“Smart data, smarter healthcare”

Last week Hugh Leslie & I spent time in Shanghai and Huangzhou at the invitation of Professor Xudong Lu and Professor Huilong Duan. It was a privilege to be invited and particpate in the very first openEHR meetings to be held in China.

It follows on from a surprise discovery of Professor Lu’s work presented at the Medinfo conference in Brazil last year, and Prof Lu attending our openEHR training in Kyoto in Janauary.

Hugh & I presented at two events:

• a seminar of invited guests and VIPs for the first openEHR meeting to be held in China on April 18 in Shanghai – an introduction to openEHR (Heather) and an overview of openEHR activity in Australia (Hugh); followed by
• an introduction to openEHR – at the China mHealth & Medical Software conference, Shanghai on April 19

Watch my introduction to openEHR, ‘Smart data, smarter healthcare’ presentation, available via slideshare:

Bridging the interop chasms

The dilemma for implementers is: how to standardise clinical content yet use it in different clinical scenarios? How to lock down clinical content yet express variation in clinician requirements. How to create clinical content standards when every day the scope, depth and detail of the content is dynamically evolving? And then if we somehow manage to specify the content enough to successfully implement our own system, how to make that solution interoperable with others?

Everyone is running around (often in circles) trying to find the holy grail of health IT, the ‘one ring to bind them all’ of standards and, more recently, the ‘Uber of healthcare’. The sheer number of approaches reflects that there is no clear single approach at this point, no clear winning strategy.

My proposal: if we want to achieve any degree of semantic interoperability in our clinical systems we need to standardise the clinical content, keeping it open and independent of any single implementation or messaging formalism.

I noticed that John Halamka, CIO at Harvard & Beth Israel Deaconess Medical Center, was quoted in a rather unappetising Forbes article yesterday – worth reading for the interesting content but please leave the title and analogy at the door. His quote:

“FHIR is the “HTML” of healthcare. It’s based on clinical modeling by experts but does not require implementer’s to understand those details. Historically healthcare standard were easy for designers and hard for implementor’s. FHIR has focused on ease of implementation.” 

Totally agree: FHIR is the newest technology on the block and indeed many are considering it the HTML of healthcare. It has created a massive buzz of excitement around the world. But remember that HTML itself is a content free zone. Without addition of content, HTML is essentially just a technical paradigm. Similarly, without high quality clinical content, FHIR runs the risk of just being a marvellous new technical approach, elegantly solving our current implementer nightmares. We need more…

I have blogged previously about my experience participating in a FHIR clinical connectathon this time last year: The challenge for FHIR: meeting real world clinician requirements. My opinion about FHIR is neutral. I am not a software engineer. However as a clinician it was not offering enough to meet my recording needs a year ago. Little of my world has been FHIRified since. No doubt the FHIR team have a strategy for that, it will evolve, sometime.

My potentially controversial position is that it does not necessarily follow logically that the clever minds who devised FHIR are best placed to develop the clinical content that will ensure FHIR’s success. The great majority of FHIR models created to date are related to the US domain and within a rather limited scope that are largely focused on supporting existing messaging requirements and simple document exchange.

This is not a FHIR-specific problem – it is ubiquitous across the healthIT ecosystem. We need to transition away from the traditional business approach where the technicians/vendors dictate what clinicians would have in their EHR, and the data structure is determined by software engineers who have to investigate, interpret and then attempt to represent the world and work of the clinician in every EHR. With the best of intentions from both sides there is still a massive semantic chasm between the two professions.

What most technical modellers haven’t yet grappled with is the huge scope of clinical content – the sheer breadth and depth is astounding and as new knowledge is discovered it is also dynamic and evolving, which only compounds an almost impossible task. Most clinical recording in systems to date is relatively simple – usually focused on the ‘one size fits all’ minimal data set approach to enable some information to be shared broadly. It certainly has had some limited success but how long will it be before we realise that we need to standardise more than the minimum data sets.

