An ontology-based hierarchical semantic modeling approach to clinical pathway workflows

A holonic approach to clinical pathway data analysis

BACKGROUND

Healthcare faces challenges due to the advancements of Industry 4.0 as large volumes of data are generated within healthcare facilities that, combined with the complex nature of healthcare environments, make it difficult to utilise and interpret this data effectively.

PURPOSE

A novel holonic approach to clinical pathway data analysis is presented and implemented as a clinical pathway digital twin. A holon is here taken to be an autonomous and co-operative building block of a software system for transforming, transporting, storing and/or validating information. The digital twin's aim is to ingest, structure and analyse the information associated with a clinical pathway to support healthcare professionals in making informed decisions, for example monitoring and predicting the duration from admission to discharge for individual patients.

METHOD

Real world observations and a review of literature led to the identification of a generic set of clinical pathway analysis needs and, derived therefrom, a set of design requirements. A proof-of-concept clinical pathway analysis digital twin was implemented using a holonic approach derived from the ARTI reference architecture. The holonic approach is evaluated in a hip and knee replacement pathway case study. The evaluation includes automated statistical analyses and machine learning predictions.

RESULTS

The evaluation demonstrates that the holonic approach provides an intuitive and extensible means to aggregate and disaggregate information tactically, and to derive context-tailored analysis features. The holonic approach enhances checking for data completion and handling data anomalies. The evaluation also demonstrates on-demand report generation, which reduces repetitive manual tasks for healthcare professionals.

CONCLUSION

The novel holonic data analysis approach facilitates context-rich analyses tailored to specific clinical pathway activities, with effective tailoring of data ingestion and analysis. Healthcare professionals can use the data analysis approach to extract valuable insights for decision-making related to clinical pathways.

Automated clinical pathway standardization using SNOMED CT- based semantic relatedness

The increasing number of patients and heavy workload drive health care institutions to search for efficient and cost-effective methods to deliver optimal care. Clinical pathways are promising care plans that proved to be efficient in reducing costs and optimizing resource usage. However, most clinical pathways are circulated in paper-based formats. Clinical pathway computerization is an emerging research field that aims to integrate clinical pathways with health information systems. A key process in clinical pathway computerization is the standardization of clinical pathway terminology to comply with digital terminology systems. Since clinical pathways include sensitive medical terms, clinical pathway standardization is performed manually and is difficult to automate using machines. The objective of this research is to introduce automation to clinical pathway standardization. The proposed approach utilizes a semantic score-based algorithm that automates the search for SNOMED CT terms. The algorithm was implemented in a software system with a graphical user interface component that physicians can use to standardize clinical pathways by searching for and comparing relevant SNOMED CT retrieved automatically by the algorithm. The system has been tested and validated on SNOMED CT ontology. The experimental results show that the system reached a maximum search space reduction of 98.9% within any single iteration of the algorithm and an overall average of 71.3%. The system enables physicians to locate the proper terms precisely, quickly, and more efficiently. This is demonstrated using case studies, and the results show that human-guided automation is a promising methodology in the field of clinical pathway standardization and computerization.

Ontological framework for standardizing and digitizing clinical pathways in healthcare information systems

BACKGROUND AND OBJECTIVE

Most healthcare institutions are reorganizing their healthcare delivery systems based on Clinical Pathways (CPs). CPs are novel medical management plans to standardize medical activities, reduce cost, optimize resource usage, and improve the quality of service. However, most CPs are still paper-based and not fully integrated with Health Information Systems (HIS). More CP computerization research is therefore needed to fully benefit from CP's practical potentials. A major contribution of this research is the vision that CP systems deserve to be placed at the centre of HIS, because within CPs lies the very heart of medical planning, treatment and impressions, including healthcare quality and cost factors.

