England’s Electronic Prescription Service: Infrastructure in an Institutional Setting

Good friends in Oslo (Margunn Aanestad, Miria Grisot, Ole Hanseth and Polyxeni Vassilakopoulou) have just launched their edited a book on Information Infrastructure within European Health Care. The book is open-access meaning you can download it for free here.  

Infrastructure Book

Our team’s contribution is chapter 8 which discusses England’s Electronic Prescription Service that we evaluated for NPfIT over a number of years. This service moved UK GPs away from paper prescriptions (FP10s – the green form) to electronic messages sent directly to the pharmacy.  We examine the making of the EPS temporally by looking at:  (1) How existing technology (the installed base) and historical actions affect the project. (2) How the present practices and the wider NPfIT programme influenced. (3) How the desired future, reflected in policy goals and visions, influenced the present actions.

To go to our article directly click here.

England’s Electronic Prescription Service

Ralph HibberdTony Cornford, Valentina Lichtner, Will Venters, Nick Barber.

Abstract

We describe the development of the Electronic Prescription Service (EPS), the solution for the electronic transmission of prescriptions adopted by the English NHS for primary care. The chapter is based on both an analysis of data collected as part of a nationally commissioned evaluation of EPS, and on reports of contemporary developments in the service. Drawing on the notion of an installed infrastructural base, we illustrate how EPS has been assembled within a rich institutional and organizational context including causal pasts, contemporary practices and policy visions. This process of assembly is traced using three perspectives; as the realization and negotiation of constraints found in the wider NHS context, as a response to inertia arising from limited resources and weak incentive structures, and as a purposive fidelity to the existing institutional cultures of the NHS. The chapter concludes by reflecting on the significance of this analysis for notions of an installed base.

Image (cc) Simon Harrod via Flickr with thanks!

Government as a Platform – an assessment framework

I’m pleased that my paper with Alan Brown, Jerry Fishenden and Mark Thompson has been published in Government Information Quarterly today! The paper draws together our collective work on platforms and government IT to develop an assessment framework for GaaP (Government as a platform). We then evaluate recent UK government’s digital projects using the framework.

Cover image Government Information Quarterly

“Appraising the impact and role of platform models and Government as a Platform (GaaP) in UK Government public service reform: Towards a Platform Assessment Framework (PAF)”

Alan Brown, Jerry Fishenden, Mark Thompson, Will Venters

https://doi.org/10.1016/j.giq.2017.03.003

Abstract

The concept of “Government as a Platform” (GaaP) (O’Reilly, 2009) is coined frequently, but interpreted inconsistently: views of GaaP as being solely about technology and the building of technical components ignore GaaP’s radical and disruptive embrace of a new economic and organisational model with the potential to improve the way Government operates – helping resolve the binary political debate about centralised versus localised models of public service delivery. We offer a structured approach to the application of the platforms that underpin GaaP, encompassing not only their technical architecture, but also the other essential aspects of market dynamics and organisational form. Based on a review of information systems platforms literature, we develop a Platform Appraisal Framework (PAF) incorporating the various dimensions that characterise business models based on digital platforms. We propose this PAF as a general contribution to the strategy and audit of platform initiatives and more specifically as an assessment framework to provide consistency of thinking in GaaP initiatives. We demonstrate the utility of our PAF by applying it to UK Government platform initiatives over two distinct periods, 1999–2010 and 2010 to the present day, drawing practical conclusions concerning implementation of platforms within the unique and complex environment of the public sector.

Keywords

  • Platform;
  • Ecosystem;
  • Government as a Platform;
  • GaaP;
  • Digital Government

Image: Maurice via Flickr (CC BY) with thanks!

The Enterprise Kindergarten for our new AI Babies? Digital Leadership Forum.

I am to be part of a panel at the Digital Leadership Forum event today discussing AI and the Enterprise.  In my opinion, the AI debate has become dominated by the AI technology and the arrival of products sold to Enterprise as “AI solutions” rather than the ecosystems and contexts in which AI algorithms will operate. It is to this that I intend to talk.

