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)

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Hospital Readmissions Among Patients Experiencing Homelessness: An Electronic Health Record Data Study.

BACKGROUND: Population-level tracking of hospital use patterns with integrated care organizations in patients experiencing homelessness has been difficult. A California law implemented in 2019 (Senate Bill 1152) aimed to ensure safety for this population after discharge from the hospital by requiring additional documentation for patients experiencing homelessness, which provides an opportunity to evaluate hospital use by this population. METHODS: In a large integrated health system in California, patients experiencing homelessness were identified through documentation change requirements associated with this law and compared with a matched group from the general population. RESULTS: Patients experiencing homelessness had increased rates of hospital readmission after discharge compared to the general population matched on demographics and medical comorbidity in 2019 and 2020. Any address change in the prior year for patients was associated with increased odds of emergency department readmission. Patients experiencing homelessness, both enrolled in an integrated delivery system and not, were successfully identified as having higher readmission rates compared with their housed counterparts. CONCLUSION: Documentation of housing status following Senate Bill 1152 has enabled improved study of hospital use among those with housing instability. Understanding patterns of hospital use in this vulnerable group will help practitioners identify timely points of intervention for further social and health care support.

Perfil de crianças e adolescentes dependentes de tecnologia em um hospital pediátrico do Sul do país / Perfil de niños e adolescentes dependientes de tecnología en un hospital pediátrico de referencia del sur del país / Profile of technology-dependent children and adolescents in a referral pediatric hospital in the south of the country

Resumo Objetivo identificar o perfil de crianças e adolescentes dependentes de tecnologia de um hospital de referência pediátrica do sul do país. Método estudo descritivo, com abordagem quantitativa. A coleta de dados ocorreu por meio da análise de prontuários, entre janeiro de 2016 e dezembro de 2019, armazenados em planilha Microsoft Excel para a análise estatística descritiva. Um projeto aprovado pelo Comitê de Ética sob o parecer 5.115.194. Resultados prevaleceu o sexo masculino (50,8%), em idade pré-escolar (30,8%), proveniente da Grande Florianópolis (60,1%). Os diagnósticos mais frequentes foram relacionados à prematuridade/período neonatal, anomalias congênitas/defeitos genéticos, doenças neurológicas e/ou neuromusculares, correspondendo a 37%, 33,2% e 18,5%. Os dispositivos tecnológicos mais utilizados foram gastrostomia (56,3%) e traqueostomia (36,6%). A utilização de medicamentos contínuos se deu em 93,4% e 49,2% utilizavam quatro ou mais medicamentos. As mães foram as principais cuidadoras (80,9%). Ocorreram 31 óbitos no período. Conclusão e implicação para a prática este grupo apresenta grande demanda de cuidados decorrentes do diagnóstico principal, dos dispositivos tecnológicos, das medicações e das possíveis complicações. A identificação do perfil das crianças e adolescentes dependentes de tecnologia contribuiu para ampliar a visibilidade de uma população que está em constante crescimento e, assim, prestar uma assistência integral, de acordo com suas especificidades e reais necessidades.