METHODS

An important contribution to the realization of this vision is to fully standardize and digitize CPs so that they become machine-readable and smoothly linkable across various HIS. To achieve this goal, this research proposes a framework for (i) CP knowledge representation and sharing using ontologies, (ii) CP standardization based on SNOMED CT and HL7, and (iii) CP digitization based on a novel coding system to encode CP data. To show the feasibility of the proposed framework we developed a prototype clinical pathway management system (CPMS) based on CPs currently in use at hospitals.

RESULTS

The results show that CPs can be fully standardized and digitized using SNOMED CT terms and codes, and the CPMS can work as an independent system, performing novel CP-related functions, including useful data analytics. CPs can be compared easily for auditing and quality management. Furthermore, the CPMS was smoothly linked to a hospital EMR and CP data were captured in EMR without any loss.

CONCLUSION

The proposed framework is promising and contributes toward solving major challenges related to CP standardization, digitization, and inclusion in today's modern computerized hospitals.

Research on the construction of the semantic model for Chinese ancient architectures based on architectural narratives

Purpose

This paper aims to reorganize the relevant information of Chinese ancient architectures with the use of Semantic Web technologies and thus facilitate its deep discovery and usage.

Design/methodology/approach

This paper proposes an ontology model for Chinese ancient architectures based on architectural narratives theory. To verify the availability of the ancient architecture ontology, we designed and implemented three experiments, including semantic retrieval based on SPARQL query, semantic reasoning with the use of Jena reasoner and visual analysis based on the Chinese Online Digital Humanities Resources Platform.

Findings

The proposed ontology provided a solution for the semantic annotation of the unstructured information of Chinese ancient architectures. On this basis, deep knowledge services such as semantic retrieval, semantic reasoning and visual analysis can be provided.

Practical implications

The proposed semantic model of ancient architectures can effectively improve the organization and access quality of the semantic content of Chinese ancient architectures.

Originality/value

This paper focuses on the semantic modelling for the unstructured information of Chinese ancient architectures to semantically describe the related entities (e.g. persons, events, places and times) and uncover their relationships, and thus it made contribution to the deep semantic annotations on ancient architectures.

Variance Risk Identification and Treatment of Clinical Pathway by Integrated Bayesian Network and Association Rules Mining

With the continuous development of data mining techniques in the medical field, variance analysis in clinical pathways based on data mining approaches have attracted increasing attention from scholars and decision makers. However, studies on variance analysis and treatment of specific kinds of disease are still relatively scarce. In order to reduce the hazard of postpartum hemorrhage after cesarean section, we conducted a detailed analysis on the relevant risk factors and treatment mechanisms, adopting the integrated Bayesian network and association rule mining approaches. By proposing a Bayesian network model based on regression analysis, we calculated the probability of risk factors determining the key factors that result in postpartum hemorrhage after cesarean section. In addition, we mined a few association rules regarding the treatment of postpartum hemorrhage on the basis of different clinical features. We divided the risk factors into primary and secondary risk factors by realizing the classification of different causes of postpartum hemorrhage after cesarean section and sorted the posterior probability to obtain the key factors in the primary and secondary risk factors: uterine atony and prolonged labor. The rules of clinical features associated with the management of postpartum hemorrhage during cesarean section were obtained. Finally, related strategies were proposed for improving medical service quality and enhancing the rescue efficiency of clinical pathways in China.

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification

INTRODUCTION

An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study.

METHODS

A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enabled identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others).

RESULTS

The proposed approach enables more a realistic and detailed simulation of the patient flow within a group of related departments. An experimental study shows an improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Almazov National Medical Research Centre in Saint Petersburg, Russia.

CONCLUSION

The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.

CPDI: An Index for measuring deviations in Clinical Pathways

Clinical Pathways (CPs) are evidence-based recommendation for treating a diagnosis and an effective instrument to decrease undesired practice variability and improve clinician performance. Deviations from CPs might just as well reduce quality of care. Moreover they can be associated to possible adverse events. In this perspective, we developed and tested a system for comparing a patient trajectory (PT) with the corresponding CP in order to recognize significant variations. To measure adherence, a Clinical Pathway Deviation Index (CPDI) was constructed as the weighted-sum of five indicators. To build the indicators three different tools for CPs modeling have been tested. Only two of them proved suitable for our system. A preliminary analysis has been carried out using data of 24 real PTs. The aim of this paper is to present the system and to characterize CPDI performances.