It’s ironic though that we should come see AI in this way – as a kind of “black-box” to be purchased and installed. If AI is about “learning” and “intelligence” then surely an enterprises “AI- Baby”, if it is to act sensibly, needs a carefully considered environment which is carefully controlled to help it learn? AI technology is about learning – nurturing even – to ensure the results are relevant. With human babies we spend time choosing the books they will learn from, making the nursery safe and secure, and allowing them to experience the world carefully in a controlled manner. But do enterprises think about investing similar effort in considering the training data for their new AI? And in particular considering the digital ecosystem (Kindergarten) which will provide such data? 

Examples of AI Success clearly demonstrate such a kindergarten approach. AlphaGo grew in a world of well understood problems (Go has logical rules) with data unequivocally relevant to that problem.  The team used experts in the game to hone its learning, and were on hand to drive its success.  Yet many AI solutions seem marketed as “plug-and-play” as though exposing the AI to companies’ messy, often ambiguous, and usually partial data will be fine.

So where should a CxO be spending their time when evaluating enterprise AI? I would argue they should seek to evaluate both the AI product and their organisation’s “AI kindergarten” in which the “AI product” will grow?

Thinking about this further we might recommend that:

  • CxOs should make sure that the data feeding AI represents the companies values and needs and is not biased or partial.
  • Ensure that the AI decisions are taken forward in a controlled way, and that there is human oversight. Ensure the organisation is comfortable with any AI decisions and that even when they are wrong (which AI sometimes will be) they do not harm the company.
  • Ensure that the data required to train the AI is available. As AI can require a huge amount of data to learn effectively so it may be uneconomic for a single company to seek to acquire that data (see UBERs woes in this).
  • Consider what would happen if the data-sources for AI degraded or changed (for example a sensor broke, a camera was changed, data-policy evolved or different types of data emerged). Who would be auditing the AI to ensure it continued to operate as required?
  • Finally, consider that the AI-baby will not live alone – they will be “social”. Partners or competitors might employ similar AI which, within the wider marketplace ecosystem, might affect the world in which the AI operates. (See my previous article on potential AI collusion). Famously the interacting algorithms of high-frequency traders created significant market turbulence dubbed the “flash-crash” with traders’ algorithms failed to understand the wider context of other algorithms interacting. Further, as AI often lacks transparency of its decision making, so this interacting network of AI may act unpredictably and in ways poorly understood.
Image Kassandra Bay (cc) Thanks

Digital infrastructures in organizational agility – Dr Florian Allwein

It was a great pleasure to see Florian Allwein, my PhD student, successfully defend his PhD today. The thesis has significant lessons for practitioners interested in the role of their digital technology in promoting agility within large organisations.

The abstract of Dr Allwein’s thesis:

Organizational agility has received much attention from practitioners and researchers in Information Systems. Existing research, however, has been criticised for a lack of variety. Moreover, as a consequence of digitalization, information systems are turning from traditional, monolithic systems to open systems defined by characteristics like modularity and generativity. The concept of digital infrastructures captures this shift and stresses the evolving, socio-technical nature of such systems. This thesis sees IT in large companies as digital infrastructures and organizational agility as a performance within them. In order to explain how such infrastructures can support performances of agility, a focus on the interactions between IT, information and the user and design communities within them is proposed. A case study was conducted within Telco, a large telecommunications firm in the United Kingdom. It presents three projects employees regarded as agile. Data was collected through interviews, observations of work practices and documents. A critical realist ontology is applied in order to identify generative mechanisms for agility. The mechanism of agilization – making an organization more agile by cultivating digital infrastructures and minding flows of information to attain an appropriate level of agility – is proposed to explain the interactions between digital infrastructures and performances of agility. It is supported by the related mechanisms of informatization and infrastructuralization. Furthermore, the thesis finds that large organizations do not strive for agility unreservedly, instead aiming for bounded agility in well-defined areas that does not put the business at risk. This thesis contributes to the literature by developing the concept of agility as a performance and illustrating how it aligns with digital infrastructures. The proposed mechanisms contribute to an emerging mid-range theory of organizational agility that will also be useful for practitioners. The thesis also contributes clear definitions of the terms “information” and “data” and aligns them to the ontology of critical realism.

