This is the first joint post in a series where Findwise & SearchExplained, together decompose Microsoft’s realm with the focus on knowledge graphs and AI. The advent of graph technologies and more specific knowledge graphs have become the epicentre of the AI hyperbole.
The use of a symbolic representation of the world, as with ontologies (domain models) within AI is by far nothing new. The CyC project, for instance, started back in the 80’s. The most common use for average Joe would be by the use of Google Knowlege Graph that links things and concepts. In the world of Microsoft, this has become a foundational platform capacity with the Microsoft Graph.
It is key to separate the wheat from the chaff since the Microsoft Graph is by no means a Knowledge Graph. It is a highly platform-centric way to connect things, applications, users and information and data. Which is good, but still it lacks the obvious capacity to disambiguate complex things of the world, since this is not its core functionality to build a knowledge graph (i.e ontology).
From a Microsoft centric worldview, one should combine the Microsoft Graph with different applications with AI to automate, and augment the life with Microsoft at Work. The reality is that most enterprises do not use Microsoft only to envelop the enterprise information landscape. The information environment goes far beyond, into a multitude of organising systems within or outside to company walls.
Question: How does one connect the dots in this maze-like workplace? By using knowledge graphs and infuse them into the Microsoft Graph realm?
The model, artefacts and pragmatics
People at work continuously have to balance between modalities (provision/find/act) independent of work practice, or discipline when dealing with data and information. People also have to interact with groups, and imaged entities (i.e. organisations, corporations and institutions). These interactions become the mould whereupon shared narratives emerge.
Knowledge Graphs (ontologies) are the pillar artefacts where users will find a level playing field for communication and codification of knowledge in organising systems. When linking the knowledge graphs, with a smart semantic information engine utility, we get enterprise-linked-data that connect the dots. A sustainable resilient model in the content continuum.
Microsoft at Work – the platform, as with Office 365 have some key building blocks, the content model that goes cross applications and services. The Meccano pieces like collections [libraries/sites] and resources [documents, pages, feeds, lists] should be configured with sound resource descriptions (metadata) and organising principles. One of the back-end service to deal with this is Managed Metadata Service and the cumbersome TermStore (it is not a taxonomy management system!). The pragmatic approach will be to infuse/integrate the smart semantic information engine (knowledge graphs) with these foundation blocks. One outstanding question, is why Microsoft has left these services unchanged and with few improvements for many years?
The unabridged pathway and lifecycle to content provision, as the creation of sites curating documents, will be a guided (automated and augmented [AI & Semantics]) route ( in the best of worlds). The Microsoft Graph and the set of API:s and connectors, push the envelope with people at centre. As mentioned, it is a platform-centric graph service, but it lacks connection to shared narratives (as with knowledge graphs). Fuzzy logic, where end-user profiles and behaviour patterns connect content and people. But no, or very limited opportunity to fine-tune, or align these patterns to the models (concepts and facts).
Akin to the provision modality pragmatics above is the find (search, navigate and link) domain in Office 365. The Search road-map from Microsoft, like a yellow brick road, envision a cohesive experience across all applications. The reality, it is a silo search still 😉 The Microsoft Graph will go hand in hand to realise personalised search, but since it is still constraint in the means to deliver a targeted search experience (search-driven-application) in the modern search. It is problematic, to say the least. And the back-end processing steps, as well as the user experience do not lean upon the models to deliver i.e semantic-search to connect the dots. Only using the end-user behaviour patterns, end-user tags (/system/keyword) surface as a disjoint experience with low precision and recall.
The smart semantic information engine will usually be a mix of services or platforms that work in tandem, an example:
The digitisation of society emerge in all sectors, and the key driver to all this is the abundance of data that needs to be brought into context and use.
When discussing digitisation, people commonly think in data highways and server farms as being the infrastructure. Access to comprehensive information resources is increasingly becoming a commodity, enabling and enhancing societal living conditions. To achieve this, sense-making of data has to be in integrative part of the digital infrastructure. Reflecting this to traditional patterns, digital roads need junctions, signs and semaphores to function, just as their physical counterparts.
The ambition with AI and smart society and cities should be for the benefit of its inhabitants, but without a blueprint to get a coherent model that will be working in all these utilities, it will all break. Second to this, benevolence, participation and sustainability, have to be the overarching theme, to contrast dystopian visions with citizen surveillance and fraudulent behaviour.
Data needs context to make sense and create value, and this frame of reference will be realised through domain models of the world, with shared vocabularies to disambiguate concepts. In short a semantic layer. It is impossible to boil the ocean, which makes us rather lean toward a layered approach.
All complex systems (or complex adaptive system, CAS) revolve around a set of autonomous agents, for example, cells in a human body or citizens in an urban city. The emergent behaviour in CAS is governed by self-organising principles. A City Information Architecture is by nature a CAS, and hence the design has to be resilient and coherent.
What infrastructural dimensions should a smart city design build upon?
Urban Environment, the physical spaces comprised of geodata means, register of cadastre (real-estate), roads and other things in the landscape.