A process mining-based investigation of adverse events in care processes

This paper proposes the Clinical Pathway Analysis Method (CPAM) approach that enables the extraction of valuable organisational and medical information on past clinical pathway executions from the event logs of healthcare information systems. The method deals with the complexity of real-world clinical pathways by introducing a perspective-based segmentation of the date-stamped event log. CPAM enables the clinical pathway analyst to effectively and efficiently acquire a profound insight into the clinical pathways. By comparing the specific medical conditions of patients with the factors used for characterising the different clinical pathway variants, the medical expert can identify the best therapeutic option. Process mining-based analytics enables the acquisition of valuable insights into clinical pathways, based on the complete audit traces of previous clinical pathway instances. Additionally, the methodology is suited to assess guideline compliance and analyse adverse events. Finally, the methodology provides support for eliciting tacit knowledge and providing treatment selection assistance.

Analysis and classification of oncology activities on the way to workflow based single source documentation in clinical information systems

BACKGROUND

Today, cancer documentation is still a tedious task involving many different information systems even within a single institution and it is rarely supported by appropriate documentation workflows.

METHODS

In a comprehensive 14 step analysis we compiled diagnostic and therapeutic pathways for 13 cancer entities using a mixed approach of document analysis, workflow analysis, expert interviews, workflow modelling and feedback loops. These pathways were stepwise classified and categorized to create a final set of grouped pathways and workflows including electronic documentation forms.

RESULTS

A total of 73 workflows for the 13 entities based on 82 paper documentation forms additionally to computer based documentation systems were compiled in a 724 page document comprising 130 figures, 94 tables and 23 tumour classifications as well as 12 follow-up tables. Stepwise classification made it possible to derive grouped diagnostic and therapeutic pathways for the three major classes - solid entities with surgical therapy - solid entities with surgical and additional therapeutic activities and - non-solid entities. For these classes it was possible to deduct common documentation workflows to support workflow-guided single-source documentation.

CONCLUSIONS

Clinical documentation activities within a Comprehensive Cancer Center can likely be realized in a set of three documentation workflows with conditional branching in a modern workflow supporting clinical information system.

Research and Development of Semantics-based Sharable Clinical Pathway Systems

The clinical pathway (CP) as a novel medical management schema is beneficial for reducing the length of stay, decreasing heath care costs, standardizing clinical activities, and improving medical quality. However, the practicability of CPs is limited by the complexity and expense of adding the standard functions of electronic CPs to existing electronic medical record (EMR) systems. The purpose of this study was to design and develop an independent clinical pathway (ICP) system that is sharable with different EMR systems. An innovative knowledge base pattern was designed with separate namespaces for global knowledge, local knowledge, and real-time instances. Semantic web technologies were introduced to support knowledge sharing and intelligent reasoning. The proposed system, which was developed in a Java integrated development environment, achieved standard functions of electronic CPs without modifying existing EMR systems and integration environments in hospitals. The interaction solution between the pathway system and the EMR system simplifies the integration procedures with other hospital information systems. Five categories of transmission information were summarized to ensure the interaction process. Detailed procedures for the application of CPs to patients and managing exceptional alerts are presented by explicit data flow analysis. Compared to embedded pathway systems, independent pathway systems feature greater feasibility and practicability and are more advantageous for achieving the normalized management of standard CPs.

Developing a semantic web model for medical differential diagnosis recommendation

In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.

