scholarly journals A novel approach to the classification of performance on inpatient perception of hospitalization experience across the US 52 states

2019 ◽  
Author(s):  
Tung-Hui Jen ◽  
Tsair-Wei Chien ◽  
Willy Chou
Keyword(s):  
Healthcare Providers ◽  
Us States ◽  
High Performing ◽  
Novel Approach ◽  
The Us ◽  
Choropleth Maps ◽  
Applied Approach ◽  
Consumer Assessment ◽  
Domain Scores

Abstract Background: Medical groups identified as high-performing with different approaches have been proposed for classification of performance in the literature, but no consistently-applied approach exists for identifying high performers for the benefit of healthcare providers. Most organizations classify hospitals using domain scores and assign a letter grade (e.g., from A to F in Leapfrog Groups) to the performance level. Whether bibliometrics can be an alternative for classifying healthcare givers’ performances is worthy of study. This study was performed to visualize survey results about inpatients’ perceptions of hospitalization experience for the US states using bibliometrics. Methods: We downloaded the 2014 summaries of HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Services) data. Four indices of h, PI, Ag, and x were applied to evaluate performance based on the core domains derived from the ten domain scores for each state in the US, and then displayed online dashboards to show hospitalization satisfaction across states on Google Maps. Choropleth maps were plotted for classifying the performnces into six grades using the quantiles method.Results: The top three states for hihg quality-of-care are Louisiana, Nebraska, and South Dakota using either x or Ag-index to assess. The Ag-index earns the mean correlation coefficient (=0.86) higher than the other three. The inpatient perception of hospitalization experience or the US states were classified and displayed on choropleth maps. Conclusions: The classification of healthcare performance is sensitive to the domain scores, the approach in classification, and the choice of metrics. The absence of a consistently-applied approach to identify healthcare performers impedes efforts to reliably compare, select, and reward high-performing providers. The x-index is recommended for quantifying the performance of healthcare givers in the foreseeable future.

scholarly journals A Quantitative Framework for Measuring Personalized Medicine Integration into US Healthcare Delivery Organizations

2021 ◽  
Vol 11 (3) ◽  
pp. 196
Author(s):  
Arushi Agarwal ◽  
Daryl Pritchard ◽  
Laura Gullett ◽  
Kristen Garner Amanti ◽  
Gary Gustavsen
Keyword(s):  
Personalized Medicine ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Significant Heterogeneity ◽  
High Performing ◽  
Methodical Approach ◽  
The Us ◽  
Longitudinal Approach ◽  
Multifactorial Approach ◽  
Internal Funding

Personalized medicine (PM) approaches have revolutionized healthcare delivery by offering new insights that enable healthcare providers to select the optimal treatment approach for their patients. However, despite the consensus that these approaches have significant value, implementation across the US is highly variable. In order to address barriers to widespread PM adoption, a comprehensive and methodical approach to assessing the current level of PM integration within a given organization and the broader healthcare system is needed. A quantitative framework encompassing a multifactorial approach to assessing PM adoption has been developed and used to generate a rating of PM integration in 153 organizations across the US. The results suggest significant heterogeneity in adoption levels but also some consistent themes in what defines a high-performing organization, including the sophistication of data collected, data sharing practices, and the level of internal funding committed to supporting PM initiatives. A longitudinal approach to data collection will be valuable to track continued progress and adapt to new challenges and barriers to PM adoption as they arise.

scholarly journals Payroll Share, Real Wage and Labor Productivity across US States

2020 ◽  
pp. 1-41
Author(s):  
Ivan Mendieta-Muñoz ◽  
Codrina Rada ◽  
Ansel Schiavone ◽  
Rudi von Arnim
Keyword(s):  
Mixture Models ◽  
Labor Productivity ◽  
Real Wage ◽  
Real Wages ◽  
Relative Prices ◽  
Us States ◽  
High Performing ◽  
Joint Distributions ◽  
The Us ◽  
Index Decomposition

This paper analyzes regional contributions to the US payroll share from 1977 to 2017 and the four major business cycles throughout this period. We implement two empirical exercises. First, we decompose the US payroll share across states. Utilizing a Divisia index decomposition technique yields exact contributions of real wages, employment structure, labor productivity and relative prices across the states to the aggregate change in the payroll share. Key findings are that the decline in the aggregate (i) is driven by decoupling between real wage and labor productivity; and (ii) is initially driven by the rust belt states, but subsequently dominated by relatively large states. Second, we employ mixture models on real wages and labor productivity across US states to discern whether distinct mechanisms appear to generate these distributions. Univariate models (iii) indicate the possibility that two distinct mechanisms generate state labor productivities, raising the question of whether regional dualism has taken hold. Lastly, we use bivariate mixture models to investigate whether such dualism and decoupling manifest in the joint distributions of payroll shares and labor productivity, too. Results (iv) are affirmative, and further suggest a tendency for high performing states to have relatively high payroll shares initially, and low payroll shares more recently.

