µ-Charts and Z: Examples and extensions

μ-Charts are a way of specifying reactive systems, i.e. systems which are in some environment to which they have to react, based on the well-established formalism Statecharts. This paper gives (very abbreviated) examples of translating μ-charts to Z, which is itself a well-established language for specifying computational systems with tried and tested methods and support tools which guide its effective use in systems development. We undertake this translation in order that investigation of the modelled system can be performed before expensive and lengthy implementation is considered. We also present an extension of the μ-charts and the related Z to deal with a simple command language, local variables and integer-valued signals

Experiences using Z animation tools.

In this paper we describe our experience of using three different animation systems. We searched for and decided to use these tools in the context of a project which involved developing formal versions (in Z) of informal requirements documents, and then showing the formal versions to people in industry who were not Z users (or users of any formal techniques). So, an animator seemed a good way of showing the behaviour of a system described formally without the audience having to learn Z. A requirement, however, that the tools used have to satisfy is that they correctly animated Z (whatever that may mean) and they behave adequately in terms of speed and presentation. We have to report that none of the tools we looked at satisfy these requirements--though to be fair all of them are still under development

Idioms for µ-charts

This paper presents an idiomatic construct for µ-charts which reflects the high-level specification construct of synchronization between activities. This, amongst others, has emerged as a common and useful idea during our use of µ-charts to design and specify commonly-occurring reactive systems. The purpose of this example, apart from any inherent interest in being able to use synchronization in a specification, is to show how the very simple language of µ-charts can used as a basis for a more expressive language built by definitional extension

The syntax and semantics of μ-Charts

μ-Charts is a language for specifying the behaviour of reactive systems. The language is a simplified variant of the well-known language Statecharts that was introduced by Harel. Development of the μ-Charts language is ongoing research undertaken under the auspices of the Formal Methods Laboratory of the Computer Science Department, University of Waikato. This paper gives a comprehensive treatment of the syntax and semantic for μ-Charts

A survey of software development practices in the New Zealand software industry

We report on the software development techniques used in the New Zealand software industry, paying particular attention to requirements gathering. We surveyed a selection of software companies with a general questionnaire and then conducted in-depth interviews with four companies. Our results show a wide variety in the kinds of companies undertaking software development, employing a wide range of software development techniques. Although our data are not sufficiently detailed to draw statistically significant conclusions, it appears that larger software development groups typically have more well-defined software development processes, spend proportionally more time on requirements gathering, and follow more rigorous testing regimes

To Link or Not to Link? Multiple Team Membership and Unit Performance

Multiple team membership is common in today’s team-based organizations, but little is known about its relationship with collective effectiveness across teams. We adopted a microfoundations framework utilizing existing individual- and team-level research to develop a higher-level perspective on multiple team membership’s relationship with performance of entire units of teams. We tested our predictions with data collected from 849 primary care units of the Veterans Health Administration serving over 4.2 million patients. In this context, we found multiple team membership is negatively associated with unit performance, and this negative relationship is exacerbated by task complexity

THOSE WITH THE MOST FIND IT HARDEST TO SHARE: EXPLORING LEADER RESISTANCE TO THE IMPLEMENTATION OF TEAM-BASED EMPOWERMENT

We use a convergent parallel mixed methods approach to explore barriers to the successful implementation of a team-based empowerment initiative within the Veterans Health Administration. Although previous research has suggested that leaders often actively obstruct empowerment initiatives, little is known about the reasons behind and effects of such hindering. Using a longitudinal quasi-experimental design, we support a hypothesis that higher-status physician leaders are less successful than lower-status nonphysician leaders in implementing team-based empowerment. In parallel, we analyze qualitative data obtained through interviews conducted during early months of the teambased empowerment initiative to identify common themes for why and how leaders facilitated or obstructed implementation. Leader identity work and leader delegation were identified as themes explaining (1) why higher-status leaders struggled with the new empowering role and (2) how specific leader actions either facilitated or inhibited sharing of tasks and leadership. Results suggest that team-based empowerment creates a status threat for high-status leaders who then struggle to protect their old identity as someone with distinct professional capabilities, which in turn leads to improper delegation behavior. Therefore, in order for team-based empowerment to succeed, leaders may need to change their perceptions of who they are before they will change what they do

Modelling care quality for patients after a transient ischaemic attack within the US Veterans Health Administration

Objective Timely preventive care can substantially reduce risk of recurrent vascular events or death after a transient ischaemic attack (TIA). Our objective was to understand patient and facility factors influencing preventive care quality for patients with TIA in the US Veterans Health Administration (VHA). Methods We analysed administrative data from a retrospective cohort of 3052 patients with TIA cared for in the emergency department (ED) or inpatient setting in 110 VHA facilities from October 2010 to September 2011. A composite quality indicator (QI score) pass rate was constructed from four process-related quality measures—carotid imaging, brain imaging, high or moderate potency statin and antithrombotic medication, associated with the ED visit or inpatient admission after the TIA. We tested a multilevel structural equation model where facility and patient characteristics, inpatient admission, and neurological consultation were predictors of the resident’s composite QI score. Results Presenting with a speech deficit and higher Charlson Comorbidity Index (CCI) were positively related to inpatient admission. Being admitted increased the likelihood of neurology consultation, whereas history of dementia, weekend arrival and a higher CCI score made neurological consultation less likely. Speech deficit, higher CCI, inpatient admission and neurological consultation had direct positive effects on the composite quality score. Patients in facilities with fewer full-time equivalent neurology staff were less likely to be admitted or to have a neurology consultation. Facilities having greater organisational complexity and with a VHA stroke centre designation were more likely to provide a neurology consultation. Conclusions Better TIA preventive care could be achieved through increased inpatient admissions, or through enhanced neurology and other care resources in the ED and during follow-up care

