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BACKGROUND: Data transfer between electronic health records (EHRs) at the point of care and electronic data capture (EDC) systems for clinical research is still mainly carried out manually, which is error-prone as well as cost- and time-intensive. Automated digital transfer from EHRs to EDC systems (EHR2EDC) would enable more accurate and efficient data capture but has so far encountered technological barriers primarily related to data format and the technological environment: in Germany, health care data are collected at the point of care in a variety of often individualized practice management systems (PMSs), most of them not interoperable. Data quality for research purposes within EDC systems must meet the requirements of regulatory authorities for standardized submission of clinical trial data and safety reports. OBJECTIVE: We aimed to develop a model for automated data transfer as part of an observational study that allows data of sufficient quality to be captured at the point of care, extracted from various PMSs, and automatically transferred to electronic case report forms in EDC systems. This required addressing aspects of data security, as well as the lack of compatibility between EHR health care data and the data quality required in EDC systems for clinical research. METHODS: The SaniQ software platform (Qurasoft GmbH) is already used to extract and harmonize predefined variables from electronic medical records of different Compu Group Medical-hosted PMSs. From there, data are automatically transferred to the validated AlcedisTRIAL EDC system (Alcedis GmbH) for data collection and management. EHR2EDC synchronization occurs automatically overnight, and real-time updates can be initiated manually following each data entry in the EHR. The electronic case report form (eCRF) contains 13 forms with 274 variables. Of these, 5 forms with 185 variables contain 67 automatically transferable variables (67/274, 24% of all variables and 67/185, 36% of eligible variables). RESULTS: This model for automated data transfer bridges the current gap between clinical practice data capture at the point of care and the data sets required by regulatory agencies; it also enables automated EHR2EDC data transfer in compliance with the General Data Protection Regulation (GDPR). It addresses feasibility, connectivity, and system compatibility of currently used PMSs in health care and clinical research and is therefore directly applicable. CONCLUSIONS: This use case demonstrates that secure, consistent, and automated end-to-end data transmission from the treating physician to the regulatory authority is feasible. Automated data transmission can be expected to reduce effort and save resources and costs while ensuring high data quality. This may facilitate the conduct of studies for both study sites and sponsors, thereby accelerating the development of new drugs. Nevertheless, the industry-wide implementation of EHR2EDC requires policy decisions that set the framework for the use of research data based on routine PMS data.

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Patient satisfaction studies have gained more and more attention, and there are many patient satisfaction studies. These studies assume that patients were selected randomly and independently, but patient satisfaction surveys are described as a multistage or hierarchically structured sample. Thus, there is a need to conduct a hierarchical linear model (HLM) analysis with a large number of hospitals. This study utilized an HLM to investigate both the individual patient-level effect on the overall satisfaction rating and the effect of hospital characteristics on the combining process of patient's overall satisfaction rating. This study used patient satisfaction data collected from 100 hospitals with the sample size of 85 766. The hospital-level characteristics include total expense per personnel, payroll expense per personnel, number of staffed beds per personnel, and number of admission per personnel. This study found that hospital characteristics influence overall rating of the hospital through the doctor, staff, and room attributes. When considering the complex nature of the overall patient rating process of hospitals, it makes more sense to analyze hospital characteristics that are interacting with attributes rather than treat hospital characteristics as independent of these factors.

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Patient satisfaction and how it is measured is an important field of study in healthcare. Consequently, there have been many patient satisfaction studies. However, there is a lack of studies that focused on how male and female patients perceive, or weigh, their overall satisfaction. Determining the weights of these attributes by gender is an important outcome to determine overall patient satisfaction and quality of care. Data were collected by applying the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey instrument to record regular patient experience at 70 United States hospitals. The data were collected between July 1, 2011 and June 30, 2012, and there are 43,938 cases in the data set. This study found that all five of the attributes (Nursing Care, Physician Care, Staff Care, Room, and Help) had an association with patient satisfaction. Among them, nursing Care had the largest influence on patient satisfaction of all of the five attributes. This study also showed a statistically significant interaction effect by gender for the interaction between Nursing care x Gender, Physician care x Gender, and Help x Gender. Female patients rated their relationship with their nurses as being more important to their overall satisfaction, while male patients rated their relationship with their doctor as being more important to overall satisfaction. Male patients also rated their relationship with help as being more important to overall satisfaction when compared with the results from female patients. Suggestions to healthcare managers are also discussed.

