Blockchain en salud: transformando la seguridad y la gestión de datos clínicos / Blockchain in health: Transforming security and clinical data management

Introducción Los avances tecnológicos continúan transformando la sociedad, incluyendo el sector de la salud. La naturaleza descentralizada y verificable de la tecnología blockchain presenta un gran potencial para abordar desafíos actuales en la gestión de datos sanitarios. Discusión Este artículo indaga sobre cómo la adopción generalizada de blockchain se enfrenta a importantes desafíos y barreras que deben abordarse, como la falta de regulación, la complejidad técnica, la salvaguarda de la privacidad y los costos tanto económicos como tecnológicos. La colaboración entre profesionales médicos, tecnólogos y legisladores es esencial para establecer un marco normativo sólido y una capacitación adecuada. Conclusión La tecnología blockchain tiene potencial de revolucionar la gestión de datos en el sector de la salud, mejorando la calidad de la atención médica, empoderando a los usuarios y fomentando la compartición segura de datos. Es necesario un cambio cultural y regulatorio, junto a más evidencia, para concluir sus ventajas frente a las alternativas tecnológicas existentes. (AU)

Introduction Technological advances continue to transform society, including the health sector. The decentralized and verifiable nature of blockchain technology presents great potential for addressing current challenges in healthcare data management. Discussion This article reports on how the generalized adoption of blockchain faces important challenges and barriers that must be addressed, such as the lack of regulation, technical complexity, safeguarding privacy, and economic and technological costs. Collaboration between medical professionals, technologists and legislators is essential to establish a solid regulatory framework and adequate training. Conclusion Blockchain technology has the potential to revolutionize data management in the healthcare sector, improving the quality of medical care, empowering users, and promoting the secure sharing of data, but an important cultural change is needed, along with more evidence, to reveal its advantages in front of the existing technological alternative. (AU)

Content of Patient Electronic Messages to Physicians in a Large Integrated System.

This quality improvement study describes the content of electronic health record messages from patients to physicians in a large integrated health care system using natural language processing algorithms.

Standardization of Emergency Department Clinical Note Templates: A Retrospective Analysis across an Integrated Health System.

BACKGROUND AND OBJECTIVE: Clinical documentation is essential for conveying medical decision-making, communication between providers and patients, and capturing quality, billing, and regulatory measures during emergency department (ED) visits. Growing evidence suggests the benefits of note template standardization; however, variations in documentation practices are common. The primary objective of this study is to measure the utilization and coding performance of a standardized ED note template implemented across a nine-hospital health system. METHODS: This was a retrospective study before and after the implementation of a standardized ED note template. A multi-disciplinary group consensus was built around standardized note elements, provider note workflows within the electronic health record (EHR), and how to incorporate newly required medical decision-making elements. The primary outcomes measured included the proportion of ED visits using standardized note templates, and the distribution of billing codes in the 6 months before and after implementation. RESULTS: In the preimplementation period, a total of six legacy ED note templates were being used across nine EDs, with the most used template accounting for approximately 36% of ED visits. Marked variations in documentation elements were noted across six legacy templates. After the implementation, 82% of ED visits system-wide used a single standardized note template. Following implementation, we observed a 1% increase in the proportion of ED visits coded as highest acuity and an unchanged proportion coded as second highest acuity. CONCLUSION: We observed a greater than twofold increase in the use of a standardized ED note template across a nine-hospital health system in anticipation of the new 2023 coding guidelines. The development and utilization of a standardized note template format relied heavily on multi-disciplinary stakeholder engagement to inform design that worked for varied documentation practices within the EHR. After the implementation of a standardized note template, we observed better-than-anticipated coding performance.

Pediatric Population Management Classification for Children with Medical Complexity.

Improving the overall care of children with medical complexity (CMC) is often beset by challenges in proactively identifying the population most in need of clinical management and quality improvement. The objective of the current study was to create a system to better capture longitudinal risk for sustained and elevated utilization across time using real-time electronic health record (EHR) data. A new Pediatric Population Management Classification (PPMC), drawn from visit diagnoses and continuity problem lists within the EHR of a tristate health system, was compared with an existing complex chronic conditions (CCC) system for agreement (with weighted κ) on identifying CCMC, as well as persistence of elevated charges and utilization from 2016 to 2019. Agreement of assignment PPMC was lower among primary care provider (PCP) populations than among other children traversing the health system for specialty or hospital services only (weighted κ 62% for PCP vs. 82% for non-PCP). The PPMC classification scheme, displaying greater precision in identifying CMC with persistently high utilization and charges for those who receive primary care within a large integrated health network, may offer a more pragmatic approach to selecting children with CMC for longitudinal care management.

