An approach to exploring associations between hospital structural measures and patient satisfaction by distance-based analysis.

BACKGROUND: Patient satisfaction studies have explored domains of patient satisfaction, the determinants of domains, and score differences of domains by patient/hospital structural measures but reports on the structure of patient satisfaction with respect to similarities among domains are scarce. This study is to explore by distance-based analysis whether similarities among patient-satisfaction domains are influenced by hospital structural measures, and to design a model evaluating relationships between the structure of patient satisfaction and hospital structural measures. METHODS: The Hospital Consumer Assessment of Healthcare Providers and Systems 2012 survey scores and their structural measures from the Hospital Compare website reported adjusted percentages of scale for each hospital. Contingency tables of nine measures and their ratings were designed based on hospital structural measures, followed by three different distance-based analyses - clustering, correspondence analysis, and ordinal multidimensional scaling - for robustness to identify homogenous groups with respect to similarities. RESULTS: Of 4,677 hospitals, 3,711 (79.3%) met the inclusion criteria and were analyzed. The measures were divided into three groups plus cleanliness. Certain combinations of these groups were shown to be dependent on hospital structural measures. High value ratings for communication and low value ratings for medication explanation, quietness and staff responsiveness were not influenced by hospital structural measures, but the varied-ratings domain group similarities, including items such as global evaluation and pain management, were affected by hospital structural measures. CONCLUSIONS: Distance-based analysis can reveal the hidden structure of patient satisfaction. This study suggests that hospital structural measures including hospital size, the ability to provide acute surgical treatment, and hospital interest in improving medical care quality are factors which may influence the structure of patient satisfaction.

Real-World Preventive Effects of Suvorexant in Intensive Care Delirium: A Retrospective Cohort Study.

OBJECTIVE: This study aimed to examine the effects of suvorexant on delirium prevention in a real-world setting. Previous studies have demonstrated the efficacy of suvorexant for delirium prevention in limited randomized clinical trial settings; however, its effectiveness in everyday clinical settings remains unknown. METHODS: A single-center, retrospective cohort study was conducted in the intensive care unit of an academic hospital. Patients (aged ≥ 3 years) admitted from January 2016 to December 2018 were eligible if they stayed in the intensive care unit for at least 72 hours. Suvorexant was prescribed by the attending physician for insomnia as part of everyday clinical practice. A Cox proportional hazards regression analysis was conducted on delirium-free survival for suvorexant users, adjusting for delirium-related covariates. As part of routine clinical practice, the Confusion Assessment Method for the Intensive Care Unit was used to detect the existence of delirium at least twice daily throughout the intensive care unit stay. RESULTS: There were 699 patients-84 suvorexant users and 615 suvorexant nonusers. Delirium was detected in 214 patients. Delirium prevalence was significantly lower in suvorexant users than in nonusers (17.9% vs 32.4%, respectively; P = .007). Cox regression analysis revealed a significantly lower hazard ratio (0.472; 95% CI, 0.268-0.832; P = .009) of delirium in suvorexant users than in nonusers. Trazodone also had a preventive effect on delirium (hazard ratio 0.345; 95% CI, 0.149-0.802; P = .013). CONCLUSIONS: The present study extends to real-world settings previous findings that suvorexant is effective for delirium prevention.

Prospective clinical trial evaluating vulnerability and chemotherapy risk using geriatric assessment tools in older patients with lung cancer.

AIM: In Japan, the number of older patients with cancer has been increasing. Assessment of performance status, cognitive function and social background is necessary for the treatment of older patients. The aims of the present study were: (i) to establish an evaluation system using electronic medical records; and (ii) to distinguish older patients as fit versus vulnerable or frail according to a geriatric assessment (GA) system score. METHODS: We incorporated GA tools in our electronic medical records system and carried out comprehensive assessments for patients with newly diagnosed lung cancer aged ≥65 years. The decision about primary treatment followed consultation with the clinical team and was not guided by GA scores. Subsequent treatment and outcomes were recorded. RESULTS: A total of 100 patients had completed GA. The average age was 75 years (range 65-94 years). Regarding GA results, 63% were positive on the Comprehensive Geriatric Assessment 7, 39% on the Vulnerable Elderly Survey-13 and 84% on the Geriatric 8. The percentage of vulnerable patients (positive on all three GA) was significantly higher in the non-standard therapy group (n = 19) than in the standard therapy group (n = 81; 78.9% vs 21.0%, P < 0.001). Among vulnerable patients who received standard therapy, 47% discontinued chemotherapy as a result of toxicity. Even if a patient was considered vulnerable based on GA scores, chemotherapy is possibly safe for those with EGFR mutations. CONCLUSIONS: We confirmed the feasibility of this system. During decision-making for older patients with cancer, a combination of GA helps prevent undertreatment or overtreatment. Geriatr Gerontol Int 2019; 19: 1108-1111.

