Open Prehospital Electronic Patient Care and Response Record

Our solution team are operational staff working for the International Commitee of the Red Cross.

To put it simply - we have a problem with the collection of good quality, timely data from developing prehospital services. Outside of high-income, developed services, accurate data collection, analysis and research is extremely limited. Prehospital responders and emergency medical staff are generally not equipped with the necessary tools to be able to submit timely and accurate clinical and operational data, from the point-of-care, thus affecting the delivery of evidence-based programming, decision-making and care.

Now, why is this important?

Well, here's what we know - "…the availability of quality prehospital care causes a significant reduction in trauma-related mortality alone. It is also the foundation for effective disaster response and management of mass casualty incidents. So, it is a critical component of health systems and is necessary to improve outcomes of injuries and other time-sensitive illness." (Bhattarai, Barone-Adesi & Hubloue, 2023). However, we also know that these systems in LMICs face significant implementation barriers and are often far from an ideal standard, suffering from a lack of infrastructure, lack of investment, lack of research (Bhattarai, Barone-Adesi & Hubloue, 2023; Quake et al., 2024).

Now, of course, we can't address every challenge at once - but there is a gap that we can address, and it's that of the research and evidence base, the improvement of which could positively contribute to a better understanding of how to improve patient outcomes in the field. Mould-Millman, Sasser & Wallis (2013) stated that outcome-based research to "demonstrate the link between prehospital initiatives and mortality and morbidity" should be a priority, alongside contextually appropriate innovation. But this lack of research and a lack of knowledge of the true function of these systems remains, often meaning that we're 'flying blind' when developing innovative interventions (Nielsen et al., 2012; Suryanto, Plummer & Boyle, 2017; Quake et al., 2024).

So, there you have it. This gap in data collection, analysis and research is something that I think can be filled by developing a simple, free, customisable, application-based point-of-care data collection tool, that can be analysed (with the help of artifical learning) to help us produce a solid and important evidence-base, across a number of unique countries and unique contexts.

Speaking more personally, I have been privileged enough to work in a number of developing prehospital settings, often on projects geared towards improving this element of the local health system - however, from Nigeria to Kenya, Yemen to Sudan, Myanmar to Bangladesh, Somalia to Tanzania, this kind of tool is something we have always lacked. And yes, whilst some "off-the-shelf" eMR systems do exist, they are prohibitively expensive for developing health contexts, they lack contextualisation, tend to have a billing-centric focus, are poorly adapted for the prehospital setting, and have a variety of interface and connectivity issues - there is thus a gap in the market for a solution underpinned by humanitarian motivation and experience. So, why don't we build something that's more appropriate, more practical, more accessible, and just better?

My proposed solution is the development of a free (i.e., openly available), simple, contextually customisable (think language, staff & locations) application that will allow frontline emergency care responders to input point-of-care clinical & operational data, which can then be submitted to central, locally-owned databases for analysis, monitoring & research.

What kind information will it collect?

• High-level case information, no names or addresses:
• Uniquely generated case ID
• Date and time case received
• DOB / Age
• Patient Sex
• Dispatch & Operational Information:
• Operational location (customisable to context, or assigned to login)
• Response team (customisable to context, or assigned to login)
• Vehicle (customisable to context, or assigned to login)
• Pickup Location (GPS or What3Words-style geocode)
• Dispatch code / acuity
• Operational timestamps
•   -Time of injury
•  - Time of response
•  - Duration at scene
•  - Duration of transport
•  - Time of handover
•  - Time of clearance
• Distance travelled to scene
• Distance travelled to facility (if relevant)
• Identified access barriers? Distance, time, cost, knowledge of service / health literacy, safety & security etc.
• Condition Information:
• Care provided on site? By whom?
• Patient acuity on assessment / triage
• Mobility status
• Mechanism of Injury / Pattern of Illness (general)
• Mechanism of Injury / Pattern of Illness (type)
• Trauma injury registry opens for traumatic mechanisms
• Observations taken / timestamped
• Auto-calculated Kampala Trauma Score or Revised Trauma Score
• Treatment consent?
• Treatment type / timestamped
• Follow up flags (i.e., SGBV, child abuse, displaced population, person with disability, mental health concerns etc.)
• Handover & Disposition:
• Patient Transported?
• Transport acuity
• Dropoff Location (GPS or What3Words-style geocode)
• Dropoff location selection (i.e., why?)
• Handover provided? If not, why?
• Name of staff receiving handover, or name of facility receiving handover.
• Signature.
• Submit button  

The form will be designed to have a simple, functional layout, and to ensure that there is little-to-no freetext input where possible, with inputs such as observations, treatments and locations codified and standardised to allow for analysis with limited need for data cleaning upon submission. Conditional logic will be built in to certain responses, opening new options, or removing options. 

