MuCIGREF: multiple computer-interpretable guideline representation and execution framework for managing multimobidity care

Clinical Practice Guidelines (CPGs) supply evidence-based recommendations to healthcare professionals (HCPs) for the care of patients. Their use in clinical practice has many benefits for patients, HCPs and treating medical centres, such as enhancing the quality of care, and reducing unwanted care variations. However, there are many challenges limiting their implementations. Initially, CPGs predominantly consider a specific disease, and only few of them refer to multimorbidity (i.e. the presence of two or more health conditions in an individual) and they are not able to adapt to dynamic changes in patient health conditions. The manual management of guideline recommendations are also challenging since recommendations may adversely interact with each other due to their competing targets and/or they can be duplicated when multiple of them are concurrently applied to a multimorbid patient. These may result in undesired outcomes such as severe disability, increased hospitalisation costs and many others. Formalisation of CPGs into a Computer Interpretable Guideline (CIG) format, allows the guidelines to be interpreted and processed by computer applications, such as Clinical Decision Support Systems (CDSS). This enables provision of automated support to manage the limitations of guidelines. This thesis introduces a new approach for the problem of combining multiple concurrently implemented CIGs and their interrelations to manage multimorbidity care. MuCIGREF (Multiple Computer-Interpretable Guideline Representation and Execution Framework), is proposed whose specific objectives are to present (1) a novel multiple CIG representation language, MuCRL, where a generic ontology is developed to represent knowledge elements of CPGs and their interrelations, and to create the multimorbidity related associations between them. A systematic literature review is conducted to discover CPG representation requirements and gaps in multimorbidity care management. The ontology is built based on the synthesis of well-known ontology building lifecycle methodologies. Afterwards, the ontology is transformed to a metamodel to support the CIG execution phase; and (2) a novel real-time multiple CIG execution engine, MuCEE, where CIG models are dynamically combined to generate consistent and personalised care plans for multimorbid patients. MuCEE involves three modules as (i) CIG acquisition module, transfers CIGs to the personal care plan based on the patient’s health conditions and to supply CIG version control; (ii) parallel CIG execution module, combines concurrently implemented multiple CIGs by performing concurrency management, time-based synchronisation (e.g., multi-activity merging), modification, and timebased optimisation of clinical activities; and (iii) CIG verification module, checks missing information, and inconsistencies to support CIG execution phases. Rulebased execution algorithms are presented for each module. Afterwards, a set of verification and validation analyses are performed involving real-world multimorbidity cases studies and comparative analyses with existing works. The results show that the proposed framework can combine multiple CIGs and dynamically merge, optimise and modify multiple clinical activities of them involving patient data. This framework can be used to support HCPs in a CDSS setting to generate unified and personalised care recommendations for multimorbid patients while merging multiple guideline actions and eliminating care duplications to maintain their safety and supplying optimised health resource management, which may improve operational and cost efficiency in real world-cases, as well

Micro-meso-macro practice tensions in using patient-reported outcome and experience measures in hospital palliative care

This article applies a micro-meso-macro analytical framework to understand clinicians’ experiences and perspectives of using patient-reported outcome and experience measures (PROMs and PREMs) in routine hospital-based palliative care. We structure our discussion through qualitative analysis of a design and implementation project for using an electronic tablet-based tool among hospital-based palliative clinicians to assess patients’ and their family caregivers’ quality of life concerns and experiences of care. Our analysis identified three categories of practice tensions shaping clinicians’ use of PROMs and PREMs in routine care: tensions surrounding implementation, tensions in standardization and quantification, and tensions that arose from scope of practice concerns. Our findings highlight that clinicians necessarily work within the confluence of multiple system priorities, that navigating these priorities can result in irreducible practice tensions, and that awareness of these tensions is a critical consideration when integrating PROMs and PREMs into routine practice

Making Medical Homes Work: Moving From Concept to Practice

Explores practical considerations for implementing a medical home program of physician practices committed to coordinating and integrating care based on patient needs and priorities, such as how to qualify medical homes and how to match patients to them

DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data

The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge\u27s main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability

Implementation of Modified Constraint-induced Therapy in Upper Limb Stroke Rehabilitation in an Inpatient Rehabilitation Hospital

Background: Despite increasing and strong evidence of modified constraint-induced therapy (mCIT) as an effective intervention approach for patients with chronic and subacute stroke in outpatient settings, it is still not widely used for the rehabilitation of patients with acute stroke who are typically admitted to inpatient rehabilitation hospitals. Purpose: The purpose of this study is to implement an evidence-based approach using mCIT in the upper extremity rehabilitation of patients with acute stroke in an inpatient rehabilitation hospital and to demonstrate its feasibility and efficacy in increasing the motor recovery, and the amount and quality of arm use when compared to traditional occupational therapy intervention. Theoretical Framework: The theoretical framework is based on behaviorist theory, and the model of human occupational (MOHO). Methods. The study is a quasi-experimental, multiple baseline, randomized, pretest-posttest control-group design study, using a dose-matched control intervention, traditional rehabilitation (TR) for comparison with mCIT. A total of six participants admitted to an inpatient rehabilitation within two weeks of their first stroke and who met the eligibility criteria were randomly assigned to the two groups. The participants were assessed on outcome measures namely the Canadian Occupational Performance Measure, the Fugl-Meyer Assessment, the Wolf Motor Function Test, and the Motor Activity Log before and after intervention. Results: Four of the 6 participants completed the study according to the study protocol. This study demonstrated significant improvement in motor recovery, improved arm function, more frequent and effective use of the affected arm, and clinically significant improvement in the participants’ perception of occupational performance and satisfaction with performance by both intervention approaches. It has also demonstrated greater improvement following intervention with mCIT compared with TR in all outcome measures studied except in the client’s perception of satisfaction with performance, with significantly greater change in affected arm motor recovery and the frequency of affected arm use. Conclusions: The findings of this study demonstrate the feasibility and efficacy of mCIT in upper limb rehabilitation of patients with acute stroke in an inpatient rehabilitation hospital. This strengthens the case for the routine implementation of this evidence-based intervention approach that has been strongly demonstrated in patients with subacute and chronic stroke

A derivative-free approach for a simulation-based optimization problem in healthcare

Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs,developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital's ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation--based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints. In this work, we develop a simulation--based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete--event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative--free optimization algorithm to modify the given setting. We report results for a real--world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach

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

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. 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. 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. 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

Physiology-Aware Rural Ambulance Routing

In emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work. In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, 201