Two‐year effects of an interdisciplinary intervention on recovery following hip fracture in older Taiwanese with cognitive impairment

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91216/1/gps2750.pd

Improving equity and cultural responsiveness with marginalized communities: a position paper.

Aim: The aim of this paper is to explore the impact of culture on health, healthcare provision and its contribution towards health inequity experienced by some marginalised communities. Background: Health inequity is a global issue, which occurs across and within countries, and is the greatest barrier to worldwide health and the development of the human race. In response to this challenge, there is an international commitment to ensure universal health coverage based on the fundamental principle that individuals should be able to access healthcare services they need. Despite this, there is clear evidence that indigenous and other cultural minorities such as New Zealand Māori and Gypsy Roma Travellers still experience far poorer health outcomes when compared to the majority population. Furthermore, when they do access health care, their experiences are often not positive and this in turn results in reluctance to access preventative health care, instead accessing health services much later, reducing treatment options and compounding higher mortality rates. What is often not explored or examined is the impact of the different cultural beliefs of individuals in these communities and the nurses caring for them. Design: This is a position paper drawing upon research experience with New Zealand Māori and Gypsy Roma Travellers. We critically review the experiences of health inequity of marginalised communities. It does so by examining how these communities may have a different world view to the nurses caring for them and it is this lack of understanding and valuing of alternative worldviews that contributes to the poorer health outcomes both communities face. Conclusion and relevance to clinical practice: As nurses work with many different individuals and groups we have to find ways of ensuring a more embracing, culturally responsive health care environment which respects and values the beliefs of others

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Learning From Experience: Avoiding Common Pitfalls in Multicenter Quality Improvement Collaboratives

Clinicians and researchers often tout the newest breakthrough or latest successful intervention. Sharing wins, however, is often done at the expense of sharing obstacles, failures, and subsequent adjustments, which are the cornerstone of quality improvement (QI).1–3 Here, we share 3 key lessons from 2 hospital-based QI initiatives—the Ohio Timely Recognition of Abuse Injuries (TRAIN) Collaborative and the University of Pittsburgh Medical Center (UPMC) Child Abuse Initiative (UPMC-CAI). Both focus on early identification, proper evaluation, and accurate reporting of child maltreatment. These are important clinical issues because many children who die or nearly die from maltreatment had been evaluated previously by a medical professional who did not recognize abuse and/or did not report it to Child Protective Services. The TRAIN Collaborative consists of 6 children’s hospitals in Ohio. Modeled after the Institute for Healthcare Improvement’s (IHI) Breakthrough Series Collaborative, TRAIN convened an expert panel and conducted an iterative series of learning sessions and rapid cycles of change. The collaborative focused on improving the health care provider’s recognition of, and response to, potentially abusive injuries in infants 6 months of age and younger. The UPMC-CAI was a collaboration between UPMC Children’s Hospital of Pittsburgh (CHP) and 13 general emergency departments (EDs) in the UPMC hospital system. Key to this initiative was a child abuse clinical decision support system consisting of a universal child abuse screen4 and triggers developed based on natural language processing and orders placed in the electronic health record (EHR), a pop-up alert for providers, a physical abuse order set, and a child abuse reporting form to assist providers in documenting necessary information for Child Protective Services. Both initiatives showed success. The TRAIN Collaborative reduced recurrent injury by nearly 75%.5 The UPMC-CAI demonstrated a 4-fold increase in identification of potentially abusive injuries in infants and toddlers.6 Although both experienced success, both also identified several setbacks related to (1) staff turnover; (2) unanticipated differences between academic and community hospitals; and (3) failure to invest early enough or robustly enough in data collection

Inequality in Quality? The selection and use of quality indicators to investigate ethnic disparities in the quality of hospital care, Aotearoa New Zealand.

