PIM is for storage

- 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.

Product attributes

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.

Simplified i3 products

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?

To summarise

  • 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
1 Gartner 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. 

Under the hood of the search engine

While using a search application we rarely think about what happens inside it. We just type a query, sometime refine details with facets or additional filters and pick one of the returned results. Ideally, the most desired result is on the top of the list. The secret of returning appropriate results and figuring out which fits a query better than others is hidden in the scoring, ranking and similarity functions enclosed in relevancy models. These concepts are crucial for the search application user’s satisfaction.

In this post we will review basic components of the popular TF/IDF model with simple examples. Additionally, we will learn how to ask Elasticsearch for explanation of scoring for a specific document and query.

Document ranking is one of the fundamental problems in information retrieval, a discipline acting as a mathematical foundation of search. The ranking, which is literally assigning a rank to a document matching search query corresponds with a term of relevance. Document relevance is a function which determines how well given document meets the search query. A concept of similarity corresponds, in turn, to the relevance idea, since relevance is a metric of similarity between a candidate result document and a search query. Continue reading

What’s new in Apache Solr 6?

Apache Solr 6 has been released recently! You need to remember about some important technical news: no more support for reading Lucene/Solr 4.x index or Java 8 is required. But what I think, the most interesting part is connected with its new features, which certainly follow world trends. I mean here: SQL engine at the top of the Solr, graph search and replicating data across different data centers.

Apache Solr

One of the most promising topic among the new features is Parallel SQL Interface. In a brief, it is possibility to run SQL queries on the top of the Solr Cloud (only Cloud mode right now). It can be very interesting to combine full-text capabilities with well-known SQL statements.
Solr uses Presto internally, which is a SQL query engine and works with various types of data stores. Presto is responsible for translating SQL statements to the Streaming Expression, since Solr SQL engine in based on the Streaming API.
Thanks to that, SQL queries can be executed at worker nodes in parallel. There are two implementations of grouping results (aggregations). First one is based on map reduce algorithm and the second one uses Solr facets. The basic difference is a number of fields used in grouping clause. Facet API can be used for better performance, but only when GROUP BY isn’t complex. If it is, better try aggregationMode=map_reduce.
From developer perspective it’s really transparent. Simple statement like “SELECT field1 FROM collection1″ is translated to proper fields and collection. Right now clauses like WHERE, ORDER BY, LIMIT, DISTINCT, GROUP BY can be used.
Solr still doesn’t support whole SQL language, but even though it’s a powerful feature. First of all, it can make beginners life easier, since relational world is commonly known. What is more, I imagine this can be useful during some IT system migrations or collecting data from Solr for further analysis. I hope to hear many different study cases in the near future.

Apache Solr 6 introduces also a topic, which is crucial, wherever a search engine is a business critical system. I mean cross data center replication (CDCR).
Since Solr Cloud has been created to support near real-time (NRT) searching, it didn’t work well when cluster nodes were distributed across different data centers. It’s because of the communication overhead generated by the leaders, replicas and synchronizations operation.

New idea is in experimental phase and still under developing, but for now we have an active-passive mode, where data is pushed from the Source DC to the Target DC. Documents can be sent in a real-time or according to the schedule. Every leader from active cluster sends asynchronously updates to the proper leader in passive cluster. After that, target leaders replicate changes to their replicas as usual.
CDCR is crucial when we think about distributed systems working in high-availability mode. It always refers to disaster recovery, scaling or avoiding single points of failure (SPOF). Please visit documentation page to find some details and plans for the future: https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=62687462

What if your business works in highly connected environment, where data relationships matter, but you still benefit from full-text searching? Solr 6 has a good news – it’s a graph traversal functionality.
A lot of enterprises know that focusing on relations between documents and graph data modeling is a future. Now you can build Solr queries which will allow you to discover information organized in nodes and edges. You can explore your collections in terms of data interactions and connections between particular data elements. We can think about the use cases from semantic search area (query augmentation, using ontologies etc.) or more prosaic, like organization security roles or access control.
Graph traversal query is still in progress, but we can use it from now and its basic syntax is really simple: fq={!graph from=parent_id to=id}id:”DOCUMENT_ID”

