Emergency transport of stroke suspects in a rural state: opportunities for improvement

Network analysis of stroke systems of care in Korea

Background

The landscape of stroke care has shifted from stand-alone hospitals to cooperative networks among hospitals. Despite the importance of these networks, limited information exists on their characteristics and functional attributes.

Methods

We extracted patient-level data on acute stroke care and hospital connectivity by integrating national stroke audit data with reimbursement claims data. We then used this information to transform interhospital transfers into a network framework, where hospitals were designated as nodes and transfers as edges. Using the Louvain algorithm, we grouped densely connected hospitals into distinct stroke care communities. The quality and characteristics in given stroke communities were analysed, and their distinct types were derived using network parameters. The clinical implications of this network model were also explored.

Results

Over 6 months, 19 113 patients with acute ischaemic stroke initially presented to 1009 hospitals, with 3114 (16.3%) transferred to 246 stroke care hospitals. These connected hospitals formed 93 communities, with a median of 9 hospitals treating a median of 201 patients. Derived communities demonstrated a modularity of 0.904 , indicating a strong community structure, highly centralised around one or two hubs. Three distinct types of structures were identified: single-hub (n=60), double-hub (n=22) and hubless systems (n=11). The endovascular treatment rate was highest in double-hub systems, followed by single-hub systems, and was almost zero in hubless systems. The hubless communities were characterised by lower patient volumes, fewer hospitals, no hub hospital and no stroke unit.

Conclusions

This network analysis could quantify the national stroke care system and point out areas where the organisation and functionality of acute stroke care could be improved.

Prehospital time of suspected stroke patients treated by emergency medical service: a nationwide study in Thailand

Background

This work was to study the prehospital time among suspected stroke patients who were transported by an emergency medical service (EMS) system using a national database.

Methods

National EMS database of suspected stroke patients who were treated by EMS system across 77 provinces of Thailand between January 1, 2015, and December 31, 2018, was retrospectively analyzed. Demographic data (i.e., regions, shifts, levels of ambulance, and distance to the scene) and prehospital time (i.e., dispatch, activation, response, scene, and transportation time) were extracted. Time parameters were also categorized according to the guidelines.

Results

Total 53,536 subjects were included in the analysis. Most of the subjects were transported during 06.00-18.00 (77.5%) and were 10 km from the ambulance parking (80.2%). Half of the subjects (50.1%) were served by advanced life support (ALS) ambulance. Median total time was 29 min (IQR 21, 39). There was a significant difference of median total time among ALS (30 min), basic (27 min), and first responder (28 min) ambulances, Holm P = 0.009. Although 91.7% and 88.3% of the subjects had dispatch time ≤ 1 min and activation time ≤ 2 min, only 48.3% had RT ≤ 8 min. However, 95% of the services were at the scene ≤ 15 min.

Conclusion

Prehospital time from EMS call to hospital was approximately 30 min which was mainly utilized for traveling from the ambulance parking to the scene and transporting patients from the scene to hospitals. Even though only 48% of the services had RT ≤ 8 min, 95% of them had the scene time ≤ 15 min.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12245-021-00361-w.

Establishing a digital health platform in an academic medical center supporting rural communities

The University of Arkansas for Medical Sciences (UAMS), like many rural states, faces clinical and research obstacles to which digital innovation is seen as a promising solution. To implement digital technology, a mobile health interest group was established to lay the foundation for an enterprise-wide digital health innovation platform. To create a foundation, an interprofessional team was established, and a series of formal networking events was conducted. Three online digital health training models were developed, and a full-day regional conference was held featuring nationally recognized speakers and panel discussions with clinicians, researchers, and patient advocates involved in digital health programs at UAMS. Finally, an institution-wide survey exploring the interest in and knowledge of digital health technologies was distributed. The networking events averaged 35-45 attendees. About 100 individuals attended the regional conference with positive feedback from participants. To evaluate mHealth knowledge at the institution, a survey was completed by 257 UAMS clinicians, researchers, and staff. It revealed that there are opportunities to increase training, communication, and collaboration for digital health implementation. The inclusion of the mobile health working group in the newly formed Institute for Digital Health and Innovation provides a nexus for healthcare providers and researches to facilitate translational research.