Hybrid electronic record: An error reduction strategy for diverse medical prescription formats.

BACKGROUND:  This project is part of a broader effort to develop a new electronic registry for ophthalmology in the KwaZulu-Natal (KZN) province in South Africa. The registry should include a clinical decision support system that reduces the potential for human error and should be applicable for our diversity of hospitals, whether electronic health record (EHR) or paper-based. METHODS:  Post-operative prescriptions of consecutive cataract surgery discharges were included for 2019 and 2020. Comparisons were facilitated by the four chosen state hospitals in KZN each having a different system for prescribing medications: Electronic, tick sheet, ink stamp and handwritten health records. Error types were compared to hospital systems to identify easily-correctable errors. Potential error remedies were sought by a four-step process. RESULTS:  There were 1307 individual errors in 1661 prescriptions, categorised into 20 error types. Increasing levels of technology did not decrease error rates but did decrease the variety of error types. High technology scripts had the most errors but when easily correctable errors were removed, EHRs had the lowest error rates and handwritten the highest. CONCLUSION:  Increasing technology, by itself, does not seem to reduce prescription error. Technology does, however, seem to decrease the variability of potential error types, which make many of the errors simpler to correct.Contribution: Regular audits are an effective tool to greatly reduce prescription errors, and the higher the technology level, the more effective these audit interventions become. This advantage can be transferred to paper-based notes by utilising a hybrid electronic registry to print the formal medical record.

Classification Accuracy and Description of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in an Integrated Health Care System, 2006-2017.

INTRODUCTION: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic illness characterized by marked functional limitations and fatigue. Electronic health records can be used to estimate incidence of ME/CFS but may have limitations. METHODS: The authors used International Classification of Diseases (ICD) diagnosis codes to identify all presumptive cases of ME/CFS among 9- to 39-year-olds from 2006 to 2017. The authors randomly selected 200 cases for medical record review to classify cases as confirmed, probable, or possible, based on which and how many current clinical criteria they met, and to further characterize their illness. The authors calculated crude annual rates of ME/CFS coding stratified by age and sex using only those ICD codes that had identified confirmed, probable, or possible ME/CFS cases in the medical record review. RESULTS: The authors identified 522 individuals with presumptive ME/CFS based on having ≥ 1 ICD codes for ME/CFS in their electronic medical record. Of the 200 cases selected, records were available and reviewed for 188. Thirty (15%) were confirmed or probable ME/CFS cases, 39 (19%) were possible cases, 119 (60%) were not cases, and 12 (6%) had no medical record available. Confirmed/probable cases commonly had chronic pain (80%) or anxiety/depression (70%), and only 13 (43%) had completed a sleep study. Overall, 37 per 100,000 had ICD codes that identified confirmed, probable, or possible ME/CFS. Rates increased between 2006 and 2017, with the largest absolute increase among those 30-39 years old. CONCLUSIONS: Using ICD diagnosis codes alone inaccurately estimates ME/CFS incidence.

Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study.

BACKGROUND: Electronic health records (EHRs) play an increasingly important role in delivering HIV care in low- and middle-income countries. The data collected are used for direct clinical care, quality improvement, program monitoring, public health interventions, and research. Despite widespread EHR use for HIV care in African countries, challenges remain, especially in collecting high-quality data. OBJECTIVE: We aimed to assess data completeness, accuracy, and timeliness compared to paper-based records, and factors influencing data quality in a large-scale EHR deployment in Rwanda. METHODS: We randomly selected 50 health facilities (HFs) using OpenMRS, an EHR system that supports HIV care in Rwanda, and performed a data quality evaluation. All HFs were part of a larger randomized controlled trial, with 25 HFs receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited the 50 HFs to collect 28 variables from the paper charts and the EHR system using the Open Data Kit app. We measured data completeness, timeliness, and the degree of matching of the data in paper and EHR records, and calculated concordance scores. Factors potentially affecting data quality were drawn from a previous survey of users in the 50 HFs. RESULTS: We randomly selected 3467 patient records, reviewing both paper and EHR copies (194,152 total data items). Data completeness was >85% threshold for all data elements except viral load (VL) results, second-line, and third-line drug regimens. Matching scores for data values were close to or >85% threshold, except for dates, particularly for drug pickups and VL. The mean data concordance was 10.2 (SD 1.28) for 15 (68%) variables. HF and user factors (eg, years of EHR use, technology experience, EHR availability and uptime, and intervention status) were tested for correlation with data quality measures. EHR system availability and uptime was positively correlated with concordance, whereas users' experience with technology was negatively correlated with concordance. The alerts for missing VL results implemented at 11 intervention HFs showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHRs and paper records (11.9%-26.7%; P<.001). Similar effects were seen on the completeness of the recording of medication pickups (18.7%-32.6%; P<.001). CONCLUSIONS: The EHR records in the 50 HFs generally had high levels of completeness except for VL results. Matching results were close to or >85% threshold for nondate variables. Higher EHR stability and uptime, and alerts for entering VL both strongly improved data quality. Most data were considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports, and alerts are recommended. The application of quality improvement techniques described in this study should benefit a wide range of HFs and data uses for clinical care, public health, and disease surveillance.

Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis.

BACKGROUND: Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians' records, it has yet to be widely applied to pharmaceutical care records. OBJECTIVE: In this study, we aimed to investigate the feasibility of automatic extraction of the information regarding patients' diseases and symptoms from pharmaceutical care records. The verification was performed using Medical Named Entity Recognition-Japanese (MedNER-J), a Japanese disease-extraction system designed for physicians' records. METHODS: MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Keio University Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F1-score. RESULTS: The F1-scores of NER for subjective, objective, assessment, and plan data were 0.46, 0.70, 0.76, and 0.35, respectively. In NER and positive-negative classification, the F1-scores were 0.28, 0.39, 0.64, and 0.077, respectively. The F1-scores of NER for objective (0.70) and assessment data (0.76) were higher than those for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F1-score of NER and positive-negative classification was high for assessment data alone (F1-score=0.64), which was attributed to the similarity of its description format and contents to those of the training data. CONCLUSIONS: MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records.

Successes and challenges of best practice alerts to identify and engage individuals living with hepatitis C virus.

Introduction: Many individuals living with hepatitis C virus (HCV) are unaware of their diagnosis and/or have not been linked to programs providing HCV care. The use of electronic medical record (EMR) systems may assist with HCV infection identification and linkage to care. Methods: In October 2021, we implemented HCV serology-focused best practice alerts (BPAs) at The Ottawa Hospital (TOH) via our EMR (EPIC). Our BPAs were programmed to identify previously tested HCV seropositive individuals. Physicians were prompted to conduct HCV RNA testing and submit consultation requests to the TOH Viral Hepatitis Program. We evaluated data post-BPA implementation to assess the design and related outcomes. Results: From 1 September 2022 to 15 December 2022, a total of 2,029 BPAs were triggered for 139 individuals. As a consequence of the BPA prompts, nine HCV seropositive and nine HCV RNA-positive individuals were linked to care. The proportion of total consultations coming from TOH physicians increased post-BPA implementation. The BPA alerts were frequently declined, and physician engagement with our BPAs varied across specialty groups. Programming issues led to unnecessary BPA prompts (e.g., no hard stop to the prompts even though the individual was treated and cured and individuals linked to care without first undergoing HCV RNA testing). A fixed 6-month lookback period for test results limited our ability to identify many individuals. Conclusion: An EMR-based BPA can assist with the identification and engagement of HCV-infected individuals in care. However, challenges including issues with programming, time commitment toward BPA configuration, productive communication between healthcare providers and the programming team, and physician responsiveness to the BPAs require attention to optimize the impact of BPAs.

Exploring the Multifactorial Landscape: Risk Factors for Dementia in a Tertiary Care Setting in Thailand.

