A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems.

The objective of this review is to describe the implementation of human factors principles for the design of alerts in clinical information systems. First, we conduct a review of alarm systems to identify human factors principles that are employed in the design and implementation of alerts. Second, we review the medical informatics literature to provide examples of the implementation of human factors principles in current clinical information systems using alerts to provide medication decision support. Last, we suggest actionable recommendations for delivering effective clinical decision support using alerts. A review of studies from the medical informatics literature suggests that many basic human factors principles are not followed, possibly contributing to the lack of acceptance of alerts in clinical information systems. We evaluate the limitations of current alerting philosophies and provide recommendations for improving acceptance of alerts by incorporating human factors principles in their design

Improving Quality of Care in Acute Cardiology

Acute cardiac care has changed dramatically over the past decennia. In coronary care and general intensive care units, information technology was introduced for arrhythmia monitoring and other signal processing. More recently, information technology has been applied to assist clinical decision making. Chapter 1 provides a definition for clinical decision support systems (CDSS) in critical care and describes factors for successful implementation of such systems. Subsequent chapters present three groups of studies designed to improve patient care (I) using information technology to assist rapid diagnosis and treatment in patients with evolving myocardial infarction, (II) better managing the multitude of monitoring alarms and (III) improving glucose regulation in patients at an intensive cardiac care unit

Guideline-based decision support in medicine : modeling guidelines for the development and application of clinical decision support systems

Guideline-based Decision Support in Medicine Modeling Guidelines for the Development and Application of Clinical Decision Support Systems The number and use of decision support systems that incorporate guidelines with the goal of improving care is rapidly increasing. Although developing systems that are both effective in supporting clinicians and accepted by them has proven to be a difficult task, of the systems that were evaluated by a controlled trial, the majority showed impact. The work, described in this thesis, aims at developing a methodology and framework that facilitates all stages in the guideline development process, ranging from the definition of models that represent guidelines to the implementation of run-time systems that provide decision support, based on the guidelines that were developed during the previous stages. The framework consists of 1) a guideline representation formalism that uses the concepts of primitives, Problem-Solving Methods (PSMs) and ontologies to represent guidelines of various complexity and granularity and different application domains, 2) a guideline authoring environment that enables guideline authors to define guidelines, based on the newly developed guideline representation formalism, and 3) a guideline execution environment that translates defined guidelines into a more efficient symbol-level representation, which can be read in and processed by an execution-time engine. The described methodology and framework were used to develop and validate a number of guidelines and decision support systems in various clinical domains such as Intensive Care, Family Practice, Psychiatry and the areas of Diabetes and Hypertension control

Effectiveness of electronic guideline-based implementation systems in ambulatory care settings - a systematic review

<p>Abstract</p> <p>Background</p> <p>Electronic guideline-based decision support systems have been suggested to successfully deliver the knowledge embedded in clinical practice guidelines. A number of studies have already shown positive findings for decision support systems such as drug-dosing systems and computer-generated reminder systems for preventive care services.</p> <p>Methods</p> <p>A systematic literature search (1990 to December 2008) of the English literature indexed in the Medline database, Embase, the Cochrane Central Register of Controlled Trials, and CRD (DARE, HTA and NHS EED databases) was conducted to identify evaluation studies of electronic multi-step guideline implementation systems in ambulatory care settings. Important inclusion criterions were the multidimensionality of the guideline (the guideline needed to consist of several aspects or steps) and real-time interaction with the system during consultation. Clinical decision support systems such as one-time reminders for preventive care for which positive findings were shown in earlier reviews were excluded. Two comparisons were considered: electronic multidimensional guidelines versus usual care (comparison one) and electronic multidimensional guidelines versus other guideline implementation methods (comparison two).</p> <p>Results</p> <p>Twenty-seven publications were selected for analysis in this systematic review. Most designs were cluster randomized controlled trials investigating process outcomes more than patient outcomes. With success defined as at least 50% of the outcome variables being significant, none of the studies were successful in improving patient outcomes. Only seven of seventeen studies that investigated process outcomes showed improvements in process of care variables compared with the usual care group (comparison one). No incremental effect of the electronic implementation over the distribution of paper versions of the guideline was found, neither for the patient outcomes nor for the process outcomes (comparison two).</p> <p>Conclusions</p> <p>There is little evidence at the moment for the effectiveness of an increasingly used and commercialised instrument such as electronic multidimensional guidelines. After more than a decade of development of numerous electronic systems, research on the most effective implementation strategy for this kind of guideline-based decision support systems is still lacking. This conclusion implies a considerable risk towards inappropriate investments in ineffective implementation interventions and in suboptimal care.</p

