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Purpose: The European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record systems has a positive impact on data quality, but improvements are not consistent across empirical studies. This study aims to analyze differences in the changes of data quality and to discuss these against distinct stages of the electronic medical record's adoption process. Methods: Paper-based and electronic medical records from three surgical departments were compared, assessing changes in data quality after the implementation of an electronic medical record system. Data quality was operationalized as completeness of documentation. Ten information that must be documented in both record types (e.g. vital signs) were coded as 1 if they were documented, otherwise as 0. Chi-Square-Tests were used to compare percentage completeness of these ten information and t-tests to compare mean completeness per record type. Results: A total of N = 659 records were analyzed. Overall, the average completeness improved in the electronic medical record, with a change from 6.02 (SD = 1.88) to 7.2 (SD = 1.77). At the information level, eight information improved, one deteriorated and one remained unchanged. At the level of departments, changes in data quality show expected differences. Conclusion: The study provides evidence that improvements in data quality could depend on the process how the electronic medical record is adopted in the affected department. Research is needed to further improve data quality through implementing new electronical medical record systems or updating existing ones. [ABSTRACT FROM AUTHOR]

Background: Research on Qi Gong (QG) supports promising health benefits. Both interest and use of QG in U.S. adults has increased over the past decade. Shared Medical Appointments (SMAs) are a novel, cost-effective, and time efficient health care delivery approach associated with patient and clinician satisfaction. Objectives: A telehealth delivered QG SMA was pilot tested among a diverse demographic population within an integrative medicine practice at an academic medical center to assess feasibility and acceptability. Methods: This was a feasibility/acceptability pilot study conducted at a large New York City academic medical center's Integrative Health and Wellness center from January to July 2023. A QG instructor-acupuncturist and an integrative medicine physician-acupuncturist co-led 3 separate series (5 Element QG, Eight-Section Brocade, and Joint-Mobilizing/Sinew Strengthening exercises) of weekly 30-45-minute sessions of QG SMA on a weekday afternoon via telemedicine. The first session included an overview of QG and Traditional Chinese Medicine (TCM) research while follow-up sessions included a check-in, didactic demonstration followed by QG practice, and a debrief to answer questions. Video links were provided for home practice. Surveys assessing satisfaction were sent pre/post series. Results: 18 sessions of QG SMA were offered over a 6-month period. A total of 40 unique participants from diverse demographics (gender, race/ethnicity, primary residence) attended, for a total of 197 virtual visits. A total of 20 participants enrolled in Series 1 (8 weeks), 23 enrolled in Series 2 (7 weeks), and 16 enrolled in Series 3 (3cweeks). For each session, group attendance ranged from 8-16 with an average of 11 participants. Attendance was high with participants attending an average of 72% of the sessions. Participants attended 88% of the first 8-week series, 54% of the second 7-week series, and 60% of the third series. Participant interest persisted over time with 35% of the 40 participants attending more than 1 series, and 12.5% attending all 3 series. Participants' diagnoses and health symptoms included pain (62.5%), cancer (45%) anxiety/depression (40%), cardiovascular disease (CVD) or metabolic conditions (32.5%), gastrointestinal (GI) symptoms/diagnoses (27.5%), stress (22.5%), osteopenia/osteoporosis (17.5%), and insomnia (17.5%). Pre-series [n = 27] participants endorsed symptoms including sleep disturbances, fatigue, pain, stress, weakness, GI symptoms, psychological symptoms, hot flashes, and brain fog. Post-program survey results [n = 11] suggested QG program addressed common symptoms including fatigue, insomnia, anxiety, stress, pain, weakness, and gastrointestinal symptoms. Participants reported incorporating QG, breathing techniques, and meditation into their daily routine. All participants reported their goals were met and that they would recommend the program to others. Regarding delivery preferences, 73% preferred telehealth, 27% hybrid, and none preferred in-person. Participants appreciated the format, new skills, community, and instructors. Conclusion: While the Virtual QG SMA series provided to diverse demographic population with mixed diagnoses is feasible, acceptable, and shows promising positive impact in this pilot, caution in interpreting the data is advised due to the low response rate of the post-program survey. Robust studies with longer follow-ups are recommended. [ABSTRACT FROM AUTHOR]

Background: This study describes how New York City (NYC) Health + Hospitals implemented a large-scale Community Health Worker (CHW) program in adult primary care clinics between January 2022 and December 2023 and established metrics to monitor program implementation. This study is timely as healthcare systems consider how to scale high-quality CHW programs. Methods: We collected metrics in the following areas: (1) Workforce demographics, team structure, and training; (2) Enrolled patient demographics; (3) Patient-centered metrics, such as patient counts (e.g. patients outreached and enrolled) and engagement (e.g. median time in program, caseloads per CHW), and goals (e.g. median number of goals identified and completed). Metrics are based on standard data elements captured through CHW documentation in the electronic health record collected during program implementation. Data cleaning is completed using SQL queries and R scripts. Results: In June 2023, there were a total of 97 CHW and 22 CHW Supervisor staff lines in adult primary care across 17 healthcare sites. There were 4.6 CHWs to 1 CHW supervisor on average though this ranged by facility from 1:1 to 1:6. Compared to the population that receives primary care at NYC H + H, CHWs served more African American/Black patients (40% vs. 32%) and an older patient population (35% older than 65 vs. 21% older than 65). From January 2022 to December 2023, 13,812 patients were outreached by CHWs. Of these, 9,069 (66%) were referred by clinicians, 7,331 (53%) were enrolled, and 5,044 (37%) successfully graduated. The median number of goals identified by patients was four, and the median number of goals completed with a CHW per patient was three. The top three goals were primary care engagement (47%), specialty care engagement (46%), and food insecurity (45%). Conclusion: Establishing clear implementation and process metrics helps to ensure that CHWs embedded in health systems can meaningfully engage adult patients in health care, address patient-centered goals, and connect patients to community and government services. [ABSTRACT FROM AUTHOR]

The article discusses the role of artificial intelligence in enhancing medical care quality and patient safety. Topics mentioned include the enhancement of electronic medical record, the reduction in administrative burden of medical care, the increase in job satisfaction of medical care personnel, the decrease in workforce shortages and burnout and the improvement in the time of physicians for caring their patients instead of documenting their care.