Online treatment compliance checking for clinical pathways

Compliance checking for clinical pathways (CPs) is getting increasing attention in health-care organizations due to stricter requirements for cost control and treatment excellence. Many compliance measures have been proposed for treatment behavior inspection in CPs. However, most of them look at aggregated data seen from an external perspective, e.g. length of stay, cost, infection rate, etc., which may provide only a posterior impression of the overall conformance with the established CPs such that in-depth and in near real time checking on the compliance of the essential/critical treatment behaviors of CPs is limited. To provide clinicians real time insights into violations of the established CP specification and support online compliance checking, this article presents a semantic rule-based CP compliance checking system. In detail, we construct a CP ontology (CPO) model to provide a formal grounding of CP compliance checking. Using the proposed CPO, domain treatment constraints are modeled into Semantic Web Rule Language (SWRL) rules to specify the underlying treatment behaviors and their quantified temporal structure in a CP. The established SWRL rules are integrated with the CP workflow such that a series of applicable compliance checking and evaluation can be reminded and recommended during the pathway execution. The proposed approach can, therefore, provides a comprehensive compliance checking service as a paralleling activity to the patient treatment journey of a CP rather than an afterthought. The proposed approach is illustrated with a case study on the unstable angina clinical pathway implemented in the Cardiology Department of a Chinese hospital. The results demonstrate that the approach, as a feasible solution to provide near real time conformance checking of CPs, not only enables clinicians to uncover non-compliant treatment behaviors, but also empowers clinicians with the capability to make informed decisions when dealing with treatment compliance violations in the pathway execution.

Creating hospital-specific customized clinical pathways by applying semantic reasoning to clinical data

OBJECTIVE

Clinical pathways (CPs) are widely studied methods to standardize clinical intervention and improve medical quality. However, standard care plans defined in current CPs are too general to execute in a practical healthcare environment. The purpose of this study was to create hospital-specific personalized CPs by explicitly expressing and replenishing the general knowledge of CPs by applying semantic analysis and reasoning to historical clinical data.

METHODS

A semantic data model was constructed to semantically store clinical data. After querying semantic clinical data, treatment procedures were extracted. Four properties were self-defined for local ontology construction and semantic transformation, and three Jena rules were proposed to achieve error correction and pathway order recognition. Semantic reasoning was utilized to establish the relationship between data orders and pathway orders.

RESULTS

A clinical pathway for deviated nasal septum was used as an example to illustrate how to combine standard care plans and practical treatment procedures. A group of 224 patients with 11,473 orders was transformed to a semantic data model, which was stored in RDF format. Long term order processing and error correction made the treatment procedures more consistent with clinical practice. The percentage of each pathway order with different probabilities was calculated to declare the commonality between the standard care plans and practical treatment procedures. Detailed treatment procedures with pathway orders, deduced pathway orders, and orders with probability greater than 80% were provided to efficiently customize the CPs.

CONCLUSIONS

This study contributes to the practical application of pathway specifications recommended by the Ministry of Health of China and provides a generic framework for the hospital-specific customization of standard care plans defined by CPs or clinical guidelines.

Clinical Pathway Analytics

Recently, medical informatics researchers have focused on supporting standardized activity coordination patterns that structure complex multi-disciplinary interventions. While the designed clinical pathways are aimed to improve the provided healthcare, this paper demonstrates that the care process of individual patients can significantly deviate from the standardized path. Examining the deviations in the individual care processes might result in the further enhancement of the provided quality of care, the evaluation of adverse events or the identification of suboptimal use of resources. This paper presents an optimal combination of process analytics for extracting valuable medical and organizational information from real-world care processes. The authors elaborate an extensive case study based on these process analytics on a series of oncological care processes.

Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances

Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective.

Creating personalised clinical pathways by semantic interoperability with electronic health records

OBJECTIVE

There is a growing realisation that clinical pathways (CPs) are vital for improving the treatment quality of healthcare organisations. However, treatment personalisation is one of the main challenges when implementing CPs, and the inadequate dynamic adaptability restricts the practicality of CPs. The purpose of this study is to improve the practicality of CPs using semantic interoperability between knowledge-based CPs and semantic electronic health records (EHRs).

