Correspondence to Dr Durk-Jouke van der Zee; [email protected]
STRENGTHS AND LIMITATIONS OF THIS STUDY
Single-source routinely collected comprehensive data, such as emergency medical services (EMS) serving an entire region, allow identification of regional interhospital transfer networks.
Geomapping of interhospital transfers showed implications of emerging transfer networks for EMS transport volumes and spatial distribution.
Combining geomapping results and hospital characteristics clarifies how increasing size and scope of transfer networks relate to hospital specialisation and affiliation with multihospital systems.
We studied EMS records for only one province in the Netherlands, while we expect that emerging transfer networks imply similar challenges for EMS providers outside the province.
Introduction
In recent years, interhospital transfers have become routine. In part, the need for these transfers is the result of coordinated changes in regional healthcare networks aimed at optimising the allocation of scarce resources. Also, interhospital transfers may be needed in response to less-coordinated changes in the region stemming from population changes.1 On a regional scale, the development of various care pathways with interhospital transfers has resulted in the emergence of regional transfer networks spanning many hospitals,2 3 allowing patients to be transferred for a myriad of medical diagnoses.1 The size, complexity and evolutionary growth of these networks tend to raise concerns with respect to their coordination,4–8 and rationale of decisions regarding allocation of health resources and services.9–13
The scarce research exploring the implications of emerging transfer networks for the workings of regional health systems thus far has concentrated on evaluating how transfers can improve patient safety and the quality of treatment. Especially, the motivation and roles of hospitals in network design and behaviour have been studied. Transfer numbers and underlying conditions of patients served were observed while considering access to higher quality care.3 5 14–16 Surprisingly, implications of the growing regional transfer networks—in both size and scope—for operating and managing emergency medical services (EMS) transportation systems received little scientific attention. Notably, EMS providers do act as the main provider for both urgent and planned interhospital transfers and ensure patients’ safe and timely access to hospital care.12 13
To fill this gap in knowledge, this study investigated an emerging and increasingly complex regional transfer network in the province of Drenthe, the Netherlands. We used routinely collected logistic data on EMS transports and resources to quantify transfer frequencies and regional spread. Adaptations in fleet management that may be necessary for safeguarding call responsiveness and resource efficiencies were also explored. Specifically, EMS service demand changes were analysed in relation to hospital specialisation and membership of a multihospital system, assumed to be main determinants.
Methods
Design
This study is a retrospective, ‘real-world’ descriptive study analysing routinely collected EMS patient records from the province of Drenthe, the Netherlands, between 2013 and 2019. Yearly demand changes in interhospital transports, following from hospital specialisation and emergence of multihospital systems, were evaluated. Demand changes are measured as numerical differences between yearly transport volumes realised for Drenthe. Corresponding structural changes in EMS fleet size and organisation were studied relying on data on resource use from patient records and supplemental organisational data on adjustments of EMS fleets. In reporting the study, the REporting of studies Conducted using Observational Routinely-collected Data (RECORD)17 guidelines for reporting on studies using observational routinely collected health data were followed; see online supplemental material.
Setting: the health care network in the province of Drenthe
The province of Drenthe, the Netherlands, has a population of 492 167, with a population density of 185 inhabitants per square kilometre.18 There are four hospitals within the province. Three of those hospitals offer basic treatment, while one hospital also offers multitrauma and advanced cardiological care. Three hospitals within Drenthe are part of a multihospital system, that is, a group of hospitals governed by a central organisation.19 Respective hospitals are split over two multihospital systems, each having partnering hospitals in neighbouring provinces. Patients in the province of Drenthe rely on hospitals in neighbouring provinces for highly specialised care, with few patients being served by nationwide hospitals at larger distances.
EMS for patients in the province of Drenthe is provided by a single EMS provider. The EMS provider is publicly financed, which is common for EMS providers in the Netherlands. Its services include both urgent transports and planned transports to hospitals, as well as planned transports to a variety of other care providers, such as nursing and care homes. Dedicated fleets of ambulances are managed for both transport modes. The advanced life support (ALS) fleet is reserved for urgent transports, as reflected in high requirements on staff and resources, allowing care for critical patients. The basic life support (BLS) fleet generally sets lower requirements for staff and resources. Planned transport may require ALS or BLS staff and resources depending on patient care needs. When those needs are limited, self-travel or taxi transport may suffice too. In case temporarily high demand for planned transport exceeds capacity of the BLS fleet, it is met by the larger ALS fleet. All transports are dispatched by a single central coordination centre, serving the provinces of Drenthe, Friesland and Groningen. Dispatch criteria for planned transports generally stress resource efficiencies including fit with service required (ALS, BLS), whereas call responsiveness is decisive in case of urgent transports.
