The effect of racial and gender concordance between physicians and patients on the assessment of hospitalist performance: a pilot study.

BACKGROUND: Lack of racial concordance between physicians and patients has been linked to health disparities and inequities. Studies show that patients prefer physicians who look like them; however, there are too few underrepresented minority physicians in the workforce. Hospitalists are Internal Medicine physicians who specialize in inpatient medicine. At our hospital, hospitalists care for 60% of hospitalized medical patients. We utilized the validated Tool to Assess Inpatient Satisfaction with Care from Hospitalists (TAISCH) to assess the effect of patient-provider race and gender concordance on patients' assessment of their physician's performance. METHODS: Four hundred thirty-seven inpatients admitted to the non-teaching hospitalist service, cared for by a unique hospitalist physician for two or more consecutive days, were surveyed using the validated TAISCH instrument. The influence of gender and racial concordance on TAISCH scores for patient - hospitalist pairs were assessed by comparing the specific dyads with the overall mean scores. T-tests were used to compare the means. Generalized estimating equations were used to account for clustering. RESULTS: Of the 34 hospitalist physicians in the analysis, 20% were African American (AA-non-Hispanic), 15% were Caucasians (non-Hispanic) and 65% were in the "other" category. The "other" category consisted of predominantly physicians of South East Asian decent (i.e. Indian subcontinent) and Hispanic. Of the 437 patients, 66% were Caucasians, and 32% were AA. The overall mean TAISCH score, as these 437 patients assessed their hospitalist provider was 3.8 (se = 0.60). The highest mean TAISCH score was for the Caucasian provider-AA patient dyads at 4.2 (se = 0.21, p = 0.05 compared to overall mean). The lowest mean TAISCH score was 3.5 (se = 0.14) seen in the AA provider/AA patient dyads, significantly lower than the overall mean (p = 0.013). There were no statistically significant differences noted between mean TAISCH scores of gender and racially concordant versus discordant doctor-patient dyads (all p's > 0.05). CONCLUSIONS: In the inpatient setting, it appears as if neither race nor gender concordance with the provider affects a patient's assessment of a hospitalist's performance.

VMFCVD: An Optimized Framework to Combat Volumetric DDoS Attacks using Machine Learning.

Despite significant development in distributed denial of service (DDoS) defense systems, the downtime caused by DDoS damages reputation, crushes end-user experience, and leads to considerable revenue loss. Volumetric DDoS attacks are the most common form of DDoS attack and are carried out by an army of infected IoT devices or by reflector servers, which increase attacks at massive scales. In this work, we propose a voting-based multimode framework to combat volumetric DDoS (VMFCVD) attacks. VMFCVD is based on a triad of fast detection mode (FDM), defensive fast detection mode (DFDM), and high accuracy mode (HAM) methods. FDM is designed to classify network traffic when the server is under attack. The highly dimensionally reduced dataset helps FDM accelerate detection speed. During our experiment, the dimension reduction for FDM was more than 97% while maintaining an accuracy of 99.9% in most cases. DFDM is an extended version of FDM that enhances malicious network traffic detection accuracy by tightening the detection technique. HAM focuses on detection accuracy, showing substantial improvement over FDM and DFDM. HAM activates when the server is stable. VMFCVD is extensively experimented on the latest benchmark DDoS and botnet datasets, namely the CICIDS2017 (BoT & DDoS), CSE-CIC-IDS2018 (BoT & DDoS), CICDDoS2019 (DNS, LDAP, SSDP & SYN), DoHBrw2020, NBaIoT2018 (Mirai), UNSW2018 BoTIoT, and UNSW NB15 datasets. The VMFCVD results show that it outperforms recent studies. VMFCVD performs exceptionally well when the server is under DDoS attack.

A Nationwide Study of the Impact of Dysphagia on Hospital Outcomes Among Patients With Dementia.

OBJECTIVES: To assess the impact of dysphagia on clinical and operational outcomes in hospitalized patients with dementia. DESIGN: Retrospective cohort study. SETTING: 2012 Nationwide Inpatient Sample. PARTICIPANTS: All patients discharged with a diagnosis of dementia (N = 234,006) from US hospitals in 2012. MEASUREMENTS: Univariate and multivariate regression models, adjusting for stroke and patient characteristics, to assess the impact of dysphagia on the prevalence of comorbidities, including pneumonia, sepsis, and malnutrition; complications, including mechanical ventilation and death; and operational outcomes, including length of stay (LOS) and total charges for patients with dementia. RESULTS: Patients having dementia with dysphagia (DWD) had significantly higher odds of having percutaneous endoscopic gastrostomy placement during the admission (odds ratio [OR]: 13.68, 95% confidence interval [CI]: 12.53-14.95, P < .001), aspiration pneumonia (OR: 6.27, 95% CI: 5.87-6.72, P < .001), pneumonia (OR: 2.84, 95% CI: 2.67-3.02, P < .001), malnutrition (OR: 2.5, 95% CI: 2.27-2.75, P < .001), mechanical ventilation (OR: 1.69, 95% CI: 1.51-1.9, P < .001), sepsis (OR: 1.52, 95% CI: 1.39-1.67, P < .001), and anorexia (OR: 1.29, 95% CI: 1.01-1.65, P = .04). Mean LOS was 2.16 days longer (95% CI: 1.98-2.35, P < .001), mean charge per case was US$10,703 higher (95% CI: US$9396-US$12,010, P < .001), and the odds of being discharged to a skilled nursing, rehabilitation, or long-term facility was 1.59 times higher (95% CI: 1.49-1.69, P < .001) in the DWD cohort compared to patients having dementia without dysphagia. CONCLUSION: Dysphagia is a significant predictor of worse clinical and operational outcomes including a 38% longer LOS and a 30% increase in charge per case among hospitalized patients with dementia. Although these findings may not be surprising, this new evidence might bring heightened awareness for the need to more thoughtfully support patients with dementia and dysphagia who are hospitalized.

