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:
There is an urgent need to iron out the social dilemmas and focus on motivational solutions that strive for cooperation and collective action. Knowledge deciphered with the notion of intelligence and emerging utilities with AI as an assistant with us humans. We the peoples!
To make a model of the world, to codify our knowledge and enable worldviews to complex data is nothing new per se. A Knowlege Graph – is in its essence a constituted shared narrative within the collective imagination (i.e organisation). Where facts of things and their inherited relationships and constraints define the model to be used to master the matrix. These concepts and topics are our communication means to bridge between groups of people. Shared nomenclatures and vocabularies.
Knowledge Engineering in practice
At work – building a knowledge graph – there are some pillars, that the architecture rests upon. First and foremost is the language we use every day to undertake our practices within an organisation. The corpus of concepts, topics and things that revolve around the overarching theme. No entity act in a vacuum with no shared concepts. Humans coordinate work practices by shared narratives embedded into concepts and their translations from person to person. This communication might be using different means, like cuneiform (in ancient Babel) or digital tools of today. To curate, cultivate and nurture a good organisational vocabulary, we also need to develop practices and disciplines that to some extent renders similarities to ancient clay-tablet librarians. Organising principles, to the organising system (information system, applications). This discipline could be defined as taxonomists (taxonomy manager) or knowledge engineers. (or information architect)
Set the scope – no need to boil the ocean
All organisations, independent of business vertical, have known domain concepts that either are defined by standards, code systems or open vocabularies. A good idea will obviously be to first go foraging in the sea of terminologies, to link, re-hash/re-use and manage the domain. The second task in this scoping effort will be to audit and map the internal terrain of content corpora. Since information is scattered across a multitude of organising systems, but within these, there are pockets of a structure. Here we will find glossaries, controlled vocabularies, data-models and the like. The taxonomist will then together with subject matter experts arrange governance principles and engage in conversations on how the outer and inner loop of concepts link, and start to build domain-specific taxonomies. Preferable using the simple knowledge organisation system (SKOS) standard
Participatory Design from inception
Concepts and their resource description will need to be evaluated and semantically enhanced with several different worldviews from all practices and disciplines within the organisation. Concepts might have a different meaning. Meaning is subjective, demographic, socio-political, and complex. Meaning sometimes gets lost in translation (between different communities of practices).
The best approach to get a highly participatory design in the development of a sustainable model is by simply publish the concepts as open thesauri. A great example is the HealthDirect thesaurus. This service becomes a canonical reference that people are able to search, navigate and annotate.
It is smart to let people edit and refine and comment (annotate) in the same manner as the Wikipedia evolves, i.e edit wiki data entries. These annotations will then feedback to the governance network of the terminologies.
Link to organising systems
All models (taxonomies, vocabularies, ontologies etc.) should be interlinked to the existing base of organising systems (information systems [IS]) or platforms. Most IS’s have schemas and in-built models and business rules to serve as applications for a specific use-case. This implies also the use of concepts to define and describe the data in metadata, as reference data tables or as user experience controls. In all these lego pieces within an IS or platform, there are opportunities to link these concepts to the shared narratives in the terminology service. Linked-enterprise-data building a web of meaning, and opening up for a more interoperable information landscape.
One omnipresent quest is to set-up a sound content model and design for i.e Office 365, where content types, collections, resource descriptions and metadata have to be concerted in the back-end services as managed-metadata-service. Within these features and capacities, it is wise to integrate with the semantic layer. (terminologies, and graphs). Other highly relevant integrations relate to search-as-a-service, where the semantic layer co-acts in the pipeline steps, add semantics, link, auto-classify and disambiguate with entity extraction. In the user experience journey, the semantic layer augments and connect things. Which is for instance how Microsoft Graph has been ingrained all through their platform. Search and semantics push the envelope 😉
Data integration and information mechanics
A decoupled information systems architecture using an enterprise service bus (messaging techniques) is by far the most used model. To enable a sustainable data integration, there is a need to have a data architecture and clear integration design. Adjacent to the data integration, are means for cleaning up data and harmonise data-sets into a cohesive matter, extract-load-transfer [etl]. Data Governance is essential! In this ballpark we also find cues to master data management. Data and information have fluid properties, and the flow has to be seamless and smooth.
