Complaint Data Research Articles

77 Results of a Novel National Emergency Department Chief Complaint Database

77 Results of a Novel National Emergency Department Chief Complaint Database

Assessment of the Relationship Between an Adverse Impact of Work on Physicians’ Personal Relationships and Unsolicited Patient Complaints

ObjectiveTo evaluate the relationship between an adverse impact of work on physicians’ personal relationships and unsolicited patient complaints about physician behavior — a well-established indicator of patient care quality. Participants and MethodsWe paired data from a physician wellness survey collected in April and May 2013 with longitudinal unsolicited patient complaint data collected independently from January 1, 2013, to December 31, 2016. Unsolicited patient complaints were used to calculate the Patient Advocacy Reporting System (PARS) score, an established predictor of clinical outcomes and malpractice suits. The primary outcome was PARS score tercile. Ordinal logistic regression mixed effects models were used to assess the association between the impact of work on a physician’s personal relationships and PARS scores. ResultsOf 2384 physicians eligible to participate, 831 (34.9%) returned surveys including 429 (51.6%) who consented for their survey responses to be linked to independent data and had associated PARS scores. In a multivariate model adjusting for gender and specialty category, each 1-point higher impact of work on personal relationships score (0-10 scale; higher score unfavorable) was associated with a 19% greater odds of being in the next higher PARS score tercile of unsolicited patient complaints (odds ratio, 1.19; 95% CI, 1.07-1.33) during the subsequent 4-year study period. ConclusionAn adverse impact of work on physicians’ personal relationships is associated with independently assessed, unsolicited patient complaints. Organizational efforts to mitigate an adverse impact of work on physicians’ personal relationships are warranted as part of efforts to improve the quality of patient experience and malpractice risk.

Classification of Public Complaints Basedon Text Mining Using Modified K-Nearest Neighbor, Naïve Bayes and C4.5 Algorithm

To improve public services, accuracy and acceleration are needed in classifying the types of complaints so that complaints can immediately get a response from the relevant regional apparatus. This public complaint data is in text form and is not balanced in each category of regional apparatus, so we contribute to research to compare the performance of different text mining-based classification algorithms. In addition, we also tested the resampling method to overcome imbalanced data. In the final stage, testing is carried out using a multiclass confusion matrix table to show accuracy, precision, recall, and f1-score. The test results show the highest value in the Naïve Bayes algorithm with the ComplementNB model without resampling data, which is 89.58% accuracy, 86.72% precision, 82.40% recall, 84.09% f1-score. However, all scores decreased when combined with SMOTE resampling of 83.66% accuracy, 67.79% precision, 80.35% recall, 71.68% f1-score. ComplementNB can be an alternative model in the classification of public complaints with imbalanced datasets

Understanding User Experience and Satisfaction with Urban Infrastructure through Text Mining of Civil Complaint Data

Understanding User Experience and Satisfaction with Urban Infrastructure through Text Mining of Civil Complaint Data

Accelerated forgetting in healthy older samples: Implications for methodology, future ageing studies, and early identification of risk of dementia.

Accelerated long-term forgetting (ALF) has been reported in healthy older individuals, and is a possible early marker for risk of developing Alzheimer's disease (AD). The Verbal Associative Learning and Memory Test (VALMT) addresses methodological weaknesses in existing clinical tests and has detected ALF in epilepsy within an hour. We used VALMT to investigate learning and forgetting in healthy Older participants. Older (60-69 years) and Younger (19-31 years) participants were compared. Using VALMT, unrelated word pairs were learnt to criterion, then cued-recall tested at delays of 5, 30, and 55 min. Unique pairs were tested at each delay. Subjective memory complaints data was gathered, and the Wechsler Memory Scale Logical Memory test (WMS-LM; a standard clinical measure) was administered. VALMT identified a significant difference in delayed recall between Younger and Older groups by 55 min (d = 1.32). While "fast-learning" Older participants scored similarly to Younger participants, "slow-learning" Older participants were impaired at all delays. Forgetting rates suggested degradation of memory starts during early synaptic consolidation rather than later system-level consolidation. Increased subjective memory complaints were associated with reduced VALMT scores. By contrast, WMS-LM failed to identify significant differences between any groups, and did not correlate with memory complaints. We conclude that VALMT may be better able than WMS-LM to identify subtle impairments in healthy older adults within a single clinical visit, and VALMT results better reflect subjective experience. Older slow-learners forget faster and report more subjective memory complaints, which may indicate a group at risk of developing AD.

