Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (735 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Ratio of output to effort and or resources, or the ratio of effort and or resources produced to energy expended.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Productivity.
Where it fits in the MeSH classification
- Broader topics
- Psychology, Industrial; Organization and Administration
- Narrower topics
- Efficiency, Organizational
Research landscape
PubMed indexes 39,436 records tagged with the MeSH heading “Efficiency” (counts retrieved 2026-10-31). 4,276 of them (10.8%) were published from 2020 onward, and the busiest period so far is 2000-2009 with 11,864 records.
Reviews account for 2,059 records (5.2%) and meta-analyses for 84. Randomized controlled trials number 456 (1.2%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 11,592 |
| 2000-2009 | 11,864 |
| 2010-2019 | 11,704 |
| 2020 onward | 4,276 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 2,059 | 5.2% |
| Meta-analyses | 84 | 0.2% |
| Randomized controlled trials | 456 | 1.2% |
Method: PubMed query "Efficiency"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in psychiatry and psychology
Research on mental health and behaviour relies on questionnaires, interviews and rating scales rather than physical measurements, so how a condition was defined and measured shapes every result. Many studies are observational or small, placebo responses can be large, and findings in one group or culture may not carry over to another. Nothing on this page is advice about diagnosis or treatment; anyone worried about their mental health should talk to a qualified clinician. Check how outcomes were measured, who took part and how long they were followed.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Impact of Long COVID on productivity and informal caregiving | 2024 | Research article Design not recorded in structured metadata | 46 | cc by PMID 38146040 · DOI · PMC11377524 |
| Artificial Intelligence in Operating Room Management | 2024 | Systematic review Design not recorded in structured metadata | 45 | cc by PMID 38353755 · DOI · PMC10867065 |
| Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency | 2025 | Research article Design not recorded in structured metadata | 44 | cc by PMID 39820845 · DOI · PMC11739231 |
| Workplace productivity losses due to multimorbidity: findings from an Australian longitudinal population survey, 2009-21 | 2026 | Research article Design not recorded in structured metadata | 2 | cc by PMID 41071372 · DOI · PMC13017662 |
Impact of Long COVID on productivity and informal caregiving [1]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundAround 2 million people in the UK suffer from Long COVID (LC).
- Closing excerpt
- ConclusionLC significantly impacts productivity losses and provision of informal care, exacerbated by high national prevalence of LC.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (243 words)
BackgroundAround 2 million people in the UK suffer from Long COVID (LC). Of concern is the disease impact on productivity and informal care burden. This study aimed to quantify and value productivity losses and informal care receipt in a sample of LC patients in the UK.MethodsThe target population comprised LC patients referred to LC specialist clinics. The questionnaires included a health economics questionnaire (HEQ) measuring productivity impacts, informal care receipt and service utilisation, EQ-5D-5L, C19-YRS LC condition-specific measure, and sociodemographic and COVID-19 history variables. Outcomes were changes from the incident infection resulting in LC to the month preceding the survey in paid work status/h, work income, work performance and informal care receipt. The human capital approach valued productivity losses; the proxy goods method valued caregiving hours. The values were extrapolated nationally using published prevalence data. Multilevel regressions, nested by region, estimated associations between the outcomes and patient characteristics.Results366 patients responded to HEQ (mean LC duration 449.9 days). 51.7% reduced paid work hours relative to the pre-infection period. Mean monthly work income declined by 24.5%. The average aggregate value of productivity loss since incident infection was £10,929 (95% bootstrap confidence interval £8,844-£13,014) and £5.7 billion (£3.8-£7.6 billion) extrapolated nationally. The corresponding values for informal caregiving were £8,726 (£6,247-£11,204) and £4.8 billion (£2.6-£7.0 billion). Multivariate analyses found significant associations between each outcome and health utility and C19-YRS subscale scores.ConclusionLC significantly impacts productivity losses and provision of informal care, exacerbated by high national prevalence of LC.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 38146040 · DOI · PMC11377524
Artificial Intelligence in Operating Room Management [2]
Abstract reading summary — source excerpts
- Opening excerpt
- This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms.
