Documentation In Electronic Health Research Articles

Creating Perinatal Nursing Care Plans Using ChatGPT: A Pathway to Improve Nursing Care Plans and Reduce Documentation Burden.

Extensive time spent on documentation in electronic health records (EHRs) impedes patient care and contributes to nurse burnout. Artificial intelligence-based clinical decision support tools within the EHR, such as ChatGPT, can provide care plan recommendations to the perinatal nurse. The lack of explicit methodologies for effectively integrating ChatGPT led to our initiative to build and demonstrate our ChatGPT-4 prompt to support nurse care planning. We employed our process model, previously tested with 22 diverse medical-surgical patient scenarios, to generate a tailored prompt for ChatGPT-4 to produce care plan suggestions for an exemplar patient presenting with preterm labor and gestational diabetes. A comparative analysis was conducted by evaluating the output against a "nurse-generated care plan" developed by our team of nurses on content alignment, accuracy of standardized nursing terminology, and prioritization of care. ChatGPT-4 delivered suggestions for nursing diagnoses, interventions, and outcomes comparable to the "nurse-generated care plan." It accurately identified major care areas, avoided irrelevant or unnecessary recommendations, and identified top priority care. Of the 24 labels generated by ChatGPT-4, 16 correctly utilized standardized nursing terminology. This demonstration of the use of our ChatGPT-4 prompt illustrates the potential of leveraging a large language model to assist perinatal nurses in creating care plans. The next steps are improving the accuracy of ChatGPT-4-generated standardized nursing terminology and integrating our prompt into EHRs. This work supports our broader goal of enhancing patient outcomes while mitigating the burden of documentation that contributes to nurse burnout.

Addressing menopause symptoms in the primary care setting: opportunity to bridge care delivery gaps.

Menopause symptoms affect quality of life and financial well-being but are often unaddressed in primary care clinics. Therefore, we evaluated the extent of menopause symptom documentation in electronic health records (EHRs) by primary health care professionals. We retrospectively reviewed adult women who reported moderate or higher vasomotor symptoms on a Mayo Clinic survey conducted from March 1, 2021, through June 30, 2021. We then assessed adequacy of menopause symptom documentation in the EHRs of these women who had primary care visits during the survey period. We reviewed the percentage of documented vasomotor symptoms from May 1, 2019, through May 1, 2021. In the Mayo Clinic Health System-Northwest Wisconsin Region, 229 women self-reported moderate or higher vasomotor symptoms in the Mayo Clinic survey. Although only 23% of these women had vasomotor symptoms listed in the EHR clinical problem lists, 60% of these women had vasomotor symptoms documented in their clinic notes from the primary care visit. Approximately 6% of women reported hormone therapy use for management of menopause symptoms, and nearly 15% reported use of nonhormone prescription therapies for vasomotor symptoms. A greater proportion of women in our study had EHR documentation of bothersome menopause symptoms than those reported in other studies, but vasomotor symptoms remain generally untreated. We need better methods for identifying midlife women with bothersome menopause symptoms in primary care clinics so that appropriate treatment options, including hormone therapy, can be discussed and offered.

Natural Language Processing for the Ascertainment and Phenotyping of Left Ventricular Hypertrophy and Hypertrophic Cardiomyopathy on Echocardiogram Reports

Extracting and accurately phenotyping electronic health documentation is critical for medical research and clinical care. We sought to develop a highly accurate and open-source natural language processing (NLP) module to ascertain and phenotype left ventricular hypertrophy (LVH) and hypertrophic cardiomyopathy (HCM) diagnoses fromechocardiogram reports within a diverse hospital network. After the initial development on 17,250 echocardiogram reports, 700 unique reports from 6 hospitals were randomly selected from data repositories within the Mass General Brigham healthcare system and manually adjudicated by physicians for 10 subtypes of LVH and diagnoses of HCM. Using an open-source NLP system, the module was formally tested on 300 training set reports and validated on 400 reports. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated to assess the discriminative accuracy of the NLP module. The NLP demonstrated robust performance across the 10 LVH subtypes, with the overall sensitivity and specificity exceeding 96%. In addition, the NLP module demonstrated excellent performance in detecting HCM diagnoses, with sensitivity and specificity exceeding 93%. In conclusion, we designed a highly accurate NLP module to determine the presence of LVH and HCM on echocardiogram reports. Our work demonstrates the feasibility and accuracy of NLP to detect diagnoses on imaging reports, even when described in free text. This module has been placed in the public domain to advance research, trial recruitment, and population health management for patients with LVH-associated conditions.

