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

Abstract

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.

Keywords: health informatics; information management; organisation of health services; risk management.

Figure 1

Unweighted, undirected patient transfer networks for four hospital departments over a 1-year period. Vertex (location) colours distinguish different functionality, that is, ED, ORs, ICUs and medical and surgical wards. Vertex size is proportional to its degree (number of other locations connected to it). Network sizes are given as edge (E) and vertex (V) counts and density. The complete list of abbreviations is found in online supplemental table S1. ED, emergency department; EDOU, emergency department observation unit; GE, gastroenterology; GIS, gastrointestinal surgery; ICU, intensive care unit; MICU, medical intensive care unit; NR, neurology; OR, operating room; OT, orthopaedics; PHDU, postoperative high-dependency unit; TCVS, thoracic and cardiovascular surgery.

Figure 2

Temporal changes in network size by hospital department and transfer type. (A) Weekly network sizes in terms of transfer pathway (edge) and location (vertex) counts. (B) Weekly sum of transfers, split by transfer type. (C) Weekly sum of transfers by type, normalised by number of patient admissions in the corresponding department that week. Study week is counted from a Monday in June 2018; hence, study weeks 1–13 denote June to August, and so forth. ED-Any: transfers from the emergency department (ED) to any other ward. Bed ward-Bed ward: transfers between regular wards. To-From Technical: transfers involving technical wards, that is, intensive care units (ICUs), high-dependency units (HDUs) and operating rooms (ORs).

Figure 3

Transfer network connectivity on weekdays and weekends. For 30 hospital wards, daily average number of hospital locations the ward received patients from (in-degree, green dots) and sent patients to (out-degree, amber dots). Data for all stays allocated to any of the four studied departments, split by weekday/weekend. Full-year network size (all four departments) is reported as mean (SD) of edge (E) and vertex (V) counts. ED, emergency department; HDU, high-dependency unit; ICU, intensive care unit; OR, operating room.