- Closing excerpt
- Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (137 words)
This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 38353755 · DOI · PMC10867065
Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research.
- Closing excerpt
- Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (175 words)
Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 39820845 · DOI · PMC11739231
Workplace productivity losses due to multimorbidity: findings from an Australian longitudinal population survey, 2009-21 [4]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundWhile productivity loss has been studied in various populations, the impact of multimorbidity on workplace productivity at a population level remains understudied.
- Closing excerpt
- ConclusionOur results underscore the need for evidence-based workplace policies to support the productivity and well-being of employees living with multimorbidity.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (180 words)
BackgroundWhile productivity loss has been studied in various populations, the impact of multimorbidity on workplace productivity at a population level remains understudied. This study estimates the productivity losses attributable to multimorbidity.MethodUsing data from four waves of the Household, Income and Labour Dynamics in Australia (HILDA) survey, we investigated the relationship between multimorbidity and productivity loss. Negative binomial and logistic regression models were employed to analyze absenteeism, presenteeism, and working hour tension as measures of productivity loss.ResultsWe found a significant association between multimorbidity and increased absenteeism, presenteeism and working hour tension. After controlling for socio-economic, demographic, health, and workplace-related factors, individuals with multimorbidity had a 1.07-fold higher rate of absenteeism (incidence rate ratios: 1.07; 95% CI: 1.02-1.13) compared to those without serious illness. Their odds of experiencing presenteeism were three times higher, and the incidence of working hour tension was 32% higher. On average, the annual cost of absenteeism was AU$265.20 higher for individuals with multimorbidity than for those without serious illness.ConclusionOur results underscore the need for evidence-based workplace policies to support the productivity and well-being of employees living with multimorbidity.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41071372 · DOI · PMC13017662
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article How to improve R&D productivity: the pharmaceutical industry's grand challenge [5]
- Research article Diagnosing the decline in pharmaceutical R&D efficiency [6]
- Research article Microbiota of the chicken gastrointestinal tract: influence on health, productivity and disease [7]
- Research article How does burnout affect physician productivity? A systematic literature review [8]
- Research article Workers' health and productivity under occupational heat strain: a systematic review and meta-analysis [9]
- Research article Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: a meta-analysis [10]
References
- Kwon J, Milne R, Rayner C, Rocha Lawrence R, Mullard J, Mir G, et al.. Impact of Long COVID on productivity and informal caregiving. . 2024. PMID 38146040 · DOI · PMC11377524
- Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami EG. Artificial Intelligence in Operating Room Management. . 2024. PMID 38353755 · DOI · PMC10867065
- Bhuyan SS, Sateesh V, Mukul N, Galvankar A, Mahmood A, Nauman M, et al.. Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency. . 2025. PMID 39820845 · DOI · PMC11739231
- Ali MA, Keramat SA, Lu CY. Workplace productivity losses due to multimorbidity: findings from an Australian longitudinal population survey, 2009-21. . 2026. PMID 41071372 · DOI · PMC13017662
- Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, et al.. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov. 2010. PMID 20168317 · DOI
- Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. 2012. PMID 22378269 · DOI
- Stanley D, Hughes RJ, Moore RJ. Microbiota of the chicken gastrointestinal tract: influence on health, productivity and disease. Appl Microbiol Biotechnol. 2014. PMID 24643736 · DOI
- Dewa CS, Loong D, Bonato S, Thanh NX, Jacobs P. How does burnout affect physician productivity? A systematic literature review. BMC Health Serv Res. 2014. PMID 25066375 · DOI · PMC4119057
- Flouris AD, Dinas PC, Ioannou LG, Nybo L, Havenith G, Kenny GP, et al.. Workers' health and productivity under occupational heat strain: a systematic review and meta-analysis. Lancet Planet Health. 2018. PMID 30526938 · DOI
- Harter JK, Schmidt FL, Hayes TL. Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: a meta-analysis. J Appl Psychol. 2002. PMID 12002955 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.