The last Solr 6 improvement, which I’m going to mention about is a new scoring algorithm – BM25. In fact, it’s a change forced by Apache Lucene 6. BM25 is now a default similarity implementation. Similarity is a process which examines which documents are similar to the query and to what extent. There are many different factors which determine document score. There are e.g.: number of search terms found in document, popularity of this search terms over the whole collection or document length. This is where BM25 improves scoring: it takes into consideration average length of the documents (fields) across the entire corpus. It also limits better an impact of terms frequency on results ranking.

As we can see, Apache Solr 6 provides us with many new features and those mentioned above are not all of them. We’re going to write more about the new functionalities soon. Until then, we encourage you to try the newest Solr on your own and remember: don’t hesitate to contact us in case of any problems!

Understanding politics with Watson using Text Analytics

To understand the topics that actually are important to different political parties is a difficult task. Can text analytics together with an search index be an approach to given a better understanding?

This blog post describes how IBM Watson Explorer Content Analytics (WCA) can be used to make sense of Swedish politics. All speeches (in Swedish: anföranden) in the Swedish Parliament from 2004 to 2015 are analyzed using WCA. In total 139 110 transcribed text documents were analyzed. The Swedish language support build by Findwise for WCA is used together with a few text analytic processing steps which parses out person names, political party, dates and topics of interest. The selected topics in this analyzed are all related to infrastructure and different types of fuels.

We start by looking at how some of the topics are mentioned over time.

Analyze of terms of interets in Swedsih parlament between 2004 and 2014.

Analyze of terms of interest in Swedish parliament between 2004 and 2014.

The view shows topic which has a higher number of mentions compared to what would be expected during one year. Here we can see among other topics that the topic flygplats (airport) has a high increase in number of mentioning during 2014.

So let’s dive down and see what is being said about the topic flygplats during 2014.

Swedish political parties mentioning Bromma Airport.

Swedish political parties mentioning Bromma Airport during 2014.

The above image shows how the different political parties are mentioning the topic flygplats during the year 2014. The blue bar shows the number of times the topic flygplats was mentioned by each political party during the year. The green bar shows the WCA correlation value which indicates how strongly related a term is to the current filter. What we can conclude is that party Moderaterna mentioned flygplats during 2014 more frequently than other parties.

Reviewing the most correlated nouns when filtering on flygplats and the year 2014 shows among some other nouns: Bromma (place in Sweden), airport and nedläggning (closing). This gives some idea what was discussed during the period. By filtering on the speeches which was held by Moderaterna and reading some of them makes it clear that Moderaterna is against a closing of Bromma airport.

The text analytics and the index provided by WCA helps us both discover trending topics over time and gives us a tool for understanding who talked about a subject and what was said.

All the different topics about infrastructure can together create a single topic for infrastructure. Speeches that are mentioning tåg (train), bredband (broadband) or any other defined term for infrastructure are also tagged with the topic infrastructure. This wider concept of infrastructure can of course also be viewed over time.

Discussions in Swedish parliament mentioning the defined terms which builds up the subject infrastructure 2004 to 2015.

Discussions in Swedish parliament mentioning the defined terms which builds up the subject infrastructure 2004 to 2015.

Another way of finding which party that are most correlated to a subject is by comparing pair of facets. The following table shows parties highly related to terms regarding infrastructure and type of fuels.

Political parties highly correlated to subjects regarding infrastructure and types of fuel.

Swedish political parties highly correlated to subjects regarding infrastructure and types of fuel.