Data
Patient transport data were collected from pseudonymised EMS records of all patient transports executed by the EMS provider for Drenthe between 1 January 2013 and 31 December 2019, specifying patient logistic and resource details. We only considered records referring to transports having an origin and/or destination in the province of Drenthe. Patient data for 2020–2022 was deliberately not included, to eliminate exceptional effects of the COVID-19 pandemic on interhospital transfers. During the pandemic, regional EMS resources were also regularly used for long-distance transfers of COVID-19 patients.
Interhospital transports were selected from the complete data set (all Drenthe EMS records for 2013–2019) by considering transports connecting hospitals only (see online supplemental figure S1). From the complete Drenthe dataset of 248 114 records relating to 137 168 patients, with transports to and from Drenthe locations (identified via zip codes), See and Treat, that is, patient calls without transportation (48 863 records), and international transports (169 records) were excluded. The resulting data set was filtered by excluding non-interhospital transports (172 756 records). Filtering included verification of hospitals and zip codes using an official list of Dutch hospitals,20 and manual checking on nursing and care homes being in close proximity of hospitals, and sharing the same zip code. The final set of selected records (24,311) included transport dates and times, origins and destinations (both hospitals), urgency level (urgent or planned transport) and resource use (as clarified by vehicle type ALS or BLS). As all EMS transports were recorded as a standard feature, no missing data were reported.
Hospitals identified as either origin or destination of an interhospital transport were categorised according to membership of a multihospital system (name of system, none) and level of care offered: general, top clinical or academic hospitals, using overviews provided by the Dutch National Institute for Public Health and the Environment20 and the Dutch Association of Top Clinical hospitals.21 Hospital names were used as a link. General hospitals are usually smaller and offer basic care, whereas top-clinical hospitals are larger hospitals offering more complex care in addition to basic care. Lastly, academic hospitals concentrate on the provision of the most complex so-called tertiary care.
Data analysis
As a starting point for analysis, and to provide a broad perspective on the way interhospital transfers influence EMS demand for service, yearly volume figures for the region were established by counting interhospital transfers on a yearly basis for different transports’ urgencies (ie, urgent and planned). Impact of demand on fleet use was assessed by considering frequencies of transports’ urgencies (urgent or planned) and levels of support provided (ALS, BLS).
Geomapping was used to detail the regional transfer network for interhospital transport, aiming to characterise impact of increased transfers on EMS operations beyond volumes, by considering spatial effects. Note that geographical factors are known determinants of potential efficiencies in EMS transportation system set-up and operations.12 Geomapping visualised the transfer network spanning regional hospitals, using arcs to mark yearly transfer volumes among these. Geomapping was done in three steps. The first step displayed all regional hospitals on the geographical map, each being labelled for its level of care offered (general, top clinical or academic hospitals) and membership of a multihospital system. The second step grouped yearly transfers per hospital pair, differentiating for transfer direction, using EMS transport data. The third step visualised grouped transfers on a geomap as arcs, clarifying origin and destination hospital, and transfer volumes involved by arc width. The procedure was repeated for transfers in years 2013 and 2019 to show emergent behaviour of the transfer network.