There are well documented differences in health outcomes between Māori and New Zealand (NZ) Europeans. Jones (2002) describes differential treatment within the health system as one determinant of ethnic inequalities: is it possible that New Zealand’s health services contribute to the differences in health status between Māori and NZ Europeans? Aim and objectives: This thesis describes an investigation into the quality of care for Māori compared with NZ Europeans in public hospitals nationally. The objectives of this study were: 1. To identify measures applicable to this study context with validity as indicators of the quality of health care. 2. To employ this/these measure(s) to compare the quality of inpatient hospital care between NZ Māori and NZ European patients, with consideration of confounding and mediating factors in order to estimate the net effect of ethnic group on the quality indicator. 3. To offer recommendations in light of the findings of this study. Methods: Literature review and three ‘study context’ criteria were used to select two indicators to represent inpatient quality of care - unplanned readmission/death within thirty days of discharge (‘readmission’) and patient satisfaction. Phase One of the research used data from the National Minimum Data Set to calculate and compare the rate of readmission for Māori and NZ European inpatients at NZ public hospitals. Characteristics of the two ethnic groups were compared with age-sex adjusted proportions, and variation in the likelihood of readmission with patient and clinical factors was explored with rate ratios. The odds of readmission for NZ Māori compared to NZ European patients (n=89,090) were calculated from a logistic regression model, with variables representing age, comorbidity, index procedure, hospital volume and socio-economic position included. In Phase Two, Māori and NZ Europeans recently discharged from one of three NZ hospitals were approached to complete the Client Satisfaction Questionnaire-8 (CSQ-8). Descriptive analyses explored the characteristics of the respondents (n=1103) according to ethnic group and mean satisfaction score. A linear regression model including variables for age and health status estimated the difference in mean CSQ-8 score for Māori compared to NZ European respondents. Results: The Phase One analyses found 16% higher odds of readmission for NZ Māori compared to NZ European patients (odds ratio (OR) 1.16, 95% CI 1.08 – 1.24; adjusted for age, index procedure, comorbidity, hospital volume, and deprivation), and 19% higher odds (OR 1.19, 95% CI 1.11 – 1.27) when the model did not include a deprivation term. Readmission was also associated with older age (OR 1.33; 95% CI 1.19-1.48, for >79 yrs compared with 18-39 yrs), higher comorbidity (OR 2.08; 95% CI 1.89-2.31 for Charlson score 3+ compared with 0) and higher hospital volume (OR 0.81; 95% CI 0.76-0.86 for lowest volume facility compared with highest). Measurement error of quality of care by readmission was the primary source of bias in this phase; sensitivity analyses suggest the contribution of ‘poor quality’ to the increased odds of readmission for Māori may be small. That is, unmeasured factors may have a comparatively greater role than quality of care in the ethnic difference of this outcome. The Phase Two multivariable model showed comparable satisfaction for Māori and NZ European respondents, with the difference in mean scores only -0.02 (95% CI -0.36 - 0.57). However, bias from differential non-response is possible – participation for Māori was 37% compared to 60% for NZ Europeans. These results may also be affected by differential or non-differential measurement error. That is, CSQ-8 score may have lower validity as a measure of health care quality in this setting and population. Conclusions: A valid measurement of quality by readmission or satisfaction is difficult, as both are highly vulnerable to error. In particular, ethnic differences in readmission may be predominantly influenced by factors other than the inpatient quality of care. However, given supporting evidence and the plausibility of quality as a component cause for health outcomes inequalities, it is likely that the increased odds of readmission for Māori compared to NZ Europeans is in part due to poorer quality of care. This study recommends protocols be developed to guide the calculation and interpretation of readmission as a proxy for quality, and suggests further research to explore the measurement of patient satisfaction in the NZ setting

Inequality in Quality? The selection and use of quality indicators to investigate ethnic disparities in the quality of hospital care, Aotearoa New Zealand.