Let’s start by explain the first row in order to understand the table. Mobilnät (mobile net) has only been mentioned 44 times by Centerpartiet, but Centerpartiet is still highly related to the term with a WCA correlation value of 3.7. This means that Centerpartiet has a higher share of its speeches mentioning mobilnät compared to other parties. The table indicates that two parties Centerpartiet and Miljöpartiet are more involved about the subject infrastructure topics than other political parties.

Swedish parties mentioning the defined concept of infrastructure.

Swedish parties mentioning the defined concept of infrastructure.

Filtering on the concept infrastructure also shows that Miljöpartiet and Centerpartiet are the two parties which has the highest share of speeches mentioning the defined infrastructure topics.

Interested to dig deeper into the data? Parsing written text with text analytics is a successful approach for increasing an understanding of subjects such as politics. Using IBM Watson Explorer Content Analytics makes it easy. Most of the functionality used in this example is also out of the box functionalities in WCA.

Migration from Google Search Appliance (GSA) in 4 easy steps

migration

 

 

Google Search Appliance is being phased out and in 2018, renewals will end. As an existing client, you can buy one-year license renewals throughout 2017. However, if fancying a change, here’s 4 simple steps for switching to Apache Solr or Elasticsearch.

1. Choose your hosting solution or servers

Wikimedia_Foundation_Servers-8055_14 

Whereas Google Search Appliance comes ready to plug in, Apache Solr and Elasticsearch need to be deployed and hosted on servers. You can choose to host Solr or Elasticsearch on your own infrastructure or in the cloud. Both platforms are highly scalable and can be massively distributed.

  • Own infrastructure

Servers and hardware requirements are highly dependent on the number of documents, documents types, search use cases and number of users. Memory, CPUs, disk and network are the main parameters to consider.

Elasticsearch hardware recommendations: https://www.elastic.co/guide/en/elasticsearch/guide/current/hardware.html

Apache Solr performance: https://wiki.apache.org/solr/SolrPerformanceProblems

Both Elasticsearch and Solr requires running java. For SolrCloud, you will also need to install Zookeeper.

  • In the cloud

You can also choose to run Solr or Elasticsearch on a cloud platform.

Elastic official cloud platform: https://www.elastic.co/cloud

2. Define your schema and mapping

In Apache Solr and Elasticsearch, fields can be indexed and processed differently according on type, language, use case … A field and its type can be defined in Elasticsearch using the mapping API or in Apache Solr with the schema.xml

Elasticsearch mapping API: https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping.html

Apache Solr schema: https://wiki.apache.org/solr/SchemaXml

3. Tune your connectors

the-cable-guy

Do you need to change all connectors?

The answer is no. Connectors sending GSA feeds can be kept, just refactor the output to match the Elasticsearch or Solr indexing syntax.

However, if you use GSA to crawl websites, you will need either to reconsider crawling as the method to get your data or to use an external webcrawler (like Norconex) Contrary to GSA, Apache Solr and Elasticsearch do not come with a webcrawler.

Elasticsearch Indexing API: https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-index_.html

4. Rewrite your queries and fetch new output

All common query functions such as filtering, sorting and dynamic navigation are standard in both Apache Solr and Elasticsearch. However, query parameters and output (XML or JSON) are different, which means queries and front-end need adaption to your new search engine.

If you are using Jellyfish by Findwise, queries and output will roughly be the same.

Elasticsearch response body: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-body.html

Apache Solr response: https://cwiki.apache.org/confluence/display/solr/Response+Writers

Google Search Appliance features equivalence

GSA feature Elasticsearch Apache Solr
Web crawling X X
Language Bundles Languages Language Analysis
Synonyms Synonyms Synonyms
Stopwords Stopwords Stopwords
Result Biasing Controlling relevance Query elevation
Suggestions Search-suggesters Suggester
Dynamic navigation Aggregations Faceting
Document preview X X
User result X X
Expert search X X
Keymatch X X
Related Queries X X
Secure search Shield Solr Security
Search reports Logstash+Kibana X
Mirroring/Distributed Scale Elastic Solr Cloud
System alert Watcher X
Email update/Alert Watcher X

X = not available outside of the box

4 quick ways to improve your search!

