Tag Archives: Accenture

Accenture and connected health

(Notes from an Executive Short Program called Digitalization for Growth and Innovation, hosted by Ragnvald Sannes and yours truly, in Sophia Antipolis right now.Disclaimer: These are my notes, I am writing fast and might get something wrong, so nothing official by Accenture or anyone else.)

Andy Greenberg is relatively new to Accenture, having a background in various technology companies involved in health and fitness monitoring.

The Internet of Things is the next era in computing, we are moving to the second half of the chessboard, Moore’s law is still active. Everything gets faster all the time, sensors cheaper, more and more connections and kinds of connections becoming available. A lot of the data growth has been driven by sensors. Smartphones everywhere, but can’t be assumed in the health space.

We need to capture the data, and we can’t send it all away – so we have to do data analytics on the edge, i.e. do analysis right away. You have to think about some things, such as engineers designing for engineers is not a good thing, and that if you can do something – such as connecting a device – it does not necessarily mean that you should do it. However, there will be 25 to 50b connected devices in the next few years – and it can deliver value. Tesla, for instance, can update its cars  instead of recalling them, improving customer satisfaction and saving money. An Airbus can send messages about needed parts, in the future they will be 3D printed at the airport before they land. There is a large gap between how many CEOs think IoT is important and how many have any kind of capability to do it.

IoT has enormous potential in health care. We have an aging population – and that is true of the health providers as well. Patients have different expectations: “health consumers are becoming consumers, comparing their experience not to the last doctor’s visit, but the last time they bought something on Amazon”. Spending on healthcare is increasing, as is the number of connected and connectable devices.

IoT enables connected health services, including merging the experience at home and at hospital, feeding data from home and feeding treatment from hospital to home after a hospital stay. The key is to understand the complexity of where people are at different times and manage accordingly, as opposed to thinking that they are either one kind of patient and another – we are all different types of patients at one time or another. Key is to focus on preventing readmission to hospital, but there might be more value in managing the healthy population – focusing only on the high risk patients may not be the right strategy. (Dee Edington – Zero Trends). It is not just about getting the ill well, but keeping the well well.

Moore’s law works both for fitness devices and medical devices. For fitness devices, wireless offloading of data makes a real difference, the holy grail is when the data offload disappear completely, if something monitors you all the time and alerts you to do something then you are more likely to use it. Medical devices have been more about diagnosis, now moving into monitoring and adherence. Proteus Digital Health, for instance, has a smart pill that monitors that it is being taken, for instance. Problem is that you need to wear a patch, and the first drug it is being applied to is one for schizophrenia – in other words, the patients that are most likely to be paranoid… There is also work done with smart devices, such as asthma inhalers, which can track how much it is used, geolocate, match to other people using inhalers same day, track pollen count etc. Find covariation from individual and communal data.

Healthcare players need to understand consumer expectations – Disney spent more than one million on wristbands to make the interaction with their parks much more frictionless. Healthcare providers should do the same thing – help their patients navigate through their services – including hospitals – to make the experience more seamless. This is happening in the pharmaceutical space: About half of all presecriptions are either not filled or taken incorrectly. Some names: Gecko Health, Propeller Health, Adherium, Inspiro medical.

When you add connectivity to the mix, it changes everything. One challenge is that even though the value is clear, the person receiving it may not be the one paying for it. This means that many device innovations are seen by their creators as a way to be unique, that will change over time because the value is much bigger of things being standardized and more widely distributed. You also need to standardize to the lowest common denominator from a connectivity perspective. Security is obviously an important issue as well.

Q: How do you make a secure app, how do you handle security?

Andy: Only a minority has a code on their phone, so you need a separate login. Security has be be part of the design from the very beginning. The biggest piece of guidance is to understand that.

Q: Can you see health care become completely digitalized?

Andy: Health care will always have a large human element, and there are huge hurdles in interoperability, but in between 5 and 10 years we will see significant action. The technology is not the problem any more, it is all about adoption.