When defining the message structure (asynchronous) in information exchange protocols and packages. It is highly desired to rely on standards, well-defined models (ontologies). As within the healthcare & life science domain using Hl7/FHIR. These standards have domain-models with entities, properties, relations and graphs. The data serialisation for data exchange might use XML or RDF (JSON-LD, Turtle etc.). The value-set (namespaces) for properties will be possible to link to SKOS vocabularies with terms.
Query the graph
Knowledge engineering is both setting the useful terminologies into action, but also load, refine and develop ontologies (information models, data models). There are many very useful open ontologies that could or should be used and refined by the taxonomists, i.e ISA2 Core Vocabularies, With data-sets stored in a graph (triplestore) there are many ways to query the graph to get results and insights (links). Either by using SPARQL (similar to SQL in schema-based systems), or combine this with SHACL (constraints) or via Restful APIs.
These means to query the knowledge graph will be one reasoning to add semantics to data integration as described above.
Adding smartness and we are all done…
Semantic AI or means to bridge between symbolic representation (semantics) and machine learning (ML), natural language processing (NLP), and deep-learning is where all thing come together.
In the works (knowledge engineering) to build the knowledge graph, and govern it, it taxes many manual steps as mapping models, standards and large corpora of terminologies. Here AI capacities enable automation and continuous improvements with learning networks. Understanding human capacities and intelligence, unpacking the neurosciences (as Lars Leksell) combined with neural-networks will be our road ahead with safe and sustainable uses of AI. Fredric Landqvistresearch blog
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.
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
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.
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.
This is the seventh post in a series (1, 2, 3, 4, 5, 6) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud. We will help you to consider the options and guide on the steps you need to take.
Starting from our first post we have covered different aspects you need to consider as you take each step including information structure and how it is managed using Office 365 and SharePoint as a technology example. Planning for migration.
Do not even think about moving into the cloud apartment without a proper cleaning of the content buckets. Moving from an architected household to a rented place, taxes a structured audit. Clean out all redundant, outdated and trivial matter (ROT). The very same habit you have cleaning up the attic when moving out from your old house.
It is also a good idea to decorate and add any features to your new cloud apartment before the content furniture is there. It means the content will fit with any new design and adapt to any extra functionality with new features like windows and doors. This can be done by reviewing and updating your publishing templates at the same time. This will save time in the future.
Leaning upon the information governance standards, it should be easy to address the cleaning before moving, for all content owners who have been appointed to a set of collections or habitats. Most organisations could use a content vacuum cleaner, or rather use the search facilities and metric means to deliver up to date reports on:
Active / in-Active habitats
No clear ownership or the owner has left the building
Metadata and link quality to content and collections to be moved across to the cloud apartments.
Review publishing templates and update features or design to be used in the Cloud
When all active habitats and qualified content buckets have been revisited by their set of curators and information owners. The preparation and use of moving boxes, should be applied.
All moving boxes do need proper tagging, so that any moving company will be able to sort out where about the stuff should be placed in the new house, or building. For collections, and habitats, this means using the very same set of questions stated for adding a new habitat or collection to the cloud apartment house. Who, why, where and so forth, through the use of a structured workflow and form. When this first cleaning steps have been addressed, there should be automatic metadata enhancement, aligned with the information management processes to be used in the new cloud.
With decent resource descriptions and cleaned up content through the audit (ROT), this last step will auto-tag content based upon the business rules applied for the collection or habitat. Then been loaded into the content moving truck, or loading dock. Ready to added to the cloud.
All content that neither have proper assigned information ownership, or are in such a shape that migration can’t be done should persist on the estate or be archived or purged. This means that all metadata and links to either content bucket or habitat that won’t be moved in the first instances, should at least have correct and unique uri:s, address, to this content. And in the case a bucket or habitat have been run down by a demolition firm, purged. All inter-linkage to that piece of content or collection have to be changed.