Comparison of the geomaps developed for 2013 and 2019 enabled easy identification and ranking of main volume changes in the transfer network and relating these to hospital labels (level of care, membership of multihospital system). Starting from the overviews provided, geomaps allowed for basic insights on the relevance of hospital specialisation and membership of a multihospital system as determinants of transfer volume changes. Moreover, information on the geographical dispersion of transfers provided by the geomaps was used to enhance understanding of challenges in fleet management going together with demand changes, for example, lengthy rides that cross regional borders may compromise EMS responsiveness and resource efficiencies.12
We further explored the role of multihospital systems and hospital specialisation. We studied the role of sending hospitals being affiliated with a multihospital system, identified as an important factor in determining transfer destinations.22 Specifically, we performed analyses observing changes over time in volumes for ‘internal’ patient transfers between hospitals in the same multihospital system versus volumes for ‘external’ patient transfers linking hospitals of different organisations, thereby differentiating for transfer urgency (urgent, planned). In a similar vein, we studied the role of hospital specialisation for urgent and planned interhospital transport. Our analysis allowed identifying changes in volumes of transfers suggesting a ‘levelling up’ of provided care, that is, the level of care offered at the destination hospital is higher than for the originating hospital, and vice-versa, suggesting a ‘levelling down’ of provided care. Such ‘levelling down’ may, for example, relate to a need for freeing beds from patients no longer requiring specialty care.1 23
Changes in transfer volumes and the spatial effects going along with hospital specialisation and multihospital systems may affect the EMS call responsiveness and resource efficiencies. In a final phase, structural changes in EMS fleet management that could be related to the observed changes in interhospital transfers were evaluated. Evaluation was performed using organisational data on adjusting EMS fleet composition and management during the study period, cross validated with resource data from the EMS records.
Patient and public involvement
Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.
Results
Quantifying EMS interhospital transports
Table 1 provides an overview of all patients being served by the EMS provider based on its records. In total, 248 114 transports were executed by the EMS between 2013 and 2019. Most transports were classified as urgent, but the volume of planned transports was large and growing. The number of interhospital transports was 24 311, making up 9.8% of overall transports over the study period. Notably, its share in overall transports increased from 8.6% in 2013 to 11.3% in 2019. Indeed, the relative increase of interhospital transports from 2670 to 4313 between 2013 and 2019 (61.5%) clearly outpaced the increase of overall EMS transports (23.0%, from 31 113 to 38 261 between 2013 and 2019). Around 80% of interhospital transports were planned transports. Urgent interhospital transports (4800) represented less than 3% of the overall urgent transport (186,622), and the growth of urgent transport was mainly due to transports from or to hospitals, as they cannot be explained by the interhospital transports (see online supplemental figure S2A). On the other hand, planned interhospital transports (19,511) comprised 31.7% of the overall planned transports (61,492), and the increase of 1418 planned interhospital transports over the study period (from 2093 to 3511) explained most of the 1570 additional overall planned transports (from 8225 to 9795), implying a considerable role of interhospital transfers in the increase of planned EMS transports. Online supplemental figure S2B clearly shows that the increase in planned interhospital transports was significant and in most years made up for the increase in planned transports.
Table 1All and interhospital emergency medical services (EMS) transports with origin and/or destination Drenthe
| Year | All transports | Interhospital transports | ||||
| Total, N | Urgent, n (%)* | Planned, n (%)† | Total, n (%)¶ | Urgent, n (%)‡ | Planned, n (%)§ | |
| 2013 | 31 113 | 22 888 (73.6) | 8225 (26.4) | 2670 (8.6) | 577 (1.9) | 2093 (6.7) |
| 2014 | 32 942 | 24 597 (74.7) | 8345 (25.3) | 3028 (9.2) | 594 (1.8) | 2434 (7.4) |
| 2015 | 34 512 | 26 491 (76.8) | 8021 (23.2) | 3112 (9.0) | 603 (1.7) | 2509 (7.3) |
| 2016 | 36 406 | 27 684 (76.0) | 8722 (24.0) | 3417 (9.4) | 712 (2.0) | 2705 (7.4) |
| 2017 | 36 932 | 28 119 (76.1) | 8813 (23.9) | 3678 (10.0) | 747 (2.0) | 2931 (7.9) |
| 2018 | 37 948 | 28 377 (74.8) | 9571 (25.2) | 4093 (10.8) | 765 (2.0) | 3328 (8.8) |
| 2019 | 38 261 | 28 466 (74.4) | 9795 (25.6) | 4313 (11.3) | 802 (2.1) | 3511 (9.2) |
| Total | 248 114 | 186 622 (75.2) | 61 492 (24.8) | 24 311 (9.8) | 4800 (1.9) | 19 511 (7.9) |
*Urgent transports as a percentage of all patient transports.
†Planned transports as a percentage of all patient transports.
‡Urgent interhospital transports as a percentage of all patient transports.
§Planned interhospital transports as a percentage of all patient transports.
¶Interhospital transports as a percentage of all patient transports.