The aim of this research is to develop a method for creating an online service site in the field of health care. The object of research is the process of developing a medical website. The developed site of online services of the medical institution is a partial implementation of the medical information system. The creation of the methodology for the development of the site of online services in the field of health care was carried out on the basis of the development of API methods (Application Programming Interface methods) and functionality on the site server, which provides the opportunity to interact with eHealth and will allow integration with other medical systems. A feature of the created medical information system is interaction with the centralized eHealth system. API documentation, which is publicly available, is used to interact with the eHealth system. The choice of tools for the development of a medical information system is substantiated. For this, alternative variants of means of developing the client side of the site are determined and the importance of each means was calculated. Super Decisions software is used to determine the importance of criteria and compare alternative options. With the help of the Super Decisions program, the React framework is chosen for the development of the client side of the site. The structure of the website of online services in the medical field is developed, the client and server sides of the product are practically implemented. Development of the server part of the site was carried out based on integration with third-party systems and creation of API methods for exchanging information on the client side. Criteria are defined and programming languages for backend development are analyzed. PHP (Hypertext Preprocessor) language is chosen based on the analysis of reasonable criteria. Symfony is chosen as the PHP framework. A graphic representation of the structure and navigation of the site is developed. The procedure for setting up the environment and raising the project according to the selected technologies is proposed. Integration of the developed site with eHealth is proposed. Experimental testing of the developed site for online services in the field of health care is performed. [ABSTRACT FROM AUTHOR]

Background: Patient portals can improve access to electronic health information and enhance patient engagement. However, disparities in patient portal utilization remain, affecting disadvantaged communities disproportionately. This study examined patient- and provider-level factors associated with portal usage among Medicaid recipients in a large federally qualified health center (FQHC) network in Texas. Methods: Deidentified electronic medical records of patients 18 years or older from a large Texas FQHC network were analyzed. The dependent variable was a binary flag indicating portal usage during the study period. Independent variables included patient- and provider-level factors. Patient-level factors included sociodemographic, geographic, and clinical characteristics. Provider characteristics included primary service line, provider type, provider language, and years in practice. Because the analysis was at the individual level, a multivariable logistic regression model focused on adjusted associations between independent variables and portal usage. Results: The analytic sample consisted of 9,271 individuals. Compared with individuals 18–39 years, patients 50 years and older had lower odds (50–64 OR: 0.60, p < 0.001; 65+ OR: 0.51, p < 0.001) of portal usage. Males were less likely to use portals (OR: 0.44, p = 0.03), and compared to Non-Hispanic Whites, Non-Hispanic Black (OR: 0.86, p = 0.02) and Hispanics (OR: 0.83, p < 0.001) were significantly less likely to use portals. Individuals with 1 or more telemedicine consults had a two-times greater odds of portal usage (OR: 1.97, p < 0.001). Compared to individuals who had clinic visits in December 2018, portal usage was significantly higher in the pandemic months (March 2020–November 2020, all p's < 0.01). Importantly, the behavioral health service line had the greatest odds (OR: 1.52, p < 0.001), whereas the dental service line had the lowest odds (OR: 0.69, p = 0.01) compared to family practice. No other provider characteristics were significant. Conclusion: Our finding of significant patient-level factors is important and can contribute to developing appropriate patient-focused health information technology approaches to ensure equitable access and maximize the potential benefits of patient portals in health care delivery. [ABSTRACT FROM AUTHOR]

While very much in its infancy in terms of becoming an established tool, the use of digital technology in community nursing is steadily growing, despite the persistent barriers to, and challenges encountered in its uptake and implementation. The mobile nature and high workload of a community nurse's daily practice should facilitate the rapid uptake of time-saving technology. However, there are indications that technology may not be the panacea it was originally proclaimed to be. Francesca Ramadan elaborates on the past and present applications of digital technology in community nursing and delves into the principles that should shape the future potential of tools such as artificial intelligence, automation technologies and clinical decision support systems. [ABSTRACT FROM AUTHOR]

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Distinguishing urgent from non-urgent communication is critical. We aimed to understand how hospital staff choose what communication technologies to use. A mixed methods study was conducted with four focus groups, with staff working on postpartum care units, and log data from a hands-free communication system was analyzed. We found that urgent communications were appropriate for the hands-free device, with less urgent communications sent through secure chat in the electronic health record. Exceptions included when the intended recipient was in the operating room and during sensitive discussions. The most common duration of calls on the hands-free device was 16--60 seconds, with few more than 5minutes. The most frequent reason for incomplete calls was the user not logging in (36%), which could be reduced by eliminating logging in and training. We recommend an interruptive communication technology for urgent information and an electronic health record chat for less urgent information that does not require an immediate response. We recommend forwarding and sending calls to a hospital-provided cellphone from the hands-free communication system for provider roles that do not align with the intended workflow for hands-free communications. [ABSTRACT FROM AUTHOR]

Objective: Quit Connect (QC), our specialty clinic smoking cessation intervention, supports clinic staff to check, advise, and connect willing patients to a state quit line or class. QC improved tobacco screening and quit line referrals 26‐fold in a predominantly White academic health care system population. Implementing QC includes education, electronic health record (EHR) reminders, and periodic audit feedback. This study tested QC's feasibility and impact in a safety‐net rheumatology clinic with a predominantly Black population. Methods: In this pre‐post study, adult rheumatology visits were analyzed 12 months before through 18 months after QC intervention (November 2019 through November 2021, omitting COVID‐19 peak April through November 2020). EHR data compared process and clinical outcomes, including offers, referrals to resources, completed referrals, and documented cessation. Clinic staff engaged in pre‐post focus groups and questionnaires regarding intervention feasibility and acceptability. Cost‐effectiveness was also assessed. Results: Visit‐level patients who smoked were 89.8% Black and 69.5% women (n = 550). Before intervention, clinic staff rarely asked patients about readiness to cut back smoking (<10% assessment). After QC intervention, staff assessed quit readiness in 31.8% of visits with patients who smoked (vs 8.1% before); 58.9% of these patients endorsed readiness to cut back or quit. Of 102 accepting cessation services, 37% (n = 17) of those reached set a quit date. Staff found the intervention feasible and acceptable. Each quit attempt cost approximately $4 to $10. Conclusion: In a safety‐net rheumatology clinic with a predominantly Black population, QC improved tobacco screening, readiness‐to‐quit assessment, and referrals and was also feasible and cost‐effective. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) is a disabling mental illness that can affect all aspects of daily life and is a leading cause of healthcare resource utilisation (HCRU). Aims: We aimed to characterise patients with MDD with moderate-to-high-suicide-intent, compare their HCRU to patients with MDD without moderate-to-high-suicide-intent, and better understand their patient pathways. Methods: This retrospective cohort study used data collected from primary care electronic health records from Clinical Practice Research Datalink (CPRD), linked to Hospital Episode Statistics, Mental Health Services Data Set, and Office for National Statistics in England. Adults diagnosed with ≥ 1 MDD diagnosis between 04/2007 and 11/2015 were categorised by suicide intent. Results: 307,476 patients with MDD were included (294,259 patients without moderate-to-high-suicide-intent and 13,217 with moderate-to-high-suicide-intent). Patients with MDD with moderate-to-high-suicide-intent were younger on average (39.0 vs. 44.8 years) and included a lower percentage of females (58% vs. 65%) compared to patients without moderate-to-high-suicide-intent. HCRU was greater among patients with moderate-to-high-suicide-intent than patients without moderate-to-high-suicide-intent during the first follow-up year for general practitioner consultations (38.5 vs. 29.4), psychiatric outpatient visits (1.5 vs. 0.1), psychiatrist visits (3.6 vs. 0.3), emergency visits (1.5 vs. 0.3), and hospitalisations (86% vs. 26%). Overall, 56% of patients with moderate-to-high-suicide-intent had an antidepressant prescription within 30 days from the initial moderate-to-high-suicide-intent. Conclusions: Patients with MDD and moderate-to-high-suicide-intent were younger, included more males and incurred greater HCRU than those without moderate-to-high-suicide-intent. These results suggest a greater need for effective medical care and appropriate treatments for patients with moderate-to-high-suicide-intent, which could help reduce associated symptoms, mortality, and HCRU. [ABSTRACT FROM AUTHOR]

