Three studies [32, 41, 44] reported fasting blood glucose levels. Two studies [32, 41] showed a bigger fasting blood glucose reduction in the intervention group than in the control group and one study [44] showed no significant difference between the two groups. Eight studies [24, 29, 40, 41, 44, 45, 56, 59] reported blood pressure. Three studies [29, 40, 41] found that compared to the control group, patients in the intervention group showed greater improvement in blood pressure levels. Five studies [24, 44, 45, 56, 59] found there were no statistically significant intergroup differences between the two groups. Four studies [41, 44, 51, 59] reported triglycerides and three studies [44, 51, 59] showed no significant differences between the groups.

For chronic kidney disease, the meta-analysis of two studies [31, 41] showed no significant differences of SCr (MD=-55.59, 95% CI -143.02 to 31.84, P = 0.21, I2 = 92%) and eGFR (MD = 9.15, 95% CI -6.53 to 24.83, P = 0.25, I2 = 98%) between the two groups (Fig. 3).

Fig. 3
figure 3

Forest plots of the outcome indicators

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Eight studies [25, 26, 29, 51,52,53, 63, 64] assessed depression scores, of which six studies [25, 26, 29, 52, 63, 64] reported a greater decrease in the intervention group than in the control group. Two studies [51, 53] showed no significant differences between the two groups.

  1. b)

    Patient-reported outcome measures

Thirty studies reported patient-reported outcomes, including self-management scores, self-efficacy scores, quality of life, satisfaction, symptom scores, health behaviours, and knowledge. Eight studies [26, 28, 36, 38, 40, 48, 50, 54] reported self-management scores and nine studies [28, 34,35,36, 38, 42, 50, 53, 54] reported self-efficacy scores. And only one study [53, 54] indicated no statistically significant difference in self-management and self-efficacy between the two groups. The meta-analysis of three studies [35, 36, 43] of patients with coronary heart disease detected no significant differences between the two groups in self-efficacy scores (SMD = 1.20, 95% CI -0.14 to 2.54, P = 0.08, I2 = 97%), indicating a high degree of heterogeneity (Fig. 3).

Quality of life was reported in 17 studies, although different scales were used for measurement. Eleven studies [26, 28, 29, 35, 37,38,39, 46, 47, 50, 62] reported that patients in the intervention group reported greater improvements in their quality of life than patients in the control group. Six studies [33, 52,53,54, 56, 60] found that the quality of life did not differ between the two groups. A meta-analysis of the two studies [37, 46] for patients with chronic heart failure showed statistically significant differences in quality of life measured by the MLHFQ scale between the two groups (MD=-4.63, 95% CI -8.67 to -0.60, P = 0.02, I2 = 0%), indicating no heterogeneity (Fig. 3).

All five studies [29, 32, 39, 46, 59] that reported satisfaction with care consistently showed that patients in the intervention group were more satisfied with care than those in the control group. Symptom scores were reported in seven studies [25, 30, 33, 47, 61,62,63]. Five studies [30, 47, 61–63] reported a greater relief of symptoms in the intervention group than in the control group, whereas the other two studies [25, 33] found no differences between the two groups.

Five studies reported effects on health behaviours and knowledge. Three studies [41, 42, 54] reported improvements in health behaviours after the intervention, including tobacco smoking, physical activity, and healthy eating habits. Two studies [35, 41] reported that scores on the coronary heart disease and hypertension knowledge and cognition questionnaire were higher in the intervention group than in the control group.

  1. (b)

    Utilisation of health services

Seventeen studies reported the effects on health service utilisation, including the appointment rate of outpatients, visits, hospitalisation days, hospital admission and re-admission, and medications. One study [32] reported that the appointment rate for outpatient services in the intervention group was higher than that in the control group. Five studies [26, 28, 33, 53, 56] reported visits. Three of them [26, 28, 33] showed the average number of physician visits per patient was lower in the intervention group. Six studies [25, 26, 37, 43, 53, 56] reported hospitalisation rate, one of them [37] found that the hospitalisation rate in the intervention group was lower than that in the control group. Five studies [27, 28, 31, 57, 60] reported re-admission rates, three of them [27, 28, 31] showed that patients in the intervention group had fewer re-admissions. There were no significant differences in the rate of medication use between the two studies [24, 37].

Hospitalisation days were reported in five studies [27, 34, 43, 46, 57]. three studies [27, 34, 43] reported shorter lengths of stay in the intervention group. A meta-analysis of patients with chronic obstructive pulmonary disease showed statistically significant differences between two groups in two studies [26, 34] (MD=-0.66, 95% CI -1.05 to -0.26, P = 0.001, I2 = 0%), with no heterogeneity (Fig. 3).

