Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance

Machine learning for administrative health records: A systematic review of techniques and applications

Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.

An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects

It is crucial to develop spatiotemporal analysis tools to mitigate risks during a pandemic. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings an interactive tool that is capable of crossing information about mobility patterns, geolocation characteristics and epidemiologic variables. To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, and epidemiologic indicators can be analyzed through multiple coordinated views. The proposal is to enable users to understand how different locations commute citizens, monitor risk over time, and understand what locations need more assistance, considering different layers of visualization, such as clusters and individual locations. The main novelty is the interactive way to construct the mobility network that defines the social distancing level and the way that risks are managed, since many different geolocation characteristics can be considered and visualized, such as socioeconomic indicators of a location, the economic importance of a set of locations, and the connection of important neighborhoods of a city with other cities. The proposed tool was built and verified by experts assembled to give scientific recommendations to the city administration of Recife, the capital city of Pernambuco. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and contamination risk, where the practical insights can also be used to tighten and relax mitigation measures in other phases of a pandemic.

Network Analysis Examining Intrahospital Traffic of Patients With Traumatic Hip Fracture

INTRODUCTION

Increased intrahospital traffic (IHT) is associated with adverse events and infections in hospitalized patients. Network science has been used to study patient flow in hospitals but not specifically for patients with traumatic injuries.

METHODS

This retrospective analysis included 103 patients with traumatic hip fractures admitted to a level I trauma center between April 2021 and September 2021. Associations with IHTs (moves within the hospital) were analyzed using R (4.1.2) as a weighted directed graph.

RESULTS

The median (interquartile range) number of moves was 8 (7-9). The network consisted of 16 distinct units and showed mild disassortativity (-0.35), similar to other IHT networks. The floor and intensive care unit (ICU) were central units in the flow of patients, with the highest degree and betweenness. Patients spent a median of 20-28 hours in the ICU, intermediate care unit, or floor. The number of moves per patient was mildly correlated with hospital length of stay (ρ = 0.26, p = .008). Intrahospital traffic volume was higher on weekdays and during daytime hours. Intrahospital traffic volume was highest in patients aged <65 years ( p = .04), but there was no difference in IHT volume by dependent status, complications, or readmissions.

CONCLUSIONS

Network science is a useful tool for trauma patients to plan IHT, flow, and staffing.

A systematic review to identify the challenges to achieving effective patient flow in public hospitals

This systematic review aims to uncover the challenges related to patient flow from a whole public hospital perspective and identify strategies to overcome these challenges. A search in Medline, Emcare and PubMed was conducted and 24 articles published in English, from 2015 to 2020, were selected in relation to patient flow challenges and strategies. Analysis of the articles was completed using a thematic approach to identify common themes in relation to the area of focus. Strategies from the literature were then aligned with the challenges to inform areas of potential improvement in relation to patient flow. The themes generated included Teamwork, Collaboration and Communication; Public Hospitals as complex systems; Timely discharge; Policy, Process and Decision-making; and Resources-capacity and demand. The key finding is that a whole system approach is required to improve patient flow in public hospitals. When effective patient flow is achieved, demand and capacity are matched, increasing patient access to the health service and enabling the resources required to provide high quality patient care. The findings will create a better understanding of improving patient flow in public hospitals.

Improving Patient Flow in a Primary Care Clinic

When patients visit primary care clinics, they can be subject to long wait times due to operational inefficiencies and bottlenecks, decreasing patient satisfaction and sometimes leading to worse health outcomes. The existing literature models primary care clinics primarily as agent-based models, which are excellent at tracking individual patients and their movements in a model of a clinic. While agent-based models can detect bottlenecks, a network flow model better detects bottlenecks in the model by correlating changes in patient flow and wait times in the healthcare network. In this paper, a network flow model is constructed, where patients flow along the capacitated edges of a network while receiving treatment at the nodes. This configuration easily identifies bottlenecks by analyzing the flow in and flow out of nodes through metrics such as efficiency and patient wait times. The capacities of the edges for this model are taken from an agent-based model of a case study of a primary care clinic and sampled as random variables. Ensemble runs of the network flow model are created to account for uncertainty in the synthetic data. By changing the topology of the network flow model, bottlenecks are removed, increasing the model efficiency and decreasing patient wait times. Finally, the model is subjected to a sensitivity analysis. The focus in this work is on the method rather than the results per se.