Francis: We are stuck in a fee for service model that is, in my opinion, broken. Should move to a value model, and digitization can help with that.

Q: Where will we see the first real use of it?

Andy: Already seeing that, pockets of it. Maybe the most interesting and recent adaptation is the use of telemedicine about mental health. The VA hospitals are doing that to allow face-to-face conversations with clients with mental health issues. The key here is having payers pay for this as legitimate treatment. Remote monitoring is coming along. Change in payment models and health plans that change prices if you carry a device also drives this.

Q: The nordics are a bit of digital laggards – what will happen here?

Andy: The nordics tend to be ahead in technology and behind in business models. The aging population is a driver and Asia is a big area for that. Regulatory constraints are going to be a big hurdle, some countries are so high on privacy that they make it almost impossible to even try. Payment is important – if governments say they are willing to pay for making the elderly stay home longer, then it will come.

Accenture on Cognitive Computing

(Notes from an Executive Short Program called Digitalization for Growth and Innovation, hosted by Ragnvald Sannes and yours truly, in Sophia Antipolis right now.Disclaimer: These are my notes, I am writing fast and might get something wrong, so nothing official by Accenture or anyone else.)

Cyrille Bataller is a managing director and the domain lead for Intelligent Computing, with the Emerging Technology group at Accenture. He leads Accenture’s exploration of the Cognitive Computing field

Cognitive Computing

Skilled work increasingly done by machines – doctors, lawyers, traders, professors etc. Large potential impact, also societal, but exciting new technology. Cognitive computing is IT systems that can sense, comprehend and act – and are perhaps the most disruptive technology on the horizon. They can interact with their environment, learn by training instead of coding, and can analyse and use enormous amounts of data. The quest for cognitive computing has a long history – and has had a varied history, with periods of advances and periods of setbacks, or at least less interest. We differentiate between “strong” AI – achieving consciousness – and “weak AI” – having the machine mimic certain capabilities of humans.

Accenture’s ambition has been to create a toolbox of cognitive computing capabilities under a single architecture, such as text analytics, research assistants, image analysis, multimedia search, cognitive robots, virtual agents, expert systems, video analytics, identity analytics, speech analytics, data visualisation, domain-specific calculations, recommendation systems and self-adjusting IT systems. These are all examples of human traits and capabilities as well…

Some detail – image analysis is about deep learning, using neural networks to mimic how the brain works. Use layers of increasingly complex rules to categorize complex shapes such as faces or animals. In one case, they used neural networks to recognize and classify images of cars as undamaged, some damage or totalled to an accuracy of 90%. By applying text analysis as well, this could be used by an insurance company to analyze insurance claims. Google has acquired a company called DeepMind to look at images on the web, eventually recognizing cats.

Another example is use of robotics in business operations – from minibots, companies such as XL and AutoHotKey to automate application software, to standard robots to using virtual assistants to do user support, for instance. You can have a team of robots in the cloud to work alongside your real back office. These robots can observe and learn and gradually take over or optimize these activities, such as updating documents (powerpoints, for instance). Could be used to observe how an accountant works, for instance, and issue certification based on following proper procedures. You could have 20-30 people in the back office and 100s of robots learning from them and doing the regular stuff.

Virtual agents: Amelia, handling a missing invoice situation. Amelia can understand, learn and solve problems. Generates a flowchart which can then be optimized and analyzed.

Video analytics is another application that is receiving lots of attention. One problem with CCTV videos is that they are almost only used forensically – checking a certain date and time – because nobody has time to watch the videos themselves. Can be used to count cars and recognize events, measure queuing times at customs and immigration, measure service times, monitor for lost objects on a train, analyze the age distribution on public transport, detect emotions, understand the hot and cold zones in the shopping center, find leaks in an industrial setting, etc.

The following framework (from the perspective document referenced below) shows a four-quadrant framework that can help guide companies in choosing the approach towards cognitive computing:

accenture-cognitive

See Accenture.com/cognitive for more information and a point of view document, written by Cyrille Bataller and Jeanne Harris.