A goal-oriented framework for specifying clinical guidelines and handling medical errors

Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines

The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.

Towards a goal-oriented methodology for clinical-guideline-based management recommendations for patients with multimorbidity: GoCom and its preliminary evaluation

Patients with chronic multimorbidity are becoming more common as life expectancy increases, making it necessary for physicians to develop complex management plans. We are looking at the patient management process as a goal-attainment problem. Hence, our aim is to develop a goal-oriented methodology for providing decision support for managing patients with multimorbidity continuously, as the patient's health state is progressing and new goals arise (e.g., treat ulcer, prevent osteoporosis). Our methodology allows us to detect and mitigate inconsistencies among guideline recommendations stemming from multiple clinical guidelines, while consulting medical ontologies and terminologies and relying on patient information standards. This methodology and its implementation as a decision-support system, called GoCom, starts with computer-interpretable clinical guidelines (CIGs) for single problems that are formalized using the PROforma CIG language. We previously published the architecture of the system as well as a CIG elicitation guide for enriching PROforma tasks with properties referring to vocabulary codes of goals and physiological effects of management plans. In this paper, we provide a formalization of the conceptual model of GoCom that generates, for each morbidity of the patient, a patient-specific goal tree that results from the PROforma engine's enactment of the CIG with the patient's data. We also present the "Controller" algorithm that drives the GoCom system. Given a new problem that a patient develops, the Controller detects inconsistencies among goals pertaining to different comorbid problems and consults the CIGs to generate alternative non-conflicted and goal-oriented management plans that address the multiple goals simultaneously. In this stage of our research, the inconsistencies that can be detected are of two types - starting vs. stopping medications that belong to the same medication class hierarchy, and detecting opposing physiological effect goals that are specified in concurrent CIGs (e.g., decreased blood pressure vs. increased blood pressure). However, the design of GoCom is modular and generic and allows the future introduction of additional interaction detection and mitigation strategies. Moreover, GoCom generates explanations of the alternative non-conflicted management plans, based on recommendations stemming from the clinical guidelines and reasoning patterns. GoCom's functionality was evaluated using three cases of multimorbidity interactions that were checked by our three clinicians. Usefulness was evaluated with two studies. The first evaluation was a pilot study with ten 6th year medical students and the second evaluation was done with 27 6th medical students and interns. The participants solved complex realistic cases of multimorbidity patients: with and without decision-support, two cases in the first evaluation and 6 cases in the second evaluation. Use of GoCom increased completeness of the patient management plans produced by the medical students from 0.44 to 0.71 (P-value of 0.0005) in the first evaluation, and from 0.31 to 0.78 (P-value < 0.0001) in the second evaluation. Correctness in the first evaluation was very high with (0.98) or without the system (0.91), with non-significant difference (P-value ≥ 0.17). In the second evaluation, use of GoCom increased correctness from 0.68 to 0.83 (P-value of 0.001). In addition, GoCom's explanation and visualization were perceived as useful by the vast majority of participants. While GoCom's detection of goal interactions is currently limited to detection of starting vs. stopping the same medication or medication subclasses and detecting conflicting physiological effects of concurrent medications, the evaluation demonstrated potential of the system for improving clinical decision-making for multimorbidity patients.

An ontology-driven framework to support the dynamic formation of an interdisciplinary healthcare team

BACKGROUND AND PURPOSE

Teamwork has become a modus operandi in healthcare and delivery of patient care by an interdisciplinary healthcare team (IHT) is now a prevailing modality of care. We argue that a formal and automated support framework is needed for an IHT to properly leverage information technology resources. Such a framework should allow for patient preferences and expand a representation of a clinical workflow with a formal model of dynamic formation of a team, especially with regards to team leader- and membership, and the assignment of tasks to team members. Our goal was to develop such a support framework, present its prototype software implementation and verify the implementation using a proof-of-concept use case. Specifically, we focused on clinical workflows for in-patient tertiary care and on patient preferences with regards to selecting team members and team leaders.