What PROMIS Scores Correlate With Severe Disability in Cervical Spine Surgery?

Institutional Review Board-approved prospective cohort study. To establish a conversion between Neck Disability Index (NDI) and Patient-Reported Outcomes Measurement Information System (PROMIS) Pain Interference (PI). As the National Institutes of Health (NIH) PROMIS is a global outcome measure, it can be difficult to interpret the clinical implications in patients with cervical spine surgery. To determine how a disease-specific legacy instrument (NDI) correlates with PROMIS PI and define levels of substantial and minimal disability, we developed a formula to convert NDI scores to PROMIS PI. Adult patients undergoing cervical spine surgery at a single institution from 2016 to 2018 were prospectively enrolled. Patients undergoing surgery for instability due to trauma were excluded. Preoperative questionnaires, demographic data, presenting complaint (radiculopathy, myelopathy, myeloradiculopathy), and procedural data were recorded. Patients were included if they had completed the NDI and PROMIS PI. Correlation and regression analyses were performed. One hundred ninety-six patients met inclusion criteria. The average age was 56.9±12.9 years. The mean NDI score was 68.6±38 and PROMIS PI was 60.9±7.3. The PROMIS PI was strongly correlated with the NDI ( r =0.76, P <0.001). Linear regression analysis revealed the following formula. We performed a regression analysis allowing for the conversion of PROMIS PI scores with NDI. In keeping with the previous studies, our analysis showed that PROMIS PI was most strongly correlated to NDI. A PROMIS PI score >66 was correlated to substantial disability (NDI=50), whereas a PROMIS PI score <59 was correlated to minimal relative impairment (NDI=28). These results provide important reference, as PROMIS reporting becomes more widespread in the literature.

Innovating into trouble: When innovation leads to customer complaints

This paper examines the unintended consequences of innovation. We show that innovative activity can have adverse outcomes in the form of increased customer complaints with the potential for reputational and financial damage. Complaints may arise directly from adverse reactions to innovative services or indirectly from service failures where firms over-prioritise innovation. Our empirical analysis focuses on legal services in England and Wales. Survey data on innovation by legal service providers is matched with complaints data from the Legal Ombudsman for England and Wales. This allows us to identify the links between innovation activity and subsequent customer complaints. Our analysis reveals that higher levels of innovation activity increase the probability and number of consumer complaints. We identify how firms can reduce the potential for consumer complaints by adopting collaborative innovation strategies. In addition, firms with international competitors are less likely to face complaints. Our results have strategic, regulatory and policy implications.

Sosialisasi Pengendalian Kualitas Pelayanan Guna Meningkatkan Kepuasan Pelanggan Pada UD. Putra Azzam Service

Business competition is everywhere. Not only in production, but also in service. UD. Putra Azzam Service is a company engaged in computer service and installation. Partners always strive to provide the best service that their customers want and try to improve their competitiveness. From observations through a survey of partner owners, it was found that there were several customer complaints related to damage to goods after purchase or other problems that arose after service. The effect is a decrease in customers in February – April 2021. Servqual and Quality Function Deployment (QFD) methods can be used to find ways to improve service to partners. This community service activity is intended for that. Activities are carried out by observing the service process, obtaining customer complaint data and analyzing the results to determine recommendations for corrective actions. These recommendations are conveyed to partners in dissemination activities. Partners welcome the suggestions given.