Disparities in cannabis use and documentation in electronic health records among children and young adults

The legalizations of medical and recreational cannabis have generated a great deal of interest in studying the health impacts of cannabis products. Despite increases in cannabis use, its documentation during clinical visits is not yet mainstream. This lack of information hampers efforts to study cannabis’s effects on health outcomes. A clear and in-depth understanding of current trends in cannabis use documentation is necessary to develop proper guidelines to screen and document cannabis use. Here we have developed and used a natural language processing pipeline to evaluate the trends and disparities in cannabis documentation. The pipeline includes a screening step to identify clinical notes with cannabis use documentation which is then fed into a BERT-based classifier to confirm positive use. This pipeline is applied to more than 23 million notes from a large cohort of 370,087 patients seen in a high-volume multi-site pediatric and young adult clinic over a period of 21 years. Our findings show a very low but growing rate of cannabis use documentation (<2%) in electronic health records with significant demographic and socioeconomic disparities in both documentation and positive use, which requires further attention.

Characterizing atrial fibrillation symptom improvement following de novo catheter ablation.

Atrial fibrillation (AF) symptom relief is a primary indication for catheter ablation, but AF symptom resolution is not well characterized. The study objective was to describe AF symptom documentation in electronic health records (EHRs) pre- and post-ablation and identify correlates of post-ablation symptoms. We conducted a retrospective cohort study using EHRs of patients with AF (n = 1293), undergoing ablation in a large, urban health system from 2010 to 2020. We extracted symptom data from clinical notes using a natural language processing algorithm (F score: 0.81). We used Cochran's Q tests with post-hoc McNemar's tests to determine differences in symptom prevalence pre- and post-ablation. We used logistic regression models to estimate the adjusted odds of symptom resolution by personal or clinical characteristics at 6 and 12 months post-ablation. In fully adjusted models, at 12 months post-ablation patients, patients with heart failure had significantly lower odds of dyspnoea resolution [odds ratio (OR) 0.38, 95% confidence interval (CI) 0.25-0.57], oedema resolution (OR 0.37, 95% CI 0.25-0.56), and fatigue resolution (OR 0.54, 95% CI 0.34-0.85), but higher odds of palpitations resolution (OR 1.90, 95% CI 1.25-2.89) compared with those without heart failure. Age 65 and older, female sex, Black or African American race, smoking history, and antiarrhythmic use were also associated with lower odds of resolution of specific symptoms at 6 and 12 months. The post-ablation symptom patterns are heterogeneous. Findings warrant confirmation with larger, more representative data sets, which may be informative for patients whose primary goal for undergoing an ablation is symptom relief.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

Interdisciplinary feeding clinic during the coronavirus disease 2019 pandemic: Implementing telehealth and social determinants of health screening.

Interdisciplinary feeding clinic during the coronavirus disease 2019 pandemic: Implementing telehealth and social determinants of health screening.

Usability of Graphical User Interfaces With Semiautomatic Delabeling Feature to Improve Drug Allergy Documentation

The quality of allergy documentation in electronic health records is frequently poor. To compare the usability of 3 graphical user interfaces (GUIs) for drug allergy documentation. Physicians tested 3 GUIs by means of 5 fictional drug allergy scenarios: the current GUI (GUI 0), using mainly free-text, and 2 new coded versions (GUI 1 and GUI 2) asking information on allergen category, specific allergen, symptom(s), symptom onset, timing of initial reaction, and diagnosis status with a semiautomatic delabeling feature. Satisfaction was measured by the System Usability Scale questionnaire, efficiency by time to complete the tasks, and effectiveness by a task completion score. Posttest interviews provided more in-depth qualitative feedback. Thirty physicians from 7 different medical specialties and with varying degrees of experience participated. The mean System Usability Scale scores for GUI 1 (77.25, adjective rating "Good") and GUI 2 (78.42, adjective rating "Good") were significantly higher than for GUI 0 (56.58, adjective rating "OK") (Z, 6.27, Padj < .001 and Z, 6.62, Padj < .001, respectively). There was no significant difference in task time between GUIs. Task completion scores of GUI 1 and GUI 2 were higher than for GUI 0 (Z, 9.59, Padj < .001 and Z, 11.87, Padj < .001, respectively). Quantitative and qualitative findings were combined to propose a GUI 3 with high usability. The usability and quality of allergy documentation was higher for the newly developed coded GUIs with a semiautomatic delabeling feature without being more time-consuming.