Processes of Care Associated With Risk of Mortality and Recurrent Stroke Among Patients With Transient Ischemic Attack and Nonsevere Ischemic Stroke

Importance: Early evaluation and management of patients with transient ischemic attack (TIA) and nonsevere ischemic stroke improves outcomes. Objective: To identify processes of care associated with reduced risk of death or recurrent stroke among patients with TIA or nonsevere ischemic stroke. Design, Setting, and Participants: This cohort study included all patients with TIA or nonsevere ischemic stroke at Department of Veterans Affairs emergency department or inpatient settings from October 2010 to September 2011. Multivariable logistic regression was used to model associations of processes of care and without-fail care, defined as receiving all guideline-concordant processes of care for which patients are eligible, with risk of death and recurrent stroke. Data were analyzed from March 2018 to April 2019. Main Outcomes and Measures: Risk of all-cause mortality and recurrent ischemic stroke at 90 days and 1 year was calculated. Overall, 28 processes of care were examined. Without-fail care was assessed for 6 processes: brain imaging, carotid artery imaging, hypertension medication intensification, high- or moderate-potency statin therapy, antithrombotics, and anticoagulation for atrial fibrillation. Results: Among 8076 patients, the mean (SD) age was 67.8 (11.6) years, 7752 patients (96.0%) were men, 5929 (73.4%) were white, 474 (6.1%) had a recurrent ischemic stroke within 90 days, 793 (10.7%) had a recurrent ischemic stroke within 1 year, 320 (4.0%) died within 90 days, and 814 (10.1%) died within 1 year. Overall, 9 processes were independently associated with lower odds of both 90-day and 1-year mortality after adjustment for multiple comparisons: carotid artery imaging (90-day adjusted odds ratio [aOR], 0.49; 95% CI, 0.38-0.63; 1-year aOR, 0.61; 95% CI, 0.52-0.72), antihypertensive medication class (90-day aOR, 0.58; 95% CI, 0.45-0.74; 1-year aOR, 0.70; 95% CI, 0.60-0.83), lipid measurement (90-day aOR, 0.68; 95% CI, 0.51-0.90; 1-year aOR, 0.64; 95% CI, 0.53-0.78), lipid management (90-day aOR, 0.46; 95% CI, 0.33-0.65; 1-year aOR, 0.67; 95% CI, 0.53-0.85), discharged receiving statin medication (90-day aOR, 0.51; 95% CI, 0.36-0.73; 1-year aOR, 0.70; 95% CI, 0.55-0.88), cholesterol-lowering medication intensification (90-day aOR, 0.47; 95% CI, 0.26-0.83; 1-year aOR, 0.56; 95% CI, 0.41-0.77), antithrombotics by day 2 (90-day aOR, 0.56; 95% CI, 0.40-0.79; 1-year aOR, 0.69; 95% CI, 0.55-0.87) or at discharge (90-day aOR, 0.59; 95% CI, 0.41-0.86; 1-year aOR, 0.69; 95% CI, 0.54-0.88), and neurology consultation (90-day aOR, 0.67; 95% CI, 0.52-0.87; 1-year aOR, 0.74; 95% CI, 0.63-0.87). Anticoagulation for atrial fibrillation was associated with lower odds of 1-year mortality only (aOR, 0.59; 95% CI, 0.40-0.85). No processes were associated with reduced risk of recurrent stroke after adjustment for multiple comparisons. The rate of without-fail care was 15.3%; 1216 patients received all guideline-concordant processes of care for which they were eligible. Without-fail care was associated with a 31.2% lower odds of 1-year mortality (aOR, 0.69; 95% CI, 0.55-0.87) but was not independently associated with stroke risk. Conclusions and Relevance: Patients who received 6 readily available processes of care had lower adjusted mortality 1 year after TIA or nonsevere ischemic stroke. Clinicians caring for patients with TIA and nonsevere ischemic stroke should seek to ensure that patients receive all guideline-concordant processes of care for which they are eligible

Care Trajectories of Veterans in the Twelve Months following Hospitalization for Acute Ischemic Stroke

Background—Recovery after a stroke varies greatly between individuals and is reflected by wide variation in the use of institutional and home care services. This study sought to classify veterans according to their care trajectories in the 12 months after hospitalization for ischemic stroke. Methods and Results—The sample consisted of 3811 veterans hospitalized for ischemic stroke in Veterans Health Administration facilities in 2007. Three outcomes—nursing home care, home care, and mortality—were modeled jointly >12 months using latent class growth analysis. Data on Veterans’ care use and cost came from the Veterans Administration and Medicare. Covariates included stroke severity (National Institutes of Health Stroke Scale), functional status (functional independence measure score), age, marital status, chronic conditions, and prestroke ambulation. Five care trajectories were identified: 49% of Veterans had Rapid Recovery with little or no use of care; 15% had a Steady Recovery with initially high nursing home or home care that tapered off; 9% had Long-Term Home Care; 13% had Long-Term Nursing Home Care; and 14% had an Unstable trajectory with multiple transitions between long-term and acute care settings. Care use was greatest for individuals with more severe strokes, lower functioning at hospital discharge, and older age. Average annual costs were highest for individuals with the Long-Term Nursing Home trajectory ($63 082), closely followed by individuals with the Unstable trajectory ($58 720). Individual with the Rapid Recovery trajectory had the lowest costs ($9271). Conclusions—Care trajectories after stroke were associated with stroke severity and functional dependency and they had a dramatic impact on subsequent costs