Background: In the digital age, maintaining patient confidentiality while ensuring effective care coordination poses significant challenges for healthcare providers, particularly nurses. Aim: To investigate the challenges and strategies associated with balancing patient confidentiality and effective care coordination in the digital age. Methods: A cross-sectional study was conducted in a general hospital in Egypt to collect data from 150 nurses across various departments with at least six months of experience in patient care. Data were collected using six tools: Demographic Form, HIPAA Compliance Checklist, Privacy Impact Assessment (PIA) Tool, Data Sharing Agreement (DSA) Framework, EHR Privacy and Security Assessment Tool, and NIST Cybersecurity Framework. Validity and Reliability were ensured through pilot testing and factor analysis. Results: Participants were primarily aged 31–40 years (45%), with 75% female and 60% staff nurses. High compliance was observed in the HIPAA Compliance Checklist, especially in Administrative Safeguards (3.8 ± 0.5), indicating strong management and training processes, with an overall score of 85 ± 10. The PIA Tool showed robust privacy management, with Project Descriptions scoring 4.5 ± 0.3 and a total score of 30 ± 3. The DSA Framework had a mean total score of 20 ± 2, with Data Protection Measures scoring highest at 4.0 ± 0.4. The EHR assessments revealed high scores in Access Controls (4.4 ± 0.3) and Data Integrity Measures (4.3 ± 0.3), with an overall score of 22 ± 1.5. The NIST Cybersecurity Framework had a total score of 18 ± 2, with the highest scores in Protect (3.8) and lower in Detect (3.6). Strong positive correlations were found between HIPAA Compliance and EHR Privacy (r = 0.70, p < 0.05) and NIST Cybersecurity (r = 0.55, p < 0.05), reflecting effective data protection practices. Conclusion: The study suggests that continuous improvement in privacy practices among healthcare providers, through ongoing training and comprehensive privacy frameworks, is vital for enhancing patient confidentiality and supporting effective care coordination. [ABSTRACT FROM AUTHOR]

In emergency department (ED) settings, rapid and precise diagnostic evaluations are critical to ensure better patient outcomes and efficient healthcare delivery. This study assesses the accuracy of differential diagnosis lists generated by the third-generation ChatGPT (ChatGPT-3.5) and the fourth-generation ChatGPT (ChatGPT-4) based on electronic health record notes recorded within the first 24 h of ED admission. These models process unstructured text to formulate a ranked list of potential diagnoses. The accuracy of these models was benchmarked against actual discharge diagnoses to evaluate their utility as diagnostic aids. Results indicated that both GPT-3.5 and GPT-4 reasonably accurately predicted diagnoses at the body system level, with GPT-4 slightly outperforming its predecessor. However, their performance at the more granular category level was inconsistent, often showing decreased precision. Notably, GPT-4 demonstrated improved accuracy in several critical categories that underscores its advanced capabilities in managing complex clinical scenarios. [ABSTRACT FROM AUTHOR]

Access to palliative care (PC) holds significance for hospital-admitted patients grappling with the symptoms of life limiting illnesses. Nonetheless, numerous such patients who could gain from PC fail to receive it promptly or even at all. We can leverage the prior year's historical data extracted from electronic health records of hospitalized patients to train a machine learning (ML) model. This model's purpose would be to prognosticate the requirement for PC consultation using real-time data. The model, operating as a semi-supervised system, will be integrated into institutional data pipelines, and utilized by a downstream display application overseen by the PC team. In cases where the PC team deems it suitable, a team member will communicate with the respective care team of the patient. The ML model's training efficacy will be assessed using the area under the curve (AUC) metric, employing a 20% reserved validation set. The threshold for PC consultations will be grounded in historical data. To enhance the ML model's precision, the pivotal variables within the model will be pinpointed, and any sources of biases or errors in the model will be identified for meticulous refinement. The AUC values of successive ML models will be juxtaposed with cross-validation data. Automatizing the referral procedure through electronic health record systems has the potential to usher in a more effective and streamlined approach to healthcare delivery. [ABSTRACT FROM AUTHOR]

Objective: The measurement and recording of vital signs may be impacted by biases, including preferences for even and round numbers. However, other biases, such as variation due to defined numerical boundaries (also known as boundary effects), may be present in vital signs data and have not yet been investigated in a medical setting. We aimed to assess vital signs data for such biases. These parameters are clinically significant as they influence care escalation. Methods: Vital signs data (heart rate, respiratory rate, oxygen saturation and systolic blood pressure) were collected from a tertiary hospital electronic medical record over a 2‐year period. These data were analysed using polynomial regression with additional terms to assess for underreporting of out‐of‐range observations and overreporting numbers with terminal digits of 0 (round numbers), 2 (even numbers) and 5. Results: It was found that heart rate, oxygen saturation and systolic blood pressure demonstrated 'boundary effects', with values inside the 'normal' range disproportionately more likely to be recorded. Even number bias was observed in systolic heart rate, respiratory rate and blood pressure. Preference for multiples of 5 was observed for heart rate and blood pressure. Independent overrepresentation of multiples of 10 was demonstrated in heart rate data. Conclusion: Although often considered objective, vital signs data are affected by bias. These biases may impact the care patients receive. Additionally, it may have implications for creating and training machine learning models that utilise vital signs data. [ABSTRACT FROM AUTHOR]

Background: Age‐friendly care, addressing what matters most, medications, mentation, and mobility, is a successful model for improving older adult care. We describe the initial outcomes of age‐friendly care implementation in five primary care clinics in an academic health system. Methods: In partnership with a regional quality improvement (QI) organization, we used practice facilitation to implement age‐friendly care from July 2020 to June 2023. Clinic workflows and electronic health record (EHR) templates were modified to capture six QI measures for patients ≥65 years:Documenting what matters most to patientsAdvance care planning (ACP)Annual cognitive screeningCaregiver referral to dementia community resourcesFall‐risk screeningCo‐prescription of opioid and sedative‐hypnotic drugs Providers were alerted if patients had positive screens and given support tools for clinical decision‐making. QI measures from January–June 2023 were compared to the year prior to implementation. Providers and staff were interviewed about implementation barriers and facilitators. Results: All six measures improved in Geriatrics and and other clinics showed improvement in ACP and cognitive screening. All clinics had high fall‐risk screening rates (≥85%). The least improved measure was co‐prescription of opioids and sedative‐hypnotics with co‐prescription rates ranging from 7% to 39%. Implementation hinged on leadership prioritization, practice facilitator guidance, clinical team buy‐in, EHR functionality, and clinical performance review. Three clinics received Age‐Friendly Health System recognition. Conclusions: A QI approach using practice facilitation and EHR templates improved some but not all age‐friendly care measures. Future interventions will focus on training in high‐risk medication tapering and elicitation of health goals. See related editorial by Lauren J Gleason and Katherine Thompson. [ABSTRACT FROM AUTHOR]

The article shares strategies associated with reengineering the core functionalities of an electronic health record (EHR) system of a Kentucky geriatric-focused primary care (PC) clinic. It notes that the strategy necessitated a shift from traditional roles of geriatrician-led PC to team-based PC. It states that the success of the strategy relied on comprehensive education for team members on age-friendly health care.

