2. semedy was founded to develop
and deliver innovative software
solutions based on semantically
robust healthcare information
management and highly specialized
knowledge management solutions.
semedy structures clinical
information and converts it into
machine-processable knowledge for
clinical decision support.
semedy offers the CKMS
Clinical Knowledge
Management System and
sem.reasoner, a powerful,
scalable reasoning engine
enabling intelligent clinical
decision support to
hospitals and healthcare
systems. CKMS was
recently implemented
successfully at Partners
Healthcare System (PHS) in
Boston (USA). semedy is
located in the US,
Switzerland and Germany.
3. „Knowledge is the fuel for future healthcare“ - Health care institutions build
large and increasing amounts of clinical knowledge assets. They are responsible
for maintaining the accuracy and transparency of the knowledge on an ongoing
basis. They have to keep all their knowledge assets up-to-date, link them to
external sources, and maintain the referential integrity throughout their insti-
tutions. Failing to do so can lead to inappropriate or sub-standard patient care.
Proper knowledge management requires standardized processes for regularly
acquiring and integrating up-to-date knowledge including proactively
reviewing, maintaining, and monitoring the embedded knowledge and its
various use throughout an institution. Such monitoring enables providers to
continuously improve and refine the knowledge; it lays the foundation to
assess the effectiveness of clinical decision support (CDS) on clinical outcomes.
Knowledge is created through a collaborative process between knowledge
engineers and clinicians who need to collaborate within the framework of a
Clinical Knowledge Management System (CKMS) to review and manage new
and existing knowledge assets.
semedy´s CKMS interacts with clinical applications and Business Intelligence
(BI) tools to support and streamline the complete
knowledge asset lifecycle. Focusing on the
design and build phase, CKMS supports
request, authorization, prioritization
and test phases, while interacting
(exporting/importing knowledge
assets) with EHR and BI systems
to monitor and evaluate the
utilization of published
knowledge assets.
CKMS serves as a central unified
repository for all types of
knowledge assets that exist in a
clinical environment ranging from
simple structures, such as simple
entities, references, forms, rules, validity
checks to more complex ones, such as order
sets, guidelines, metrics or complex algorithms. It
provides a generic and flexible approach to support the evolution of content
without breaking relationships among the different knowledge assets.
4. CKMS content can evolve independent of other systems guaranteeing growth
and ease of maintenance for future years. The different knowledge assets can
be revised and follow a configurable lifecycle workflow.
Data integrity
At any point in time CKMS guarantees the integrity of the managed knowledge
assets. CKMS permits only operations on the knowledge assets that do not
violate the data integrity. The system supports semantically enriched processes
leading towards more robust levels of semantic interoperability. A variety of
integrity checks is already included in the basic CKMS version. In addition CKMS
supports the creation of custom integrity checks.
The embedded inference engine ensures the structural integrity and semantic
consistency. Content-focused integrity checks verify existing constraints, such
as the uniqueness of certain codes or checking the interdependencies among
different knowledge assets.
Search & Navigate
As the number of knowledge assets in the
repository grows, the need for sophisticated
search functionalities increases which allows
finding and retrieving related content quickly
and efficiently. CKMS displays query results of
knowledge assets allowing users to traverse
relationships among knowledge assets and
easily navigate through the content. A
graphical visualization component displays a
specific knowledge asset including all
connected assets.
5. Import
CKMS includes two standard import
formats, one XML-based and a JSON-
based format. Alternatively custom
import plugins can be created and
integrated with CKMS through its
flexible plugin framework.
During the import process CKMS
validates the content and provides
feedback if the content does not
successfully pass the validation. The
import process also takes care of
creating new revisions of content that
already existed in CKMS.
User permissions
CKMS is shipped with a very flexible and
fine-grained permission system that
supports the various management and
maintenance responsibilities of different
types of users and their functions.
It is for example possible to configure
user permissions to create new
knowledge assets depending on the asset
type. Thus, establishing user permissions
and roles is highly flexible to reflect and
adapt to the various functions of team
members (knowledge engineers,
clinicians, administrators, etc.) and the
requirements of a complex healthcare
institution.
Create and maintain content
A large collection of features is provided to manage and control all knowledge assets
when content is created manually or existing content requires maintenance.
Various users can share responsibilities for knowledge assets which promotes a
team-oriented and expert-driven knowledge management cycle. This includes the
process of promoting assets through the different lifecycle states. When users
violate state dependencies the CKMS provides feedback about which entities caused
the operation to fail and assists them to resolve the discrepancies. CKMS includes a
pre-configured lifecycle state transition graph, which can be fully customized to fit
any customer needs.
Modular and extensible templates assist users to create new knowledge entities and
the associated metadata. A flexible metadata scheme supports a high level of
customization. New assets can be created in CKMS using the standard forms
delivered with CKMS, or using user-defined forms for specific asset types. While
completing the input forms during knowledge asset creation, the user is supported
through a field level validation, which directly recognizes and helps to resolve
modeling errors. During editing the user can trigger a full manual validation to let
the application check if all constraints and dependencies are correct.
