Sharing Clinical Notes while Protecting Adolescent Confidentiality and Maintaining Parental Insight.

BACKGROUND: The 21st Century Cures Act mandates sharing electronic health records (EHRs) with patients. Health care providers must ensure confidential sharing of medical information with adolescents while maintaining parental insight into adolescent health. Given variability in state laws, provider opinions, EHR systems, and technological limitations, consensus on best practices to achieve adolescent clinical note sharing at scale is needed. OBJECTIVES: This study aimed to identify an effective intervention process to implement adolescent clinical note sharing, including ensuring adolescent portal account registration accuracy, across a large multihospital health care system comprising inpatient, emergency, and ambulatory settings. METHODS: A query was built to assess portal account registration accuracy. At a large multihospital health care system, 80.0% of 12- to 17-year-old patient portal accounts were classified as inaccurately registered (IR) under a parent or registration accuracy unknown (RAU). To increase accurately registered (AR) accounts, the following interventions were pursued: (1) distribution of standardized portal enrollment training; (2) patient outreach email campaign to reregister 29,599 portal accounts; (3) restriction of access to remaining IR and RAU accounts. Proxy portal configurations were also optimized. Subsequently, adolescent clinical note sharing was implemented. RESULTS: Distribution of standardized training materials decreased IR and increased AR accounts (p = 0.0492 and 0.0058, respectively). Our email campaign (response rate: 26.8%) was most effective in decreasing IR and RAU accounts and increasing AR accounts (p < 0.002 for all categories). Remaining IR and RAU accounts, 54.6% of adolescent portal accounts, were subsequently restricted. Postrestriction, IR accounts continued declining significantly (p = 0.0056). Proxy portal enhancements with interventions deployed increased proxy portal account adoption. CONCLUSION: A multistep intervention process can be utilized to effectively implement adolescent clinical note sharing at a large scale across care settings. Improvements to EHR technology, portal enrollment training, adolescent/proxy portal settings, detection, and automation in reenrollment of inaccurate portal accounts are needed to maintain integrity of adolescent portal access.

A Practical Approach to Optimize Computerized Provider Order Entry Systems and Reduce Clinician Burden: Pre-Post Evaluation of Vendor-Derived "Order Friction" Data.

Computerized provider order entry (CPOE) systems have been cited as a significant contributor to clinician burden. Vendor-derived measures and data sets have been developed to help with optimization of CPOE systems. We describe how we analyzed vendor-derived Order Friction (OF) EHR log data at our health system and propose a practical approach for optimizing CPOE systems by reducing OF. We also conducted a pre-post intervention study using OF data to evaluate the impact of defaulting the frequency of urine, stool and nasal swab tests and found that all modified orders had significantly fewer changes required per order (p<0.01). Our proposed approach is a six-step process: 1) understand the ordering process, 2) understand OF data elements contextually, 3) explore ordering user-level factors, 4) evaluate order volume and friction from different order sources, 5) optimize order-level design, 6) identify high volume alerts to evaluate for appropriateness.

Clinician Acceptance of Order Sets for Pain Management: A Survey in Two Urban Hospitals.

BACKGROUND: Order sets are a clinical decision support (CDS) tool in computerized provider order entry systems. Order set use has been associated with improved quality of care. Particularly related to opioids and pain management, order sets have been shown to standardize and reduce the prescription of opioids. However, clinician-level barriers often limit the uptake of this CDS modality. OBJECTIVE: To identify the barriers to order sets adoption, we surveyed clinicians on their training, knowledge, and perceptions related to order sets for pain management. METHODS: We distributed a cross-sectional survey between October 2020 and April 2021 to clinicians eligible to place orders at two campuses of a major academic medical center. Survey questions were adapted from the widely used framework of Unified Theory of Acceptance and Use of Technology. We hypothesize that performance expectancy (PE) and facilitating conditions (FC) are associated with order set use. Survey responses were analyzed using logistic regression. RESULTS: The intention to use order sets for pain management was associated with PE to existing order sets, social influence (SI) by leadership and peers, and FC for electronic health record (EHR) training and function integration. Intention to use did not significantly differ by gender or clinician role. Moderate differences were observed in the perception of the effort of, and FC for, order set use across gender and roles of clinicians, particularly emergency medicine and internal medicine departments. CONCLUSION: This study attempts to identify barriers to the adoption of order sets for pain management and suggests future directions in designing and implementing CDS systems that can improve order sets adoption by clinicians. Study findings imply the importance of order set effectiveness, peer influence, and EHR integration in determining the acceptability of the order sets.