Up to 40% of care provided to patients is either wasteful or harmful. The practice of audit and feedback can help identify where care can be improved. However, such audits must be executed in a systematic way that engages with clinicians to maximize the impact of feedback, ultimately improving patient outcomes. Currently, audit training is not integrated into formal education pathways and clinicians need guidance to support them in this activity. This paper explores contemporary research, with the aim of providing practical advice for recommendations to maximize the impact of audit and feedback. [ABSTRACT FROM AUTHOR]

Our study assessed the characteristics of people living with HIV (PLWH) detected via opportunistic screening in Valencia (Spain) to determine diagnoses potentially missed under a more restrictive, indicator-condition diagnostic strategy. We conducted a retrospective analysis of electronic health records of 97 PLWH diagnosed between April 2019 and August 2022. The main outcomes reported were patient CD4+ T cell count, known HIV risk factors at diagnosis, and missed opportunities for diagnosis, defined as the failure of a previously untested patient to undergo HIV testing despite attending previous visits to healthcare facilities prior to diagnosis. Successful linkage to care was achieved for 95.9% of diagnosed patients. Half of the PLWH were diagnosed late, while 47.8% did not meet the criteria for indicator-condition-driven HIV diagnosis at the time of their diagnosis. Additionally, 52.2% did not receive HIV testing despite an average of 5.1 ± 6.0 healthcare visits in the 12 months prior to diagnosis. Spaniards had more missed opportunities for diagnosis than foreigners (64% vs. 40%, p = 0.02). Depending solely on an indicator-condition-driven HIV diagnosis approach could result in 47.8% of cases being missed. Including "migrants" as a testing criterion could lower missed diagnoses to 25.3% but might create inequities in prevention access. In conclusion, our findings provide valuable insights to enhance HIV testing, early diagnosis, and linkage to care. While it is crucial to uphold the indicator-condition-driven HIV diagnosis as baseline practice, improving screening strategies will decrease late diagnoses and missed opportunities, thereby effectively contributing to end the epidemic. [ABSTRACT FROM AUTHOR]

Background: Attaining a good quality of life (QOL) is a priority for people living with HIV (PLHIV). We explored the interaction between QOL and the associated demographic, behavioural and clinical factors for PLHIV attending an outer-metropolitan clinical setting in Western Sydney, Australia. The clinic's cohort of PLHIV is characterised by relatively high proportions of women, heterosexual men, and patients from culturally and linguistically diverse (CALD) communities. Methods: We assessed QOL using the PozQol tool that is specifically designed for PLHIV. QOL scores and de-identified socio-demographic and clinical data were extracted from the electronic and paper medical records of PLHIV who completed a PozQol tool (September 2020–March 2022). We performed descriptive analyses and logistic regression to identify associations. Results: Among 188 patients, there were 77.7% men, 21.3% women, 1.1% transwomen; 67.0% were born overseas, 85.1% spoke English, 84.4% were Medicare-eligible, 85.9% were employed, 58.5% were diagnosed with HIV 6–20 years ago, and 33.0% within the past 5 years. Overall, 58.0% had a high or very high QOL. A low score in any domain was associated with Medicare-ineligibility. Low QOL scores in specific domains were associated with the following factors: health (being born overseas, having partners of both sexes), psychological (unemployment, having a mental health condition, having a viral load >20 copies/mL), social (unemployment), and functional (Medicare-eligibility, unemployment, having a viral load >20 copies/mL). Conclusions: The PozQol tool has enhanced understanding of factors impacting on QOL for PLHIV attending our service in Western Sydney. Identifying patients with low QOL scores allows targeted clinical interventions to improve QOL, and re-alignment of clinical services to better support PLHIV. Quality of life (QOL) is an important priority for people living with HIV (PLHIV). Using a patient-reported outcome measure (PozQol tool), we found that 58% of PLHIV reported a high or very high QOL, and socio-demographic factors such as Medicare-ineligibility and unemployment were associated with lower QOL scores. PozQol may assist services by enhancing understanding of QOL among PLHIV, guide local and national policy, health service provision, and promote empowerment of individuals to better understand their own QOL. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) poses a significant health challenge globally, necessitating accurate and timely diagnosis for effective management. Conventional diagnostic methods often struggle to address the multifaceted nature of diabetes and the requisite lifestyle adjustments. In this study, we propose a data-driven approach utilizing machine learning techniques to enhance diabetes diagnosis. By leveraging extensive patient attributes and medical records, machine learning algorithms can uncover intricate patterns and correlations. Our methodology, validated on the PIMA India dataset, demonstrates promising results. The random forest model achieved the highest accuracy of 87%, followed closely by gradient boost at 90%. Notably, XGBoost and CATBoost models attained a peak accuracy of 90.9%. These findings underscore the potential of machine learning in transforming diabetes diagnosis. Beyond improving diagnostic accuracy, our approach aims to guide individuals towards healthier lifestyles. Intelligent systems driven by machine learning hold promise for revolutionizing diabetes management, ultimately leading to better patient outcomes and more effective health care delivery. [ABSTRACT FROM AUTHOR]

Background: Clinical laboratory testing, essential for medical diagnostics, represents a significant part of healthcare activity, influencing around 70% of critical clinical decisions. The automation of laboratory equipment has expanded test menus and increased efficiency to meet the growing demands for clinical testing. However, concerns about misutilization remain prevalent. In Belgium, primary care has seen a dramatic increase in lab test usage, but recent utilization data is lacking. Methods: We conducted a comprehensive retrospective analysis of laboratory test utilization trends within the primary care settings of Belgium over a ten-year period, spanning from 2012 to 2021, incorporating a vast dataset of 189 million test records for almost 1.5 million persons. This was the first study to integrate the metadata from both the INTEGO & THIN databases, which are derived from the two major electronic medical record (EMR) systems used in primary care in Belgium, providing a comprehensive national perspective. This research provides crucial insights into patient-level patterns, test-level utilization, and offers international perspectives through comparative analysis. Results: We found a subtle annual increase in the average number of laboratory tests per patient (ranging from approximately 0.5-1%), indicative of a deceleration in growth in laboratory test ordering when compared to previous decades. We also witnessed stability and consistency of the most frequently ordered laboratory tests across diverse patient populations and healthcare contexts over the years. Conclusions: These findings emphasize the need for continued efforts to optimize test utilization, focusing not only on tackling overutilization but on enhancing the diagnostic relevance of tests ordered. The frequently ordered tests should be prioritized in these initiatives to ensure their continued effectiveness in patient care. By consolidating extensive datasets, employing rigorous statistical analysis, and incorporating international perspectives, this study provides a solid foundation for evidence-based strategies aimed at refining laboratory test utilization practices. These strategies can potentially improve the quality of healthcare delivery while simultaneously addressing cost-effectiveness concerns in healthcare. [ABSTRACT FROM AUTHOR]

PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System. METHODS: Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage. To that end, a clinician annotated MM stage within over 5,000 short snippets of clinical notes, and annotated MM stage at MM treatment initiation for 200 patients. These were allocated into snippet- and patient-level development and validation sets. We developed MM stage extraction and roll-up algorithms within the development sets. After the algorithms were finalized, we validated them using standard measures in held-out validation sets. RESULTS: We developed algorithms for three different MM staging systems that have been in widespread use (Revised International Staging System [R-ISS], International Staging System [ISS], and Durie-Salmon [DS]) and for stage reported without a clearly defined system. Precision and recall were uniformly high for MM stage at the snippet level, ranging from 0.92 to 0.99 for the different MM staging systems. Performance in identifying for MM stage at treatment initiation at the patient level was also excellent, with precision of 0.92, 0.96, 0.90, and 0.86 and recall of 0.99, 0.98, 0.94, and 0.92 for R-ISS, ISS, DS, and unclear stage, respectively. CONCLUSION: Our MM stage extraction algorithm uses rule-based NLP and data aggregation to accurately measure MM stage documented in oncology notes and pathology reports in VA's national EHR system. It may be adapted to other systems where MM stage is recorded in clinical notes. Stage is a key factor in assessment of multiple myeloma (MM) risk, but large databases lack the necessary laboratory and pathology data for staging. We developed a rules-based NLP algorithm to extract oncologist-determined MM stage from VA EHR data. The algorithm demonstrated accurate assignment of MM stage when validated against clinician annotation. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic highlighted the need for a nationwide health information technology solution that could improve upon manual case reporting and decrease the clinical and administrative burden on the US health care system. We describe the development, implementation, and nationwide expansion of electronic case reporting (eCR), including its effect on public health surveillance and pandemic readiness. Methods: Multidisciplinary teams developed and implemented a standards-based, shared, scalable, and interoperable eCR infrastructure during 2014-2020. From January 27, 2020, to January 7, 2023, the team conducted a nationwide scale-up effort and determined the number of eCR-capable electronic health record (EHR) products, the number of reportable conditions available within the infrastructure, and technical connections of health care organizations (HCOs) and jurisdictional public health agencies (PHAs) to the eCR infrastructure. The team also conducted data quality studies to determine whether HCOs were discontinuing manual case reporting and early results of eCR timeliness. Results: During the study period, the number of eCR-capable EHR products developed or in development increased 11-fold (from 3 to 33), the number of reportable conditions available increased 28-fold (from 6 to 173), the number of HCOs connected to the eCR infrastructure increased 143-fold (from 153 to 22 000), and the number of jurisdictional PHAs connected to the eCR infrastructure increased 2.75-fold (from 24 to 66). Data quality reviews with PHAs resulted in select HCOs discontinuing manual case reporting and using eCR-exclusive case reporting in 13 PHA jurisdictions. The timeliness of eCR was <1 minute. Practice Implications: The growth of eCR can revolutionize public health case surveillance by producing data that are more timely and complete than manual case reporting while reducing reporting burden. [ABSTRACT FROM AUTHOR]

Background: Novel hospital diversion strategies are needed to support a growing number of patients with dementia living in the community. One promising model is community paramedicine (CP), which deploys paramedics to the home, who consult with a physician to coordinate treatment and assess disposition. While evidence suggests CP can manage many patients without escalation to the emergency department (ED), no studies have evaluated optimal CP utilization for patients with dementia. Therefore, we compare the use and outcomes of CP for homebound patients with and without dementia. Methods: This retrospective cohort study examines 251 homebound patients receiving home‐based primary care, who utilized a physician‐led CP service between March 2017 and May 2022. Linked electronic health record data included patient demographics, clinical characteristics, and CP encounter details. Dementia status and CP outcomes, including rates of ED transport, over‐transport (i.e., transported, but not hospitalized), and under‐transport (i.e., not transported, but ED visit within 3 days), were determined via chart review. Using logistic regression, we modeled the association of dementia status with over‐ and under‐transport, adjusting for age, sex, and chief complaint. Results: Fifty‐three percent of CP patients had dementia. Their most common chief complaints were dyspnea (24.3%), altered mental status (17.9%), and generalized weakness (9.8%). We found no significant difference in ED transport rates by dementia status (25.4 vs. 22.8%, p = 0.54). Dementia diagnosis was associated with lower rates of over‐transport (OR = 0.21, p = 0.03, CI [0.05, 0.85]) and comparable rates of under‐transport (OR = 0.70, p = 0.47, CI [0.27, 1.83]) in adjusted models. Conclusions: CP has effectively managed a diverse population of homebound patients with dementia cared for via home‐based primary care. Future work should examine potential cost savings and use of CP in dementia care across geographic and healthcare settings. See related article by Kim et al. in this issue. [ABSTRACT FROM AUTHOR]

Introduction: Among older adults undergoing surgery, postoperative delirium is the most common complication. Cognitive impairment and dementia are major risk factors for postoperative delirium, yet they are frequently under‐recognized. It is well established that applying delirium preventive interventions to at‐risk individuals can reduce the likelihood of delirium by up to 40%. The aim of this study was to evaluate how often delirium preventive interventions are missing in patients at risk for delirium due to baseline cognitive impairment. Methods: We conducted a retrospective study using data from the observational study Perioperative Anesthesia Neurocognitive Disorder Assessment‐Geriatric (PANDA‐G) and clinical data from the University of California San Francisco delirium prevention bundle. Patients age 65+ received preoperative multidomain cognitive assessment as part of a research protocol prior to undergoing inpatient spine surgery at a single major academic institution. Results of the cognitive testing were not available to clinical teams. Using electronic medical records, we evaluated if patients who were cognitively impaired at baseline received delirium prevention orders, sleep orders, and avoidance of AGS Beers Criteria® potentially inappropriate medications. Results: Of the 245 patients included in the study, 42% were women. The mean [SD] age was 72 [5.2] years. Preoperative cognitive impairment was identified in 40% of participants (N = 98), and of these, 34% had postoperative delirium. Of patients with preoperative cognitive impairment, 45% did not receive delirium preventive orders, 43% received PIMs, and 49% were missing sleep orders. At least one of the three delirium preventive interventions was missing in 70% of the patients. Discussion: Undiagnosed preoperative cognitive impairment among older adults undergoing spine surgery is common. When cognitive test results were not available to clinicians, patients with baseline cognitive impairment frequently did not receive evidence‐based delirium preventive interventions. These findings highlight an opportunity to improve perioperative brain health care via preoperative cognitive assessment and clinical communication. [ABSTRACT FROM AUTHOR]

