Wisdom within: unlocking the potential of big data for nursing regulators

Nurse Leader Perceptions of Data in the Veterans Health Administration: A Qualitative Evaluation

Healthcare systems and nursing leaders aim to make evidence-based nurse staffing decisions. Understanding how nurses use and perceive available data to support safe staffing can strengthen learning healthcare systems and support evidence-based practice, particularly given emerging data availability and specific nursing challenges in data usability. However, current literature offers sparse insight into the nature of data use and challenges in the inpatient nurse staffing management context. We aimed to investigate how nurse leaders experience using data to guide their inpatient staffing management decisions in the Veterans Health Administration, the largest integrated healthcare system in the United States. We conducted semistructured interviews with 27 Veterans Health Administration nurse leaders across five management levels, using a constant comparative approach for analysis. Participants primarily reported using data for quality improvement, organizational learning, and organizational monitoring and support. Challenges included data fragmentation, unavailability and unsuitability to user need, lack of knowledge about available data, and untimely reporting. Our findings suggest that prioritizing end-user experience and needs is necessary to better govern evidence-based data tools for improving nursing care. Continuous nurse leader involvement in data governance is integral to ensuring high-quality data for end-user nurses to guide their decisions impacting patient care.

Completion of electronic nursing documentation of inpatient admission assessment: Insights from Australian metropolitan hospitals

INTRODUCTION

Electronic nursing documentation is an essential aspect of inpatient care and multidisciplinary communication. Analysing data in electronic medical record (eMR) systems can assist in understanding clinical workflows, improving care quality, and promoting efficiency in the healthcare system. This study aims to assess timeliness of completion of an electronic nursing admission assessment form and identify patient and facility factors associated with form completion in three metropolitan hospitals.

MATERIALS AND METHODS

Records of 37,512 adult inpatient admissions (November 2018-November 2019) were extracted from the hospitals' eMR system. A dichotomous variable descriptive of completion of the nursing assessment form (Yes/No) was created. Timeliness of form completion was calculated as the interval between date and time of admission and form completion. Univariate and multivariate multilevel logistic regression were used to identify factors associated with form completion.

RESULTS

An admission assessment form was completed for 78.4% (n = 29,421) of inpatient admissions. Of those, 78% (n = 22,953) were completed within the first 24 h of admission, 13.3% (n = 3,910) between 24 and 72 h from admission, and 8.7% (n = 2,558) beyond 72 h from admission. Patient length of hospital stay, admission time, and admitting unit's nursing hours per patient day were associated with form completion. Patient gender, age, and admitting unit type were not associated with form completion.

DISCUSSION

Form completion rate was high, though more emphasis needs to be placed on the importance of timely completion to allow for adequate patient care planning. Staff education, qualitative understanding of delayed form completion, and streamlined guidelines on nursing admission and eMR use are recommended.

Pain Information Model and Its Potential for Predictive Analytics: Applicability of a Big Data Science Framework

PURPOSE

To describe the application of a big data science framework to develop a pain information model and to discuss the potential for its use in predictive modeling.

DESIGN AND METHOD

This is an application of a cross-industry standard process for a data mining adapted framework (the Applied Healthcare Data Science Framework) to build an information model on pain management and its potential for predictive modeling. Data were derived from electronic health records and were composed of approximately 51,000 records of unique adult patients admitted to clinical and surgical units between July 2015 and June 2019.

FINDINGS

The application of the Applied Healthcare Data Science Framework steps allowed the development of an information model on pain management, considering pain assessment, interventions, goals, and outcomes. The developed model has the potential to be used for predicting which patients are most likely to be discharged with self-reported pain.

CONCLUSIONS

Through the application of the framework, it is possible to support health professionals' decision making on the use of data to improve the effectiveness of pain management.

CLINICAL RELEVANCE

In the long term, the framework is intended to guide data science methodologies to personalize treatments, reduce costs, and improve health outcomes.

Information Model on Pain Management: An Analysis of Big Data

PURPOSE

To develop an information model to support secondary use of data using electronic health records.

DESIGN

Retrospective observational data-driven study with secondary use of data. The sample was composed of structured data from all adults admitted to clinical and surgical inpatient units of a public university hospital. Data between June 2014 and July 2019 were included, totaling approximately 51,000 unique patients.

METHODS

Six systematic steps of the Applied Healthcare Data Science Roadmap were applied.

FINDINGS

An information model on pain management was developed.

CONCLUSIONS

The data science methodology used allowed the development of information model in pain management, mapping attributes about pain management and to categorize them into assessment and reassessment, goals, interventions, and outcomes.

CLINICAL RELEVANCE

Based on the information model developed, it is possible to optimize the electronic health system and improve the quality of patient care delivery in pain management.

Construction and usability of community health nursing database in rural north-eastern Thailand

INTRODUCTIONS

Digitization could be incorporated in rural areas of resource-poor countries because information gathered by nurses working on-site could be better used.

BACKGROUND

For effective management of community health information, the usage and maintenance of digital records are important. Digitization of information provides essential information for informing health policy.

AIM

To develop a sustainable database to effectively collect and manage community health information and nursing practice.

METHODS

This study used a mixed method design. Phase 1 involved the development of a database system through repeated systematic focus group discussions with community health nurses. Phase 2 involved a practical trial examination of the developed system with both objective and subjective evaluations.

RESULTS

A nursing database system was developed with templates designed for the major health problems of communities. The templates were composed of multiple-choice items and a free-text field that allowed records to be more detailed than handwritten records and maintained in standardized formats. This enables accumulation of data that were less likely to be influenced by the variance of ability in each nurse.

DISCUSSION AND CONCLUSION

A multifaceted evaluation of the database system suggested that it could improve the efficiency of information management and contribute to the improvement of nursing care quality through standardization of the recording pattern.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

The nursing database will enable high-quality information storage that will potentially better inform health and healthcare policies as well as enable visualization of data concerning nursing care challenges and activities within the relevant communities. This information is essential for policy development and implementation in areas of human and fiscal resource allocations and meeting training/education needs.