Movable Objects, with mobile sensing platforms capturing things like vehicles, traffic and more, in short, the dynamics of a city environment.
Human actor networks, the social economic mobility, culture and community in the habitat
Virtual Urban Systems augmented and immersive platforms to model the present or envision future states of the city environment
Each of these organising systems and categories holds many different types of data, but the data flows also intertwine. Many of the things described in the geospatial and urban environment domain, might be enveloped in a set of building information models (BIM) and geographical information systems (GIS). The resource descriptions link the objects, moving from one building to a city block or area. Similar behaviour will be found in the movable object’s domain because the agents moving around will by nature do so in the physical spaces. So when building information infrastructures, the design has to be able to cross-boundaries with linked-models for all useful concepts. One way to express this is through a city information model (CIM).
When you add the human actor networks layer to your data, things will become messy. In an urban system, there are many organisations and some of these act as public agencies to serve the citizens all through the life and business events. This socially knitted interaction model, use the urban environment and in many cases moveble objects. The social life of information when people work together, co-act and collaborate, become the shared content continuum. Lastly, data from all the above-mentioned categories also feeds into the virtual urban system, that either augment the perceived city real environment, or the city information modelling used to create instrumental scenarios of the future state of the complex system.
Everything is deeply intertwingled
Connect people and things using semantics and artificial intelligence (AI) companions. There will be no useful AI without a sustainable information architecture (IA). Interoperability on all levels is the prerequisite; systemic (technical and semantic), organisational (process and climate).
Only when we follow the approach of integration and the use of a semantic layer to glue together all the different types and models – thereby linking heterogeneous information and data from several sources to solve the data variety problem – are we able to develop an interoperable and sustainable City Information Model (CIM).
Such model can not only be used inside one city or municipality – it should be used also to interlink and exchange data and information between cities as well as between cities and provinces, regions, countries and societal digitalisation transformation.
A semantic layer completes the four-layered Data & Content Architecture that usual systems have in place:
Use standards (as ISA2), and meld them into contextual schemas and models (ontologies), disambiguate concepts and link these with verbatim thesauri and taxonomies (i.e SKOS). Start making sense and let AI co-act as companions (Deep-learning AI) in the real and virtual smart city, applying semantic search technologies over various sources to provide new insights. Participation and engagement from all actor-networks will be the default value-chain, the drivers being new and cheaper, more efficient smart services, the building block for the city innovation platform.
We have all heard about the totally unhip GDPR and the potential wave of fines and lawsuits. The long arm of the law and it’s stick have been noted. Less talked about but infinitely more exciting is the other side. Turn over the coin and there’s a whole A-Z of organisational and employee carrots. How so?
We all leave digital trails behind us, trails about us. Others that have access to these trails can use our data and information. The new European General Data Protection Regulation (GDPR) intends the usage of such Personal Identifiable Information (PII) to be correct and regulated, with the power to decide given to the individual.
Some organisations are wondering how on earth they can become GDPR compliant when they already have a business to run. But instead of a chore, setting a pathway to allow for some more principled digital organisational housekeeping can bring big organisational gains sooner rather than later.
Many enterprises are now beginning to realise the extra potential gains of having introduced new organisational principles to become compliant. The initial fear of painful change soon subsides when the better quality data comes along to make business life easier. With the further experience of new initiatives from new data analysis, NLP, deep learning, AI, comes the feeling: why we didn’t we just do this sooner?
Most organisations have a system(s) in place holding PII data, even if getting the right data out in the right format remains problematical. The organisation of data for GDPR compliance can be best achieved so that it becomes transformed to be part of a semantic data layer. With such a layer, knowing all the related data from different sources you have on Joe Bloggs becomes so much easier when he asks for a copy of the data you have about him. Such a semantic data layer will also bring other far-reaching and organisation-wide benefits.
Semantic Data Layer
For example, heterogeneous data in different formats and from different sources can become unified for all sorts of new smart applications, new insights and new innovation that would have been previously unthinkable. Data can stay where it is… no need to change that relational database yet again because of a new type of data. The same information principles and technologies involved in keeping an eye on PII use, can also be used to improve processes or efficiencies and detect consumer behaviour or market changes.
But it’s not just the business operations that benefit, empowered employees become happier having the right information at hand to do their job. Something that is often difficult to achieve, as in many organisations, no one area “owns” search, making it is usually somebody else’s problem to solve. For the Google-loving employee, not finding stuff at work to help them in their job can be downright frustrating. Well ordered data (better still in a semantic layer) can give them the empowering results page they need. It’s easy to forget that Google only deals with the best structured and linked documentation, why shouldn’t we do the same in our organisations?
Just as the combination of (previously heterogeneous) datasets can give us new insights for innovation, we also observe that innovation increasingly comes in the form of external collaboration. Such collaboration of course increases the potential GDPR risk through data sharing, Facebook being a very current point in case. This brings in the need for organisational policy covering data access, the use and handling of existing data and any new (extra) data created through its use. Such policy should for example cover newly created personal data from statistical inference analysis.