(c) Dr Florian Allwein

 

Image: (cc)Erick Pleitez (Thanks)

Anti-competitive Artificial Intelligence (AI) – [FT.com]

Yesterday’s FT provides a fascinating article (available here) on the role algorithms may increasingly plan in price-rigging and collusion. While previously humans have colluded to fix prices, today’s algorithms which seek profit maximization may end up colluding in a way which is hard to detect and difficult to stop. Indeed a recent OECD report states:

“Finding ways to prevent collusion between self-learning algorithms might be one of the biggest challenges that competition law enforcers have ever faced… [Algorithms and Big Data] “may pose serious challenges to competition authorities in the future, as it may be very difficult, if not impossible, to prove an intention to co-ordinate prices, at least using current antitrust tools”.

While algorithmic trading has proliferated in financial services (reported in many popular books such as “Dark Pools”), it is their increasing use in consumer marketplaces which concerns the article’s authors – airline booking, hotels, and online retailing.

The problem for regulation is that “All of the economic models are based on human incentives and what we think humans rationally will do.” (Terrell McSweeny US FTC) while an AI algorithm which “learns” that its most profitable course of action is price coordination are poorly represented in our understanding.

“What happens if the machines realise it is in their interest to systematically and quickly raise prices in a co-ordinated way without deviating?” (Terrell McSweeny)

Indeed we might ask whether an algorithm which uses huge databases of historical demand and supply data, and detailed data of the competitive marketplace, to arrive at its most profitable price in the milliseconds of a webpage loading is acting competitively in keeping with market principles or against the consumer (who could never undertake similar analysis and therefore faces huge information asymmetry challenges).

An interesting example in the article is an App to track petrol pricing whereby, because the app highlights instantly to competitors that a price has been cut (and they can match the price cut before demand shifts), so it removes the incentive for anyone to discount.

The article even states: “the availability of perfect information, a hallmark of free market theory, might harm rather than empower consumers”

 

(Image (cc) Keith Cooper – thanks)

Professorship in Information Systems at the LSE

It’s exciting that the LSE Department of Management is recruiting another Professor in information systems… For details see…  http://bit.ly/LSEProfIS

“We welcome applicants with a successful research record in areas of digital innovation such as digital platforms, service innovation and e-business, social media and the digital economy, and information infrastructures and digital ecosystems. Scholarship on big data as a key component of digital innovation will be desirable. We expect research that demonstrates strong relevance for understanding the complexity of social or organizational processes and the institutional patterns within which digital innovation is embedded”

Artificial Intelligence and human work.

The best computer is a man, and it’s the only one that can be mass-produced by unskilled labour.” (Wernher von Braun)

Last night I began to think further about the role of AI and humans in society while attending Future Advocacy’s launch of a report on “Maximising the opportunities and minimising the risks of artificial intelligence in the UK”. While a very useful contribution which I recommend, my friend Rose Luckin[1] (Prof in Education Technology @ UCL) rightly criticised the lack of specific focus on improving education and pointed out that our current education strategy centres around teaching children things computers do really well (basic maths, repetition, remembering things) rather than those AI will struggle with – creativity, critical thinking etc. This left me wondering what work humans are going to provide, and whether we really understand the skills requirement of a world with AI?

In thinking about this I recalled the quote from Wernher von Braun, the German rocket scientist that “The best computer is a man, and it’s the only one that can be mass-produced by unskilled labour.”  Since the onset of the industrial revolution mechanisation has replaced human skill and as Prof Murray Shanahan[2] said last night, already replaced many jobs. After all, only around 5% of us work on agriculture today. It is therefore not a question of whether, but the degree to which new AI technology will replace jobs – and the economic efficiency of that replacement.

