Nursing Value User Stories: A Value Measurement Method for Linking Nurse Contribution to Patient Outcomes.

The use of nursing big data sets for value-based measurement is novel. Nursing value measurement depends on the availability of essential data attributes in the electronic health record related to nursing care delivered (what happened, when, and the result seen). Key in measuring value is a standardized structure and format of these attributes for enabling uniform consistent analysis, along with data sets that are sharable and comparable across individuals and groups, time, organization, and practice focus. The foundation of such sharable and comparable data sets would represent at a minimum individual essential nurse care actions and the resulting patient outcome(s). While nurses generate an extraordinary amount of health-related data, healthcare information systems are not designed to collect structured data that reflect the unique attributes of nursing care or support nursing analytic activities that would measure value. More important, the multidimensional features of the nursing process are difficult to untangle and differentiate from other healthcare workers and nonnursing care activities. The complexity of nursing knowledge work has limited the development of nursing data science methods like value measurement and discouraged value versus cost discussions. This article sets out to describe nursing value measurement and an approach that nurse scientists are maximizing through methods adapted from agile project management, including user stories, and business analysis processes to recognize nurses as primary contributors to patient outcomes and value generation. Nursing Value User Story methods deconstruct complex nursing scenarios into user stories that capture nursing actions as standardized data that can be mapped to a common nursing data model. Methods described here are being used in pilot research at Los Angeles Children's Hospital, and results will be available in 2019.

Measuring Nursing Care Value.

The value of nursing care as well as the contribution of individual nurses to clinical outcomes has been difficult to measure and evaluate. Existing health care financial models hide the contribution of nurses; therefore, the link between the cost and quality o nursing care is unknown. New data and methods are needed to articulate the added value of nurses to patient care. The final results and recommendations of an expert workgroup tasked with defining and measuring nursing care value, including a data model to allow extraction of key information from electronic health records to measure nursing care value, are described. A set of new analytic metrics are proposed.

Nursing Care Value-Based Financial Models.

Nursing care makes up one of the largest expenditures in the health care system, yet patient-level nursing intensity and costs are essentially unknown. Prior efforts to define nursing care value have been stymied by a lack of available data; however, emerging information from electronic health records provide an opportunity to measure nursing care in ways that have not been possible. New metrics using these data will allow improved measurement of cost, quality, and intensity at the level of each nurse and patient across many different settings which can be used to inform operational and clinical decision making. In this article, the initial results and recommendations of an expert panel tasked with defining and measuring nursing care value as part of a larger effort to address evolving issues related to big data and nursing knowledge development are described.

Powering Big Data for Nursing Through Partnership.

The Big Data Principles Workgroup (Workgroup) was established with support of the Healthcare Information and Management Systems Society. Building on the Triple Aim challenge, the Workgroup sought to identify Big Data principles, barriers, and challenges to nurse-sensitive data inclusion into Big Data sets. The product of this pioneering partnership Workgroup was the "Guiding Principles for Big Data in Nursing-Using Big Data to Improve the Quality of Care and Outcomes."

Health Information Technology, Patient Safety, and Professional Nursing Care Documentation in Acute Care Settings.

The electronic health record (EHR) is a documentation tool that yields data useful in enhancing patient safety, evaluating care quality, maximizing efficiency, and measuring staffing needs. Although nurses applaud the EHR, they also indicate dissatisfaction with its design and cumbersome electronic processes. This article describes the views of nurses shared by members of the Nursing Practice Committee of the Missouri Nurses Association; it encourages nurses to share their EHR concerns with Information Technology (IT) staff and vendors and to take their place at the table when nursing-related IT decisions are made. In this article, we describe the experiential-reflective reasoning and action model used to understand staff nurses' perspectives, share committee reflections and recommendations for improving both documentation and documentation technology, and conclude by encouraging nurses to develop their documentation and informatics skills. Nursing issues include medication safety, documentation and standards of practice, and EHR efficiency. IT concerns include interoperability, vendors, innovation, nursing voice, education, and collaboration.

Excellence and evidence in staffing: a data-driven model for excellence in staffing (2nd edition).

The Patient Protection and Affordable Care Act (PPACA, 2010) and the Institute of Medicine's (IOM, 2011) Future of Nursing report have prompted changes in the U.S. health care system. This has also stimulated a new direction of thinking for the profession of nursing. New payment and priority structures, where value is placed ahead of volume in care, will start to define our health system in new and unknown ways for years. One thing we all know for sure: we cannot afford the same inefficient models and systems of care of yesterday any longer. The Data-Driven Model for Excellence in Staffing was created as the organizing framework to lead the development of best practices for nurse staffing across the continuum through research and innovation. Regardless of the setting, nurses must integrate multiple concepts with the value of professional nursing to create new care and staffing models. Traditional models demonstrate that nurses are a commodity. If the profession is to make any significant changes in nurse staffing, it is through the articulation of the value of our professional practice within the overall health care environment. This position paper is organized around the concepts from the Data-Driven Model for Excellence in Staffing. The main concepts are: Core Concept 1: Users and Patients of Health Care, Core Concept 2: Providers of Health Care, Core Concept 3: Environment of Care, Core Concept 4: Delivery of Care, Core Concept 5: Quality, Safety, and Outcomes of Care. This position paper provides a comprehensive view of those concepts and components, why those concepts and components are important in this new era of nurse staffing, and a 3-year challenge that will push the nursing profession forward in all settings across the care continuum. There are decades of research supporting various changes to nurse staffing. Yet little has been done to move that research into practice and operations. While the primary goal of this position paper is to generate research and innovative thinking about nurse staffing across all health care settings, a second goal is to stimulate additional publications. This includes a goal of at least 20 articles in Nursing Economic$ on best practices in staffing and care models from across the continuum over the next 3 years.

