Tonya Hongsermeier

Creating and sharing clinical decision support content with Web 2.0

Clinical decision support is a powerful tool for improving healthcare quality and patient safety. However, developing a comprehensive package of decision support interventions is costly and difficult. If used well, Web 2.0 methods may make it easier and less costly to develop decision support. Web 2.0 is characterized by online communities, open sharing, interactivity and collaboration. Although most previous attempts at sharing clinical decision support content have worked outside of the Web 2.0 framework, several initiatives are beginning to use Web 2.0 to share and collaborate on decision support content. We present case studies of three efforts: the Clinfowiki, a world-accessible wiki for developing decision support content; Partners Healthcare eRooms, web-based tools for developing decision support within a single organization; and Epic Systems Corporations Community Library, a repository for sharing decision support content for customers of a single clinical system vendor. We evaluate the potential of Web 2.0 technologies to enable collaborative development and sharing of clinical decision support systems through the lens of three case studies; analyzing technical, legal and organizational issues for developers, consumers and organizers of clinical decision support content in Web 2.0. We believe the case for Web 2.0 as a tool for collaborating on clinical decision support content appears strong, particularly for collaborative content development within an organization.

A Description and Functional Taxonomy of Rule-based Decision Support Content at a Large Integrated Delivery Network

OBJECTIVE This study sought to develop a functional taxonomy of rule-based clinical decision support. DESIGN The rule-based clinical decision support content of a large integrated delivery network with a long history of computer-based point-of-care decision support was reviewed and analyzed along four functional dimensions: trigger, input data elements, interventions, and offered choices. RESULTS A total of 181 rule types were reviewed, comprising 7,120 different instances of rule usage. A total of 42 taxa were identified across the four categories. Many rules fell into multiple taxa in a given category. Entered order and stored laboratory result were the most common triggers; laboratory result, drug list, and hospital unit were the most frequent data elements used. Notify and log were the most common interventions, and write order, defer warning, and override rule were the most common offered choices. CONCLUSION A relatively small number of taxa successfully described a large body of clinical knowledge. These taxa can be directly mapped to functions of clinical systems and decision support systems, providing feature guidance for developers, implementers, and certifiers of clinical information systems.

A pilot study of distributed knowledge management and clinical decision support in the cloud

OBJECTIVE Implement and perform pilot testing of web-based clinical decision support services using a novel framework for creating and managing clinical knowledge in a distributed fashion using the cloud. The pilot sought to (1) develop and test connectivity to an external clinical decision support (CDS) service, (2) assess the exchange of data to and knowledge from the external CDS service, and (3) capture lessons to guide expansion to more practice sites and users. MATERIALS AND METHODS The Clinical Decision Support Consortium created a repository of shared CDS knowledge for managing hypertension, diabetes, and coronary artery disease in a community cloud hosted by Partners HealthCare. A limited data set for primary care patients at a separate health system was securely transmitted to a CDS rules engine hosted in the cloud. Preventive care reminders triggered by the limited data set were returned for display to clinician end users for review and display. During a pilot study, we (1) monitored connectivity and system performance, (2) studied the exchange of data and decision support reminders between the two health systems, and (3) captured lessons. RESULTS During the six month pilot study, there were 1339 patient encounters in which information was successfully exchanged. Preventive care reminders were displayed during 57% of patient visits, most often reminding physicians to monitor blood pressure for hypertensive patients (29%) and order eye exams for patients with diabetes (28%). Lessons learned were grouped into five themes: performance, governance, semantic interoperability, ongoing adjustments, and usability. DISCUSSION Remote, asynchronous cloud-based decision support performed reasonably well, although issues concerning governance, semantic interoperability, and usability remain key challenges for successful adoption and use of cloud-based CDS that will require collaboration between biomedical informatics and computer science disciplines. CONCLUSION Decision support in the cloud is feasible and may be a reasonable path toward achieving better support of clinical decision-making across the widest range of health care providers.

The morningside initiative: collaborative development of a knowledge repository to accelerate adoption of clinical decision support.

