Technology Myth Busters for Nurse Leaders.

Technology is frequently viewed as a barrier to workflow and efficiency rather than as a tool that can be used to improve the quality of our care, increase efficiency, or enhance patient outcomes. However, when technology is applied effectively, nurse leaders can leverage tools such as clinical decision support to avoid errors, inform decision making, and boost provider and patient satisfaction. These topics were discussed at the AONE annual meeting in a preconference session. In this article, AONE session leaders detail ways to ensure that information technology is a valuable tool-and not an obstacle-for effective nursing practice.

Mapping Perinatal Nursing Process Measurement Concepts to Standard Terminologies.

The use of standard terminologies is an essential component for using data to inform practice and conduct research; perinatal nursing data standardization is needed. This study explored whether 76 distinct process elements important for perinatal nursing were present in four American Nurses Association-recognized standard terminologies. The 76 process elements were taken from a valid paper-based perinatal nursing process measurement tool. Using terminology-supported browsers, the elements were manually mapped to the selected terminologies by the researcher. A five-member expert panel validated 100% of the mapping findings. The majority of the process elements (n = 63, 83%) were present in SNOMED-CT, 28% (n = 21) in LOINC, 34% (n = 26) in ICNP, and 15% (n = 11) in CCC. SNOMED-CT and LOINC are terminologies currently recommended for use to facilitate interoperability in the capture of assessment and problem data in certified electronic medical records. Study results suggest that SNOMED-CT and LOINC contain perinatal nursing process elements and are useful standard terminologies to support perinatal nursing practice in electronic health records. Terminology mapping is the first step toward incorporating traditional paper-based tools into electronic systems.

EHR Documentation: The Hype and the Hope for Improving Nursing Satisfaction and Quality Outcomes.

The phenomenon of "data rich, information poor" in today's electronic health records (EHRs) is too often the reality for nursing. This article proposes the redesign of nursing documentation to leverage EHR data and clinical intelligence tools to support evidence-based, personalized nursing care across the continuum. The principles consider the need to optimize nurses' documentation efficiency while contributing to knowledge generation. The nursing process must be supported by EHRs through integration of best care practices: seamless workflows that display the right tools, evidence-based content, and information at the right time for optimal clinical decision making. Design of EHR documentation must attain a balance that ensures the capture of nursing's impact on safety, quality, highly reliable care, patient engagement, and satisfaction, yet minimizes "death by data entry." In 2014, a group of diverse informatics leaders from practice, academia, and the vendor community formed to address how best to transform electronic documentation to provide knowledge at the point of care and to deliver value to front line nurses and nurse leaders. As our health care system moves toward reimbursement on the basis of quality outcomes and prevention, the value of nursing data in this business proposition will become a key differentiator for health care organizations' economic success.

Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.

Background: Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. Objective: To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). Methods: We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. Results: A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). Conclusions: We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.

Using Technology to Facilitate Evidence-Based Practice During the COVID-19 Pandemic.

The COVID-19 pandemic brought many challenges to the health care workforce. A novel infectious disease, COVID-19 uncovered information gaps that were essential for frontline staff, including nurses, to care for patients and themselves. The authors developed a Web-based solution consisting of saved searches from PubMed on clinically relevant topics specific to nurses' information needs. This article discusses the objectives, development, content, and usage of this Internet resource and also provides tips for hospitals of all sizes to implement similar tools to evidence-based practice during infectious disease outbreaks.

Impact of COVID-19 on New Graduate Nurses' Transition to Practice: Loss or Gain?

BACKGROUND: The COVID-19 pandemic disrupted nursing education and required modification of instructional methods and clinical experiences. Given the variation in education, rapid transition to virtual platforms, and NCLEX-RN testing stressors, this cohort faced unique losses and gains influencing their transition into clinical practice. PURPOSE: This study examined the impact of COVID-19 and preparedness for professional practice of 340 new graduate nurses (NGNs) at an academic medical center. METHODS: This was a mixed-methods descriptive study focusing on how clinical experience loss or gains in the final semester affected the fears, concerns, and recommendations for NGNs. RESULTS: More than half (67.5%, n = 295) of NGNs reported changes to clinical experiences, ranging from 0 to 240 hours transitioned to virtual (n = 187; median, 51; interquartile range, 24-80). NGNs fear missing important details or doing something wrong in providing patient care. They identified the need for preceptor support, guidance, teaching, and continued practice of skills. CONCLUSION: Recommendations are clear communication with leadership, advocacy from the nurse residency program, and targeted clinical and emotional support for NGNs.