MATERIALS AND METHODS

Drawing on the research on clinical teamwork we defined the conceptual foundations for the proposed framework. Then, we designed its architecture and used ontology-driven design and first-order logic with associated reasoning methods to create and operationalize architectural elements. Finally, we incorporated existing solutions for business workflow modeling and execution as a backend for implementing the proposed framework.

RESULTS

We developed a Team and Workflow Management Framework (TWMF) with semantic components that allow for formalizing and operationalizing team formation in in-patient tertiary care setting and support provider-related patient preferences. We also created a prototype software implementation of TWMF using the IBM Business Process Manager platform. This implementation was evaluated in several simulated patient scenarios.

CONCLUSIONS

TWMF integrates existing workflow technologies and extends them with the capabilities to support dynamic formation of an IHT. Results of this research can be used to support real-time execution of clinical workflows, or to simulate their execution in order to assess the impact of various conditions (e.g., patterns of work shifts, staffing) on IHT operations.

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.

Evidence-based Draft Guideline for Prevention of Midwifery Malpractices based on Referred Cases to the Forensic Medicine Commission and the Medical Council from 2006-2011

BACKGROUND

Medical errors are the main concerns in health systems, which considering their ascending rate in the recent years, especially in the field of midwifery, have caused a medical crisis. Considering the importance of evidence-based health services as a way to improve health systems, the aim of this study was to suggest a guideline for preventing malpractice in midwifery services.

MATERIALS AND METHODS

In this cross-sectional study that was conducted in 2013, we investigated 206 cases that were referred to the Isfahan Legal Medicine Organization and Medical Council of Forensic Medicine from 2006-2011. Data were collected by a checklist and were analyzed using SPSS-16 software. Descriptive statistical tests (mean, maximum, minimum, standard deviation, frequency, and percentage agreement) were used to describe the data. Then, we used the Delphi technique with the participation from 17 experts in midwifery, gynecology, and legal medicine to provide an evidence-based draft guideline for prevention of midwifery errors.

RESULTS

A total of 206 cases were reviewed. In 66 cases (32%) the verdict for malpractice in midwifery services was approved. A practical draft guideline for preventing clinical errors for midwifery in the fields of pregnancy, delivery, and postpartum period was developed.

CONCLUSIONS

This evidence-based draft guideline can improve the attention of all the healthcare providers, especially midwives and physicians to prevent urgent problems and offer effective health services for mothers and infants.

Critical factors influencing physicians' intention to use computerized clinical practice guidelines: an integrative model of activity theory and the technology acceptance model

Background

With the widespread use of information communication technologies, computerized clinical practice guidelines are developed and considered as effective decision supporting tools in assisting the processes of clinical activities. However, the development of computerized clinical practice guidelines in Taiwan is still at the early stage and acceptance level among major users (physicians) of computerized clinical practice guidelines is not satisfactory. This study aims to investigate critical factors influencing physicians’ intention to computerized clinical practice guideline use through an integrative model of activity theory and the technology acceptance model.

Methods

The survey methodology was employed to collect data from physicians of the investigated hospitals that have implemented computerized clinical practice guidelines. A total of 505 questionnaires were sent out, with 238 completed copies returned, indicating a valid response rate of 47.1 %. The collected data was then analyzed by structural equation modeling technique.

Results

The results showed that attitudes toward using computerized clinical practice guidelines (γ = 0.451, p < 0.001), organizational support (γ = 0.285, p < 0.001), perceived usefulness of computerized clinical practice guidelines (γ = 0.219, p < 0.05), and social influence (γ = 0.213, p < 0.05) were critical factors influencing physicians’ intention to use computerized clinical practice guidelines, and these factors can explain 68.6 % of the variance in intention to use computerized clinical practice guidelines.

Conclusions

This study confirmed that some subject (human) factors, environment (organization) factors, tool (technology) factors mentioned in the activity theory should be carefully considered when introducing computerized clinical practice guidelines. Managers should pay much attention on those identified factors and provide adequate resources and incentives to help the promotion and use of computerized clinical practice guidelines. Through the appropriate use of computerized clinical practice guidelines, the clinical benefits, particularly in improving quality of care and facilitating the clinical processes, will be realized.