BACKGROUND: Dementia poses a significant public health challenge worldwide, necessitating a deeper understanding of its risk factors to inform preventive strategies. METHOD: This retrospective longitudinal study leveraged clinical data from a tertiary care database to investigate the risk factors associated with an incident dementia diagnosis. The study cohort comprised individuals aged 50 years and older. Key variables including age, income, comorbidities such as depressive disorder, osteoporosis, stroke, and metabolic conditions like type 2 diabetes and hypertension were analyzed by using Cox regression analysis. RESULT: The study cohort included 127,016 adults 50 years and older. The results revealed that advancing age, with individuals aged 70-79 years having a hazard ratio (HR) of 3.9 (95% confidence interval (CI), 2.6-5.8), and those aged 80 years and above having an HR of 11.6 (95% CI, 7.7-17.3), lower income status (patients with no income or occupation had a notably higher risk of dementia diagnosis, with an HR of 2.0 (95% CI, 1.4-2.8)), depressive disorder (HR of 3.3 (95% CI, 3.3-3.7)), osteoporosis (HR of 1.2 (95% CI, 1.1-1.4)), and stroke (HR of 2.5 (95% CI, 2.3-2.7)) were significantly associated with an increased risk of incident dementia. However, no significant associations were observed for type 2 diabetes, hypertension, obesity, or underweight status managed in tertiary care. CONCLUSION: The findings underscore the importance of considering a wide range of factors in understanding dementia risk and highlight the potential utility of routinely collected clinical data for comprehensive risk assessment. Further investigation into additional variables and multi-center studies may provide deeper insights into the complex interplay of risk factors contributing to dementia onset.

Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.

OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS: The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS: The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION: The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION: The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

Electronic medical records imputation by temporal Generative Adversarial Network.

The loss of electronic medical records has seriously affected the practical application of biomedical data. Therefore, it is a meaningful research effort to effectively fill these lost data. Currently, state-of-the-art methods focus on using Generative Adversarial Networks (GANs) to fill the missing values of electronic medical records, achieving breakthrough progress. However, when facing datasets with high missing rates, the imputation accuracy of these methods sharply deceases. This motivates us to explore the uncertainty of GANs and improve the GAN-based imputation methods. In this paper, the GRUD (Gate Recurrent Unit Decay) network and the UGAN (Uncertainty Generative Adversarial Network) are proposed and organically combined, called UGAN-GRUD. In UGAN-GRUD, it highlights using GAN to generate imputation values and then leveraging GRUD to compensate them. We have designed the UGAN and the GRUD network. The former is employed to learn the distribution pattern and uncertainty of data through the Generator and Discriminator, iteratively. The latter is exploited to compensate the former by leveraging the GRUD based on time decay factor, which can learn the specific temporal relations in electronic medical records. Through experimental research on publicly available biomedical datasets, the results show that UGAN-GRUD outperforms the current state-of-the-art methods, with average 13% RMSE (Root Mean Squared Error) and 24.5% MAPE (Mean Absolute Percentage Error) improvements.

From Clinic to Kitchen to Electronic Health Record: The Background and Process of Building a Culinary Medicine eConsult Service.

Diet plays a pivotal role in health outcomes, influencing various metabolic pathways and accounting for over 20% of risk-attributable disability adjusted life years (DALYs). However, the limited time during primary care visits often hinders comprehensive guidance on dietary and lifestyle modifications. This paper explores the integration of electronic consultations (eConsults) in Culinary Medicine (CM) as a solution to bridge this gap. CM specialists, with expertise in the intricate connections between food, metabolism, and health outcomes, offer tailored dietary recommendations through asynchronous communication within the electronic health record (EHR) system. The use of CM eConsults enhances physician-patient communication and fosters continuous medical education for requesting clinicians. The benefits extend directly to patients, providing access to evidence-based nutritional information to address comorbidities and improve overall health through patient empowerment. We present a comprehensive guide for CM specialist physicians to incorporate CM eConsults into their practices, covering the historical context of eConsults, their adaptation for CM, billing methods, and insights from the implementation at UT Southwestern Medical Center. This initiative delivers expanded access to patient education on dietary risks and promotes interprofessional collaboration to empower improved health.

What you eat significantly impacts your health, affecting various aspects including weight, blood sugar, and inflammation. This paper highlights how health-related issues are linked to diet and presents one solution to help doctors guide patients more effectively. Often, the limited time during medical visits makes it challenging for doctors to provide detailed advice on lifestyle changes. Additional common barriers are that many doctors lack nutrition expertise, and access to nutrition experts such as registered dietitian nutritionists can be limited geographically and financially. This paper introduces the concept of electronic consultations (eConsults) in Culinary Medicine (CM) to help overcome this challenge. CM specialists are licensed healthcare professionals who understand how food influences the body and can use eConsults to offer personalized dietary recommendations. EConsults occur via a secure electronic medical record system that connects doctors and specialists, ensuring efficient communication. Patients benefit by gaining access to reliable nutritional information tailored to their specific health needs. This innovative approach also enhances communication between doctors and patients and helps doctors stay updated on the new research about how nutrition and food impact health. The paper provides a practical guide for doctors to integrate CM eConsults into their practices, making it easier to give valuable advice on dietary risks and promote healthier lifestyles. Overall, this initiative represents a significant step in improving patient nutrition education and fostering positive changes in health through the power of informed dietary choices.