  1. (c)

    Costs

Eight studies reported on costs, including medical costs, program costs, unplanned hospitalisation costs, total hospital costs, home care and outpatient service costs, and cost-effectiveness. Two studies [55, 58] reported program cost and cost-effectiveness analyses. One study [58] indicated that patients in the intervention group had lower total medical costs, with no reduction in their quality of life. One study [55] found that interventions might be more cost-effective when the program targets populations with poor care levels. Three studies [31, 34, 59] suggested that medical costs, unplanned hospitalisation costs, and total hospital costs in the intervention group were lower than those in the control group. One study [53] reported that home care and outpatient service costs were higher in the intervention group than in the control group because intervention costs were included in these costs.

Discussion

To the best of our knowledge, this is the first systematic review to describe and evaluate multidisciplinary teamwork in non-hospital settings. We documented key characteristics of multidisciplinary teams and interventions in the literature, synthesised the effects of interventions, and quantified the effects of interventions on patients with specific conditions.

Main findings and comparison with other studies

It shows that multidisciplinary teamwork is effective in improving most patient-reported outcomes (satisfaction, health behaviours, and knowledge). However, there is inconsistent evidence regarding the effects of improving clinical outcomes and reducing the utilisation of health services and costs.

Thirty-nine studies suggest that multidisciplinary teamwork is not only advocated but also implemented worldwide as a strategy to improve the management of chronic conditions. The consistent focus on community settings in the included studies provides empirical justification for prioritizing primary care systems for patients with chronic conditions. This finding resonates with Hickman’s review, which positions community-based care as critical for post-hospitalization populations. These studies, targeting chronic conditions and comorbidities, reflect the empirical consensus on the necessity for integrated care and inter-professional collaboration.

Variations in multidisciplinary team members resulted from the health demands of patients with different chronic conditions rather than differences among countries or regions. We found that one-third of the included studies reported key roles in teams, such as team leader, care coordinator, or manager, most of which were assigned to trained nurses. In the included studies, 85% of multidisciplinary teams had nurses who played a key role in the teams. In primary healthcare settings, nurses are more available and flexible than specialists for chronic conditions. They play an important role in team coordination and the management of chronic conditions. This is consistent with the findings in palliative care and residential long-term care by Dorja [65] and Kathrin [66], who found that nurses play a central role in care coordination.

Interventions in the teams focus on Delivery Arrangement in the EPOC taxonomy. Among the five subcategories of delivery arrangements, who provide care and how the healthcare workforce is managed are the most reported subcategories, rather than the Coordination of Care and Management of Care Process. In addition to teams and care coordination among different providers, role expansion and staffing models are among the most commonly reported interventions. This is also supported by Dubois and Singh [67], who found that the staff mix is a key element of integrated workforce management. Strategically ‘ensuring the right people, with the right skills, are in the right place at the right time’ enables optimal outcomes for patients, healthcare professionals and health systems (primarily costs and utilisation of health services). Notably, over half of the multidisciplinary teams promoted responsibility for healthcare or disease management to patients, respected patients’ autonomy, and maximally responded to each patient’s unique priorities. It is estimated that individuals and family carers perform more than 99% of daily care for chronic conditions [68]. Whereas, in our study, only two of the 39 studies involved informal caregivers in a multidisciplinary team. Over half of the multidisciplinary teams outreach the services of different providers in community primary care institutions. Outreach not only facilitates health services and improves patients’ experiences of people-centred care but also decreases travel costs and medical expenses.

This review showed that a multidisciplinary team is effective in improving most patient-reported outcomes. Compared with clinical outcomes, patient-reported outcomes are more important in demonstrating the impact of interventions on the outcomes most meaningful to patients and in informing policymaking from the perspective of patients and their families [69]. Moreover, patient-reported outcome measures were viewed as enablers of people-centred health care [70]. People-centeredness is one of the key aims of integrated multidisciplinary care. Therefore, it is reasonable to conclude that a multidisciplinary team is effective in improving most patient-reported outcomes. In the context of the primary healthcare delivery system, the sustained service and attention of a multidisciplinary team will enable chronic patients to improve their self-efficacy, quality of life, satisfaction, and health evaluation. From the perspective of the modern medical model, multidisciplinary teams are useful for enhancing overall health. However, evidence of its effects on clinical outcomes, utilisation of health services, and costs remains mixed. This is consistent with the findings of studies targeting some chronic conditions [71]. Hickman’s review demonstrated that multidisciplinary team interventions could reduce re-admission rates, mortality, and function decline in older patients [72]. Different healthcare settings and times of intervention partially resulted in different findings. Hickman’s review targeted acute care settings; the multidisciplinary team members were from different hospital units, and participants were inpatients with high disease severity facing a high risk of mortality and re-admission. Most teams in our review provided care in community settings for patients in a stable stage of chronic conditions with a low risk of mortality during follow-up. Therefore, the teamwork of multidisciplinary teams in acute and non-acute care settings is quite different, such as the effects on clinical outcomes and utilisation of care in the short term.