Modeling Healthcare Processes Using Commitments: An Empirical Evaluation

The two primary objectives of this paper are: (a) to demonstrate how Comma, a business modeling methodology based on commitments, can be applied in healthcare process modeling, and (b) to evaluate the effectiveness of such an approach in producing healthcare process models. We apply the Comma approach on a breast cancer diagnosis process adapted from an HHS committee report, and presents the results of an empirical study that compares Comma with a traditional approach based on the HL7 Messaging Standard (Traditional-HL7). Our empirical study involved 47 subjects, and two phases. In the first phase, we partitioned the subjects into two approximately equal groups. We gave each group the same requirements based on a process scenario for breast cancer diagnosis. Members of one group first applied Traditional-HL7 and then Comma whereas members of the second group first applied Comma and then Traditional-HL7—each on the above-mentioned requirements. Thus, each subject produced two models, each model being a set of UML Sequence Diagrams. In the second phase, we repartitioned the subjects into two groups with approximately equal distributions from both original groups. We developed exemplar Traditional-HL7 and Comma models; we gave one repartitioned group our Traditional-HL7 model and the other repartitioned group our Comma model. We provided the same changed set of requirements to all subjects and asked them to modify the provided exemplar model to satisfy the new requirements. We assessed solutions produced by subjects in both phases with respect to measures of flexibility, time, difficulty, objective quality, and subjective quality. Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student’s t-test to the 10% level of significance. Comma is a promising new approach for modeling healthcare processes. Further gains could be made through improved tooling and enhanced training of modeling personnel.

Computer-interpretable clinical guidelines: a methodological review

Clinical practice guidelines (CPGs) aim to improve the quality of care, reduce unjustified practice variations and reduce healthcare costs. In order for them to be effective, clinical guidelines need to be integrated with the care flow and provide patient-specific advice when and where needed. Hence, their formalization as computer-interpretable guidelines (CIGs) makes it possible to develop CIG-based decision-support systems (DSSs), which have a better chance of impacting clinician behavior than narrative guidelines. This paper reviews the literature on CIG-related methodologies since the inception of CIGs, while focusing and drawing themes for classifying CIG research from CIG-related publications in the Journal of Biomedical Informatics (JBI). The themes span the entire life-cycle of CIG development and include: knowledge acquisition and specification for improved CIG design, including (1) CIG modeling languages and (2) CIG acquisition and specification methodologies, (3) integration of CIGs with electronic health records (EHRs) and organizational workflow, (4) CIG validation and verification, (5) CIG execution engines and supportive tools, (6) exception handling in CIGs, (7) CIG maintenance, including analyzing clinician's compliance to CIG recommendations and CIG versioning and evolution, and finally (8) CIG sharing. I examine the temporal trends in CIG-related research and discuss additional themes that were not identified in JBI papers, including existing themes such as overcoming implementation barriers, modeling clinical goals, and temporal expressions, as well as futuristic themes, such as patient-centric CIGs and distributed CIGs.

A three stage ontology-driven solution to provide personalized care to chronic patients at home

PURPOSE

The goal of this work is to contribute to personalized clinical management in home-based telemonitoring scenarios by developing an ontology-driven solution that enables a wide range of remote chronic patients to be monitored at home.

METHODS

Through three stages, the challenges of integration and management were met through the ontology development and evaluation. The first stage dealt with the ontology design and implementation. The second stage dealt with the ontology application study in order to specifically address personalization issues. For both stages, interviews and working sessions were planned with clinicians. Clinical guidelines and MDs (medical device) interoperability were taken into account as well during these stages. Finally the third stage dealt with a software prototype implementation.

RESULTS

An ontology was developed as an outcome of the first stage. The structure, based on the autonomic computing paradigm, provides a clear and simple manner to automate and integrate the data management procedure. During the second stage, the application of the ontology was studied to monitor patients with different and multiple morbidities. After this task, the ontology design was successfully adjusted to provide useful personalized medical care. In the third and final stage, a proof-of-concept on the software required to remote monitor patients by means of the ontology-based solution was developed and evaluated.