While having a semantic layer may in fact make human error in data usage potentially more possible through increased access, it also provides a better potential solution to prevent misuse as metadata can be baked into the data to classify both information “sensitivity” and control user accessibility rights.
Secondly start small, apply organising principles by focusing on the low-hanging fruit: the already structured data within systems. The creation of quality data with added metadata in a semantic layer can have a magnetic effect within an organisation (build that semantic platform and they will come).
Step three: start being creative and agile.
A case story
A recent case, within the insurance industry reveals some cues to why these set of tools will improve signals and attention for becoming more compliant with regulations dealing with PII. Our client knew about a set of collections (file shares) where PII might be found. Adding search, and NLP/ML opened up the pandoras box with visual analytic tools. This is the simple starting point, finding i.e names or personal number concepts in the text. Second to this will be to add semantics, where industry standard terminologies and ontologies can further help define the meaning of things.
In all corporate settings, there exist both well-cultivated and governed collections of information resources, but usually also a massive unmapped terrain of content collections, where no one has a clue if there might be PII hidden amongst it. The strategy using a semantic data layer should always be combined with operations to narrowing down the collections to become part of the signalling system – it is generally not a good idea to boil the whole-data-ocean in the enterprise information environment. Rather through such work practices, workers are aware of the data hot-spots, the well-cultivated collections of information and that unmapped terrain. Having the additional notion of PII to contend with will make it that just bit easier to recognise those places where semantic enhancement is needed.
not a good idea to boil the whole-data-ocean
Running with the same pipeline (with the option of further models to refine and improve certain data) will not only allow for the discovery of multiple occurrences of named entities (individuals) but also the narrative and context in which they appear.
Having a targeted model & terminology for the insurance industry will only go to improve this semantic process further. This process can certainly ease what may be currently manual processes or processes that don’t exist because of their manual pain: for example, finding sensitive textual information from documents within applications or from online textual chats. Developing such a smart information platform enables the smarter linking of other things from the model, such as service packages, service units / or organisational entities, spatial data as named places or timelines, or medical treatments, things perhaps currently you have less control over.
There’s not much time before the 25th May and the new GDPR, but we’ll still be here afterwards to help you with a compliance burden or a creative pathway, depending on your outlook.
People and the Post, Postal History from the Smithsonian’s National Postal Museum
Mankind’s preoccupation for much of this century has to become fully digitalized. Utilities, software, services and platforms are all becoming an ‘intertwingled’ reality for all of us. Being mobile, the blurring of the borders between the workplace and recreational life plus the ease of digital creation are creating information overloads and (out-of-sight) digital landfills. While digital content creation is cheaper to create and store, its volume and its uncared for status makes it harder for everyone else to find and consume the bits they really need (and have some provenance for peace of mind).
Fear not. A collection of emerging digital technologies exist that can both support and maintain future sustainable digital recycling – things like: Cognitive Computing, Artificial Intelligence; Natural Language Processing; Machine Learning and the like, Semantics adding meaning to shared concepts, and Graphs linking our content and information resources. With good information management practice and having the appropriate supporting tools to tinker with, there is a great opportunity to not only automate knowledge digitization but to augment it.
In the content continuum (from its creation to its disposal) there is a great need for automating processes as much as possible in order to reduce the amount of obsolete or hidden (currently value-less) digital content. Digital knowledge recycling is difficult as nearly every document or content creator is, by nature, reluctant to add further digital tags (a.k.a. metadata) describing their content or documents once they have been created. What’s more experience shows this is inefficient on a number of accounts, one of which is inconsistency.
Most digital documents (and most digital content, unless intended to sell something publicly) therefore lack the proper recycling resource descriptors that can help with e.g. classification, topic description or annotation with domain specific (shared, consistent) concepts. Such descriptions add appropriate meaning or context to content, aiding its further digital reuse (consumption). Without them, the problem of findability is likely to remain omnipresent across many intranets and searched resources.
Smartphones generate content automatically, often without the user thinking or realizing. All kinds of resource descriptors (time, place etc.) are created automatically through movement and mobile usage. With the addition of further machine learning and algorithms, online services such as Google Photos use these descriptors (and some automatic annotation of their own) to add more contextual data before classifying pictures into collections. This improved data quality (read: metadata addition and improved findability) allows us to find the pictures or timeline we want more easily.
In the very same manner, workplace content or documents can now have this same type of supporting technical platform that automatically adds additional business specific context and meaning. This could include data from users: their profiles, departments or their system user behaviour patterns.
For real organizational agility though a further extra layer of automatic annotation (tagging) and classification is needed – achieved using shared models of the business. These models can be expressed through a combination of various controlled vocabularies (taxonomies) that can be further joined through relationships (ontologies) and finally published (publicly or privately) as domain models as linked data (in graphs). Within this layer exist not just synonyms, but alternative and preferred labels, and more importantly relationships can be expressed between concepts – hence the graph: concepts being the dots (nodes) with relationships the joining lines (vertices). Using certain tools, the certain relationships between concepts can be further given a weighting.