S54 Improving Influenza and Pneumococcal Vaccination Rates in an Inflammatory Bowel Disease Clinic at Tan Tock Seng Hospital, Singapore

Background: Despite a higher risk of infections (such as influenza and pneumococcal disease) with or without immunosuppressive or biologic therapies, vaccination rates remain low among inflammatory bowel disease (IBD) patients in Tan Tock Seng Hospital. Specifically, our 3 months’ retrospective data revealed that ≤15% of patients had completed their influenza and pneumococcal vaccinations. Hence, in January 2021, a team comprising of 3 IBD physicians and 2 new IBD nurses came together to work on a quality improvement (QI) project and streamline the vaccination processes. The primary aim was to improve vaccination rates through a nurse-led vaccination screening, assessment and referral exercise as well as addressing vaccination hesitancy. Methods: A cause and effect QI tool (fish-bone diagram) was adopted to assess and study areas for improvement. Through this root cause analysis, the team was able to identify gaps which contributed to poor vaccination uptake. They included system (unavailability of IBD vaccination protocol, no written education materials for IBD patients, inconvenient vaccination access), physician (limited physician time consult, incomplete vaccination screening or assessment) and patient factors (patient’s perceived lack of benefit, vaccine misconceptions, fear of side effects). In order to address aforementioned factors causing the poor vaccination rates, vaccination guidelines were first protocolised based on national and international clinical guidelines. An information pamphlet was also prepared to help patients better understand the importance and benefits of vaccinations. At each outpatient appointment, IBD nurses would first establish the patient’s vaccination eligibility based on age, disease activity, use of immunosuppressive therapy and vaccination history. Where appropriate, vaccination counselling and pamphlet will be provided to the patient. With patient’s consent, patients would then be referred to the treatment room at the clinic for vaccination administration, all within the same clinical setting. Results: There were 246 patients attended IBD clinic from June to November 2021. Our 6-months post-intervention study reported the following: 1. Clear and systematic vaccination screening and assessment- Vaccination documentation in electronic health records increased from 30% to 100% - a 70% increase compared to pre-intervention 2. Influenza vaccination- 60% of IBD patients fulfilled vaccination eligibility - Influenza vaccination completion increased from 13% to 55% – a 42% increase compared to pre-intervention 3. Pneumococcal vaccination- 28% of IBD patients fulfilled vaccination eligibility - Pneumococcal vaccination completion increased from 2% to 48% - a 46% increase compared to pre-intervention. Conclusion(s): The role of vaccination screening and recommendations were traditionally performed by physicians and pharmacists. However, with the implementation of nurse-led vaccination exercise, it had helped to improve and better identify eligible patients for vaccination in a systematic and concise manner in this one-stop service. It further empowered the 2 new IBD nurses in exercising their clinical judgement skills while freeing up time for physicians to address patients who may have higher medical needs requiring longer consultations. Most importantly, with increasing vaccination rates, IBD patients could be better protected against these vaccines-preventable diseases. We believe that we can achieve higher rates of vaccinations in due time through education and being a vaccine(s) advocate to further reduce vaccine hesitancy.

Physician Note Composition Patterns and Time on the EHR Across Specialty Types: a National, Cross-sectional Study.

The burden of clinical documentation in electronic health records (EHRs) has been associated with physician burnout. Numerous tools (e.g., note templates and dictation services) exist to ease documentation burden, but little evidence exists regarding how physicians use these tools in combination and the degree to which these strategies correlate with reduced time spent on documentation. To characterize EHR note composition strategies, how these strategies differ in time spent on notes and the EHR, and their distribution across specialty types. Secondary analysis of physician-level measures of note composition and EHR use derived from Epic Systems' Signal data warehouse. We used k-means clustering to identify documentation strategies, and ordinary least squares regression to analyze the relationship between documentation strategies and physician time spent in the EHR, on notes, and outside scheduled hours. A total of 215,207 US-based ambulatory physicians using the Epic EHR between September 2020 and May 2021. Percent of note text derived from each of five documentation tools: SmartTools, copy/paste, manual text, NoteWriter, and voice recognition and transcription; average total and after-hours EHR time per visit; average time on notes per visit. Six distinct note composition strategies emerged in cluster analyses. The most common strategy was predominant SmartTools use (n=89,718). In adjusted analyses, physicians using primarily transcription and dictation (n=15,928) spent less time on notes than physicians with predominant Smart Tool use. (b=-1.30, 95% CI=-1.62, -0.99, p<0.001; average 4.8 min per visit), while those using mostly copy/paste (n=23,426) spent more time on notes (b=2.38, 95% CI=1.92, 2.84, p<0.001; average 13.1 min per visit). Physicians' note composition strategies have implications for both time in notes and after-hours EHR use, suggesting that how physicians use EHR-based documentation tools can be a key lever for institutions investing in EHR tools and training to reduce documentation time and alleviate EHR-associated burden.