AN ELECTRONIC MEDICAL RECORD PREDICTION MODEL TO IDENTIFY INADEQUATE BOWEL PREPARATION IN PATIENTS AT OUTPATIENT COLONOSCOPY.

Background and Aims: Inadequate bowel preparation during colonoscopy is associated with decreased adenoma detection, increased costs, and patient procedural risks. This study aimed to develop a prediction model for identifying patients at high risk of inadequate bowel preparation for potential clinical integration into the EMR. Methods: A retrospective study was conducted using outpatient screening/surveillance colonoscopies at the University of North Carolina (UNC) from 2017 to 2022. Data were extracted from the EMRs of Epic and ProVation, including demographic, socioeconomic, and clinical variables. Logistic regression, LASSO regression, and gradient boosting machine (GBM) models were evaluated and validated in a held-out testing set. Results: The dataset included 23,456 colonoscopies, of which 6.25% had inadequate bowel preparation. The reduced LASSO regression model demonstrated an area under the curve (AUC) of 0.65 [95% CI 0.63-0.67] in the held-out testing set. The relative risk of inadequate bowel prep in the high-risk group determined by the model was 2.42 (95% CI 2.07-2.82), compared to patients identified as low risk. The model calibration in the testing set revealed that among patients categorized as having 0-11%, 11-22%, and 22-33% predicted risk of inadequate prep, the respective proportions of patients with inadequate prep were 5.5%, 19.3%, and 33.3%. Using the reduced LASSO model, a rudimentary code for a potential Epic FHIR application called PrepPredict was developed. Conclusions: This study developed a prediction model for inadequate bowel preparation with the potential to integrate into the EMR for clinical use and optimize bowel preparation to improve patient care.

Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis.

BACKGROUND: Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals. OBJECTIVE: This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout. METHODS: We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management. RESULTS: The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57). CONCLUSIONS: The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173.

Characterization of Primary IGF-1 Deficiency in a Cohort of Canadian Children with Short Stature Using a Novel Algorithm Tailored to Electronic Medical Records.

(1) Background: Severe primary insulin-like growth factor-I deficiency (SPIGFD) is a rare disorder causing short stature in children due to low insulin-like growth factor 1 (IGF-1) levels. Given the sparsity of reported cases of SPIGFD worldwide, the condition may be underdiagnosed, potentially preventing affected children from receiving therapy with recombinant human IGF-1 (rhIGF-1). Our objective was to determine the prevalence of SPIGFD among children with short stature at a large pediatric tertiary care center through the use of a novel electronic medical record (EMR) algorithm. (2) Methods: We queried our EMR using an algorithm that detected all children seen at our center between 1 November 2013 and 31 August 2021 with short stature and low IGF-1. We then conducted chart reviews, applying established diagnostic criteria for those identified with potential SPIGFD. (3) Results: From a cohort of 4863 children with short stature, our algorithm identified 30 (0.6%) patients with potential SPIGFD. Using chart reviews, we determined that none of these patients had SPIGFD. (4) Conclusions: Our algorithm can be used in other EMRs to identify which patients are likely to have SPIGFD and thus benefit from treatment with rhIGF-1. This model can be replicated for other rare diseases.

Security Risk Assessment for Patient Portals of Hospitals: A Case Study of Taiwan.

