Standardizing Physiologic Assessment Data to Enable Big Data Analytics

Standardizing nursing data extracted from electronic health records for integration into a statewide clinical data research network

BACKGROUND

Care plans documented by nurses in electronic health records (EHR) are a rich source of data to generate knowledge and measure the impact of nursing care. Unfortunately, there is a lack of integration of these data in clinical data research networks (CDRN) data trusts, due in large part to nursing care being documented with local vocabulary, resulting in non-standardized data. The absence of high-quality nursing care plan data in data trusts limits the investigation of interdisciplinary care aimed at improving patient outcomes.

OBJECTIVE

To map local nursing care plan terms for patients' problems and goals in the EHR of one large health system to the standardized nursing terminologies (SNTs), NANDA International (NANDA-I), and Nursing Outcomes Classification (NOC).

METHODS

We extracted local problems and goals used by nurses to document care plans from two hospitals. After removing duplicates, the terms were independently mapped to NANDA-I and NOC by five mappers. Four nurses who regularly use the local vocabulary validated the mapping.

RESULTS

83% of local problem terms were mapped to NANDA-I labels and 93% of local goal terms were mapped to NOC labels. The nurses agreed with 95% of the mapping. Local terms not mapped to labels were mapped to the domains or classes of the respective terminologies.

CONCLUSION

Mapping local vocabularies used by nurses in EHRs to SNTs is a foundational step to making interoperable nursing data available for research and other secondary purposes in large data trusts. This study is the first phase of a larger project building, for the first time, a pipeline to standardize, harmonize, and integrate nursing care plan data from multiple Florida hospitals into the statewide CDRN OneFlorida+ Clinical Research Network data trust.

CE: Assessing Movement-Evoked Pain

ABSTRACT

One of the most common and nuanced tasks that nurses perform is pain assessment, particularly in acute postoperative settings where frequent reassessments are needed. Most assessments are limited to obtaining a pain intensity score with little attention paid to the conditions necessitating the assessment or the factors contributing to the pain. Pain is frequently assessed during rest, but seldom during periods of movement or activity, which is a crucial omission given that acute postoperative movement-evoked pain (MEP) is intense and a common barrier to healing and restoration of function. In addition to physical limitations, MEP can impede cognitive, emotional, and social functioning in ways that can contribute to chronic pain, mood disorders, and disability. Professional and regulatory standards are moving away from a focus on pain intensity to an emphasis on its context, impact on function, and associated distress. Thus, there are many driving forces compelling nurses to integrate MEP assessments into practice to expedite the restoration of biopsychosocial functioning in postoperative patients. The authors discuss the clinical significance of a MEP assessment as well as protocols and tools for completing such assessments.

SNOMED-CT as a standardized language system model for nursing: an integrative review

OBJECTIVE

To describe the use of the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) as a model for interoperability of the nursing terminology in the national and international contexts.

METHODS

This is an integrative literature review according to Cooper, which searched for articles in Portuguese, English and Spanish, published between September 2011 and November 2018 in the BVS, PubMed, SCOPUS, CINAHL, EMBASE, and Web of Science databases, ending in a sample of 15 articles.

RESULTS

The SNOMED-CT is a multi-professional nomenclature used by nursing in different care contexts, being associated with other standardized languages of the discipline, such as ICNP®, NANDA-I, and the Omaha System.

CONCLUSION

This review has shown that the use of SNOMED- CT is incipient in the national context, justifying the need to develop studies aimed at mapping the interoperability of existing systems of standardized language, especially NANDA-I, ICNP and Omaha System, in order to adapt the implementation of SNOMED-CT.

The use of SNOMED CT, 2013-2020: a literature review

OBJECTIVE

This article reviews recent literature on the use of SNOMED CT as an extension of Lee et al's 2014 review on the same topic. The Lee et al's article covered literature published from 2001-2012, and the scope of this review was 2013-2020.

MATERIALS AND METHODS

In line with Lee et al's methods, we searched the PubMed and Embase databases and identified 1002 articles for review, including studies from January 2013 to September 2020. The retrieved articles were categorized and analyzed according to SNOMED CT focus categories (ie, indeterminate, theoretical, pre-development, implementation, and evaluation/commodity), usage categories (eg, illustrate terminology systems theory, prospective content coverage, used to classify or code in a study, retrieve or analyze patient data, etc.), medical domains, and countries.