Table 2 reports the resource use (ie, use of ALS or BLS fleet) for planned and urgent interhospital transports. As can be seen, most of the increase in planned interhospital transport was facilitated by the ALS fleet, as the BLS fleet was operating at maximum capacity already. However, the BLS fleet was expanded and used more intensively since 2018.
Table 2Interhospital transport typified for urgency and resource use (ALS, BLS)
| Year | Resource use considering urgency of healthcare | ||||
| Urgent | Planned | ||||
| Interhospital transports, n | Fleet use | Interhospital transports, n | Fleet use | ||
| ALS | ALS | BLS | |||
| 2013 | 577 | 577 | 2093 | 1445 | 648 |
| 2014 | 594 | 594 | 2434 | 1810 | 624 |
| 2015 | 603 | 603 | 2509 | 1880 | 629 |
| 2016 | 712 | 712 | 2705 | 2068 | 637 |
| 2017 | 747 | 747 | 2931 | 2317 | 614 |
| 2018 | 765 | 765 | 3328 | 2481 | 847 |
| 2019 | 802 | 802 | 3511 | 2695 | 816 |
| Total | 4800 | 4800 | 19 511 | 14 696 | 4815 |
ALS, advanced life support; BLS, basic life support.
The geomapping analysis of interhospital transports is presented in figure 1. Figure 1 shows interhospital transport volumes by identifying related hospitals within and outside the province of Drenthe. Hospital markings are added to indicate their level of care offered (ie, general, top clinical, or academic), and to clarify their affiliation with multihospital systems. Figure 1 reveals how patient transports among hospitals affiliated with a multihospital system contributed strongly to the increase in interhospital transport volumes from 2013 to 2019. In addition, figure 1 shows that despite significant transport volumes within Drenthe, most interhospital transports were cross-border (1927 in 2013 vs 2990 in 2019). Especially, hospital relationships linking hospitals within the province of Drenthe to hospitals in neighbouring regions were strong and further increased over the study period. Furthermore, many incidental or low-volume relationships of Drenthe hospitals with nationwide hospitals were identified, which can be mainly explained by specialised care only offered at a national level and patients that visited Drenthe as tourists, returning to their ‘home’ hospital. These results hint at important spatial effects going together with increases in the volume of interhospital transports.
Enlarge this image.Figure 1. Geomapping interhospital transports with origin and/or destination Drenthe.
Analysing interhospital transports
Tables 3 and 4 show cumulative transport volumes over time for related hospitals, being part of a multihospital group, both for urgent and planned interhospital transports. In a similar vein, tables 5 and 6 typify cumulative transport volumes ensuing from specialisation of care, that is, level of care offered at destination hospital versus origin hospital, both for urgent and planned interhospital transports.
Table 3Urgent transport volumes between hospitals within the same multihospital system and between hospitals not in the same multihospital system
| Year | All urgent interhospital transports, N | Between hospitals not in the same multihospital system, n (%)* | Between hospitals within the same multihospital system, n (%)† | Between hospitals within multihospital system I, n (%)‡ | Between hospitals within multihospital system II, n (%)§ |
| 2013 | 577 | 386 (66.9) | 191 (33.1) | 89 (15.4) | 102 (17.7) |
| 2014 | 594 | 364 (61.3) | 230 (38.7) | 125 (21.0) | 105 (17.7) |
| 2015 | 603 | 368 (61.0) | 235 (39.0) | 109 (18.1) | 126 (20.9) |
| 2016 | 712 | 396 (55.6) | 316 (44.4) | 199 (27.9) | 117 (16.4) |
| 2017 | 747 | 429 (57.4) | 318 (42.6) | 194 (26.0) | 124 (16.6) |
| 2018 | 765 | 419 (54.8) | 346 (45.2) | 216 (28.2) | 130 (17.0) |
| 2019 | 802 | 410 (51.1) | 392 (48.9) | 245 (30.5) | 147 (18.3) |
| Total | 4800 | 2772 (57.8) | 2028 (42.3) | 1177 (24.5) | 851 (17.7) |
| Mean (SD) | 685 (85) | 396 (23) | 289 (67) | 168 (55) | 121 (14) |
*Percentage of all urgent interhospital transfers relating to transfers between hospitals not in the same multihospital system.
†Percentage of all urgent interhospital transfers relating to transfers between hospitals within the same multihospital system.