PURPOSE Information about social determinants of health (SDOH) is essential for primary care clinicians in the delivery of equitable, comprehensive care, as well as for program planning and resource allocation. SDOH are rarely captured consistently in clinical settings, however. Artificial intelligence (AI) could potentially fill these data gaps, but it needs to be designed collaboratively and thoughtfully. We report on a codesign process with primary care clinicians to understand how an AI tool could be developed, implemented, and used in practice. METHODS We conducted semistructured, 50-minute workshops with a large urban family health team in Toronto, Ontario, Canada asking their feedback on a proposed AI-based tool used to derive patient SDOH from electronic health record data. An inductive thematic analysis was used to describe participants' perspectives regarding the implementation and use of the proposed tool. RESULTS Fifteen participants contributed across 4 workshops. Most patient SDOH information was not available or was difficult to find in their electronic health record. Discussions focused on 3 areas related to the implementation and use of an AI tool to derive social data: people, process, and technology. Participants recommended starting with 1 or 2 social determinants (income and housing were suggested as priorities) and emphasized the need for adequate resources, staff, and training materials. They noted many challenges, including how to discuss the use of AI with patients and how to confirm their social needs identified by the AI tool. CONCLUSIONS Our codesign experience provides guidance from end users on the appropriate and meaningful design and implementation of an AI-based tool for social data in primary care. [ABSTRACT FROM AUTHOR]

The article focuses on improving diabetic foot exam rates in primary care, highlighting a successful quality improvement initiative in a rural Alabama clinic. Topics include the clinic's adoption of a comprehensive diabetic foot screening toolkit, utilization of registered nurses' full scope of practice, and significant improvements in adherence to American Diabetes Association guidelines for foot exams, resulting in a 31 percent increase in completed exams compared to baseline data.

The aim of this work was to examine the profile and treatment outcomes of patients with dual pathology depending on whether the patients were attending addiction centers or are being treated in a coordinated model by mental health services. Data from 7225 dual diagnosis patients were used, of whom 2417 (33.5%) received treatment in the mental health coordinated modality. Clinical information was taken from the patients' electronic health record. Differences were found in patients' sociodemographic and comorbidity profiles according to treatment modality. In general, coordinated care yielded favorable outcomes (higher attendance and lower dropout rates but no differences in retention). The logistic regression analysis identified predictors of patient profiles in coordinated care, emphasizing having a severe mental health disorder (OR = 3.878, 95% CI [3.443, 4.368]; p =.000), being referred by social/health services, or having retired status. Main differences were observed according to the comorbid diagnosis presented, particularly in cases in which the patient had impulse control, hyperkinetic, or cluster C personality disorder. While therapeutic outcomes are influenced by associated comorbidities, the disorders prognosis can be favorable with appropriate treatment. Furthermore, analysis of differences according to treatment modality allows for predicting the type of patient who will receive a particular service, which enables the development of tailored treatments. [ABSTRACT FROM AUTHOR]

In modern healthcare systems, digitalizing medical records is crucial for optimizing patient care. This paper investigates the creation and execution of a website designed to merge patients' medical records, with the goal of offering a holistic and patient-focused approach to healthcare management. The platform aims to streamline the exchange of information among healthcare providers, enhance treatment decision-making processes, and empower patients to take an active role in their healthcare. By centralizing healthcare data on a digital platform, this initiative aims to revolutionize traditional healthcare practices and ultimately improve patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Critical nursing shortages have required many health care organizations to restructure nursing care delivery models. At a tertiary health care center, 150 registered practical nurses were integrated into acute inpatient care settings. Problem: A mechanism to continuously monitor the impact of this staffing change was not available. Approach: Leveraging current literature and consultation with external peers, metrics were compiled and categorized according to Donabedian's Structure Process Outcome Framework. Consultation with internal subject matter experts determined the final metrics. Outcomes: The Patient care, Utility, Logistics, Systemic Evaluation (PULSE) electronic dashboard was developed, capturing metrics from multiple internal databases and presenting real-time composites of validated indicators. Conclusion: The PULSE dashboard is a practical means of enabling nursing leadership to evaluate the impact of change and to make evidence-informed decisions about nursing care delivery at our organization. [ABSTRACT FROM AUTHOR]

Nowadays, smart healthcare and Electronic Health Record (EHR) play a significant role in simplifying the experience of medical treatment and cuts down the trivial work of paramedics. However, this poses significant challenges for organizations, including the management, transfer and protection of EHR which is vital for correct detection. To overcome these challenges, cloud computing presented which offers various attractive features for EHR management such as data availability, elasticity, and ubiquitous computation. However, the use of cloud computing introduces confidentiality and privacy concerns which can lead to human health threat. A trivial solution for this problem is encryption which makes searching a desired document infeasible, more specifically in big data scenarios. Although searchable encryption is a candidate solution, it suffers from keyword guessing attacks, malicious service providers, lack of trust, data integrity, and fairness. In this paper, we propose BACASE-SH framework that leverages blockchain technology to EHR integrity and availability insurance, fair payment, and establish trust among the involved parties. Moreover, we present a certificate-less authenticated asymmetric searchable encryption scheme to address challenges such as data owner (patient) and data user (physician) identity and keyword guessing attacks. The scheme is implemented using a smart contract, and the evaluation results show its efficiency. [ABSTRACT FROM AUTHOR]

Background: Many countries observed a sharp decline in the use of general practice services after the outbreak of the COVID-19 pandemic. However, research has not yet considered how changes in healthcare consumption varied among regions with the same restrictive measures but different COVID-19 prevalence. Aim: To investigate how the COVID-19 pandemic affected healthcare consumption in Dutch general practice during 2020 and 2021, among regions with known heterogeneity in COVID-19 prevalence, from a pre-pandemic baseline in 2019. Design & setting: Population-based cohort study using electronic health records. The study was undertaken in Dutch general practices involved in regional research networks. Method: An interrupted time-series analysis of changes in healthcare consumption from before to during the pandemic was performed. Descriptive statistics were used on the number of potential COVID-19-related contacts, reason for contact, and type of contact. Results: The study covered 3 595 802 contacts (425 639 patients), 3 506 637 contacts (433 340 patients), and 4 105 413 contacts (434 872 patients) in 2019, 2020, and 2021, respectively. Time-series analysis revealed a significant decrease in healthcare consumption after the outbreak of the pandemic. Despite interregional heterogeneity in COVID-19 prevalence, healthcare consumption decreased comparably over time in the three regions, before rebounding to a level significantly higher than baseline in 2021. Physical consultations transitioned to phone or digital over time. Conclusion: Healthcare consumption decreased irrespective of the regional prevalence of COVID-19 from the start of the pandemic, with the Delta variant triggering a further decrease. Overall, changes in care consumption appeared to reflect contextual factors and societal restrictions rather than infection rates. [ABSTRACT FROM AUTHOR]

Background: To combat increasing levels of violence in the emergency department (ED), hospitals have implemented several safety measures, including behavioral flags. These electronic health record (EHR)‐based notifications alert future clinicians of past incidents of potentially threatening patient behavior, but observed racial disparities in their placement may unintentionally introduce bias in patient care. Little is known about how patients perceive these flags and the disparities that have been found in their placement. Objective: This study aims to investigate patient perceptions and perceived benefits and harms associated with the use of behavioral flags. Methods: Twenty‐five semistructured qualitative interviews were conducted with a convenience sample of patients in the ED of a large, urban, academic medical center who did not have a behavioral flag in their EHR. Interviews lasted 10–20 min and were recorded then transcribed. Thematic analysis of deidentified transcripts took place in NVivo 20 software (QSR International) using a general inductive approach. Results: Participant perceptions of behavioral flags varied, with both positive and negative opinions being shared. Five key themes, each with subthemes, were identified: (1) benefits of behavioral flags, (2) concerns and potential harms of flags, (3) transparency with patients, (4) equity, and (5) ideas for improvement. Conclusions: Patient perspectives on the use of behavioral flags in the ED vary. While many saw flags as a helpful tool to mitigate violence, concerns around negative impacts on care, transparency, and equity were also shared. Insights from this stakeholder perspective may allow for health systems to make flags more effective without compromising equity or patient ideals. [ABSTRACT FROM AUTHOR]

