Global Telehealth 2012

The Global Telehealth conference was a great success last week.

We heard from a wide range of views, and several international groups. Half of the submission were international and one of the key messages seemed to be, there’s nothing particularly unique about the Australian experience and problems. Presenters looked into how business cases could be built and sustained, and avoiding pilot-itis

NICTA was a sponsor of the 2nd International Conference on Global Telehealth, which will be held from 26-28 November 2012 in Sydney. Speakers including myself and Hanna Suominen delivered peer-reviewed technical presentations on telehealth research, practice and evidence. The event covered a wide range of telehealth related topics including telemedicine, teleconsutation, telediagnosis, telemonitoring, telecare, and teletraining.

For more information on the conference visit Global Telehealth.

The Australian Telehealth Society (supporter for the conference) announced the Ministerial opening of the conference, and will put up slides soon.

CSIRO also launched its white-paper in the delivery of telehealth via satellite services. A good review can be found in the ABC interview. The report is available from the CSIRO site.

Storify

This week I’m also experimenting with Storify. The Storify version of the social discussion around the #GT2012 tag is available.

Health Worforce Australia: reviewing telehealth

Health Workforce Australia is looking to perform a review of telehealth skills across Austraslia, some material from HWA is below

Health Workforce Australia (HWA) was established as a national health workforce agency through the National Partnership Agreement on Hospital and Health Workforce reform. HWA drives a strategic long-term program which addresses the future challenges of providing a skilled, flexible and innovative health workforce.

The reforms are needed to address workforce shortages and to ensure Australia’s health workforce can meet increased demands for services resulting from an ageing population, increasing levels of chronic diseases and community expectations.

Recent research by HWA, Health Workforce 2025, indicates the need for co-ordinated, long-term reforms of the health sector workforce and the way it currently operates in order to ensure that Australia’s health workforce can meet increasing demands for health services effectively and efficiently.

More information will become available from Health Workforce Australia.

Big data: what does it mean for healthcare?

Last week I was presenting on big data and the impact on eHealth at the CeBIT Big Data Conference.

CeBIT Big Data

The slides are now available via slideshare.

Gartner have put Big Data on the upslope of the hype cycle, stating that Big data is one of the most hyped terms in the market today. Some (admittedly with an interest in this field grow) have declared Gartner wrong, but for me, a telling measure of `hype’ is that I’ve presented at three Australian big-data industry workshops in last the few weeks. Nonetheless, of the technologies on the hype cycle most likely to change the world, big data is at the top of my list: and the biggest change is likely to be felt in Health.

Big Data changes everything for Health

The Big Data that I’m talking about is not the data sitting on a billing system server, it’s the breadcrumbs of information about everything that is known and recorded about everything to do with my health. And that includes the health of anyone who is anything like me to give an understanding of what interventions could (or should) apply to improve my health. And it’s not just the existence of that data, Big Data is about turning all that data into actionable information. Although actionable information grew from business intelligence, the concept is directly applicable to health care services.

Another thing that’s quite interesting is the whole big data and analytics side. It is quite hyped up right now, but the promise is there. Companies are able to process information that they were not able to do before – unstructured information – but it’s also about the speed at which they can do this

Gartner, via Marketing Mag

Part of Big Health Data is the massive data of genomics (about a terabyte per person) and proteomics (maybe hundreds of terabytes per person). The main problem for that data is massive pattern matching, and the main advances are through building efficient signal processing that can crunch those patterns on commodity hardware. This is the so-called “lab at the bedside” translational medicine that NICTA and others are working on. For the ‘omics, the problem is to either crush the data (reduce storage) or crunch the data faster, producing the equivalent of supercomputing with commodity hardware.

For me, the more interesting aspect is making all the unstructured data — the stuff that shows up in the other field of SQL databases — become actionable.

That is, the myriad small pieces of clinical notes and information floating around describing the whole-of-person and whole-of-nation health. Sebastian Seung has suggest this might be called my connectome. In this sense, Big Data is not a new technology, it’s a new philosophy. It’s not that the data is suddenly available: we’ve had electronic records and digitally share-able information forever. It’s the demand that we must combine all this unstructured data and use it and the expectation that this is possible that is the key.

Three tennets for Big Unstructured Health Data

Value the other

Current medical informations systems are often designed with relational databases in mind: fields are created that characterise the information, and perhaps with a view to allowing researchers access later. Eventually, the designer creates a field other where free text information is placed. This field captures the main value in the health record. In some cases many other fields are left blank, and the full notes placed in other. For this reason, natural language and unstructured data mining approaches for the other data are required. The key message: some structure is good, but forced structure is not needed if sufficiently powerful analytic approaches are used.

Social innovation

The next generation of informatics leader is not likely to be working in the same health service that needs the next generation of informatics software. The key here is social innovation: building structures that facilitate the social innovation ecosystem will drive major advances. The objective here is to harness the best of the open innovation community to the task at hand.

Some emerging areas are starting to appear, such as Kaggle who have coined the phase Making Data Science a Sport and have developed the approach of data analytics as a competitive sport. The $3m Heritage Health Prize, where NICTA’s combined team is in the top 20 of 1,441 teams, is one example.

Analytics as a service

There is growing interest in the idea of Machine Learning as a Service. This is where web-like (RESTful) API’s are built to allow the best algorithms access to data in a safe way, that allows end-users to build inference engines in much the same way that a child might build a Lego city. Google has announced a similar approach, and an excellent review is available. Analytics as a service for health expands the data mining and social innovation concepts to allow anyone who wants analysis done to get the best results without first employing a team of data analysts. The approach combines

• A data service that exposes API’s and de-identified data to analytics engines
• An analytics service that allows algorithms to be safely housed and applied to the data
• Storage for the data, meta-data and privacy-preserving techniques (middleware) that ensures the service conforms to specifications

This allows medical data users to treat analytics as a commodity resource, in the same way that one might view IT as a commodity. or telephone carriers as a commodity.

Opening the unstructured data of health will allow data triage and highlighting the actionable information from the raw data of clinical records. We are not at the point that Google suggested for a 2008 April Fool’s joke: searching tomorrow’s internet today but we are moving toward analysing tomorrow’s health today.

Changing the 80-20 rule of health

There are many references to the concept that the software catechism of 80-20 applies to health. In software development, there is a belief that about 80% of the processor power is chewed up by about 20% of the code. In health, similar statistics exist: about 80% of the spending of health is directed toward around 20% of the health outcomes — such as, the massive spending on acute care. Conversely, around 80% of the need (eg. preventative care) receives 20% of the attention. Actually, preventative care receives much less than 20% of health spending, see the previous post. But, accessing the unstructured big data of health may start to shift this balance, without requiring a substantial budget shift, at least, that’s the hope for Big Data.