RESULTS

After applying inclusion and exclusion criteria, 622 articles were selected for final review. Compared to the papers published between 2001 and 2012, papers published between 2013 and 2020 revealed an increase in more mature usage of SNOMED CT, and the number of papers classified in the "implementation" and "evaluation/commodity" focus categories expanded. When analyzed by decade, papers in the "pre-development," "implementation," and "evaluation/commodity" categories were much more numerous in 2011-2020 than in 2001-2010, increasing from 169 to 293, 30 to 138, and 3 to 65, respectively.

CONCLUSION

Published papers in more mature usage categories have substantially increased since 2012. From 2013 to present, SNOMED CT has been increasingly implemented in more practical settings. Future research should concentrate on addressing whether SNOMED CT influences improvement in patient care.

Nursing Praxis for Reducing Documentation Burden Within Nursing Admission Assessments

The purpose of this quality improvement project was to conduct a scholarly assessment of the information collected within the nursing admission encounter and implement content revisions across three pilot medical surgical units. The guiding principles were to preserve regulatory information, identify nurse-sensitive data, and eliminate nonessential information. The goal was to decrease the number of clicks and time expended to document electronically an acute admission encounter by 20% and to project the number of hours returned to patient care as a result of decreasing computer clicks. A second goal was to quantify the projected costs of completing a nursing admission encounter. This quality improvement project leveraged nurse executive competencies to intersect the nursing process to develop a nursing documentation praxis. This author's praxis reduced nursing documentation burden in clicks by 29% and reduced time to document on an admission encounter by 34%. This restored the focus on nurse-patient interactions by returning 1016 hours per year to patient care activities, across three pilot units, as well as quantified the costs of completing a nursing admission assessment to utilize in future cost analysis of nursing tasks.

A Methodology to Generate Longitudinally Updated Acute-On-Chronic Liver Failure Prognostication Scores From Electronic Health Record Data

Queries of electronic health record (EHR) data repositories allow for automated data collection. These techniques have not been used in hepatology due to the inability to capture hepatic encephalopathy (HE) grades, which are inputs for acute-on-chronic liver failure (ACLF) models. Here, we describe a methodology to use EHR data to calculate rolling ACLF scores. We examined 239 patient admissions with end-stage liver disease from July 2014 to June 2019. We mapped EHR flowsheet data to determine HE grades and calculated two longitudinally updated ACLF scores. We validated HE grades and ACLF diagnoses by chart review and calculated sensitivity, specificity, and Cohen's kappa. Of 239 patient admissions analyzed, 37% were women, 46% were non-Hispanic white, median age was 60 years, and the median Model for End-Stage Liver Disease-Na score at admission was 25. Of the 239, 7% were diagnosed with ACLF as defined by the North American Consortium for the Study of End-Stage Liver Disease (NACSELD) diagnostic criteria at admission, 27% during the hospitalization, and 9% at discharge. Forty percent were diagnosed with ACLF by the European Association for the Study of the Liver- Chronic Liver Failure Consortium (CLIF-C) diagnostic criteria at admission, 51% during the hospitalization, and 34% at discharge. From the chart review of 51 admissions, we found sensitivities and specificities for any HE (grades 1-4) were 92%-97% and 76%-95%, respectively; for severe HE (grades 3-4), sensitivities and specificities were 100% and 78%-98%, respectively. Cohen's kappa between flowsheet and chart review of HE grades ranged from 0.55 to 0.72. Sensitivities and specificities for NACSELD-ACLF diagnoses were 75%-100% and 96%-100%, respectively; for CLIF-C-ACLF diagnoses, these were 91%-100% and 96-100%, respectively. We generated approximately 28 unique ACLF scores per patient per admission day. Conclusion: We developed an informatics-based methodology to calculate longitudinally updated ACLF scores. This opens new analytic potentials, such as big data methods, to develop electronic phenotypes for patients with ACLF.

A refined methodology for validation of information models derived from flowsheet data and applied to a genitourinary case

Use of electronic health record data is expanding to support quality improvement and research; however, this requires standardization of the data and validation within and across organizations. Information models (IMs) are created to standardize data elements into a logical organization that includes data elements, definitions, data types, values, and relationships. To be generalizable, these models need to be validated across organizations. The purpose of this case report is to describe a refined methodology for validation of flowsheet IMs and apply the revised process to a genitourinary IM created in one organization. The refined IM process, adding evidence and input from experts, produced a clinically relevant and evidence-based model of genitourinary care. The refined IM process provides a foundation for optimizing electronic health records with comparable nurse sensitive data that can add to common data models for continuity of care and ongoing use for quality improvement and research.

