scholarly journals Ordinal outcome analysis improves the detection of between-hospital differences in outcome

2020 ◽  
Author(s):  
Iris E. Ceyisakar ◽  
Nikki van Leeuwen ◽  
Diederik W.J. Dippel ◽  
Ewout W. Steyerberg ◽  
Hester F. Lingsma
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Outcome Measures ◽  
Statistical Power ◽  
Ordinal Scale ◽  
Efficiency Gain ◽  
Number Of Patients ◽  
Dichotomous Outcomes ◽  
Ordinal Outcome

Abstract Background There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. Methods We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples. We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers. Results In the IMPACT study (8,799 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale (‘unfavorable outcome’), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1,657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37% to 63% less patients.Conclusions Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements.

scholarly journals Ordinal outcome analysis improves the detection of between-hospital differences in outcome

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
I. E. Ceyisakar ◽  
N. van Leeuwen ◽  
Diederik W. J. Dippel ◽  
Ewout W. Steyerberg ◽  
H. F. Lingsma
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Outcome Measures ◽  
Statistical Power ◽  
Ordinal Scale ◽  
Efficiency Gain ◽  
Number Of Patients ◽  
Dichotomous Outcomes ◽  
Ordinal Outcome

Abstract Background There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples. Methods We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers. Results In the IMPACT study (9578 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale (‘unfavorable outcome’), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37 to 63% less patients. Conclusions Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements. Trial registration We do not report the results of a health care intervention.

scholarly journals Ordinal outcome analysis improves the detection of between-hospital differences in outcome

2020 ◽  
Author(s):  
Iris E. Ceyisakar ◽  
Nikki van Leeuwen ◽  
Diederik W.J. Dippel ◽  
Ewout W. Steyerberg ◽  
Hester F. Lingsma
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Outcome Measures ◽  
Statistical Power ◽  
Ordinal Scale ◽  
Efficiency Gain ◽  
Number Of Patients ◽  
Dichotomous Outcomes ◽  
Ordinal Outcome

Abstract Background There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. Methods We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples. We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers. In the IMPACT study (8,799 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale (‘unfavorable outcome’), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1,657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37% to 63% less patients. Conclusion Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements.

scholarly journals Adoption and Performance of Complementary Clinical Information Technologies: Analysis of a Survey of General Practitioners (Preprint)

2019 ◽  
Author(s):  
Placide Poba-Nzaou ◽  
Sylvestre Uwizeyemungu ◽  
Xuecheng Liu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Quality Of Care ◽  
Clinical Information ◽  
Performance Outcomes ◽  
Health Records ◽  
Number Of Patients ◽  
Practice Performance ◽  
Strong Cluster