Characterizing Multitasking and Workflow Fragmentation in Electronic Health Records among Emergency Department Clinicians: Using Time-Motion Data to Understand Documentation Burden.

BACKGROUND: The impact of electronic health records (EHRs) in the emergency department (ED) remains mixed. Dynamic and unpredictable, the ED is highly vulnerable to workflow interruptions. OBJECTIVES: The aim of the study is to understand multitasking and task fragmentation in the clinical workflow among ED clinicians using clinical information systems (CIS) through time-motion study (TMS) data, and inform their applications to more robust and generalizable measures of CIS-related documentation burden. METHODS: Using TMS data collected among 15 clinicians in the ED, we investigated the role of documentation burden, multitasking (i.e., performing physical and communication tasks concurrently), and workflow fragmentation in the ED. We focused on CIS-related tasks, including EHRs. RESULTS: We captured 5,061 tasks and 877 communications in 741 locations within the ED. Of the 58.7 total hours observed, 44.7% were spent on CIS-related tasks; nearly all CIS-related tasks focused on data-viewing and data-entering. Over one-fifth of CIS-related task time was spent on multitasking. The mean average duration among multitasked CIS-related tasks was shorter than non-multitasked CIS-related tasks (20.7 s vs. 30.1 s). Clinicians experienced 1.4 ± 0.9 task switches/min, which increased by one-third when multitasking. Although multitasking was associated with a significant increase in the average duration among data-entering tasks, there was no significant effect on data-viewing tasks. When engaged in CIS-related task switches, clinicians were more likely to return to the same CIS-related task at higher proportions while multitasking versus not multitasking. CONCLUSION: Multitasking and workflow fragmentation may play a significant role in EHR documentation among ED clinicians, particularly among data-entering tasks. Understanding where and when multitasking and workflow fragmentation occurs is a crucial step to assessing potentially burdensome clinician tasks and mitigating risks to patient safety. These findings may guide future research on developing more scalable and generalizable measures of CIS-related documentation burden that do not necessitate direct observation techniques (e.g., EHR log files).

Clinical informatics during the COVID-19 pandemic: Lessons learned and implications for emergency department and inpatient operations.

In response to a pandemic, hospital leaders can use clinical informatics to aid clinical decision making, virtualizing medical care, coordinating communication, and defining workflow and compliance. Clinical informatics procedures need to be implemented nimbly, with governance measures in place to properly oversee and guide novel patient care pathways, diagnostic and treatment workflows, and provider education and communication. The authors' experience recommends (1) creating flexible order sets that adapt to evolving guidelines that meet needs across specialties, (2) enhancing and supporting inherent telemedicine capability, (3) electronically enabling novel workflows quickly and suspending noncritical administrative or billing functions in the electronic health record, and (4) using communication platforms based on tiered urgency that do not compromise security and privacy.

Lean Process Improvement in the Emergency Department.

Lean engineering is based on a process improvement strategy originally developed at Toyota and has been used in many different industries to maximize efficiency by minimizing waste. Lean improvement projects are frequently instituted in emergency departments in an effort to improve processes and thereby improve patient care. Such projects have been undertaken with success in many emergency departments in order to improve metrics such as door-to-provider time, left without being seen rate, and patient length of stay. By reducing waste in the system, Lean processes aim to maximize efficiency and minimize delay and redundancy to the extent possible.

Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets.

Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integrating clinical knowledge with machine learning can facilitate effective development and maintenance of order sets while promoting best practices in ordering. To this end, we simulated the revision of an "AM Lab Order Set" under 6 revision approaches. Revisions included changes in the order set content or default settings through 1) population statistics, 2) individualized prediction using machine learning, and 3) clinical knowledge. Revision criteria were determined using electronic health record (EHR) data from 2014 to 2015. Each revision's clinical appropriateness, workload from using the order set, and generalizability across time were evaluated using EHR data from 2016 and 2017. Our results suggest a potential order set revision approach that jointly leverages clinical knowledge and machine learning to improve usability while updating contents based on latest clinical knowledge and best practices.