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)

View Fredric Landqvist's LinkedIn profileFredric Landqvist research blog
View Peter Voisey's LinkedIn profilePeter Voisey

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