Ideas Blog

Artificial Intelligence: the great unknown


Artificial intelligence has arrived, is here to stay, and is likely to transform our work-lives, personal lives and social structures. Exactly how no one is entirely sure.

The potential of AI was very apparent from discussions at the IBM Watson Summit in Auckland on August 16, 2017, and the New Zealand AI Forum ‘Connect’ event that followed.

With the development of data analysis that uses natural conversation as commands rather than code, expert practitioners in various disciplines who are not trained in programming will be able to navigate complex data structures to gain evidence-based insight without the need for analysts. Neural networks can be programmed without coding by using IBM’s Darviz tool.

For more on the future of analysis and AI see 13 year old Tanmay Bakshi’s YouTube channel with over 100 instructional videos.

The attendees at IBM’s event were at pains to point out that AI will not replace humans but will augment what humans can do. However, I wonder how truck drivers feel about autonomous vehicles?

Later in the day at the AI Forum event, New Zealand lawyer Bruce McClintock used historic case law to demonstrate how the issues of foreseeability and negligence are well covered by existing law. But how will we negotiate the issues around human autonomy and freedom that AI is likely to impinge upon. These are societal and moral rather than legal issues.

It is clear that much more thought and research is needed into the social, psychological, ethical, and legal aspects of AI and it’s rapid introduction into our lives.

At Adapt Research, we are very interested in this space, and in collaboration with one of our clients we’ve submitted an opinion piece for publication on these issues (details to come). We will update this blog with further commentary as it emerges.

Click here to contact us if you would like a copy of our report once it is published.

For information on AI issues, see for example:

Strategic Implications:

Societal Response:

Further research needed:

Read back is critical for healthcare communication and safe teamwork

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Safe and effective healthcare is frustrated by failures in communication. We know that double checking drug names and doses and using checklists are huge boons to patient safety. Effective communication is important too.

Repeating back important information (read back) enhances the effectiveness of communication across many industries.

However, formal communication protocols are uncommon in healthcare teams.

In our study we quantified the effect of read back on the transfer of information between members of a healthcare team during a simulated clinical crisis.

To do this we gave post-anaesthesia care unit nurses and anaesthetic assistants clinically relevant items of information at the start of simulations. A clinical crisis was prompted so that participants called an anaesthetist, who had no prior knowledge of the patient.

We analysed video recordings of the simulations and found that anaesthetists who read back the information were eight times times more likely to know the information at the end of the scenario compared to times when they didn’t respond.

Anaesthetists who gave any response at all were still three times more likely to know the information compared with no verbal response.

This means that in a critical healthcare situation, if information is not read back, there is a good chance that communication has failed.

Training healthcare teams to use read-back techniques should increase information transfer between team members with the potential for improved patient safety.

Catheter Ablation is cost-saving if we choose the right patients with Atrial Fibrillation


Catheter ablation (CA) for atrial fibrillation (AF) is a procedure with high up-front costs but is superior to pharmacologic treatments for reducing symptoms1 and hospital presentations2. In patients with mild symptoms or few hospitalisations the cost of CA may not be justified.

However, for patients with severe symptoms and/or frequent hospital admissions CA could be preferred when downstream health system costs and quality of life are taken into account. Several international cost effectiveness analyses have been published on CA for AF3, but few have stratified the target patient group by hospitalisations avoided, or by heterogeneity of quality of life gained.

Adapt Research developed a macroeconomic model to define a patient population for whom CA is economically rational.

We compared scenarios where CA is offered to different sub-groups of patients with AF. International literature and local New Zealand health system data informed heterogeneity of procedure success by type of AF, time since procedure, and age of patient. Disability weight and number of hospital presentations were varied. Costs of CA, downstream outpatient care, and subsequent hospitalisations were estimated from New Zealand health datasets and international literature. CA and pharmacologic management were compared to obtain incremental cost-effectiveness ratios (ICERs). Scenarios were modeled over five years and no difference in the rate of mortality or stroke was assumed between CA and drug treatment.

It turns out that the ICER for CA compared to pharmacologic management ranged from cost-saving to NZD$169,308 (USD$112,680).

Variables tending to increase the ICER were: lower cost of drug treatment, increased cost of CA, offering CA to older patients, and to those with non-paroxysmal AF.

Variables tending to decrease the ICER were lower procedure cost, increased disability weight assigned to AF, and increased number of hospitalisations avoided.

The ICER under present provision in New Zealand is estimated to be NZD$55,994 (USD$37,249). Targeting only those patients with the most severe symptoms reduces the ICER to NZD$35,750 (USD$23,782).

CA is cost-saving for patients having more than one hospitalisation per year for AF.

If QALYs and absentee days are monetized using GDP, then CA for a wide range of patients is cost-saving from a societal perspective. Time to recoup costs ranged from zero to 17 years.

So it seems that the cost-effectiveness of CA for AF is highly dependent on the patient population to whom CA is offered. This is important given heterogeneity of the target population. Using severity of AF scales4, which have been validated against quality of life metrics and number of hospital presentations, could help identify an appropriate target patient group.

Click here to request a copy of our full technical report.

References: 1 Shi, LZ. et al. 2015. Exp Ther Med, 10(2):816-22. 2 Bulkova, V. et al. 2014. J Am Heart Assoc, 3(4) e000881. 3 Neyt, M. et al. 2013. BMC Cardiovasc Disord, 13(78). 4 Ha, AC. et al. 2013. J Interv Card Electrophysiol, 36(2):177-84.

Should we close borders in a pandemic?


There will almost certainly be future pandemic diseases that pose a grave threat to human lives. Pandemic influenza, novel emerging infectious agents and possible synthetic bioweapons all pose serious risks. It seems biologically plausible that a new infectious agent might have the transmission characteristics of influenza and the death rate of Ebola.

In our modeling study we explored the costs and benefits of complete border closure to protect the island nation of New Zealand during a global pandemic.

Our cost-benefit analysis took a societal perspective and included case-study specific epidemiological data from past influenza pandemics. Country-specific healthcare cost data, valuation of life, and lost tourism revenue as well as a complete end to trade.

Even in the face of a complete end to tourism, exports and imports, a net benefit was estimated for scenarios where the mortality rate was very high at 2.75% of the country’s population dying. In this situation the net benefit was NZ$54 billion. Even for lower mortality rates there was a period of closure between 12 and 26 weeks at which the net benefit switched from favorable to unfavorable.

This “proof-of-concept” modeling work suggests that in some extreme pandemic scenarios it may make sense for New Zealand to close its borders.

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