Resumen Objetivo identificar el perfil de niños y adolescentes dependientes de tecnología atendidos en un hospital de referência pediátrica del sur del país. Método estudio descriptivo con enfoque cuantitativo. La recolección de datos ocurrió através del análisis de las historias clínicas, desde enero de 2016 hasta diciembre de 2019, almacenadas en una hoja de cálculo de Microsoft Excel para el análisis estadístico descriptivo. El proyecto fue aprobado por el Comité de Ética bajo el parecer 5.115.194. Resultados predominaron varones (50,8%), en período de desarrollo preescolar (30,8%), la región más frecuentada de la Gran Florianópolis (60,1%). Los diagnósticos más frecuentes estuvieron relacionados con prematuridad/el período neonatal, anomalías congénitas/defectos genéticos, enfermedades neurológicas y/o neuromusculares, correspondiendo al 37%, 33,2% y 18,5%, respectivamente. Los dispositivos tecnológicos más utilizados fueron la gastrostomía (56,3%) y la traqueotomía (36,6%). El uso de medicación continua ocurrió en el 93,4% y el 49,2% utilizó cuatro o más medicamentos. Las madres fueron las principales cuidadoras en 80,9% de los casos, ocurriendo 31 óbitos en el período. Conclusión e implicación para la práctica este grupo tiene una alta demanda de atención debido al diagnóstico principal, dispositivos tecnológicos, medicamentos y posibles complicaciones. Identificar el perfil de niños y adolescentes dependientes de tecnología contribuye a aumentar la visibilidad de una población en constante crecimiento y, por lo tanto, calificar la asistencia, de acuerdo com sus especificidades y reales necesidades.

Abstract Objective to identify the profile of technology-dependent children and adolescents at a pediatric referral hospital in southern Brazil. Method a descriptive study with a quantitative approach. Data was collected by analyzing medical records between January 2016 and December 2019 and stored in a Microsoft Excel spreadsheet for descriptive statistical analysis. The project was approved by the Ethics Committee under protocol number 5.115.194. Results: The prevalence was male (50.8%), pre-school age (30.8%), from Greater Florianópolis (60.1%). The most frequent diagnoses were related to prematurity/neonatal period, congenital anomalies/genetic defects, and neurological and/or neuromuscular diseases, corresponding to 37%, 33.2%, and 18.5%. The most commonly used technological devices were gastrostomy (56.3%) and tracheostomy (36.6%). 93.4% used continuous medication and 49.2% used four or more medications. Mothers were the main caregivers (80.9%). There were 31 deaths during the period. Conclusion and implications for practice this group has a high demand for care due to the main diagnosis, technological devices, medications, and possible complications. Identifying the profile of technology-dependent children and adolescents has helped to increase the visibility of a population that is constantly growing and thus provides comprehensive care according to their specific needs.

Adoption of Electronic Health Record Among Substance Use Disorder Treatment Programs: Nationwide Cross-Sectional Survey Study.

BACKGROUND: Electronic health record (EHR) systems have been shown to be associated with improvements in care processes, quality of care, and patient outcomes. EHR also has a crucial role in the delivery of substance use disorder (SUD) treatment and is considered important for addressing SUD crises, including the opioid epidemic. However, little is known about the adoption of EHR in SUD treatment programs or the organizational-level factors associated with the adoption of EHR in SUD treatment. OBJECTIVE: We examined the adoption of EHR in SUD programs, with a focus on changes in adoption from 2014 to 2017, and identified organizational-level factors associated with EHR adoption. METHODS: We used data from the 2014 and 2017 National Drug Abuse Treatment System Surveys. Our analysis included 1027 SUD programs (531 in 2014 and 496 in 2017). We used chi-square and Mann-Whitney U tests for categorical and continuous variables, respectively, to assess changes in EHR adoption, technology use, program, and client characteristics. We also investigated differences in characteristics and barriers to adoption by EHR adoption status (adopted EHR vs had not adopted or were planning to adopt EHR). We then conducted multivariate logistic regressions to examine internal and external factors associated with EHR adoption. RESULTS: The adoption of EHR increased significantly from 57.6% (306/531) in 2014 to 69.2% (343/496) in 2017 (P<.001), showing that nearly one-third (153/496, 30.8%) of SUD programs had not yet adopted an EHR system by 2017. We identified a significant increase in technology use and ownership by a parent company (P=.01 and P<.001) and a decrease in the percentage of uninsured patients in 2017 (P<.001), compared to 2014. Our analysis further showed significant differences by adoption status for three major barriers to adoption: (1) start-up costs, (2) ongoing financial costs, and (3) privacy or security concerns (P<.001). Programs that used computerized scheduling (adjusted odds ratio [AOR] 3.02, 95% CI 2.23-4.09) and billing systems (AOR 2.29, 95% CI 1.62-3.25) were more likely to adopt EHR. Similarly, ownership type, such as private nonprofit (AOR 1.86, 95% CI 1.31-2.65) and public (AOR 2.14, 95% CI 1.27-3.67), or interest in participating in a patient-centered medical home (AOR 1.93, 95% CI 1.29-2.92), were associated with an increased likelihood to adopt EHR. Overall, SUD programs were more likely to adopt an EHR system in 2017 compared to 2014 (AOR 1.44, 95% CI 1.07-1.94). CONCLUSIONS: Our findings highlighted that SUD programs may be on track to achieve widespread EHR adoption. However, there is a need for focused strategies, resources, and policies explicitly designed to systematically address barriers and tackle obstacles to expanding the adoption of EHR systems. These efforts must be holistic and address factors at multiple organizational levels.