A Novel Approach to Improving Patient Experience in Orthopedics

2017 ◽  
Vol 32 (6) ◽  
pp. 655-660 ◽  
Author(s):  
Spencer M. Stein ◽  
Sarav S. Shah ◽  
Alanna Carcich ◽  
Marlena McGill ◽  
Isaac Gammal ◽  
...  
Keyword(s):  
Patient Experience ◽  
Healthcare Providers ◽  
Physician Communication ◽  
Value Based Purchasing ◽  
Quality Health Care ◽  
Novel Approach ◽  
Quality Health ◽  
Physician Awareness ◽  
Hospital Consumer Assessment ◽  
Consumer Assessment

The patient experience domain comprises a significant portion of the Hospital Value-Based Purchasing program. This study investigated whether an intervention focusing on attending physician awareness, resident and physician assistant education, and multidisciplinary patient-centric care had an effect on patient perceived physician communication and overall hospital ratings. Responses to the Hospital Consumer Assessment of Healthcare Providers and Systems survey were reviewed in 2014 and 2015. Patients’ perceptions that the physician explained their condition in ways they understood and the overall hospital rating improved significantly after implantation of the model ( P < .05). Patient-physician communication is important for high-quality health care and is becoming increasingly more important in hospital economics. These methods may serve as a protocol for other institutions to improve the patient experience.

scholarly journals An improved methodology for estimating the prevalence of SARS-CoV-2

2020 ◽  
Author(s):  
Virag Patel ◽  
Catherine McCarthy ◽  
Rachel A Taylor ◽  
Ruth Moir ◽  
Louise A Kelly ◽  
...  
Keyword(s):  
Age Distribution ◽  
Healthcare Providers ◽  
Case Fatality ◽  
The United States ◽  
Control Measures ◽  
Us States ◽  
Disease Patterns ◽  
Prevalence Estimates ◽  
The Us ◽  
The Usa

Since the identification of Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019, there have been more than 17 million cases of the disease in 216 countries worldwide. Comparisons of prevalence estimates between different communities can inform policy decisions regarding safe travel between countries, help to assess when to implement (or remove) disease control measures and identify the risk of over-burdening healthcare providers. Estimating the true prevalence can, however, be challenging because officially reported figures are likely to be significant underestimates of the true burden of COVID-19 within a community. Previous methods for estimating the prevalence fail to incorporate differences between populations (such as younger populations having higher rates of asymptomatic cases) and so comparisons between, for example, countries, can be misleading. Here, we present an improved methodology for estimating COVID-19 prevalence. We take the reported number of cases and deaths (together with population size) as raw prevalence for the population. We then apply an age-adjustment to this which allows the age-distribution of that population to influence the case-fatality rate and the proportion of asymptomatic cases. Finally, we calculate the likely underreporting factor for the population and use this to adjust our prevalence estimate further. We use our method to estimate the prevalence for 166 countries (or the states of the United States of America, hereafter referred to as US state) where sufficient data were available. Our estimates show that as of the 30th July 2020, the top three countries with the highest estimated prevalence are Brazil (1.26%, 95% CI: 0.96 - 1.37), Kyrgyzstan (1.10%, 95% CI: 0.82 - 1.19) and Suriname (0.58%, 95% CI: 0.44 - 0.63). Brazil is predicted to have the largest proportion of all the current global cases (30.41%, 95%CI: 27.52 - 30.84), followed by the USA (14.52%, 95%CI: 14.26 - 16.34) and India (11.23%, 95%CI: 11.11 - 11.24). Amongst the US states, the highest prevalence is predicted to be in Louisiana (1.07%, 95% CI: 1.02 - 1.12), Florida (0.90%, 95% CI: 0.86 - 0.94) and Mississippi (0.77%, 95% CI: 0.74 - 0.81) whereas amongst European countries, the highest prevalence is predicted to be in Montenegro (0.47%, 95% CI: 0.42 - 0.50), Kosovo (0.35%, 95% CI: 0.29 - 0.37) and Moldova (0.28%, 95% CI: 0.23 - 0.30). Our results suggest that Kyrgyzstan (0.04 tests per predicted case), Brazil (0.04 tests per predicted case) and Suriname (0.29 tests per predicted case) have the highest underreporting out of the countries in the top 25 prevalence. In comparison, Israel (34.19 tests per predicted case), Bahrain (19.82 per predicted case) and Palestine (9.81 tests per predicted case) have the least underreporting. The results of this study may be used to understand the risk between different geographical areas and highlight regions where the prevalence of COVID-19 is increasing most rapidly. The method described is quick and easy to implement. Prevalence estimates should be updated on a regular basis to allow for rapid fluctuations in disease patterns.

scholarly journals Classifying inpatient perception of hospitalization experience across the US 50 states using the convolutional neural networks

2020 ◽  
Author(s):  
Tung-Hui Jen ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Po-Hsin Chou
Keyword(s):  
Neural Networks ◽  
Service Quality ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Healthcare Providers ◽  
Area Under The Curve ◽  
Hospital Service ◽  
The Us ◽  
Sensitivity Specificity ◽  
Consumer Assessment