Low rates of structured advance care planning documentation in electronic health records: results of a single-center observational study

BackgroundProper advance care planning (ACP) documentation both improves patient care and is increasingly seen as a marker of high quality by governmental payers. The transition of most medical documentation to electronic health records (EHR) allows for ACP documents to be rapidly disseminated across diverse ambulatory practice settings. At the same time, the complexity and heterogeneity of the EHR, as well as the multiple potential storage locations for documentation, may lead to confusion and inaccessibility. There has been movement to promote structured ACP (S-ACP) documentation within the EHR.MethodsWe performed a retrospective cohort study at a single, large university medical center in California to analyze rates of S-ACP documentation. S-ACP was defined as ACP documentation contained in standardized locations, auditable, and not in free-text format. The analytic cohort composed of all patients 65 and older with at least one ambulatory encounter at Stanford Health Care between 2012 and 2020, and without concurrent hospice care. We then analyzed clinic-level, provider-level, insurance, and temporal factors associated with S-ACP documentation rate.ResultsOf 187,316 unique outpatient encounters between 2012 and 2020, only 7,902 (4.2%) contained S-ACP documentation in the EHR. The most common methods of S-ACP documentation were through problem list diagnoses (3,802; 40.3%) and scanned documents (3,791; 40.0%). At the clinic level, marked variability in S-ACP documentation was observed, with Senior Care (46.6%) and Palliative Care (25.0%) demonstrating highest rates. There was a temporal trend toward increased S-ACP documentation rate (p < 0.001).ConclusionThis retrospective, single-center study reveals a low rate of S-ACP documentation irrespective of clinic and specialty. While S-ACP documentation rate should not be construed as a proxy for ACP documentation rate, it nonetheless serves as an important quality metric which may be reported to payers. This study highlights the need to both centralize and standardize reporting of ACP documentation in complex EHR systems.

Abstract 13012: Atrial Fibrillation Symptom Improvement Following De Novo Catheter Ablation: Trajectories and Predictors of Symptom Resolution

Introduction: Atrial fibrillation (AF) symptom relief is a primary indication for catheter ablation, but the impact of ablation on AF symptoms is not well characterized. The objective of this study was to describe AF symptom documentation in electronic health records (EHRs) pre- and post-ablation and identify correlates of post-ablation symptoms. Methods: We evaluated EHRs of AF patients (n=1293) in a large, urban health system from 2010-2020 who underwent ablation and had at least one encounter note within 30 days of the ablation. We extracted structured data about patient demographics, medications, diagnoses, and encounters, and symptom data from unstructured clinical notes using natural language processing. We used Cochran’s Q tests with post-hoc McNemar’s tests to determine whether the prevalence of symptoms differed significantly pre- and 3, 6, 9, and 12 months post-ablation. We used bivariate logistic regression models to estimate the unadjusted odds of symptom prevalence by personal or clinical characteristics at 6 and 12 months post-ablation. Results: Compared to pre-ablation, the prevalence of any AF symptom decreased but anxiety and weakness increased post-ablation (Table 1). The unadjusted odds of any symptoms 12 months post-ablation were associated with Black/African-American (odds ratio [OR]=0.35; 95% confidence interval [CI]=0.14-0.92) or another race (OR=0.39, 95% CI=0.16-0.95) versus Asian race, Hispanic/Latino ethnicity (OR=2.40, 95% CI=1.28-4.39) versus non-Hispanic/Latino, antiarrhythmic medication (OR=0.62, 95% CI=0.46-0.80) versus no medication, smoking history (OR=0.70, 95% CI=0.53-0.94) versus no history, and heart failure (OR=0.73, 95% CI=0.55-0.97) versus no heart failure. Conclusion: Symptom patterns post-ablation are heterogeneous. Findings warrant confirmation with larger, more representative datasets, which may be informative for patients whose primary goal for undergoing an ablation is symptom relief.

