Medical doctors continue to drown in unnecessary data coming from the EHR, in the meantime, an aversion towards digital health has been developed due to this overflow of information. What is necessary? When is it too much and when is it not enough?
We all remember the NHS Electronic Health Record Project from 2005, a program that was supposed to have every patient in the United Kingdom enrolled by 2010 and eventually spent more than US$24bn in its implementation and roll out but ultimately failed and had to be dismantled.
When we think about which lessons were learned from this entire project, we can start by thinking of two special indicators we didn’t count on at the time: adoption and acceptance.
Although very interrelated, these two concepts are independent forces that decide whether there is clinical readiness or not for a new technology.
Jhonatan Bringas Dimitriades
When we talk about using data and analysing/processing data (or about data-driven decisions), we usually don’t consider what the day-to-day Medical Practitioner has to say about these concepts.
Most technologists I have had the pleasure to speak with underestimate the influence of medical doctors in the procurement (or tech implementation) processes of the hospitals and in the development of work-flow guidelines that involve the use of new technologies.
The reality is that doctors are not happy about “abundance of data” and there is a clear reason why.
The EHR experience of doctors has not been as successful as the experience of the general population with an iphone, EHR systems have overflown doctors with bureaucratic information (Codes, requirements, patient insurance information, QA information and so on) that have no implication in the daily medical decision making and that only create volumes of chores and chaos in the Medical Practice.
In a study from 2014, it was reported that doctors in the USA spend 8.6h a week on administrative chores (We are talking about an entire work day a week!)
How do we deliver medical relevance in the data we present to the doctors? How do we leverage acceptance?
As spoken before in my article about digital biomarkers, there are considerable types of data packages and information that, if taken in consideration for clinical practice, would revolutionise and enhance the way we perform medicine, increase the level of “precision” in precision medicine and help us develop new types of Therapeutic compounds, devices and products.
So, how do we provide this information? The answer to this question exists in articles like this one that tell us about what doctors want to see when given information. Images, concise information, information that can be immediately generating a specific action or intervention.
The way to unburden the doctors from data is not to stop giving them data but to refine that type and amount of information they receive.
A good example of this is the dashboards that have been created for remote patient monitoring systems, enabling doctors and nurses to be able to understand, in seconds, the status of a patient.
The next step for data acquisition, processing and delivery is to understand something even bigger than medical work-flow, that is pathophysiology. This is the way a disease’s process works, we can call it the physiology of illness.
This concept can help us understand which information is most valuable to doctors in a specific situation at a specific time. Just to give a quick example, we can say that heart rate and blood pressure might not be the most important parameters when we monitor a patient with a migraine, but they might be vital to prognose shock in a patient that has lost blood in an accident.
Understanding pathophysiology and being able to tailor our data solutions to a more specific usage of technology will help doctors adopt and accept these new advancements and will make medical decision making data-driven.
As I always conclude in my articles, I would like to offer this phrase again:
The future of medicine is precision-based, tailored according to each patient, their physiology, gender, ethnicity, genetics and so on. Building technology for everyone means building for each one of us, in an individualised, precise way.
