New Coding Guidelines Reduce Emergency Department Note Bloat But More Work Is Needed.

STUDY OBJECTIVE: The length and redundancy of notes authored by clinicians has significantly increased, giving rise to the term "note bloat." We analyzed the impact of new coding guidelines and documentation best practices on the length of emergency department (ED) notes and the amount of time clinicians spent documenting. METHODS: In a large, multisite health care delivery organization, we retrospectively evaluated the length of all ED provider notes and the amount of time clinicians spent documenting between February 2018 and June 2023. In January 2023, we implemented changes to the standardized note template to align with the new coding guidelines from the American Medical Association and the Centers for Medicare & Medicaid Services. The primary outcomes were the length of provider notes and the amount of time spent documenting. RESULTS: Our study sample consisted of 1,679,762 ED provider notes. Six months after the intervention, the average note length decreased by 872 words (95% confidence interval 867 to 877 words), whereas the amount of time clinicians spent documenting did not change. CONCLUSIONS: Embracing new guidelines and practices, we reduced the length of ED provider notes by 872 words. Despite this, the time clinicians spent documenting did not change significantly. We provide an early report of success in reducing note bloat in the ED to help guide future efforts to reduce overall documentation burden.

Variation in pediatric and adolescent electronic health data sharing practices under the 21st Century Cures Act.

OBJECTIVE: To describe real-world practices and variation in implementation of the Information Blocking provisions amongst healthcare organizations caring for pediatric patients. MATERIALS AND METHODS: An online survey regarding implementation practices was distributed to representatives from 10 participating US healthcare organizations located in 6 different states. The survey was followed by structured interviews conducted through video conference. Information was gathered about implementation practices at each organization, with a focus on patient and proxy portal access to, and segmentation capabilities of, certain data classes listed in the United States Core Data for Interoperability Version 1. RESULTS: All organizations had implemented the information blocking provisions at their institution. All organizations utilized different portal account types for proxies and users. All organizations reported the capability of sharing labs, medications, problem lists, imaging, and notes with the parent/guardian of the non-adolescent minor user with differences in how sensitive elements within the data classes were protected. Variability existed in how data was shared with the remaining user types. DISCUSSION: Significant variability exists in how organizations have implemented the information blocking rules. Variation in data sharing and data access between institutions can result in privacy breaches and create confusion about completeness of data for patients and families. CONCLUSION: Healthcare organizations have utilized varying strategies to comply with the information blocking provisions of the 21st Century Cures Act. Increased clarity from the Office of the National Coordinator for Health Information Technology on minor, adolescent, and caregiver privacy and improved segmentation capabilities from Electronic Health Record vendors is needed.

Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments.

OBJECTIVE: Understand the perceived role of electronic health records (EHR) and workflow fragmentation on clinician documentation burden in the emergency department (ED). METHODS: From February to June 2022, we conducted semistructured interviews among a national sample of US prescribing providers and registered nurses who actively practice in the adult ED setting and use Epic Systems' EHR. We recruited participants through professional listservs, social media, and email invitations sent to healthcare professionals. We analyzed interview transcripts using inductive thematic analysis and interviewed participants until we achieved thematic saturation. We finalized themes through a consensus-building process. RESULTS: We conducted interviews with 12 prescribing providers and 12 registered nurses. Six themes were identified related to EHR factors perceived to contribute to documentation burden including lack of advanced EHR capabilities, absence of EHR optimization for clinicians, poor user interface design, hindered communication, increased manual work, and added workflow blockages, and five themes associated with cognitive load. Two themes emerged in the relationship between workflow fragmentation and EHR documentation burden: underlying sources and adverse consequences. DISCUSSION: Obtaining further stakeholder input and consensus is essential to determine whether these perceived burdensome EHR factors could be extended to broader contexts and addressed through optimizing existing EHR systems alone or through a broad overhaul of the EHR's architecture and primary purpose. CONCLUSION: While most clinicians perceived that the EHR added value to patient care and care quality, our findings underscore the importance of designing EHRs that are in harmony with ED clinical workflows to alleviate the clinician documentation burden.

How Are Leading Research Institutions Engaging with Data Sharing Tools and Programs?

