Implications of interhospital patient transfers for emergency medical services transportation systems in the Netherlands: a retrospective study.

OBJECTIVES: Interhospital patient transfers have become routine. Known drivers are access to specialty care and non-clinical reasons, such as limited capacity. While emergency medical services (EMS) providers act as main patient transfer operators, the impact of interhospital transfers on EMS service demand and fleet management remains understudied. This study aims to identify patterns in regional interhospital patient transfer volumes and their spatial distribution, and to discuss their potential implications for EMS service demand and fleet management. DESIGN: A retrospective study was performed analysing EMS transport data from the province of Drenthe in the Netherlands between 2013 and 2019 and public hospital listings. Yearly volume changes in urgent and planned interhospital transfers were quantified. Further network analysis, including geomapping, was used to study how transfer volumes and their spatial distribution relate to hospital specialisation, and servicing multihospital systems. Organisational data were considered for relating transfer patterns to fleet changes. SETTING: EMS in the province of Drenthe, the Netherlands, 492 167 inhabitants. PARTICIPANTS: Analyses are based on routinely collected patient data from EMS records, entailing all 248 114 transports (137 168 patients) of the Drenthe EMS provider (2013-2019). From these interhospital transports were selected (24 311 transports). RESULTS: Interhospital transfers represented a considerable (9.8%) and increasing share of transports (from 8.6% in 2013 to 11.3% in 2019). Most transfers were related to multihospital systems (47.3%, 11 509 transports), resulting in a considerable growth of planned EMS transports (from 2093 in 2013 to 3511 in 2019). Geomapping suggests increasing transfer distances and diminishing resource efficiencies due to lacking follow-up rides. Organisational data clarify how EMS fleets were adjusted by expanding resources and reorganising fleet operation. CONCLUSIONS: Emerging interhospital network transfers play an important role in EMS service demand. Increased interhospital transport volumes and geographical spread require a redesign of current EMS fleets and management along regional lines.

Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data.

BACKGROUND: Tracing frequent users of health care services is highly relevant to policymakers and clinicians, enabling them to avoid wasting scarce resources. Data collection on frequent users from all possible health care providers may be cumbersome due to patient privacy, competition, incompatible information systems, and the efforts involved. OBJECTIVE: This study explored the use of a single key source, emergency medical services (EMS) records, to trace and reveal frequent users' health care consumption patterns. METHODS: A retrospective study was performed analyzing EMS calls from the province of Drenthe in the Netherlands between 2012 and 2017. Process mining was applied to identify the structure of patient routings (ie, their consecutive visits to hospitals, nursing homes, and EMS). Routings are used to identify and quantify frequent users, recognizing frail elderly users as a focal group. The structure of these routes was analyzed at the patient and group levels, aiming to gain insight into regional coordination issues and workload distributions among health care providers. RESULTS: Frail elderly users aged 70 years or more represented over 50% of frequent users, making 4 or more calls per year. Over the period of observation, their annual number and the number of calls increased from 395 to 628 and 2607 to 3615, respectively. Structural analysis based on process mining revealed two categories of frail elderly users: low-complexity patients who need dialysis, radiation therapy, or hyperbaric medicine, involving a few health care providers, and high-complexity patients for whom routings appear chaotic. CONCLUSIONS: This efficient approach exploits the role of EMS as the unique regional "ferryman," while the combined use of EMS data and process mining allows for the effective and efficient tracing of frequent users' utilization of health care services. The approach informs regional policymakers and clinicians by quantifying and detailing frequent user consumption patterns to support subsequent policy adaptations.

Tracing frequent users of regional care services using emergency medical services data: a networked approach.