An interpretable predictive deep learning platform for pediatric metabolic diseases.

OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications. MATERIALS AND METHODS: No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients. Here, we use interpretable deep learning, leveraging longitudinal clinical measurements, demographical data, and diagnosis codes from electronic health record data from a large integrated health system to predict the onset of prediabetes, type 2 diabetes (T2D), and metabolic syndrome in pediatric cohorts. RESULTS: The cohort included 49 517 children with overweight or obesity aged 2-18 (54.9% male, 73% Caucasian), with a median follow-up time of 7.5 years and mean body mass index (BMI) percentile of 88.6%. Our model demonstrated area under receiver operating characteristic curve (AUC) accuracies up to 0.87, 0.79, and 0.79 for predicting T2D, metabolic syndrome, and prediabetes, respectively. Whereas most risk calculators use only recently available data, incorporating longitudinal data improved AUCs by 13.04%, 11.48%, and 11.67% for T2D, syndrome, and prediabetes, respectively, versus models using the most recent BMI (P < 2.2 × 10-16). DISCUSSION: Despite most risk calculators using only the most recent data, incorporating longitudinal data improved the model accuracies because utilizing trajectories provides a more comprehensive characterization of the patient's health history. Our interpretable model indicated that BMI trajectories were consistently identified as one of the most influential features for prediction, highlighting the advantages of incorporating longitudinal data when available.

Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records.

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

Perspective: Leveraging Electronic Health Record Data Within Food Is Medicine Program Evaluation: Considerations and Potential Paths Forward.

Government, health care systems and payers, philanthropic entities, advocacy groups, nonprofit organizations, community groups, and for-profit companies are presently making the case for Food is Medicine (FIM) nutrition programs to become reimbursable within health care services. FIM researchers are working urgently to build evidence for FIM programs' cost-effectiveness by showing improvements in health outcomes and health care utilization. However, primary collection of this data is costly, difficult to implement, and burdensome to participants. Electronic health records (EHRs) offer a promising alternative to primary data collection because they provide already-collected information from existing clinical care. A few FIM studies have leveraged EHRs to demonstrate positive impacts on biomarkers or health care utilization, but many FIM studies run into insurmountable difficulties in their attempts to use EHRs. The authors of this commentary serve as evaluators and/or technical assistance providers with the United States Department of Agriculture's Gus Schumacher Nutrition Incentive Program National Training, Technical Assistance, Evaluation, and Information Center. They work closely with over 100 Gus Schumacher Nutrition Incentive Program Produce Prescription FIM projects, which, as of 2023, span 34 US states and territories. In this commentary, we describe recurring challenges related to using EHRs in FIM evaluation, particularly in relation to biomarkers and health care utilization. We also outline potential opportunities and reasonable expectations for what can be learned from EHR data and describe other (non-EHR) data sources to consider for evaluation of long-term health outcomes and health care utilization. Large integrated health systems may be best positioned to use their own data to examine outcomes of interest to the broader field.

Graph neural networks for clinical risk prediction based on electronic health records: A survey.

OBJECTIVE: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. METHODS: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. RESULTS: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. CONCLUSION: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

The impact of nuance DAX ambient listening AI documentation: a cohort study.

OBJECTIVE: To assess the impact of the use of an ambient listening/digital scribing solution (Nuance Dragon Ambient eXperience (DAX)) on caregiver engagement, time spent on Electronic Health Record (EHR) including time after hours, productivity, attributed panel size for value-based care providers, documentation timeliness, and Current Procedural Terminology (CPT) submissions. MATERIALS AND METHODS: We performed a peer-matched controlled cohort study from March to September 2022 to evaluate the impact of DAX in outpatient clinics in an integrated healthcare system. Primary outcome measurements included provider engagement survey results, reported patient safety events related to DAX use, patients' Likelihood to Recommend score, number of patients opting out of ambient listening, change in work relative values units, attributed value-based primary care panel size, documentation completion and CPT code submission deficiency rates, and note turnaround time. RESULTS: A total of 99 providers representing 12 specialties enrolled in the study; 76 matched control group providers were included for analysis. Median utilization of DAX was 47% among active participants. We found positive trends in provider engagement, while non-participants saw worsening engagement and no practical change in productivity. There was a statistically significant worsening of after-hours EHR. There was no quantifiable effect on patient safety. DISCUSSION: Nuance DAX use showed positive trends in provider engagement at no risk to patient safety, experience, or clinical documentation. There were no significant benefits to patient experience, documentation, or measures of provider productivity. CONCLUSION: Our results highlight the potential of ambient dictation as a tool for improving the provider experience. Head-to-head comparisons of EHR documentation efficiency training are needed.