Website Layanan Pengaduan Masyarakat Pada Bina Marga Kota Palangka Raya

The public complaint service website is a support system primarily for the community who wish to report road and bridge damage complaints in the Public Works Department. In handling community complaints, the Public Works Department has a mechanism for handling complaints that can be done directly or using WhatsApp applications. This results in complaint handling still being manual and not computerized. The Waterfall software development methodology was used in designing and building this system. The stages involved were: Requirements Definitions, System and Software Design, Implementation and Unit Testing, Integration and System Testing, and Operation and Maintenance. The programming language used in developing the website was PHP, the database used was MySQL, and blackbox testing was performed. After testing, the website can run its functions well, which is to manage complaint data entered into the database. The aim is for the Public Works Department to plan road and bridge construction and repairs in a more structured manner and to help with road and bridge monitoring. This website can facilitate the public in reporting the location of damaged roads and bridges using the Google Maps API, making it easier for users to determine the location of damaged roads and bridges.

SP4N-LAPOR AS AN ONLINE INFORMATION AND COMMUNICATION SERVICE FOR THE PEOPLE OF PONOROGO REGENCY

SP4N-LAPOR is a facility provided to provide information and seek information from the public on the performance of the government. This facility is made based on the mandate of the Law of the Republic of Indonesia. The Ponorogo Regional Government since 2017 has provided this facility. Narrative research is used to determine the quality of service utilization of LAPOR and the follow-up of the data that has been collected by this facility. The research method used are surveys, observation, and interviews. The survey was conducted on people who use SP4N-LAPOR facilities and come to the offices of Regional Apparatus Organizations. The number of survey data obtained is 162 units. The results of statistically processing the data according to the guidelines of the PANRB Ministerial Regulation of the Republic of Indonesia number 14 of 2017 obtained an average value of 81.56. This value indicates that the service quality is included in the good category. Observation and interview methods were conducted on employees who are responsible for processing complaint data made by the public through SP4N-LAPOR. The performance of the employee affects the time for problem-solving, while the community demands that problems that occur immediately be followed up. Obstacles like this will make a poor assessment of the performance of services that have been provided by the government. This fact is in accordance with the value of the questionnaire for handling complaints, suggestions, and inputs, which is 2.895 which is included in the poor category.

Identify the Cause of Inpatient Complaints at Islamic Hospital Surabaya Ahmad Yani Using Root Cause Analysis Method

Background: Surabaya Islamic Hospital is under the auspices of the Surabaya Islamic Hospital Foundation (YARSIS). In 2019, there were 559 complaints reported by PR staff. Meanwhile, during the pandemic in 2020 there was a decrease in complaints because there were only 261 customers who submitted complaints about the services they received at the Surabaya Islamic Hospital. Objectives: The general objective of this study was to identify the cause of patient complaints in the inpatient setting of the Surabaya Islamic Hospital using Root Cause Analysis (RCA) Methods: Researchers collect data through Surabaya Islamic Hospital complaint data in 2020. The data is processed through simple RCA stages and analyzed with fishbone diagrams for determining the root cause. Results: The results of the Root Cause Analysis of the complaints of inpatients at the Surabaya Islamic Hospital prove that there are at least 8 root problems and 6 solutions that can be done to overcome them. The most interventions/improvements must be carried out in the HR sector, namely the level of discipline, skills, empathy, communication, and the limitations of medical and non medical staff. The development of an Integrated Hospital Management Information System is also feasible to be implemented to provide more effective and efficient services. In addition, improvements in supporting facilities and enforcement of rules for inpatient visitors will make visitors feel more comfortable in the inpatient room. Conclusions: It is important for hospital to analyze the complaints received. The results of this study can be used by hospitals in Indonesia as references to improve their services.

