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Throughout a child's day, five minutes can go by quickly (when you are on a roller coaster) or slowly (when you are in the dentist's chair).

BackgroundEuropean Society of Cardiology (ESC) guideline suggests non-ST elevation myocardial infarction (NSTEMI) patients should have invasive angiography within 24 hours of admission whereas National Institute of Clinical Excellence (NICE) guideline recommends invasive angiography within 72 hours of admission. The aim of this quality improvement project was to identify the time frame and culprits of the delays from local trust level and to implement action plans to reduce these delays.MethodsWe analyzed the data of 50 patients requiring invasive angiography for NSTEMI admitted to Tameside General Hospital from August 2022 to January 2023. We identified numbers of time points and delays from admission to discharge. We highlighted on time frame from admission to invasive angiography, time frame from admission to referral to local cardiologist in acute medical patient (AMU) stream, time frame from admission to referral to coronary angiogram transfer system (CATS) and time frame from CATS referral to invasive angiography in regional network primary coronary intervention (PCI) center.Results50 patients with NSTEMI were included in our study. The median time frame from admission to invasive angiography was 6 days. 2 out of 50 patients had invasive angiography within 3 days as per NICE guideline and only 1 patient had invasive angiography within 24hr as per ESC guideline. 76% of patients admitted to acute coronary unit (ACU) and 24% of patients admitted to acute medical unit (AMU) from emergency department. AMU patient stream was referred to local cardiologist in median 1 day from admission. Median time frame from referral to CATS was 2 days from admission in both AMU and ACU patient streams. Median acceptance day for invasive coronary angiography in regional network PCI centre from CATS referral was 4 days (Figure 1). In the local trust level, three main culprits had been identified since the day of admission including delay referral to CATS, incomplete details on CATS referral and delay in echocardiogram for these patients.In terms of action plans, firstly, we implemented the new rapid access acute coronary syndrome (RAACS) pathway for high-risk acute coronary syndrome (ACS) patients to aim 24 hours target from admission to invasive angiography after collaboration with regional network PCI center. We also created new referral system for these patients: dedicated bleep during office hours (8:30-16:30) and email referral for out of office hours (16:30-8:30). The referrals will be reviewed by ACS nurses or cardiology registrars from 8:30-16:30 from Monday to Friday and transferred to RAACS pathway. Secondly, audit presentations were delivered in local departmental teaching and grand round to increase the awareness of new RAACS pathway by staffs. Thirdly, collaboration with cardiorespiratory investigation (CRI) department to prioritise echocardiogram for NSTEMI patients.ConclusionTiming of invasive angiography in NSTEMI patients from Tameside General Hospital is significantly delayed compared to NICE and ESC recommendations. We identified the culprits of these delays from local trust level and implemented the interventions as above to reduce these delays. After that, we planned to do second audit plan, do, study, act (PDSA) cycle to check the result. Delays from regional network level are still needed to investigate in details in order to meet guideline recommended timing.Abstract 70 Figure 1Conflict of InterestNo

We have always been conscious of the wait for life-changing messages, whether it be the time it takes to receive a text message from your love, for a soldier's family to learn news from the front, or for a space probe to deliver data from the far reaches of the solar system. In this book in praise of wait times, award-winning author Jason Farman passionately argues that the delay between call and answer has always been an important part of the message. Traveling backward from our current era of Twitter and texts, Farman shows how societies have worked to eliminate waiting in communication and how they have interpreted those times' meanings. Exploring seven eras and objects of waiting--including pneumatic mail tubes in New York, Elizabethan wax seals, and Aboriginal Australian message sticks--Farman offers a new mindset for waiting. In a rebuttal to the demand for instant communication, Farman makes a powerful case for why good things can come to those who wait.

Background It is globally agreed that a well-designed health system deliver timely and convenient access to health services for all patients. Many interventions aiming to reduce waiting times have been implemented in Chinese public tertiary hospitals to improve patients’ satisfaction. However, few were well-documented, and the effects were rarely measured with robust methods. Methods We conducted a longitudinal study of the length of waiting times in a public tertiary hospital in Southern China which developed comprehensive data collection systems. Around an average of 60,000 outpatients and 70,000 prescribed outpatients per month were targeted for the study during Oct 2014-February 2017. We analyzed longitudinal time series data using a segmented linear regression model to assess changes in levels and trends of waiting times before and after the introduction of waiting time reduction interventions. Pearson correlation analysis was conducted to indicate the strength of association between waiting times and patient satisfactions. The statistical significance level was set at 0 . 05. Results The monthly average length of waiting time decreased 3 . 49 min ( P  = 0 . 003) for consultations and 8 . 70 min ( P  = 0 . 02) for filling prescriptions in the corresponding month when respective interventions were introduced. The trend shifted from baseline slight increasing to afterwards significant decreasing for filling prescriptions ( P  =0.003). There was a significant negative correlation between waiting time of filling prescriptions and outpatient satisfaction towards pharmacy services ( r  = −0 . 71, P  = 0 . 004). Conclusions The interventions aimed at reducing waiting time and raising patient satisfaction in Fujian Provincial Hospital are effective. A long-lasting reduction effect on waiting time for filling prescriptions was observed because of carefully designed continuous efforts, rather than a one-time campaign, and with appropriate incentives implemented by a taskforce authorized by the hospital managers. This case provides a model of carrying out continuous quality improvement and optimizing management process with the support of relevant evidence.

When a child anticipates a long-awaited day with cousins and grandparents, the child discovers every day is filled with surprises and joy, which can easily be lost if one is not present.