Disappearing expertise in clinical automation: Barcode medication administration and nurse autonomy.

OBJECTIVE: Using the case of barcode medication administration (BCMA), our objective is to describe the challenges nurses face when informatics tools are not designed to accommodate the full complexity of their work. MATERIALS AND METHODS: Autonomy is associated with nurse satisfaction and quality of care. BCMA organizes patient information and verifies medication administration. However, it presents challenges to nurse autonomy. Qualitative fieldwork, including observations of everyday work and interviews, was conducted during the implementation of BCMA in a large academic medical center. Fieldnotes and interview transcripts were coded and analyzed to describe nurses' perspectives on medication safety. RESULTS: Nurses adopt orienting frames to structure work routines and require autonomy to ensure safe task completion. Nurses exerted agency by trusting their own judgment over system information when the system did not consider workload complexity. Our results indicate that the system's rigidity clashed with adaptive needs embodied by nurses' orienting frames. DISCUSSION: Despite the fact that the concept of nurse as knowledge worker is foundational to informatics, nurses may be perceived as doers, rather than knowledge workers. In practice, nurses not only make decisions, but also engage in highly complex task-related work that is not well supported by process-oriented information technology tools. CONCLUSIONS: Information technology developers and healthcare organization managers should engage and better understand nursing work in order to develop technological and social systems to support it.

Defining an Essential Clinical Dataset for Admission Patient History to Reduce Nursing Documentation Burden.

BACKGROUND: Documentation burden, defined as the need to complete unnecessary documentation elements in the electronic health record (EHR), is significant for nurses and contributes to decreased time with patients as well as burnout. Burden increases when new documentation elements are added, but unnecessary elements are not systematically identified and removed. OBJECTIVES: Reducing the burden of nursing documentation during the inpatient admission process was a key objective for a group of nurse experts who collaboratively identified essential clinical data elements to be documented by nurses in the EHR. METHODS: Twelve health care organizations used a data-driven process to evaluate inpatient admission assessment data elements to identify which elements were consistently deemed essential to patient care. Processes used for the twelve organizations to reach consensus included identifying: (1) data elements that were truly essential, (2) which data elements were explicitly required during the admission process, and (3) data elements that must be documented by a registered nurse (RN). RESULT: The result was an Admission Patient History Essential Clinical Dataset (APH ECD) that reduced the amount of admission documentation content by an average of 48.5%. Early adopters experienced an average reduction of more than two minutes per admission history documentation session and an average reduction in clicks of more than 30%. CONCLUSION: The creation of the essential clinical dataset is an example of combining evidence from nursing practice within the EHR with a set of predefined guiding principles to decrease documentation burden for nurses. Establishing essential documentation components for the adult admission history and intake process ensures the efficient use of bedside nurses' time by collecting the right (necessary) information collected by the right person at the right time during the patient's hospital stay. Determining essential elements also provides a framework for mapping components to national standards to facilitate shareable and comparable nursing data.

Exploring the Perceived Value of a Personalized Informatics Tool to Anticipate and Mitigate Patient Risk.

Health care systems are implementing a myriad of strategies to improve patient outcomes and reduce both adverse events and unplanned readmissions. These approaches include interventions related to people, processes, and technology. This article describes the development of a technology-based model in the form of an actionable risk profile, which was used by one inpatient surgical unit (people) during a daily care team briefing (process). The risk profile was evaluated for its perceived value to the care team as a method of organizing care and anticipating patients' needs. Preliminary findings of the proof of concept are presented along with recommendations for practice and research.

Measuring non-administration of ordered medications in the pediatric inpatient setting.

