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.

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 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 Guideline-based indicators for adult patients with myelodysplastic syndromes

2020 ◽  
Vol 4 (16) ◽  
pp. 4029-4044 ◽  
Author(s):  
Kristina Stojkov ◽  
Tobias Silzle ◽  
Georg Stussi ◽  
David Schwappach ◽  
Juerg Bernhard ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Myelodysplastic Syndromes ◽  
Best Practice ◽  
Health Care Systems ◽  
Performance Outcomes ◽  
Practice Performance ◽  
Patient Reported ◽  
Agreement Score ◽  
Care Systems

Myelodysplastic syndromes (MDSs) represent a heterogeneous group of hematological stem cell disorders with an increasing burden on health care systems. Evidence-based MDS guidelines and recommendations (G/Rs) are published but do not necessarily translate into better quality of care if adherence is not maintained in daily clinical practice. Guideline-based indicators (GBIs) are measurable elements for the standardized assessment of quality of care and, thus far, have not been developed for adult MDS patients. To this end, we screened relevant G/Rs published between 1999 and 2018 and aggregated all available information as candidate GBIs into a formalized handbook as the basis for the subsequent consensus rating procedure. An international multidisciplinary expert panel group (EPG) of acknowledged MDS experts (n = 17), health professionals (n = 7), and patient advocates (n = 5) was appointed. The EPG feedback rates for the first and second round were 82% (23 of 28) and 96% (26 of 27), respectively. A final set of 29 GBIs for the 3 domains of diagnosis (n = 14), therapy (n = 8), and provider/infrastructural characteristics (n = 7) achieved the predefined agreement score for selection (&gt;70%). We identified shortcomings in standardization of patient-reported outcomes, toxicity, and geriatric assessments that need to be optimized in the future. Our GBIs represent the first comprehensive consensus on measurable elements addressing best practice performance, outcomes, and structural resources. They can be used as a standardized instrument with the goal of assessing, comparing, and fostering good quality of care within clinical development cycles in the daily care of adult MDS patients.

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

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.

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 Study on the relationship of depression, anxiety, lifestyle and eating habits with the severity of reflux esophagitis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rongxin Wang ◽  
Jing Wang ◽  
Shuiqing Hu
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Reflux Esophagitis ◽  
Eating Habits ◽  
Positive Correlation ◽  
Relationship Of ◽  
The Relationship

Abstract Background The etiology of reflux esophagitis (RE) is multi-factorial. This study analyzed the relationship of depression, anxiety, lifestyle and eating habits with RE and its severity and further explored the impact of anxiety and depression on patients’ symptoms and quality of life. Methods From September 2016 to February 2018, a total of 689 subjects at Xuanwu Hospital Capital Medical University participated in this survey. They were divided into the RE group (patients diagnosed with RE on gastroscopy, n = 361) and the control group (healthy individuals without heartburn, regurgitation and other gastrointestinal symptoms, n = 328). The survey included general demographic information, lifestyle habits, eating habits, comorbidities, current medications, the gastroesophageal reflux disease (GERD) questionnaire (GerdQ), the Patient Health Questionnaire-9 depression scale and the General Anxiety Disorder-7 anxiety scale. Results The mean age and sex ratio of the two groups were similar. Multivariate logistic regression analysis identified the following factors as related to the onset of RE (p < 0.05): low education level; drinking strong tea; preferences for sweets, noodles and acidic foods; sleeping on a low pillow; overeating; a short interval between dinner and sleep; anxiety; depression; constipation; history of hypertension; and use of oral calcium channel blockers. Ordinal logistic regression analysis revealed a positive correlation between sleeping on a low pillow and RE severity (p = 0.025). Depression had a positive correlation with the severity of symptoms (rs = 0.375, p < 0.001) and patients’ quality of life (rs = 0.306, p < 0.001), whereas anxiety showed no such association. Conclusions Many lifestyle factors and eating habits were correlated with the onset of RE. Notably, sleeping on a low pillow was positively correlated with RE severity, and depression was positively related to the severity of symptoms and patients’ quality of life.

scholarly journals Annual Disease Experience by Type and Correlations with Unmet Healthcare Needs among ROK Military Personnel

2020 ◽  
Vol 185 (7-8) ◽  
pp. e944-e951
Author(s):  
Hwi Jun Kim ◽  
Sarah So Yeon Oh ◽  
Dong Woo Choi ◽  
Sun Yeong Won ◽  
Hae Jung Kim ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Military Personnel ◽  
Private Insurance ◽  
Multiple Logistic Regression Analysis ◽  
Healthcare Needs ◽  
Unmet Healthcare Needs ◽  
Number Of Patients ◽  
Disease Experience

Abstract Introduction The National Statistical Yearbook of Defense 2018 issued by the Republic of Korea (ROK) Ministry of National Defense reported that the number of patients using military hospitals steadily increased from 2008 to 2017. However, in the outpatient clinic statistics for years 2015–2017 from the ROK Armed Forces Medical Command, the amount of medical care received from some medical departments, such as the infection medicine, surgery, and anesthesiology departments, decreased. Therefore, the purpose of this study was to observe the differences in incidence of military personnel’s unmet healthcare needs according to number of diseases by type. Materials and Methods The study used data from the Military Health Survey, which was conducted from 2014 to 2015 and included 5162 responses from ROK military personnel. The number of diseases by type and unmet healthcare needs were self-reported. A multiple logistic regression analysis was used to examine the validity of the annual disease experience by type and correlations with unmet healthcare needs. Results Of the 5162 military personnel, 25.2% experienced unmet healthcare needs, and the more people with the number of disease by type, the more likely they were to experience unmet healthcare needs (1: 13.4%, 2: 22.9%, 3: 29.2%, 4: 34.5%, 5: 41.4%). The logistic regression analysis also revealed significant differences (1 = REF, 2 odds ratio (OR) = 1.83, 95% confidence interval (CI): 1.50–2.24; 3 OR = 2.53, 95% CI: 2.05–3.11, 4 OR = 3.10, 95% CI = 2.49–3.85; ≥5 OR = 3.85, 95% CI = 3.08–4.81). In addition, subgroup analysis showed that female military personnel are more likely to experience unmet healthcare needs than are male military personnel. We have also confirmed that working areas and private insurance can affect unmet healthcare needs. Conclusion This study suggests that unmet healthcare needs are influenced by the number of disease by the type of ROK military personnel. It is therefore necessary to strive to reduce the number of military personnel who experience unmet healthcare needs through this data.

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