Using network analysis to model the effects of the SARS Cov2 pandemic on acute patient care within a healthcare system

Consolidation of healthcare in the US has resulted in integrated organizations, encompassing large geographic areas, with varying services and complex patient flows. Profound changes in patient volumes and behavior have occurred during the SARS Cov2 pandemic, but understanding these across organizations is challenging. Network analysis provides a novel approach to address this. We retrospectively evaluated hospital-based encounters with an index emergency department visit in a healthcare system comprising 18 hospitals, using patient transfer as a marker of unmet clinical need. We developed quantitative models of transfers using network analysis incorporating the level of care provided (ward, progressive care, intensive care) during pre-pandemic (May 25, 2018 to March 16, 2020) and mid-pandemic (March 17, 2020 to March 8, 2021) time periods. 829,455 encounters were evaluated. The system functioned as a non-small-world, non-scale-free, dissociative network. Our models reflected transfer destination diversification and variations in volume between the two time points - results of intentional efforts during the pandemic. Known hub-spoke architecture correlated with quantitative analysis. Applying network analysis in an integrated US healthcare organization demonstrates changing patterns of care and the emergence of bottlenecks in response to the SARS Cov2 pandemic, consistent with clinical experience, providing a degree of face validity. The modelling of multiple influences can identify susceptibility to stress and opportunities to strengthen the system where patient movement is common and voluminous. The technique provides a mechanism to analyze the effects of intentional and contextual changes on system behavior.

Graph Network Techniques to Model and Analyze Emergency Department Patient Flow

This article moves beyond analysis methods related to a traditional relational database or network analysis and offers a novel graph network technique to yield insights from a hospital’s emergency department work model. The modeled data were saved in a Neo4j graphing database as a time-varying graph (TVG), and related metrics, including degree centrality and shortest paths, were calculated and used to obtain time-related insights from the overall system. This study demonstrated the value of using a TVG method to model patient flows during emergency department stays. It illustrated dynamic relationships among hospital and consulting units that could not be shown with traditional analyses. The TVG approach augments traditional network analysis with temporal-related outcomes including time-related patient flows, temporal congestion points details, and periodic resource constraints. The TVG approach is crucial in health analytics to understand both general factors and unique influences that define relationships between time-influenced events. The resulting insights are useful to administrators for making decisions related to resource allocation and offer promise for understanding impacts of physicians and nurses engaged in specific patient emergency department experiences. We also analyzed customer ratings and reviews to better understand overall patient satisfaction during their journey through the emergency department.

Effects of patient-level risk factors, departmental allocation and seasonality on intrahospital patient transfer patterns: network analysis applied on a Norwegian single-centre data set

OBJECTIVES

Describe patient transfer patterns within a large Norwegian hospital. Identify risk factors associated with a high number of transfers. Develop methods to monitor intrahospital patient flows to support capacity management and infection control.

DESIGN

Retrospective observational study of linked clinical data from electronic health records.

SETTING

Tertiary care university hospital in the Greater Oslo Region, Norway.

PARTICIPANTS

All adult (≥18 years old) admissions to the gastroenterology, gastrointestinal surgery, neurology and orthopaedics departments at Akershus University Hospital, June 2018 to May 2019.

METHODS

Network analysis and graph theory. Poisson regression analysis.

OUTCOME MEASURES

Primary outcome was network characteristics at the departmental level. We describe location-to-location transfers using unweighted, undirected networks for a full-year study period. Weekly networks reveal changes in network size, density and key categories of transfers over time. Secondary outcome was transfer trajectories at the individual patient level. We describe the distribution of transfer trajectories in the cohort and associate number of transfers with patient clinical characteristics.

RESULTS

The cohort comprised 17 198 hospital stays. Network analysis demonstrated marked heterogeneity across departments and throughout the year. The orthopaedics department had the largest transfer network size and density and greatest temporal variation. More transfers occurred during weekdays than weekends. Summer holiday affected transfers of different types (Emergency department-Any location/Bed ward-Bed ward/To-From Technical wards) differently. Over 75% of transferred patients followed one of 20 common intrahospital trajectories, involving one to three transfers. Higher number of intrahospital transfers was associated with emergency admission (transfer rate ratio (RR)=1.827), non-prophylactic antibiotics (RR=1.108), surgical procedure (RR=2.939) and stay in intensive care unit or high-dependency unit (RR=2.098). Additionally, gastrosurgical (RR=1.211), orthopaedic (RR=1.295) and neurological (RR=1.114) patients had higher risk of many transfers than gastroenterology patients (all effects: p<0.001).

