Implementation of workflow engine technology to deliver basic clinical decision support functionality

BACKGROUND: Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. RESULTS: We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. CONCLUSIONS: We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform

The Center for Medicare and Medicaid Innovation: Activity on Many Fronts

Provides an overview of the Innovation Center's organization, differences from CMS's traditional demonstration authority, payment and delivery reform initiatives, and first-year efforts to solicit and promote new ideas and collaborate with other payers

A prospective cohort study assessing clinical referral management & workforce allocation within a UK regional medical genetics service

Abstract Ensuring patient access to genomic information in the face of increasing demand requires clinicians to develop innovative ways of working. This paper presents the first empirical prospective observational cohort study of UK multi-disciplinary genetic service delivery. It describes and explores collaborative working practices including the utilisation and role of clinical geneticists and non-medical genetic counsellors. Six hundred and fifty new patients referred to a regional genetics service were tracked through 850 clinical contacts until discharge. Referral decisions regarding allocation of lead health professional assigned to the case were monitored, including the use of initial clinical contact guidelines. Significant differences were found in the cases led by genetic counsellors and those led by clinical geneticists. Around a sixth, 16.8% (109/650) of referrals were dealt with by a letter back to the referrer or re-directed to another service provider and 14.8% (80/541) of the remaining patients chose not to schedule an appointment. Of the remaining 461 patients, genetic counsellors were allocated as lead health professional for 46.2% (213/461). A further 61 patients did not attend. Of those who did, 86% (345/400) were discharged after one or two appointments. Genetic counsellors contributed to 95% (784/825) of total patient contacts. They provided 93.7% (395/432) of initial contacts and 26.8% (106/395) of patients were discharged at that point. The information from this study informed a planned service re-design. More research is needed to assess the effectiveness and efficiency of different models of collaborative multi-disciplinary working within genetics services. Keywords (MeSH terms) Genetic Services, Genetic Counseling, Interdisciplinary Communication, Cohort Studies, Delivery of Healthcare, Referral and Consultation

Safer clinical systems : interim report, August 2010

Safer Clinical Systems is the Health Foundation’s new five year programme of work to test and demonstrate ways to improve healthcare systems and processes, to develop safer systems that improve patient safety. It builds on learning from the Safer Patients Initiative (SPI) and models of system improvement from both healthcare and other industries. Learning from the SPI highlighted the need to take a clinical systems approach to improving safety. SPI highlighted that many hospitals struggle to implement improvement in clinical areas due to inherent problems with support mechanisms. Clinical processes and systems, rather than individuals, are often the contributors to breakdown in patient safety. The Safer Clinical Systems programme aimed to measure the reliability of clinical processes, identify defects within those processes, and identify the systems that result in those defects. Methods to improve system reliability were then to be tested and re-developed in order to reduce the risk of harm being caused to patients. Such system-level awareness should lead to improvements in other patient care pathways. The relationship between system reliability and actual harm is challenging to identify and measure. Specific, well-defined, small-scale processes have been used in other programmes, and system reliability has been shown to have a direct causal relationship with harm (e.g. care bundle compliance in an intensive care unit can reduce the incidence of ventilator-associated pneumonia). However, it has become evident that harm can be caused by a variety of factors over time; when working in broader, more complex and dynamic systems, change in outcome can be difficult to attribute to specific improvements and difficulties are also associated with relating evidence to resulting harm. The overall aim of Phase 1 of the Safer Clinical Systems programme was to demonstrate proof-of-concept that using a systems-based approach could contribute to improved patient safety. In Phase 1, experienced NHS teams from four locations worked together with expert advisers to co-design the Safer Clinical Systems programme

Use of electronic medical records and biomarkers to manage risk and resource efficiencies

Peer reviewedPublisher PD

Multicenter Evaluation of the QIAstat-Dx Respiratory Panel for the Detection of Viruses and Bacteria in Nasopharyngeal Swab Specimens

The QIAstat-Dx Respiratory Panel (QIAstat-Dx RP) is a multiplex in vitro diagnostic test for the qualitative detection of 20 pathogens directly from nasopharyngeal swab (NPS) specimens. The assay is performed using a simple sample-to-answer platform with results available in approximately 69 min. The pathogens identified are adenovirus, coronavirus 229E, coronavirus HKU1, coronavirus NL63, coronavirus OC43, human metapneumovirus A and B, influenza A, influenza A H1, influenza A H3, influenza A H1N1/2009, influenza B, parainfluenza virus 1, parainfluenza virus 2, parainfluenza virus 3, parainfluenza virus 4, rhinovirus/enterovirus, respiratory syncytial virus A and B, Bordetella pertussis, Chlamydophila pneumoniae, and Mycoplasma pneumoniae. This multicenter evaluation provides data obtained from 1,994 prospectively collected and 310 retrospectively collected (archived) NPS specimens with performance compared to that of the BioFire FilmArray Respiratory Panel, version 1.7. The overall percent agreement between QIAstat-Dx RP and the comparator testing was 99.5%. In the prospective cohort, the QIAstat-Dx RP demonstrated a positive percent agreement of 94.0% or greater for the detection of all but four analytes: coronaviruses 229E, NL63, and OC43 and rhinovirus/enterovirus. The test also demonstrated a negative percent agreement of ≥97.9% for all analytes. The QIAstat-Dx RP is a robust and accurate assay for rapid, comprehensive testing for respiratory pathogens