UI / UX Design Model for Student Complaint Handling Application Using Design Thinking Method (Case Study: STMIK Rosma Karawang)

The development of information technology is currently very rapid and unavoidable, especially in the world of education. Global demands require the education sector to constantly adjust technological developments with efforts to improve the quality of education, adjust the use of information and communication technology. STMIK Rosma is a university that utilizes computer technology. However, there are still shortcomings, namely the absence of a student complaint application so that the process of submitting student complaints is still running manually. This results in not recording student complaints, student complaint data not well documented, so that the college leadership of the campus cannot monitor the complaints submitted by students. This study aims to analyze the functional requirements of the STMIK Rosma student complaint application. The method used in this research is Design Thinking. The result of this research is the UI / UX design of the student complaint handling application (mobile application) at STMIK Rosma Karawang which is designed using the classic mockuplus tools. This research is expected to provide suggestions to universities to improve the process of handling student complaints.

ANALISIS KLASIFIKASI REKAPITULASI PENGADUAN PELANGGAN UP3 PT. PLN SEMARANG MENGGUNAKAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE)

Every company must have a way to solve the problems faced by its customers, PT. PLN Persero, the Indonesian national energy utility, must have a method to handle consumer complaints. PT. PLN Persero has a recovery time strategy for resolving consumer concerns, but it is not always effective in doing so. The QUEST algorithm (Quick, Unbiased, and Efficient Statistical Tree) approach is used to classify the problem of the recovery time policy failing on specific complaints. Classification of complaint data in order to obtain characteristics and factors as the main influence on the complaints and be able to provide new opinions for PT. PLN to address customer complaints. The QUEST method is a classification tree technique with two nodes per split that yields an unbiased variable. The QUEST method may be used with both category and numerical data. QUEST uses three stages to create a classification tree: picking the splitting variable, identifying the split point, and pausing the split. The classification tree generated has a tree depth of four layers and obtained three essential factors in the classification, namely weather, the number of customers experiencing the same event, and distance from the site. The classification tree accuracy level is 0.851 (or 85.1%), with a prediction error rate of 0.149 (or 14.9%).Keywords: binary classification tree, recovery time, QUEST algorithm.

Leveraging data analytics to understand the relationship between restaurants’ safety violations and COVID-19 transmission

This paper leverages natural language processing, spatial analysis, and statistical analysis to examine the relationship between restaurants’ safety violations and COVID-19 cases. We used location-based consumers’ complaints data during the early stage of business reopening in Florida, USA. First, statistical analysis was conducted to examine the correlation between restaurants’ safety violations and COVID-19 transmission. Second, a neural network-based deep learning model was developed to perform topic modeling based on consumers’ complaints. Third, spatial modeling of the complaints’ geographic distributions was performed to identify the hotspots of consumers’ complaints and COVID-19 cases. The results reveal a positive relationship between consumers’ complaints about restaurants’ safety violations and COVID-19 cases. In particular, consumers’ complaints about personal protection measures had the highest correlation with COVID-19 cases, followed by environmental safety measures. Our analytical methods and findings shed light on customers’ behavioral shifts and hospitality businesses’ adaptive practices during a pandemic.

PENERAPAN METODE DSS (MAUT &amp; IRR) DALAM MENENTUKAN KELAYAKAN PENGADUAN

The research and development center for mineral and coal technology or abbreviated as Puslitbang tekMIRA is located in Bandung, West Java, which has asset management, namely the rental of rooms or buildings. In asset management, there is a complaint page if a customer has a complaint. Based on the complaint data that came in at the same time, the authors saw that there were problems that were considered to need attention, namely which complaints would be resolved first and whether or not the complaint was feasible or not to be processed. This study uses the DSS (Decision Support System) method, namely Multi Attribute Utility Theory (MAUT) and IRR (Internal Rate of Return). The MAUT method is a method that converts some importance into a numerical value from a scale of 0-1 which represents the worst and best values ​​and the final result is a ranking. While the IRR method is an indicator of the efficiency level of an investment that is used to provide an overview of whether the complaint is feasible or not to be followed up. The result of the application of this method is the feasibility of the complaint.