Clinical impacts of an integrated electronic health record-based smoking cessation intervention during hospitalisation.

OBJECTIVE: To assess the effects of an electronic health record (EHR) intervention that prompts the clinician to prescribe nicotine replacement therapy (NRT) at hospital admission and discharge in a large integrated health system. DESIGN: Retrospective cohort study using interrupted time series (ITS) analysis leveraging EHR data generated before and after implementation of the 2015 EHR-based intervention. SETTING: Kaiser Permanente Northern California, a large integrated health system with 4.2 million members. PARTICIPANTS: Current smokers aged ≥18 hospitalised for any reason. EXPOSURE: EHR-based clinical decision supports that prompted the clinician to order NRT on hospital admission (implemented February 2015) and discharge (implemented September 2015). MAIN OUTCOMES AND MEASURES: Primary outcomes included the monthly percentage of admitted smokers with NRT orders during admission and at discharge. A secondary outcome assessed patient quit rates within 30 days of hospital discharge as reported during discharge follow-up outpatient visits. RESULTS: The percentage of admissions with NRT orders increased from 29.9% in the year preceding the intervention to 78.1% in the year following (41.8% change, 95% CI 38.6% to 44.9%) after implementation of the admission hard-stop intervention compared with the baseline trend (ITS estimate). The percentage of discharges with NRT orders increased acutely at the time of both interventions (admission intervention ITS estimate 15.5%, 95% CI 11% to 20%; discharge intervention ITS estimate 13.4%, 95% CI 9.1% to 17.7%). Following the implementation of the discharge intervention, there was a small increase in patient-reported quit rates (ITS estimate 5.0%, 95% CI 2.2% to 7.8%). CONCLUSIONS: An EHR-based clinical decision-making support embedded into admission and discharge documentation was associated with an increase in NRT prescriptions and improvement in quit rates. Similar systemic EHR interventions can help improve smoking cessation efforts after hospitalisation.

Effectiveness of the live zoster vaccine during the 10 years following vaccination: real world cohort study using electronic health records.