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The purpose of this study was to investigate how patients' self-rated health status (SRHS) is associated with their attribute reaction integration process and, in turn, their overall ratings of hospitals. We collected patient satisfaction data from 70 hospitals by means of a patient satisfaction questionnaire. The sample included patients who were 18 years or older and discharged from the hospital from July 1, 2011, through June 30, 2012. Data for 36,528 patients were available for analysis. We conducted multiple linear regression analysis with patients' SRHS and interaction effects with nursing care, physician care, staff care, and room, while controlling for age, gender, race, and education. Study findings showed an association between SRHS levels and the patient's overall rating of the hospital; they also revealed interaction effects with nursing care, physician care, and staff care variables in the model. The statistically significant interaction effects indicate that for patients whose SRHS was less than excellent, physician care became more important and nursing care and staff care became less important compared with patients whose SRHS was excellent. When we consider the nature of medical care, this transition seems reasonable. We also found that it is reasonable to categorize patients into two groups: those whose SRHS is excellent and those whose SRHS is less than excellent (i.e., very good, good, fair, or poor). As the study findings show, these two groups of patients combined their attribute reactions differently.

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The authors evaluated whether a patient's perceived pain control influenced the relationships between four attributes (nursing, physician, staff, and environment) and patient satisfaction. Multiple linear regression analyses were conducted to examine overall satisfaction and intention to recommend, controlling for race, gender, age, and education. The authors found that no matter the level of pain control, nursing was always the most influential attribute in patient satisfaction. The influence of the other attributes varied, depending on the patients' pain control. Hospital managers may improve patient satisfaction by focusing on pain management nursing care.

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CONTEXT: Health care environments have been changing rapidly, and one of the changes is to emphasize patient satisfaction. However, most studies assume that all patients integrate their health care attribute reactions in the same way to arrive at their satisfaction. OBJECTIVE: The objective of this study is to investigate how patients' experience of pain influences their attribute reaction integration process and their overall rating of the hospital. DESIGN: Patient satisfaction data were collected using a mailed questionnaire. Multiple linear regression analyses with a dichotomous (yes/no) pain variable and its interaction effects with nursing care, physician care, staff care, and hospital room were conducted with control variables. MAIN OUTCOME MEASURES: The pain variable was statistically significant and also revealed interaction effects with the physician care and the staff care variables in the model. Patients who needed medicine for pain showed lower overall rating of the hospitals than patients who did not need medicine. RESULTS: The statistically significant interaction effects indicate that for patients who needed medicine for pain, staff care becomes more important and physician care becomes less important compared to patients who do not need medicine for pain. All 4 attributes (nursing care, physician care, staff care, and hospital room) are not equally influential. CONCLUSION: Implementing policies and procedures related to these interaction effects would lead to the most efficient and effective improvement outcomes. These findings suggest that future policies should be modified to enhance nursing and staff care to provide more direct care for patients with pain.

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Health-care managers have to address many aspects of the organization, and patient satisfaction is clearly one of the critical aspects for managers. To respond to the need of health-care managers, there have been many patient satisfaction studies. However, these studies focus on which attributes (factors such as nursing care and physician care) are more influential; they do not provide specific aspects for each attribute. In order to develop an effective intervention programme to improve patient satisfaction, more specific research outcomes are needed. This study utilized data collected between January 2007 and June 2008 from 32 hospitals representing a large, national private not-for-profit hospital system. The patient satisfaction survey included the Consumer Assessment of Healthcare Providers and Systems, Hospital version questionnaire items, and there are 31,471 cases. Two-stage multiple linear regression analyses were conducted with control variables (age, gender, perceived health, education and race). It was found that patients' highest priority is to be treated with courtesy and respect by nurses and physicians. An effective intervention programme to improve patient satisfaction would include a training programme, where care providers understand that patients want them to show courtesy and respect. Then, well-trained and empathetic nurses and staff members can comfort patients, and consequently improve patient satisfaction.