CONCLUSIONS

Our proposed ontology provides an understandable and simple solution to address integration and personalized care challenges in home-based telemonitoring scenarios. Furthermore, our three-stage approach contributes to enhance the understanding, re-usability and transferability of our solution.

A comparison of clinical practice guidelines for proximal femoral fracture

Clinical practice guidelines are designed to assist clinical decision-making by summarising evidence and forming recommendations. The number of available guidelines is vast and they vary in relevance and quality. We reviewed guidelines relevant to the management of a patient with a fractured neck of femur and explored similarities and conflicts between recommendations. As guidelines are often produced in response to an area of clinical uncertainty, recommendations differ. This can result in a situation where the management of a particular clinical problem will depend upon which guideline is followed. We explore the reasons for such differences.

Supporting adaptive clinical treatment processes through recommendations

OBJECTIVES

Efficient clinical treatment processes is considered a key factor of medical quality control. Current IT solutions are far away from this perspective since they typically have difficulty supporting the variances occurring in clinical practices, and providing adequate flexible support of clinical processes.

METHODS

This paper proposes a hybrid approach based on rough set theory and case-based reasoning to allow physicians to rapidly adjust patients' treatment processes to changes of patients' clinical states. In detail, the proposed approach recommends appropriate treatment plans in clinical process execution by adopting a similarity measure to select appropriate clinical treatment plans executed on patients who presented similar features to the current one. Such clinical treatment plans are then applied to suggest which actions to perform next in clinical treatment process execution.

RESULTS

As a motivating scenario, this study performs the experiments of type 2 diabetes patient's treatment process. The results show that the proposed approach is feasible to recommend suitable clinical treatment plans in clinical process execution, which makes adaptive clinical treatment processes possible.

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.

Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems

OBJECTIVE

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways.

METHODS

A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation 'challenge' themes.

RESULTS

One hundred and eight relevant studies were selected for review. Twenty-five underlying 'challenge' themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed.

DISCUSSION AND CONCLUSION

We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings.

Patterns for collaborative work in health care teams

OBJECTIVE

The problem of designing and managing teams of workers that can collaborate working together towards common goals is a challenging one. Incomplete or ambiguous specification of responsibilities and accountabilities, lack of continuity in teams working in shifts, inefficient organization of teams due to lack of information about workers' competences and lack of clarity to determine if the work is delegated or assigned are examples of important problems related to collaborative work in healthcare teams. Here we address these problems by specifying goal-based patterns for abstracting the delegation and assignment of services. The proposed patterns should provide generic and reusable solutions and be flexible enough to be customizable at run time to the particular context of execution. Most importantly the patterns should support a mechanism for detecting abnormal events (exceptions) and for transferring responsibility and accountability for recovering from exceptions to the appropriate actor.

METHOD

To provide a generic solution to the problematic issues arising from collaborative work in teams of health workers we start from definitions of standard terms relevant for team work: competence, responsibility, and accountability. We make explicit the properties satisfied by service assignment and delegation in terms of competences, responsibilities, and accountability in normal scenarios and abnormal situations that require the enactment of recovery strategies. Based on these definitions we specify (1) a basic terminology, (2) design patterns for service assignment and delegation (with and without supervision), and (3) an exception manager for detecting and recovering from exceptions. We use a formal framework to specify design patterns and exceptions.

RESULTS

We have proved using Owicki-Gries Theory that the proposed patterns satisfy the properties that characterize service assignment and delegation in terms of competence, responsibility and accountability in normal and abnormal (exceptional) scenarios. We show that although abstract, the proposed patterns can be instantiated in an executable COGENT prototype, and can be mapped into the Tallis tool that enacts PROforma language specifications of medical guidelines.

CONCLUSIONS

The proposed patterns are generic and abstract enough to capture the normal and abnormal scenarios of assignment and delegation of tasks in collaborative work in health care teams.