BACKGROUND The benefits from the combination of 4 clinical information systems (CISs)—electronic health records (EHRs), health information exchange (HIE), personal health records (PHRs), and telehealth—in primary care depend on the configuration of their functional capabilities available to clinicians. However, our empirical knowledge of these configurations and their associated performance implications is very limited because they have mostly been studied in isolation. OBJECTIVE This study aims to pursue 3 objectives: (1) characterize general practitioners (GPs) by uncovering the typical profiles of combinations of 4 major CIS capabilities, (2) identify physician and practice characteristics that predict cluster membership, and (3) assess the variation in the levels of performance associated with each configuration. METHODS We used data from a survey of GPs conducted throughout the European Union (N=5793). First, 4 factors, that is, EHRs, HIE, PHRs, and Telehealth, were created. Second, a cluster analysis helps uncover clusters of GPs based on the 4 factors. Third, we compared the clusters according to five performance outcomes using an analysis of variance (ANOVA) and a Tamhane T2 post hoc test. Fourth, univariate and multivariate multinomial logistic regressions were used to identify predictors of the clusters. Finally, with a multivariate multinomial logistic regression, among the clusters, we compared performance in terms of the number of patients treated (3 levels) over the last 2 years. RESULTS We unveiled 3 clusters of GPs with different levels of CIS capability profiles: <i>strong</i> (1956/5793, 37.36%), <i>medium</i> (2764/5793, 47.71%), and <i>weak</i> (524/5793, 9.04%). The logistic regression analysis indicates that physicians (younger, female, and less experienced) and practice (solo) characteristics are significantly associated with a weak profile. The ANOVAs revealed a strong cluster associated with significantly high practice performance outcomes in terms of the quality of care, efficiency, productivity, and improvement of working processes, and two noncomprehensive medium and weak profiles associated with medium (equifinal) practice performance outcomes. The logistic regression analysis also revealed that physicians in the weak profile are associated with a decrease in the number of patients treated over the last 2 years. CONCLUSIONS Different CIS capability profiles may lead to similar equifinal performance outcomes. This underlines the importance of looking beyond the adoption of 1 CIS capability versus a cluster of capabilities when studying CISs. GPs in the strong cluster exhibit a comprehensive CIS capability profile and outperform the other two clusters with noncomprehensive profiles, leading to significantly high performance in terms of the quality of care provided to patients, efficiency of the practice, productivity of the practice, and improvement of working processes. Our findings indicate that medical practices should develop high capabilities in all 4 CISs if they have to maximize their performance outcomes because efforts to develop high capabilities selectively may only be in vain.

scholarly journals Adoption and Performance of Complementary Clinical Information Technologies: Analysis of a Survey of General Practitioners

10.2196/16300 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e16300
Author(s):  
Placide Poba-Nzaou ◽  
Sylvestre Uwizeyemungu ◽  
Xuecheng Liu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Quality Of Care ◽  
Clinical Information ◽  
Performance Outcomes ◽  
Health Records ◽  
Number Of Patients ◽  
Practice Performance ◽  
Strong Cluster

Background The benefits from the combination of 4 clinical information systems (CISs)—electronic health records (EHRs), health information exchange (HIE), personal health records (PHRs), and telehealth—in primary care depend on the configuration of their functional capabilities available to clinicians. However, our empirical knowledge of these configurations and their associated performance implications is very limited because they have mostly been studied in isolation. Objective This study aims to pursue 3 objectives: (1) characterize general practitioners (GPs) by uncovering the typical profiles of combinations of 4 major CIS capabilities, (2) identify physician and practice characteristics that predict cluster membership, and (3) assess the variation in the levels of performance associated with each configuration. Methods We used data from a survey of GPs conducted throughout the European Union (N=5793). First, 4 factors, that is, EHRs, HIE, PHRs, and Telehealth, were created. Second, a cluster analysis helps uncover clusters of GPs based on the 4 factors. Third, we compared the clusters according to five performance outcomes using an analysis of variance (ANOVA) and a Tamhane T2 post hoc test. Fourth, univariate and multivariate multinomial logistic regressions were used to identify predictors of the clusters. Finally, with a multivariate multinomial logistic regression, among the clusters, we compared performance in terms of the number of patients treated (3 levels) over the last 2 years. Results We unveiled 3 clusters of GPs with different levels of CIS capability profiles: strong (1956/5793, 37.36%), medium (2764/5793, 47.71%), and weak (524/5793, 9.04%). The logistic regression analysis indicates that physicians (younger, female, and less experienced) and practice (solo) characteristics are significantly associated with a weak profile. The ANOVAs revealed a strong cluster associated with significantly high practice performance outcomes in terms of the quality of care, efficiency, productivity, and improvement of working processes, and two noncomprehensive medium and weak profiles associated with medium (equifinal) practice performance outcomes. The logistic regression analysis also revealed that physicians in the weak profile are associated with a decrease in the number of patients treated over the last 2 years. Conclusions Different CIS capability profiles may lead to similar equifinal performance outcomes. This underlines the importance of looking beyond the adoption of 1 CIS capability versus a cluster of capabilities when studying CISs. GPs in the strong cluster exhibit a comprehensive CIS capability profile and outperform the other two clusters with noncomprehensive profiles, leading to significantly high performance in terms of the quality of care provided to patients, efficiency of the practice, productivity of the practice, and improvement of working processes. Our findings indicate that medical practices should develop high capabilities in all 4 CISs if they have to maximize their performance outcomes because efforts to develop high capabilities selectively may only be in vain.