Ensuring the security and availability of a hospital wireless LAN system.

Wireless technologies as part of the data communication infrastructure of modern hospitals are being rapidly introduced. Even though there are concerns about problems associated with wireless communication security, the demand is remarkably large. Herein we discuss security countermeasures that must be taken and issues concerning availability that must be considered to ensure safe hospital/business use of wireless LAN systems, referring to the procedures introduced at a university hospital. Security countermeasures differ according to their purpose, such as preventing illegal use or ensuring availability, both of which are discussed. The main focus of the availability discussion is on signal reach, electromagnetic noise elimination, and maintaining power supply to the network apparatus. It is our hope that this information will assist others in their efforts to ensure safe implementation of wireless LAN systems, especially in hospitals where they have the potential to greatly improve information sharing and patient safety.

Installation of secure, always available wireless LAN systems as a component of the hospital communication infrastructure.

Wireless technologies as part of the data communication infrastructure of modern hospitals are being rapidly introduced. Even though there are concerns about problems associated with wireless communication security, the demand is remarkably large. In addition, insuring that the network is always available is important. Herein, we discuss security countermeasures and points to insure availability that must be taken to insure safe hospital/business use of wireless LAN systems, referring to the procedures introduced at Shimane University Hospital. Security countermeasures differ according to their purpose, such as for preventing illegal use or insuring availability, both of which are discussed. It is our hope that this information will assist others in their efforts to insure safe implementation of wireless LAN systems, especially in hospitals where they have the potential to greatly improve information sharing and patient safety.

Evaluating learning models with transitions of human interests based on objective rule evaluation indices.

This paper presents a method to support the evaluation procedure of a data mining process using human-system interaction. The post-processing of mined results is one of the key factors for successful data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset containing noise. We have designed a method based on objective rule evaluation indices to support the rule evaluation procedure; the indices are calculated to evaluate each if-then rule mathematically. We have evaluated five representative learning algorithms to construct rule evaluation models of the actual data mining results from a chronic hepatitis data set. Further, we discuss the relationship between the transitions of the subjective criterion of a medical expert and the performances of the rule evaluation models.

Evaluation of rule interestingness measures in medical knowledge discovery in databases.

OBJECTIVE: We discuss the usefulness of rule interestingness measures for medical KDD through experiments using clinical datasets, and, based on the outcomes of these experiments, also consider how to utilize these measures in postprocessing. METHODS AND MATERIALS: We first conducted an experiment to compare the evaluation results derived from a total of 40 various interestingness measures with those supplied by a medical expert for rules discovered in a clinical dataset on meningitis. We calculated and compared the performance of each interestingness measure to estimate a medical expert's interest using f-measure and correlation coefficient. We then conducted a similar experiment for hepatitis. RESULTS AND CONCLUSION: The comprehensive results of experiments on meningitis and hepatitis indicate that the interestingness measures, accuracy, chi-square measure for one quadrant, relative risk, uncovered negative, and peculiarity, have a stable, reasonable performance in estimating real human interest in the medical domain. The results also indicate that the performance of interestingness measures is influenced by the certainty of a hypothesis made by the medical expert, and that the combinational use of interestingness measures will contribute to support medical experts to generate and confirm their hypotheses through human-system interaction.

Cluster analysis of trajectory data on hospital laboratory examinations.

Large-scale cross-patient analysis of time series laboratory tests is a challenging task because of the temporal irregularity of data, coexistence of various events, and multidimensionalty of examinations. In this paper, we present a novel cluster analysis method for multidimensional, inhomogeneous time series based on the trajectory comparison technique. Application to the chronic hepatitis dataset delivered some interesting findings, for example, there existed some patterns in ALB-PLT trajectories that took similar temporal courses, and the clusters well corresponded to the fibrotic stages.

Multiscale analysis of long time-series medical databases.

Data mining in time-series medical databases has been receiving considerable attention since it provides a way of revealing useful information hidden in the database; for example relationships between the temporal course of examination results and onset time of diseases. This paper presents a new method for finding similar patterns in temporal sequences based on multiscale matching. Multiscale matching enables us the cross-scale comparison of sequences, namely, it enable us to compare temporal patterns by partially changing observation scales. We examined the usefulness of the method on the chronic hepatitis dataset and found some interesting patterns. On GPT sequences, we found patterns that may represent the effectiveness of interferon (IFN) treatment. On platelet count sequences, we found that, if IFN treatment was ineffective, platelet count kept decreasing following the progress of liver fibrosis, while it started increasing if the treatment was effective.