Transforming Synergy Care Delivery Model into an Effective Nursing Shift Report Tool

Background: The complexity of hospitalized patients with acute and chronic illnesses poses challenges for effective communication among nurses. Nursing shift report (NSR) plays a crucial role in communicating and planning patient care to ensure continuity, quality, and safety of patient care. Suboptimal shift reports may misdirect nursing surveillance and fail to recognize/interpret serious patient complications. A preliminary study involving interviews of staff nurses on current shift report practices revealed that NSRs lacked structure, was task-oriented and inconsistent. Nurses desired a standardized, efficient and systematic shift report. Our study aimed to develop a NSR tool using the American Association for Critical Care Nurses (AACN) Synergy model, systematically capturing the holistic care needs of patients and their families. Method: The Synergy Model-integrated NSR tool was developed with thirteen staff nurses working at seven different units in a hospital. The nurses were first educated about the Synergy model and its eight patient characteristics (Vulnerability, Stability, Resiliency, Predictability, Resource Availability, Participation in Care, Participation in Decision-making, Complexity). Then nurses integrated the patient characteristics into the NSR tool. Results: Identified were unit specific and common care elements across units to be included onto a NSR, matching them with each patient characteristics. A synergy rating scale was incorporated into the tool to allow nurses to score patient's conditions and care needs. The total sum would help nurses quickly judge the overall severity of the patient's condition. Nurses felt that structured and focused information of the tool would prevent information being left out of the reporting process. Conclusion/Implications: Nurses perceived the new NSR tool would be an effective, systematic process with widespread implications for improvements in quality of care and patient safety. The conversion of this paper-based shift report into a mobile shift reporting tool is in progress to support safe, efficient, and patient-centered bedside handoffs

Development of an Automated Mapping Tool to Transform Nursing Narrative Information into Quantifiable Nursing Data

poster abstractBackground: Inspecting the effectiveness of health care has been a central focus of health care professionals challenged by a system with aggressive cost constraints and increasing demands for quality of care. This focus has highlighted the importance of having health care data and facilitated the use of large data sets. It is crucial that nurses clearly verify the economic and clinical values of nursing interventions for the improvement of patient outcomes. However, rarely has effectiveness of nursing care in hospitals been demonstrated due to nurse scientists’ inability to electronically obtain valid and comparable nursing data. The importance of “computable” nursing data and databases have been long recognized and led to the development of standardized nursing terminologies (SNTs) to represent nursing interventions and outcomes. Yet, a majority of nursing information systems in hospitals is still using nurses’ free-text records to document care processes and patient outcomes. Free-text records, which may produce rich information on nursing phenomena yet incomputable, have been of limited use for generating nursing information and knowledge. Therefore, the study aimed at the development of an automated mapping tool to extract and transform the narrative nursing notes to quantifiable data in SNTs. Method: The nursing narrative notes were collected from a retrospective nursing record review of patients who were admitted to a community hospital with the diagnosis of Septicemia. The Nursing Interventions Classification and the Nursing Outcome Classification were the SNTs used for mapping. The automated mapping tool was developed using natural language processing; the Graphic User Interface was designed using NetBeans IDE and Perl programming language. Tokenizing each sentence to identify single word term candidates, stemming them, lexical collocations to coordinate the words into meaningful information (phrases/sentences), and mapping them into labels and indicators of SNTs were accomplished by using Regular Expressions. The validation of the tool was completed by comparing the result from the use of the tool with the result from the manual mapping by 2 nursing experts, which was considered as the gold standard. Results: The interface features of the automated mapping tool included data entry options (i.e., browse/upload files or type-in each nursing narrative sentence), mapping sources to select NIC and NOC dictionaries, their domains and classes by their hierarchical classification structure, and output options (i.e., nursing representation with the mapped terms, Frequency of the mapped terms). A total of 25588 words from nursing narrative records of 14 patients were used. On average 52 parsed phrases or sentences per nursing record were mapped. In total, 768 labels of NIC and 4733 indicators of NOC, including the duplicates. Compared with the manually mapped terms (the gold standard), the automated mapping tool showed the accuracy rates ((True positive + True negative)/The Overall mapped), 80.6% with NIC and 74.8% with NOC. The most frequently mapped descriptors of NIC were ‘Report changes in patient status’ under the label of ‘Physician Support (7710).’ The most commonly mapped indicators of NOC were ‘Coughing (041019)’ under the label of ‘Respiratory Status: Airway Patency.’ Nurses were likely to document their observations of patient status than what nursing interventions were provided. Conclusion/Implications: The new automated mapping tool showed high performance at the initial stage. The validation of the tool will be continuously tested with more nursing narratives data. It is expected that the tool will be useful for transforming nursing information with SNTs into quantifiable and comparable data, which consequently can be used for nursing effectiveness research. It can be used for outcomes analyses, regulatory quality report generation, and text analysis for finding appropriate nursing literature and capturing nursing concepts in qualitative research. The study findings can also contribute to the development and refinement of SNTs to more accurately represent nursing practice

Weighted Orlicz regularity estimates for fully nonlinear elliptic equations with asymptotic convexity

We prove interior Hessian estimates in the setting of weighted Orlicz spaces for viscosity solutions of fully nonlinear, uniformly elliptic equations F(D2u,x)=f(x)in B1 under asymptotic assumptions on the nonlinear operator F. The results are further extended to fully nonlinear, asymptotically elliptic equations.</i

Nursing activities and factors influential to nurse staffing decision-making

Objective: There is limited published research supporting the effectiveness of nursing workload measurement to comprehensively measure nursing workload and to formulate nurse resource need. Predictive accuracy is impaired due to variation in direct and indirect care-related activities across measurement instruments. This study aimed to (1) identify common nursing activities considered by nurse managers for staffing decision-making, (2) systematically review such nursing activities in relation to existing nursing workload instruments and Nursing Intervention Classification taxonomy, and (3) describe challenges perceived by managers in staffing decision-making. Methods: A survey was developed from an inclusive review of 20 nursing workload instruments collectively measuring 502 nursing activities. Nurse managers in 13 medical-surgical and two intensive care units at a Midwest healthcare organization identified nursing activities considered daily for staffing decision-making. Results: Twenty-one activities were commonly considered by at least 90 percent of managers (n = 13) for daily staffing decisionmaking, although none of the instruments reviewed included all 21 activities. Conclusions: Lack of a standardized framework for nursing workload measurement might have led to nurse managers’ different perceptions about appropriate determinants of these measurements. A standardized approach for measuring nursing workload would facilitate benchmarking for estimating nurse resource need. Further research is needed to design a systematic infrastructure that ensures staffing to meet patient care need. A process is also needed to alleviate the challenges in staffing decision-making that nurse managers face, such as fluctuations in census and patient acuity, nurse competency-based patient assignments, and limited information resources for staffing estimation