The Morningside Initiative is a public-private activity that has evolved from an August, 2007, meeting at the Morningside Inn, in Frederick, MD, sponsored by the Telemedicine and Advanced Technology Research Center (TATRC) of the US Army Medical Research Materiel Command. Participants were subject matter experts in clinical decision support (CDS) and included representatives from the Department of Defense, Veterans Health Administration, Kaiser Permanente, Partners Healthcare System, Henry Ford Health System, Arizona State University, and the American Medical Informatics Association (AMIA). The Morningside Initiative was convened in response to the AMIA Roadmap for National Action on Clinical Decision Support and on the basis of other considerations and experiences of the participants. Its formation was the unanimous recommendation of participants at the 2007 meeting which called for creating a shared repository of executable knowledge for diverse health care organizations and practices, as well as health care system vendors. The rationale is based on the recognition that sharing of clinical knowledge needed for CDS across organizations is currently virtually non-existent, and that, given the considerable investment needed for creating, maintaining and updating authoritative knowledge, which only larger organizations have been able to undertake, this is an impediment to widespread adoption and use of CDS. The Morningside Initiative intends to develop and refine (1) an organizational framework, (2) a technical approach, and (3) CDS content acquisition and management processes for sharing CDS knowledge content, tools, and experience that will scale with growing numbers of participants and can be expanded in scope of content and capabilities. Intermountain Healthcare joined the initial set of participants shortly after its formation. The efforts of the Morningside Initiative are intended to serve as the basis for a series of next steps in a national agenda for CDS. It is based on the belief that sharing of knowledge can be highly effective as is the case in other competitive domains such as genomics. Participants in the Morningside Initiative believe that a coordinated effort between the private and public sectors is needed to accomplish this goal and that a small number of highly visible and respected health care organizations in the public and private sector can lead by example. Ultimately, a future collaborative knowledge sharing organization must have a sustainable long-term business model for financial support.

Definitions management: a semantics-based approach for clinical documentation in healthcare delivery

Structured Clinical Documentation is a fundamental component of the healthcare enterprise, linking both clinical (e.g., electronic health record, clinical decision support) and administrative functions (e.g., evaluation and management coding, billing). Documentation templates have proven to be an effective mechanism for implementing structured clinical documentation. The ability to create and manage definitions, i.e., definitions management, for various concepts such as diseases, drugs, contraindications, complications, etc. is crucial for creating and maintaining documentation templates in a consistent and cohesive manner across the organization. Definitions management involves the creation and management of concepts that may be a part of controlled vocabularies, domain models and ontologies. In this paper, we present a real-world implementation of a semantics-based approach to automate structured clinical documentation based on a description logics (DL) system for ontology management. In this context we will introduce the ontological underpinnings on which clinical documents are based, namely the domain, document and presentation ontologies. We will present techniques that leverage these ontologies to render static and dynamic templates that contain branching logic. We will also evaluate the role of these ontologies in the context of managing the impact of definition changes on the creation and rendering of these documentation templates, and the ability to retrieve documentation templates and their instances precisely in a given clinical context.

Partners healthcare order set schema: An information model for management of clinical content

Developed by the Clinical Knowledge Management and Decision Support Group at Partners HealthCare and the Decision Systems Group at Harvard Medical School, the XML-based Order Set Schema presented in this chapter is the result of a broader enterprise-wide knowledge management effort to enhance quality, safety, and efficiency of provided care at Partners HealthCare while maximizing the use of new clinical information technology. We are in the process of deploying the Order Set Schema at two Partners-based, Harvard-affiliated academic medical centers the Brigham & Women’s Hospital (BWH) and Massachusetts General Hospital (MGH), Boston, MA, so that existing content in the Computerized Physician Order Entry (CPOE) systems at these two institutions can be successfully extracted and mapped into the proposed schema. In this way, “hardwired” knowledge could be mapped into taxonomies of relevant terms, definitions and associations, resulting in formalized conceptual models and ontologies with explicit, consistent, user-meaningful relationships among concepts to support collaboration, and content management that will promote systematic (a) conversion of reference content into a form that approaches specifications for decision support content; (b) development and reuse of clinical content while ensuring consistency in the information; and (c) support an open and distributed review process among leadership, content matter experts, and end-users. Further, incorporating metadata into our unified content strategy will improve workflow by enabling timely review and updating of content, knowledge life-cycle management, and knowledge encoding; reduce costs and; aid authors to identify relevant elements for reuse while reducing redundant and spurious content. Ultimately, we view our knowledge management infrastructure as a key element for knowledge discovery.