METHODS

Simple protocol and resource description framework query language is used to gather patient information from semantic EHRs. The gathered patient information is entered into the CP ontology represented by web ontology language. Then, after reasoning over rules described by semantic web rule language in the Jena semantic framework, we adjust the standardised CPs to meet different patients' practical needs.

RESULTS

A CP for acute appendicitis is used as an example to illustrate how to achieve CP customisation based on the semantic interoperability between knowledge-based CPs and semantic EHRs. A personalised care plan is generated by comprehensively analysing the patient's personal allergy history and past medical history, which are stored in semantic EHRs. Additionally, by monitoring the patient's clinical information, an exception is recorded and handled during CP execution. According to execution results of the actual example, the solutions we present are shown to be technically feasible.

CONCLUSION

This study contributes towards improving the clinical personalised practicality of standardised CPs. In addition, this study establishes the foundation for future work on the research and development of an independent CP system.

Summarizing clinical pathways from event logs

OBJECTIVE

Clinical pathway analysis, as a pivotal issue in ensuring specialized, standardized, normalized and sophisticated therapy procedures, is receiving increasing attention in the field of medical informatics. Research in clinical pathway analysis has so far mostly focused on looking at aggregated data seen from an external perspective, and only provide very limited insight into the pathways. In some recent work, process mining techniques have been studied in discovering clinical pathway models from data. While it is interesting, discovered models may provide too much detail to give a comprehensive summary of the pathway. Moreover, the number of patterns discovered can be large. Alternatively, this article presents a new approach to build a concise and comprehensive summary that describes the entire structure of a clinical pathway, while revealing essential/critical medical behaviors in specific time intervals over the whole time period of the pathway.

METHODS

The presented approach summarizes a clinical pathway from the collected clinical event log, which regularly records all kinds of patient therapy and treatment activities in clinical workflow by various hospital information systems. The proposed approach formally defines the clinical pathway summarization problem as an optimization problem that can be solved in polynomial time by using a dynamic-programming algorithm. More specifically, given an input event log, the presented approach summarizes the pathway by segmenting the observed time period of the pathway into continuous and overlapping time intervals, and discovering frequent medical behavior patterns in each specific time interval from the log.

RESULTS

The proposed approach is evaluated via real-world data-sets, which are extracted from Zhejiang Huzhou Central hospital of China with regard to four specific diseases, i.e., bronchial lung cancer, colon cancer, gastric cancer, and cerebral infarction, in two years (2007.08-2009.09). Although the medical behaviors contained in these logs are very diverse and heterogeneous, experimental results indicates that the presented approach is feasible to construct condensed clinical pathway summaries in polynomial time from the collected logs, and have a linear scalability against the increasing size of the logs.

CONCLUSION

Experiments on real data-sets illustrate that the presented approach is efficient and discovers high-quality results: the observed time period of a clinical pathway is correctly segmented into a set of continuous and overlapping time intervals, in which essential/critical medical behaviors are well discovered from the event log to form the backbone of a clinical pathway. The experimental results indicate that the generated clinical pathway summary not only reveals the global structure of a pathway, but also provides a thorough understanding of the way in which actual medical behaviors are practiced in specific time intervals, which might be essential from the perspectives of clinical pathway analysis and improvement.

Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA

Background

Hospitals nowadays have to serve numerous patients with limited medical staff and equipment while maintaining healthcare quality. Clinical pathway informatics is regarded as an efficient way to solve a series of hospital challenges. To date, conventional research lacks a mathematical model to describe clinical pathways. Existing vague descriptions cannot fully capture the complexities accurately in clinical pathways and hinders the effective management and further optimization of clinical pathways.

Method

Given this motivation, this paper presents a clinical pathway management platform, the Imperial Clinical Pathway Analyzer (ICPA). By extending the stochastic model performance evaluation process algebra (PEPA), ICPA introduces a clinical-pathway-specific model: clinical pathway PEPA (CPP). ICPA can simulate stochastic behaviours of a clinical pathway by extracting information from public clinical databases and other related documents using CPP. Thus, the performance of this clinical pathway, including its throughput, resource utilisation and passage time can be quantitatively analysed.

