The Relationship Between Staffing Levels and Consumer Complaints in Nursing Homes.

While research tends to find an association of nurse staffing with quality in nursing homes, few studies examine complaints as a quality measure or account for ancillary staff. This study used federal nursing home complaint data to examine how key explanatory variables including nursing and ancillary staffing were associated with numbers of complaints and the likelihood of receiving a complaint. Results support that nursing home staffing is associated with quality. While direct care staffing was associated with fewer complaints, larger effects were found for social service and activities staffing. Increasing ancillary staffing may be a cost-effective way to reduce complaints.

Unveiling the Negative Customer Experience in Diagnostic Centers: A Data Mining Approach.

Introduction: This study aims to identify the negative customer experiences reflected in complaints against diagnostic centers using data mining tools. Methods: Analyzing customer complaints from a consumer complaints website, the Apriori algorithm was employed to uncover frequent patterns and identify key areas of concern. The frequency and distribution of terms used in complaints were also analyzed, and word clouds were generated to visualize the findings. Results: The study revealed that major areas of unfavorable customer experience included delayed test reports, erroneous test results, difficulties scheduling appointments, staff incivility, subpar service, and medical negligence. Discussion: These findings and the proposed model can guide diagnostic centers in incorporating data mining tools for customer experience analysis, enabling managers to proactively address issues and view complaints as opportunities for service improvement rather than legal liabilities.

Taxonomic diversity and biomass of the invertebrate fauna of nine drinking water treatment plants and their non-chlorinated distribution systems.

Invertebrates such as Asellus aquaticus, halacarid mites, copepods and cladocerans are known to regularly occur in drinking water distribution systems (DWDS). An eight-year study investigated the biomass and taxonomic composition of invertebrates in the finished water of nine Dutch drinking water treatment plants (using surface water, ground water or dune-infiltrated water) and their non-chlorinated distribution systems. The main aims of the study were to examine the source waters' influence on invertebrate biomass and composition in the distribution networks and to describe invertebrate ecology in relation to the habitat of filters and the DWDS. Invertebrate biomass of the finished drinking waters of the surface water treatment plants was significantly higher than in the finished waters of the other treatment plants. This difference was due to the higher nutrient levels of the source water. The main part of the biomass in the finished water of the treatment plants consisted of rotifers, harpacticoid copepods, copepod larvae, cladocerans and oligochaetes, which are small-sized, euryoecious and tolerate broad environmental conditions. Most of them reproduce asexually. Most species found in the DWDS are known to be detritivores, but all are benthic and euryoecious organisms, many of which have a cosmopolitan distribution. The euryoeciousness of these freshwater species was also shown by their occurrence in brackish waters and ground or hyporheic waters and the ability of many eurythermic species to overwinter in the DWDS habitat. These species are preadapted to the oligotrophic environment of the DWDS and can develop stable populations there. Most species can reproduce asexually and the sexually reproducing invertebrates (Asellus aquaticus, cyclopoids and probably also halacarids) have obviously overcome the potential problem of finding a mating partner. This study also showed a significant correlation of DOC in the drinking water with the invertebrate biomass. A. aquaticus was the dominant biomass component in six out of nine locations and was highly correlated with the Aeromonas counts in the DWDS. Thus, invertebrate monitoring in DWDS is an important additional parameter in understanding biological stability conditions in non-chlorinated DWDS.

When Hotel Guests Complain About Tobacco, Electronic Cigarettes, and Cannabis: Lessons for Implementing Smoking Bans.

We analyzed reviews on tripadvisor.com from a random sample of N = 477 hotels in ten large cities in the US to examine how well existing policies protect guests from exposure to tobacco, electronic cigarette, and cannabis (TEC) smoke. We examined the association between complaints per 100 reviews with hotel smoking policies, star rating, cost, brand, and location. Of all TEC complaints, 80% were associated with thirdhand smoke residue lingering in hotels from previous guests. Compared to the hotel brands with the best records, the two worst-performing brands had 3.4- and 3.6-times higher complaint rates (P < .001). Hotels with ≤2-star ratings had twice the complaints as higher-rated hotels (P < .001). Compared to 100% smokefree hotels, those offering designated smoking rooms had a 35% higher rate of complaints (P < .05). The success of some hotel brands demonstrates it is feasible to protect guests by fully committing to, implementing, and enforcing 100% smokefree building policies.

Detecting Foodborne Disease Outbreaks in Florida through Consumer Complaints.

ABSTRACT: The Florida Complaint and Outbreak Reporting System (FL-CORS) database is used by the Florida Department of Health's Food and Waterborne Disease Program as one of the tools to detect foodborne disease outbreaks (FBOs). We present a descriptive and spatial network analysis of FL-CORS data collected during 2015 to 2018. We also quantified FBOs that were investigated and confirmed because of a filed complaint and the etiological agents involved in these outbreaks. An increasing number of unique complaints filed in FL-CORS was observed during 2015 to 2018, with a sharp increase during 2017 to 2018 and a different seasonal pattern in 2018. The preferred mechanism of reporting varied by age group, with younger people more frequently filing complaints online and older people preferring reporting in person or by phone. Spatial network analysis revealed that 87% of complaints had the same county of residence and county of presumed exposure. Frequency of complaints was negatively associated with linear distance between place of residence and place of exposure at the zip code level. Counties located in North and Central Florida, as well as some coastal areas in South Florida, had higher incidence rates of complaints. Those counties tend to have a large population density, and some are popular vacation destinations. On average, 96 FBOs were reported in Florida annually, of which 60% were confirmed with successful identification of the causative agent. The 56% of the confirmed FBOs were triggered by a complaint. Throughout the years, 2.4 to 2.8 FBOs and 1.4 confirmed FBOs were identified per 100 complaints. Ciguatera toxin was the cause of 40% of all FBOs in Florida, and only 28% of outbreaks were detected through complaints. In contrast, complaints were the main source of identifying outbreaks of norovirus, nontyphoidal Salmonella enterica, and scombroid food poisoning, as well as rare outbreaks of Clostridium perfringens, Cryptosporidium spp., Shigella spp., and Vibrio vulnificus.

Contamination source identification in water distribution networks using convolutional neural network.

Contamination source identification (CSI) is significant for water quality security and social stability when a contamination intrusion event occurs in water distribution systems (WDSs). However, in research, this is an extremely challenging task for many reasons, such as limited number of water quality sensors and their limitations in detecting contaminants. Hence, some researchers have introduced consumers' complaint information as an alternative of sensors for CSI. But the problem with this approach is that the uncertainty of complaint delay time has a great impact on the identification accuracy. To address this issue, this study constructed complaint matrices to present the spatiotemporal characteristics of consumer complaints in an intrusion event and proposed a new methodology employing convolution neural network (CNN)-a deep learning algorithm-for the purpose of pattern recognition. CNN aimed to explore the inherent characteristics of complaint patterns corresponding to different contaminant intrusion nodes and to improve the performance of identifying the contamination source based on consumer complaint information. Two case studies illustrated methodology effectiveness in WDSs of various scales, even with the high uncertainties of complaint delay time. The comparison between CNN and a back-propagation artificial neural network algorithm demonstrates that the former framework possesses stronger robustness and higher accuracy for CSI.