This year as CEO of the Health Information Management Association of Australia, HIMAA, I have had the opportunity to attend a number of national and international events related to our healthcare system, aged care system, digital health and AI.
As I reflect on what I have heard and the conversations I have been part of, it strikes me that we continue to have an unsung hero in our midst.
This hero is somewhat assumed, present in all settings and interactions, part of every interoperability message, database and AI action. The true hero of all digital systems and the underpinning of modern life itself – DATA.
Like a drop of water at its inception, a single piece of data created to describe and represent facts at a point in time finds itself part of a small trickle as it co-exists with other data, is joined together with other data like a stream, being pushed forward into rivers and eventually the ocean, where it loses its source identity and integrity (where did it come from? where was it first collected? What were the parameters of it? What is the quality?) to be ubiquitous – it’s just “data”. It’s pushed around, is the fuel for systems, and is the reason for decisions. Our health and care ecosystems can’t exist without it.
There is an insatiable thirst for more data, more meaningful data, more precise data, more access to data. Remote monitoring, precision medicine, data integration and interoperability, the application of AI are all fueled by this thirst.
Means to an end
And yet, data is not the end game, it is a means to an end. It provides and enables the rationale behind insights, knowledge and decisions.
As a result of data, decisions are made every day that affect patient outcomes, health and care outcomes, funding and services.
I have heard an increasing recognition of the importance of data and its integrity, sentiments shared by the Australian Commission on Safety and Quality in Health Care, the Australian Institute of Health and Welfare, the Independent Health and Aged Care Pricing Authority, the Australian Digital Health Agency, and the Australian Government Department of Health, Disability and Ageing.
Australia’s ongoing aged care reform demands an increased focus on data capture, integrity, use and reporting to demonstrate accountability and improve operational efficiency.
Data integrity
In the midst of ongoing technological advancement, if we don’t have data integrity all the effort is in vain, including AI.
There needs to be integrity of pre-existing/re-used data, and newly created data including by technological means. Practices and methods at data creation, transformation, exchange, and analysis are vital to data integrity.
Dialogue, shared experience and research need to occur to determine and publish best practices for data integrity in the health and care sectors within the context of technological advancements.
I believe it is time to give more attention to, value, and advocate for our unsung hero of the health, disability and ageing sector, data, to ensure its integrity, to optimise its value, and to achieve its purpose – better health and wellbeing for all.
People see a graph and ‘believe’ it without question – despite absence of definitions of data variables or any details of the methodology behind the data capture. No idea of data quality but if it’s in a graphic, it must be true.
Possibly, it is limiting to apply this question only to ‘health’.
Yes: Quality data requires consistency of metrics and measures as well as diligent lodgement to provide continuity. The Department seems to obsessed with fragmented funding source metrics without any rationalisation or means of tracking equivalents across programs avoids credible assessment of outcomes (time series), value and effectiveness.
A problem with AI is that it treats all data as equal, seeing as so much health and related data is inconsistent obfuscating rubbish, especially at the population level and prevalence assessment. An essential component is to requires some quality metadata (with location information) to be included with data feeds. In its current “free for all” state AI will probably dis-inform rather than inform. Statements about the use of AI and the qualifications and experience of the user/interpreter should be mandatory for any report or opinion.
YES: People see a graph and ‘believe’ it without question – despite absence of definitions of data variables or any details of the methodology behind the data capture. No idea of data quality but if it’s in a graphic, it must be true.
YES: I think we dont consider the value that the HIM provides, they just get on with their roles and responsibilities, they certainly are the engine room of clinical outcomes.
YES: For a long time, health has functioned in an applications driven world, with the assumption that the systems ensure the right data is collected and is accurate. Furthermore, we have relied on integration to move data about, resulting in a lot of duplication. When this data is brought back together, how do you know what piece of data is right? AI is just another tool but is being sold as the panacea for everything. More than ever, we need a data driven approach in a healthcare setting that is provided by a community, each with their own commitment to collecting good quality data that contributes to a rich data fabric for AI and other tools to use. Alternatively, you could go and buy one solution that fits all that generally comes with a high cost with no easy exit pathway and unexpected constraints.
There is a large amount of data that is never utilised to it’s full extent nor is it understood accurately. This is where HIMs come into play, as we are the experts on the data, how to interpret it and utilise the information to be advantageous for our health system. HIMs are your conduit between clinicians and IT, the governor of health information and data, the protector of privacy and we are underutilised and under recognised.