‡Percentage of all urgent interhospital transfers relating to transfers between hospitals A, B in multihospital system I.
§Percentage of all urgent interhospital transfers relating to transfers between hospitals C, D and E in multihospital system II.
Table 4Planned transport volumes between hospitals within the same multihospital system and between hospitals not in the same multihospital system
| Year | All planned interhospital transports, N | Between hospitals not in the same multihospital system, n (%)* | Between hospitals within the same multihospital system, n (%)† | Between hospitals within multihospital system I, n (%)‡ | Between hospitals within multihospital system II, n (%)§ |
| 2013 | 2093 | 1456 (69.6) | 637 (30.4) | 242 (11.6) | 395 (18.9) |
| 2014 | 2434 | 1476 (60.6) | 958 (39.4) | 418 (17.2) | 540 (22.2) |
| 2015 | 2509 | 1437 (57.3) | 1072 (42.7) | 379 (15.1) | 693 (27.6) |
| 2016 | 2705 | 1370 (50.6) | 1335 (49.4) | 452 (16.7) | 883 (32.6) |
| 2017 | 2931 | 1426 (48.7) | 1505 (51.3) | 528 (18.0) | 977 (33.3) |
| 2018 | 3328 | 1410 (42.4) | 1918 (57.6) | 767 (23.0) | 1151 (34.6) |
| 2019 | 3511 | 1455 (41.4) | 2056 (58.6) | 877 (25.0) | 1179 (33.6) |
| Total | 19 511 | 10 030 (51.4) | 9481 (48.6) | 3663 (18.8) | 5818 (29.8) |
| Mean (SD) | 2787 (467) | 1432 (32) | 1354 (475) | 523 (207) | 831 (278) |
*Percentage of all planned interhospital transfers relating to transfers between hospitals not in the same multihospital system.
†Percentage of all planned interhospital transfers relating to transfers between hospitals within the same multihospital system.
‡Percentage of all planned interhospital transfers relating to transfers between hospitals A and B in multihospital system I.
§Percentage of all planned interhospital transfers relating to transfers between hospitals C, D and E in multihospital system II.
Table 5Urgent interhospital transport by transfer level
| Year | All urgent interhospital transports, N | Level down, n (%)* | Level up, n (%)† | Same level, n (%)‡ |
| 2013 | 577 | 3 (0.5) | 375 (65.0) | 199 (34.5) |
| 2014 | 594 | 2 (0.3) | 424 (71.4) | 168 (28.3) |
| 2015 | 603 | 6 (1.0) | 382 (63.3) | 215 (35.7) |
| 2016 | 712 | 6 (0.8) | 518 (72.8) | 188 (26.4) |
| 2017 | 747 | 2 (0.3) | 554 (74.2) | 191 (25.6) |
| 2018 | 765 | 5 (0.7) | 576 (75.3) | 184 (24.1) |
| 2019 | 802 | 3 (0.4) | 595 (74.2) | 204 (25.4) |
| Total | 4800 | 27 (0.6) | 3424 (71.3) | 1349 (28.1) |
| Mean (SD) | 685 (86) | 3 (2) | 489 (87) | 193 (14) |
*Percentage of all urgent interhospital transfers to hospitals providing lower levels of care.
†Percentage of all urgent interhospital transfers to hospitals providing higher levels of care.
‡Percentage of all urgent interhospital transfers to hospitals providing the same level of care.
Table 6Planned interhospital transport by transfer level
| Year | All planned interhospital transports, N | Level down, n (%)* | Level up, n (%)† | Same level, n (%)‡ |
| 2013 | 2093 | 413 (19.7) | 905 (43.2) | 775 (37.0) |
| 2014 | 2434 | 436 (17.9) | 1116 (45.9) | 882 (36.2) |
| 2015 | 2509 | 383 (15.3) | 1052 (41.9) | 1074 (42.8) |
| 2016 | 2705 | 434 (16.0) | 1018 (37.6) | 1253 (46.3) |
| 2017 | 2931 | 442 (15.1) | 1192 (40.7) | 1297 (44.3) |
| 2018 | 3328 | 481 (14.5) | 1395 (41.9) | 1452 (43.6) |
| 2019 | 3511 | 469 (13.4) | 1482 (42.2) | 1560 (44.4) |
| Total | 19 511 | 3058 (15.7) | 8160 (41.8) | 8293 (42.5) |
| Mean (SD) | 2787 (467) | 437 (30) | 1166 (192) | 1185 (268) |
*Percentage of all planned interhospital transfers to hospitals providing lower levels of care.