Results

A typical clinical pathway on stroke extracted from a UK hospital is used to illustrate the effectiveness of ICPA. Three application scenarios are tested using ICPA: 1) redundant resources are identified and removed, thus the number of patients being served is maintained with less cost; 2) the patient passage time is estimated, providing the likelihood that patients can leave hospital within a specific period; 3) the maximum number of input patients are found, helping hospitals to decide whether they can serve more patients with the existing resource allocation.

Conclusions

ICPA is an effective platform for clinical pathway management: 1) ICPA can describe a variety of components (state, activity, resource and constraints) in a clinical pathway, thus facilitating the proper understanding of complexities involved in it; 2) ICPA supports the performance analysis of clinical pathway, thereby assisting hospitals to effectively manage time and resources in clinical pathway.

Ontology-driven execution of clinical guidelines

Clinical guidelines (CG) contain general descriptions, defined by health care organisations, of the way in which a particular pathology should be treated. Their adoption in daily care offers several benefits to both patients and practitioners, such as the standardisation of the delivered care and the reduction of errors, but, at the same time, there are several issues that limit their application. CGs are designed to cover a disease taking into account the available evidence but are not designed to be deployed in a particular hospital or healthcare institution. CGs include general recommendations that should be translated according the particular settings before adoption in daily care. This adoption should also specify accountable information about the responsible actors of performing actions in healthcare teams in order to avoid errors arising during delegation/assignment of tasks. In addition, this enactment is not performed taking into account a central knowledge base or a single actor. This paper proposes the combination of a multi-agent system modelling complex healthcare organisations and knowledge representation techniques in order to build a general framework for enabling the enactment of CGs in the context of a medical centre. As a main contribution, the ontological paradigm and the expressiveness of modern ontology languages are used to design, implement and exploit a medico-organisational ontology aimed to provide the semantics required to support the execution of clinical guidelines. The knowledge-driven guideline enactment is managed by a multi-agent system modelling in a distributed fashion the clinical entities involved in the care delivery.

On mining clinical pathway patterns from medical behaviors

OBJECTIVE

Clinical pathway analysis, as a pivotal issue in ensuring specialized, standardized, normalized and sophisticated therapy procedures, is receiving increasing attention in the field of medical informatics. Clinical pathway pattern mining is one of the most important components of clinical pathway analysis and aims to discover which medical behaviors are essential/critical for clinical pathways, and also where temporal orders of these medical behaviors are quantified with numerical bounds. Even though existing clinical pathway pattern mining techniques can tell us which medical behaviors are frequently performed and in which order, they seldom precisely provide quantified temporal order information of critical medical behaviors in clinical pathways.

METHODS

This study adopts process mining to analyze clinical pathways. The key contribution of the paper is to develop a new process mining approach to find a set of clinical pathway patterns given a specific clinical workflow log and minimum support threshold. The proposed approach not only discovers which critical medical behaviors are performed and in which order, but also provides comprehensive knowledge about quantified temporal orders of medical behaviors in clinical pathways.

RESULTS

The proposed approach is evaluated via real-world data-sets, which are extracted from Zhejiang Huzhou Central hospital of China with regard to six specific diseases, i.e., bronchial lung cancer, gastric cancer, cerebral hemorrhage, breast cancer, infarction, and colon cancer, in two years (2007.08-2009.09). As compared to the general sequence pattern mining algorithm, the proposed approach consumes less processing time, generates quite a smaller number of clinical pathway patterns, and has a linear scalability in terms of execution time against the increasing size of data sets.

CONCLUSION

The experimental results indicate the applicability of the proposed approach, based on which it is possible to discover clinical pathway patterns that can cover most frequent medical behaviors that are most regularly encountered in clinical practice. Therefore, it holds significant promise in research efforts related to the analysis of clinical pathways.