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace

BACKGROUND

Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace activity, but little is known about the ability of these data to predict RN strain.

OBJECTIVES

The purpose of this study was to assess the utility of ambient workplace data, defined as time-stamped transaction records and log file data produced by non-electronic health record care delivery tools (e.g., nurse call systems, communication devices), as an information channel for automated sensing of RN strain.

METHODS

In this exploratory retrospective study, ambient data for a 1-year time period were exported from electronic nurse call, medication dispensing, time and attendance, and staff communication systems. Feature sets were derived from these data for supervised machine learning models that classified work shifts by unplanned overtime. Models for three timeframes -8, 10, and 12 hours-were created to assess each model's ability to predict unplanned overtime at various points across the work shift.

RESULTS

Classification accuracy ranged from 57 to 64% across three analysis timeframes. Accuracy was lowest at 10 hours and highest at shift end. Features with the highest importance include minutes spent using a communication device and percent of medications delivered via a syringe.

CONCLUSION

Ambient data streams can serve as information channels that contain signals related to unplanned overtime as a proxy indicator of RN strain as early as 8 hours into a work shift. This study represents an initial step toward enhanced detection of RN strain and proactive prevention of missed or delayed patient care.

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.

Gap Analysis and Refinement Recommendations of Skin Alteration and Pressure Ulcer Enterprise Reference Models against Nursing Flowsheet Data Elements

Reference models are an essential instrument to provide structure and guidance in the creation and use of data elements within an organizations' electronic health record (EHR). Standardization of data elements is imperative to ensure clinical data is consistently and reliably captured for use in clinical documentation, care communication, and a variety of downstream data uses. Ongoing assessment and refinement of reference models and data elements are necessary to ascertain clinical data capture is applicable and inclusive across a variety of caregivers and domains. We performed a gap analysis on current state nursing data elements against two validated interprofessional reference models: skin alteration and pressure ulcer assessments. We present our findings along with recommendations for reference model refinements. We also highlight additional findings of inconsistencies and redundancies within data elements used for nursing documentation and highlight recommendations for improvement.

Modeling Flowsheet Data to Support Secondary Use

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

Surgical data science: The new knowledge domain

Healthcare in general, and surgery/interventional care in particular, is evolving through rapid advances in technology and increasing complexity of care, with the goal of maximizing the quality and value of care. Whereas innovations in diagnostic and therapeutic technologies have driven past improvements in the quality of surgical care, future transformation in care will be enabled by data. Conventional methodologies, such as registry studies, are limited in their scope for discovery and research, extent and complexity of data, breadth of analytical techniques, and translation or integration of research findings into patient care. We foresee the emergence of surgical/interventional data science (SDS) as a key element to addressing these limitations and creating a sustainable path toward evidence-based improvement of interventional healthcare pathways. SDS will create tools to measure, model, and quantify the pathways or processes within the context of patient health states or outcomes and use information gained to inform healthcare decisions, guidelines, best practices, policy, and training, thereby improving the safety and quality of healthcare and its value. Data are pervasive throughout the surgical care pathway; thus, SDS can impact various aspects of care, including prevention, diagnosis, intervention, or postoperative recovery. The existing literature already provides preliminary results, suggesting how a data science approach to surgical decision-making could more accurately predict severe complications using complex data from preoperative, intraoperative, and postoperative contexts, how it could support intraoperative decision-making using both existing knowledge and continuous data streams throughout the surgical care pathway, and how it could enable effective collaboration between human care providers and intelligent technologies. In addition, SDS is poised to play a central role in surgical education, for example, through objective assessments, automated virtual coaching, and robot-assisted active learning of surgical skill. However, the potential for transforming surgical care and training through SDS may only be realized through a cultural shift that not only institutionalizes technology to seamlessly capture data but also assimilates individuals with expertise in data science into clinical research teams. Furthermore, collaboration with industry partners from the inception of the discovery process promotes optimal design of data products as well as their efficient translation and commercialization. As surgery continues to evolve through advances in technology that enhance delivery of care, SDS represents a new knowledge domain to engineer surgical care of the future.