The Emergency Department is a key facet of modern healthcare facilities and is responsible for bringing in a lot of patient volume. This healthcare asset is prone to a lot of shortcomings, primarily long waiting times. Though, patients primarily go to the emergency department in emergency scenarios, they still have a choice on which hospital they choose to attend. As such, it may be important for patients to be informed on the expected wait times at hospitals. Though the wait times vary each day, it may be important to know the average expected wait times, time till sent home, and number of violations by hospital type. Using the hospital data published by Medicare, the hospitals were grouped into 3 categories (Not for profit, For profit, and Academic) and data analysis was conducted and Analysis of Variance (ANOVA)was used to determine if the results were statistically significant. The analysis revealed that Academic hospital emergency departments had the highest time before admission, highest time until admission, and highest number of violations. The ANOVA revealed that the difference observed was significant in both time until admission (p-value=.0867) and number of violations (p-value = .011). The results of the study suggest that patients should consider attending a for-profit hospital emergency department if waiting time is a major concern. Analysis of the number of violations (indicative of poor care quality) suggested that for-profit hospitals do not have worse care quality and patients should not be worried about compromised quality care for the shorter wait time.

Improvement of quality care is a major goal for modern healthcare. Quality of care is often measured through readmission rates for specific conditions such as heart failure, pneumonia, total arthroplasty of knee/hip, etc. This data has been used by the Center for Medicare and Medicaid Services (CMS)`to adjust reimbursement rates for hospitals with excessive readmissions. Different hospital operation and management models possess structural differences that may impact the rates of readmission. This study investigated whether there were significant differences in the readmission rates for Academic, Non-Profit, and For-Profit hospitals. The results may be important in reshaping guidelines to assess hospitals based on readmission rates. The average excess readmissions for heart failure were Non-Profit hospitals for 1.0047, For Profit for 1.013, and Academic for .975. The Anova for this set returned a p-value of 1.70284E-05 meaning that the results were statistically significant. As such, Academic hospitals have statistically lower readmission rates for heart failure. The excess readmission rates for pneumonia yielded 1.025 for Non-Profit, 1.024 for For-Profit, and .99 for Academic hospitals. The Anova returned a p-value of 2.4899E-09 which suggests the differences seen are statistically significant. As such, academic hospitals also have a statistically lower rate of pneumonia readmissions. The study possess implications on on consumer decision making in choosing a hospital. In addition to this, algorithms for benchmarking as well as CMS adjustments to reimbursement rates may consider factoring the hospital ownership type.

Falls are a serious concern among the elderly community and contribute danger through immediate harm as well as persistent risk that carries on after the fall. One in four elderly adults fall every year resulting in 32,000 annual deaths due to falls. After a fall, there may be weakness, ongoing pain, concussive symptoms, and loss of confidence. Falls are most likely to occur while attempting to stand up or sit down. Currently, there are many interventions in use to prevent falls such as alarms, restraints, hip protectors, and lowering bed height. Studies have found these interventions to be ineffective at reducing fall rates and fall injury. These measures are applicable for falls that occur while sitting or lying down and as such do not account for falls that occur while standing or while attempting to stand. International efforts using staff have shown promising results in reducing falls. This model may be applicable to the American nursing home. In this model, staff assist residents in everyday tasks and are available to help when residents need help standing up or sitting down. Currently, nursing homes are short staffed. Factors such as low wages, poor working conditions, and too many tasks per worker have been cited as contributing to low employment. Additionally, loose governmental regulations on the number of residents per nurse allow for nursing homes to be operational despite being understaffed. Prevention of falls may be best addressed through an active staff that routinely visits resident rooms and assists in walking, getting up, and sitting down. Staff should be responsible for eliminating trip hazards in patient rooms. The Biden administration announced efforts to improve nursing home conditions in early 2022. Governmental interventions in the form of mandating increased staffing and wages for nursing home staff may be effective at reducing fall rates within nursing homes.

Introduction:Approximately 1 in 3 women will develop breast cancer each year. The key to the treatment of breast cancer is early detection and intervention. Analysis of the data on breast cancer rates and demographics may be useful in predicting which groups are more likely to develop breast cancer and recommending frequent and early screenings. Methods:The study utilized data from the Breast Cancer Surveillance Consortium which publishes data on breast cancer rates with demographic data which they compiled through collaboration with clinicians across the United States. Data analysis tools available through the JMP 16 software were used to look into the different categorical demographic groups and to assess which groups were statistically more predisposed to developing breast cancer. Analysis of the distributions as well as One-way Analysis of Variance of the different categories (year, age group, race/ethnicity, age of menarche, age of firstborn, breast density, current heart rate, age of menopause, body mass index (BMI), and breast cancer history) was conducted. Results:The major results come from the Anova and Tukey-HSD test which was used to discern statistically unique groups. The study found that having no hormone replacement therapy, pre/peri-menopausal, BMI group 1(10-24.99), Non-Hispanic White, and Scattered fibro glandular densities were all observed with having statistically greater breast cancer rates. Conclusion:The results suggest that an individual belonging to one or more categories (having no hormone replacement therapy, pre/peri-menopausal, BMI group 1(10-24.99), Non-Hispanic White, and Scattered fibro glandular densities) should have more frequent and earlier screenings.

Introduction:The use of telemedicine has long been accepted as an alternative to traditional healthcare visits. In recent years, there has been an increase in wearable healthcare technology and remote monitoring software. With this increase, more healthcare services can be provided while patient remains at home. The question remains whether patients are satisfied with telemedicine-based healthcare. Methods:This systemic literature review aims to consult existing primary source literature on patient satisfaction with telemedicine. Utilizing the Google Scholar and PubMed databases, 35 articles were found through a key word search of “telehealth”, “patient satisfaction” as well as “patient satisfaction with telemedicine”. Exclusion criteria was established based on whether the article primarily discussed the patient perspective and discussed patient preference for telehealth in relation to traditional setting interventions. Of the initial 35 articles found using the key word search, 16 articles were analyzed to determine the patient satisfaction with the telemedicine intervention. Concluding statementsThe study suggests that patients are satisfied with telemedicine and many patients prefer the telemedicine to the traditional visit. Given this data, telemedicine may serve as an important medical tool in the years to come. The key benefits noted were shorter visit, quicker access to medical professional, and lack of travel costs. Future studies may investigate investments needed to establish telehealth in underserved areas. Additionally, the physician and provider perspective on the effectiveness of telehealth consultation may also be important prior to advocating for shifting greater services to telehealth settings.