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Beatrice J. Kalisch - One of the best experts on this subject based on the ideXlab platform.
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Correlates and predictors of missed Nursing Care in hospitals.
Journal of Clinical Nursing, 2017Co-Authors: Helga Bragadóttir, Beatrice J. Kalisch, Gudný Bergthora TryggvadóttirAbstract:Aims and objectives To identify the contribution of hospital, unit, staff characteristics, staffing adequacy and teamwork to missed Nursing Care in Iceland hospitals. Background A recently identified quality indicator for Nursing Care and patient safety is missed Nursing Care defined as any standard, required Nursing Care omitted or significantly delayed, indicating an error of omission. Former studies point to contributing factors to missed Nursing Care regarding hospital, unit and staff characteristics, perceptions of staffing adequacy as well as Nursing teamwork, displayed in the Missed Nursing Care Model. Design This was a quantitative cross-sectional survey study. Methods The samples were all registered nurses and practical nurses (n = 864) working on 27 medical, surgical and intensive Care inpatient units in eight hospitals throughout Iceland. Response rate was 69·3%. Data were collected in March–April 2012 using the combined MISSCare Survey-Icelandic and the Nursing Teamwork Survey-Icelandic. Descriptive, correlational and regression statistics were used for data analysis. Results Missed Nursing Care was significantly related to hospital and unit type, participants’ age and role and their perception of adequate staffing and level of teamwork. The multiple regression testing of Model 1 indicated unit type, role, age and staffing adequacy to predict 16% of the variance in missed Nursing Care. Controlling for unit type, role, age and perceptions of staffing adequacy, the multiple regression testing of Model 2 showed that Nursing teamwork predicted an additional 14% of the variance in missed Nursing Care. Conclusions The results shed light on the correlates and predictors of missed Nursing Care in hospitals. This study gives direction as to the development of strategies for decreasing missed Nursing Care, including ensuring appropriate staffing levels and enhanced teamwork. Relevance to clinical practice By identifying contributing factors to missed Nursing Care, appropriate interventions can be developed and tested.
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Correlates and predictors of missed Nursing Care in hospitals.
Journal of Clinical Nursing, 2017Co-Authors: Helga Bragadóttir, Beatrice J. Kalisch, Gudný Bergthora TryggvadóttirAbstract:To identify the contribution of hospital, unit, staff characteristics, staffing adequacy and teamwork to missed Nursing Care in Iceland hospitals. A recently identified quality indicator for Nursing Care and patient safety is missed Nursing Care defined as any standard, required Nursing Care omitted or significantly delayed, indicating an error of omission. Former studies point to contributing factors to missed Nursing Care regarding hospital, unit and staff characteristics, perceptions of staffing adequacy as well as Nursing teamwork, displayed in the Missed Nursing Care Model. This was a quantitative cross-sectional survey study. The samples were all registered nurses and practical nurses (n = 864) working on 27 medical, surgical and intensive Care inpatient units in eight hospitals throughout Iceland. Response rate was 69·3%. Data were collected in March-April 2012 using the combined MISSCare Survey-Icelandic and the Nursing Teamwork Survey-Icelandic. Descriptive, correlational and regression statistics were used for data analysis. Missed Nursing Care was significantly related to hospital and unit type, participants' age and role and their perception of adequate staffing and level of teamwork. The multiple regression testing of Model 1 indicated unit type, role, age and staffing adequacy to predict 16% of the variance in missed Nursing Care. Controlling for unit type, role, age and perceptions of staffing adequacy, the multiple regression testing of Model 2 showed that Nursing teamwork predicted an additional 14% of the variance in missed Nursing Care. The results shed light on the correlates and predictors of missed Nursing Care in hospitals. This study gives direction as to the development of strategies for decreasing missed Nursing Care, including ensuring appropriate staffing levels and enhanced teamwork. By identifying contributing factors to missed Nursing Care, appropriate interventions can be developed and tested. © 2016 John Wiley & Sons Ltd.
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Errors of Omission: Missed Nursing Care.
Western Journal of Nursing Research, 2014Co-Authors: Beatrice J. Kalisch, Boqin XieAbstract:A series of studies on missed Nursing Care (i.e., required standard Nursing Care that is not completed) is summarized. Missed Nursing Care is substantial and similar levels are found across hospitals. Reasons for missed Nursing Care are staffing resources, material resources, and communication and these are also similar across hospitals. The higher the staffing levels, the fewer occurrences of missed Nursing Care. Magnet status and higher levels of teamwork are associated with less missed Nursing Care, and more missed Care leads to a lower level of staff satisfaction. Missed Nursing Care has been found to be a mediator between staffing levels and patient falls. Patient identified missed Nursing Care predicts adverse events (i.e., falls, pressure ulcers, new infections etc.).