†Percentage of all planned interhospital transfers to hospitals providing higher levels of care.
‡Percentage of all planned interhospital transfers to hospitals providing the same level of care.
Tables 3 and 4, and figure 2A,B show how the number of transports between hospitals part of the same multihospital system increased from 2013 to 2019. It doubled for the urgent interhospital transports (ie, from 191 to 392 between 2013 and 2019) and it tripled for the planned interhospital transports (ie, from 637 to 2056 between 2013 and 2019). For both urgent and planned interhospital transport, the share of transports between hospitals part of the same multihospital system increased. As can be seen in figure 2A,B, the increase in urgent and planned interhospital transports was almost completely accountable to the transports between hospitals part of the same multihospital system. Tables 3 and 4 clarify how the largest absolute volume changes were observed for planned interhospital transports, as indicated by yearly figures and their spread (SD).
Enlarge this image.Figure 2. (A) Urgent interhospital transport volumes. (B) Planned interhospital transport volumes.
Further detail on spatial effects of specialisation on interhospital transports is provided in tables 5 and 6. Table 5 shows that, as expected, most urgent transports were associated with a levelling up of care being provided (71.3%). Table 6 shows that most planned transports were associated with a levelling up (41.8%) or the same level of care (42.5%) being provided.
Enriching tables 5 and 6 with the geomaps in figure 1, we see how ‘levelling up or down’ as indicated by the level of care provided by the hospitals involved largely determined the geographical direction of transports. Most interhospital transports (47.6%) were associated with a levelling up of care being provided. These were often cross border, as seen in the large volumes of patients transported out of Drenthe to provinces of Groningen and Overijssel (figure 1). Increased distances to hospitals added to EMS workload, and the unbalanced demand for service might have complicated EMS resource efficiencies. Note that most transports between hospitals of equal level were related to general hospitals, and especially general hospital C (see markings in figure 1). This can be largely explained by its role as the largest hospital within a multihospital system and its facilities for treating multitrauma and advanced cardiological care. Hence, transports to and from hospital C could be related to hospital specialisation, though at a more complex level than considered in this study.
Fleet management in response to changes in interhospital transports
At the start of the study period, the resources of the EMS provider comprised a large fleet of ALS ambulances, including staff, vehicles and equipment capable of providing ALS, and relatively few resources for BLS, respectively. This composition of fleets was tailored to a large demand for urgent transports, and low demand for planned transports.
Recognising the considerable increase in interhospital transports, including the spatial effects, and their negative influence on call responsiveness and resource efficiencies, the EMS provider adjusted the composition and organisation of the fleet in various ways. A first adjustment was made in 2018, when the EMS provider extended the fleet of BLS ambulances, including staff and resources, in direct response to increased demand for this service. This was meant to alleviate the pressure of the ALS fleet too, as demand for urgent transports was increasing as well, which impeded using ALS vehicles as backup for BLS demand.
Further changes in managing the fleet were motivated by a growing spatial imbalance for planned (interhospital) transports. Increasingly, ambulances—including their staff and equipment—transported patients from the EMS provider’s actual region of operation (ie, Drenthe) to other regions, often without the possibility to transport the same or another patient back into the region on its return. In response, the EMS provider initiated cooperation with two other EMS providers serving the neighbouring provinces of Friesland and Groningen. Changes involved the joint offering of BLS transports for the overall Northern Netherlands region (Drenthe, Friesland and Groningen), and investment in a joint subsidiary company, being made responsible for nationwide transports.
Discussion
EMS providers are an important actor in interhospital transfers, facilitating the timely and safe transport of patients from one hospital to another. Surprisingly, the effects of regional changes in interhospital transfer relationships on the underlying transportation system operated by EMS providers have hardly been studied. The results of our study in the province of Drenthe, the Netherlands, confirm findings from prior research that patient transfer volumes are increasing.24 Indeed, the yearly increase for interhospital transports in Drenthe (ranging from 2.8% to 13.4%) is much higher compared with the 1.56% yearly increase found by Hernandez-Boussard.24 Interhospital transports made up a large and strongly increasing part (from 25.4% to 35.8% over the period observed) of overall planned transports and explained most of the growth in overall planned transports. On the other hand, the share of urgent interhospital transports in overall urgent transports was and remained small (<3%).