OBJECTIVES: To assess the effectiveness of live zoster vaccine during more than 10 years after vaccination; and to describe methods for ascertaining vaccine effectiveness in the context of waning. DESIGN: Real world cohort study using electronic health records. SETTING: Kaiser Permanente Northern California, an integrated healthcare delivery system in the US, 1 January 2007 to 31 December 2018. POPULATION: More than 1.5 million people aged 50 years and older followed for almost 9.4 million person years. MAIN OUTCOME MEASURE: Vaccine effectiveness in preventing herpes zoster, postherpetic neuralgia, herpes zoster ophthalmicus, and admission to hospital for herpes zoster was assessed. Change in vaccine effectiveness by time since vaccination was examined using Cox regression with a calendar timeline. Time varying indicators were specified for each interval of time since vaccination (30 days to less than one year, one to less than two years, etc) and adjusted for covariates. RESULTS: Of 1 505 647 people, 507 444 (34%) were vaccinated with live zoster vaccine. Among 75 135 incident herpes zoster cases, 4982 (7%) developed postherpetic neuralgia, 4439 (6%) had herpes zoster ophthalmicus, and 556 (0.7%) were admitted to hospital for herpes zoster. For each outcome, vaccine effectiveness was highest in the first year after vaccination and decreased substantially over time. Against herpes zoster, vaccine effectiveness waned from 67% (95% confidence interval 65% to 69%) in the first year to 15% (5% to 24%) after 10 years. Against postherpetic neuralgia, vaccine effectiveness waned from 83% (78% to 87%) to 41% (17% to 59%) after 10 years. Against herpes zoster ophthalmicus, vaccine effectiveness waned from 71% (63% to 76%) to 29% (18% to 39%) during five to less than eight years. Against admission to hospital for herpes zoster, vaccine effectiveness waned from 90% (67% to 97%) to 53% (25% to 70%) during five to less than eight years. Across all follow-up time, overall vaccine effectiveness was 46% (45% to 47%) against herpes zoster, 62% (59% to 65%) against postherpetic neuralgia, 45% (40% to 49%) against herpes zoster ophthalmicus, and 66% (55% to 74%) against admission to hospital for herpes zoster. CONCLUSIONS: Live zoster vaccine was effective initially. Vaccine effectiveness waned substantially yet some protection remained 10 years after vaccination. After 10 years, protection was low against herpes zoster but higher against postherpetic neuralgia. TRIAL REGISTRATION: ClinicalTrials.gov number NCT01600079; EU PAS register number EUPAS17502.

Protocol to evaluate sequential electronic health record-based strategies to increase genetic testing for breast and ovarian cancer risk across diverse patient populations in gynecology practices.

BACKGROUND: Germline genetic testing is recommended by the National Comprehensive Cancer Network (NCCN) for individuals including, but not limited to, those with a personal history of ovarian cancer, young-onset (< 50 years) breast cancer, and a family history of ovarian cancer or male breast cancer. Genetic testing is underused overall, and rates are consistently lower among Black and Hispanic populations. Behavioral economics-informed implementation strategies, or nudges, directed towards patients and clinicians may increase the use of this evidence-based clinical practice. METHODS: Patients meeting eligibility for germline genetic testing for breast and ovarian cancer will be identified using electronic phenotyping algorithms. A pragmatic cohort study will test three sequential strategies to promote genetic testing, two directed at patients and one directed at clinicians, deployed in the electronic health record (EHR) for patients in OB-GYN clinics across a diverse academic medical center. We will use rapid cycle approaches informed by relevant clinician and patient experiences, health equity, and behavioral economics to optimize and de-risk our strategies and methods before trial initiation. Step 1 will send patients messages through the health system patient portal. For non-responders, step 2 will reach out to patients via text message. For non-responders, Step 3 will contact patients' clinicians using a novel "pend and send" tool in the EHR. The primary implementation outcome is engagement with germline genetic testing for breast and ovarian cancer predisposition, defined as a scheduled genetic counseling appointment. Patient data collected through the EHR (e.g., race/ethnicity, geocoded address) will be examined as moderators of the impact of the strategies. DISCUSSION: This study will be one of the first to sequentially examine the effects of patient- and clinician-directed strategies informed by behavioral economics on engagement with breast and ovarian cancer genetic testing. The pragmatic and sequential design will facilitate a large and diverse patient sample, allow for the assessment of incremental gains from different implementation strategies, and permit the assessment of moderators of strategy effectiveness. The findings may help determine the impact of low-cost, highly transportable implementation strategies that can be integrated into healthcare systems to improve the use of genomic medicine. TRIAL REGISTRATION: ClinicalTrials.gov. NCT05721326. Registered February 10, 2023. https://www. CLINICALTRIALS: gov/study/NCT05721326.