The ENT emergency clinic: does senior input matter?

2012 ◽  
Vol 127 (1) ◽  
pp. 15-19 ◽  
Author(s):  
A Mirza ◽  
L McClelland ◽  
M Daniel ◽  
N Jones
Keyword(s):  
Quality Of Care ◽  
Discharge Rate ◽  
Surrogate Marker ◽  
Operating Theatre ◽  
Number Of Children ◽  
Discharge Rates ◽  
Time Period ◽  
Number Of Patients

AbstractBackground:Many ENT conditions can be treated in the emergency clinic on an ambulatory basis. Our clinic traditionally had been run by foundation year two and specialty trainee doctors (period one). However, with perceived increasing inexperience, a dedicated registrar was assigned to support the clinic (period two). This study compared admission and discharge rates for periods one and two to assess if greater registrar input affected discharge rate; an increase in discharge rate was used as a surrogate marker of efficiency.Method:Data was collected prospectively for patients seen in the ENT emergency clinic between 1 August 2009 and 31 July 2011. Time period one included data from patients seen between 1 August 2009 and 31 July 2010, and time period two included data collected between 1 August 2010 and 31 July 2011.Results:The introduction of greater registrar support increased the number of patients that were discharged, and led to a reduction in the number of children requiring the operating theatre.Conclusion:The findings, which were determined using clinic outcomes as markers of the quality of care, highlighted the benefits of increasing senior input within the ENT emergency clinic.

scholarly journals Pain management after lung surgery

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Maria Frödin ◽  
Margareta Warrén Stomberg
Keyword(s):  
Quality Of Care ◽  
Pain Management ◽  
Postoperative Pain ◽  
Severe Pain ◽  
Postoperative Pain Management ◽  
Lung Surgery ◽  
Moderate Pain ◽  
Number Of Patients ◽  
Severe Postoperative Pain

Pain management is an integral challenge in nursing and includes the responsibility of managing patients’ pain, evaluating pain therapy and ensuring the quality of care. The aims of this study were to explore patients’ experiences of pain after lung surgery and evaluate their satisfaction with the postoperative pain management. A descriptive design was used which studied 51 participants undergoing lung surgery. The incidence of moderate postoperative pain varied from 36- 58% among the participants and severe pain from 11-26%, during their hospital stay. Thirty-nine percent had more pain than expected. After three months, 20% experienced moderate pain and 4% experienced severe pain, while after six months, 16% experienced moderate pain. The desired quality of care goal was not fully achieved. We conclude that a large number of patients experienced moderate and severe postoperative pain and more than one third had more pain than expected. However, 88% were satisfied with the pain management. The findings confirm the severity of pain experienced after lung surgery and facilitate the apparent need for the continued improvement of postoperative pain management following this procedure.

scholarly journals Quality of Care in Long-Term Hemodialysis Patients in Mexico

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Torres-Díaz JA ◽  
◽  
Gonzalez-Gonzalez JG ◽  
Zúniga-Hernández JA ◽  
Olivo-Gutiérrez MC ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
World Literature ◽  
Logistic Regression Model ◽  
Hospital Admissions ◽  
Cross Sectional