The application will have a smartphone & tablet front-end interface, governed by a provided login (by the participating organisation), with a web-based application for customisation/contextualisation (of language, staff, vehicles, locations etc.), analytics & data sharing. The application should be able to be operated both offline and online, with data stored on the device (for a set period), and submitted when connectivity is available.

Backend analysis will be automatically compiled, and artifical/algorithmic learning techniques can be used to identify trends, gaps, care pathways etc. at both the clinical level, and at the operational level - providing insight not only into patient outcomes and protocols, but also into access to care trends and service efficiency.

This solution is primarily designed to be used in contexts affected by fragility, conflict, a low- or middle-income status, or contexts where the prehospital / emergency care system is developing. For my organisation, ICRC, this is the case in the majority of contexts in which we are operating - with our obvious concentration on conflict areas meaning that these areas are of particular interest.

The target population/s for the solution will be both the response teams / organisations, and the members of their health catchment populations - with the data collected able to be used to improve the performance of the responders, identify supply gaps within the catchment, and explore mortality and morbidity trends pertaining to emergencies (think conflict or accident-related trauma, obstetric emergencies, NCD-related emergencies & disease outbreaks). Additionally, this solution would be of academic benefit in these contexts - providing a tool that can collect frontline data and feed this information into research, analysis and innovation.

As touched upon in the problem statement, we know that prehospital systems face a variety of implementation barriers in these contexts, and we stand to be able to address one of the key gaps, which could go on to provide the evidence to underpin future investment, future development and new initiatives. The improvement in these services could thus have a wide ranging impact on the lives of people living in a variety of contexts, potentially contributing to health system changes that reduce mortality and morbidity in the cities, countries and towns that are currently disproportionately affected.

Working with the ICRC means that both myself, and my teams are deployed directly to the countries and contexts where this solution could be of most benefit. We have direct contact with the beneficiaries and organisations that could put the tool to use, and already run health projects in many of these contexts that could stand to benefit from the improved data collection methods - particularly in Africa, Asia, the Middle East and the Pacific. 

More broadly, ICRC has missions is over 100 countries, and the wider Red Cross & Red Crescent (RCRC) movement has over 190 member societies, many of which could benefit from this solution, or that could help to advocate for its use in contexts that have emergency care systems not tied to the RCRC movement (i.e., government / public services).

Our organisation roots are in emergency response, and I believe that this solution suits our history, operational priorities and our unique mandate.

We have already built and trialed a number of iterations of this tool and we know that it enables evidence-based practices, it aids the improvement of care quality, and the effectiveness and sustainability of the health systems within which we work. 

The initial trial was conducted in Myanmar (and is still operational), starting in Yangon before being expanded to 2 further areas - initially using a QR code placed in the back of ambulances to allow responders to quickly access an online form. We used the findings to develop tailor-made training programmes for prehospital responders. According to responders from our original pilot,“This QR tool simply makes us document what we did. Especially after a few days when we look back at what we documented, we can see pretty much everything we should have done better." (Name Redacted, Thaketa, Myanmar). Another responder said, “I used to be in a flurry when it comes to treating patients. After attending ICRC's highly practical trainings in 2021 and 2022, I became calm and composed because I feel confident in what I need to do and how I do it" (Name Redacted, Thaketa, Myanmar).

A second trial was conducted in Ukraine, focusing on our ambulance teams in Odessa & Mykolaiv, allowing us to hone the collection points and test the solution in a context of higher clinical function.

A third trial is currently underway in Somalia, beginning in Mogadishu and currently looking to expand to 2 more regions. It has allowed for simple auditing of response records, and is also leading to new training programmes and service expansion. 

Through these trials we have been able to collect more than 14,000 records since 2022, and we have begun analysing the trends and using the findings to inform new initiatives and programme approaches. However, we have been limited in terms of scalability and analysis power due to the nature of the build platforms that we've been using - thus the reason for the pitch.