An Electronic Health Record-integrated Web Application Augments a QI-directed Morbidity & Mortality Conference and Improves Quality of Care.

BACKGROUND: In 2014, we developed a QI-directed Morbidity and Mortality (M&M) Conference, prioritizing discussion of individual and system failures, as well as development of action items to prevent failure recurrence. However, due to a reliance on individual electronic documents to store M&M data, our ability to assess trends in failures and action item implementation was hindered. To address this issue, in 2019, we created a secure electronic health record (EHR)-integrated web application (web app) to store M&M data. STUDY DESIGN: In this study, we assessed the impact of our web app on efficient review and tracking of M&M data, including system failure occurrence and closure of action items. Additionally, in 2021, it was discovered that a backlog of action items existed. To address this issue, we implemented a QI initiative to reduce the backlog, and used the web app to compare action item closure over time. RESULTS: Use of the web app dramatically improved review of M&M data. During the study period, there was a 67.0% reduction in the occurrence of the most common system failures. Additionally, our QI initiative resulted in a 97.7% reduction in the duration of time to complete a single action item and a 61.1% increase in the on-time closure rate for action items. CONCLUSIONS: Integration of a web app into a QI-directed M&M Conference enhanced our ability to track system level failures and action item closure over time. Using this web app, we demonstrated that our M&M Conference achieved its intended goal of improving the quality of patient care. LEVEL OF EVIDENCE: IV.

Risk of Corneal Graft Rejection and Vaccination: A Matched Case-Control Study From a United States Integrated Health Care System.

PURPOSE: Data on vaccine-associated corneal transplant rejections are limited. We examined the association between graft rejection and vaccination. DESIGN: Matched case-control METHODS: We used electronic health records to identify corneal transplant recipients between January 2008 and August 2022 at Kaiser Permanente Southern California. Cases were transplant recipients who experienced a graft rejection (outcome) during the study period. Randomly selected controls who did not experience a corneal graft rejection at their matched cases' index date (rejection date) were matched in a 3:1 ratio to cases. For controls, index date was determined by adding the number of days between transplant and graft rejection of their matched case to the control's transplant date. RESULTS: The study included 601 cases and 1803 matched controls (mean age 66 years [s.d. 17.0], 52% female, 47% non-Hispanic white). Twenty-three% of cases and 22% of controls received ≥1 vaccinations within 12 weeks prior to the index date. The adjusted odds ratio (aOR) for vaccination in the 12 weeks prior to index date, comparing cases to controls was 1.17 (95% CI: 0.91, 1.50]). The aOR was 1.09 (0.84, 1.43) for 1 vaccination, 1.53 (0.90, 2.61) for 2 vaccinations, and 1.79 (0.55, 5.57) for ≥3 vaccinations. The aOR was 1.60 (0.81, 3.14) for mRNA vaccines, and 1.19 (0.80, 1.78) for adjuvanted/high dose vaccines. CONCLUSIONS: We found no evidence to suggest an association between vaccination and graft rejection. Our findings provide support for the completion of recommended vaccinations for corneal transplant recipients, without significantly increasing the risk of graft rejection.

Building Efficiency and Scaling With a Remote Genetic Counseling Program.

