Longitudinal Time Series Data Research Articles

Development of a Machine Learning Model to Estimate US Firearm Homicides in Near Real Time

Firearm homicides are a major public health concern; lack of timely mortality data presents considerable challenges to effective response. Near real-time data sources offer potential for more timely estimation of firearm homicides. To estimate near real-time burden of weekly and annual firearm homicides in the US. In this prognostic study, anonymous, longitudinal time series data were obtained from multiple data sources, including Google and YouTube search trends related to firearms (2014-2019), emergency department visits for firearm injuries (National Syndromic Surveillance Program, 2014-2019), emergency medical service activations for firearm-related injuries (biospatial, 2014-2019), and National Domestic Violence Hotline contacts flagged with the keyword firearm (2016-2019). Data analysis was performed from September 2021 to September 2022. Weekly estimates of US firearm homicides were calculated using a 2-phase pipeline, first fitting optimal machine learning models for each data stream and then combining the best individual models into a stacked ensemble model. Model accuracy was assessed by comparing predictions of firearm homicides in 2019 to actual firearm homicides identified by National Vital Statistics System death certificates. Results were also compared with a SARIMA (seasonal autoregressive integrated moving average) model, a common method to forecast injury mortality. Both individual and ensemble models yielded highly accurate estimates of firearm homicides. Individual models' mean error for weekly estimates of firearm homicides (root mean square error) varied from 24.95 for emergency department visits to 31.29 for SARIMA forecasting. Ensemble models combining data sources had lower weekly mean error and higher annual accuracy than individual data sources: the all-source ensemble model had a weekly root mean square error of 24.46 deaths and full-year accuracy of 99.74%, predicting the total number of firearm homicides in 2019 within 38 deaths for the entire year (compared with 95.48% accuracy and 652 deaths for the SARIMA model). The model decreased the time lag of reporting weekly firearm homicides from 7 to 8 months to approximately 6 weeks. In this prognostic study of diverse secondary data on machine learning, ensemble modeling produced accurate near real-time estimates of weekly and annual firearm homicides and substantially decreased data source time lags. Ensemble model forecasts can accelerate public health practitioners' and policy makers' ability to respond to unanticipated shifts in firearm homicides.

INVESTMENT IN INFRASTRUCTURE AS A KEY TO SUSTAINABLE ECONOMIC RECOVERY: THE ROLE OF THE BUILDING INDUSTRY

The focus of this paper is the role of the building industry in post-COVID-19 economic recovery plans. Investment in infrastructure forms a major part of many countries’ strategies to engender economic growth and construction is the pivotal industry in enabling the implementation of the plans in a sustainable manner. This study looks at the effects of investment in infrastructure on the economy with reference to the role of the construction industry in delivering this investment. Basic issues are considered, particularly concerning how relevant investment can be measured and how the contribution of the construction sector can be realistically assessed. A review of data sources and empirically peer-reviewed papers is undertaken. Based on longitudinal time series data from national statistics agencies and international organizations, analysis is undertaken to discover the relationship between infrastructure investment and economic growth. The study focuses on the UK, but comparisons are made with other countries to consider alternative approaches to stimulus investment policies with digitalization, and sustainability and green investment being a growing feature of investment plans. Potential issues of these approaches are examined and the main barriers to their achievement are identified. Emerging trends and a set of policy agendas are proposed to guide future directions.

Impact of Corporate Governance on Financial Performance of Financial Holding Companies in Nigeria

The paper impact of corporate governance on financial performance of financial Holding Companies in Nigeria.The paper used corporate governance mechanism proxied by board size, board independence, board composition, board meetings and board number of committees on Return on Assets. Data were obtained from three financial holding companies in Nigeria Annual Reports for the period 2012 to 2019. A strongly balanced longitudinal time series data was employed for the analysis, while the Hausman test was conducted for selection of fixed or random effects. The random effect was chosen,and the results indicates that variables of corporate governance such as board size and board Independence have negative relationship with financial performance of Financial Holding Companies (Holdcos) in Nigeria. Board composition and Board Committees had positive nexus with financial performance of financial Holdcos . While board meeting had a negative effect on financial performance of financial Holdcos in Nigeria. Consequent upon the results obtained, the study recommends that the board size should be moderate, board members (executive, non-executive and independence) should be well experience and qualified as well as available to attend to its oversight functions. High frequency of meeting of the board should be restrained from as it may not necessarily engenders increase in financial performance. Also, Holdcos should have more Board Committees to ensure effective oversight as it leads to better financial performance.

