Improving Accuracy of Handoff by Implementing an Electronic Health Record–generated Tool: An Improvement Project in an Academic Neonatal Intensive Care Unit

Evaluation of activities of daily living using an electronic version of the Longshi Scale in patients with stroke: reliability, consistency, and preference

BACKGROUND

The Longshi Scale is a pictorial assessment tool for evaluating activities of daily living (ADL) in patients with stroke. The paper-based version presents challenges; thus, the WeChat version was created to enhance accessibility. Herein, we aimed to validate the inter-rater and test-retest reliabilities of the WeChat version of the Longshi Scale and explore its potential clinical applications.

METHODS

We recruited 115 patients with stroke in the study. The ADL results of each patient were assessed using both the WeChat and paper-based version of the Longshi Scale; each evaluation was conducted by 28 health professionals and 115 caregivers separately. To explore the test-retest reliability of the WeChat version, 22 patients were randomly selected and re-evaluated by health professionals using the WeChat version. All evaluation criteria were recorded, and all evaluators were surveyed to indicate their preference between the two versions.

RESULTS

Consistency between WeChat and the paper-based Longshi Scale was high for ADL scores by health professionals (ICC2,1 = 0.803-0.988) and caregivers (ICC2,1 = 0.845-0.983), as well as for degrees of disability (κw = 0.870 by professionals; κw = 0.800 by caregivers). Bland-Altman analysis showed no significant discrepancies. The WeChat version exhibited good test-retest reliability (κw = 0.880). The WeChat version showed similar inter-rater reliability in terms of the ADL score evaluated using the paper-based version (ICC2,1 = 0.781-0.941). The time to complete assessments did not differ significantly, although the WeChat version had a shorter information entry time (P < 0.001, 95% confidence interval: -43.463 to -15.488). Health professionals favored the WeChat version (53.6%), whereas caregivers had no significant preference.

CONCLUSIONS

The WeChat version of the Longshi Scale is reliable and serves as a suitable alternative for health professionals and caregivers to assess ADL levels in patients with stroke. The WeChat version of the Longshi Scale is considered user-friendly by health professionals, although it is not preferred by caregivers.

TRIAL REGISTRATION

This study was approved by the Ethics Committee of the Second People's Hospital of Shenzhen (approval number: 20210812003-FS01) and registered on the Clinical Trial Register Center website: clinicaltrials.gov on January 31, 2022 (registration no.: NCT05214638).

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

BACKGROUND

Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research.

OBJECTIVE

The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences.

METHODS

We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results.

RESULTS

We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized.

CONCLUSIONS

The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

Shift-to-shift handoffs in the NICU: lessons learned from a large scale audit

OBJECTIVE

Describe the frequency of best practice behaviors during NICU provider and nursing shift-to-shift handoffs and identify strengths and opportunities for improvement.

STUDY DESIGN

Observational study of handoff characteristics among 40 centers participating in a learning collaborative over a 10-month period. Data were gathered using a handoff audit tool that outlined best practices. Comparisons of behaviors between nurse-to-nurse and provider-to-provider handoffs were made where appropriate.

RESULTS

Overall, 946 audits of shift-to-shift handoffs were analyzed. While many behaviors were demonstrated reliably, differences between nurse-to-nurse vs provider-to-provider handoffs were noted. Families were present for 5.9% of handoffs and, among those who were present, 48.2% participated by contributing information, asking questions, and sharing goals.

CONCLUSIONS

Observation and measurement of handoff behaviors can be used to identify opportunities to improve handoff communication, family participation, and human factors that support handoff. Auditing handoffs is feasible and necessary to improve these critical transitions in infants' care.

Using machine learning for process improvement in sepsis management

INTRODUCTION

In the U.S., sepsis afflicts 1.7 million adults, causing 270,000 deaths each year. Early detection of sepsis could decrease the number of deaths by 92,000 annually and decrease hospital expenditures by 1.5 billion USD. Few prior studies and reviews have presented a holistic understanding of the relationship between machine learning and existing process improvement measures. This study, in addition to discussing machine learning and existing process improvements measures, elaborates on the disadvantages and the barriers to integrating machine learning into the clinic. This article synthesizes previous studies to educate healthcare professionals on effectively managing sepsis by leveraging the benefits of machine learning.

METHODS

This study used the PubMed database. Search terms include sepsis antibiotics, sepsis process improvement, sepsis machine learning. Our search criteria included previous studies published between January 1, 2017, and February 1, 2022.

RESULTS/DISCUSSION

Although machine learning algorithms have better predictive capabilities, their effectiveness in the clinical setting is limited as studies show mixed results because the medical staff often fails to intervene. To overcome poor interventional response, clinicians need to work with the facility's IT department to ensure integration into clinical workflow and minimize alert-fatigue. Algorithms should enhance the productivity of clinical teams, not attempt to replace them entirely.

CONCLUSION

Hospitals can employ process improvement measures that effectively utilize machine learning algorithms to ensure integration into clinical workflows. Healthcare professionals can utilize workflow tools in addition to the predictive capabilities of machine learning to enhance clinical decisions in sepsis.

