A scalable architecture for incremental specification and maintenance of procedural and declarative clinical decision-support knowledge

System for Context-Specific Visualization of Clinical Practice Guidelines (GuLiNav): Concept and Software Implementation

Background

Clinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model.

Objective

Here we present a novel approach based on dynamic guideline visualization that incorporates the individual patient’s current treatment context.

Methods

We derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine.

Results

We implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians.

Conclusions

The novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.

The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review

Clinical practice guidelines (CPGs) document evidence-based information and recommendations on treatment and management of conditions. CPGs usually focus on management of a single condition; however, in many cases a patient will be at the centre of multiple health conditions (multimorbidity). Multiple CPGs need to be followed in parallel, each managing a separate condition, which often results in instructions that may interact with each other, such as conflicts in medication. Furthermore, the impetus to deliver customised care based on patient-specific information, results in the need to be able to offer guidelines in an integrated manner, identifying and managing their interactions. In recent years, CPGs have been formatted as computer-interpretable guidelines (CIGs). This enables developing CIG-driven clinical decision support systems (CDSSs), which allow the development of IT applications that contribute to the systematic and reliable management of multiple guidelines. This study focuses on understanding the use of CIG-based CDSSs, in order to manage care complexities of patients with multimorbidity. The literature between 2011 and 2017 is reviewed, which covers: (a) the challenges and barriers in the care of multimorbid patients, (b) the role of CIGs in CDSS augmented delivery of care, and (c) the approaches to alleviating care complexities of multimorbid patients. Generating integrated care plans, detecting and resolving adverse interactions between treatments and medications, dealing with temporal constraints in care steps, supporting patient-caregiver shared decision making and maintaining the continuity of care are some of the approaches that are enabled using a CIG-based CDSS.

An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management

OBJECTIVE

Chronic diseases are complex and persistent clinical conditions that require close collaboration among patients and health care providers in the implementation of long-term and integrated care programs. However, current solutions focus partially on intensive interventions at hospitals rather than on continuous and personalized chronic disease management. This study aims to fill this gap by providing computerized clinical decision support during follow-up assessments of chronically ill patients at home.

METHODS

We proposed an ontology-based framework to integrate patient data, medical domain knowledge, and patient assessment criteria for chronic disease patient follow-up assessments. A clinical decision support system was developed to implement this framework for automatic selection and adaptation of standard assessment protocols to suit patient personal conditions. We evaluated our method in the case study of type 2 diabetic patient follow-up assessments.

RESULTS

The proposed framework was instantiated using real data from 115,477 follow-up assessment records of 36,162 type 2 diabetic patients. Standard evaluation criteria were automatically selected and adapted to the particularities of each patient. Assessment results were generated as a general typing of patient overall condition and detailed scoring for each criterion, providing important indicators to the case manager about possible inappropriate judgments, in addition to raising patient awareness of their disease control outcomes. Using historical data as the gold standard, our system achieved a rate of accuracy of 99.93% and completeness of 95.00%.

CONCLUSIONS

This study contributes to improving the accessibility, efficiency and quality of current patient follow-up services. It also provides a generic approach to knowledge sharing and reuse for patient-centered chronic disease management.

Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7's Virtual Medical Record standard

OBJECTIVE

We show how the HL7 Virtual Medical Record (vMR) standard can be used to design and implement a data integrator (DI) component that collects patient information from heterogeneous sources and stores it into a personal health record, from which it can then retrieve data. Our working hypothesis is that the HL7 vMR standard in its release 1 version can properly capture the semantics needed to drive evidence-based clinical decision support systems.

MATERIALS AND METHODS

To achieve seamless communication between the personal health record and heterogeneous data consumers, we used a three-pronged approach. First, the choice of the HL7 vMR as a message model for all components accompanied by the use of medical vocabularies eases their semantic interoperability. Second, the DI follows a service-oriented approach to provide access to system components. Third, an XML database provides the data layer.Results The DI supports requirements of a guideline-based clinical decision support system implemented in two clinical domains and settings, ensuring reliable and secure access, high performance, and simplicity of integration, while complying with standards for the storage and processing of patient information needed for decision support and analytics. This was tested within the framework of a multinational project (www.mobiguide-project.eu) aimed at developing a ubiquitous patient guidance system (PGS).