With widespread electronic health record (EHR) adoption and improvements in health information interoperability in the United States, troves of data are available for knowledge discovery. Several data sharing programs and tools have been developed to support research activities, including efforts funded by the National Institutes of Health (NIH), EHR vendors, and other public- and private-sector entities. We surveyed 65 leading research institutions (77% response rate) about their use of and value derived from ten programs/tools, including NIH's Accrual to Clinical Trials, Epic Corporation's Cosmos, and the Observational Health Data Sciences and Informatics consortium. Most institutions participated in multiple programs/tools but reported relatively low usage (even when they participated, they frequently indicated that fewer than one individual/month benefitted from the platform to support research activities). Our findings suggest that investments in research data sharing have not yet achieved desired results.

Analyzing Patient-Provided Responses to Improve Collection of Health Equity Data Elements.

Self-report is purported to be the gold standard for collecting demographic information. Many entry forms include a free-text "write-in" option in addition to structured responses. Balancing the flexibility of free-text with the value of collecting data in a structured format is a challenge if the data are to be useful for measuring and mitigating health disparities. While much work has been done to improve collection of race and ethnicity information, how to best collect data related to sexual and gender minority status and military veteran status has been less commonly studied. We analyzed 3,381 patient-provided free-text responses collected via a patient portal for gender identity, sexual orientation, pronouns, and veteran experiences. We identified common responses to better understand our patient population and help improve future iterations of data collection tools.

Providers Electing to Receive Electronic Result Notifications: Demographics and Motivation.

BACKGROUND: Automated electronic result notifications can alert health care providers of important clinical results. In contrast to historical notification systems, which were predominantly focused on critical laboratory abnormalities and often not very customizable, modern electronic health records provide capabilities for subscription-based electronic notification. This capability has not been well studied. OBJECTIVES: The purpose of this study was to develop an understanding of when and how a provider decides to use a subscription-based electronic notification. Better appreciation for the factors that contribute to selecting such notifications could aid in improving the functionality of these tools. METHODS: We performed an 8-month quantitative assessment of 3,291 notifications and a qualitative survey assessment of 73 providers who utilized an elective notification tool in our electronic health record. RESULTS: We found that most notifications were requested by attending physicians (∼60%) and from internal medicine specialty (∼25%). Most providers requested only a few notifications while a small minority (nearly 5%) requested 10 or more in the study period. The majority (nearly 30%) of requests were for chemistry laboratories. Survey respondents reported using the tool predominantly for important or time-sensitive laboratories. Overall opinions of the tool were positive (median = 7 out of 10, 95% confidence interval: 6-9), with 40% of eligible respondents reporting the tool improved quality of care. Reported examples included time to result review, monitoring of heparin drips, and reviewing pathology results. CONCLUSION: Developing an understanding for when and how providers decide to be notified of clinical results can help aid in the design and improvement of clinical tools, such as improved elective notifications. These tools may lead to reduced time to result review which could in turn improve clinical care quality.

Pandemic Telemedicine Adoption Trends in a Predominantly Rural Integrated Health System.

Between March 2020 and February 2022, use of telemedicine services in the U.S. shifted dramatically in response to the evolving SARS-CoV2 pandemic. The initial wave caused many non-emergent clinical services to be postponed, including specialty care clinic visits, which were rapidly converted to telemedicine encounters. Telemedicine use ebbed and flowed with subsequent pandemic waves. This paper describes trends in telemedicine use from March 2020-February 2022 at Geisinger, a predominantly rural integrated health system. It highlights characteristics of 5,390 virtual vs. 15,740 in-person clinic visits to neurosurgery and gastroenterology specialists in December 2021 and January 2022. Differences in ordering of diagnostic testing and prescription medications, as well as post-clinic-visit utilization, varied by specialty. Virtual visits in these specialties saved patients from traveling over 174,700 miles/month to attend appointments. Analyzing telemedicine use patterns can inform future resource allocation and determine when virtual encounters can complement or replace in-person specialty care visits.

Comparison of Ordering Tools on Adherence to Treatment Protocols in the Emergency Department.

OBJECTIVE: To assess adherence to clinical protocols in the emergency department after the implementation of embedded order panels in the electronic health record. MATERIALS AND METHODS: Due to infrequent use, a subset of order sets were redesigned as embedded order panels in a menu-style quick list. Usage was measured before and after implementation at seven departments. RESULTS: There were 2,247 applicable encounters during the pre-intervention period and 1,723 post-intervention. The use of order sets increased significantly after implementation (14% vs. 33% or encounters, p < 0.001). Traditional order sets required at least seven keystrokes or mouse clicks, while embedded order panels required only two. DISCUSSION AND CONCLUSION: Use of order sets increased after implementation of embedded order panels; however, they were still only used for about one-third of applicable encounters suggesting that more work is needed to increase treatment protocol adherence and electronic health record efficiency.