Findwise has recently published its annual report about Enterprise Search and Findability. We can see that a lot of people are complaining that the search engine is running poorly. There were 36% dissatisfied (users) in 2015. Is there any simple recipe for that? I bet there are some things that can be applied almost immediately!

 boy-cry

Data

It is quite common that the reason for bad results is content quality – there is simply no good content in a search engine!

Solution = editors and cleaning: Educate editors to produce better content! Decide on the main message, set informative titles and be consistent with internal wording and phrasing. Also, look over your index, archive outdated content and remove duplicates. Don’t be afraid to remove entire data sources from your index.


Metadata

If you already have data indexed, it is much easier to search using additional filters. Let’s say, when we are looking for a new washing machine on any good online store we can easily filter out the features such as energy class, manufacturer, price range etc. The same can happen to corporate data, provided that our documents are tagged in a consistent manner.

Solution = tagging: Check tags and metadata consistency for documents, which we search through. If the quality of the tagging leaves much to be desired, it should be corrected (note: this can be done automatically to the large extent!). Then you should consider what filters are the most useful for your company search and implement them in your browser.

 

Accuracy

Users’ expectations are very important. If they ask and search, they usually want and need, eg. current lunch menu, financial settlement form, a specific procedure for calculating credit risk, sales report for the previous quarter, etc. This unique need of each user is expressed through a simple query. And here we encounter significant problem: these queries are not always well interpreted by the search engine. If you don’t see the desired document/answer in the first five slots of the search results list, even after 2-3 trials by using various queries, you quickly come to the conclusion that the search engine doesn’t not work (well).

Solution = user feed-back: It is fundamental to regularly collect users feed-back on the search engine. If you receive signals that something does not work, then you absolutely need to examine what specific search scenarios aren’t functioning well. These things can be usually fixed pretty easily by using synonyms, promotions or by changing the order of results display.


Monitoring

It is not easy to gather the opinion of everyone in large organiations, as there might be thousands of them. A search engine, like everything else, sometimes breaks down, answers too long for queries and gives silly results, or even no result at all. Additionally, it’s not certain if such a thing contributes to our organization or not, and who makes the use of our search at all.

Solution = logging: Log analysis gives a lot of information about the real use of search engines by the users. Logs tell us how many people are looking for something, what they are asking for, how fast search engine responds, when it gives zero results. It’s priceless information to understand what works, who really benefits, what are the most popular contents and questions, what needs to be improved. It’s crucially important to do it on a regular basis.

 boy-happy

Summary

And now, when you fixed all these four points related to the search engine please tell me that it continues to be malfunctioning. I’ve yet to hear such a case :-)

Generational renewal at work – a search challenge

The big generational shift

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.

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.

Written by: Maria “Ia” Björk & Joar Svensson

Sensemaking or Digital Despair

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.

So finally, How do we make sense of any mess?

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.

View Fredric Landqvist's LinkedIn profileFredric Landqvist research blog

Improving User Intelligence with the ELK Stack at SCA

SCA is a leading global hygiene and forest products company, employing around 44,000 people worldwide. The Group (all companies within SCA) develops and produces sustainable personal care, tissue and forest products. Sales are conducted in about 100 countries under many strong brands. Each brand each has its own website and its own search.

At SCA we use Elasticsearch, Logstash, and Kibana to record searches, clicks on result documents and user feedback, on both the intranet and external sites. We also collect qualitative metrics by asking our public users a question after showing search results: “Did you find what you were looking for?” The user has the option to give a thumbs up or down and also write a comment.

What is logged?

All search parameters and results information is recorded for each search event: the query string, paging, sorting, facets, the number of hits, search response time, the date and time of the search, etc. Clicking a result document also records a multitude of information: the position of the document in the result list, the time it took from search to click and various document metadata (such as URL, source, format, last modified, author, and more). A click event also gets connected with the search event that generated it. This is also the case for feedback events.