DISCUSSION

The vMR model with its extension mechanism is demonstrated to be effective for data integration and communication within a distributed PGS implemented for two clinical domains across different healthcare settings in two nations.

Service oriented architecture for clinical decision support: a systematic review and future directions

The use of a service-oriented architecture (SOA) has been identified as a promising approach for improving health care by facilitating reliable clinical decision support (CDS). A review of the literature through October 2013 identified 44 articles on this topic. The review suggests that SOA related technologies such as Business Process Model and Notation (BPMN) and Service Component Architecture (SCA) have not been generally adopted to impact health IT systems' performance for better care solutions. Additionally, technologies such as Enterprise Service Bus (ESB) and architectural approaches like Service Choreography have not been generally exploited among researchers and developers. Based on the experience of other industries and our observation of the evolution of SOA, we found that the greater use of these approaches have the potential to significantly impact SOA implementations for CDS.

Integration of Personalized Healthcare Pathways in an ICT Platform for Diabetes Managements: A Small-Scale Exploratory Study

The availability of new tools able to support patient monitoring and personalized care may substantially improve the quality of chronic disease management. A personalized healthcare pathway (PHP) has been developed for diabetes disease management and integrated into an information and communication technology system to accomplish a shift from organization-centered care to patient-centered care. A small-scale exploratory study was conducted to test the platform. Preliminary results are presented that shed light on how the PHP influences system usage and performance outcomes.

Exploration of patterns predicting renal damage in patients with diabetes type II using a visual temporal analysis laboratory

Objective To analyze the longitudinal data of multiple patients and to discover new temporal knowledge, we designed and developed the Visual Temporal Analysis Laboratory (ViTA-Lab). In this study, we demonstrate several of the capabilities of the ViTA-Lab framework through the exploration of renal-damage risk factors in patients with diabetes type II.

Materials and methods The ViTA-Lab framework combines data-driven temporal data mining techniques, with interactive, query-driven, visual analytical capabilities, to support, in an integrated fashion, an iterative investigation of time-oriented clinical data and of patterns discovered in them. Patterns discovered through the data mining mode can be explored visually, and vice versa. Both analysis modes are supported by a rich underlying ontology of clinical concepts, their relations, and their temporal properties. The knowledge enables us to apply a temporal-abstraction pre-processing phase that abstracts in a context-sensitive manner raw time-stamped data into interval-based clinically meaningful interpretations, increasing the results’ significance. We demonstrate our approach through the exploration of risk factors associated with future renal damage (micro-albuminuria and macro-albuminuria) and their relationship to the hemoglobin A1C (HbA1C ) and creatinine level concepts, in the longitudinal records of 22 000 patients with diabetes type II followed for up to 5 years.

Results The iterative ViTA-Lab analysis process was highly feasible. Higher ranges of either normal albuminuria or normal creatinine values and their combination were shown to be significantly associated with future micro-albuminuria and macro-albuminuria. The risk increased given high HbA1C levels for women in the lower range of normal albuminuria, and for men in the higher range of albuminuria.

Conclusions The ViTA-Lab framework can potentially serve as a virtual laboratory for investigations of large masses of longitudinal clinical databases, for discovery of new knowledge through interactive exploration, clustering, classification, and prediction.

Adapting heart failure guidelines for nursing care in home health settings: challenges and solutions

BACKGROUND

Nurses provide most of home health services for patients with heart failure, and yet there are no evidence-based practice guidelines developed for home health nurses.

PURPOSE

The purpose of this article was to review the challenges and solutions for adapting generally available HF clinical practice guidelines to home health nursing.

METHODS

Appropriate HF guidelines were identified and home health nursing-relevant guidelines were extracted by the research team. In addition, a team of nursing academic and practice experts evaluated the extracted guidelines and reached consensus through Delphi rounds.