Purpose A third-party telemedicine (TM) genetic counseling program was initiated at a large community oncology practice spanning 35 clinical sites with 110 clinicians and 97 advanced practice providers throughout Tennessee and Georgia. Patients and Methods Appropriate patients were referred through the electronic health record (EHR) based on current National Comprehensive Cancer Network guidelines. A combination of TM and genetic counseling assistants enhanced convenience, broadened access, and decreased no-show rates. Physician education for mutation-positive screening recommendations was provided through deep integration of dedicated genetic counseling notes in the EHR. Results From 2019 to 2022, the program expanded from 1 to 20 clinics with referrals growing from 195 to 885. An average of 82% of patients completed genetic counseling consultations over TM with more than 70% completing genetic testing. The average was 4 to 6 days from referral to consultation. The no-show rate was maintained at less than 7%. In 2023, this model supported all 35 clinics across the state. Conclusion Our program illustrates how remote genetic counseling programs are an effective choice for scaling genetics care across a large community oncology practice. Deep integration of TM genetic counseling within the EHR helps identify patients who are high risk and improves test adoption, patient keep rate, and turnaround time, helping to achieve better patient outcomes.

Consore: A Powerful Federated Data Mining Tool Driving a French Research Network to Accelerate Cancer Research.

BACKGROUND: Real-world data (RWD) related to the health status and care of cancer patients reflect the ongoing medical practice, and their analysis yields essential real-world evidence. Advanced information technologies are vital for their collection, qualification, and reuse in research projects. METHODS: UNICANCER, the French federation of comprehensive cancer centres, has innovated a unique research network: Consore. This potent federated tool enables the analysis of data from millions of cancer patients across eleven French hospitals. RESULTS: Currently operational within eleven French cancer centres, Consore employs natural language processing to structure the therapeutic management data of approximately 1.3 million cancer patients. These data originate from their electronic medical records, encompassing about 65 million medical records. Thanks to the structured data, which are harmonized within a common data model, and its federated search tool, Consore can create patient cohorts based on patient or tumor characteristics, and treatment modalities. This ability to derive larger cohorts is particularly attractive when studying rare cancers. CONCLUSIONS: Consore serves as a tremendous data mining instrument that propels French cancer centres into the big data era. With its federated technical architecture and unique shared data model, Consore facilitates compliance with regulations and acceleration of cancer research projects.

Medical and genetic correlates of long-term buprenorphine treatment in the electronic health records.

Despite the benefits associated with longer buprenorphine treatment duration (i.e., >180 days) (BTD) for opioid use disorder (OUD), retention remains poor. Research on the impact of co-occurring psychiatric issues on BTD has yielded mixed results. It is also unknown whether the genetic risk in the form of polygenic scores (PGS) for OUD and other comorbid conditions, including problematic alcohol use (PAU) are associated with BTD. We tested the association between somatic and psychiatric comorbidities and long BTD and determined whether PGS for OUD-related conditions was associated with BTD. The study included 6686 individuals with a buprenorphine prescription that lasted for less than 6 months (short-BTD) and 1282 individuals with a buprenorphine prescription that lasted for at least 6 months (long-BTD). Recorded diagnosis of substance addiction and disorders (Odds Ratio (95% CI) = 22.14 (21.88-22.41), P = 2.8 × 10-116), tobacco use disorder (OR (95% CI) = 23.4 (23.13-23.68), P = 4.5 × 10-111), and bipolar disorder (OR(95% CI) = 9.70 (9.48-9.92), P = 1.3 × 10-91), among others, were associated with longer BTD. The PGS of OUD and several OUD co-morbid conditions were associated with any buprenorphine prescription. A higher PGS for OUD (OR per SD increase in PGS (95%CI) = 1.43(1.16-1.77), P = 0.0009), loneliness (OR(95% CI) = 1.39(1.13-1.72), P = 0.002), problematic alcohol use (OR(95%CI) = 1.47(1.19-1.83), P = 0.0004), and externalizing disorders (OR(95%CI) = 1.52(1.23 to 1.89), P = 0.0001) was significantly associated with long-BTD. Associations between BTD and the PGS of depression, chronic pain, nicotine dependence, cannabis use disorder, and bipolar disorder did not survive correction for multiple testing. Longer BTD is associated with diagnoses of psychiatric and somatic conditions in the EHR, as is the genetic score for OUD, loneliness, problematic alcohol use, and externalizing disorders.

The impact of spiritual care delivered by nurses on patients' comfort: A propensity score matched cohort utilizing electronic health record data.