Background Long waiting time in hospital leads to patient’s low satisfaction. In addition to reducing the actual waiting time (AWT), we can also improve satisfaction by adjusting the expected waiting time (EWT). Then how much can the EWT be adjusted to attribute a higher satisfaction? Methods This study was conducted though experimental with hypothetical scenarios. A total of 303 patients who were treated by the same doctor from August 2021 to April 2022 voluntarily participated in this study. The patients were randomly divided into six groups: a control group ( n  = 52) and five experimental groups ( n  = 245). In the control group, the patients were asked their satisfaction degree regarding a communicated EWT (T 0 ) and AWT (T a ) under a hypothetical situation. In the experimental groups, in addition to the same T 0 and T a as the control group, the patients were also asked about their satisfaction degree with the extended communicated EWT (T 1 ). Patients in five experimental groups were given T 1 values with 70, 80, 90, 100, and 110 min respectively. Patients in both control and experiment groups were asked to indicate their initial EWT, after given unfavorable information (UI) in a hypothetical situation, the experiment groups were asked to indicate their extended EWT. Each participant only participated in filling out one hypothetical scenario. 297 valid hypothetical scenarios were obtained from the 303 hypothetical scenarios given. Results The experimental groups had significant differences between the initial indicated EWT and extended indicated EWT under the effect of UI (20 [10, 30] vs. 30 [10, 50], Z = -4.086, P  < 0.001). There was no significant difference in gender, age, education level and hospital visit history ( χ 2  = 3.198, P  = 0.270; χ 2  = 2.177, P  = 0.903; χ 2  = 3.988, P  = 0.678; χ 2  = 3.979, P  = 0.264) in extended indicated EWT. As for patient’s satisfaction, compared with the control group, significant differences were found when T 1  = 80 min ( χ 2  = 13.511, P  = 0.004), T 1  = 90 min ( χ 2  = 12.207, P  = 0.007) and T 1  = 100 min ( χ 2  = 12.941, P  = 0.005). When T 1  = 90 min, which is equal to the T a , 69.4% (34/49) of the patients felt “very satisfied”, this proportion is not only significantly higher than that of the control group (34/ 49 vs. 19/52, χ 2  = 10.916, P  = 0.001), but also the highest among all groups. When T 1  = 100 min (10 min longer than T a ), 62.5% (30/48) of the patients felt “very satisfied”, it is significantly higher than that of the control group (30/ 48 vs. 19/52, χ 2  = 6.732, P  = 0.009). When T 1  = 80 min (10 min shorter than T a ), 64.8% (35/54) of the patients felt “satisfied”, it is significantly higher than that of the control group (35/ 54 vs. 17/52, χ 2  = 10.938, P  = 0.001). However, no significant difference was found when T 1  = 70 min ( χ 2  = 7.747, P  = 0.052) and T 1  = 110 min ( χ 2  = 4.382, P  = 0.223). Conclusions Providing UI prompts can extend the EWT. When the extended EWT is closer to the AWT, the patient’s satisfaction level can be improved higher. Therefore, medical institutions can adjust the EWT of patient’s through UI release according to the AWT of hospitals to improve patient’s satisfaction.

As pressure increases on public health systems globally, a potential consequence is that this is transferred to patients in the form of longer waiting times to receive care. In this review, we overview what waiting for health care encompasses, its measurement, and the data available in terms of trends and comparability. We also discuss whether waiting time is equally distributed according to socioeconomic status. Finally, we discuss the policy implications and potential approaches to addressing the burden of waiting time. Waiting time for elective surgery and emergency department care is the best described type of waiting time, and it either increases or remains unchanged across multiple developed countries. There are many challenges in drawing direct comparisons internationally, as definitions for these types of waiting times vary. There are less data on waiting time from other settings, but existing data suggest waiting time presents a significant barrier to health care access for a range of health services. There is also evidence that waiting time is unequally distributed to those of lower socioeconomic status, although this may be improving in some countries. Further work to better clarify definitions, identify driving factors, and understand hidden waiting times and identify opportunities for reducing waiting time or better using waiting time could improve health outcomes of our health services.

The Last Mile Problem refers to the provision of travel service from the nearest public transportation node to a home or office. We study the supply side of this problem in a stochastic setting, with batch demands resulting from the arrival of groups of passengers who request last-mile service at urban rail stations or bus stops. Closed-form approximations are derived for the performance of Last Mile Transportations Systems (LMTS) as a function of the fundamental design parameters of such systems. An initial set of results is obtained for the case wherein a fleet of vehicles of unit capacity provides the Last-Mile service, and each delivery route consists of a simple round trip between the rail station or bus stop and a single passenger’s destination. These results are then extended to the general case in which the capacity of a vehicle is a small number (up to 20). It is shown through comparisons with simulation results that the approximations perform consistently well for a broad and realistic range of input values and conditions. These expressions can therefore be used for the preliminary planning and design of an LMTS, especially for determining approximate resource requirements, such as the number of vehicles/servers needed to achieve some prespecified level of service, as measured by the expected waiting time until a passenger is picked up from the station or delivered to her destination.

Rate-limiting steps in the dark-to-light transition of Photosystem II (PSII) were discovered by measuring the variable chlorophyll-a fluorescence transients elicited by single-turnover saturating flashes (STSFs). It was shown that in diuron-treated samples: (i) the first STSF, despite fully reducing the QA quinone acceptor molecule, generated only an F1(

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