Purpose: Maternity leave is a critical employee benefit that allows mothers to recover from the stress of pregnancy and childbirth and bond with their new baby. We aimed to examine the association between the extension of a maternity leave policy and maternal use of mental health services and prescription drugs in a universal public healthcare system. Methods: This study uses administrative medical records from 18,000 randomly selected women who gave birth three months before and after an extension of the maternity leave policy. More specifically, mothers who gave birth after January 1st 2001, were entitled to 50 weeks of paid maternity leave, while mothers who gave birth before that date were entitled to only 26 weeks of paid maternity leave. Medical records were analyzed over a seven-year period (i.e., from October 1998 to March 2006). We examined the number and costs of mothers' medical visits for mental health care in the five years following delivery, as well as maternal use of prescribed medication for mental health problems. Results: We found that mothers with extended maternity leave had − 0.12 (95%CI=-0.21; -0.02) fewer medical visits than mothers without a more generous maternity leave and that the cost of mental health services was Can$5 less expensive per women. These differences were found specifically during the extended maternity leave period. Conclusions: The extra time away from work may help mothers to balance new family dynamics which may result in less demand on the healthcare system. [ABSTRACT FROM AUTHOR]

The integration of genetic information (GI) into the electronic health record (EHR) seems inevitable as the mainstreaming of genomics continues. Such newly provided accessibility to GI could be beneficial for improving health care, as well as for supporting clinical decision‐making and health management. Notwithstanding these promising benefits, the automatic integration of GI into the EHR, allowing unrestricted access to one's GI through patient portals, carries various knowledge‐related risks for patients. This article is focused on the potential case of inadvertently revealing misattributed parentage through such practice. The article aims to identify key clinical and ethical implications of such revelation for adult patients. Clinical implications include, for example, altering the physician‐patient interaction and the need to enhance physician's genetic literacy to improve genetic‐information‐specific communication skills. Ethical implications yield arguments supporting disclosure of MP, such as autonomy, individuals' right to know medical information pertaining to them, and the right to know one's genetic origins. Arguments opposing disclosure of MP centre on the right not to know GI and concerns for post‐disclosure family relationships. Following the clinical and ethical analyses of these respective implications, we consider how such integration of GI into the EHR ought to be carried out, ethically. We therefore suggest a solution, featuring an autonomy‐based approach, built around EHR users' right not to know. Our solution of nuanced consent options (including a 'genetic ignorance option') is designed to enable patients' informed exposure to GI through the EHR, allowing them some control over their self‐ and familial narrative. [ABSTRACT FROM AUTHOR]

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements—well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms—to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders. [ABSTRACT FROM AUTHOR]

Background Objective Methods Results Conclusions Dermatologic conditions are estimated to account worldwide for approximately 8% of all visits at emergency departments (EDs). Although rarely life‐threatening, several dermatologic emergencies may have a high morbidity. Little is known about ED consultations of patients with dermatological emergencies and their subsequent hospital disposal.We explore determinants and clinical variables affecting patients' disposal and hospitalization of people attending the ED at a Swiss University Hospital, over a 56‐month observational period, for a dermatological problem.De‐identified patients' information was extracted from the hospital electronic medical record system. Generalized estimating equations were used to explore determinants of patient's disposition.Out of 5096 consecutive patients with a dermatological main problem evaluated at the ED, 79% of patients were hospitalized after initial assessment. In multivariable analyses, factors which were significantly associated with an increased admission rate included length of ED stay, age ≥ 45 years, male sex, distinct vital signs, high body mass index, low oxygen saturation, admission time in the ED and number and type of dermatological diagnoses. Only 2.2% of the hospitalized patients were admitted to a dermatology ward, despite the fact that they had dermatological diagnoses critically determining the diagnostic related group (DRG) payment. The number of patients managed by dermatologists during in‐patient treatment significantly decreased over the study period.Our study identifies a number of independent predictors affecting the risk of hospital admission for patients with dermatological conditions, which may be useful to improve patients' disposal in EDs. The results indicate that the dermatological specialty is becoming increasingly marginalized in the management of patients in the Swiss hospital setting. This trend may have significant implications for the delivery of adequate medical care, outcomes and cost‐effectiveness. Dermatologists should be more engaged to better position their specialty and to effectively collaborate with nondermatologists to enhance patient care. [ABSTRACT FROM AUTHOR]

Background: Telemedicine use increased with the Covid-19 pandemic. The impact of telemedicine on resource use in pulmonary clinics is unknown. Methods: This retrospective cohort study identified adults with pulmonary clinic visits at the University of Miami Hospital and Clinics (January 2018-December 2021). The primary exposure was telemedicine versus in-person visits. Standard statistics were used to describe the cohort and compare patients stratified by visit type. Multivariable logistic regression models evaluated the association of telemedicine with resource use (primarily, computed tomography [CT] orders placed within 7 days of visit). Results: 21,744 clinic visits were included: 5,480 (25.2%) telemedicine and 16,264 (74.8%) in-person. In both, the majority were < 65-years-old, female, and identified as Hispanic white. Patients seen with telemedicine had increased odds of having CT scans ordered within 7 days (adjusted odds ratio [aOR] 1.34, [95% confidence interval 1.04–1.74]); and decreased odds of chest x-rays (aOR 0.37 [0.23–0.57]). Telemedicine increased odds of contact of any kind with our healthcare system within 30-days (aOR 1.56 [1.29–1.88]) and 90-days (aOR 1.39 [1.17–1.64]). Specifically, telemedicine visits had decreased odds of emergency department visits and hospitalizations (30 days: aOR 0.54 [0.38–0.76]; 90 days: aOR 0.68 [0.52–0.89]), but increased odds of phone calls and electronic health record inbox messages (30 days: aOR 3.44 [2.73–4.35]; 90 days: aOR 3.58 [2.95–4.35]). Conclusions: Telemedicine was associated with an increased odds of chest CT order with a concomitant decreased odds of chest x-ray order. Increased contact with the healthcare system with telemedicine may represent a larger time burden for outpatient clinicians. [ABSTRACT FROM AUTHOR]

The very rapid development in health services today requires that every health service provider organize medical records electronically with the principles of security and confidentiality of data and information. However, in practice, Elecronic Medical Records is still experiencing many legal issues that are detrimental to both the Hospital and especially the patients themselves. Therefore this study aims to analyze the legal basis of hospital obligations for the security and confidentiality of electronic medical record patient data and analyze the responsibility of hospitals for the security and confidentiality of patient’s EMR data. The type of research used in this thesis is normative juridical research with statutory approach and conceptual approach. The results still found some legal wins, ambiguities and some shortcomings in implementing this EMR in full. [ABSTRACT FROM AUTHOR]

