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

Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.http://deepblue.lib.umich.edu/bitstream/2027.42/78267/1/1748-5908-5-26.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/2/1748-5908-5-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/3/1748-5908-5-26-S3.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/4/1748-5908-5-26-S2.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/5/1748-5908-5-26-S1.TIFFPeer Reviewe

The use of Natural Language Processing techniques to support Health Literacy: an evidence-based review

Background and objectives: To conduct a literature search and analysis of the existing research using natural language processing for improving or helping health literacy, as well as to discuss the importance and potentials of addressing both fields in a joint manner. This review targets researchers who are unfamiliar with natural language processing in the field of health literacy, and in general, any researcher, regardless of his or her background, interested in multi-disciplinary research involving technology and health care. Methods: We introduce the concepts of health literacy and natural language processing. Then, a thorough search is performed using relevant databases and well-defined criteria. We review the existing literature addressing these topics, both in an independent and joint manner, and provide an overview of the state of the art using natural language processing in health literacy. We additionally discuss how the different issues in health literacy that are related to the comprehension of specialised health texts can be improved using natural language processing techniques, and the challenges involved in these processes. Results: The search process yielded 235 potential relevant references, 49 of which fully fulfilled the established search criteria, and therefore they were later analysed in more detail. These articles were clustered into groups with respect to their purpose, and most of them were focused on the development of specific natural language processing modules, such as question answering, information retrieval, text simplification or natural language generation in order to facilitate the understanding of health information.This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the projects, "Tratamiento inteligente de la informacion para la ayuda a la toma de decisiones" (GRE12-44), "Explotacion y tratamiento de la informacion disponible en Internet para la anotacion y generacion de textos adaptados al usuario" (GRE13-15), DIIM2.0 (PROMETEOII/2014/001), ATTOS (TIN2012-38536-C03-03), LEGOLANG-UAGE (TIN2012-31224), SAM (FP7-611312), and FIRST (FP7-287607)

Identifying Health Facilities outside the Enterprise: Challenges and Strategies for Supporting Health Reform and Meaningful Use

Objective: To support collation of data for disability determination, we sought to accurately identify facilities where care was delivered across multiple, independent hospitals and clinics. Methods: Data from various institutions' electronic health records were merged and delivered as continuity of care documents to the United States Social Security Administration (SSA). Results: Electronic records for nearly 8000 disability claimants were exchanged with SSA. Due to the lack of standard nomenclature for identifying the facilities in which patients received the care documented in the electronic records, SSA could not match the information received with information provided by disability claimants. Facility identifiers were generated arbitrarily by health care systems and therefore could not be mapped to the existing international standards. Discussion: We propose strategies for improving facility identification in electronic health records to support improved tracking of a patient's care between providers to better serve clinical care delivery, disability determination, health reform and meaningful use. Conclusion: Accurately identifying the facilities where health care is delivered to patients is important to a number of major health reform and improvement efforts underway in many nations. A standardized nomenclature for identifying health care facilities is needed to improve tracking of care and linking of electronic health records

Introducing realist ontology for the representation of adverse events

The goal of the REMINE project is to build a high performance prediction, detection and monitoring platform for managing Risks against Patient Safety (RAPS). Part of the work involves developing in ontology enabling computer-assisted RAPS decision support on the basis of the disease history of a patient as documented in a hospital information system. A requirement of the ontology is to contain a representation for what is commonly referred to by the term 'adverse event', one challenge being that distinct authoritative sources define this term in different and context-dependent ways. The presence of some common ground in all definitions is, however, obvious. Using the analytical principles underlying Basic Formal Ontology and Referent Tracking, both developed in the tradition of philosophical realism, we propose a formal representation of this common ground which combines a reference ontology consisting exclusively of representations of universals and an application ontology which consists representations of defined classes. We argue that what in most cases is referred to by means of the term 'adverse event' - when used generically - is a defined class rather than a universal. In favour of the conception of adverse events as forming a defined class are the arguments that (1) there is no definition for 'adverse event' that carves out a collection of particulars which constitutes the extension of a universal, and (2) the majority of definitions require adverse events to be (variably) the result of some observation, assessment or (absence of) expectation, thereby giving these entities a nominal or epistemological flavour

Direct susceptibility testing by disk diffusion on clinical samples : a rapid and accurate tool for antibiotic stewardship

We compared the accuracy of direct susceptibility testing (DST) with conventional antimicrobial susceptibility testing (AST), both using disk diffusion, on clinical samples. A total of 123 clinical samples (respiratory tract samples, urine, vaginal and abdominal abscess discharges, bile fluid and a haematoma punctate) were selected on various indications; direct inoculation on Mueller-Hinton agar and antibiotic paper disks were applied. In parallel, standard culture, identification and AST on the colonies grown overnight was executed. Both AST and DST were interpreted after identification of the isolates. The results from both AST and DST for 11 antibiotics tested on 97 samples with Gram-negative rods showed 93.4 % total agreement, 1.6 % minor discordances, 4.6 % major discordances and 0.4 % very major discordances. Analysing the discordant results, DST predominantly resulted in more resistant isolates than AST. This was mostly due to the presence of resistant mutants or an additional isolate. The remaining discordances were seen for isolates with inhibition zones close to the clinical breakpoint. For the 26 samples yielding staphylococci, a total agreement of 100 % was observed for the nine antibiotics tested. Overall, the highest percentage of discordant results occurred for the beta-lactam antibiotics amoxicillin-clavulanate (13.4 %) and cefuroxime (12.4 %). When used selectively and interpreted carefully, DST on clinical samples is potentially very useful in the management of critically ill patients, as the time to results is shortened by approximately 24 h. However, we recommend to communicate results with reservations and confirm by conventional AST