Background: Growing cyberattacks have made it more challenging to maintain healthcare information system (HIS) security in medical institutes, especially for hospitals that provide patient portals to access patient information, such as electronic health record (EHR). Objective: This work aims to evaluate the patient portal security risk of Taiwan's EEC (EMR Exchange Center) member hospitals and analyze the association between patient portal security, hospital location, contract category and hospital type. Methods: We first collected the basic information of EEC member hospitals, including hospital location, contract category and hospital type. Then, the patient portal security of individual hospitals was evaluated by a well-known vulnerability scanner, UPGUARD, to assess website if vulnerable to high-level attacks such as denial of service attacks or ransomware attacks. Based on their UPSCAN scores, hospitals were classified into four security ratings: absolute low risk, low to medium risk, medium to high risk and high risk. Finally, the associations between security rating, contract category and hospital type were analyzed using chi-square tests. Results: We surveyed a total of 373 EEC member hospitals. Among them, 20 hospital patient portals were rated as "absolute low risk", 104 hospital patient portals as "low to medium risk", 99 hospital patient portals as "medium to high risk" and 150 hospital patient portals as "high risk". Further investigation revealed that the patient portal security of EEC member hospitals was significantly associated with the contract category and hospital type (P<0.001). Conclusion: The analysis results showed that large-scale hospitals generally had higher security levels, implying that the security of low-tier and small-scale hospitals may warrant reinforcement or strengthening. We suggest that hospitals should pay attention to the security risk assessment of their patient portals to preserve patient information privacy.

Factors Associated With Tobacco Cessation Advice Recall and Quit Rates in Vascular Surgery Patients. A Single Center Study.

OBJECTIVES: Smoking is an important modifiable risk factor in all vascular diseases and verbal advice from providers has been shown to increase rates of tobacco cessation. We sought to identify factors that will improve tobacco cessation and recall of receiving verbal cessation advice in vascular surgery patients at a single institution. METHODS: The study is a retrospective cohort study. Patients seen in outpatient vascular surgery clinic who triggered a tobacco Best Practice Advisory (BPA) during their office visits over a 10-month period were contacted post-clinic and administered surveys detailing smoking status, cessation advice recall, and validated scales for nicotine dependence and willingness to quit smoking. This BPA is a "hard stop" that requires providers to document actions taken. Charts were reviewed for tobacco cessation documentation. Nine-digit zip-codes identified the area deprivation index, a measure of socioeconomic status. Univariate analysis was used to identify factors associated with cessation and advice recall. RESULTS: One hundred out of 318 (31.4%) patients responded to the survey. Epic Slicer Dicer found 97 BPA responses. To dismiss the BPA, 89 providers (91.8%) selected "advised tobacco cessation" and "Unable to Advise" otherwise. Of the 318 patients, 115 (36.1%) had cessation intervention documented in their provider notes and 151 (47.5%) received written tobacco cessation advice. Of survey respondents, 70 recalled receiving verbal advice, 27 recalled receiving written advice, 28 reported receiving offers of medication/therapy for cessation. 55 patients reported having tobacco cessation plans, and among those 17 reported having quit tobacco. Recall of receiving written advice (P < .001) and recall of receiving medication/therapy (P = .008) were associated with recall of receiving verbal cessation advice. CONCLUSIONS: Providing patients with tobacco cessation medication/therapy and written tobacco cessation education during office visits is associated with increased patients' recall of tobacco cessation advice. Vascular surgeons should continue to provide directed tobacco cessation advice.

Development of Recommendations for the Digital Sharing of Notes With Adolescents in Mental Health Care: Delphi Study.

Background: In many countries, health care professionals are legally obliged to share information from electronic health records with patients. However, concerns have been raised regarding the sharing of notes with adolescents in mental health care, and health care professionals have called for recommendations to guide this practice. Objective: The aim was to reach a consensus among authors of scientific papers on recommendations for health care professionals' digital sharing of notes with adolescents in mental health care and to investigate whether staff at child and adolescent specialist mental health care clinics agreed with the recommendations. Methods: A Delphi study was conducted with authors of scientific papers to reach a consensus on recommendations. The process of making the recommendations involved three steps. First, scientific papers meeting the eligibility criteria were identified through a PubMed search where the references were screened. Second, the results from the included papers were coded and transformed into recommendations in an iterative process. Third, the authors of the included papers were asked to provide feedback and consider their agreement with each of the suggested recommendations in two rounds. After the Delphi process, a cross-sectional study was conducted among staff at specialist child and adolescent mental health care clinics to assess whether they agreed with the recommendations that reached a consensus. Results: Of the 84 invited authors, 27 responded. A consensus was reached on 17 recommendations on areas related to digital sharing of notes with adolescents in mental health care. The recommendations considered how to introduce digital access to notes, write notes, and support health care professionals, and when to withhold notes. Of the 41 staff members at child and adolescent specialist mental health care clinics, 60% or more agreed with the 17 recommendations. No consensus was reached regarding the age at which adolescents should receive digital access to their notes and the timing of digitally sharing notes with parents. Conclusions: A total of 17 recommendations related to key aspects of health care professionals' digital sharing of notes with adolescents in mental health care achieved consensus. Health care professionals can use these recommendations to guide their practice of sharing notes with adolescents in mental health care. However, the effects and experiences of following these recommendations should be tested in clinical practice.