A Stochastic Multivariate Irregularly Sampled Time Series Imputation Method for Electronic Health Records

Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do not consider the importance of temporal information embedded in EHR data. Besides, most time-dependent neural networks such as recurrent neural networks (RNNs) inherently consider the time steps to be equal, which in many cases, is not appropriate. This study presents a method using the gated recurrent unit (GRU), neural ordinary differential equations (ODEs), and Bayesian estimation to incorporate the temporal information and impute sporadically observed time series measurements in high-dimensional EHR data.

Sustainable growth research – A study on the telecom operators in China

In recent years, the telecom industry has faced digital transformation challenges and fierce market competition. The challenges push telecom operators to grow their subscriber bases by offering lower prices and improved services and new features, which puts pressure on operators’ profitability. In addition, the rise of Internet companies gradually erodes the profit of the traditional telecom operators. Therefore, paying attention to the critical factors impacting firm sustainable growth can help operators get out of the predicament. Based on the resource-based view (RBV), this study explores the factors that influence the firm sustainable growth. Multiple regression model is applied to empirically test the hypotheses with longitudinal time-series panel data from major telecom operators in China. The study provides empirical evidence for sustainable growth research and useful insights for practitioners on the way to keep sustainable growth.

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study.

BackgroundAnxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored.ObjectiveWe aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up.MethodsThe individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status.ResultsIndividual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group.ConclusionsPatients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.

Forecasting suicide rates in India: An empirical exposition.

IntroductionSuicide is a major social and health issue in India. Yearly statistics show a concerning increasing pattern of suicidal deaths in India which is higher in comparison to the global trend. There is limited evidence regarding historical analysis of suicide or any forecasting for suicide in India towards predicting the possible risks of death due to suicide.MethodsThis paper examines the trend of suicide rate and characteristics of suicide victims in India, based on the longitudinal time series data over the last 50 years—collected from the National Crime Record Bureau Reports (1969 to 2018) of the Government of India. In our analysis, we have used the time series model to forecast the suicide rates in India for the next decade. ARIMA (4,1,0) model is found to be the best fit model for forecasting the data.FindingsThere has been an observable and rising trend of suicide rates in India over the last five decades. The forecast indicates a continuance of rising suicide cases for an upcoming couple of years in India with a limited decline in the following years. The prediction model indicates a future relatively consistent pattern of suicide in India which does not seem to be a very encouraging trend. As we have not included the period staring the year 2020 onwards affected by Covid-19 and which has several disruptions in personal and family spaces, the projected suicide trend during the period of next two to three years (2020–22) may rise far high and then it may show a declining path. Along with this, there is a shift in means of suicide in the last couple of decades. Constituting the second-highest number of cases, Illness associated suicide was visibly a serious concern.ConclusionThe present analysis finds that there is no visible substantial relief for suicide deaths during the coming years in India. On the other hand, more extensive exploration of sample cases may provide important information for suicide prevention. Availability of detailed and more inclusive data will be highly useful for analysis and suicide preventive policies. Investment in public health care and other welfare activities like education and employment generation will yield visible positive results in suicide control.

A cross-cultural exploratory analysis of pandemic growth: The case of COVID-19.