Introduction: The End Stage Renal Disease (ESRD) is one of the leading causes of mortality in Mexico. The quality of care these patients receive remains uncertain. Methods: This is a descriptive, single-center and cross-sectional cohort study. The KDOQI performance measures, hemoglobin level >11 g/dL, blood pressure <140/90 mmHg, serum albumin >4 g/dL and use of arteriovenous fistula of patients with ESRD on hemodialysis were analyzed in a period of a year. The association between mortality and the KDOQI objectives was evaluated with a logistic regression model. A linear regression model was also performed with the number of readmissions. Results: A total of 124 participants were included. Participants were categorized by the number of measures completed. Fourteen (11.3%) of the participants did not meet any of the goals, 51 (41.1%) met one, 43 (34.7%) met two, 11 (8.9%) met three, and 5 (4%) met the four clinical goals analyzed. A mortality of 11.2% was registered. In the logistic regression model, the number of goals met had an OR for mortality of 1.1 (95% CI 0.5-2.8). In the linear regression model, for the number of readmissions, a beta correlation with the number of KDOQI goals met was 0.246 (95% CI -0.872-1.365). Conclusion: The attainment of clinical goals and the mortality rate in our center is similar to that reported in the world literature. Our study did not find a significant association between compliance with clinical guidelines and mortality or the number of hospital admissions in CKD patients on hemodialysis.

Abstract W P287: Does Comprehensive Stroke Certification Demonstrate a Significant Effect on Volume and Quality of Care?

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Laurie Paletz ◽  
Shlee Song ◽  
Nili Steiner ◽  
Betty Robertson ◽  
Nicole Wolber ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Quality Of Care ◽  
Acute Stroke ◽  
Acute Ischemic Stroke ◽  
Turnaround Time ◽  
Joint Commission ◽  
Stroke Patients ◽  
Number Of Patients ◽  
The Impact

Introduction/Background information: At the onset of acute stroke symptoms, speed, capability, safety and skill are essential-lost minutes can be the difference between full recoveries, poor outcome, or even death. The Joint Commission's Certificate of Distinction for Comprehensive Stroke Centers recognizes centers that make exceptional efforts to foster better outcomes for stroke care. While many hospitals have been surveyed, Cedars Sinai was the 5 th hospital in the nation to receive this certification. Researchable question: Does Comprehensive stroke certification (CSC) demonstrate a significant effect on volume and quality of care? Methods: We assembled a cross-functional, multidisciplinary expert team representing all departments and skill sets involved in treating stroke patients. We carefully screened eligible patients with acute ischemic stroke We assessed the number of patients treated at Cedars-Sinai with IV-T-pa t 6 months before and then 6 months after CSC and the quality of their care including medical treatment and door to needle time. Results: In the 6 months prior to Joint Commissions Stroke Certification we treated 20 of 395acute stroke patients with t-PA with an average CT turnaround time of 31±19minutes and an average Door to needle time (DTNT) of 68±32minutes. In the 6 months since Joint Commission Stroke Certification we have increased the number of acute stroke patients treated by almost double. There were 37 out of 489(P=0.02, Chi Square) patients treated with IV t-PA with an average CT turnaround time of 22±7minutes (p=0.08, t-test, compared to pre-CSC) and an average DTNT of 61± 23minutes (not different than pre-CSC). Conclusion: We conclude that Joint Commission Certification for stroke was associated with an increased rate of treatment with IV rt-PA in acute ischemic stroke patients. We were not able to document an effect on quality of care. Further studies of the impact of CSC certification are warranted.

scholarly journals LO68: Patterns and predictors of emergency physician productivity

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S30-S31
Author(s):  
S. Campbell ◽  
S. Weerasinghe
Keyword(s):  
Emergency Department ◽  
Quality Of Care ◽  
Ct Scan ◽  
Resource Utilization ◽  
Emergency Physician ◽  
X Ray ◽  
Workload Measurement ◽  
Number Of Patients ◽  
Return Visits