Overcoming technical and cultural challenges to delivering equitable care for LGBTQ+ individuals in a rural, underserved area.

The lesbian, gay, bisexual, transgender, queer, or questioning (LGBTQ+) community is vulnerable to health-care disparities. Many health-care organizations are working to collect sexual orientation and gender identity in their electronic health records (EHRs), with the goal of providing more inclusive care to their LGBTQ+ patients. There are significant human and technical barriers to making these efforts successful. Based on our 5-year experience at Geisinger (an integrated health system located in a rural, generally conservative area), this case report provides insights to overcome challenges in 4 critical areas: (1) enabling the EHR to collect and use information to support the health-care needs of LGBTQ+ patients, (2) building a culture of awareness and caring, empowering members of the health-care team to break down barriers of misunderstanding and mistrust, (3) developing services to support the needs of LGBTQ+ patients, and (4) partnering with local communities to become a trusted health-care provider.

Effect of Electronic Health Record Reminders for Routine Immunizations and Immunizations Needed for Chronic Medical Conditions.

BACKGROUND: Immunization reminders in electronic health records (EHR) provide clinical decision support (CDS) that can reduce missed immunization opportunities. Little is known about using CDS rules from a regional immunization information system (IIS) to power local EHR immunization reminders. OBJECTIVE: This study aimed to assess the impact of EHR reminders using regional IIS CDS-provided rules on receipt of immunizations in a low-income, urban population for both routine immunizations and those recommended for patients with chronic medical conditions (CMCs). METHODS: We built an EHR-based immunization reminder using the open-source resource used by the New York City IIS in which we overlaid logic regarding immunizations needed for CMCs. Using a randomized cluster-cross-over pragmatic clinical trial in four academic-affiliated clinics, we compared captured immunization opportunities during patient visits when the reminder was "on" versus "off" for the primary immunization series, school-age boosters, and adolescents. We also assessed coverage of CMC-specific immunizations. Up-to-date immunization was measured by end of quarter. Rates were compared using chi square tests. RESULTS: Overall, 15,343 unique patients were seen for 26,647 visits. The alert significantly impacted captured opportunities to complete the primary series in both well-child and acute care visits (57.6% on vs. 54.3% off, p = 0.001, and 15.3% on vs. 10.1% off, p = 0.02, respectively), among most age groups, and several immunization types. Captured opportunities for CMC-specific immunizations remained low regardless of alert status. The alert did not have an effect on up-to-date immunization overall (89.1 vs. 88.3%). CONCLUSION: CDS in this population improved captured immunization opportunities. Baseline high rates may have blunted an up-to-date population effect. Converting Centers for Disease Control and Prevention (CDC) rules to generate sufficiently sensitive and specific alerts for CMC-specific immunizations proved challenging, and the alert did not have an impact on CMC-specific immunizations, potentially highlighting need for more work in this area.

Published models that predict hospital readmission: a critical appraisal.

INTRODUCTION: The number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness. OBJECTIVE: To critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation. METHODS: We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models. Guided by CAMPR, two researchers independently appraised published readmission models in two recent systematic reviews and concurrently extracted data to generate reference lists of eligibility criteria and risk factors. RESULTS: We found that published models (n=81) followed 6.8 recommendations (45%) on average. Many models had weaknesses in their development, including failure to internally validate (12%), failure to account for readmission at other institutions (93%), failure to account for missing data (68%), failure to discuss data preprocessing (67%) and failure to state the model's eligibility criteria (33%). CONCLUSIONS: The high prevalence of weaknesses in model development identified in the published literature is concerning, as these weaknesses are known to compromise predictive validity. CAMPR may support researchers, clinicians and administrators to identify and prevent future weaknesses in model development.

A deep database of medical abbreviations and acronyms for natural language processing.

The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6-14% increase in abbreviation coverage; 28-52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations .

A Practical Approach for Monitoring the Use of Copy-Paste in Clinical Notes.