This added layer generates a higher quality of automated context, meaning and consistency for the annotation (tagging) of content and documents alike. The very same layer feeds information architecture in the navigation of resources (e.g. websites). In Search, it helps to disambiguate between queries (e.g. apple the fruit, or apple the organization?).
This digital helper application layer works very much in the same smooth manner as e.g. Google Photos, i.e. in the background, without troubling the user.
This automation however, will not work without sustainable organizing principles, applied in information management practices and tools. We still need a bit of human touch! (Just as Google Photos added theirs behind the scenes earlier, as a work in progress)
This codification or digitalization of knowledge allows content to be annotated, classified and navigated more efficiently. We are all becoming more aware of the Google Knowledge Graph or the Microsoft Graph that can connect content and people. The analogy of connecting the dots in a graph is like linking digital concepts and their known relationships or values.
Augmentation can take shape in a number of forms. A user searching for a particular query can be presented not only with the most appropriate search results (via the sense-making connections and relationships) but also can be presented with related ideas they had not thought of or were unaware of – new knowledge and serendipity!
Search, semantic, and cognitive platforms have now reached a much more useful level than in earlier days of AI. Through further techniques new knowledge can also be discovered by inference, using the known relationships within the graph to fill in missing knowledge.
Key to all of this though is the building of a supporting back-end platform for continuous improvement in the content continuum. Technically, something that is easier to start than one may first suspect.
Sustainable Organising Principles to the Digital Workplace
As we look back at 2016, we can say that it has been an exciting and groundbreaking year that has changed how we handle information. Let’s look back at the major developments from 2016 and list key focus areas that will play an important role in 2017.
3 trends from 2016 that will lay basis for shaping the year 2017
There has been a massive shift towards cloud, not only using the cloud for hosting services but building on top of cloud-based services. This has affected all IT projects, especially the Enterprise Search market when Google decided to discontinue GSA and replace it with a cloud based Springboard. More official information on Springboard is still to be published in writing, but reach out to us if you are keen on hearing about the latest developments.
There are clear reasons why search is moving towards the cloud. Some of the main ones being machine learning and the amount of data. We have an astonishing amount of information available, and the cloud is simply the best way to handle this overflow. Development in the cloud is faster, the cloud gives practically unlimited processing power and the latest developments available in the cloud are at an affordable price.
3 predictions for key developments happening in 2017
As we move towards the year 2017, we see that these trends from 2016 have positive effects on how information will be handled. We will have even more data and even more effective ways to use it. Here are three predictions for how we will see the information space evolve in 2017.
The collaboration with computers are changing. For decades, we have been giving tasks to computers and waited for their answer. This is slowly changing so that we start to collaborate with computers and even expect computers to take the initiative. The developments behind this is in machine learning and human language understanding. We no longer only index information and search it with free text. Nowadays, we can build a computer understanding information. This information includes everything from IoT data points to human created documents and data from other AI systems. This enables building an insight engine that can help us formulate the right question or even giving us insight based on information to a question we never ask. This will revolutionize how we handle our information how we interact with our user interfaces.
We will see virtual private assistants that users will be happy to use and train so that they can help us to use information like never before in our daily live. Google Now, in its current form, is merely the first step of something like this, being active towards bringing information to the user.
The way we use and interact with data is changing. With collected information about pretty much anything, we have almost any information right at our fingertips and need effective ways to learn from this – in real time. In 2017, we will see a shift away from classic BI systems towards search-driven evolutions of this. We already have Kibana Dashboards with TimeLion and ThoughtSpot but these are only the first examples of how search is revolutionizing how we interact with data. Advanced analytics available for anyone within the organization, to get answers and predictions directly in graphs and diagrams, is what 2017 insights will be all about.
We have seen the rise of Chatbots in 2016. In 2017, this trend will also take on how we interact with enterprise systems. A smart conversational user interface builds on top of the same foundations as an enterprise search platform. It is highly personalized, contextually smart and builds its answers from information in various systems and information in many forms.
Imagine discussing future business focus areas with a machine that challenges us in our ideas and backs everything with data based facts. Imagine your enterprise search responding to your search with a question asking you to detail what you actually are achieving.
What are your thoughts on the future developement?
How do you see the 2017 change the way we interact with our information? Comment or reach out in other ways to discuss this further and have a Happy Year 2017!
– Add search for distribution, customization and seamless multichannel experiences.
Retailers, e-commerce and product data Having met a number of retailers to discuss information management, we’ve noticed they all experience the same problem. Products are (obviously) central and information is typically stored in a PIM or DAM system. So far so good, these systems do the trick when it comes to storing and managing fundamental product data. However, when trying to embrace current trends1 of e-commerce, such as mobile friendliness, multi-channel selling and connecting products to other content, PIM systems are not really helping. As it turns out, PIM is great for storage but not for distribution.