Minimum data sets are woefully inadequate when we need to represent the detail that clinicians record as part of day-to-day patient care, for patient-specific data exchange with other clinicians, to drive complex decision support, for data querying, aggregation, analysis. Patients vary, so that no one approach will suit all – the needs for a surgical patient, a neonate, a pregnant woman vary hugely. Clinicians vary, so that no one approach will suit all – generalist vs specialist; medical and nursing notes may focus on the same things but from a completely different point of view. Clinical contexts vary, so no one approach will suit all. Many aspects of clinical recording needs recurring, fractal patterns. There are homunculi, questionnaires, normal statements, graphs, video, audio. The ‘one size fits all’ approach to health data standards is an absolute myth – in fact, the truth is actually that one size fits none.

Take the timing of medications as an example. Just this one seemingly simple data element raises a huge number of challenges for EHRs. I challenge you to show me one system in the world that enables prescribing today at the following level of detail:

This is the level of detail that clinicians need today in their clinical work. Yet most clinical systems only cater for the ability to prescribe to the level of complexity where someone can take one tablet, three times a day, after meals. Even if you try to prescribe a skin cream, many systems are unable to do anything but represent it in the most rudimentary way.

Below is the mind map of the current Medication Order archetype:

You can see and download the corresponding archetype that is currently undergoing review here. This clinical content specification is the result of years of research, investigation of existing clinical systems, engagement with vendors and standards organisations and direct clinical informatician experience. The amount of work involved in specifying this common clinical order should not be underestimated, nor should it need to be undertaken ever again if we can get appropriate levels of agreement through international domain expert review! (Please register in CKM and adopt the archetype to participate in the international review.) It is massive. It is hugely complex.

Via a similar process, we have just achieved a consensus view on our Adverse Reaction Risk archetype: 12 review rounds completed; 91 participants from 16 countries; 182 total reviews; 0 face-to-face meetings! A core concept in any clinical system, with every system implementing it slightly differently. Now we have a line drawn in the sand, a starting point for being able to share adverse reaction data that has been designed and verified by clinicians, yet immediately computable. This work was done in collaboration with the FHIR/HL7 patient care community – note the joint copyright!

Archetypes bridge the semantic chasm between clinicians and software engineers.

Using archetypes as the means to represent clinical knowledge in an open, non-proprietary way is major breakthrough in our quest to “help kill the “golden goose” of proprietary EHR data”, as the Forbes article phrases it.

One does not have to be a rocket scientist to recognise that if we leverage the 400+ existing archetypes already in the international CKM, which already incorporates a huge breadth of clinician knowledge and expertise, we can kickstart any serious development of implementable resources for any technical paradigm willing to join in.

There is a powerful logic in actively separating out development of clinical content from the implementation formalism. In that way we can develop, agree and verify the clinical content specifications once. The resulting archetypes become the free, open standard representing the clinician’s knowledge in an implementation agnostic way. Subsequently technical transformations will be able to deliver the content to the implementer in any formalism that they choose. No longer just one archetype for openEHR implementation alone, but leverage the content to develop FHIR, CIMI, CDA, v2, UML resources… Now there is a vision!

Archetypes bridge the semantic chasm between implementation formalisms.

IMO this is probably the greatest benefit : the archetyped clinical content is created once, verified as fit for use by our clinicians, and remains as a governed infostructure resource for the next years, decades and beyond. Governance processes ensure that the archetypes are maintained and can evolve within a robust versioning framework. Different implementations will have the same, standardised clinical content.

One solid truth that we can rely on in the world of technology is that FHIR, HL7v2, HL7v3, CIMI, UML, AML, openEHR and other implementation formalisms will likely be overtaken at some time in the future. As sure as death and taxes. We really don’t want to start the process of building content for the each and every new technical invention that comes along.

Archetypes bridge the chasms created by the volatile fads and fashions in IT: our resulting ehealth INFOstructure will be a non-proprietary representation of clinical knowledge that can withstand and outlast the inevitable waves of technology as they come and go. 