Although the novel coronavirus that has resulted in more than 3 million deaths and 140 million cases of infection worldwide has wreaked havoc globally, some nations were more successful than others in curbing growth in their number of cases, thereby saving lives. In this research note, we integrate insights from cross-cultural research with inquiry in social psychology and public health literatures to advance a theoretically grounded and culturally derived explanation of cross-national variance in the growth rate of COVID-19. Our multi-level analyses, based on longitudinal time series data from 107 nations, and focused on the first 91 days of this pandemic in different nations, illustrate the direct and interactive effects of culture. Specifically, we find that individualism and uncertainty avoidance have a positive impact, while power distance and masculinity have a negative impact, on the growth rate of COVID-19 cases. Three-way interaction analyses between time, government stringency, and culture indicate that early government stringency attenuated pandemic growth, and this attenuation effect was more significant in collectivistic than in individualistic nations, and in high rather than low power distance nations. Our findings provide evidence that can enable policymakers and organizations to develop strategies that not only conform to science but that also consider the cultural orientation of nations.

Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point Detection.

With the arrival of the internet of things, smart environments are becoming increasingly ubiquitous in our everyday lives. Sensor data collected from smart home environments can provide unobtrusive, longitudinal time series data that are representative of the smart home resident's routine behavior and how this behavior changes over time. When longitudinal behavioral data are available from multiple smart home residents, differences between groups of subjects can be investigated. Group-level discrepancies may help isolate behaviors that manifest in daily routines due to a health concern or major lifestyle change. To acquire such insights, we propose an algorithmic framework based on change point detection called Behavior Change Detection for Groups (BCD-G). We hypothesize that, using BCD-G, we can quantify and characterize differences in behavior between groups of individual smart home residents. We evaluate our BCD-G framework using one month of continuous sensor data for each of fourteen smart home residents, divided into two groups. All subjects in the first group are diagnosed with cognitive impairment. The second group consists of cognitively healthy, age-matched controls. Using BCD-G, we identify differences between these two groups, such as how impairment affects patterns of performing activities of daily living and how clinically-relevant behavioral features, such as in-home walking speed, differ for cognitively-impaired individuals. With the unobtrusive monitoring of smart home environments, clinicians can use BCD-G for remote identification of behavior changes that are early indicators of health concerns.

Implementation of a referral and expert advice call Center for Maternal and Newborn Care in the resource constrained health system context of the Greater Accra region of Ghana

BackgroundReferral and clinical decision-making support are important for reducing delays in reaching and receiving appropriate and quality care. This paper presents analysis of the use of a pilot referral and decision making support call center for mothers and newborns in the Greater Accra region of Ghana, and challenges encountered in implementing such an intervention.MethodsWe analyzed longitudinal time series data from routine records of the call center over the first 33 months of its operation in Excel.ResultsDuring the first seventeen months of operation, the Information Communication Technology (ICT) platform was provided by the private telecommunication network MTN. The focus of the referral system was on maternal and newborn care. In this first phase, a total of 372 calls were handled by the center. 93% of the calls were requests for referral assistance (87% obstetric and 6% neonatal). The most frequent clinical reasons for maternal referral were prolonged labor (25%), hypertensive diseases in pregnancy (17%) and post-partum hemorrhage (7%). Birth asphyxia (58%) was the most common reason for neonatal referral. Inadequate bed space in referral facilities resulted in only 81% of referrals securing beds. The national ambulance service was able to handle only 61% of the requests for assistance with transportation because of its resource challenges. Resources could only be mobilized for the recurrent cost of running the center for 12 h (8.00 pm – 8.00 am) daily. During the second phase of the intervention we switched the use of the ICT platform to a free government platform operated by the National Security. In the next sixteen-month period when the focus was expanded to include all clinical cases, 390 calls were received with 51% being for medical emergency referrals and 30% for obstetrics and gynaecology emergencies. Request for bed space was honoured in 69% of cases.ConclusionsThe call center is a potentially useful and viable M-Health intervention to support referral and clinical decision making in the LMIC context of this study. However, health systems challenges such inadequacy of human resources, unavailability of referral beds, poor health infrastructure, lack of recurrent financing and emergency transportation need to be addressed for optimal functioning.