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The Relationship Between Electronic Nursing Care Reminders and Missed Nursing Care.
CIN: Computers Informatics Nursing, 2014Co-Authors: Ronald Piscotty, Beatrice J. KalischAbstract:The purpose of the study was to explore relationships between nurses' perceptions of the impact of health information technology on their clinical practice in the acute Care setting, their use of electronic Nursing Care reminders, and episodes of missed Nursing Care. The study aims were accomplished with a descriptive design using adjusted correlations. A convenience sample (N = 165) of medical and/or surgical, intensive Care, and intermediate Care RNs working on acute Care hospital units participated in the study. Nurses from 19 eligible Nursing units were invited to participate. Adjusted relationships using hierarchical multiple regression analyses indicated significant negative relationships between missed Nursing Care and Nursing Care reminders and perceptions of health information technology. The adjusted correlations support the hypotheses that there is a relationship between Nursing Care reminder usage and missed Nursing Care and a relationship between health information technology and missed Nursing Care. The relationships are negative, indicating that nurses who rate higher levels of reminder usage and health information technology have decreased reports of missed Nursing Care. The study found a significant relationship between Nursing Care reminders usage and decreased amounts of missed Nursing Care. The findings can be used in a variety of improvement endeavors, such as encouraging nurses to utilize Nursing Care reminders, aid information system designers when designing Nursing Care reminders, and assist healthCare organizations in assessing the impact of technology on Nursing practice.
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Hospital Variation in Missed Nursing Care
American Journal of Medical Quality, 2011Co-Authors: Beatrice J. Kalisch, Dana Tschannen, Hyunhwa Lee, Christopher R. FrieseAbstract:Quality of Nursing Care across hospitals is variable, and this variation can result in poor patient outcomes. One aspect of quality Nursing Care is the amount of necessary Care that is omitted. This article reports on the extent and type of Nursing Care missed and the reasons for missed Care. The MISSCare Survey was administered to Nursing staff (n = 4086) who provide direct patient Care in 10 acute Care hospitals. Missed Nursing Care patterns as well as reasons for missing Care (labor resources, material resources, and communication) were common across all hospitals. Job title (ie, registered nurse vs Nursing assistant), shift worked, absenteeism, perceived staffing adequacy, and patient work loads were significantly associated with missed Care. The data from this study can inform quality improvement efforts to reduce missed Nursing Care and promote favorable patient outcomes.
Kyung Hee Lee - One of the best experts on this subject based on the ideXlab platform.
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do staffing levels predict missed Nursing Care
International Journal for Quality in Health Care, 2011Co-Authors: Beatrice J. Kalisch, Dana Tschannen, Kyung Hee LeeAbstract:Objective. To examine whether actual nurse staffing predicts missed Nursing Care, controlling for other unit characteristics. Design. This study utilized a cross-sectional, descriptive design. Setting. Ten hospitals in the Midwestern region of the USA. Participants. Nursing staff members with direct Care responsibilities (n ¼ 4288) on 110 Care units. Main Outcome Measures. The MISSCare Survey was utilized to capture respondents’ perceptions of missed Nursing Care as well as other unit characteristics (i.e. demographics, work schedules and absenteeism). Actual staffing data (hours per patient day [HPPD], registered nurse hours per patient day [RN HPPD], skill mix) and unit level case mix index were collected from the participating hospitals for the mean scores of 2 months during survey distribution. Results. HPPD was a significant predictor of missed Nursing Care (b ¼ 20.45, P ¼ 0.002). Conclusions. Findings from this study suggest that missed Nursing Care may explain, at least in part, the relationship between staffing levels and patient outcomes.
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the impact of teamwork on missed Nursing Care
Nursing Outlook, 2010Co-Authors: Beatrice J. Kalisch, Kyung Hee LeeAbstract:Previous studies have shown that missed Nursing Care is a significant problem in acute Care hospitals. Other studies have demonstrated that teamwork is a critical element in assuring patient safety and quality of Care. The purpose of this study was to determine if the level of Nursing teamwork impacts the extent and nature of missed Nursing Care. A sample of 2 216 Nursing staff members on 50 acute Care patient Care units in 4 hospitals completed the Nursing Teamwork Survey and the MISSCare Survey. The response rate was 59.7%. Controlling for occupation of staff members (eg, RN/LPN, NA) and staff characteristics (eg, education, shift worked, experience, etc), teamwork alone accounted for about 11% of missed Nursing Care. The results of this study show that the level of Nursing teamwork impacts the nature and extent of missed Nursing Care. The study results point to a need to invest in methods of enhancing teamwork in these settings.