Physicians' needs for drug allergy documentation in electronic health records and allergy alert systems: Results of an end user's survey.

Physicians' needs for drug allergy documentation in electronic health records and allergy alert systems: Results of an end user's survey.

Developing Structured Data Entry Forms for Food Allergy Clinical Documentation in The Electronic Health Record

Food allergy (FA) clinical documentation in electronic health records (EHRs) varies widely due to extensive use of free-text entry and lack of a common FA terminology. This limits FA research and integration of FA data across institutions. Structured data entry (SDE) forms are needed to improve FA documentation in EHRs.

COVID-19 prevalence, symptoms, and sociodemographic disparities in infection among insured pregnant women in Northern California.

BackgroundResearch on COVID-19 during pregnancy has mainly focused on women hospitalized for COVID-19 or other reasons during their pregnancy. Little is known about COVID-19 in the general population of pregnant women.ObjectiveTo describe the prevalence of COVID-19, symptoms, consequent healthcare use, and possible sources of COVID-19 exposure among a population-based sample of pregnant women residing in Northern California.MethodsWe analyzed data from 19,458 members of Kaiser Permanente Northern California who were pregnant between January 2020 and April 2021 and responded to an online survey about COVID-19 testing, diagnosis, symptoms, and their experiences during the COVID-19 pandemic. Medical diagnosis of COVID-19 during pregnancy was defined separately by self-report and by documentation in electronic health records (EHR). We examined relationships of COVID-19 with sociodemographic factors, underlying comorbidities, and survey measures of COVID-19-like symptoms, consequent healthcare utilization, and possible COVID-19 exposures.ResultsAmong 19,458 respondents, the crude prevalence of COVID-19 was 2.5% (n = 494) according to self-report and 1.4% (n = 276) according to EHR. After adjustment, the prevalence of self-reported COVID-19 was higher among women aged <25 years compared with women aged ≥35 years (prevalence ratio [PR], 1.75, 95% CI: 1.23, 2.49) and among Hispanic women compared with White women (PR, 1.91, 95% CI: 1.53, 2.37). Prevalence of self-reported COVID-19 was higher among women affected by personal or partner job loss during the pandemic (PR, 1.23, 95% CI: 1.02, 1.47) and among women living in areas of high vs. low neighborhood deprivation (PR, 1.74, 95% CI: 1.33, 2.27). We did not observe differences in self-reported COVID-19 between women with and without underlying comorbidities. Results were similar for EHR-documented COVID-19. Loss of smell or taste was a unique and common symptom reported among women with COVID-19 (42.3% in self-reported; 54.0% in EHR-documented). Among women with symptomatic COVID-19, approximately 2% were hospitalized, 71% had a telehealth visit, and 75% quarantined at home. Over a third of women with COVID-19 reported no known exposure to someone with COVID-19.ConclusionsObserved COVID-19 prevalence differences by sociodemographic and socioeconomic factors underscore social and health inequities among reproductive-aged women. Women with COVID-19 reported unique symptoms and low frequency of hospitalization. Many were not aware of an exposure to someone with COVID-19.

Diagnosis Documentation of Critically Ill Children at Admission to a PICU.

Multidisciplinary PICU teams must effectively share information while caring for critically ill children. Clinical documentation helps clinicians develop a shared understanding of the patient's diagnosis, which informs decision-making. However, diagnosis-related documentation in the PICU is understudied, thus limiting insights into how pediatric intensivists convey their diagnostic reasoning. Our objective was to describe how pediatric critical care clinicians document patients' diagnoses at PICU admission. Retrospective mixed methods study describing diagnosis documentation in electronic health records. Academic tertiary referral PICU. Children 0-17 years old admitted nonelectively to a single PICU over 1 year. None. One hundred PICU admission notes for 96 unique patients were reviewed. In 87% of notes, both attending physicians and residents or advanced practice providers documented a primary diagnosis; in 13%, primary diagnoses were documented by residents or advanced practice providers alone. Most diagnoses (72%) were written as narrative free text, 11% were documented as problem lists/billing codes, and 17% used both formats. At least one rationale was documented to justify the primary diagnosis in 91% of notes. Diagnostic uncertainty was present in 52% of notes, most commonly suggested by clinicians' use of words indicating uncertainty (65%) and documentation of differential diagnoses (60%). Clinicians' integration and interpretation of information varied in terms of: 1) organization of diagnosis narratives, 2) use of contextual details to clarify the diagnosis, and 3) expression of diagnostic uncertainty. In this descriptive study, most PICU admission notes documented a rationale for the primary diagnosis and expressed diagnostic uncertainty. Clinicians varied widely in how they organized diagnostic information, used contextual details to clarify the diagnosis, and expressed uncertainty. Future work is needed to determine how diagnosis narratives affect clinical decision-making, patient care, and outcomes.