ESMvis: a tool for visualizing individual Experience Sampling Method (ESM) data

PurposeThe experience sampling method (ESM) is used for intensive longitudinal time-series data collection during normal daily life. ESM data give information on momentary affect, activities and (social) context of, for example, patients suffering from mental disorders, and allows for person-specific feedback reports. However, current personalized feedback reports only display a selection of measured variables, and typically involve only summary statistics, thus not reflecting the dynamic fluctuations in affect and its influencing factors. To address this shortcoming, we developed a tool for dynamically visualizing ESM data.MethodsWe introduce a new framework, ESMvis, for giving descriptive feedback, focusing on direct visualization of the dynamic nature of raw data. In this ESM feedback approach, raw ESM data are visualized using R software. We applied ESMvis to data collected for over 52 weeks on a patient diagnosed with an obsessive–compulsive disorder with comorbid depression.ResultsWe provided personalized feedback, in which both the overall trajectory and specific time moments were captured in a movie format. Two relapses during the study period could be visually determined, and subsequently confirmed by the therapist. The therapist and patient evaluated ESMvis as an insightful add-on tool to care-as-usual.ConclusionESMvis is a showcase on providing personalized feedback by dynamic visualization of ESM time-series data. Our tool is freely available and adjustable, making it widely applicable. In addition to potential applications in clinical practice, ESMvis can work as an exploratory tool that can lead to new hypotheses and inform more complex statistical techniques.

Ultradian, circadian, and circaseptan rhythms in the patterns of usage of Facebook messenger

ABSTRACT The Facebook Messenger (FBM) is one of the most popular instant messaging social apps, launched by Facebook in 2010. As of October 2019, there were about 1.3 billion FBM users worldwide. In this study, we analyzed periodicities in the online activity patterns of users in FBM. We did not recruit any subjects in this study; rather four of us used our own FBM accounts to reveal the presence of any rhythms with τ = 12 h or τ = 24 h or τ = 168 h in the patterns FBM usage among our FB friends. We log-transformed the time series data and subjected those to Cosinor rhythmometry. The peaks in the daily pattern of FBM usages, revealed from Cosinor analyses, and harmonics curve fittings validated the presence of multi-frequency rhythms in the longitudinal time-series data captured over a period of 16 days. The underlying basis of the observed multi-frequency rhythms could be attributed to the phenomenon of social synchronization. The current findings might bear commercial applications with special reference to targeted content advertising (TCA).

Comparing groups of time dependent data using locally weighted scatterplot smoothing alpha-adjusted serial T-tests

BackgroundIn statistical analysis of time series researchers often pick key points from curves and run the venerable analysis of variance (ANOVA) to determine if a difference exists between groups. However, this approach fails to compare most of the data across time and thereby may throw out potentially valuable inferences. Research questionThis study illustrates a novel method termed LOESS alpha-adjusted serial t-testing (LAAST). LAAST employs locally weighted scatterplot smoothing (LOESS) on the data, serial correlation to make alpha adjustments, and point-wise Welch's t-tests to determine regional significance when comparing groups of time dependent data. It was expected that LAAST gives similar results to random field theory (RFT) based inferences while overcoming its shortcomings with respect to longitudinal data analysis. MethodsTwo data sets were analyzed with LAAST and RFT. The first contained two groups of five simulated random sinusoidal waveforms such that both inline time-series and equivalent time-offset longitudinal conditions were represented. The second data set was comprised of publicly available medial gastrocnemius forces from individuals with (N = 27) and without (N = 16) pain. ResultsResults for both data sets indicated similar corrected alpha levels regardless of analysis type, but the applied alpha level corrections were less conservative for LAAST than RFT or Holm-Bonferroni corrections, but often more conservative than Hochberg corrections. SignificanceAnalysis methods employing functional ANOVA and RFT have enabled researchers to effectively run comparisons between groups at all points within the time series and are gaining popularity. However, in some correction methods for multiple comparisons the alpha level correction can in turn lead to inflation of type II error. These results suggest that LAAST is comparable to RFT while also being appropriate for longitudinal type time series data analysis. Additionally, its use of Welch’s t-tests improves its validity on non-normally distributed data.