There are well documented differences in health outcomes between Māori and New Zealand (NZ) Europeans. Jones (2002) describes differential treatment within the health system as one determinant of ethnic inequalities: is it possible that New Zealand’s health services contribute to the differences in health status between Māori and NZ Europeans? Aim and objectives: This thesis describes an investigation into the quality of care for Māori compared with NZ Europeans in public hospitals nationally. The objectives of this study were: 1. To identify measures applicable to this study context with validity as indicators of the quality of health care. 2. To employ this/these measure(s) to compare the quality of inpatient hospital care between NZ Māori and NZ European patients, with consideration of confounding and mediating factors in order to estimate the net effect of ethnic group on the quality indicator. 3. To offer recommendations in light of the findings of this study. Methods: Literature review and three ‘study context’ criteria were used to select two indicators to represent inpatient quality of care - unplanned readmission/death within thirty days of discharge (‘readmission’) and patient satisfaction. Phase One of the research used data from the National Minimum Data Set to calculate and compare the rate of readmission for Māori and NZ European inpatients at NZ public hospitals. Characteristics of the two ethnic groups were compared with age-sex adjusted proportions, and variation in the likelihood of readmission with patient and clinical factors was explored with rate ratios. The odds of readmission for NZ Māori compared to NZ European patients (n=89,090) were calculated from a logistic regression model, with variables representing age, comorbidity, index procedure, hospital volume and socio-economic position included. In Phase Two, Māori and NZ Europeans recently discharged from one of three NZ hospitals were approached to complete the Client Satisfaction Questionnaire-8 (CSQ-8). Descriptive analyses explored the characteristics of the respondents (n=1103) according to ethnic group and mean satisfaction score. A linear regression model including variables for age and health status estimated the difference in mean CSQ-8 score for Māori compared to NZ European respondents. Results: The Phase One analyses found 16% higher odds of readmission for NZ Māori compared to NZ European patients (odds ratio (OR) 1.16, 95% CI 1.08 – 1.24; adjusted for age, index procedure, comorbidity, hospital volume, and deprivation), and 19% higher odds (OR 1.19, 95% CI 1.11 – 1.27) when the model did not include a deprivation term. Readmission was also associated with older age (OR 1.33; 95% CI 1.19-1.48, for >79 yrs compared with 18-39 yrs), higher comorbidity (OR 2.08; 95% CI 1.89-2.31 for Charlson score 3+ compared with 0) and higher hospital volume (OR 0.81; 95% CI 0.76-0.86 for lowest volume facility compared with highest). Measurement error of quality of care by readmission was the primary source of bias in this phase; sensitivity analyses suggest the contribution of ‘poor quality’ to the increased odds of readmission for Māori may be small. That is, unmeasured factors may have a comparatively greater role than quality of care in the ethnic difference of this outcome. The Phase Two multivariable model showed comparable satisfaction for Māori and NZ European respondents, with the difference in mean scores only -0.02 (95% CI -0.36 - 0.57). However, bias from differential non-response is possible – participation for Māori was 37% compared to 60% for NZ Europeans. These results may also be affected by differential or non-differential measurement error. That is, CSQ-8 score may have lower validity as a measure of health care quality in this setting and population. Conclusions: A valid measurement of quality by readmission or satisfaction is difficult, as both are highly vulnerable to error. In particular, ethnic differences in readmission may be predominantly influenced by factors other than the inpatient quality of care. However, given supporting evidence and the plausibility of quality as a component cause for health outcomes inequalities, it is likely that the increased odds of readmission for Māori compared to NZ Europeans is in part due to poorer quality of care. This study recommends protocols be developed to guide the calculation and interpretation of readmission as a proxy for quality, and suggests further research to explore the measurement of patient satisfaction in the NZ setting

Development and Utilisation of a Real-Time Display of Logged in Radiology Information System Users

In radiology departments with multiple geographically separated reporting areas, locating radiologists can be challenging. We have developed an in-house solution to minimise the time spent looking for radiologists utilising near real-time data stored with our radiology information system (RIS). An auto updating Extensible Markup Language (XML) data feed from our RIS provider provides information about users logged into the RIS. It includes user names, their contact details and specialty interests, their location within the department, and a time stamp of last recorded dictation or report verification. The information is then displayed on our internal intranet and on a self-refreshing screen in our main department corridor. In order to estimate time savings made through the tools creation, usage statistics were calculated and combined with assessments of time taken to find a named radiologist prior to the tools implementation. Over the month of April 2009, there were 2,798 hits on the locator page. Radiologists were responsible for 1,248 hits and radiology administration staff for 1,550 hits. The average time for using the tool was calculated at 5 s. Reviewing a roster and calling/paging a radiologist took on average 30 s, and a full walk around of the department took 195 s. Through utilisation of near real-time data available within our RIS system and display of these data in an accessible form, we have developed a tool that has realised savings of up to 16 h of radiologist reporting time per week