Retailers need to distribute product information across various channels – online stores, mobile and desktop, spreadsheet exports, subsets of data with adjustments for different markets and industries. They also need connecting products to availability, campaigns, user generated content and fast changing business rules. Add to this the need for closing the analytics feedback loop, and the IT department realises that PIM (or DAM) is not the answer.
Adding search technology for distribution Whereas PIM is great for storage, search technology is the champ not only for searching but also for distribution. You may have heard the popular Create Once Publish Everywhere? Well, search technology actually gives meaning to the saying. Gather any data (PIM, DAM, ERP, CMS), connect it to other data and display it across multiple channels and contexts.
Also, with the i32 package of components you can add information (metadata) or logic that is not available in the PIM system. This whilst source data stay intact – there is no altering, copying or moving.
Combined with a taxonomy for categorising information you’re good to go. You can now enrich products and connect them to other products and information (processing service). Categorise content according to product taxonomy and be done. Performance will be super high, as content is denormalised and stored in the search engine, ready for multi channel distribution. Also, with this setup you can easily also add new sources to enrich products or modify relevance. Who knows what information will be relevant for products in the future?
PIM for input, search for output. Design for distribution!
Use PIM for managing products, not for managing business rules.
Add metadata and taxonomies to tailor product information for different channels.
Connect products to related content.
Use stand-alone components based on open source for strong TCO and flexibility.
References 1Gartner for marketers 2The Findwise i3 package of components (for indexing, processing, searching and analysing data) is compatible with the open source search engines Apache Solr and Elasticsearch.
There have been discussions surrounding the great generational renewal in the workplace for a while. The 50’s generation, who have spent a large part of their working lives within the same company, are being replaced by an agile bunch born in the 90’s. We are not taken by tabloid claims that this new generation does not want to work, or that companies do not know how to attract them. What we are concerned with is that businesses are not adapting fast enough to the way the new generation handle information to enable the transfer of knowledge within the organisation.
Working for the same employer for decades
Think about it for a while, for how long have the 50’s generation been allowed to learn everything they know? We see it all the time, large groups of employees ready to retire, after spending their whole working lives within the same organisation. They began their careers as teenagers working on the factory floor or in a similar role, step by step growing within the company, together with the company. These employees have tended to carry a deep understanding of how their organisation work and after years of training, they possess a great deal of knowledge and experience. How many companies nowadays are willing to offer the 90’s workers the same kind of journey? Or should they even?
2016 – It’s all about constant accessibility
The world is different today, than 50 years ago. A number of key factors are shaping the change in knowledge-intense professions:
Information overload – we produce more and more information. Thanks to the Internet and the World Wide Web, the amount of information available is greater than ever.
Education has changed. Employees of the 50’s grew up during a time when education was about learning facts by rote. The schools of today focus more on teaching how to learn through experience, to find information and how to assess its reliability.
Ownership is less important. We used to think it was important to own music albums, have them in our collection for display. Nowadays it’s all about accessibility, to be able to stream Spotify, Netflix or an online game or e-book on demand. Similarly we can see the increasing trend of leasing cars over owning them. Younger generations take these services and the accessibility they offer for granted and they treat information the same way, of course. Why wouldn’t they? It is no longer a competitive advantage to know something by heart, since that information is soon outdated. A smarter approach of course is to be able to access the latest information. Knowing how to search for information – when you need it.
Factors supporting the need for organising the free flow of the right information:
Employees don’t stay as long as they used to in the same workplace anymore, which for example, requires a more efficient on boarding process. It’s no longer feasible to invest the same amount of time and effort on training one individual since he/she might be changing workplace soon enough anyway.
It is much debated whether it is possible to transfer knowledge or not. Current information on the other hand is relatively easy to make available to others.
Access to information does not automatically mean that the quality of information is high and the benefits great.
Organisations lack the right tools
Knowing a lot of facts and knowledge about a gradually evolving industry was once a competitive advantage. Companies and organisations have naturally built their entire IT infrastructure around this way of working. A lot of IT applications used today were built for a previous generation with another way of working and thinking. Today most challenges involve knowing where and how to find information. This is something we experience in our daily work with clients. Organisations more or less lack the necessary tools to support the needs of the newer generation in their daily work.
To summarize the challenge: organisations need to be able to supply their new workforce with the right tools to constantly find (and also manipulate) the latest and best information required for them to shine.
Success depends on finding the right information
In order for the new generation to succeed, companies must regularly review how information is handled plus the tools supporting information-heavy work tasks.
New employees need to be able to access the information and knowledge left by retiring employees, while creating and finding new content and information in such a way that information realises its true value as an asset.
Efficiency, automation… And Information Management!
There are several ways of improving efficiency, the first step is often to investigate if parts, or perhaps the entire creating and finding process can be automated. Secondly, attack the information challenges.
What kind of information is it?
Where is the information located?
What is important, the information objects in their entirety or the subsets?
How will the information be consumed?
What prior knowledge is needed to interpret the information?