Each event is written to a log file that is being monitored by Logstash, which then creates a document from each event and pushes them to Elasticsearch where the data is visualized in Kibana.

Why?

Due to the extent of information that is indexed, we can answer questions from the very simple, such as “What are the ten most frequent queries during the past week?” and “Users who click on document X, what do they search for?” to the more complex like “What is the distribution of clicked documents’ last modified dates, coming from source S, on Wednesdays? The possibilities are almost endless!

The answers to these questions allow us to tune the search to meet the needs of the users to an even greater extent and deliver even greater value. Today, we use this analysis for everything from adjusting the relevance model, to adding new facets or removing old ones, or changing the layout of the search and result pages.

Experienced value – more than “just” logs

Recording search and click events are common practice, but at SCA we have extended this to include user feedback, as mentioned above. This increases the value of the statistics even more. It allows an administrator to follow up on negative feedback in detail, e.g. by recreating the scenario. It also enables implicitly evaluated trial periods for change requests. If a statistically significant increase in the share of positive feedbacks is observed, then that change made it easier for users to find what they were looking for. We can also find the answer to new questions, such as “What’s the feedback from the users who experience zero hits?” and “Are users more likely to find what they are looking for if they use facets?”

And server monitoring as well!

This setup is not only used to record information about user behavior, we also monitor the health of our servers. Every few seconds we index information about each server’s CPU, memory and disk usage. The most obvious gain is the historic aspect. Not only can we see the resource usage at a specific point in time, we can also see trends that would not be noticeable if we only had access to data from right now. This can of course be correlated with the user statistics, e.g. if a rise in CPU usage can be correlated to an increase in query volume.

Benefits of the ELK Stack

What this means for SCA is that they get a search that is ever improving. We, the developers and administrators of the search system, are no longer in the dark regarding what changes actually change things for the better. The direct feedback loop between the users and administrators of the system creates a sense of community, especially when users see that their grievances are being tended to. Users find what they are looking for to a greater and greater extent, saving them time and frustration.

Conclusion

We rely on Elasticsearch, Logstash and Kibana as the core of our search capability, and for the insight to continually improve. We’re excited to see what the 2.0 versions bring. The challenge is to know what information you are after and create a model that will meet those needs. Getting the ELK platform up and running at SCA was the part of the project that took the least amount of our time, once the logs started streaming out of our systems.

A Health Care Information Commons Vision: from frozen assets to liquid gold

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.

Healthcare Information Commons

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?

Semantic Transformer

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.

In the data entry or capture point, there will be options to add semantic layers and attributes to the type of content and data provisioned. An easy way to illustrate this, is the emerging use of schema.org templated entities and properties for the MedicalTypes, MedicalConditions, Drugs, Guidelines, Codes from controlled vocabularies like SnoMedCT, Mesh, ICD10 and the like.

 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.

A Personal Health Record (PHR) , is a health record where health data and information related to the care of a patient is maintained by the patient

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.

Concluding remarks

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
  • Standard Sets for Medical Conditions by international collaboration at ICHOM, and in Sweden Sveus. Standards from Hl7 FHIR, W3C and Web of Data / Semantic Web. The Swedish National Board of Health and Welfare, have an embroic information structure (not in semantic machine readible, RDF, format). Information intermediaries like Google have settle for simple schemas for health and medicin.
  • 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.
  • The technology stack with smarter devices, sensors and things, along with Internet anywhere with cognitive computing and computational knowledge on-top of the commons will bring forward semantic translators.
  • New leaps in collaborative work and development with the use of the notebook theme, language and platform agnostic ways.

Making sense, defrosting health data into liguid gold improving healthcare for all.

For more information on Findwise research, please visit KConnect and Orios (Open Standards)

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