As I just touched on above, despite the success of the prototypes and trials, we are limited in terms of scalability, sustainability, deployability and analysis power due to the nature of the build tools that we've had to use. Until now, I have had to build, pitch and trial all prototypes myself, using build platforms based on licenced software, and hobby-coders to help with backend analysis - but, coming from a clinical background, I lack the technical skills to be able to fully realise the standalone, complete version of the tool. So, we could benefit from assistance with the technology and build, as well as help developing the analytical capabilities and outputs behind the scenes. This support could either be technical and in-kind, or financial to pursue a developer - though from my perspective, a technical partnership would be preferred.

When I was first developing this solution, I pitched it to a number of developers, research bodies and even our own internal innovations team - and there was significant interest. However, a mixture of budget cuts and my own impatience (I like to see things move quickly!) meant that I 'went it alone' to get the proof-of-concept pilot moving. As it stand now, I believe the concept has been proven, and we're ready to take it to the next level. It seems like Solve, looking at the track record, could be the right organisation to help us realise the dream, and help us create a solution that could be of great value to the emergency care community.

As touched upon in the problem statement and solution description, I believe that the development of an electronic solution to prehospital data collection and analysis in developing contexts is a relatively novel concept, and a concept that can contribute more than its weight in value when looking at the knock-on benefits in analysis, evidence and research. 

Speaking from experience, underdeveloped health systems often rely on analogue / paper-based record keeping systems, if they even have record keeping systems at all. These systems are difficult to audit, difficult to analyse, and trends can't be observed in real time. Additionally, records are often rudimentary, and don't collect clinical or operational information in any usable depth - part of the reason that there is a dearth of good quality analysis in many contexts, and part of the reason behind poor information continuity between the prehospital phase and in-hospital phase of care. 

Now, I can't pretend that there aren't already organisations that have explored system-level prehospital improvement through application-based solutions - I could point to Trek Medics (US), Ural EMS (Bangladesh) and Good SAM (UK) who have all looked at providing solutions for dispatching prehospital responders (formal or informal) through location-based software. However, we still see a gap in good quality record collection at the point-of-care, and the subsequent analysis - so this is where our solution looks to innovate, as well as potentially being of value to other application-based prehospital solutions, and the services and systems that use them. 

I believe our solution will have an impact on the problem in a number of ways.

1. Provision of a new tool to aid prehospital data collection at the point-of-care. <-- This addresses the problem of there not being a viable tool for this purpose, suited to the contexts within which we work. This tool will be free, mitigating a key financial barrier, and being digital in nature in can be a catalyst towards the digital transition for a health system.

2. Improvement of the point-of-care record keeping workflow. <-- The implementation of this tool, combined with simple training, will lead to faster and cleaner (i.e., more consistent) record keeping at the point-of-care. In turn, this will allow for more time with patients and faster clearance times - improving the care quality and efficiency of a service. 

3. Data analysis will lead to novel academic research. <-- Access to up-to-date, good quality, consistent date will make big data analysis and medical research easier and more accessible. 

4. Trend analysis and findings will lead to evidence-based training, education & protocol development. <-- Prehospital training initiatives and clinical practice protocols are often developed in high-income settings before being distributed to other contexts without adaptation. Having access to real-time trends will allow for tailored, contextualised training, education and protocol development, based on true evidence, to be conceived of in the contexts that it will be applied, creating local ownership of knowledge transfer practices.

5. Trend analysis and findings will lead to evidence-based operational changes and improved efficiency. <-- Through improved understanding of operational timestamps, patient hot zones, frequently visited facilities and access-barriers, organisations will be able to make informed decisions about ambulance positioning, shift structures and route selections to improve the unit-hour-utilisation (UHU) of their vehicles - leading to cost savings.

6. An emphasis on better record keeping at prehospital level will improve the continuum of care. <-- New tools require change management through training and education, and through these initiatives we can influence the sharing of information (i.e., handover practices) at the prehospital interface between responders and their in-facility counterparts. Improved information sharing means an improvement in patient understanding, and an improvement in care.

7. Trend analysis and findings will lead to an improved understanding of the prevailing health problems in the field. <-- Better understanding of prevailing health problems will lead to health promotion and public education initiatives to begin addressing health concerns via a public health approach. Prevention is better than the cure.