Reiko Sakashita - One of the best experts on this subject based on the ideXlab platform.
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SMC - New feature definition for improvement of Nursing-Care text classification
2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012Co-Authors: Manabu Nii, Atsuko Uchinuno, Yoshinori Hirohata, Reiko SakashitaAbstract:Recently, “Web based Nursing-Care Quality Improvement System” have been proposed and operating continuously for improving the Nursing-Care quality in Japan. For evaluating actual Nursing-Care process, freestyle Japanese texts which are called “Nursing-Care texts” are collected through the Internet. The Nursing-Care experts read the collected Nursing-Care texts Carefully to evaluate actual Nursing-Care process. Then they make a recommendation which includes some improvements, and send it to each nurse. The number of Nursing-Care experts who can evaluate the Nursing-Care texts is a few. Hence, it is hard to perform the above mentioned evaluation process because of a large number of nurses. In order to assist Nursing-Care experts in evaluating the Nursing-Care texts, we have been developing a computer aided Nursing-Care text classification system. In this paper, first, we introduce our computer aided Nursing-Care text classification system. Then we propose a method to improve the classification performance of the Nursing-Care text classification system. In our proposed method, dependency relation between terms is extracted from the Nursing-Care text The extracted dependency is used as a feature value which represents characteristics of each Nursing-Care text. From some experimental results for the actual Nursing-Care text sets, we show that our proposed feature definition is effective for improving the classification performance.
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Feature extraction from Nursing-Care texts for classification
2008Co-Authors: Manabu Nii, Y. Takahashi, Atsuko Uchinuno, Shigeru Ando, Reiko SakashitaAbstract:The Nursing Care quality improvement is very important in the medical field. Currently, Nursing-Care freestyle texts (Nursing-Care data) are collected from many hospitals in Japan by using Web applications and stored into the database. Some Nursing-Care experts evaluate the collected data to improve Nursing Care quality. For evaluating the Nursing-Care data, experts need to read all freestyle texts Carefully and then classified them into four classes. However, it is a very hard task for each expert to evaluate the data because of huge number of Nursing-Care data in the database. In order to reduce workloads evaluating Nursing-Care data, we have proposed a support vector machine (SVM) based classification system. In this paper, to improve the classification performance, we propose a feature extraction method for generating numerical data from collected Nursing-Care texts. In our proposed method, the frequency in use of a term in the term list is used for selecting features which contribute to the classification. And then, the Nursing-Care numerical data are classified by the SVM based classification system. From computer simulation results, we show the effectiveness of our proposed method.
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Nursing-Care Data Classification using Neural Networks
2007 IEEE ICME International Conference on Complex Medical Engineering, 2007Co-Authors: Manabu Nii, Y. Takahashi, Atsuko Uchinuno, Reiko SakashitaAbstract:Nursing-Care data in this paper are Japanese texts written by nurses which consist of answers for questions about Nursing-Care. The Nursing-Care data are collected via WWW application from many hospitals in Japan. The collected data are stored into the database. The Nursing-Care experts evaluate the collected data to improve Nursing-Care quality. Currently, the collected data are evaluated by experts reading all texts Carefully. It is difficult, however, for experts to evaluate the data because there are huge number of Nursing-Care data in the database. In this paper, to reduce workloads for the evaluation of Nursing-Care data, neural networks are used for classifying Nursing-Care data instead of fuzzy classification system. We use standard three-layer feedforward neural networks with back-propagation type learning. First, we extract attribute values (i.e., training data) from texts written by nurses. And then, we train a neural network using the training data. From computer simulations, we show the effectiveness of our proposed system using the leaving-one out method.
Dana Tschannen - One of the best experts on this subject based on the ideXlab platform.
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Hospital Variation in Missed Nursing Care
American Journal of Medical Quality, 2011Co-Authors: Beatrice J. Kalisch, Dana Tschannen, Hyunhwa Lee, Christopher R. FrieseAbstract:Quality of Nursing Care across hospitals is variable, and this variation can result in poor patient outcomes. One aspect of quality Nursing Care is the amount of necessary Care that is omitted. This article reports on the extent and type of Nursing Care missed and the reasons for missed Care. The MISSCare Survey was administered to Nursing staff (n = 4086) who provide direct patient Care in 10 acute Care hospitals. Missed Nursing Care patterns as well as reasons for missing Care (labor resources, material resources, and communication) were common across all hospitals. Job title (ie, registered nurse vs Nursing assistant), shift worked, absenteeism, perceived staffing adequacy, and patient work loads were significantly associated with missed Care. The data from this study can inform quality improvement efforts to reduce missed Nursing Care and promote favorable patient outcomes.