SEMPATH Ontology: modeling multidisciplinary treatment schemes utilizing semantics

A dramatic increase of demand for provided treatment quality has occurred during last decades. The main challenge to be confronted, so as to increase treatment quality, is the personalization of treatment, since each patient constitutes a unique case. Healthcare provision encloses a complex environment since healthcare provision organizations are highly multidisciplinary. In this paper, we present the conceptualization of the domain of clinical pathways (CP). The SEMPATH (SEMantic PATHways) Oontology comprises three main parts: 1) the CP part; 2) the business and finance part; and 3) the quality assurance part. Our implementation achieves the conceptualization of the multidisciplinary domain of healthcare provision, in order to be further utilized for the implementation of a Semantic Web Rules (SWRL rules) repository. Finally, SEMPATH Ontology is utilized for the definition of a set of SWRL rules for the human papillomavirus) disease and its treatment scheme.

Ontology-based clinical pathways with semantic rules

Clinical Pathways (CP) enhance the quality of patient care, and are thus important in health management. However, there is a need to address the challenge of adaptation of treatment procedures in CP-that is, the treatment schemes must be re-modified once the clinical status and other care conditions of patients in the healthcare setting change, which happen frequently. In addition, the widespread and frequent use of Electronic Medical Records (EMR) implies an increasing need to combine CP with other healthcare information systems, especially EMR, in order to greatly improve healthcare quality and efficiency. This study proposed an ontology-based method to model CP: ontology was used to model CP domain terms; Semantic Web Rule language was used to model domain rules. In this way, the CP could reason over the rules, knowledge, and information collected, and provides automated error checking for the next steps of the treatment in runtime, which is adaptive to treatment procedures. To evaluate our method, we built a Lobectomia Pulmonalis CP and realized it based on an EMR system.

Using recommendation to support adaptive clinical pathways

Clinical pathways are among the main tools used to manage the quality in health-care concerning the standardization of care processes. This paper deals with a recommendation service to support adaptive clinical pathways. The proposed approach can guide physicians in clinical pathways by providing recommendations on possible next steps based on the measurement of the target patient status and medical knowledge from completed clinical cases. The efficiency and usability of the proposed method is validated by experiments referring to a real data set extracted from Electronic Patient Records. The experimental results indicate that the recommendation service can provide its users with advice rationales that remain consistent even when patient status has changed. This makes adaptive clinical pathways possible.

A holistic environment for the design and execution of self-adaptive clinical pathways

One of the main challenges to be confronted by modern health care, so as to increase treatment quality, is the personalization of treatment. The treatment personalization requires the continuous reconfiguration and adaptation of the selected treatment schemes according to the "current" clinical status of each patient and "current" circumstances inside a health care organization that change rapidly, as well as the updated medical knowledge. In this paper, we present an innovative software environment that provides an integrated IT solution concerning the adaptation of health care processes (clinical pathways) during execution time. The software comprises a health care process execution engine assisted by a semantic infrastructure for reconfiguring the clinical pathways. During the execution of clinical pathways, the system reasons over the rules and reconfigures the next steps of the treatment. A graphical designer interface is implemented for the definition of the rule-set for the clinical pathways adaptation in a user-friendly way.

Variances handling method of clinical pathways based on T-S fuzzy neural networks with novel hybrid learning algorithm

Clinical pathways' variances present complex, fuzzy, uncertain and high-risk characteristics. They could cause complicating diseases or even endanger patients' life if not handled effectively. In order to improve the accuracy and efficiency of variances handling by Takagi-Sugeno (T-S) fuzzy neural networks (FNNs), a new variances handling method for clinical pathways (CPs) is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm. And the optimal structure and parameters can be achieved simultaneously by integrating the random cooperative decomposing particle swarm optimization algorithm (RCDPSO) and discrete binary version of PSO (DPSO) algorithm. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy CP is used to validate the proposed method. The result demonstrates that T-S FNNs based on the proposed algorithm achieves superior performances in efficiency, precision, and generalization ability to standard T-S FNNs, Mamdani FNNs and T-S FNNs based on other algorithms (CPSO and PSO) for variances handling of CPs.