A social sensing approach for everyday urban problem-handling with the 12345-complaint hotline data

The emergence of various types of everyday urban problems poses severe challenges to the conventional top-down designed urban problem-handling protocols. Social sensing data, such as the urban problem complaint hotline records, constitute good sources for collecting bottom-up information on urban problems. Nevertheless, a partially obsolete urban problem categorization standard, a manually operated and experience-dependent workflow for problem classification and pushing, and an unoptimized problem-handling responsibility designation among the municipal departments have been impairing the sufficient utilization of the collected complaint data toward more efficient urban problem-handling performance. In this paper, based on the 12345-hotline data collected in Sanya City, China, we 1) construct an urban problem tree that presents a new and expandable classification of everyday urban problems with 7 super-types, 34 types, and 105 sub-types of events, thus complementing that in the existing National Standard which merely contains 6 types and 66 sub-types; 2) design an auto-classification algorithm for the complaint records with Natural Language Processing and Supervised Machine Learning methods, which achieves an accuracy of up to 75% at the super-type level; and 3) optimize the urban problem-handling responsibility designation among the municipal departments for improved departmental function distinction and balanced workloads, which combined with the auto-classification algorithm yield a decrease of the problem pushing failure ratio from the current 73.8% to 14.6%. We integrate all the above functions in a web-based information system to establish a digital platform for practical urban problem-handling. We conclude the paper with discussions on the paper's implications of an overhaul of urban legislative and executive systems following a bottom-up paradigm and reflections on the paper's limitations and theoretical implications from a complex-system theory perspective. Significance•An urban problem tree constructed from the “12345” Hotline is a complement to the National Standard.•Developing NLP and supervised ML algorithms for effective classification of crowd-sourced hotline complaints.•Optimizing the local government structure for urban problem-handling from a bottom-up approach.•Implementing a web-based system for hotline handling which decreases the failed pushes from 73.8% to 14.6%.

Sewerage infrastructure asset management based on a consumer-centric approach.

In most developed countries, such as the USA, the E.U., and East Asia, the importance of public infrastructure asset management has been stressed for a long time. Among the various types of public infrastructure, sewerage systems are one of the most cost-intensive facilities to manage. Sewerage systems are considered highly difficult to manage due to the undetermined level of service needed, different standards of user satisfaction, and the large gap of service understanding between experts and users. To address these issues, this study aims to define the appropriate target level of service improvement by combining consumers’ expected level of service and complaint data. In this study, the case of the inland flood management project in South Korea is investigated because of the global trend of increasing flood damage. The complaint data represent the frequency of flood damage in the area. Using the contingent valuation method, we found that people want to use 25% of their current monthly sewage bill on the management project. In addition, the results of this study demonstrate that people prefer to deal with the problems caused by old service infrastructure when it can be handled at a lower cost during early stages.

Prediction of bacteremia at the emergency department during triage and disposition stages using machine learning models

Bacteremia is a common but critical condition with high mortality that requires timely and optimal treatment in the emergency department (ED). The prediction of bacteremia at the ED during triage and disposition stages could support the clinical decisions of ED physicians regarding the appropriate treatment course and safe ED disposition. This study developed and validated machine learning models to predict bacteremia in the emergency department during triage and disposition stages. This study enrolled adult patients who visited a single tertiary hospital from 2016 to 2018 and had at least two sets of blood cultures during their ED stay. Demographic information, chief complaint, triage level, vital signs, and laboratory data were used as model predictors. We developed and validated prediction models using 10 variables at the time of ED triage and 42 variables at the time of disposition. The extreme gradient boosting (XGB) model was compared with the random forest and multivariable logistic regression models. We compared model performance by assessing the area under the receiver operating characteristic curve (AUC), test characteristics, and decision curve analysis. A total of 24,768 patients were included: 16,197 cases were assigned to development, and 8571 cases were assigned to validation. The proportion of bacteremia was 10.9% and 10.4% in the development and validation datasets, respectively. The Triage XGB model (AUC, 0.718; 95% confidence interval (CI), 0.701-0.735) showed acceptable discrimination performance with a sensitivity over 97%. The Disposition XGB model (AUC, 0.853; 95% CI, 0.840-0.866) showed excellent performance and provided the greatest net benefit throughout the range of thresholds probabilities. The Triage XGB model could be used to identify patients with a low risk of bacteremia immediately after initial ED triage. The Disposition XGB model showed excellent discriminative performance.