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do staffing levels predict missed Nursing Care
International Journal for Quality in Health Care, 2011Co-Authors: Beatrice J. Kalisch, Dana Tschannen, Kyung Hee LeeAbstract:Objective. To examine whether actual nurse staffing predicts missed Nursing Care, controlling for other unit characteristics. Design. This study utilized a cross-sectional, descriptive design. Setting. Ten hospitals in the Midwestern region of the USA. Participants. Nursing staff members with direct Care responsibilities (n ¼ 4288) on 110 Care units. Main Outcome Measures. The MISSCare Survey was utilized to capture respondents’ perceptions of missed Nursing Care as well as other unit characteristics (i.e. demographics, work schedules and absenteeism). Actual staffing data (hours per patient day [HPPD], registered nurse hours per patient day [RN HPPD], skill mix) and unit level case mix index were collected from the participating hospitals for the mean scores of 2 months during survey distribution. Results. HPPD was a significant predictor of missed Nursing Care (b ¼ 20.45, P ¼ 0.002). Conclusions. Findings from this study suggest that missed Nursing Care may explain, at least in part, the relationship between staffing levels and patient outcomes.
Manabu Nii - One of the best experts on this subject based on the ideXlab platform.
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SMC - New feature definition for improvement of Nursing-Care text classification
2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012Co-Authors: Manabu Nii, Atsuko Uchinuno, Yoshinori Hirohata, Reiko SakashitaAbstract:Recently, “Web based Nursing-Care Quality Improvement System” have been proposed and operating continuously for improving the Nursing-Care quality in Japan. For evaluating actual Nursing-Care process, freestyle Japanese texts which are called “Nursing-Care texts” are collected through the Internet. The Nursing-Care experts read the collected Nursing-Care texts Carefully to evaluate actual Nursing-Care process. Then they make a recommendation which includes some improvements, and send it to each nurse. The number of Nursing-Care experts who can evaluate the Nursing-Care texts is a few. Hence, it is hard to perform the above mentioned evaluation process because of a large number of nurses. In order to assist Nursing-Care experts in evaluating the Nursing-Care texts, we have been developing a computer aided Nursing-Care text classification system. In this paper, first, we introduce our computer aided Nursing-Care text classification system. Then we propose a method to improve the classification performance of the Nursing-Care text classification system. In our proposed method, dependency relation between terms is extracted from the Nursing-Care text The extracted dependency is used as a feature value which represents characteristics of each Nursing-Care text. From some experimental results for the actual Nursing-Care text sets, we show that our proposed feature definition is effective for improving the classification performance.
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Feature extraction from Nursing-Care texts for classification
2008Co-Authors: Manabu Nii, Y. Takahashi, Atsuko Uchinuno, Shigeru Ando, Reiko SakashitaAbstract:The Nursing Care quality improvement is very important in the medical field. Currently, Nursing-Care freestyle texts (Nursing-Care data) are collected from many hospitals in Japan by using Web applications and stored into the database. Some Nursing-Care experts evaluate the collected data to improve Nursing Care quality. For evaluating the Nursing-Care data, experts need to read all freestyle texts Carefully and then classified them into four classes. However, it is a very hard task for each expert to evaluate the data because of huge number of Nursing-Care data in the database. In order to reduce workloads evaluating Nursing-Care data, we have proposed a support vector machine (SVM) based classification system. In this paper, to improve the classification performance, we propose a feature extraction method for generating numerical data from collected Nursing-Care texts. In our proposed method, the frequency in use of a term in the term list is used for selecting features which contribute to the classification. And then, the Nursing-Care numerical data are classified by the SVM based classification system. From computer simulation results, we show the effectiveness of our proposed method.
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Nursing-Care Data Classification using Neural Networks
2007 IEEE ICME International Conference on Complex Medical Engineering, 2007Co-Authors: Manabu Nii, Y. Takahashi, Atsuko Uchinuno, Reiko SakashitaAbstract:Nursing-Care data in this paper are Japanese texts written by nurses which consist of answers for questions about Nursing-Care. The Nursing-Care data are collected via WWW application from many hospitals in Japan. The collected data are stored into the database. The Nursing-Care experts evaluate the collected data to improve Nursing-Care quality. Currently, the collected data are evaluated by experts reading all texts Carefully. It is difficult, however, for experts to evaluate the data because there are huge number of Nursing-Care data in the database. In this paper, to reduce workloads for the evaluation of Nursing-Care data, neural networks are used for classifying Nursing-Care data instead of fuzzy classification system. We use standard three-layer feedforward neural networks with back-propagation type learning. First, we extract attribute values (i.e., training data) from texts written by nurses. And then, we train a neural network using the training data. From computer simulations, we show the effectiveness of our proposed system using the leaving-one out method.