Introduction: Emergency Physician (EP) performance comprises both quality of care and quantity of patients seen in a set time. Emergency Department (ED) overcrowding increases the importance of the ability of EPs to see patients as rapidly as is safely possible. Maximizing efficiency requires an understanding of variables that are associated with individual physician performance. While using the incidence of return visits within 48 hours as a quality measure is controversial, repeat visits do consume ED resources. Methods: We analysed the practice variables of 85 EPs working at a single academic ED, for the period from June 1, 2013 to May 31, 2017, using data from an emergency department information system (EDIS). Variables analysed included: number of shifts worked, number of patients seen per hour (pt/hr), an adjusted workload measurement (assigning a higher score to CTAS 1-3 patients), percentage of patients whose care involved an ED learner, and the percentage of patients who returned to the ED within 48 hours of ED discharge. Resource utilization was measured by percentage of diagnostic imaging (ultra sound (US), CT scan (CT), x-ray (XR)) ordered and percentage of patients referred to consulting services. We performed principal component analyses to identify bench marks of resource use, demographic (age, EM qualification, gender) and other practice related predictors of performances. Results: Mean pt/hr differed significantly by EM Qualification for CTAS 2-4, with 1.71/hr (95% Confidence Interval=1.63-1.77) by FRCPS physicians, compared to 1.89/hr by CCFP(EM) (CI=1.81-1.97). There were no differences for CTAS 1 and 5. Other variables associated with a significantly lower pt/hr, included a greater use of imaging, (CT: p=0.0003, XR: p=0.0008) although this was did not reach statistical significance with US (p=0.06%). Female gender, older age, number of patient consultations for CTAS 3 and more patients seen by a learner were all associated with lower pt/hr. Pt/hr was a better predictor (R2=45%) for EP resource utilization than adjusted workload measurement (R2 =35%). Higher use of CT was associated with fewer return visits in <48 hrs (0.13% lower). Male gender, younger age, number of patient consultation for CTAS 3 and fewer patients seen by a learner were all associated with an increase in return visits. Conclusion: We found a significant difference in pt/hr rates and return visits within 48 hours between EPs with different age ranges, gender, and EM certification. Increased use of CT scan and x-ray, and consultation for patients CTAS 3 were associated with lower pt/hr. Return visit rates also varied in association with diagnostic imagine use, age, gender and number of patients seen by a learner. Further research is needed to assess the association with these variables on quality of care.

Proposed Agenda for the Measurement of Quality-of-Care Outcomes in Oncology Practice

1999 ◽  
Vol 17 (8) ◽  
pp. 2614-2614 ◽  
Author(s):  
Jeanne S. Mandelblatt ◽  
Patricia A. Ganz ◽  
Katherine L. Kahn
Keyword(s):  
Breast Cancer ◽  
Quality Of Care ◽  
Outcome Measures ◽  
Cancer Care ◽  
The United States ◽  
Leadership Role ◽  
Institute Of Medicine ◽  
Oncology Practice ◽  
The Impact

ABSTRACT: Cancer is an important disease, and health care services have the potential to improve the quality and quantity of life for cancer patients. The delivery of these services also has recently been well codified. Given this framework, cancer care presents a unique opportunity for clinicians to develop and test outcome measures across diverse practice settings. Recently, the Institute of Medicine released a report reviewing the quality of cancer care in the United States and called for further development and monitoring of quality indicators. Thus, as we move into the 21st century, professional and regulatory agencies will be seeking to expand process measures and develop and validate outcomes-oriented measures for cancer and other diseases. For such measures to be clinically relevant and feasible, it is key that the oncology community take an active leadership role in this process. To set the stage for such activities, this article first reviews broad methodologic concerns involved in selecting measures of the quality of care, using breast cancer to exemplify key issues. We then use the case of breast cancer to review the different phases of cancer care and provide examples of phase-specific measures that, after careful operationalization, testing, and validation, could be used as the basis of an agenda for measuring the quality of breast cancer care in oncology practice. The diffusion of process and outcome measures into practice; the practicality, reliability, and validity of these measures; and the impact that these indicators have on practice patterns and the health of populations will be key to evaluating the success of such quality-of-care paradigms. Ultimately, improved quality of care should translate into morbidity and mortality reductions.

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