The Journal of mHealth Vol 2 Issue 4 (August) - Page 6

Digital Health: Making Sense of the Data Digital Health: Making Sense of the Data By Dr Alexander Graham Dr Alex Graham is a medical doctor by background, having trained in London before entering the business world. He is currently a founding partner at AbedGraham, a research and strategy consultancy which assists global IT corporates to navigate the clinical, organisational and commercial complexities of the UK’s National Health Service (NHS). He is also medical director of EMEA for Imprivata. One of the better analogies I have come across regarding healthcare data is the one with the faulty car engine. How can we know so little about ourselves, when even our car dashboard tells us immediately when something even small goes wrong, such as a flat tyre or a broken light? How often do otherwise healthy people take their blood pressure or check their blood sugar? I must admit I have no idea what mine is, and I used to tell people how to manage their conditions! We are approaching an inflection point in data gathering for healthcare. The advent of sensors, internal and external, electronic health records and patient portals means that the sheer number of discrete data points will only rise exponentially. One of my favourite statistics is that the world is producing as much data every two days as it was in the whole of time up to the year 2003, which is truly staggering. Of course, healthcare always lags behind most other industries but the avalanche of numbers, diagnoses, treatments, interactions and histories is coming and we are not prepared for it at all as things stand currently. Think about how much data is produced from a single patient even now. Primary care records, hospital admissions and treatments, community care, day-on-day and year-on-year. And what do we do with all that data? For the most part, hold it in user and institution specific silos without any attempt to glean real benefit from it. I remember in A&E, the sheer number of patients who came from primary care, the community or even just walk-ins that you couldn’t find so much as an allergy status about. Not only are we not realising the benefits of data, we are causing harm both clinically and financially. 4 August 2015 So data is the key then to a successful healthcare system? No, data on its own is almost entirely useless. The mere collection of numbers, statistics and records serves almost no purpose if there is not a concerted course of action to turn that into tangible knowledge. Take a patient’s allergy status, for example. Having the data as a stand-alone point on the EHR or a drug chart is fine, but it only becomes real knowledge when the prescription is made (or rather hopefully not made) for that particular drug. Or when we look at cohorts of patients and see whether heart failure as a side effect is higher or lower than the status quo. Or how symptoms quantitatively respond to the latest medication. That is the real challenge here, making sense of the data so that patients and healthcare professionals can actively change the way they look at their health. Here are a couple of things I think are important in data in healthcare. Structured and Unstructured Data The issue with most data is that it is not in nice neat columns in the same software packages and the same for every single patient. Most is concealed in the sprawling mass of handwritten notes and siloed departments. Data analytics as much as possible requires the collection of structured data that is easier to work with on a mass scale. The advent of single-provider EHRs or even the use of integration engines in the best of breed (multiple disparate providers doing different packages) model mean that a push towards standardisation can be made, although this must be considered as a viable entity in the design and adoption of these upcoming tech- nologies. Even if data is unstructured in nature however, such as patient scans or handwritten notes, the advent of technologies such as machine learning and natural language processing mean that we can start to generate actionable insights. Incentivisation The main question however, is not can the technology handle it (even with its limited use in healthcare, I trust advanced technology to do what it says on the label almost completely) but how do we handle the human element of data and knowledge? How do we get all the individual stakeholders in the system to jump on the bandwagon? Because the first question I have for any revolutionary analytics tool or the like, is how are you going to benefit everyone in the system? How will you convince the patient to wear their sensor? How can you make sure you don’t add to the workload of a community nurse? How can you make sure (cynical I know, but let’s not pretend this doesn’t matter), that a hospital doesn’t lose revenue or incur greater costs? Those are the real questions around what is ‘actionable’ data, because if you have the greatest collection and analytics machine in the world without incentivised individuals, then data will never become knowledge. I have no doubt that analytics platforms running on a constant stream of digital data underneath will help to change the way we practice medicine but until we refine the catchment and transformation into knowledge and understand how to fit data into workers’ working patterns, we will continue to lose out on the possible benefits. n