BACKGROUND: Spiritual care has been associated with better health outcomes. Despite increasing evidence of the benefits of spiritual care for older patients coping with illness and aggressive treatment, the role of spirituality is not well understood and implemented. Nurses, as frontline holistic healthcare providers, are in a position to address patients' spiritual needs and support them in finding meaning in life. This study aimed to identify spiritual care by analyzing nursing data and to compare the psychological and physical comfort between older chronically ill patients who received spiritual care versus those who did not receive spiritual care. MATERIAL AND METHODS: A propensity score matched cohort utilizing nursing care plan data was used to construct balanced groups based on patient characteristics at admission. 45 older patients (≥65 years) with chronic illnesses received spiritual care with measured psychological or physical comfort and 90 matched controls. To ensure the robustness of our results, two sensitivity analyses were performed. Group comparisons were performed to assess the average treatment effect of spiritual care on psychological and physical comfort outcomes. RESULTS: The mean psychological comfort was 4.3 (SD = 0.5) for spiritual care receivers and 3.9 (SD = 0.9) for non-receivers. Regression analysis showed that spiritual care was associated with better psychological comfort (estimate = 0.479, std. error = 0.225, p = 0.041). While its effect on physical comfort was not statistically significant (estimate = -0.265, std. error = 0.234, p = 0.261). This study provides suggestive evidence of the positive impact of nurses' spiritual care in improving psychological comfort for older patients with chronic illnesses. CONCLUSION: Using interoperable nursing data, our findings suggest that spiritual care improves psychological comfort in older patients facing illness. This finding suggests that nurses may integrate spiritual care into their usual care to support patients experiencing distress.

An ICD for the Digital World: What Does the ICD-11 Research Show?

The 11th revision of the International Classification of Diseases (ICD) is now available for use. A literature search was conducted to review and summarize the research conducted to date. In addition to the ease of integration into electronic health records using standard digital tools such as uniform resource identifiers and application programming interfaces, ICD-11 and the World Health Organization provided linearization for mortality and morbidity, ICD-11-MMS, promise improved backward compatibility to ICD-10; increased availability in multiple languages; greater detail for clinical use, including traditional Chinese medicine; and enhanced maintenance for continued relevance. The studies reviewed here support the superior content and utility of ICD-11-MMS. Meaningful planning for implementation has begun, including the provision of a framework. It is time for the world to adopt a digitally prepared ICD.

Safe and sound: Strengthening cybersecurity in healthcare through robust staff educational programs.

This article emphasizes the importance of comprehensive cybersecurity education programs in the healthcare industry. The rapid development of technology in healthcare has brought numerous advantages, including electronic health records and telehealth services. However, these advancements also expose the healthcare industry to significant cybersecurity risks. The healthcare industry is an attractive target for cybercriminals due to the presence of sensitive personal and financial information. Current regulations, such as HIPAA and PIPEDA, are in place to protect patient information, but 95% of healthcare industry breaches result from human error. Healthcare organizations must prioritize robust cybersecurity measures and implement comprehensive education programs for all healthcare professionals. This article recommends tailoring educational content to different healthcare roles and incorporating ongoing learning and awareness as essential elements of cybersecurity education. Overall, it calls for a holistic approach to cybersecurity education in healthcare to protect patient information and mitigate cyberthreats.

Psychiatric and substance use disorders among adults over age 50 who use cannabis: A matched cohort study using electronic health record data.

INTRODUCTION: Adults over age 50 increasingly use cannabis, but few studies have examined co-occurring psychiatric and substance use disorders (SUDs) in this population. The current study utilized electronic health record (EHR) data to compare adults age 50 + with ICD-10 cannabis codes (cases) and matched controls on common psychiatric and SUDs from 2016 to 2020. METHOD: Patients age 50 + from an integrated healthcare system in Hawai'i were identified using ICD-10 codes for cannabis (use, abuse, and dependence) from 2016 to 2018. In a matched cohort design, we selected non-cannabis-using controls (matched on sex and age) from the EHR (n = 275) and compared them to cases (patients with an ICD-10 cannabis code; n = 275) on depressive and anxiety disorders and SUDs (i.e., tobacco, opioid, and alcohol use disorders) over a two-year follow-up period. RESULTS: Participants were 62.8 years (SD = 7.3) old on average; and were White (47.8 %), Asian American (24.4 %), Native Hawaiian or Pacific Islander (19.3 %), or Unknown (8.5 %) race/ethnicity. Conditional multiple logistic regression was used to estimate odds ratios comparing cases vs controls. Participants with an ICD-10 cannabis code had a significantly greater risk of major depressive disorder (OR = 10.68, p < 0.0001) and any anxiety disorder (OR = 6.45, p < 0.0001), as well as specific anxiety or trauma-related disorders (e.g., generalized anxiety disorder, PTSD) and SUDs (ORs 2.72 - 16.00, p < 0.01 for all). CONCLUSIONS: Over a two-year period, diverse adults age 50 + in Hawai'i with ICD-10 cannabis codes experienced higher rates of subsequent psychiatric and SUDs compared to controls. These findings can guide efforts to inform older adults about possible cannabis-related risks.