When we get a grip of the information we are to handle, it’s time to look into the supporting IT systems. How are employees supposed to find what they are looking for? How do they want to?
We have gotten used to find answers by searching online. This is in the DNA of the 90’s employee. By investing in a great search platform and developing processes to ensure high information quality within the organisation, we are certain the organisation will not only manage the generational renewal but excel in continuously developing new information centric services.
Finding our way in the bright, futuristic, data-driven & intertwined world, often taxes us and our digital-hungry senses. Fast rewind to the recent FindabilityDay 2015 and the parade of brilliant speaker talents on stage. Starting of with our dear friend and peer, Martin White, on the topic the future of search.
Human factors, from idea inception to design and practical UX of our digital artifacts. The key has been make-do and ship. This is the reason the more technically-advanced mobiles fell by the wayside 8 years ago Apple’s iPhone.
The social life with information, shapes our daily lives, in a hyper-connected world. It’s still very hard to find that information needle in the haystack, and most days we feel despair when losing the scent of information nuggets. The results from the Findability Survey, spoke clearly. Without sound organising principles to information and data, and a pliable recorded vision, we won’t find anything of value.
Next, moving into an old business model, with Luna’s and Sara’s presentation, a great example, where we see that the orchestration and choreography of their data assets will determine their survival or demise – in conjunction with infused means to information management practices, processes and tools. They showed a new set of facets to delivering on their mission in their line-of business.
Regardless of the line of business, it becomes clear that our fragmented workplace setting now only partly “on tap”. It makes our daily lives a mess, since things do not interoperate. The vision should show the way to a shared information commons, where we all cultivate.
Answer: Architect a place where you can find comfort with social conventions shared on the information used. Abby Covert, laid out a beautiful tapestry of things we all need to take on, to make sense in everyday life, and life at work. With clear and distinct guardrails, and signposts we don’t feel so distracted or lost. Her talk was a true enlightenment for me, being of the same profession, Information Architect.
This is the second post in a series (1), unpacking interoperability in the healthcare system. The basis in this post is semantic and technical interoperability, hence a systemic overview.
The future of health care relies on the improved flow of captured patient health information across the whole care continuum. This means a shared information system linking systems and devices from participating health care organisations while maintaining patient privacy and security standards. Such a realization would not only enhance the clinician and patient experience but also enable faster treatment and better care coordination for patients.
Information Commons is an information system, …, that exists to produce, conserve, and preserve information for current and future generations.
A seamless and secure hub, heavily-linked, providing point-of-care access to critical patient data and care decision support information for the delivery of timely care, reducing the duplication of tests and procedures.
All in all, this has to be built upon a participatory community paradigm, where clinicians, policy makers and leaders, and patients share a vision to create an interoperable information space – that is sustainable, regardless of previous lock-in mechanisms set by different technical, and semantic standards, vendors and process and policy making.
How do we create a interoperability climate?
Changes for interoperability lie in the development of new pilots with strong collaboration. They are generally more successful where they are based on patient or illness groups, value-orientated, open and scalable. Post requirements phase, iteration based on early adopters’ feedback can identify the need for improvements and enhancements around the relevancy, format and visual display of data and information, the usability of the solution and provide insight into workflow impact. The Information Commons is also a good arena for clinicians to share positive anecdotes from their experiences upon which scalable pilots can be expanded.
Such developed infrastructure and services can also support or be leveraged by other national or regional health initiatives.
Technical Layers of interoperability
Interoperability can cover many layers but at its basis would be an interoperable access layer that integrates and securely shares clinical data from multiple sources giving one point of access. The user interface (GUI) could then provide and display data and information based on stakeholder users and medical/situational context.
Such a layer would have to accommodate and support various data from the distributed system of actors, aligning both to open standards while at the same time being plastic enough in design and instantiation.
Interoperability not only covers the sharing of information but also its usage. This may include added functionality by the EHR vendor themselves or the creation of further value-adding knowledge layers that can take advantage of both structured and (the untapped wealth of) unstructured data within EHRs.
Findwise in its EU funded KConnect project is doing just that. It is currently collecting use case studies from Jönköping (RJI/Qulturum) in order to create a pilot solution for clinicians to take advantage of ‘hidden’ textual data.
Questions of interoperability also lie in the physical user experience of the systems themselves. Should the basic layer provided by EHR vendors be open to include value-added software from other parties, should it be embedded or be made into another GUI? Which ultimately is best for the clinician workflow and the agility of software solutions in supporting new value-based outcomes and reiteration for improvements in efficiency and effectiveness?
The annotations made in the healthcare systems across different domains, all have very similar outset, but lack coherent interoperable mechanism to work smoothly outside the local context. On a international, and national and regional level there should be services that acts as the electric grid to provide society with energy to be used in many contexts. A semantic grid that host controlled vocabularies within the domain, but also share practices and processes. With the use of open standards these could bridge across organisational boundaries and help clean the current messy Healthcare information space.