Digital clinical guidelines modelling

Oliveira T., Costa A., Neves J., Novais P., Digital Clinical Guidelines Modelling, Modelling and Simulation 2011, Novais P., Machado J., Analide C., Abelha A., (Eds.) (ESM’2011 – The 2011 European Simulation and Modelling Conference, Guimarães, Portugal) EUROSIS Publisher, ISBN: 978-9077381-66-3, pp 392-398, 2011.Healthcare environments are very demanding, because practitioners are required to consult many patients in a short period of time, increasing the levels of stress which usually harms the outcome of healthcare processes. The short time practitioners have with their patients does not facilitate informed decision making and checking all possibilities. A possible solution is the use of guideline-based applications, because they have the potential of being an effective means of both changing the process of healthcare and improving its outcomes. However, current Clinical Guidelines are available in text format as long documents, which render them difficult to consult and to integrate in clinical Decision Support Systems. With this paper we present a new model for guideline interpretation, in order to facilitate de development of guideline-based Decision Support Systems and to increase the availability of Clinical Guidelines at the moment of the clinical process. This model will also provide mechanisms to comply with cases where incomplete and uncertain information is present. The development and implementation of this model will be presented in the following pages

Clinical decision support in emergency medicine : exploring the prerequisites

A clinical decision support system is a technical system that combines individual patient data and evidence-based clinical knowledge to give advice and support to clinicians. For quite a long time, the emergence of such systems has been predicted and expected to impact health care dramatically by improving both quality and productivity. Three factors make Swedish emergency medicine an interesting context which could be mature for the introduction of clinical decision support systems. Firstly, Sweden is a leader in the implementation of health care information technology, and the coverage of electronic health records is around 100% in the country. Secondly, emergency medicine is a field with high patient turnover, frequent decisions, and substantial impact on patient outcome. Thirdly, although there are abundant publications on clinical decision support system development and implementation in general, there is less knowledge of such systems in the urgent care context. Therefore, this doctoral project aimed to explore the prerequisites prior to implementation of clinical decision support systems in emergency medicine. This thesis is based on a mixed-methods design and consists of four individual studies. Proctor’s conceptual model of implementation research was used as a framework for the project. Study I included semi-structured interviews with 16 medical doctors and nurses from nine Swedish emergency departments. Content analysis was used to describe factors affecting vital sign data quality in emergency care. Study II extracted vital signs from 330 000 emergency department visits to assess the effects of different documentation workflows on data quality. Study III prospectively explored 200 vital sign measurements from 50 emergency care visits to evaluate the impact of manual and automated documentation on vital sign data quality. Study III also used data from an adapted NASA TLX questionnaire to compare the workload of clinical staff (n=70) in manual and automatic documentation. Study IV used semi-structured interviews with 14 emergency medicine physicians from three different sites. Content analysis was used to explore participants’ expectations and concerns regarding clinical decision support systems. There are three main results and conclusions from the research. Firstly, documentation of vital signs in the emergency department is still surprisingly paper-based, which makes vital sign data unfit for reuse in clinical decision support. Secondly, automation of vital sign documentation is feasible in emergency care and should improve data quality and reduce workload. Thirdly, enthusiasts towards decision support are at risk of disappointment with the level of innovation in the currently available decision support systems, and this may affect the implementation strategy negatively

Clinical Decision Support: Strategies for Success

AbstractClinical Decision Support Systems (CDSS) are considered essential tools of evidence-based medicine. These systems provide physicians, caregivers and also patients with clinical knowledge needed and patient or disease specific information to help them make effective decisions that would enhance patient care and improve clinical outcomes. The lack of well-described success factors is the main challenge facing design, development and implementation of CDSS. We need to learn more about the factors that can help in increasing usability and acceptance. The medical informatics department at King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia worked on identifying and describing best strategies and requirements for success of CDSS building a detailed plan for development and implementation. The explored recommendations were categorized into ten main topics that should be addressed. These include the right content of CDSS, delivering valid and reliable information, delivering simple messages, providing users with references, saving users’ time, integrating with clinical workflow, improving system response and speed, adopting active and passive alert mechanisms, integrating with other hospital information systems (HIS) and proper management of CDSS knowledge

WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

The increase of infections caused by resistant bacteria has become one of the major health-care problems worldwide. The creation of multidisciplinary teams dedicated to the implementation of antimicrobial stewardship programmes (ASPs) is encouraged by all clinical institutions to cope with this problem. In this chapter, we describe the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on providing support for ASP teams. WASPSS gathers the required information from other hospital systems in order to provide decision support in antimicrobial stewardship from both patient-centered and global perspectives. To achieve this, it combines business intelligence techniques with a rule-based inference engine to integrate the data and knowledge required in this scenario. The system provides functions such as alerts, recommendations, antimicrobial prescription support and global surveillance. Furthermore, it includes experimental modules for improving the adoption of clinical guidelines and applying prediction models related with antimicrobial resistance. All these functionalities are provided through a multi-user web interface, personalized for each role of the ASP team

Can computerized clinical decision support systems improve practitioners' diagnostic test ordering behavior? A decision-maker-researcher partnership systematic review

<p>Abstract</p> <p>Background</p> <p>Underuse and overuse of diagnostic tests have important implications for health outcomes and costs. Decision support technology purports to optimize the use of diagnostic tests in clinical practice. The objective of this review was to assess whether computerized clinical decision support systems (CCDSSs) are effective at improving ordering of tests for diagnosis, monitoring of disease, or monitoring of treatment. The outcome of interest was effect on the diagnostic test-ordering behavior of practitioners.</p> <p>Methods</p> <p>We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for eligible articles published up to January 2010. We included randomized controlled trials comparing the use of CCDSSs to usual practice or non-CCDSS controls in clinical care settings. Trials were eligible if at least one component of the CCDSS gave suggestions for ordering or performing a diagnostic procedure. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of test ordering outcomes.</p> <p>Results</p> <p>Thirty-five studies were identified, with significantly higher methodological quality in those published after the year 2000 (<it>p </it>= 0.002). Thirty-three trials reported evaluable data on diagnostic test ordering, and 55% (18/33) of CCDSSs improved testing behavior overall, including 83% (5/6) for diagnosis, 63% (5/8) for treatment monitoring, 35% (6/17) for disease monitoring, and 100% (3/3) for other purposes. Four of the systems explicitly attempted to reduce test ordering rates and all succeeded. Factors of particular interest to decision makers include costs, user satisfaction, and impact on workflow but were rarely investigated or reported.</p> <p>Conclusions</p> <p>Some CCDSSs can modify practitioner test-ordering behavior. To better inform development and implementation efforts, studies should describe in more detail potentially important factors such as system design, user interface, local context, implementation strategy, and evaluate impact on user satisfaction and workflow, costs, and unintended consequences.</p

Computerised decision support systems for healthcare professionals:An interpretative review

<p><strong>Purpose</strong> Computerised decision support systems are designed to support clinicians in making decisions and thereby enhance the quality and safety of care. We aimed to undertake an interpretative review of the empirical evidence on computerised decision support systems, their contexts of use, and summarise evidence on the effectiveness of these tools and insights into how these can be successfully implemented and adopted.</p><p><strong>Methods</strong> We systematically searched the empirical literature to identify systematic literature reviews on computerised decision support applications and their impact on the quality and safety of healthcare delivery over a 13-year period (1997–2010). The databases searched included: MEDLINE, EMBASE, The Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, The Cochrane Central Register of Controlled Trials, The Cochrane Methodology Register, The Health Technology Assessment Database, and The National Health Service (NHS) Economic Evaluation Database. To be eligible for inclusion, systematic reviews needed to address computerised decision support systems, and at least one of the following: impact on safety; quality; or organisational, implementation or adoption considerations.</p><p><strong>Results</strong> Our searches yielded 121 systematic reviews relating to eHealth, of which we identified 41 as investigating computerised decision support systems. These indicated that, whilst there was a lack of investigating potential risks, such tools can result in improvements in practitioner performance in the promotion of preventive care and guideline adherence, particularly if specific information is available in real time and systems are effectively integrated into clinical workflows. However, the evidence regarding impact on patient outcomes was less clear-cut with reviews finding either no, inconsistent or modest benefits.</p><p><strong>Conclusions</strong> Whilst the potential of clinical decision support systems in improving, in particular, practitioner performance is considerable, such technology may also introduce new risks resulting not only from technical challenges (such as data inaccuracies) but also from disruption of clinical workflows. Moving forward, there is a need for system development, procurement and implementation to be characterised by a user ‘pull’ and then tailor systems to the needs of users.</p