Cancer Registry Enrichment via Linkage with Hospital-Based Electronic Medical Records: A Pilot Investigation.

Background: Hospital electronic medical record (EMR) systems are becoming increasingly integrated for management of patient data, especially given recent policy changes issued by the Centers for Medicaid and Medicare Services. In addition to data management, these data provide evidence for patient-centered outcomes research for a range of diseases, including cancer. Integrating EMR patient data with existing disease registries strengthens all essential components for assuring optimal health outcomes. Objectives: To identify the mechanisms for extracting, linking, and processing hospital EMR data with the Florida Cancer Data System (FCDS); and to assess the completeness of existing registry treatment data as well as the potential for data enhancement. Methods: A partnership among the Florida Department of Health, FCDS, and a large Florida hospital system was established to develop methods for hospital EMR extraction and transmission. Records for admission years between 2007 and 2010 were extracted using ICD-9-CM codes as the trigger and were linked with the cancer registry for patients with invasive cancers of the breast. Results: A total of 11,506 unique patients were linked with a total of 12,804 unique breast tumors. Evaluation of existing registry treatment data against the hospital EMR produced a total of 5% of registry records with updated surgery information, 1% of records with updated radiation information, and 7% of records updated with chemotherapy information. Enhancement of registry treatment information was particularly affected by the availability of chemotherapy medications data. Conclusion: Hospital EMR linkages to cancer disease registries is feasible but challenged by lack of standards for data collection, coding and transmission, comprehensive description of available data, and the exclusion of certain hospital datasets. The FCDS standard treatment data variables are highly robust and complete but can be enhanced by the addition of detailed chemotherapy regimens that are commonly used in patient centered outcomes research.

Understanding provider use of a new clinical decision support tool aimed at reducing excess telemetry in an academic health system: A retrospective study.

RATIONALE: Cardiac monitoring has often been identified as an area of overutilization and remains a limited resource in many hospitals. With the aim of reducing telemetry overuse, we added clinical decision support to our health system's telemetry order with guidance on appropriate indications for monitoring. The new order requires selection of an appropriate clinical indication. AIMS AND OBJECTIVES: In this study, we aimed to understand provider engagement with this tool by assessing concordance between selected indications within the order and the clinical presence of those conditions as documented within the patient chart. METHODS: We randomly selected 100 telemetry orders from July to October 2022 across four different hospitals at NYU Langone Health. Two independent, blinded reviewers used a structured protocol to identify documentation of actual indications for telemetry in each selected chart. We calculated the rate of concordance between indications selected in the order and indications that were determined to be clinically present on chart review. RESULTS: There were 30,839 telemetry orders placed during the study timeframe. Overall concordance between the selection within the order and the actual indication was 48% (95% confidence interval [CI], 38.21%-57.79%). We observed especially low concordance rates for vague indications, such as 'Other', and for 'Confirmed Stroke', which was the only indication allowing for indefinite telemetry. CONCLUSION: The overall low concordance suggests a disconnect between the support tool and clinical practice. Providers are more likely to select an indication that reduces downstream work regardless of a patient's true clinical indication.

MMpowerment: Empowering patients with multiple myeloma for shared decision-making by developing an intervention to integrate personal preferences into digital care pathways.