The healthcare information commons, do not per se have to be one system, but rather an interoperable set of services/systems that share standards to be able to exchange information and data. Very similar to they way Internet and linked data work today – not restricted by walled gardens. The governance of the commons, should be a matter of public services, with sustainable resources and open governance agenda that can invite participation and engagement. No single actor in the network, be it a large hospital, private caretaker or regional public governing body will be able take care of this single-handedly. It should be a true “commons” undertaking!
The infusion of the Information Commons into everyday healthcare provisioning use cases with semantic transformer applications could be in several modalities: finding and acting upon information or contributing in the local context.
Analogously using digital cameras from smartphones or other devices, means that the user might add “some” metadata or tags about the picture. Devices and sensors add more layers of granularity with attributes that most end-users, never see or bother about. These extra resource descriptions, will interplay with cloud based services as Google Photos – where different algorithms reformat, package the content into new forms, as contextual albums, scenes and so forth.
A set of semantic transformer application layers should be intertwingled with the Healthcare Information Commons. Firstly to make easy linkages between data sets – as the Web of Data scenarios and Linked Data propose – but also to provide smarter integration points in back-end supporting processes in the Healthcare systems where more private and locked-in data-sets exist about the patient conditions, treatments and drugs etc.
The semantic transformer applications could both be open api:s developed by the community for the commons, but also could be commercial applications provided by line-of-business specialist software vendors. As long as all of these layers, are compliant with the open standards!
For such legacy systems as EHR , and off-the-shelf healthcare applications and business applications that are semantically impaired, these semantic transformer applications could work as a repair-kit for already old broken systems. Consequently there would be no need to overhaul all legacy software within the caretaker’s organisation. A kind of smoother migration path to interoperability.
There also exists the need for semantic interoperability between the contextual patient information within the EHR and the provision of clinical decision support information. This could be in the form of internal medical guidelines and best practices, or from external resources such as medical journals or clinical trial reports.
The KConnect project are providing semantic annotation and semantic search services in different languages for clinicians and researchers to access the very latest in medical literature. This is achievable by semantically annotating required medical information (EHRs, guidelines, journals etc) and having the semantic search engine take full advantage of known key medical entities/concepts and their relationships.
Through the indexing of new information about drug usage, best practices, guidelines, new clinical trials and journals, clinicians then access up-to-date relevant information whenever they need.
In the near future to maximise both clinician and patient user engagement with EHRs, different uses and views of the EHR will have to be driven by suitable context and stakeholder semantics.
Shared Decision making
When moving into valued-based health care and outcome measurement, (as presented here by Sveus), it is critical that all actors participate on a connected level field, so that communication between healthcare practitioners and patients and their social networks works. This includes the need for shared norms and definitions as well as systems to support the decision making – and obviously a harmonised set of metrics to measure outcomes.
As presented by Peter Ubel, in his talks and recent book on Critical Decisions, it is key that we are able to share a common view between the clinician and the patient. All practitioners share jargon that do not always communicate well to the receiver. Hence there are plenty of communication breakdowns recorded in the everyday practices, leading to “malpractice” in the worst cases for the patient. In the last couple of decades, there has been a shift in power relations between healthcare professionals and patients and their families. Patient empowerment is a good thing, but if things get lost in translation, there is the risk that critical decisions are not fully supported.
With a Healthcare Information Commons pool of resources, there lays opportunities to guide patients and practitioners in their critical decision making. But also to strengthen the learning and innovation within the communities of practice, with open feedback loops to the pool.
Privacy & Security upfront
Just as data interoperability can be seen as the sharing of data, data security can be seen as the sharing of data in the right way and data privacy seen as the sharing of data with the right person in the right way. We are naturally concerned as to who may be using our data and want to be able to control its use.
The boundary between citizens’ App data and their medical data is blurring rapidly as App developments and sensors continue to provide new and different data that the individual, health care and clinical research can capitalise on in the effort to move towards better wellbeing and more value-based healthcare.
While data privacy and security have become the headline darlings of the media, they can often be distractors of innovation, often masking the true benefits of the flow of information. Just as with physical assets there are best practices for data misuse prevention, protection and policing. The majority of misuse or abuse of personal data is more often caused by human error and misjudgement than by the failure of technology.
Data interoperability can be better supported when services have clear guidelines to inform citizens as to who, when and how their data is shared, for what purpose and the available steps to alter said process. A better informed public would then see more free data resources being used for clinical research e.g. the Million Hearts initiative in the US where citizen data is being used to lower heart attacks and strokes.
Open regulations, collaboration and co-ordination along with risk assessment and protection practices such as encryption, anonymisation and de-identification, all can go a long way to allowing secure data interoperability, be it personal or aggregated data. IT has the potential too of rule-based access and forensic data access reports. No system can be made fool-proof, however precautions and the presence of well-designed data breach response plan are achievable.
Obviously we do not want all our healthcare records to be open in the air for anybody to use or read, as little as we want our financial records to be in the open. Privacy is really key! The means with the Information Commons should work with aggregated data. Not the singular set of records for one patient.