This is typically a perfect quality report, to the information owners and content editors, that they need to work through prior to actually loading the content on the content dock.
Finally when all rotten data, deserted habitats and unmanageable buckets have been weeded out. It is time to prepare the moving truck, sending the content into its new destination.
Our final thread will cover how will the organisation and it habitants will be able to find content in this mix of clouds, and things left behind on the old estate? Cloud Search and Enterprise Search, seamless or a nightmare?
This is the sixth post in a series (1, 2, 3, 4, 5, 7) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud. We will help you to consider the options and guide on the steps you need to take.
Starting from our first post we have covered different aspects you need to consider as you take each step including information structure and how it is managed using Office 365 and SharePoint as a technology example. We will cover more about SharePoint in this post, and placemaking in the cloud.
In SharePoint there are a set of logic chunks. One could decompose the digital workplace into intranet sites, as departmental and organisational buckets; team sites where groups collaborate, and lastly your personal domain being the my site collection. Navigating between these, is a mix of traditional information architecture and search driven content. When being within a such a habitat as a teamsite, it is not always obvious how to cross-link or navigate to other domains within the digital workplace hosted in Sharepoint.
One way to overcome this, is to render different forms of portals, based upon dynamic navigation. These intersections and aggregates help users to move around the maze of buckets and collections of the content. Sharepoint have very good features, and options to create search-based content delivery mechanisms.
A metadata and search-based content model, gives us cues for the future design of the digital workplace, with connected habitats and sustainable information architecture. Where people don’t get lost, and have wayfinding means to survive everyday work practices.
This is where how you manage the content in SharePoint and Office 365 is critical. As we said in our first post it is important you have a good information architecture combined with a good governance framework that helps you to transform your buckets of content from the estate into the cloud. We have covered information architecture so we now move more towards how governance completes the picture for you.
There are three approaches to the governance your organisation needs to have with SharePoint and Office 365. You don’t have to use just one. You can combine some of each to find the right blend for your organisation. What works best for you will depend on a number of different factors. Among them:
Restricting use – stopping some features from being used e.g. SharePoint Designer
Encouraging best practice – guidance and training available
Preventing problems – checking content before it is published
Each of these approaches can support your governance strategy. The key is to understand what you need to use.
You need to be clear why your organisation is using SharePoint and Office 365 and the benefits expected. This will shape how tight or loose your governance needs to be.
Once you are clear on this, you then need to consider the strategic benefits and drawbacks such as SharePoint Designer and site collection administration rights.
You control what is being used.
You decide who uses a feature e.g. SharePoint Designer.
You manage the level of autonomy each site owner has.
You find out why someone needs to use a feature.
You monitor costs for licences, users, servers, etc.
You measure who is using what and why for reporting.
You stifle innovation by not allowing people to test out ideas.
You stop legitimate use by asking for permission to use features.
You prevent people being able to share knowledge how they wish to.
You may be unable to realise the maximum potential of SharePoint.
You create unnecessary administration.
You risk adding costs without any value to offset them with.
You need to get the balance right with governance that gives you maximum value for the effort needed managing SharePoint and Office 365.
Encourage best practice
The goal from implementing SharePoint and Office 365 is to have an environment that enables employees to publish, share, find and use information easily to help with their work. They are confident the information is reliable and appropriate, whatever their need for it is. People also feel comfortable using these tools rather than alternative methods like calling helpdesks or emailing other employees for help.
Encouraging best practice by giving them the opportunity to test to meet their needs is one approach to achieving this. There are factors you need to consider that can help or hinder the success of using this approach.
You inform employees of all the benefits to be gained.
You train people to use the right tools.
You design a registration process to direct people to the right tools.
You point employees to guidance on how to follow best practice.
You encourage innovation by giving everyone freedom of use.
You can’t prevent people using different tools to those you recommend.
You risk confusing employees using content unsure of its integrity.
You can’t prevent everyone ignoring best practice when publishing.
You may make it difficult for people to share knowledge effectively.