OBJECTIVE: As part of a quality improvement initiative in the context of value-based health care we aimed to optimize the shared decision-making (SDM) process in the care pathway for Multiple Myeloma as part of a digital care pathway (DCP). For this, more insight was needed in health care professionals' (HCPs') perspectives on SDM, and how SDM elements could be addressed in a DCP for MM to facilitate HCPs' performance of SDM. METHODS: HCPs were interviewed as per the theory of planned behaviour and the model of organizational context and SDM (phase 1). Multidisciplinary development sessions were organized to discuss concepts of the solution with HCPs (phase 2). The solution was evaluated with two patients from the quality improvement team. RESULTS: In phase 1, ten interviews were held. HCPs' attitudes and the subjective norm towards SDM were positive, and the intention to perform SDM was high. The clinical environment (physical context, disease characteristics, assumptions about patient characteristics, and workflows) for MM posed challenges on the actual SDM behavior. Education and use of the DCP to create awareness of SDM were seen as possible facilitators for SDM. A prepared and active patient would facilitate the SDM process. In phase 2, three concept solutions were developed before arriving at the final solution. The final solution consisted of three elements to incorporate SDM steps in the DCP: 1) creating patient awareness and activation with two questions about their preferences prior to a consultation, 2) visualisation of preferences centrally in the DCP to trigger HCP to discuss them, 3) monitoring and improving SDM with patient-questionnaires after decision-making. Patients and HCPs were willing to implement it. CONCLUSION: HCPs intention to engage in SDM was high, but their actual behaviour was challenged by the clinical environment. A 3-element DCP-based intervention was developed to increase SDM. PATIENT OR PUBLIC CONTRIBUTION: Input on the solution was obtained from end-users including two patients and ten healthcare professionals.

Identification of mycoplasma pneumonia in children based on fusion of multi-modal clinical free-text description and structured test data.

Mycoplasma pneumonia may lead to hospitalizations and pose life-threatening risks in children. The automated identification of mycoplasma pneumonia from electronic medical records holds significant potential for improving the efficiency of hospital resource allocation. In this study, we proposed a novel method for identifying mycoplasma pneumonia by integrating multi-modal features derived from both free-text descriptions and structured test data in electronic medical records. Our approach begins with the extraction of free-text and structured data from clinical records through a systematic preprocessing pipeline. Subsequently, we employ a pre-trained transformer language model to extract features from the free-text, while multiple additive regression trees are used to transform features from the structured data. An attention-based fusion mechanism is then applied to integrate these multi-modal features for effective classification. We validated our method using clinic records of 7157 patients, retrospectively collected for training and testing purposes. The experimental results demonstrate that our proposed multi-modal fusion approach achieves significant improvements over other methods across four key performance metrics.

A Multi-centered Retrospective Study on the Efficacy of Pulsed Radiofrequency Nerve Ablation in the Treatment of Recalcitrant Plantar Fasciitis: A Mid-term Outcome.

Background Plantar fasciitis, a condition marked by persistent and often excruciating heel pain, frequently poses a formidable hurdle when conservative treatment approaches fall short. This multi-centered retrospective study embarks on a journey to explore the potential effectiveness of pulsed radiofrequency nerve ablation (RFNA), an innovative and less invasive procedure, as a novel avenue for treating recalcitrant plantar fasciitis. This investigation centers around a group of 24 patients who have faced the persistence of this challenging ailment. By meticulously scrutinizing patient outcomes and conducting a comprehensive analysis of safety aspects, this study aspires to offer enlightening revelations regarding the promise and practicality of pulsed RFNA as a therapeutic solution for tackling this intricate and tenacious condition. Methods This retrospective study included 24 patients who had undergone pulsed RFNA for recalcitrant plantar fasciitis between June 1, 2020, and June 1, 2022, at Hospital Pengajar Universiti Putra Malaysia (HPUPM), Hospital Universiti Teknologi Mara (UiTM), and Hospital Serdang. Patients were selected from the Orthopedic Clinics at HPUPM, Hospital UiTM, and Hospital Serdang and were screened according to the inclusion and exclusion criteria. Patient data was extracted from the hospital information system and electronic medical records. Pre-procedure and post-procedure assessments were conducted at one, three, and six months on the selected patients using the visual analog scale and American Orthopaedic Foot and Ankle Society Ankle-Hindfoot Scoring systems. All selected patient data was traced and tabulated accordingly. Results This study evaluates the effectiveness of pulsed RFNA in treating recalcitrant plantar fasciitis in 24 participants (39 feet). Results show a significant reduction in pain and improvement in functionality at one, three, and six months post-RFNA. Demographic factors (age, gender, and specific diagnosis) did not significantly impact outcomes. The study supports pulsed RFNA as an effective treatment for recalcitrant plantar fasciitis, emphasizing consistent benefits across various patient characteristics. Conclusion In conclusion, the study demonstrates the notable effectiveness of pulsed RFNA in improving pain reduction and functional outcomes for individuals with recalcitrant plantar fasciitis. The findings, consistent across various demographic factors, support pulsed RFNA as a promising and uniform treatment option for those who do not respond to conservative measures.