Objective: Opioid stewardship interventions promote appropriate use of opioids. We synthesized evidence from recent studies on the effectiveness of opioid stewardship interventions in United States healthcare settings on opioid prescribing and clinical outcomes. Methods: We followed the Evidence-based Practice Center Program's rapid review processes. We searched PubMed and Cochrane for original studies meeting specified inclusion criteria from January 2016 (correlating with the Centers for Disease Control and Prevention Pain Guidelines) to April 2023, supplemented by gray literature searches. Findings: Our search identified 13 randomized controlled trials and 6 nonrandomized studies. Randomized controlled trials addressed interventions involving multiple components (typically combining prescriber education, care management, and facilitated access to resources), clinical decision support (CDS) or electronic health records (EHR), and patient education and engagement, mainly in ambulatory chronic pain. Multicomponent interventions and opioid stewardship practices involving CDS or EHR were associated with decreased opioid prescribing or reduced doses and no increases in pain, emergency department visits, or hospitalizations (low strength of evidence for all outcomes). Patient engagement and education interventions had mixed results for opioid prescribing outcomes (insufficient strength of evidence) and no increases in pain, emergency department visits, or hospitalizations (low strength of evidence). Conclusions: Selected opioid stewardship interventions may be effective for reducing opioid prescribing without adversely affecting clinical outcomes. Interventions to reduce opioid prescribing should monitor unintended consequences and include access to nonpharmacological pain management resources with patient education and engagement. [ABSTRACT FROM AUTHOR]

Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients' engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients' movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system. [ABSTRACT FROM AUTHOR]

Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain. [ABSTRACT FROM AUTHOR]

Niraparib was recently funded in Canada for the maintenance treatment of ovarian cancer following platinum-based chemotherapy. However, the drug's safety profile in the real world remains uncertain. We conducted a cohort study to describe the patient population using niraparib and the proportion that experienced adverse events between June 2019 and December 2022 in four Canadian provinces (Ontario, Alberta, British Columbia [BC], and Quebec). We used administrative data and electronic medical records from Ontario Health, Alberta Health Services, and BC Cancer, and registry data from Exactis Innovation. We summarized baseline characteristics using descriptive statistics and reported safety outcomes using cumulative incidence. We identified 514 patients receiving niraparib. Mean age was 67 years and most were initiated on a daily dose of 100 or 200 mg/day. Grade 3/4 anemia, neutropenia, and thrombocytopenia occurred in 11–16% of the cohort. In Ontario, the three-month cumulative incidence of grade 3/4 thrombocytopenia was 11.6% (95% CI, 8.3–15.4%), neutropenia was 7.1% (95% CI, 4.6–10.4%), and anemia was 11.3% (95% CI, 8.0–15.2%). Cumulative incidences in the remaining provinces were similar. Initial daily dose and proportions of hematological adverse events were low in the real world and may be related to cautious prescribing and close monitoring by clinicians. [ABSTRACT FROM AUTHOR]

Background: Pre-visit planning entails completing necessary tasks prior to clinic appointments. Community pharmacists (CPs) have unique knowledge about patients' medication use but do not routinely provide drug therapy reviews before clinic visits. Objectives: (1) Create and implement a business partnership between a CP and family medicine clinic (FMC) for CP provision of pre-visit medication reviews, and (2) describe the billing experience for shared patients in the FMC chronic care management (CCM) program. Methods: A prospective 8-month study in one community pharmacy and FMC in Iowa. Eligible patients were enrolled in the clinic CCM program and received their prescriptions at the CP. CPs were granted access to the clinic electronic health record (EHR), performed medication reviews, and recorded drug therapy recommendations (DTRs) in the clinic EHR. FMC physicians reviewed CP DTRs before the patient encounter. Time tracking software in the EHR recorded CP and FMC time performing CCM services. CCM revenue was prorated between parties. FMC physicians completed a survey about their experience. Results: Overall, there were 129 CP reviews performed for 95 patients. These reviews resulted in 169 DTRs and 76% were accepted by the physician. There were 71 CCM claims billed and CCM revenue was $3596 ($1796 FMC, $1800 CP). More than 90% of physicians (N = 11) indicated they reviewed CP DTRs before the patient encounter and agreed they were helpful to their practice. Conclusion: CPs completed pre-visit medication reviews and made accepted medication therapy recommendations. CCM billing provided a mechanism for CPs to receive revenue for their services. [ABSTRACT FROM AUTHOR]

Objective: Hypoglycemia is a serious complication of type 2 diabetes (DM2), but its severity and incidence and the healthcare services used are usually underreported in clinical practice. We aimed to describe the severity and frequency of hypoglycemia episodes, the proactive behaviors adopted, and healthcare services used by administering the Hypoglycemia Awareness Questionnaire (HypoA-Q). Methods: In this cross-sectional, multicenter, validation study, demographic data and clinical variables were retrieved from electronic records, and the HypoA-Q questionnaire was filled out. Questions 1 and 2 assessed the frequency of hypoglycemia events and the healthcare services used. Questions 4c, 4d, 9, 16b, and 16c evaluated the proactive behaviors of the patient and/or caregiver during the occurrence of hypoglycemic events. Results: In total, 502 DM2 patients treated with insulin and with a history of hypoglycemia were included in this study [54.6% women, median age: 65 years (interquartile range: 58–73); 55.4% receiving the basal-bolus regimen]. 281 (56%) patients reported at least one hypoglycemic event in the last year; 25% reported at least one severe hypoglycemia event; and 8.7% required hospitalization. Only 52% of patients monitored their capillary blood glucose levels to assess for hypoglycemia. Conclusion: The use of HypoA-Q questionnaire in usual clinical practice could facilitate the characterization of hypoglycemia events and the identification of at-risk populations by healthcare professionals and payers. [ABSTRACT FROM AUTHOR]

The harms that occur to patients in the hospital are relatively well documented. In contrast, less information is available about the harms that occur to outpatients, even though the amount of health care being delivered in outpatient settings and the complexity of that care are increasing. This article describes the frequency and types of harm that occurred at 11 outpatient sites in Massachusetts during 1 year. Visual Abstract. The Safety of Outpatient Health Care: The harms that occur to patients in the hospital are relatively well documented. In contrast, less information is available about the harms that occur to outpatients, even though the amount of health care being delivered in outpatient settings and the complexity of that care are increasing. This article describes the frequency and types of harm that occurred at 11 outpatient sites in Massachusetts during 1 year. Background: Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. Objective: To measure AEs in the outpatient setting. Design: Retrospective review of the electronic health record (EHR). Setting: 11 outpatient sites in Massachusetts in 2018. Patients: 3103 patients who received outpatient care. Measurements: Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. Results: The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care–associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, −0.05 [CI, −0.09 to −0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. Limitation: Retrospective EHR review may miss AEs. Conclusion: Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. Primary Funding Source: Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions. [ABSTRACT FROM AUTHOR]