OBJECTIVES: This study shows how a networked approach relying on 'real-world' emergency medical services (EMS) records might contribute to tracing frequent users of care services on a regional scale. Their tracing is considered of importance for policy-makers and clinicians, since they represent a considerable workload and use of scarce resources. While existing approaches for data collection on frequent users tend to limit scope to individual or associated care providers, the proposed approach exploits the role of EMS as the network's 'ferryman' overseeing and recording patient calls made to an entire network of care providers. DESIGN: A retrospective study was performed analysing 2012-2017 EMS calls in the province of Drenthe, the Netherlands. Using EMS data, benefits of the networked approach versus existing approaches are assessed by quantifying the number of frequent users and their associated calls for various categories of care providers. Main categories considered are hospitals, nursing homes and EMS. SETTING: EMS in the province of Drenthe, the Netherlands, serving a population of 491 867. PARTICIPANTS: Analyses are based on secondary patient data from EMS records, entailing 212 967 transports and 126 758 patients, over 6 years (2012-2017). RESULTS: Use of the networked approach for analysing calls made to hospitals in Drenthe resulted in a 20% average increase of frequent users traced. Extending the analysis by including hospitals outside Drenthe increased ascertainment by 28%. Extending to all categories of care providers, inside Drenthe, and subsequently, irrespective of their location, resulted in an average increase of 132% and 152% of frequent users identified, respectively. CONCLUSIONS: Many frequent users of care services are network users relying on multiple regional care providers, possibly representing inefficient use of scarce resources. Network users are effectively and efficiently traced by using EMS records offering high coverage of calls made to regional care providers.

From data to knowledge: a method for modeling hospital logistic processes.

When modeling or redesigning a process, the knowledge-management perspective is seldomly used. Using the knowledge categorization developed by van Heusden and Jorna, we propose a knowledge-management perspective to provide a strategy for modeling and redesigning a business process. As an illustration of our approach, we use hospital data of multidisciplinary patients. This specific group of patients requires the involvement of different specialisms for their medical treatment that leads to more efforts regarding the coordination of care for these patients. In order to increase the care efficiency, knowledge that supports the reorganization of care for multidisciplinary patients should be provided. We use the above-mentioned knowledge-management perspective for creating new multidisciplinary units, in which different specialisms coordinate the treatment of specific groups of patients.

Logistic-based patient grouping for multi-disciplinary treatment.

Present-day healthcare witnesses a growing demand for coordination of patient care. Coordination is needed especially in those cases in which hospitals have structured healthcare into specialty-oriented units, while a substantial portion of patient care is not limited to single units. From a logistic point of view, this multi-disciplinary patient care creates a tension between controlling the hospital's units, and the need for a control of the patient flow between units. A possible solution is the creation of new units in which different specialties work together for specific groups of patients. A first step in this solution is to identify the salient patient groups in need of multi-disciplinary care. Grouping techniques seem to offer a solution. However, most grouping approaches in medicine are driven by a search for pathophysiological homogeneity. In this paper, we present an alternative logistic-driven grouping approach. The starting point of our approach is a database with medical cases for 3,603 patients with peripheral arterial vascular (PAV) diseases. For these medical cases, six basic logistic variables (such as the number of visits to different specialist) are selected. Using these logistic variables, clustering techniques are used to group the medical cases in logistically homogeneous groups. In our approach, the quality of the resulting grouping is not measured by statistical significance, but by (i) the usefulness of the grouping for the creation of new multi-disciplinary units; (ii) how well patients can be selected for treatment in the new units. Given a priori knowledge of a patient (e.g. age, diagnosis), machine learning techniques are employed to induce rules that can be used for the selection of the patients eligible for treatment in the new units. In the paper, we describe the results of the above-proposed methodology for patients with PAV diseases. Two groupings and the accompanied classification rule sets are presented. One grouping is based on all the logistic variables, and another grouping is based on two latent factors found by applying factor analysis. On the basis of the experimental results, we can conclude that it is possible to search for medical logistic homogenous groups (i) that can be characterized by rules based on the aggregated logistic variables; (ii) for which we can formulate rules to predict to which cluster new patients belong.