Satellite-Linked Remote Physiologic Monitoring During Simulated Rural Ground Ambulance and Rotor Wing Transports

Abstract

Telemedicine and remote monitoring are rapidly gaining momentum in health care, including in-home and postdischarge monitoring. However, remote monitoring for telemedicine teams and receiving medical centers is yet to reach emergency medical services. When transporting a critically ill patient, continuous vital sign monitoring may be helpful to enhance receiving center collaboration and change care delivery before arrival when necessary. Our primary objective was to demonstrate successful transmission and reception of real-time physiologic summary data to a hospital base station during ambulance transport using a low-energy Bluetooth wireless wearable device linked to iridium satellite communications. We completed our proof-of-concept study on 2 simulated ground ambulance transports and 1 helicopter flight in rural areas surrounding Rochester, Minnesota. Each simulated transport used Mayo Clinic–developed wearable devices paired one-to-one with a physiologic communication kit. Electrocardiographic and photoplethysmographic waveforms were processed on the wearables every 5 minutes, with vital sign data generated and transmitted to an iridium satellite and relayed to a base station network–protected computer and mobile application with real-time display of summary and location. Data and geolocation were tracked in real time with a mobile application during the simulated transport, and summary data packets were saved on Mayo Clinic servers. During the 3 simulated rural transports, all (n=7; 100%) wearables successfully captured, recorded, and transmitted raw waveform physiologic signals. All devices transmitted successfully via the iridium satellite to the base station network–protected computer and mobile application. In all, 106 (69%) of 154 transmissions were successfully processed. Our project demonstrated successful remote monitoring of physiologic vital signs during transport in rural, low–cellular signal areas. The use of live remote monitoring should be explored to understand its utility in practice and outcomes.

Telemedicine and telehealth, which include remote monitoring (RM), are rapidly gaining momentum across the health care spectrum, including in-home and postdischarge monitoring. However, real-time or near real-time RM for telemedicine teams and receiving medical centers is yet to reach emergency medical services (EMS) with any consistency. Literature searches found no prospective studies examining real-time transmitted RM in the prehospital space apart from a theoretical study examining the use of wearables and artificial intelligence for triage during mass casualty incidents.¹

The primary objective of this proof-of-concept study was to deploy and functionally test a privately developed remote vital sign monitor using electrocardiographic (ECG) and photoplethysmographic (PPG) waveforms during rural simulated patient transports in both a ground ambulance and helicopter. Relying on both a low-energy Bluetooth (BLE) personal area network and global area network satellite communication technologies (Iridium network; Iridium Communication Inc), we tested a novel, remote vital sign monitor on volunteers during simulated rural ambulance transports and helicopter flights.

According to the National Association of State Emergency Medical Services Officials, in 2020, there were 18,200 local EMS agencies that responded to approximately 28.5 million 911 requests across 41 states, using approximately 73,500 ambulances and 750 helicopters or fixed-wing aircrafts.²

Often, patients are transferred long distances from remote rural locations, requiring significant time.³ During the transport period, physiologic data and evolving patient clinical information are often unknown to the emergency medicine (EM), trauma, and receiving inpatient teams unless transport team personnel call or radio with the concerning clinical change. Theoretically, having real-time or near real-time notification of clinical deterioration will enhance patient safety through hospital-based telehealth and prehospital team collaboration and improve clinical outcomes by earlier resuscitative, procedural, or pharmacologic intervention. To our knowledge, there is no routinely used product on the market allowing for real-time vital signs to be automatically transmitted over long distances during transport. Therefore, we see significant potential for a technology that will change both rural and urban prehospital medicine and enhance the safety of our patients being transferred for higher-level and specialized care.

Methods

Design

This study involves proof-of-concept pilot testing of the feasibility of a BLE physiologic monitor transmitting vital signs via a satellite-linked communication network to an Android-powered mobile application for geolocation and a base station hospital server.

Participants

Seven individuals, including physicians, engineers, and helicopter pilots, volunteered as “patients” to wear the BLE monitor during ground and air ambulance live transport simulations. No personal identifiable information was collected or transmitted.