The economic value of health care data.

The amount of health care data in our world has been exploding, and the ability to store, aggregate, and combine data and then use the results to perform deep analyses have become ever more important. "Big data," large pools of data that can be captured, communicated, aggregated, stored, and analyzed, are now part of every sector and function of the global economy. While most research into big data thus far has focused on the question of their volume, there is evidence that the business and economic possibilities of big data and their wider implications are important for consideration. It is even offering the possibility that health care data could become the most valuable asset over the next 5 years as "secondary use" of electronic health record data takes off.

Staffing based on evidence: can health information technology make it possible?

Health care leaders must balance nurse staffing between financial viability and quality of care. The potential to use health information technology as a tool to assess effective nurse staffing decisions is a rather new phenomena explored by some of the thought leaders in nursing informatics. This preliminary pilot study is one of a few attempts at engineering health IT to identify factors that lead to a meaningful model for predicting nurse intensity. The Pilot Study provided richness to the design of a new model Clinical Demand Index to calculate nurse intensity by: (a) identifying the factors of how nurses spend their time; (b) using health IT data mining techniques to determine data types for abstraction; and (c) identifying variables that are most closely related to nursing intensity of how nurses spend their time. The CDI Model and health IT can make staffing based on evidence a reality and thus play an important role in demonstrating that clinical data from the electronic health record can be abstracted real time and used to objectively calculate nurse intensity and continue to engineer a learning health system.

Using Electronic Health Record Activity to Represent Interdisciplinary Care Teams and Examining their Contribution to Hospital Length of Stay.

Modeling variance in patient outcomes using medical claims and other forms of aggregated administrative data may ignore significant contributions associated with providers who are not recorded in billing transactions. We examined the association between interdisciplinary provider factors and length of stay (LOS) for 1,099 lumbar spine surgery patients. Interdisciplinary provider "dose" (number of providers/case), "workload" (care of other patients), and "activity" factors were defined and generated. Hierarchical Regression models were used to test the impact of these provider factors controlling for the effect of socio-demographic and clinical factors. Interdisciplinary provider factors explained 12% of additional variance in LOS. EHR-based interdisciplinary care team representations hold promise in contributing to our understanding of health care delivery and quality. Keywords: interdisciplinary care, nursing documentation, workload, length of stay, electronic health records (EHR).

Population health: a nursing action plan.

The passage of the Affordable Care Act shifted the focus of health care from individual, patient specific, episodic care, towards health management of groups of people with an emphasis on primary and preventive care. Population health management assists to attain and maintain health while improving quality and lowering costs. The recent Catalyst for Change report creates an urgent call for harnessing the power of nurses-in our communities, schools, businesses, homes and hospitals-to build capacity for population health. Informatics Nurse Specialists are prepared to bridge roles across practice, research, education, and policy to support this call. Each year, the AMIA Nursing Informatics Working Group convenes an expert panel to reflect on the "hot topics" of interest to nursing. Not surprisingly, the 2017 topic was on the current state and challenges of population health. The following summary reflects the panel's perspectives and recommendations for action.

Measuring Nursing Value from the Electronic Health Record.

We report the findings of a big data nursing value expert group made up of 14 members of the nursing informatics, leadership, academic and research communities within the United States tasked with 1. Defining nursing value, 2. Developing a common data model and metrics for nursing care value, and 3. Developing nursing business intelligence tools using the nursing value data set. This work is a component of the Big Data and Nursing Knowledge Development conference series sponsored by the University Of Minnesota School Of Nursing. The panel met by conference calls for fourteen 1.5 hour sessions for a total of 21 total hours of interaction from August 2014 through May 2015. Primary deliverables from the bit data expert group were: development and publication of definitions and metrics for nursing value; construction of a common data model to extract key data from electronic health records; and measures of nursing costs and finance to provide a basis for developing nursing business intelligence and analysis systems.

Can big data transform electronic health records into learning health systems?

In the United States and globally, healthcare delivery is in the midst of an acute transformation with the adoption and use of health information technology (health IT) thus generating increasing amounts of patient care data available in computable form. Secure and trusted use of these data, beyond their original purpose can change the way we think about business, health, education, and innovation in the years to come. "Big Data" is data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it.

Engineering a learning healthcare system: using health information technology to develop an objective nurse staffing tool.

Nurses represent the largest proportion of direct healthcare providers. Overstaffed or understaffed units will have implications for the quality, cost, patient, and nurse satisfaction. It is vital that nurses are armed with appropriate instruments and data to help them plan and implement efficient and effective nursing teams. A compelling case is made for the association between nursing care and clinical, quality, and financial outcomes. Even though there is a great body of work on the correlation, there is little agreement on the best approach to determine the correct balance between the patient-to-nurse ratios. The sheer number of variables depicted in the literature suggests why precise evidenced based formulas are difficult to achieve. This paper will describe a practice based knowledge generation mixed methods study using detailed observation and electronic health record abstraction to generate a structural equation for use in predicting staffing needs.