Your governance model may be ineffective and need improving.
Getting the balance right between encouraging best practice and the level of governance to deter behaviour which can destroy the value from using SharePoint and Office 365 is critical.
As well as encouraging best practice, preventing problems helps to reduce time and costs wasted on sorting out unnecessary issues. While that is the aim of most organisations the practical realities as it is rolled out can divert plans from achieving this.
You need to get the right level of governance in place to prevent problems. Is it encouraging innovation and keeping governance light touch? Is it a heavier touch to prevent the ‘wrong’ behaviour and minimise risk of your brand and reputation being damaged? How much do you want to spend preventing problems? What does your cost/benefit analysis show?
People using SharePoint and Office 365 have a great experience (especially the first time they use it).
Everyone is confident they can use it for what they need it for without experience problems.
Employees don’t waste time calling the helpdesk because many problems have been prevented.
Effective governance encourages early adoption and increased knowledge sharing.
Costs spent preventing problems are justified by increased productivity and reduced risk of errors.
People find registering difficult and lengthy because of extra steps taken to prevent problems and don’t bother.
People find it too restrictive for their needs and it stifles innovation.
People turn to other tools (maybe not approved) to meet their needs and ask other people for help to use them.
Too restrictive governance prevents most beneficial use by raising the barrier too high for people to use.
Costs of preventing problems are higher than benefits to be gained and not justified.
You need to consider the potential benefits and drawbacks before deciding on the level of governance that is right for your organisation.
Remember, it is possible and probably desirable to have different levels of governance for each feature. It may be lighter for personal views and opinions expressed in MyProfile and MySite but tighter for policies and formal news items in TeamSites.
That is the challenge! You have so much flexibility to configure the tools to meet your organisation’s needs. Don’t be afraid to test out on part of your intranet to see what effect it has and involve employees to feed back on their experience before launching it.
The way forward is to create a sustainable information architecture, that supports an information environment that is available on any platform, everywhere, anytime and on any device. A governance framework can show roles and responsibilities, how they fit with a strategy and plan with publishing standards as the foundation to a consistently good user experience.
Combining a governance framework and information architecture with the same scope avoids any gaps in your buckets of content being managed or not being found. It helps you transform from your estate to the cloud successfully.
In our last concluding posts we will dive into more design oriented topics with a helping hand from findability experts and developers. Adding migration thoughts in next post. But first navigating the social graph being people centric, leaving some outstanding questions. How will the graph interoperate if your business runs several clouds, and still have buckets of content elsewhere?
This is the fifth post in a series (1, 2, 3, 4, 6, 7 ) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud. We will help you to consider the options and guide on the steps you need to take.
Starting from our first post we have covered different aspects you need to consider as you take each step including information structure and how it is managed using Office 365 and SharePoint as a technology example. We will cover governance and how content should be managed in the cloud in this post.
Content created within a context, as either a departmental site, or team habitat has usually only reach and bearing for the local context of fellow members of staff within this unit. Other pieces of content have a coverage that stretches all parts of the business. One simple example, is the bucket of content that makes up the management system, with governing principles, strategies, policies and guidelines that describes the core processes, activities, roles and so forth within an organisation.
Yet other content, as the outcome from a project, will build a bucket of content that either lives in a new context, improves a bucket of content or feeds into yet another following project.
From an information management perspective, it is vital that you have organising principles to all your content, where all these layers have been covered. Both reach, and the life cycle to the set of content.
You need a governance framework that reaches out to every bucket of content. This covers what is still on your estate as well as the growing amount in the cloud. All content needs to be managed to remove risks of leakage of sensitive information and prevent people having an inconsistent user experience as they move from one bucket of content in the cloud to another content bucket still on the estate.
You need to make sure people do not see the difference between buckets of content on the estate from content buckets in the cloud. People using your content to help with their work don’t need to know where the content is kept. They need to find it as easily as before, preferably even easier! Content in the cloud should feel the same and be a natural extension to the digital environment people are already used to. Manage it with a governance framework that covers every bucket of content and make it more easy to adopt quicker and use more often without caution or delay.