The use of copy-paste in authoring clinical notes has been widely embraced by busy providers, but inappropriate copy-paste has been lambasted by critics for introducing risks related to patient safety and regulatory compliance. At an integrated academic health system with over 4,100 providers writing notes, we developed a pragmatic approach to assess the use of copy-paste. From January 1-December 31, 2020, approximately 2.3M inpatient notes and 6.6M ambulatory clinic notes were authored in our electronic health record. Of the inpatient notes, 42% used copy-paste, and 19% of overall note content was copied; in ambulatory notes, 18% used copy-paste and 12% of note content was copied. We describe an approach for including providers' copy-paste usage statistics into the ongoing professional practice evaluation process required for hospital accreditation, thereby offering individual training opportunities related to the lack of use of copy-paste or its potential overuse.

Ten principles for data sharing and commercialization.

Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing.

Characterizing non-heroin opioid overdoses using electronic health records.

INTRODUCTION: The opioid epidemic is a modern public health emergency. Common interventions to alleviate the opioid epidemic aim to discourage excessive prescription of opioids. However, these methods often take place over large municipal areas (state-level) and may fail to address the diversity that exists within each opioid case (individual-level). An intervention to combat the opioid epidemic that takes place at the individual-level would be preferable. METHODS: This research leverages computational tools and methods to characterize the opioid epidemic at the individual-level using the electronic health record data from a large, academic medical center. To better understand the characteristics of patients with opioid use disorder (OUD) we leveraged a self-controlled analysis to compare the healthcare encounters before and after an individual's first overdose event recorded within the data. We further contrast these patients with matched, non-OUD controls to demonstrate the unique qualities of the OUD cohort. RESULTS: Our research confirms that the rate of opioid overdoses in our hospital significantly increased between 2006 and 2015 (P < 0.001), at an average rate of 9% per year. We further found that the period just prior to the first overdose is marked by conditions of pain or malignancy, which may suggest that overdose stems from pharmaceutical opioids prescribed for these conditions. CONCLUSIONS: Informatics-based methodologies, like those presented here, may play a role in better understanding those individuals who suffer from opioid dependency and overdose, and may lead to future research and interventions that could successfully prevent morbidity and mortality associated with this epidemic.

Impact of Hearing Loss on Patient Falls in the Inpatient Setting.

INTRODUCTION: The objectives of this study were to investigate an association between the risk of patient falls and self-reported hearing loss and to examine whether self-reported hearing loss with versus without hearing aids predicts patient falls in an inpatient setting. METHODS: This retrospective cohort analysis was conducted in 2018 in a large, urban, academic medical center. Participants included unique inpatients (N=52,805) of adults aged >18 years between February 1, 2017, and February 1, 2018. Outcome measures were falls in the inpatient setting and hearing loss with versus without hearing aids as predictors for patient falls. RESULTS: Self-reported hearing loss was associated with falls in the inpatient setting (OR=1.74, 95% CI=1.46, 2.07, p<1.43 × 10-9). Among patients with hearing impairment, a lack of hearing aids increased the risk for falls in the inpatient setting (OR=2.70, 95% CI=1.64, 4.69, p<1.41 × 10-5). After accounting for the risk of fall using the Morse Fall Scale (which does not include hearing impairment) and controlling for age and sex, patients with hearing loss and no hearing aids were significantly more likely to fall (OR=2.44, 95% CI=1.002, 5.654, p<0.042), but patients with hearing loss who did have hearing aids were not significantly more likely to fall (p<0.889). Hearing loss together with the Morse Fall Scale better predicted falls than the Morse Fall Scale alone (p<0.017). CONCLUSIONS: In the inpatient setting, there was a positive association between hearing loss and falls. However, among patients with hearing loss, only those without hearing aids were significantly more likely to fall, accounting for the Morse Fall Scale score and demographics characteristics. These findings support adding hearing loss as a modifiable risk factor in risk assessment tools for falls and exploring the use of amplification devices as an intervention.

Bringing Cultural Competency to the EHR: Lessons Learned Providing Respectful, Quality Care to the LGBTQ Community.

The lesbian, gay, bisexual, transgender, queer (LGBTQ) community is vulnerable to healthcare disparities. Many healthcare organizations are contemplating efforts to collect sexual orientation and gender identity in the electronic health record (EHR), with a goal of providing more respectful, inclusive, high-quality care to their LGBTQ patients. There are significant human and technical barriers that must be overcome to make these efforts successful. Based on our four-year experience at Geisinger (an integrated health system located in a rural, generally conservative area), we provide insights to overcome challenges in two critical areas: 1) enabling the EHR to collect and use information to support the healthcare needs of LGBTQ patients, and 2) building a culture of awareness and caring, empowering members of the healthcare team to break down barriers of misunderstanding and mistrust.