CONCLUSIONS

Network and transfer chain analysis applied on patient location data revealed logistic and clinical associations highly relevant for hospital capacity management and infection control.

Geographic accessibility to cancer treatment in Brazil: A network analysis

Background

Geographic accessibility to healthcare services is a fundamental component in achieving universal health coverage, the central commitment of the Brazilian Unified Health System (SUS). For cancer patients, poor accessibility has been associated with inadequate treatment, worse prognosis, and poorer quality of life.

Methods

We explored nationwide healthcare data from the SUS health information systems, and mapped the geographic accessibility to cancer treatment in two time-frames: 2009-2010 and 2017-2018. We applied social network analysis (SNA) to estimate the commuting route, flow, and distances travelled by cancer patients to undergo surgical, radiotherapy, and chemotherapy treatment.

Findings

A total of 12,751,728 treatment procedures were analyzed. Overall, more than half of the patients (49·2 to 60·7%) needed to travel beyond their municipality of residence for treatment, a fact that did not change over time. Marked regional differences were observed, as patients living in the northern and midwestern regions of the country had to travel longer distances (weighted average of 296 to 870 km). Cancer care hubs and attraction poles were mostly identified in the southeast and northeast regions, with Barretos being the main hub for all types of treatment throughout time.

Interpretation

Important regional disparities in the accessibility to cancer treatment in Brazil were revealed, suggesting the need to review the distribution of specialized care in the country. The data presented here contribute to ongoing research on improving access to cancer care and can provide reference to other countries, offering relevant data for oncological and healthcare service evaluation, monitoring, and strategic planning.

Funding

This work was funded by the Oswaldo Cruz Foundation - Fiocruz (Inova - no. 8451635123 to BPF) and the National Council for Scientific and Technological Development - CNPq (no. 407060/2018-9 to BPF); Coordination for the Improvement of Higher Education Personnel - CAPES (scholarship to PCA, Finance Code 001); and Instituto Nacional de Ciência e Tecnologia de Inovação em Doenças de Populações Negligenciadas (INCT-IDPN).

Resumo

A acessibilidade geográfica aos serviços de saúde é um componente fundamental para o alcance da cobertura universal de saúde, compromisso central do Sistema Único de Saúde (SUS). Para pacientes com câncer, a baixa acessibilidade aos serviços especializados tem sido associada ao tratamento inadequado, piora no prognóstico e na qualidade de vida.Neste estudo, dados de saúde dos sistemas de informação em saúde do SUS foram utilizados para mapear a acessibilidade geográfica ao tratamento do câncer em dois períodos: 2009-2010 e 2017-2018. Aplicamos a análise de redes sociais (ARS) para estimar os fluxos de deslocamento e as distâncias percorridas por pacientes com câncer para receberem tratamento cirúrgico, radioterápico e quimioterápico.Um total de 12.751.728 procedimentos de tratamento foram analisados. Em geral, mais da metade dos pacientes (49,2 a 60,7%) precisaram se deslocar de seus municípios de residência para receber tratamento, fato que não mudou comparando os dois períodos de tempo analisados. Foram observadas importantes diferenças regionais no acesso. Pacientes residentes das regiões norte e centro-oeste do país tiveram que percorrer maiores distâncias para alcançar os serviços (média ponderada = 296 a 870 km). A maioria dos hubs e polos de atração para atendimento oncológico foram identificados nas regiões Sudeste e Nordeste, sendo o município de Barretos o principal hub para todos os tipos de tratamento ao longo do tempo.As disparidades de acessibilidade para o tratamento de câncer, alertam para a necessidade de revisar a distribuição dos serviços de atenção especializada no país. A metodologia e os resultados apresentados neste estudo contribuem para as pesquisas sobre a melhoria do acesso ao tratamento do câncer e podem servir como referência para outros países, oferecendo dados relevantes para avaliação, monitoramento e planejamento estratégico de serviços oncológicos e de saúde em geral.