Effectiveness of Medical Music Therapy Practice: Integrative Research Using the Electronic Health Record: Rationale, Design, and Population Characteristics.

Background: Several clinical trials support the efficacy of music therapy (MT) for improving outcomes in hospitalized patients, but few studies have evaluated the real-world delivery and integration of MT across multiple medical centers. This article describes the rationale, design, and population characteristics of a retrospective study examining the delivery and integration of MT within a large health system. Methods: A retrospective electronic health record (EHR) review was conducted of hospitalized patients seen by and/or referred to MT between January 2017 and July 2020. MT was provided across ten medical centers, including an academic medical center, a freestanding cancer center, and eight community hospitals. Discrete demographic, clinical, and MT treatment and referral characteristics were extracted from the EHR, cleaned, and organized using regular expressions functions, and they were summarized using descriptive statistics. Results: The MT team (average 11.6 clinical fulltime equivalent staff/year) provided 14,261 sessions to 7378 patients across 9091 hospitalizations. Patients were predominantly female (63.7%), White (54.3%) or Black/African American (44.0%), 63.7 ± 18.5 years of age at admission, and insured under Medicare (51.1%), Medicaid (18.1%), or private insurance (14.2%). Patients' hospitalizations (median length of stay: 5 days) were primarily for cardiovascular (11.8%), respiratory (9.9%), or musculoskeletal (8.9%) conditions. Overall, 39.4% of patients' hospital admissions included a mental health diagnosis, and 15.4% were referred to palliative care. Patients were referred by physicians (34.7%), nurses (29.4%), or advanced practice providers (24.7%) for coping (32.0%), anxiety reduction (20.4%), or pain management (10.1%). Therapists provided sessions to patients discharged from medical/surgical (74.5%), oncology (18.4%), or intensive care (5.8%) units. Conclusions: This retrospective study indicates that MT can be integrated across a large health system for addressing the needs of socioeconomically diverse patients. However, future research is needed to assess MT's impact on health care utilization (i.e., length of stay and rates of readmission) and immediate patient-reported outcomes.

Identification of recurrent atrial fibrillation using natural language processing applied to electronic health records.

AIMS: This study aimed to develop and apply natural language processing (NLP) algorithms to identify recurrent atrial fibrillation (AF) episodes following rhythm control therapy initiation using electronic health records (EHRs). METHODS AND RESULTS: We included adults with new-onset AF who initiated rhythm control therapies (ablation, cardioversion, or antiarrhythmic medication) within two US integrated healthcare delivery systems. A code-based algorithm identified potential AF recurrence using diagnosis and procedure codes. An automated NLP algorithm was developed and validated to capture AF recurrence from electrocardiograms, cardiac monitor reports, and clinical notes. Compared with the reference standard cases confirmed by physicians' adjudication, the F-scores, sensitivity, and specificity were all above 0.90 for the NLP algorithms at both sites. We applied the NLP and code-based algorithms to patients with incident AF (n = 22 970) during the 12 months after initiating rhythm control therapy. Applying the NLP algorithms, the percentages of patients with AF recurrence for sites 1 and 2 were 60.7% and 69.9% (ablation), 64.5% and 73.7% (cardioversion), and 49.6% and 55.5% (antiarrhythmic medication), respectively. In comparison, the percentages of patients with code-identified AF recurrence for sites 1 and 2 were 20.2% and 23.7% for ablation, 25.6% and 28.4% for cardioversion, and 20.0% and 27.5% for antiarrhythmic medication, respectively. CONCLUSION: When compared with a code-based approach alone, this study's high-performing automated NLP method identified significantly more patients with recurrent AF. The NLP algorithms could enable efficient evaluation of treatment effectiveness of AF therapies in large populations and help develop tailored interventions.