Archetypes are a pragmatic and sustainable approach to interoperability: between professions; between implementations; beyond our current technologies!

 

 

 

 

 

 

The ultimate PHR?

I’ve been interested in the notion of a Personal Health Record for a long time.

I was involved in the development of HotHealth, which launched at the end of 2000, a not-so-auspicious year, given the dot com crash! By the time HotHealth was completed , all the potential competitors identified in the pre-market environmental scan were defunct. It certainly wasn’t easy to get any traction for HotHealth take-up and yet only recently it has been retired. For a couple of years it was successfully used at the Royal Children’s Hospital, cut down and re-branded as BetterDiabetes to support teenagers self-manage their diabetes and communicate with their clinicians, but it wasn’t sustained.

This is not an uncommon story for PHRs. It is somewhat comforting to see that the course of those such HealthVault and GoogleHealth have also not been smooth and fabulously successful 🙂

Why is the PHR so hard?

In recent years I participated in the development of the ISO Technical Report 14292:2012: Personal health records — Definition, scope and context. In this my major contribution seemed to be introducing the idea of a health information continuum.

However in the past year or so, my notion of an ideal PHR has moved on a little further again. It has arisen on the premise of a health record platform in which standardised health information persists independently of any one software application and can be accessed by any compliant applications, whether consumer- or clinician-focused. And the record of health information can be contributed to by any number of compliant systems – whether a clinical system, a PHR or smartphone app. The focus is on the data, the health record itself; not the applications. You will have seen a number of my previous posts, including here & here!

So, in this kind of new health data utopia, imagine if all my weights were automatically uploaded to my Weight app on my smartphone wirelessly each morning. Over time I could graph this and track my BMI etc. Useful stuff, and this can be done now – but only into dead-end silos of data within a given app.

And what if a new fandangled weight management application came along that I liked better – perhaps it provided more support to help me lose weight. And I want to lose weight. So I add the new app to my smartphone and, hey presto, it can immediately access all my previous weights – all because the data structure in both apps is identical. Thus the data can be unambiguously understood and computed upon within the second app without any data manipulation. Pretty cool. No more data silos; no more data loss. Simply delete the first app from the system, and elect to keep the data within my smartphone health record.

And as I add apps that suit my lifestyle, health needs, and fitness goals etc, I’m gradually accumulating important health information that is probably not available anywhere else. And consider that only I actually know what medicines I’m taking, including over the counter and herbals. The notion of a current medication list is really not in the remit of any clinician, but the motivated consumer! And so if I add an app to start to manage my medications or immunisations this data could be also used across in yet another compliant chronic disease support app for my diabetes or asthma or…

I can gradually build up a record of health information that is useful to me to manage my health, and that is also potentially useful to share with my healthcare providers.

Do you see the difference to current PHR systems?

I can choose apps that are ‘best of breed’ and applicable to my need or interest.

I’m not locked in to any one app, a mega app that contains stuff I don’t want and will never use, with all the overheads and lack of flexibility.

I can ‘plug & play’ apps into my health record, able to change my mind if I find features, a user interface or workflow that I like better.

And yet the data remains ready for future use and potentially for sharing with my healthcare providers, if and when I choose. How cool is that?

Keep in mind that if those data structures were the same as being used by my clinician systems, then there is also potential for me to receive data from my clinicians and incorporate it into my PHR; similarly there is also potential for me to send data to my clinician and give them the choice of incorporating this into their systems – maybe my blood glucose records directly obtained from my glucometer, my weight measurements, etc. Maybe, one day, even MY current medicine list!

In this proposed flexible data environment we are avoiding the ‘one size fits all’, behemoth approach, which doesn’t seem to have worked well in many situations, both clinical systems or personal health records. Best of all the data is preserved in the non-proprietary, shared format – the beginnings of a universal health record or, at least, a health record platform fully supporting data exchange.

What do you think?