Structured representation for core elements of common clinical decision support interventions to facilitate knowledge sharing.

At present, there are no widely accepted, standard approaches for representing computer-based clinical decision support (CDS) intervention types and their structural components. This study aimed to identify key requirements for the representation of five widely utilized CDS intervention types: alerts and reminders, order sets, infobuttons, documentation templates/forms, and relevant data presentation. An XML schema was proposed for representing these interventions and their core structural elements (e.g., general metadata, applicable clinical scenarios, CDS inputs, CDS outputs, and CDS logic) in a shareable manner. The schema was validated by building CDS artifacts for 22 different interventions, targeted toward guidelines and clinical conditions called for in the 2011 Meaningful Use criteria. Custom style sheets were developed to render the XML files in human-readable form. The CDS knowledge artifacts were shared via a public web portal. Our experience also identifies gaps in existing standards and informs future development of standards for CDS knowledge representation and sharing.

Can Semantic Web Technologies Enable Translational Medicine

The success of new innovations and technologies are very often disruptive in nature. At the same time, they enable novel next generation infrastructures and solutions. These solutions often give rise to creation of new commercial markets and/or introduce great efficiencies in the form of efficient processes and the ability to create, organize, share and manage knowledge effectively. This benefits both researchers and practitioners in a given field of activity. In this chapter, we explore the area of Translational Medicine which aims to improve communication between the basic and clinical sciences so that more therapeutic insights may be derived from new scientific ideas - and vice versa. Translation research goes from bench to bedside, where theories emerging from preclinical experimentation are tested on disease-affected human subjects, and from bedside to bench, where information obtained from preliminary human experimentation can be used to refine our understanding of the biological principles underpinning the heterogeneity of human disease and polymorphism(s). Informatics in general and semantic web technologies in particular, has a big role to play in making this a reality. We present a clinical use case and identify critical requirements, viz., data integration, clinical decision support and knowledge maintenance and provenance, which should be supported to enable translational medicine. Solutions based on semantic web technologies for these requirements are also presented. Finally, we discuss research issues motivated by the gaps in the current state of the art in semantic web technologies: (a) The impact of expressive data and knowledge models and query languages; (b) The role played by declarative specifications such as rules, description logics axioms and the associated querying and inference mechanisms based on these specifications; (c) Architectures for data integration, clinical decision support and knowledge management in the context of the application use case.

A study of diverse clinical decision support rule authoring environments and requirements for integration

BackgroundEfficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs), Software Engineers (SEs), and Subject Matter Experts (SMEs) to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE) in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules.MethodsThe authors evaluated RAEs in active use across Partners Healthcare, including enterprise wide, ambulatory only, and system specific tools, with a focus on rule editors for reminder and medication rules. We conducted meetings with users of these RAEs to discuss their general experience and perceived advantages and limitations of these tools.ResultsWhile the overall rule authoring process is similar across the 10 separate RAEs, the system capabilities and architecture vary widely. Most current RAEs limit the ability of the clinical decision support (CDS) interventions to be standardized, sharable, interoperable, and extensible. No existing system meets all requirements defined by knowledge management users.ConclusionsA successful, scalable, integrated rule authoring environment will need to support a number of key requirements and functions in the areas of knowledge representation, metadata, terminology, authoring collaboration, user interface, integration with electronic health record (EHR) systems, testing, and reporting.

Knowledge management infrastructure: Evolution at partners healthcare system

Publisher Summary This chapter represents a snapshot of a multiyear undertaking to develop a knowledge management infrastructure that must, by necessity, serve the needs of a large, extremely heterogeneous application environment with an enormous inventory of homegrown content in production. The infrastructure needed to support knowledge transparency and content governance have been built, and the focus is now on tackling the deeper challenges of knowledge engineering that undermine the ability to expand or change content in production once it is determined that such changes are necessary. To accomplish this, new integration approaches are being pursued among vendor solutions for content management, business rules management, terminology management, and ontology management. This approach is essential to support managing knowledge at the speed of change anticipated, particularly with the advent of molecular medicine now. It is common in health care to focus on the computing power required for analytics or for meeting the event management requirements of patient data transactions. A knowledge management infrastructure can be viewed as a knowledge-event management framework that will support structured knowledge discovery, acquisition, and maintenance for the era of personalized medicine.