Our analysis shows that most interhospital transports were dispatched within multihospital systems. A likely explanation is the greater possibilities of multihospital systems for organising their healthcare services through resource allocation beyond individual hospitals seeking efficiencies at their internal system level.25 Accordingly, the volume of interhospital transports within multihospital systems increased considerably while volumes of interhospital transports outside multihospital systems remained stable over the study period. Geomapping together with numerical analysis showed how geographical direction of interhospital transports was largely determined by level of care provided by the hospitals. Not surprisingly, as higher level hospitals tend to be thinly scattered, ‘levelling up or down’ added to EMS work load due to longer rides to distant locations. Moreover, as transports were often cross regional borders, EMS resource efficiencies might be complicated.
Increased volume and geographical spread of interhospital transports, as clarified through geomapping, had important implications for EMS fleet management. To safeguard call responsiveness and resource efficiencies, several measures were taken. First, vehicles, staff and resources were expanded. Second, the fleet for BLS transports was reorganised at an interregional level, combining fleets for three neighbouring provinces in Northern Netherlands, and founding a subsidiary company that takes care of joint long-distance transports outside Northern Netherlands. These changes underscore that the increase of interhospital transports affect EMS providers and may force a reconsideration of their fleet management while seeking efficiencies beyond its current region. Indeed, as changes to accommodate growing interhospital transport may have merit, clearly, the bigger trade-off lies in weighing the benefits of changes in regional care networks in terms of quality of care and costs at hospital level against investments in the EMS transportation systems required to facilitate growing interhospital transports that may result from those changes. Reasoning along the axes of interhospital transport volumes and geographical spread may be informative as part of this more comprehensive cost-effectiveness analysis.
Our study has some limitations. First, as our study only involved patient data made available by the EMS provider for Drenthe, patient transports to or from Drenthe by other EMS providers were not included in the study. As those transportation volumes can be anticipated to be relatively low, no large impact on the study results and findings is expected. Another limitation is the health infrastructure within the Drenthe region, comprising general hospitals only. Hence, the spatial effects involved with transfers to hospitals outside the region offering higher levels of care may be relatively large, potentially impacting fleet organisation to a greater extent than would be the case for a region comprising hospitals offering higher levels of care. Indeed, EMS providers may arrive at different solutions in addressing interhospital transports seeking the right scale in realising new efficiencies. Third, our study shows how routinely collected EMS records allow for an efficient analysis of evolving regional transfer networks, considering their overall impact on EMS operations, and providing broad insights on their key determinants. These insights are useful for policy-making. Explaining why patients travel between hospitals, being influenced by patients’ and health services’ characteristics, is a natural follow-up question, which cannot be answered by identification of patient referral networks based on administrative health data.26 27
Conclusions
Emerging interhospital network transfers play an increasing role in EMS service demand. In seeking call responsiveness and resource efficiencies, increased interhospital transport volumes and geographical spread may require redesign of current of EMS fleets, and organisation along regional lines.
The authors would like to thank Harriëtte Holt from the University Medical Center Groningen Ambulancezorg for providing input on the manuscript as domain expert and Coert Schrijver, Rivka de Vries, Christine Pelletier, Gregor Krause and Patrick Lynn for their technical support.
Data availability statement
No data are available.
Ethics statements
Patient consent for publication
Not applicable.
Contributors All authors contributed to the concept and design of the study and critical revision of the manuscript for important intellectual content. D-JvdZ, LM and PB drafted the manuscript. LM and MA-V analysed and interpreted the data. All authors read and approved the final manuscript. D-JvdZ acts as a guarantor.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
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Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
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; Maruster, Laura 2
; Buijs, Paul 2 ; Aerts-Veenstra, Marjolein 2 ; Hatenboer, Jaap 3 ; Buskens, Erik 1
1 Department of Operations, University of Groningen, Faculty of Economics and Business, Groningen, The Netherlands; Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands
2 Department of Operations, University of Groningen, Faculty of Economics and Business, Groningen, The Netherlands
3 Ambulancezorg, University Medical Center Groningen, Tynaarlo, The Netherlands