I was inspired to write this article after reading Dr. Curtis Kennedy’s feature in AIMed Magazine 01, where he shared his experience from the Pediatric Intensive Care Unit (PICU) at Texas Children’s Hospital.

I am not a Pediatric Intensive Care doctor by training. I have been trained so far as a General Pediatrician. Yet many times as a Pediatric Registrar, in the United Kingdom (UK) where I trained, I have had to step up to the best of my ability and, with the support of my senior team, play this role.

This happens in District General Hospitals (the largest hospital in a given region of the UK which provides a broad range of services but not specialist services) where there is no PICU and children present acutely unwell.

The local Pediatric teams have the duty of care to step up to the role of Intensive Care, at least for a few hours until the child is stabilized and transferred to the nearest PICU or Neonatal Intensive Care Unit (NICU), if the child is a neonate.

Like all trainees, I have received rigorous, standardized, high quality and continuous training to do this efficiently. However, anything more in terms of expertise, knowledge, data and latest evidence-based research that could empower me would significantly impact the outcome of the children I care for.

The power of knowledge and training, the power of the data and the power of a network ecosystem all play a huge role in delivering better care. Experts who work in Pediatric Intensive Care Units from all around the UK supported me and all my colleagues day in and day out.

In the NHS where I practiced, teams at District General Hospitals are empowered to deliver high standards of care with the support of teams such as the Children Acute Transfer Service (CATS) and the Acute Neonatal Team Service (ANTS) to name just two with which I had the honor to collaborate many times.

We have arrived at another turning point. We will now also be able to learn from this data and automate the process. Machine learning will increase the footprint of doctors, amplify their efforts and impact people that are not within their immediate reach.

In this way, virtual multidisciplinary teams from different hospitals come to form around the child that is most at need, even though not all of them are at the patient’s bedside initially. Therefore, communication and sharing of data between the teams is crucial.

In intensive scenarios, a big flow of data must be communicated back and forth from both sides. In my case, information such as patients’ past medical history, observations, blood results and many more were communicated via phone. I had to read out observations and blood results over the phone and I had to ask for scans to be transferred in a CD sometimes. All this friction would take my focus away from the bedside.

We had no other choice. The synthesis and analysis of this framework of data and other information allows for optimized clinical decision-making and appropriate action.

However, as my esteemed colleague Dr. Kennedy explained, the technology now exists that allows for this data to be taken effortlessly. Technology in the form of telemedicine exists that allows for an effortless communication and data flow.

We have a duty of care to empower our colleagues with better tools (improved monitoring, point of care diagnostics and augmented cognitive capacity tools). Our Hippocratic oath is towards mankind.

But most importantly we have arrived at another turning point. We will now also be able to learn from this data and automate the process. Machine learning will increase the footprint of doctors, amplify their efforts and impact people that are not within their immediate reach.

I often wonder what happens in parts of the world which don’t benefit from the kind of support ecosystem that I have access to.

Technology can now empower the physician or healthcare professional present at the time of need to deliver care to the best of their ability by empowering them with triage tools, clinical decision tools, risk prediction tools and many more.

Automated data processing and machine learning can empower the individual to deliver care in parts of the world where healthcare infrastructure is non-existent or broken – remote areas of the world, situations of natural disaster, humanitarian crises, war.

People step up in adverse situations to deliver care with whatever tools they have. We have a duty of care to empower our colleagues with better tools (improved monitoring, point of care diagnostics and augmented cognitive capacity tools). Our Hippocratic oath is towards mankind.

But the power of the data goes beyond constantly putting out fires. The heart of medicine lies in prevention, using data to predict disease and outcomes.

In these times of healthcare transformation, we have the power to be the best advocates for our patients by influencing how the power of the data will be used. Use them to do good.

The use of digital biomarkers and big data will allow us to diagnose and capture disease accurately at an earlier stage, at its pre-symptomatic form and intervene with the aim of reducing morbidity, mortality and complications.

We can use predictive analytics to build mortality and morbidity stratification scores, pick up high-risk populations, direct strategy and individualize treatments in a new era of precision health. We can facilitate better chronic disease management and remote self-care. We can use data to better quantify the impact of the social determinants of health and influence public health policy and strategy.

We need to be open and inspired. In these times of healthcare transformation, we have the power to be the best advocates for our patients by influencing how the power of the data will be used. Use them to do good.

 

This article originally appeared in AIMed Magazine issue 03. To download the full magazine click here.

 

pediatric artificial intelligence medicine

By Dr Lida Papadopoulos
Lida Papadopoulos, MD, EMBA
Lida Papadopoulos is currently the Business Development Lead for neuroFit. She completed her medical training at the University of Siena, Italy. She received Pediatric training at the London School of Pediatrics and obtained her Diploma in Child Health (DCH) and Membership with the Royal College of Pediatrics and Child Health (MRCPCH). During her clinical training she was involved in Child Public Health Projects and was Media Spokesperson for the RCPCH. She also served as a Pediatric Simulation Facilitator with SCAMPS (Sick Children Acute Multi-professional Simulation) at the Anglia Ruskin University. Her drive for innovation brought her to Silicon Valley where she completed an Executive MBA (EMBA) at the Berkeley-Haas School of Business.