The Development of the Rheumatology Informatics System for Effectiveness Learning Collaborative for Improving Patient-Reported Outcome Collection and Patient-Centered Communication in Adult Rheumatology.

ObjectivePatient‐reported outcomes (PROs) are an integral part of treat‐to‐target approaches in managing rheumatoid arthritis (RA). In clinical practice, however, routine collection, documentation, and discussion of PROs with patients are highly variable. The RISE LC (Rheumatology Informatics System for Effectiveness Learning Collaborative) was established to develop and share best practices in PRO collection and use across adult rheumatology practices in the United StatesMethodsThe goals of the RISE LC were developed through site surveys and in‐person meetings. Participants completed a baseline survey on PRO collection and use in their practices. RISE LC learning sessions focused on improving communication around PROs with patients and enhancing shared decision‐making in treatment plans. During the coronavirus disease 2019 (COVID‐19) pandemic, the RISE LC pivoted to adapt PRO tools for telehealth.ResultsAt baseline, all responding sites (n = 15) had established workflows for collecting PROs. Most sites used paper forms alone. PRO documentation in electronic health records was variable, with only half of the sites using structured data fields. To standardize and improve the use of PROs, participants iteratively developed a Clinical Disease Activity Index–based RA Disease Activity Communication Tool to solicit treatment goals and improve shared decision‐making across sites. The COVID‐19 pandemic necessitated developing a tool to gauge PROs via telehealth.ConclusionThe RISE LC is a continuous, structured method for implementing strategies to improve PRO collection and use in rheumatological care, initially adapting from the Learning Collaborative model and extending to include features of a learning network. Future directions include measuring the impact of standardized PRO collection and discussion on shared decision‐making and RA outcomes.

Prescribing errors among adult patients in a large tertiary care system in Saudi Arabia.

BACKGROUND: Multiple studies have investigated medication errors in hospitals in Saudi Arabia; however, prevalence data on prescribing errors and associated factors remains uncertain.OBJECTIVE: Assess the prevalence, type, severity, and factors associated with prescribing errors.DESIGN: Retrospective database review.SETTING: Large tertiary care setting in Riyadh.PATIENTS AND METHODS: We described and analyzed data related to prescribing errors in adults (>14 years of age) from the Medication Error Electronic Report Forms database for the two-year period from January 2017 to December 2018.MAIN OUTCOME MEASURE: The prevalence of prescribing errors and associated factors among adult patients.SAMPLE SIZE: 315 166 prescriptions screened.RESULTS: Of the total number of inpatient and outpatient prescriptions screened, 4934 prescribing errors were identified for a prevalence of 1.56%. The most prevalent types of prescribing errors were improper dose (n=1516; 30.7%) and frequency (n=987; 20.0%). Two-thirds of prescribing errors did not cause any harm to patients. Most prescribing errors were made by medical residents (n=2577; 52%) followed by specialists (n=1629; 33%). Prescribing errors were associated with a lack of documenting clinical information (adjusted odds ratio: 14.1; 95% CI 7.7-16.8, P<.001) and prescribing anti-infective medications (adjusted odds ratio 2.9; 95% CI 1.3-5.7, P<.01).CONCLUSION: Inadequate documentation in electronic health records and prescribing of anti-infective medications were the most common factors for predicting prescribing errors. Future studies should focus on testing innovative measures to control these factors and their impact on minimizing prescribing errors.LIMITATIONS: Polypharmacy was not considered; the data are from a single healthcare system.CONFLICT OF INTEREST: None.

Academic Electronic Health Records in Undergraduate Nursing Education: Mixed Methods Pilot Study.

