ArticleBMC medicine2018
Do healthcare services behave as complex systems? Analysis of patterns of attendance and implications for service delivery.
Article in BMC medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Factors associated with persistent high use of emergency departments: a nationwide retrospective cohort study.BMJ open · 2026Article
- Activating knowledge-based practice through healthcare leadership development: insights from the second study of a broader action research project.BMC health services research · 2026Article
- Article
- Mapping healthcare leadership interventions and their performance in sub-Saharan Africa.Journal of public health in Africa · 2025Review
- Simulation to optimize the laboratory diagnosis of bacteremia.Microbiology spectrum · 2024Article
- Diagnosing an overcrowded emergency department from its Electronic Health Records.Scientific reports · 2024Article
- Feedback System Analysis of a Multicomponent Intervention on Dyads of Home-Dwelling Persons With Dementia and Their Caregivers: Results From the LIVE@Home.Path Trial.Innovation in aging · 2024Article
- Quantifying the Resilience of a Healthcare System: Entropy and Network Science Perspectives.Entropy (Basel, Switzerland) · 2023Article
- Using Relational Community Engagement within the Digital Health Intervention (DHI) to Improve Access and Retention among People Living with HIV (PLWH): Findings from a Mixed-Method Study in Cambodia.International journal of environmental research and public health · 2023Article
- The Power Law in Operating Room Management.Journal of medical systems · 2021Article
- Mapping Research Trends of Universal Health Coverage From 1990 to 2019: Bibliometric Analysis.JMIR public health and surveillance · 2021Article
- When complexity matters: a step-by-step guide to incorporating a complexity perspective in guideline development for public health and health system interventions.BMC medical research methodology · 2020Review
- The influence of complexity: a bibliometric analysis of complexity science in healthcare.BMJ open · 2019Article
- Statistical complexity of reasons for encounter in high users of out of hours primary care: analysis of a national service.BMC health services research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe science of complex systems has been proposed as a way of understanding health services and the demand for them, but there is little quantitative evidence to support this. We analysed patterns of healthcare use in different urgent care settings to see if they showed two characteristic statistical features of complex systems: heavy-tailed distributions (including the inverse power law) and generative burst patterns.
methodsWe conducted three linked studies. In study 1 we analysed the distribution of number of contacts per patient with an urgent care service in two settings: emergency department (ED) and primary care out-of-hours (PCOOH) services. We hypothesised that these distributions should be heavy-tailed (inverse power law or log-normal) in keeping with typical complex systems. In study 2 we analysed the distribution of bursts of contact with urgent care services by individuals: correlated bursts of activity occur in complex systems and represent a mechanism by which overall heavy-tailed distributions arise. In study 3 we replicated the approach of study 1 using data systematically identified from published sources.
resultsStudy 1 involved data from a PCOOH service in Scotland (725,000) adults, 1.1 million contacts) and an ED in New Zealand (60,000 adults, 98,000 contacts). The total number of contacts per individual in each dataset was statistically indistinguishable from an inverse power law (p > 0.05) above 4 contacts for the PCOOH data and 3 contacts for the ED data. Study 2 found the distribution of contact bursts closely followed a heavy-tailed distribution (p < 0.008), indicating the presence of correlated bursts. Study 3 identified data from 17 studies across 8 countries and found distributions similar to study 1 in all of them.
conclusionsUrgent healthcare use displays characteristic statistical features of large complex systems. These studies provide strong quantitative evidence that healthcare services behave as complex systems and have important implications for urgent care. Interventions to manage demand must address drivers for consultation across the whole system: focusing on only the highest users (in the tail of the distribution) will have limited impact on efficiency. Bursts of attendance - and ways to shorten them - represent promising targets for managing demand.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.