Ambulatory Virtual Care During a Pandemic: Patient Safety Considerations.

The COVID-19 pandemic prompted sudden and fundamental changes in health care, including a rapid rise in the utilization of telehealth services in the ambulatory setting. With the unprecedented and significant decline in traditional office-based visits and procedures, novel patient safety risks and challenges emerged. The ambulatory practices at our quaternary care, academic medical center experienced a 200-fold increase in virtual visit volume between February and April 2020. We convened a multidisciplinary working group dedicated to evaluating quality and safety when providing virtual visits during a pandemic. Our primary outcome was patient experience with virtual care delivery, which was assessed by leveraging patient complaint data and patient satisfaction survey data. For our main focus of patient experience and satisfaction, survey data were analyzed from the approximately 76,616 virtual visit encounters that occurred between March 1, 2020, and April 21, 2020. During this period, 5 patient complaints were filed to the Patient Advocacy Department. Overall, patient satisfaction with telehealth remained stable and high at >93% from February to May 2020. As we assessed these data each month, our working group developed risk mitigation strategies in response to the novel challenges presented by the use of telemedicine due to the COVID-19 pandemic while working to maintain patient satisfaction with care. We identified quality and safety issues around patient factors including optimal triage of patients and use of technology. We also evaluated accessibility to virtual platforms and logistics such as coordination of care for diagnostic testing. Finally, a guidance document was created and communicated to our diverse ambulatory practices to support clinicians. Ambulatory virtual care delivery requires a dynamic, flexible model of care through continuous rapid-cycle process improvement to mitigate patient safety risks during a pandemic, incorporating both provider and patient perspectives.

What are the causes of non-tolerance to new spectacles and how can they be avoided?

PurposeTo investigate non‐tolerance cases from several UK practices to determine their likely causes and how they might have been avoided.MethodsPatient complaint and refraction data were collected from non‐tolerance recheck examinations. For one practice, clinical data were also collected retrospectively to investigate the quality of the eye examinations.ResultsData for 279 rechecks were gathered from 10 practices and a recheck frequency of 2.3% was found. The mean patient age was 60 (SD 16) years, with cylinder changes responsible for 38% of prescription‐related causes of rechecks, overplusing or underminusing 26%, and underplusing or overminusing just 11%. An assessment of 242 recheck corrections found that 40% were unsatisfactory (e.g., failed to address initial or recheck symptoms, N = 45) and retrospective analysis of 217 case records showed many limitations (e.g., 61% or 28% recorded no uncorrected or habitual visual acuity (VA) at either initial examination or recheck).ConclusionsGiven that overplus‐underminus was a much bigger proportion of prescription‐related cases than overminus‐underplus (26% vs. 11%), the refraction mantra of “maximum plus for maximum VA” should be balanced by increased teaching of the problems of overplusing and underminusing, and the use of prescribing guidelines. In addition, continuing professional development regarding the basics of the recheck examination, refraction, visual acuity and prism determination is needed. Changes of oblique cylinders should be carefully considered in older patients as this is a common cause of non‐tolerance. In addition, if the “if it ain't broke, don't fix it” and related maxims had been applied to all patients who were asymptomatic at the original examination, one third of all non‐tolerance cases could have been avoided. Finally, it would seem appropriate for practices to develop a system to deal better with non‐tolerance cases. Perhaps an experienced clinician should examine all patients with non‐tolerance and provide feedback to the original clinician.