BackgroundTeaching students about electronic health records presents challenges for most nursing programs, primarily because of the limited training opportunities within clinical practice settings. A simulated electronic health record is an experiential, learner-centered strategy that enables students to acquire and apply the informatics knowledge needed for working with electronic records in a safe learning environment before the students have encounters with real patients.ObjectiveThe aim of this study is to provide a preliminary evaluation of the Lippincott DocuCare simulated electronic health record and determine the feasibility issues associated with its implementation.MethodsWe used one-group pretest-posttest, surveys, and focus group interviews with students and instructors to pilot the DocuCare simulated electronic health record within an undergraduate nursing program in Western Canada. Volunteering students worked through 4 case scenarios during a 1-month pilot. Self-reported informatics knowledge and attitudes toward the electronic health record, accuracy of computerized documentation, satisfaction, and students’ and educators’ experiences were examined. Demographic and general information regarding informatics learning was also collected.ResultsAlthough 23 students participated in this study, only 13 completed surveys were included in the analysis. Almost two-thirds of the students indicated their overall understanding of nursing informatics as being fair or inadequate. The two-tailed paired samples t test used to evaluate the impact of DocuCare on students’ self-reported informatics knowledge and attitudes toward the electronic health record revealed a statistically significant difference in the mean score of knowledge before and after using DocuCare (before: mean 2.95, SD 0.58; after: mean 3.83, SD 0.39; t12=5.80, two-tailed; P<.001). There was no statistically significant difference in the mean scores of attitudes toward the electronic health record before and after using DocuCare (before: mean 3.75, SD 0.40; after: mean 3.70, SD 0.34; t12=0.39, two-tailed; P=.70). Students’ documentation scores varied from somewhat accurate to completely accurate; however, performance improved for the majority of students as they progressed from case scenarios 1 to 4. Both the faculty and students were highly satisfied with DocuCare and highly recommended its integration. Focus groups with 7 students and 3 educators revealed multiple themes. The participants shared suggestions regarding the DocuCare product customization and strategies for potential integration in undergraduate nursing programs.ConclusionsThis study demonstrated the feasibility and suitability of the DocuCare program as a tool to enhance students’ learning about informatics and computerized documentation in electronic health records. Recommendations will be made to academic leadership in undergraduate programs on the basis of this study. Furthermore, a controlled evaluation study will be conducted in the future.

Associations of psychosocial factors, knowledge, attitudes and practices with hospitalizations in internal medicine divisions in different population groups in Israel

BackgroundInequalities in healthcare utilization exist across ethnic groups; however, the contributions of health-related knowledge and psychosocial factors to these inequalities remain unclear. We examined associations of social determinants of health, psychological factors, knowledge, attitudes and health practices, with hospitalizations in internal medicine divisions, among Israeli adults, Jews and Arabs, with non-communicable diseases, in a setting of universal health insurance.MethodsA retrospective study was undertaken among 520 Jews and Arabs aged 40 years or older with non-communicable diseases, members of a large health maintenance organization. Hospitalization (at least once during 2008) in an internal medicine division was determined based on documentation in electronic health records. Participants were randomly selected in strata of sex, population-group and hospitalization status (yes/no). Data were collected from medical records and via face-to-face interviews using a structured questionnaire. Main independent variables included comorbidity burden, health behaviors, mental health wellbeing and self-rated health. Scales measuring health knowledge and attitudes/beliefs were constructed using factor analysis.ResultsComorbidity burden (OR 1.41 [95% CI 1.24–1.61]) and self-rated health (not good vs. good) (OR 1.88 [95% CI 1.13–3.12]) were positively associated with hospitalizations in an internal medicine division, while an inverse association was found with better mental health wellbeing (OR 0.98 [95% CI 0.96–0.99, for each 1-point score increase). Among Jewish participants, positive associations were found of the number of offspring, comorbidity burden and perceived difficulty, with hospitalizations. No significant associations were found with hospitalizations of other sociodemographics, health behaviors, knowledge and attitudes/beliefs.ConclusionsComorbidity burden was the main risk factor of hospitalizations in internal medicine divisions. Psychosocial factors, such as self-rated health, a complex variable affected by social capital, mental wellbeing, the number of offspring, and perceived burden and difficulty, seem also to contribute. These findings suggest the involvement of broad family and social factors, beyond individual level characteristics and medical needs, in hospitalizations in internal medicine divisions. Interventions to reduce hospitalizations should be comprehensive and integrate aspects of mental health wellbeing; they should build on familial characteristics (e.g., number of offspring), factors related to social capital such as self-rated health, and perceived burden and difficulty.