Part of your governance needs to cover publishing standards based on business needs so it is easy to access from any device e.g laptops, tablets and smartphones, and to view without unnecessary authentication levels. This helps to create that consistent good user experience that encourages people to use your content whether the bucket is in the cloud or not.
A professional team from group HR, might work in their local teamsite, with on-going conversations, work-in-progress documents and so forth. Pieces of their content production leads to governing policies that have a global reach within the organisation, and needs to be linked from the corporate intranet spaces. with versioning and good quality to resource descriptions (meta data). This practice and professional network of HR people, do also share content on a departmental site. With links and resources, that have direct impact on their internal processes. The group of people, have outreaching triggers, and in-bound conversations. And have to balance these two states.
When it comes to temporal content buckets, like a project team site. There are several considerations one have to capture. First where will the outcome and result be stored, when the project is finished. In which context will these content pieces contribute. Second, what should be captured from all on-going conversations (social elements) and work-in-progress and drafts developed during the projects lifecycle? Should a project habitat, be searchable after closing down? Or do the habitat change status, hence all documentation stay within the collection, but the overarching state to the habitat changes? Within Sharepoint these temporal states, versions, workflow and properties. All sum up the organising principles.
If these principles haven’t been ironed out, and been described and decided. Inevitable there will be emerging ghost towns, of dead habitats and lost collections of content. With no governance or ownership whatsoever. All this will become a digital landfill.
This is the fourth post in a series (1, 2, 3,5, 6, 7) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud. We will help you to consider the options and guide on the steps you need to take.
In the first post we set out the most common challenges you are likely to face and how you may overcome these. In the second post we focused on how Office 365 and SharePoint can play a part in moving to the cloud. In the third post we covered how they can help join up your organisation online using their collaboration tools and features.
In this post we will cover engagement and how sorting and categorisation of artifacts, according to a simple-to-understand and easy-to-use standard, will form the bits and parts of the curation and cultivation process.
All document libraries should have one standard listing of all items – with two very distinct audiences: being either actors within the habitat or the people contributing, acting and joining the daily conversation; and secondly, those visitors who pass-by the habitat to collect, link and act upon the content presented within the habitats realm.
This makes it very easy for visitors to find their way around a habitat, if the visitors’ area (business lounge) is pretty much aligned to the overarching theme of the site… and all artifacts that the project team like to share wider, have been listed in a virtual bookshelf, with major versions only. The visitors’ area, has all the relevant data, presented upfront. Basically the answers to the questions set when starting the project. The visitors’ area shouldn’t be a backdrop, but rather a storefront. The content has to be of good quality. Then there should be options to engage with the inner-living-room of the habitat, and enter the messy on-going conversations, depending on access-rights. But the default setting, should always be openfor unexpected “internal” (within the realm of the organisation) visitors. If the visitors’ area is compiled in a nice and easy to use manner, most visitors are just happy to pick the best-read from the bookshelf, or at least raise a questions for the team! The social construct for this is “welcoming a stranger”, since that visitor might link to your team’s content, cross-linking into his social-spaces.
The habitat’s livingroom and social conversations, will address new context-specific organising principles. A team might want to add new list-items, sort categories or introduce very local what-goes-where themes. This may be especially so when the team consists of actors who have different roles and responsibilities with regard to the overall outcome. And because of this, there may be a certain mix of tools or services in this one habitat of many, where they hang-out for project tasks.
The contextual adjustment is where the curator has to work on a cultivation process that glues the team together. The shared terminology within a group conversation, is what match their practices together. At inception, the curator picks a bouquet of on-topic terms from the controlled vocabularies. Mixing this with everyday use, and contributions from all members, this can be the fruitful and semantically-enhanced conversations with end-user generated tags or “folksonomies”. The same goes for interior design of links, tools, chosen content types and other forms of artifacts that the team will be needing to fulfill their goals and outcome.
The governance of the habitat, leans very much on the shared experiences in the group, and assigned responsibilities for stewardship and curation – where publishing standards, guidelines and training should be part of the mix.