Automation of Longitudinal Time Series Data Visualization using Plot Specifications obtained from XL file

Longitudinal Time Series data visualization plays important role in all sector of business decision making [9]. With enormous amount of complex data [11] from cloud and business requirement, number of graphs needed for decision making increased many folds. Generating enormous number of plots manually with more human input is tedious, time consuming and error prone. To avoid these issues, suitable visualization techniques with solid design principles become very important. We conceptualized and designed a novel method for automation of these processes. R-GGPLOT2[7] package and XL specifications file were primarily used to achieve this goal. We here show as how we can create multiple plots from time series data, plots specifications-XL file and R package GGPLOT2[7] in a single run. Since all required information are entered in XL sheet, R function can be run with no modification. Multiple plots can be generated by using enormous data available in production and service sectors such as finance, healthcare, transportation and food industries etc.

Does capitation prepayment based Integrated County Healthcare Consortium affect inpatient distribution and benefits in Anhui Province, China? An interrupted time series analysis.

Objective:This study aims to compare the level and trend changes of inpatient and funds distribution, as well as inpatient benefits before and after the official operation of the ICHC in Anhui.Methods:A total of 1,013,815 inpatient cases were collected from the hospitalisation database in two counties in Anhui Province, China, during the course of the study from January 2014 to June 2017. The effect of the reform was assessed beginning with its formal operation in February 2016. Longitudinal time series data were analysed using segmented linear regression of an interrupted time series analysis.Results:The average hospitalisation expenses showed a decreasing trend and the actual compensation ratio increased significantly (p-value < 0.01). Most of the indicators in the two counties performed well, and the effect of ICHC policy was better in Funan County than in Dingyuan County. The distribution of inpatients and NRCMS funds outside the county after the reform in Dingyuan showed an increasing trend (0.27, 95%CI: 0.12 to 0.42, p-value < 0.01; 0.70, 95%CI: 0.32 to 1.09, p-value < 0.01) and the distribution of inpatients and NRCMS funds in THs showed a more obvious upward trend after the reform in Funan (0.44, 95%CI: 0.22 to 0.67, p-value < 0.001; 0.34, 95%CI: 0.23 to 0.45, p-value < 0.001).Conclusions:This study suggests that the ICHC policy provides effective strategies in promoting the integration of the healthcare delivery system in China. These strategies include strengthening family doctor signing service system and health management, developing telemedicine technology, reducing the weak points of the healthcare services, and introducing private hospitals to form new ICHCs.

Modelling rein tension during riding sessions using the generalised additive modelling technique

General additive modelling (GAM-modelling) is an exploratory technique that can be used on longitudinal (time series) data, e.g. rein tension, over a period of time. The aim was to apply GAM-modelling to investigate changes in rein tension during a normal flatwork training session. Six riders each rode two or three of their horses (n=17 horses) during a normal flatwork/dressage training session with video recordings and rein tension measurements (128 Hz). Training sessions were classified according to rider position, stride length and whether horses were straight, bent to the left or bent to the right. The rein tension data were split into strides and for each stride minimal (MIN) and maximal (MAX) rein tension were determined and the area under the rein tension curve (AUC) was calculated. Using data on a contact the three outcome variables MIN, MAX and AUC rein tension were modelled by horse and rein (left/right), and time within the session was modelled as a smooth function. Two additional sets of models were constructed; one set using data within-rein with gait as a fixed effect and one set with rein and gait as fixed effects. Mean ± standard deviation values were MIN: 8.0±7.7 N, AUC: 180±109 Ns, and MAX: 49±31 N. GAM-modelling extracted visually interpretable information from the originally chaotic rein tension signals. Modelled data suggest that MIN, AUC and MAX follow the same pattern within horse. In general, rein tension was lowest in walk, intermediate in trot and highest in canter. Evaluating the entire ride, 12/17 horses systematically showed higher tension in the right rein. It is concluded that GAM-models may be useful for detecting patterns through time in biomechanical data.