Procedure

We completed 3 total simulated prehospital transports in rural areas surrounding Rochester, Minnesota: 2 ground ambulances with a combined distance of more than 100 miles (approximately 50 miles each) and a 120-minute rotor wing flight (Eurocopter EC-145 aircraft).

Each simulated transport tested the BLE to satellite to server link using Mayo Clinic–developed wearable devices paired one-to-one with a physiologic communication kit (PCK) for a total of 7 recordings (Figure 1). The PCK is a Mayo Clinic–manufactured small, handheld antenna communicating with iridium satellites. Electrocardiographic and PPG waveforms were captured and processed continuously on the wearables at 1-second intervals and transmitted as a packet of data via BLE to the paired PCK. To conserve bandwidth, the recorded vital sign data packets were transmitted every 5 minutes from the PCK to the satellite communications system, which, in turn, relayed data to a base station network–protected computer and mobile application with real-time display of summary data and location (Figures 1 and 2). The 5-minute transmission time interval is the software default but is programmable to a specific time interval depending on the patient or clinical need.

Figure 1.

Data transmission and flow.

Figure 2.

An Android screenshot of the helicopter’s location.

The PCKs were placed on the vehicle dashboard in the ambulance and on a window in the helicopter to optimize the “line of sight” for satellite transmissions.

Results

During the simulated transports, all (n=7; 100%) wearables successfully captured, recorded, and extracted raw ECG and PPG waveform physiologic signals from volunteers. All devices transmitted successfully via satellite communications to both the base station network–protected computer and the mobile application (Table). In all, 106 of 154 vital sign data packets via iridium transmissions (69%) were successfully received at Mayo Clinic servers and the Android operating system, noting a higher proportion during ground transport than that during flight transport.

Table.

Data Transmission by Transport Mode

	Distance/Altitudea	Hours	No. of monitors used	Satellite data packets transmitted (n)	Packets received successfully, n (%)
Ground run 1	∼50 miles	2	1	22	17 (77)
Ground run 2	∼50 miles	2.5	4	120	83 (69)
Flight	2500 feet	1	2	12	6 (50)
Total/mean %			7	154	106 (69)

^aSI conversion factors: To convert distance (miles) values to kilometers, multiply by 1.60934; to convert altitude (feet) values to meters, multiply by 0.3048.

As outlined in the Table and Figure 2, summary and geolocation data were monitored in real time with a mobile application during the simulated transport, and vital sign summary data were saved on Mayo Clinic servers.

Discussion

Telehealth has seen explosive growth in utilization and market value since the onset of the coronavirus disease 2019 pandemic. The US Department of Health and Human Services studied Medicare beneficiaries, not only showing a 63-fold increase in telehealth utilization in 2020 but also reporting disparities in utilization between rural and urban locales.^4,5 The US telehealth market was estimated at $90 billion in 2021, with a projected growth to $636 billion in 2028.⁶ Similarly, RM has also seen an incredible growth following the need to monitor and track patients at home with coronavirus disease 2019.^7,8

However, despite the demand and financial growth, telehealth has minimally penetrated the prehospital area of operations. There are published works on developing a vital sign sharing system, shared transmission of ECG waveforms for acute cardiac care, and rural ultrasound images.9, 10, 11

The new Emergency Triage, Treatment, and Transport payment reform model, which requires a telemedicine link, is currently being studied by the Center for Medicare and Medicaid Services.¹² However, to our knowledge, there is no reported, consistent, prehospital utilization of telehealth services or support. Commercial prehospital RM systems have been developed¹³; however, a search of the National Library of Medicine resulted in no publications or data on specific devices.

Critically ill patients are often transported directly from a scene or from a hospital to higher levels of care for specialty needs or resources unavailable to local care facilities. These transports can be lengthy, and patients with severe injury or illness have a rapidly changing physiology. Our helicopter transport times between facilities showed a median and maximum of 29 minutes and 3.5 hours, respectively. Our ground transport times between facilities showed a median and maximum of 55 minutes and 7.5 hours, respectively. In addition, in rural transport service areas, cellular service signals may be limited or nonexistent to transmit data in ground ambulances and may not be optimal or achievable in a helicopter.