Patient security derives the need to a more free flow of data between actor systems. The medical conditions and contexts sets the standards for sharing, where extracts or segments should be possible to share aligned with privacy policies.
Future real-life experience exposé
Having a recent Swedish report on diabetes care and outcome measurement in mind. It makes sense, to illustrate the case of a diabetes patient living and acting in Göteborg, West of Sweden. They have a medical condition, being a lifelong journey with an endocrine system out of order. This has a great impact on the patient’s everyday life, and diabetes related complications. With good life balance to training, exercise and eating habits, it is possible to keep the glucose patterns in such a way that your life expectancy will equal to anybody else.
The use of personal choices to trigger improved behavior, gives the person options to chose selected wellbeing (e.g. Weight Watchers), fitness (e.g. Runkeeper) and health monitoring applications. In most cases these are closed down ecosystems, e.g. iOS included Health app, with options to share in social-media (about your progress, in terms of eating well, or improve your personal training). Many Life Science corporations are developing medical condition / disease area / treatment specific Health monitoring applications (e.g. FreeStyle Libre from Abbot for improving Glucose Monitoring) that clinicians recommend during patient consultations.
For clinical researchers there are ecosystem specific toolkits, like the open-sourced Apple Research Kit. The existence of a closed ecosystem naturally makes it more problematic to share and exchange data. In this space a Open Standards based on the idea Information Commons makes sense too – where semantic translators could improve the transmission of data from one closed ecosystem to another, without privacy infringement.
In a future more seamlessly interoperable world, the citizen / patient should be provided one-secure-access point to his/hers health account, e.g. in Sweden 1177 and Mina Vårdkontakter and Hälsa för mig.
The outstanding question: How to get interoperability between PHR and Wellbeing, Fitness and Health apps where it is easy to share vital data bits in a sound manner?
In this scene, open standards should be applied to create a make-do semantic transformation.
Lastly – interoperability within the Professional Clinician Workplace?
The statements and real-life stories from the trenches in any clinical workplace, show a mess of supporting information systems. EHRs that by no means either cooperate or interoperate. Many clinicians realise that they have to do data provision into a handful of systems with significant double manual workload. This comes with risks, given the stressful environment, and many “malpractice” incidents can arise from this workplace disorder.
Each system support its part of the process. While some software suites try to close-down into one-system to ‘rule them all paradigm,’ they still barely lean upon any open standards and they lack semantic and structured ways for the use of data and information outside of the supporting system’s narrow scope.
A diabetes nurse (post patient consultation) has to enter data into more than 10 different areas, including quality assurance and measurement systems e.g. NDR in Sweden. In some cases there have been integrated point-to-point solutions put in place, but mostly this is not the case and so unnecessary frustration is created.
In every intervention where clinicians and patients communicate, regardless of it being online, remote, on-site, there should be opportunities to tap into the Healthcare Information Commons space. With the potential to find recent new medical treatments, emerging standards/guidelines, breaking news for clinicians as well as patient-oriented and formatted communications. In the best of worlds, semantic translator applications will bridge between ecosystems inside the personal health space as well as into the workplace environment for clinicians – helping, guiding and improving all dimensions of interoperability.
Having value-based Healthcare and Outcome Measurement domain as a specific health care change driver, will push the use of standards on all levels to the limit. In the following blog post in this series, the ambition is to unpack information governance, since the data ownership and trust also have to be ironed out. And as stated by Prof Michael E. Porter, the capture of data to do proper Outcome Measurement is one of the major road-blocks ahead. The orchestration of all resources and governance still have to be unfolded. Happily some building blocks to the Healthcare Information Commons have emerged, so we do not need to reinvent the wheel:
Wikimedia realm “commons“- with all entries of semantic useful data in wikidata.org
Open Innovation, and the “open” paradigm, will change evidence based medicine, Bad Pharma and Science on a sociatal level, as stated by Ben Goldacre (TED) where we as patient together with clinicians are able to question treatments based on open data, and improve quality to Healthcare Information Commons.
Today, in fact most days, end-users feel bewildered when using the intranet.It is to some extent impossible to navigate.There exists a hodgepodge of mixed user experiences, given that the intranet often serves as the access point to several tools. And findability too is low! With a coherent, smooth and interoperable workplace, users should be able to find information and data, peers and colleagues to solve their everyday tasks, in an efficient way… anywhere, on any device and anytime.
The authors’ narrative describes how the intranet can best be used to produce beneficial business transformation, by including detailed chapters on: strategy, content & information architecture, search/findability, governance and stakeholder management, end-user engagement and adaptation. Measures and metrics are also included to qualify the sought after business values.
Findwise have contributed to the sections relating to organising principles. Put simply, it should be easy for a user to know where and how to contribute with information and content in a good manner, so that others are able to find and co-act on such codified knowledge.
Without sound and sustainable organising principles there will be no findability: shit in = shit out! Regardless of the technology platform employed for search or intranet
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