Responsible Marketing and Its Impact on Business Performance: A Longitudinal Study

ABSTRACTThis research article explores, analyzes, and justifies the requirement of socially responsible activities of the corporation in the light of alternative approach to marketing promotion. In fact, this research claims for more societal activity-based promotion (responsible marketing) in place of product driven advertisement-based marketing (traditional) only. Thus, this research refutes the combined impact of societal activities (donation and employee expenditure) and advertisement (Advertisement Expenditure) on business performance (Profit/Sales). This has been analyzed with longitudinal (time series) data for 15 year period of the available 9 companies in capitaline database. Appropriate methodologies in regards to time series data have been performed. The findings are thought-provoking since the evidence of combined effect is not that much visible, though its impact in comparison with marginal effect is quite more. Finally, to conclude this research refutes the utility of advertising as a means of promotion, but at the same time, it indicates the changing direction of advertisement content from product feature-based to more societal activity-based.

Agenda-Setting With Satire: How Political Satire Increased TTIP’s Saliency on the Public, Media, and Political Agenda

Agenda-setting has mostly been investigated as the cognitive process set in motion by the salience of political issues in the traditional news media. The question, though, remained whether political entertainment shows—political satire, specifically—can also set the agenda. The current study investigates whether two episodes of Dutch satire show Zondag met Lubach (ZML) about the European Union-United States trade agreement Transatlantic Trade Investment and Partnership (TTIP) have triggered first-level agenda-setting effects. For that purpose, three studies have been conducted to investigate the three-step agenda-setting process (i.e., learning, understanding, acting) from saliency in satire to saliency on the public agenda, but also on the media and political agenda. Study 1: A two-wave panel survey shows that consumption of the satire show positively affected knowledge acquisition about TTIP, which is the first step in the cognitive process underlying agenda-setting. Study 2: A randomized experiment demonstrated that exposure to ZML increased the perceived understanding of TTIP, which subsequently had a positive impact on the saliency of TTIP on the public agenda. Study 3: Longitudinal time-series data provide evidence that the saliency of TTIP on the public agenda—short term—and on the political agenda—long term—were positively affected by the ZML satire episodes. The study, altogether, demonstrates satire’s ability to set the agenda of both the individual and aggregate level and emphasizes the persistent relevance of agenda-setting theory in today’s high-choice media environment.

Reducing waiting time and raising outpatient satisfaction in Chinese public tertiary hospitals: an interrupted time-series study

BackgroundIt is globally agreed that a well-designed health system delivers 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 interventions were well documented, and the effects were rarely measured with robust methods. MethodsWe did a longitudinal study of the length of waiting times in a public tertiary hospital in southern China, which developed comprehensive data collection systems. Data were extracted from the hospital's information systems and patient satisfaction surveys. We analysed longitudinal time-series data using a segmented linear regression model with SPSS 21·0 to assess changes in levels and trends of waiting times before and after the introduction of waiting time reduction interventions. Pearson correlation analysis was done to indicate the strength of association between waiting times and patient satisfactions. The statistical significance level was set at 0·05. FindingsThe monthly average length of waiting time decreased by 3·49 min (p=0·003) for consultations and by 8·70 min (p=0·02) for filling prescriptions in the corresponding month in which respective interventions were introduced. The trend shifted from baseline from a slight increase to a significant decrease in time for filling prescriptions (p=0·003). We found a significant negative correlation between waiting time of filling prescriptions and outpatient satisfaction towards pharmacy services (r=–0·71; p=0·004). InterpretationThe interventions aimed at reducing waiting time and raising patient satisfaction in Fujian Provincial Hospital are effective. A long-lasting reduction 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 authorised by the hospital managers. This case provides a model of continuous quality improvement and optimisation of management process with the support of relevant evidence. FundingNational Health and Family Planning Commission of China.

Reducing waiting time and raising outpatient satisfaction in a Chinese public tertiary general hospital-an interrupted time series study

BackgroundIt 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.MethodsWe 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.ResultsThe 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).ConclusionsThe 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.