What is AI? – part 3

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This is the third of three blog posts about Appreciative Inquiry, from Simon Blake.

Many overviews of AI begin in the same place: AI started life within the context of a healthcare organisation. Through my attempt to gain an up-to-date overview of the presence and use of AI within healthcare contexts I have, though, managed to inadvertently become embroiled in something that doesn’t sit very easily. The perceived problem? So much of the relevant literature I have come across and read seems to come from authors with a nursing background. Resistance to any conclusion that doctors just don’t seem to do (much) AI drove me on – and in any case, this is backed by the certain knowledge that the early work on AI came from a researcher working closely with doctors in the US.

David Cooperrider’s founding work – a ‘conceptual reconfiguration of action research’ – centred upon a case study at a private (but non-profit) healthcare facility in the US. It’s worth reflecting upon why it was that he used a healthcare organisation – the Cleveland Clinic (CC). Other than the convenience of the close proximity of CC to Case Western Reserve University – where he was registered as a student – a long-standing relationship had developed between the two organisations (propinquity, perhaps?). This connection was driven by the innovative doctor-led and -managed co-operative structure that the CC had taken from the 1970s onwards. A number of other graduate students had already conducted their research projects at the Clinic. Indeed, Christopher Johnston suggests that it was the good fortune for Cooperrider of having his supervisor, Professor Suresh Srivastva, point him in the direction of another student’s study into the innovative doctor (physician) leadership at the Clinic, that inspired the development of AI.

 

But does this genesis provide an indication that healthcare organisations, and the ways that healthcare professionals engage in organising their work, are particularly suited to appreciative approaches? I have wondered what would have happened had Cooperrider conducted his research in an organisation in a different sector, say, an investment bank. Would we even have AI (at least in the form and spirit that we do today)?

Healthcare organisations are, after all, different in many ways from other organisations; private, public and Third sectors. In addition, hospitals are quite different from high reliability organisations, (despite some recent claims to the contrary). Although, clearly if arguably, there are also some similarities, traces, resonances, with other organisations, one commonality is the claimed relevance to them all of an appreciative approach. And the same line of argument might follow for the healthcare professions, too.

This is perhaps an obviousness, given AI’s roots in organisational development (OD). Organisations from any sector might consider and use AI in their OD processes. However, health researchers are now making a point of asserting a clear distinction in the use of AI as a research approach (as distinct from an OD tool). Suza Trajkovski, a specialist neonatal nurse and academic at the University of Western Sydney, Australia, asserts and demonstrates the power of AI as a healthcare research approach which moves beyond (negative) problem-focussed towards positive inquiries. Suza has devised a structured review of the usage in healthcare of AI as a research methodology. Her conclusion is that AI is an “engaging, inclusive, and collaborative” way of exploring issues pertinent to healthcare, especially so because it aligns neatly with the current health sectoral interest in the Safety 2 paradigm.

 

Others see the relevance in a slightly different light. For instance,Professor Bernie Carter in the UK suggests it is the “high degree of involvement, participation, goodwill and collaboration apparently engendered by using AI” that make it a good fit. From a US perspective, Nancy Shendell-Falik and her colleagues at the Newark Beth Israel Medical Center, assert that AI’s strength as a novel healthcare approach is because it is “evidence based (using people’s experiences)” as well as being focussed upon relationships. They deepen the connection drawing attention to AI practitioners’ abilitiy to encourage “people to identify, engage, and strengthen the core values and ‘‘life-giving forces’’ within the (healthcare) organization”.

These researchers have deployed AI in areas as diverse as exploring family-centred care in a neonatal unit, clinical handover within an adult care hospital, and multi-agency complex care service provision for children. The applications and uses by others of AI in healthcare extend across specialities and organisations from maternity service reprovision to outpatient cancer care service innovations. Trajkowski’s review identified only 9 research papers which detailed how they’d used the 4D cycle in healthcare, but her wider review unearthed over 750 published works (to the end of 2011).

Those ‘in the know’ might already have noticed that the selection of significant, recent ‘AI-in-healthcare’ research offered so far has all been authored by Registered Nurses. Indeed, in searching for and through the AI literature, there appears to be a subtle, yet discernible, ‘healthcare professions bias’. As you might hope and expect (given Cooperrider’s initial focus upon physicians) that doctors and the allied healthcare professions are also present.

For example, in the UK, a key study of AI use in the NHS, published in 2005, was driven by Dr. Alastair Baker, a London-based consultant paediatric specialist. Whilst the AI interviews were conducted by Baker, and the study indicates a noticeably greater involvement by nurses. However, the research analysis and discussion was rather mute on this professions issue. Two leading figures in the use of AI in healthcare settings in the US are Drs. Julie Haizlip and Margaret Pews-Ogan of the University of Virginia. They have been involved in a key project which has had a wide-ranging impact across a number of organisations – driven by research, medical education and training centred at the Center for Appreciative Practice. This has included the acknowledgement of particular features of medicine that necessitated adaptations to AI in order to enhance the chances of attaining a successful transformation of the culture across four key organisations. Internal AI champions and clinical change agents facilitated the moulding of language, expectations and infrastructure. A model for other healthcare organisations? Certainly, the thoroughness of their work is indicative of a balanced and realistic management of the potential for and actual resistance to the AI approach.

As with the earlier adoption of AI by many nurse academics and leaders, so more latterly with medicine. Recent applications address the reform of medical student education, identifying exemplars of patient transfer (sign-out) when designing new handover tools, and finding out best practice in medical professionalism when using social media.

Against the backdrop of continued resourcing, policy and other (political) pressures, is this the environment within which to take an appreciative approach to healthcare? Some would say most definitely “Yes!”. Recall Gervaise Bushe’s injunction that AI isn’t only about the ‘positive’. In addition, the time appears right to enhance the impact of AI through alignment with other approaches such as mindful inquiry, compassionate healthcare and narrative medicine, as well as incorporating it into other offshoots of action research such as co-inquiry. Drawing upon interesting work within community development, a critical AI approach may also be a ‘good fit’ in healthcare settings. Indeed, if you work within the healthcare sector, can you think of a reason why you should not be appreciative in your approach?

Simon Blake
November 2015

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