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Many patient satisfaction studies consider patient satisfaction and intention to recommend as the same constructs. However, we propose to investigate the two constructs separately. This study has utilized patient satisfaction data with 32 hospitals in different locations to investigate how hospital discharged patients combine their attribute reactions to arrive at their evaluation of hospital care and their intention to recommend to others. A multiple linear regression analysis with a scatter term was conducted while controlling for age, gender, perceived health, educational attainment and race. The interpretation of the scatter term is presented with a story. There were 31,471 cases for the analysis, and the results show some similarities and critical differences between the two models. In conclusion, hospital managers are well advised to focus on improving nursing care first, and then staff care and environment in order to increase patient evaluation of hospital care and intention to recommend.

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BACKGROUND: Patient satisfaction studies have received strong attention from both health care managers and researchers. Many of them investigate how patients combine their health care attribute reactions to arrive at their overall satisfaction. These studies, however, did not specifically investigate a possible different combining process among different racial groups. Thus, these minority populations were statistically marginalized in the outcomes of the analyses. It means that parameter estimates of the attributes were assumed to be the same for all racial groups. PURPOSE: This study investigated a possible different combining process between Caucasian and African American patients. The objective of this study was to discover how differently Caucasian and African American patients combine their attribute reactions to arrive at their overall satisfaction. METHODOLOGY/APPROACH: Data used for this analysis were collected from 32 hospitals by using a patient satisfaction questionnaire, the Consumer Assessment of Healthcare Providers and Systems, Hospital version. There were 29,684 cases. A multiple linear regression analysis with a scatter term was conducted while controlling for age, gender, perceived health, and educational attainment. FINDINGS: Caucasian and African American patients recognize the same four attribute reactions (nursing care, staff care, physician care, and environment). However, when paying attention to the sizes of the parameter estimates (weights), these two groups show a clear difference. It is also found that Caucasian patients are risk averse. On the other hand, African American patients are likely to average out their attributes reactions proportionately. PRACTICE IMPLICATIONS: The critical differences found in this study provide a very important implication for health care managers developing an intervention program to improve patient satisfaction. A one-size-fits-all strategy would not work for all patients. Caucasian and African American patients are different, and each requires a different intervention program to improve their overall evaluation of their hospital stay.

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BACKGROUND: Endothelial cells can be protected against cytokine-induced toxicity by volatile anesthetics. The authors tested whether inhalation of sevoflurane at subanesthetic concentrations provides protection against postocclusive endothelial dysfunction induced by ischemia-reperfusion injury of the forearm in humans. METHODS: Five healthy male volunteers were enrolled in this study with crossover design. Each subject was randomly exposed to 15 min of forearm ischemia in the presence or absence of sevoflurane. Sevoflurane was inhaled at 0.5-1 vol% end-tidal concentrations from 15 min before ischemia until 5 min after the onset of reperfusion. Hyperemic reaction, an indicator of ischemic injury and endothelial function, was determined at 15 and 30 min of reperfusion using venous occlusion plethysmography. Also, markers of leukocyte activation (CD11b, CD42b) were measured by flow cytometry during reperfusion. RESULTS: Fifteen minutes of forearm ischemia followed by reperfusion diminished postocclusive endothelium-dependent hyperemic reaction at 15 and 30 min of reperfusion. Peri-ischemic inhalation of sevoflurane, targeted at 0.5-1 vol% end-tidal concentrations, markedly improved postocclusive hyperemic reaction. In addition, inhalation of sevoflurane attenuated activation of leukocytes, as measured by CD11b expression, after ischemia-reperfusion injury. No changes in CD42b expression were observed after ischemia-reperfusion of the forearm. CONCLUSIONS: These data suggest that human endothelium, a key component of all vital organs, is receptive to protection by sevoflurane in vivo. Peri-ischemic administration of sevoflurane mimics a combination of pharmacologic preconditioning and postconditioning and protects at even low sedative concentrations (< 1 vol%). Inhibition of leukocyte adhesion is likely to be involved in the protection.

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