Assessment of early warning signs in hospital projects' front-end phase

PurposeThe front-end phase plays an important role in achieving project success, and establishment of performance measurement systems considering project challenges or pitfalls is a way of keeping track of this phase. Early warning signs, a type of proactive performance indicators, may serve as means for improving decision-making and project processes aiming for short- and long-term project success. In this paper, the authors present findings from a study on early warning signs (EWS) in hospital projects' front-end. A preliminary systematisation of identified signs as a contribution to front-end improvement is provided.Design/methodology/approachThe paper is based on a mixed methods approach, using a sequential, exploratory research design comprising document studies, interviews and a survey.FindingsThe authors identified 62 challenges for hospital projects' front-end performance and further established four categories of EWS as follows: (1) structure and tools, (2) context and frame factors, (3) management and (4) relational factors and properties. This mirrors the presence of hard and soft issues from previous studies. There is need for clarifying terminology and raising consciousness on EWS. Processual approaches to identify EWS are considered more useful than subsequent established indicators.Originality/valueThe findings from this paper provide insight into EWS in hospital projects' front-end phase. This adds to the general understanding of EWS and contributes to more knowledge on the front-end phase in general.

Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows

The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to use statistical process control (SPC) technology to monitor the BCS of dry cows, to evaluate the effect of control charts on nutritional strategies, and to explore the utility of SPC in predicting the incidence of postpartum subclinical ketosis (SCK). The BCS and SCK data of 286 cows from the dry off period to 60 days postpartum were collected to set up the early warning function. Three control charts, including a control chart for the average BCS of the herds, for the BCS of each dry cow, and for individual BCS, were established. The early warning signs for postpartum SCK development were: (1) an individual BCS more than 3.5 that remained unchanged for six weeks; (2) a capability index (CPK), an SPC tool, greater than −0.52. Using these parameters, the early warning signs of SCK development were verified in 429 dry cows. The results showed that the accuracy of early warning signal was 0.64 and the precision was 0.26. The control chart showed that the average BCS of dry cows was consistently higher than the expected upper limit of BCS during the experimental period, and that the addition of new cows to the herds increased the average BCS. In summary, the application of SPC technology to monitor the BCS of dry cows was not a good tool for the prediction of postpartum SCK occurrence but was an appropriate tool for guiding positive nutrition strategies.

AWARENESS OF EARLY WARNING SIGNS OF STROKE IN RURAL AND URBAN POPULATION

The objectives of our study are: 1. To find out the awareness of early warning signs of stroke in rural population. 2. To find out the awareness of early warning signs of stroke in urban population. 3. To find out the difference between the level of awareness in rural and urban population. It was a survey - based study in which the population fulfilling the criteria were given the stroke questionnaire with Prior consent. Based on the responses, the percentage of awareness among the population was calculated. The difference between the level of awareness in rural and urban population was calculated. The total Sample was 162 including rural (81) and urban (81). The sample size was derived using formula . Were, p=44.3%, q=100 - p, L= 12% (p= prevalence rate, q= 100 - p, L= allowable error). Based on stroke questionnaire it was seen that the rural population had more prevalence rate of stroke and were familiar with the term stroke or paralysis whereas urban population (12%) was unaware of the warning signs of the stroke. Awareness was quiet high in Rural Population (19%). The level of awareness was less in both the population but was less in urban population as compared to rural population.

Can Colony Size of Honeybees (Apis mellifera) Be Used as Predictor for Colony Losses Due to Varroa destructor during Winter?

For more than three decades, honeybee colonies (Apis mellifera) have experienced high losses during winter and these losses are still continuing. It is crucial that beekeepers monitor their colonies closely and anticipate losses early enough to apply mitigating actions. We tested whether colony size can be used as early predictor for potential colony losses, in particular due to the parasitic mite Varroa destructor. V. destructor is one of the most important causes of these losses. Such an early predictor for potential V. destructor induced losses is especially relevant as measuring V. destructor load in colonies is difficult and cumbersome. Over three years, we monitored colonies with high and low V. destructor loads from July until March of the next year. We found that differences in colony size were only visible after November, even though we lost almost all colonies every winter in the group with a high V. destructor load. In the Northern hemisphere, November is considered to be too late for beekeepers to strengthen colonies in preparation for winter. We therefore argue that early warning signs for potential colony losses due to V. destructor are urgently needed to allow beekeepers to prevent winter losses. We discuss the role of precision apiculture in monitoring the health and productivity of beehive colonies.

Fear of psychotic relapse: exploring dynamic interactions with common early warning signs of relapse using experience sampling methodology

Background: Fear of relapse is an independent risk factor for future relapse events indicating its importance in clinical management and early warning signs-based relapse prevention monitoring.Methods: 25 participants who were taking part in a clinical trial of relapse prevention in schizophrenia responded to daily ecological momentary assessment prompts assessing common early warning signs of relapse and self-reported positive experiences like feeling supported by others. We conducted multilevel vector auto-regression using common symptoms assessed in early warning signs monitoring relapse prevention while controlling for positive self-reported experiences like feeling supported by others to estimate three networks (to explore concurrent, temporal and mean levels across the whole time period).Results: Reporting fear of relapse was positively associated (within the same cross-sectional time window) with hearing voices, alongside anxiety, negative affect and sleep change. Fear of relapse appeared to predict anxiety and negative affect on the next consecutive day. Experiencing fear of relapse on one day meant being more likely to also experience fear of relapse again at the next consecutive time point. However, none of the common early warning signs predicted fear of relapse within the temporal window and all observed relationships were small. Discussion: Early warning signs themselves appear poor predictors of experiencing fear of relapse but because fear of relapse predicts later anxiety and negative affect (even to a small degree) it may be a valuable intervention target within the daily life of people diagnosed with schizophrenia.