Abstract Backgrounds The grade of hospital-service quality is required for the classification which is never applicable in the literature. We aimed to classify the grade of inpatients’ perceptions of patients’ hospitalization experience of states in the US using convolutional neural networks(CNN).Methods We downloaded HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Services) data from the 2007-2014 summaries of survey results. The data collection was carried out to the k-mean and the CNN that were used as unsupervised and supervised learnings for (1) dividing the US sates into two classes (n = 19 and 32 of lower and higher grades) and (2) building a hospital service predictive model to estimate 38 parameters. We calculated the sensitivity, specificity, and receiver operating characteristic curve [area under the curve (AUC)] across studies for comparison. An app predicting the hospital service for a specific US state was developed involving the model’s 38 estimated parameters for a website assessment.Results We observed that (1) the two-year 20-item model yields a higher accuracy rate (0.98) with an AUC (0.99; 95% CI 0.95–1.00) based on the 51 states; and (2) an available app for predicting hospital-service quality was successfully developed and demonstrated in this study. A smartphone app was designed to classify the grade of hospital service for each US state.Conclusions The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of the grade about the inpatients’ perceptions of patients’ hospitalization experience of states in the US for hospital service. An app developed for helping patients’ self-assess hospital service quality in each US state is required for application in the future.

scholarly journals A dashboard online on Google Maps showing inpatient perception of hospitalization experience across the US 52 states (Preprint)

2018 ◽  
Author(s):  
Tsair-Wei Chien ◽  
Yang Shao ◽  
Willy Chou
Keyword(s):  
Healthcare Providers ◽  
Report Card ◽  
Service Improvement ◽  
Google Maps ◽  
The Us ◽  
Hospital Consumer Assessment ◽  
Survey Results ◽  
Patient Responses ◽  
Consumer Assessment ◽  
Image Format

BACKGROUND The quality of health care is always an important topic we concern in the medical settings. There are many ways to report healthcare quality to the public. However, those professional indicators are unfamiliar to patients when using a static table or image format which is hard to know where to get the best care. OBJECTIVE This study is to visualize survey results about inpatients’ perceptions of patients’ hospitalization experience of states in the US using Google Maps. METHODS We downloaded HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Services) data from the 2007-2014 summaries of survey results. The data collection was carried out to (1) show the significant improvement point over the years using the method of Hotelling's T-square paired sample and present the trends for each domain about service improvement on Google maps, (2) display online dashboards to show hospitalization satisfaction for each US state on Google Maps, and (3) demonstrate an online assessment that uses smartphones for gathering perceptions of their hospitalization experience.. RESULTS The year of the inflection point for service improvement is at 2013. The domain of Discharge Information makes a significant improvement in performance over the years. A visual dashboard shows on Google Maps for understanding inpatients’ perceptions of hospitalization experience of states in the US. A smartphone APP was designed to get feedback directly from patients. CONCLUSIONS We demonstrated a dynamic reporting of patient hospitalization experiences across 50 US states on Google Maps, which is superior to the traditional report card with a static table or image format. The visual feedback to patient responses can be promptly displayed on Google Maps. The HCAHPS can improve the report card of the patient hospitalization experience in the future. CLINICALTRIAL Not available

scholarly journals Perceived Barriers to COVID-19 Testing

2021 ◽  
Vol 18 (5) ◽  
pp. 2278
Author(s):  
Pearl A. McElfish ◽  
Rachel Purvis ◽  
Laura P. James ◽  
Don E. Willis ◽  
Jennifer A. Andersen
Keyword(s):  
Analytical Approach ◽  
Nasal Swab ◽  
Healthcare Providers ◽  
Research Participant ◽  
Perceived Barriers ◽  
Lower Income ◽  
The Us ◽  
Qualitative Descriptive ◽  
Descriptive Method ◽  
Background Prior

(1) Background: Prior studies have documented that access to testing has not been equitable across all communities in the US, with less testing availability and lower testing rates documented in rural counties and lower income communities. However, there is limited understanding of the perceived barriers to coronavirus disease 2019 (COVID-19) testing. The purpose of this study was to document the perceived barriers to COVID-19 testing. (2) Methods: Arkansas residents were recruited using a volunteer research participant registry. Participants were asked an open-ended question regarding their perceived barriers to testing. A qualitative descriptive analytical approach was used. (3) Results: Overall, 1221 people responded to the open-ended question. The primary barriers to testing described by participants were confusion and uncertainty regarding testing guidelines and where to go for testing, lack of accessible testing locations, perceptions that the nasal swab method was too painful, and long wait times for testing results. (4) Conclusions: This study documents participant reported barriers to COVID-19 testing. Through the use of a qualitative descriptive method, participants were able to discuss their concerns in their own words. This work provides important insights that can help public health leaders and healthcare providers with understanding and mitigating barriers to COVID-19 testing.

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