Our primary goal was to develop a RM platform for telehealth and receiving clinical teams to have continuous awareness of patients’ vital signs during their transport to collaborate with transport care providers, if necessary, well before arrival at the hospital. Transporting patients over rural areas presents a key barrier to data transmission simply because of limited cellular infrastructure for ground transport teams and an inability to reliably use a cellular signal in an aircraft. Our technology’s key differentiator is that we use a Mayo Clinic–developed, BLE, iridium satellite–linked communication platform that can, conceivably, be used anywhere. For this test, the vital sign data packet transmission was set at every 5 minutes; however, the wearable devices can be configured to send data more frequently as needed. Flexible and adjustable data transmission timing is important for patients with critical illness or hemodynamic compromise.

Observation medicine is routine in EM. However, with the new Centers of Medicare and Medicaid Services Emergency Triage, Treatment, and Transport reimbursement model, conceptually, patients deemed to be treated on scene and not require transport to a hospital could be monitored by EM telehealth teams for a short time using field RM. Emergency Triage, Treatment, and Transport is a new, voluntary payment model launched in 2020 allowing for participating EMS systems to be reimbursed if a patient is treated and not transported or transported to an “alternate” destination other than an emergency department (ED).¹² Normally, EMS are only reimbursed if patients are “…transported to a hospital, skilled nursing facility, or dialysis center.”¹² Prehospital RM may allow for more patients to remain in place rather than be transported to an ED. Consider an example of a patient with mild asthma or congestive heart failure who needs only therapeutic bronchodilators or diuretics living in a rural area with low cellular coverage. Frequently, these patients travel long distances, are seen and treated in an ED, and are discharged. These patients could potentially be monitored at home for symptomatic improvement after treatment by paramedic teams.

Currently used vital signs have been demonstrated to poorly predict shock and may change little owing to combined physiologic responses (tachycardia and vasoconstriction).¹⁴ Hence, the concept of “compensatory reserve” has been established and studied but requires machine learning to aggregate clinical data elements.14, 15, 16, 17, 18, 19 An uncontrolled hemorrhage simulation study from 2013 among paramedics demonstrated significant reduction in the time required to recognize hemodynamic instability using a machine learning–derived warning system.²⁰ We aim to bring early detection with collaboration and specialty expertise to transport teams while enroute with patients, long before arrival to the hospital, with the goal of reducing morbidity and mortality with early intervention or guidance when necessary. With earlier clinical change detection and collaborative intervention before arrival at the hospital, we could potentially realize a reduction in intensive care unit and hospital stays by changing the physiologic course and patient trajectory, resulting in health care cost reduction and indirect revenue for systems with hospital beds opening sooner. This will require future study.

Challenges/Future

One challenge with satellite communications is that the technology relies on the line of sight with an unobstructed view of the sky. Our combined successful reception percentage of 69% (Table) was lower than anticipated because of our team not accounting for transmissions beginning while waiting in buildings to load the ambulance or helicopter. We believe the successful transmission reception was and will be much higher during the specific transport time, and we aim to demonstrate that in an upcoming prospective trial.

The next critical steps in development are to complete a prospective, observational trail in transported patients with eventual development of machine learning algorithms to detect subtle changes in transmitted vitals and to alert receiving teams of changing physiology. Theoretically, in the long term, with enough physiologic and patient data elements, artificial intelligence could support transport and receiving teams by predicting the likelihood of clinical deterioration or even cardiopulmonary collapse.

Conclusion

Our team demonstrated successful capture and transmission of remote monitor ECG and PPG waveforms of vital signs during transport in rural, low –cellular signal areas. Future development and research should include prospective testing in patients transported and monitored by telehealth physicians; transmission of other clinically meaningful metrics, such as measurements of cardiovascular and respiratory function; and development of machine learning clinical alarms. Having the capability to transmit, receive, and interpret physiologic data on patients will help develop monitoring processes, allowing for an enhanced care model by coordinating prehospital, telehealth, and receiving clinical teams.

Potential Competing Interests

Dr Russi reports travel support to present this project at a National Meeting (January 2023) from Mayo Clinic. Dr Noel reports patents planned, issued, or pending, not related to the current research project, with Securisyn Medical and Sense Neuro Diagnostics and reports participation, not related to the current research project, on the boards of MTEC (nonprofit) and Entegrion (for profit).