8. Above all, the purpose of this tool is to contribute to an improvement of patient outcomes (i.e., decreases in mortality and morbidity) through evidence-based practices. 

I'll reference the above impact statements when discussing the goals, indicators and measurements.

1. The success of the provision of the tool will be able to be measured through uptake - in terms of organisations, users, regions and countries. A wide footprint will be seen as a success measure.

2. The benefit to the point-of-care workflow can be measured in time-saved versus the pre-tool baseline (per responder), and the time-saved by removing the need to have dedicated data entry personnel to log and audit paper-based records (if they are kept).

3. The publication of novel research, focused on prehospital care or emergency care systems in our target contexts will be seen as the success measure here. Additionally, has the number of publications generated from a given context changed as a result of the tool's implementation?

4, 5 & 7. Creation of new training programmes, new clinical or operational protocols, or the improvement of UHU, response times or catchment population coverage (including reduction of access barriers) as a result of trend analysis and findings. 

6. Handover compliance and in-facility satisfaction (with shared information) can be used to measure the success of interface improvements. 

8. Reduction of mortality & morbidity associated with particular presentations. This could be measured against selected prediction scores (e.g. Kampala Trauma Score, Rapid Emergency Medicine Score etc.), or DALYs to examine a reduction of the wider health burden. 

The next iteration of the solution will be application with a smartphone/tablet-based front-end user interface, accompanied by a web-based application to drive customisations, analytics and data sharing. Backend analysis will be automatically compiled, and artificial/algorithmic learning techniques can be used to identify trends, gaps, care pathways etc., at both the clinical and operational level - providing valuable insight not only into patient outcomes, but also access to care trends, efficiency and even compliance to protocols. Interfaces could also be developed to ensure continuity with other tools (e.g., DHIS2, PEARL etc.). These are not new technologies by any stretch of the imagination, but the use of application-based, electronic record collection in developing prehospital settings is a novel concept, particularly when combined with machine-driven analysis. 

Through testing, we're also confident that even in developing and fragile contexts, access to connectivity and smartphone/tablet utility is relatively high. Additionally, as frontline responders tend to skew younger in terms of their age demographic, we're able to leverage the 'digital native' generation to improve the uptake, and to ease through the transition from analogue records (if they exist) to digital records.

Currently, I am primary driver of the solution. However, I'm supported with analysis and change management by 5 Health Field Officers in the contexts where we're running the trials. I am also supported by an innovation unit at our organisation's HQ in Geneva.

I started developing the concept in 2021, when I arrived in Myanmar, and have been developing it since then. The catalyst was my frustration that we were unable to show any value or impact following more than 5 years of prehospital interventions, due to the fact we hadn't been collecting any useful information, and we hadn't been using our data to inform programme developments. Since implementation, we moved from 0 patient records in 5 years, to more than 10,000 in 3 years.

Now, whilst I have primarily been the driver of the solution - I have been careful to ensure that any decision making pertaining to the development of the tool has been highly consultative, with the end-users and local field officers able to influence the direction, content, interface and workflow. This means that suggestions and changes have come directly from the field, in Myanmar, Ukraine and Somalia, by users and staff from these contexts. The success of a solution like this is hugely dependent on local acceptance, and this process of consultation and involvement will continue. 

Now, whilst our 'business model' does not have revenue at its core - I do believe that our solution has the ability to provide exceptional value to organisations and beneficiaries alike - through the various 'impact goals' described in the previous section. 

Currently, we support a variety of systems, organisations and beneficiaries through our health support programmes. We'd like to compliment this support by providing tools that will add value to local systems, and begin encouraging sustainability and innovation. This could and should (eventually) be achieved independent of our influence, though we're able to help kick start these initiatives across a healthy footprint of contexts. 

Furthermore, government services, private services and NGOs could also use and benefit from this kind of tool. Again, with the primary value being in the contribution to an improved health system, and improved patient outcomes. 

This solution does not have a profit at its centre. Simply, we're looking to create the next iteration of a solution that will be validated, fully operational, standalone and free for organisations and partners to use.

Of course, we can influence the utilisation of the solution through our various health projects, as we have during the trials - but the longer-term goal is to create a solution that can be of value to a variety of health systems, whilst not being a financial burden on them.