RESULTS

We identified 172 recommendations relevant to home health nursing from the American Heart Association and Heart Failure Society of America guidelines. The recommendations were divided into 5 groups (generic, minority populations, normal ejection fraction, reduced ejection fraction, and comorbidities) and further subgroups. Experts agreed that 87% of the recommendations selected by the research team were relevant to home health nursing and rejected 6% of the selected recommendations. Experts' opinions were split on 7% of guideline recommendations. Experts mostly disagreed on recommendations related to HF medication and laboratory prescription as well as HF patient assessment. These disagreements were due to lack of patient information available to home health nurses as well as unclear understanding of scope of practice regulations for home health nursing. After 2 Delphi rounds over 8 months, we achieved 100% agreement on the recommendations. The finalized guideline included 153 recommendations.

CONCLUSIONS

Guideline adaptation projects should include a broad scope of nursing practice recommendations from which home health agencies can customize relevant recommendations in accordance with available information and state and agency regulations.

Temporal abstraction and temporal Bayesian networks in clinical domains: a survey

OBJECTIVES

Temporal abstraction (TA) of clinical data aims to abstract and interpret clinical data into meaningful higher-level interval concepts. Abstracted concepts are used for diagnostic, prediction and therapy planning purposes. On the other hand, temporal Bayesian networks (TBNs) are temporal extensions of the known probabilistic graphical models, Bayesian networks. TBNs can represent temporal relationships between events and their state changes, or the evolution of a process, through time. This paper offers a survey on techniques/methods from these two areas that were used independently in many clinical domains (e.g. diabetes, hepatitis, cancer) for various clinical tasks (e.g. diagnosis, prognosis). A main objective of this survey, in addition to presenting the key aspects of TA and TBNs, is to point out important benefits from a potential integration of TA and TBNs in medical domains and tasks. The motivation for integrating these two areas is their complementary function: TA provides clinicians with high level views of data while TBNs serve as a knowledge representation and reasoning tool under uncertainty, which is inherent in all clinical tasks.

METHODS

Key publications from these two areas of relevance to clinical systems, mainly circumscribed to the latest two decades, are reviewed and classified. TA techniques are compared on the basis of: (a) knowledge acquisition and representation for deriving TA concepts and (b) methodology for deriving basic and complex temporal abstractions. TBNs are compared on the basis of: (a) representation of time, (b) knowledge representation and acquisition, (c) inference methods and the computational demands of the network, and (d) their applications in medicine.

RESULTS

The survey performs an extensive comparative analysis to illustrate the separate merits and limitations of various TA and TBN techniques used in clinical systems with the purpose of anticipating potential gains through an integration of the two techniques, thus leading to a unified methodology for clinical systems. The surveyed contributions are evaluated using frameworks of respective key features. In addition, for the evaluation of TBN methods, a unifying clinical domain (diabetes) is used.

CONCLUSION

The main conclusion transpiring from this review is that techniques/methods from these two areas, that so far are being largely used independently of each other in clinical domains, could be effectively integrated in the context of medical decision-support systems. The anticipated key benefits of the perceived integration are: (a) during problem solving, the reasoning can be directed at different levels of temporal and/or conceptual abstractions since the nodes of the TBNs can be complex entities, temporally and structurally and (b) during model building, knowledge generated in the form of basic and/or complex abstractions, can be deployed in a TBN.

Developing nursing computer interpretable guidelines: a feasibility study of heart failure guidelines in homecare

Homecare is the fastest growing healthcare sector and evidence based information systems are critically needed. Nurses provide most of the care in homecare setting, yet there is a lack of knowledge on the feasibility of applying existing methodologies to generate computer interpretable nursing guidelines for home care. This study examined the feasibility of encoding homecare nursing heart failure guideline into a computer interpretable format. First, we achieved experts' consensus on the relevant guideline. Then, after training on the graphical tool for gradual knowledge specification (Gesher), we generated a comprehensive, hierarchical and time-oriented computer interpretable guideline using one of the guideline modeling languages (Asbru). The final guideline included 167 recommendations and experts' evaluation confirmed the adequacy of guideline knowledge representation. Future work should expand the applicability of our methodology and tools to nursing specialties other than heart failure and develop methods for comprehensive quality evaluation of the resulting guidelines.