Medication stewardship using computerized clinical decision support: A case study on intravenous immunoglobulins.

Background: Healthcare delivery organizations face increasing pressure to manage the use of medications in terms of safety, waste reduction, and cost containment. Objective: To describe a computerized provider order entry (CPOE) system intervention to optimize use of a commonly ordered, high-cost therapeutic: intravenous immune globulin (IVIG). Design: Description of IVIG order configuration, medication use patterns, and subsequent order set configuration development in a CPOE system. Measurements: IVIG orders were extracted from the CPOE system before and after the implementation of a specialty orderset to determine the indications for use, dosing, and duration of therapy. Orders were compared to a theoretical dosing schedule created from published evidence and data from a prior medication use evaluation. Results: During 36 months before the implementation of the IVIG order set, 1965 IVIG orders were reviewed. The prescribed IVIG dose varied considerably from the expected dose (mean = -1.8, range = -4.9-1.5). In the 27 months after order set implementation, 848 IVIG orders were reviewed. The prescribed IVIG dose was closer to the expected dose (mean = -1.2, range = -3.9-2.6, P < .0001). Conclusions: Order configuration processes are cumbersome and time-consuming, but can be streamlined to enhance a medication's usage in the healthcare system. A better understanding of institution-specific ordering patterns may facilitate more efficient and effective order configuration and optimize drug use.

Risk of Wrong-Patient Orders Among Multiple vs Singleton Births in the Neonatal Intensive Care Units of 2 Integrated Health Care Systems.

IMPORTANCE: Multiple-birth infants in neonatal intensive care units (NICUs) have nearly identical patient identifiers and may be at greater risk of wrong-patient order errors compared with singleton-birth infants. OBJECTIVES: To assess the risk of wrong-patient orders among multiple-birth infants and singletons receiving care in the NICU and to examine the proportion of wrong-patient orders between multiple-birth infants and siblings (intrafamilial errors) and between multiple-birth infants and nonsiblings (extrafamilial errors). DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study was conducted in 6 NICUs of 2 large, integrated health care systems in New York City that used distinct temporary names for newborns per the requirements of The Joint Commission. Data were collected from 4 NICUs at New York-Presbyterian Hospital from January 1, 2012, to December 31, 2015, and 2 NICUs at Montefiore Health System from July 1, 2013, to June 30, 2015. Data were analyzed from May 1, 2017, to December 31, 2017. All infants in the 6 NICUs for whom electronic orders were placed during the study periods were included. MAIN OUTCOMES AND MEASURES: Wrong-patient electronic orders were identified using the Wrong-Patient Retract-and-Reorder (RAR) Measure. This measure was used to detect RAR events, which are defined as 1 or more orders placed for a patient that are retracted (ie, canceled) by the same clinician within 10 minutes, then reordered by the same clinician for a different patient within the next 10 minutes. RESULTS: A total of 10 819 infants were included: 85.5% were singleton-birth infants and 14.5% were multiple-birth infants (male, 55.8%; female, 44.2%). The overall wrong-patient order rate was significantly higher among multiple-birth infants than among singleton-birth infants (66.0 vs 41.7 RAR events per 100 000 orders, respectively; adjusted odds ratio, 1.75; 95% CI, 1.39-2.20; P < .001). The rate of extrafamilial RAR events among multiple-birth infants (36.1 per 100 000 orders) was similar to that of singleton-birth infants (41.7 per 100 000 orders). The excess risk among multiple-birth infants (29.9 per 100 000 orders) appears to be owing to intrafamilial RAR events. The risk increased as the number of siblings receiving care in the NICU increased; a wrong-patient order error occurred in 1 in 7 sets of twin births and in 1 in 3 sets of higher-order multiple births. CONCLUSIONS AND RELEVANCE: This study suggests that multiple-birth status in the NICU is associated with significantly increased risk of wrong-patient orders compared with singleton-birth status. This excess risk appears to be owing to misidentification between siblings. These results suggest that a distinct naming convention as required by The Joint Commission may provide insufficient protection against identification errors among multiple-birth infants. Strategies to reduce this risk include using given names at birth, changing from temporary to given names when available, and encouraging parents to select names for multiple births before they are born when acceptable to families.