Variation in referral rates to emergency departments and inpatient services from a GP out of hours service and the potential impact of alternative staffing models

Introduction

Out of hours (OOHs) primary care is a critical component of the acute care system overnight and at weekends. Referrals from OOH services to hospital will add to the burden on hospital assessment in the ED and on-call specialties.

Methods

We studied the variation in referral rates (to the ED and direct specialty admission) of individual clinicians working in the Oxfordshire, UK OOH service covering a population of 600 000 people. We calculated the referral probability for each clinician over a 13-month period of practice (1 December 2014 to 31 December 2015), stratifying by clinician factors and location and timing of assessment. We used Simul8 software to determine the range of hospital referrals potentially due to variation in clinician referral propensity.

Results

Among the 119 835 contacts with the service, 5261 (4.4%) were sent directly to the ED and 3474 (3.7%) were admitted directly to specialties. More referrals were made to ED by primary care physicians if they did not work in the local practices (5.5% vs 3.5%, p=0.011). For clinicians with >1000 consultations, percentage of patients referred varied from 1% to 21% of consultations. Simulations where propensity to refer was made less extreme showed a difference in maximum referrals of 50 patients each week.

Conclusions

There is substantial variation in clinician referral rates from OOHs primary care to the acute hospital setting. The number of patients referred could be influenced by this variation in clinician behaviour. Referral propensity should be studied including casemix adjustment to determine if interventions targeting such behaviour are effective.

IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks

Nanoparticles are useful antimicrobial drug-release systems, but some nanoparticles also exhibit antibacterial activity. However, investigation of their antibacterial activity is a difficult and slow process due to the numerous combinations of nanoparticle size, shape, and composition vs. biological tests, assay organisms, and multiple activity parameters to be measured. Additionally, the overuse of antibiotics has led to the emergence of resistant bacterial strains with different metabolic networks. Computational models may speed up this process, but the models reported to date do not to consider all the previous factors, and the data sources are dispersed and not curated. Thus, herein, we used an information fusion, perturbation-theory machine learning (IFPTML) approach, which is introduced by us for the first time, to fit a model for the discovery of antibacterial nanoparticles. The dataset studied had 15 classes of nanoparticles (1-100 nm) with most cases in the range of 1-50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a linear logistic regression equation that could model 4 biological activity parameters using only 8 variables with χ2 = 2265.75, p-level <0.05, sensitivity, Sn = 79.4, and specificity, Sp = 99.3, for 3213 cases (nanoparticle-bacteria pairs) in the training series. The model had Sn = 80.8 and Sp = 99.3 for 2114 cases in the external validation series. We also developed a random forest non-linear model with higher values of Sn and Sp = 98-99% in the training/validation series, although it was more complicated to use. SOFT.PTML has been demonstrated to be a useful tool for the analysis of complex data in nanotechnology. We also introduced a new anabolism-catabolism unbalance index of metabolic networks to reveal the biological connotation of the IFPTML predictions for antibacterial nanoparticles. These new models open a new door for the discovery of NPs vs. new bacterial species and strains with different topological structures of their metabolic networks.

Can network science reveal structure in a complex healthcare system? A network analysis using data from emergency surgical services

INTRODUCTION

Hospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Metrics of performance have to date focused on the performance of individual elements rather than the whole system. Manipulation of individual elements of a complex system without an integrative understanding of its function is undesirable and may lead to counterintuitive outcomes and a holistic metric of hospital function might help design more efficient services.

OBJECTIVES

We aimed to use network analysis to characterise the structure of the system of perioperative care for emergency surgical admissions in our tertiary care hospital.

DESIGN

We constructed a weighted directional network representation of the emergency surgical services using patient location data from electronic health records.

SETTING

A single-centre tertiary care hospital in the UK.

PARTICIPANTS

We selected data from the retrospective electronic health record data of all unplanned admissions with a surgical intervention during their stay during a 3.5-year period, which resulted in a set of 16 500 individual admissions.

METHODS

We then constructed and analysed the structure of this network using established methods from network science such as degree distribution, betweenness centrality and small-world characteristics.

RESULTS

The analysis showed the service to be a complex system with scale-free, small-world network properties. We also identified such potential hubs and bottlenecks in the system.

CONCLUSIONS

Our holistic, system-wide description of a hospital service may provide tools to inform service improvement initiatives and gives us insights into the architecture of a complex system of care. The implications for the structure and resilience of the service is that while being robust in general, the system may be vulnerable to outages at specific key nodes.