Introducing the Hospitalist Morale Index: A new tool that may be relevant for improving provider retention.

OBJECTIVE: To develop a valid instrument to assess morale and explore the relationship between morale and intent to leave employment due to unhappiness. PATIENTS AND METHODS: An expert panel identified 46 drivers of hospitalist morale. In May 2009, responders of our single-site pilot survey rated each driver in terms of current contentment and importance to their morale. With exploratory factor analysis, a 28-item/7-factor instrument emerged. In May 2011, the refined scale was distributed to 108 hospitalists from 2 academic and 3 community hospitals. Confirmatory factor analysis (CFA) was used for internal validation and refinement of the Hospitalist Morale Index. Importance ratings and contentment assessments were used to generate item scores, which were then combined to generate factor scores and personal morale scores. Results were used to validate the instrument and evaluate the relationship between hospitalist morale and intent to leave due to unhappiness. RESULTS: The 2011 response rate was 86%. The final CFA resulted in a 5-factor and 5-stand-alone-item model. Personal morale scores were normally distributed (mean = 2.79, standard deviation = 0.58). For every categorical increase on a global question that assessed overall morale, personal morale scores rose 0.23 points (P < 0.001). Each 1-point increase in personal morale score was associated with an 85% decrease (odds ratio: 0.15, 95% confidence interval: 0.05-0.41, P < 0.001) in the odds of intending to leave because of unhappiness. CONCLUSION: The Hospitalist Morale Index is a validated instrument that evaluates hospitalist morale across multiple dimensions of morale. The Hospitalist Morale Index may help program leaders monitor morale and develop customized and effective retention strategies. Journal of Hospital Medicine 2016;11:425-431. © 2016 Society of Hospital Medicine.

Exploratory qualitative study for community management and control of tuberculosis in India.

Tuberculosis (TB) is a major public health problem in India which accounts for nearly one-fifth of the global TB burden. Though India has been gaining success in eliminating TB, the disease still kills 1000 people daily. It is of prime importance to control the TB situation in India. Motivated by the need to explore factors influencing TB, a qualitative study was conducted with 14 doctors and key TB informants in India over a period of one month involving face-to-face interviews. The interviewees came from diverse backgrounds and vocations, thus providing a rich data on varied issues in controlling the spread of TB in India for enhanced patient care. The data was coded and analyzed. The findings suggest the need to address mental and social well-being of the TB patients through three main themes, namely, Alerts, Care and Education, in order to control the TB situation in India.

The Creating Incentives and Continuity Leading to Efficiency staffing model: a quality improvement initiative in hospital medicine.

OBJECTIVE: To determine the effect of a hospitalist-developed, continuity-centered hospitalist staffing model on patient outcomes and resource use. METHODS: The Creating Incentives and Continuity Leading to Efficiency (CICLE) staffing model was conceived by a group of hospitalists who sought to improve continuity of inpatient care. Using a retrospective, observational, pre-post study design, we compared patient-level data for all discharges from our hospitalist service from 6 months after implementation of the CICLE staffing model (September 1, 2009, through February 28, 2010; n=1585) with data from those same months in the prior year (September 1, 2008, through February 28, 2009; n=1808). We used the number of unique hospitalists who documented an encounter during the admission as a measure of continuity of care. Length of stay and hospital charges per admission constituted the measures of resource use. RESULTS: The odds of having a single hospitalist for the entire hospitalization nearly doubled under the CICLE model (odds ratio, 1.87; 95% confidence interval, 1.60-2.2; P<.001). Mean length of stay decreased 7.5% (from 2.92 before to 2.70 days after initiation of the model; P<.001). Mean hospital charge per admission decreased 8.5% (from $7224.33 before to $6607.79 after initiation of the model; P<.001). Thirty-day readmission rates were not substantially affected by the CICLE model (15.0% before to 17.3% after initiation of the model; P=.08). CONCLUSION: Improved continuity of care among hospitalists was associated with reductions in length of stay and lower health care costs. These benefits were realized without substantially affecting readmission rates. The staffing model can be achieved by reorganizing existing hospitalists and may not require the hiring of additional personnel. The CICLE staffing model is a viable option for hospitalist groups that are aiming to diminish resource use and improve quality of care.