There are well-rehearsed arguments about the loss of jobs and plenty of books written on the subject[3]. Some jobs are clearly at risk such as professional driving in the face of self-driving technology[4]. Other jobs are safer as they involve complex unusual actions – plumbing, for example, is messy, contingent and complex (and Prof Shanahan argued this might be the last to go).

What is however lacking is a discussion of the new jobs that AI will create. Throughout history, we have underestimated the jobs created by digital technology. In 1943 IBM’s Thomas Watson predicted a worldwide-market of 5 computers, and in the 1980s people laughed at Bill Gates vision of a computer in every home.  Today we have spending forecasts for IT in the trillions[5]. With Bank of America anticipating that the “robots and AI solutions market will grow to US$153Bn by 2020” [6] it clear that disruptive innovation (Christensen, 1997) through these advanced algorithms will have a strong impact in creating new unimagined opportunity.

Since the rise of the industrial revolution we have created new jobs to replace those lost as people stopped working on farms and in factories: our grandparents would hardly imagine so many baristas, chefs, landscape gardeners, software engineering, financiers and marketers within modern society. What is interesting then is how AI might enhance and expand existing jobs, and create new ones. For instance, an AI supported lawyer might handle more cases so reducing the lawyer’s fees while maintaining their wages. This reduction may well mean more people can access the law rather than reducing the work for lawyers[7]. Similarly, we might imagine interior decorators “virtually” visiting our homes and recommending tasteful designs using AI and online stores. While I, like many others, are not currently prepared to pay designers fees for my small London home, if a store offered the service for a low fee I might well jump at the chance so creating new jobs in this area.

In this way, AI can offer huge efficiency savings which we should not necessarily be scared about. This is not however to downplay the risks to society – particularly as the distribution of this value may be inequitable with low-paid/low-skilled employees most at risk. If, however, we can ensure that those unable to capitalise on this opportunity aren’t left behind then I am cautiously optimistic.  We should also be aware that AI will likely create low-paid, low-skilled jobs as well. Someone will need to hold the 3d camera in my house for the AI designer to work. Someone will need to deliver parcels to my house for Amazon. Someone is needed to service the computers or clean up the data needed by the AI algorithm. And someone will need to make us all great coffee.

I am not trying to present a Utopian vision here – clearly there will be problems. But it is not the end of work either. After all, society has been very good at creating new work that involves sitting in front of computers shuffling files, writing text, and editing spreadsheets and PowerPoints – for people like me. Further, as Wernher von Braun’s quote reminds us – we humans are extremely good value in providing some extremely important intellegent activities: dealing with emotion and having empathy,  thinking creatively, interacting with other humans, understanding our human society and traditions. It will be a very long time, if even, before any AI can provide such intelligence. The problem is often that we underestimate the importance of these in modern work downplaying their significance in modern economic enterprise and thus overplaying the value technocratic automated AI might provide.

(This blog is an opinion piece based on personal musings rather than report on research)

CHRISTENSEN, C. M. 1997. The innovator’s dilemma: when new technologies cause great firms to fail, Harvard Business Press.

[1] https://iris.ucl.ac.uk/iris/browse/profile?upi=RLUCK37

[2] Prof Shanahan has a new book out which looks interesting:https://mitpress.mit.edu/books/technological-singularity

[3] E.g. The Rise of the Robots (Martin Ford)

[4] This is particularly pertinent for industrial driving such as farming and mining where self-driving technology is arriving already http://www.digitaltrends.com/cool-tech/self-driving-tractors/

[5] http://www.gartner.com/technology/research/it-spending-forecast/

[6]https://www.bofaml.com/content/dam/boamlimages/documents/PDFs/robotics_and_ai_condensed_primer.pdf

[7] For a full analysis of this debate read https://www.amazon.co.uk/Future-Professions-Technology-Transform-Experts/dp/0198713398  or listen to the podcast of their talk at the LSE

Image (cc) Rolf obermaier – thanks!