OBJECTIVE: Medication compliance in inpatient settings shows some significant gaps for adult patients. In pediatric settings prescribing and other administration errors have been studied but missed doses have not been specifically studied in the pediatric inpatient setting. We intended to apply health information technology and data processing methods to study the medication compliance for pediatric patients at our institution. STUDY DESIGN: We collected medication ordering, dispensing, and administration data spanning 42 months (7/1/2010 through 12/31/2013) for pediatric inpatients admitted to a major tertiary pediatric hospital. We analyzed the orders for which either the corresponding administration record was missing or the records indicated non-administration. RESULTS: There were only 596 medication orders without corresponding administration records, accounting for less than 0.05% of 1.6 Million orders for 56,000 patients. There were 40,999 orders with corresponding administration records indicating non-administration (or less than 3% of all orders). Overall order compliance of the nursing staff was 97.35%, with another 2.6% of orders having a documented reason for non-administration The top two medication classes comprising the missed and non-administered orders were "Alimentary tract and metabolism drugs" and "Nervous system drugs". CONCLUSION: Measurement of medication compliance is an important quality measure of patient safety and quality of care. Our study found a small proportion of non-administered medication orders and discovered corresponding reasons illustrating how health information technology can help to measure the quality of the medication process from ordering and dispensing to administration at a major healthcare institution.

Blueprint for Advancing High-Value Maternity Care Through Physiologic Childbearing.

The Blueprint for Advancing High-Value Maternity Care Through Physiologic Childbearing charts an efficient pathway to a maternity care system that reliably enables all women and newborns to experience healthy physiologic processes around the time of birth, to the extent possible given their health needs and informed preferences. The authors are members of a multistakeholder, multidisciplinary National Advisory Council that collaborated to develop this document. This approach preventively addresses troubling trends in maternal and newborn outcomes and persistent racial and other disparities by mobilizing innate capacities for healthy childbearing processes and limiting use of consequential interventions. It provides more appropriate care to healthier, lower-risk women and newborns who often receive more specialized care, though such care may not be needed and may cause unintended harm. It also offers opportunities to improve the care, experience and outcomes of women with health challenges by fostering healthy perinatal physiologic processes whenever safely possible.

Standardizing Maternity Care Data to Improve Coordination of Care.

The amount of data generated by health information technology systems is staggering, and using those data to make meaningful care decisions that improve patient outcomes is difficult. The purpose of this article is to describe the Maternal Health Information Initiative, a multidisciplinary group of maternity care stakeholders charged with standardizing maternity care data. Complementary strategies that practicing clinicians can use to support this initiative and improve the usability of maternity care data are provided.

The role of health care technology in support of perinatal nurse staffing.

Health care technology can generate massive amounts of data. However, when data are generated from disparate, uncoordinated systems, using them to make decisions related to staffing can be a challenge. In this article, I describe the importance of data standardization, system interoperability, standard terminologies that support nursing practice, and nursing informatics expertise as tools for improving the usefulness of electronic systems for informing staffing decisions.

Lean in to our profession.

Nurses play a critical role in the delivery of high-quality, evidence-based health care. Nurses can "lean in" to our professional by voicing our opinions, contributing to decisions affecting health care practice and policy, and assuming leadership roles.

Standardizing the words nurses use to document elements of perinatal failure to rescue.

OBJECTIVE: To reach consensus for words used by nurses to document elements of a perinatal failure to rescue process measurement tool. DESIGN: Exploratory study with mixed methods. SETTING: Virtual. Participants were recruited through an online perinatal nursing discussion list and completed Internet-based electronic surveys. PARTICIPANTS: Twenty-nine (29) labor and delivery nurses with at least 5 years of bedside nursing experience and additional expertise in fetal heart monitoring. METHODS: Modified Delphi study with three rounds. Qualitative methods were used to analyze study results for round one. Rounds 2 and 3 were analyzed quantitatively with a desired level of consensus of 75%. RESULTS: Twenty-seven of 29 participants completed all three study rounds. Seventy-six distinct data elements related to careful monitoring, timely identification of problems, appropriate intervention, and activation of a team response were defined by consensus. Because classification of maternal and fetal risk determines assessment frequency in labor, specific criteria for classifying a woman or fetus as high risk or low risk were included in the definitions for which participants reached consensus. CONCLUSION: Achieving consensus about the actual words used to document perinatal nursing elements provides the foundation for incorporating paper-based process measurement tools, such as perinatal failure to rescue (P-FTR) into electronic documentation systems. Standardizing the words perinatal nurses use in documentation facilitates data retrieval and analysis and increases the usefulness of process measurement tools such as perinatal failure to rescue. Further, building process measurement tools into electronic systems may facilitate real-time rather than retrospective recognition of process deficiencies and improve perinatal outcomes.