Implication for further research and practice

The following four implications are provided for further research: First, the low rate of reporting of care coordinators or managers in the included studies reinforces the need for playing the role of qualified care coordinators in teams. As explained by Glouberman and Mintzberg [73], the health workforce delivers health care in different professional “worlds” in terms of setting (e.g. acute or chronic care) or service focus (e.g. cure or care). Qualified care coordinators have been proven to enhance multisectoral communication and promote professionalism with clear roles and tasks [65, 74]. Second, self-management interventions are core components of high-quality chronic disease care. The perspectives and behaviours of informal caregivers are as crucial as those of patients. Incorporating informal caregivers into multidisciplinary care teams is a promising approach to enhancing the appropriate perspectives and behaviours of informal caregivers [75]. Third, once the appropriate staff mix and skill management strategies are identified and the group of providers with a certain skill is assembled, we still need to organise the way in which the group cooperates and delivers care in practice. In addition to the multidisciplinary staff, multidisciplinary protocols, pathways, and team meetings are two distinct aspects of multidisciplinary teamwork. Therefore, it is necessary to develop multidisciplinary protocols for teams. Fourth, the effects of a multidisciplinary team may require a longer detection period, especially regarding clinical outcomes and health service utilisation of patients with chronic conditions. Regarding effect measurement, intermediate- and long-term outcomes are both suggested at different periods of intervention implementation.

Limitations

However, this study has three limitations. First, there were some methodological deficiencies in the meta-analysis. The heterogeneity of the interventions and the relatively small number of studies and participants may have led to insufficient power to detect intervention effects. Second, we categorised the interventions based on the EPOC taxonomy and could not exclude the possibility of missing other interventions. Moreover, we were unable to estimate the specific effect size of the various EPOC interventions because each multidisciplinary team implemented several interventions. Third, there may be potential selection bias. Due to language limitations, our literature search was restricted to Chinese and English databases, excluding those in other languages. This may result in the overlook of evidence in other languages.

Conclusion

Multidisciplinary teamwork can improve patient-reported outcomes such as satisfaction, health behaviour, and knowledge for patients with chronic conditions in community, clinic, and home settings. However, the effects on clinical outcomes, health utilisation, and costs are not evident in a relatively short period.

Data availability

The authors confrm that the data supporting the findings of this study areavailable within the article. The datasets analysed are available from the corresponding author, upon reasonable request.

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Acknowledgements

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Funding

This study was supported by the Shandong Provincial Natural Science Foundation (ZR2021QG051).

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Authors and Affiliations

  1. School of Public Health, Sun Yat-Sen University, No.74, the 2nd Zhongshan Road, Guangzhou, Guangdong Province, 510080, China

    Yanli Shi & Xin Wang

  2. School of Public Health, Jining Medical University, Jining, China

    Hongmin Li

  3. China Center for Health Development Studies Peking University, Beijing, China

    Beibei Yuan

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  1. Yanli Shi
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  2. Hongmin Li
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Contributions

X.W. and B.B.Y. conceived the idea of the study; Y.L.S., H.M.L. & X.W. screened the studies, extracted the data and quality assessment; Y.L.S carried out the statistical analysis; X.W. supervised the analysis. Y.L.S., H.M.L. & X.W. interpreted the findings and drafted the manuscript. Y.L.S., H.M.L., X.W. & B.B.Y. edited and revised the manuscript. All authors critically reviewed and revised the manuscript for final submission. Y.L.S., H.M.L., X.W. & B.B.Y. have verified the underlying data. All authors reviewed and approved the final the manuscript.

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Correspondence to Xin Wang.

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Shi, Y., Li, H., Yuan, B. et al. Effects of multidisciplinary teamwork in non-hospital settings on healthcare and patients with chronic conditions: a systematic review and meta-analysis. BMC Prim. Care 26, 110 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12875-025-02814-0

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BMC Primary Care

ISSN: 2731-4553

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