Implementation of ED I-PASS as a Standardized Handoff Tool in the Pediatric Emergency Department

INTRODUCTION

Communication, failures during patient handoffs are a significant cause of medical error. There is a paucity of data on standardized handoff tools for intershift transitions of care in pediatric emergency medicine (PEM). The purpose of this quality improvement (QI) initiative was to improve handoffs between PEM attending physicians (i.e., supervising physicians ultimately responsible for patient care) through the implementation of a modified I-PASS tool (ED I-PASS). Our aims were to: (1) increase the proportion of physicians using ED I-PASS by two-thirds and (2) decrease the proportion reporting information loss during shift change by one-third, over a 6-month period.

METHODS

After literature and stakeholder review, Expected Disposition, Illness Severity, Patient Summary, Action List, Situational Awareness, Synthesis by Receiver (ED I-PASS) was implemented using iterative Plan-Do-Study-Act cycles, incorporating: trained "super-users"; print and electronic cognitive support tools; direct observation; and general and targeted feedback. Implementation occurred from September to April of 2021, during the height of the COVID-19 pandemic, when patient volumes were significantly lower than prepandemic levels. Data from observed handoffs were collected for process outcomes. Surveys regarding handoff practices were distributed before and after ED I-PASS implementation.

RESULTS

82.8% of participants completed follow-up surveys, and 69.6% of PEM physicians were observed performing a handoff. Use of ED I-PASS increased from 7.1% to 87.5% ( p < .001) and the reported perceived loss of important patient information during transitions of care decreased 50%, from 75.0% to 37.5% ( p = .02). Most (76.0%) participants reported satisfaction with ED I-PASS, despite half citing a perceived increase in handoff length. 54.2% reported a concurrent increase in written handoff documentation during the intervention.

CONCLUSION

ED I-PASS can be successfully implemented among attending physicians in the pediatric emergency department setting. Its use resulted in significant decreases in reported perceived loss of patient information during intershift handoffs.

Increasing early exposure to mother's own milk in premature newborns

OBJECTIVE

Increase the proportion of ≤33 weeks newborns exposed to mother's own milk (MOM) oral care by 12 h of age by 20% over 2 years to support a healthier microbiome.

STUDY DESIGN

We implemented interventions to support early expression of colostrum and reliable delivery of resultant MOM to premature newborns. Statistical process control charts were used to track progress and provide feedback to staff. Proportions of newborns exposed to MOM by 12 h were compared relative to baseline.

RESULTS

There were 46, 66, and 46 newborns in the baseline, implementation, and sustainability periods, respectively. The primary outcome improved from 48% to 61% in the implementation period (relative change 1.27, 95% CI 0.89, 1.81, p = 0.2), to 69% in sustainability period (relative to baseline 1.45, 95% CI 1.02, 2.08, p = 0.03).

CONCLUSION

An interdisciplinary team-based, multicycle, quality improvement intervention resulted in increased rates of early exposure to MOM.

The Impact of Electronic Handoff Tool on Sign-Out Practices in an Internal Medicine Residency Program

High-quality and efficient sign-outs are essential to ensure patient safety. To evaluate the impact of a new handoff tool by objective measures of handoff quality and residents' subjective experiences. Internal medicine residents working on a medical ward service completed a handoff clinical evaluation exercise (CEX) questionnaire and an anonymous survey on handoff quality and experiences prior to implementing a new handoff tool and at 2 and 6 weeks after implementation. CEX scores significantly improved from 5.3 ± 1.1 to 6.9 ± 0.7 in 6 weeks ( P < 0.05). Residents reported that they were contacted less frequently after work, information needed by the receiving resident was more often found in the sign-out, and that tasks signed out to the oncoming team were more often executed. Before implementing the new handoff tool, 87% of residents reported that they were contacted after work hours 1-2 times per week with questions, while 75% of participants reported that they were almost never contacted after work hours after the new tool was implemented. A standardized handoff tool that utilizes smart phrases to provide residents with templates for sign-out significantly improved the quality and experience of sign-out in a short time period.

A Quality Improvement Approach to Improving Discharge Documentation

Introduction

Accurate discharge documentation is critical to ensuring a safe and effective transition of care following hospitalization, yet many discharge summaries do not meet consensus standards for content. A local needs assessment demonstrated gaps in documentation of 3 essential elements: discharge diagnosis, discharge medications, and follow-up appointments. This study aimed to increase the completion of three discharge elements from a baseline of 45% by 20 percentage points over 16 months for patients discharged from the general pediatrics service.

Methods

Ten discharge summaries were randomly selected and analyzed during each successive 2-week time period. Plan-Do-Study-Act cycles aimed to improve provider knowledge of essential discharge summary content, clarify communication during rounds, and create electronic health record shortcuts and quick-reference tools.

Results

The percentage of discharge summaries containing all 3 required elements increased from 45% to 73%. Specifically, documentation increased for discharge diagnosis (65%-87%), discharge medications (71%-90%), and follow-up appointments (88%-93%). There was no significant delay in discharge summary completion.

Conclusions

Discharge summaries are meaningfully and sustainably improved through provider education, workflows for clear communication, and electronic health record optimization.