Risk of diabetes and expected years in life without diabetes among adults from an urban community in India: findings from a retrospective cohort.

BACKGROUND: Diabetes prevalence has increased over the past few decades, and the shift of the burden of diabetes from the older population to the younger population has increased the exposure of longer durations in a morbid state. The study aimed at ascertaining the likelihood of progression to diabetes and to estimate the onset of diabetes within the urban community of Mumbai. METHODS: This study utilized an observational retrospective non-diabetic cohort comprising 1629 individuals enrolled in a health security scheme. Ten years of data were extracted from electronic medical records, and the life table approach was employed to assess the probability of advancing to diabetes and estimate the expected number of years lived without a diabetes diagnosis. RESULTS: The study revealed a 42% overall probability of diabetes progression, with age and gender variations. Males (44%) show higher probabilities than females (40%) of developing diabetes. Diabetes likelihood rises with age, peaking in males aged 55-59 and females aged 65-69. Males aged 30-34 exhibit a faster progression (10.6 years to diagnosis) compared to females (12.3 years). CONCLUSION: The study's outcomes have significant implications for the importance of early diabetes detection. Progression patterns suggest that younger cohorts exhibit a comparatively slower rate of progression compared to older cohorts.

Effect of Performance-Based Nonfinancial Incentives on Data Quality in Individual Medical Records of Institutional Births: Quasi-Experimental Study.

BACKGROUND: Despite the potential of routine health information systems in tackling persistent maternal deaths stemming from poor service quality at health facilities during and around childbirth, research has demonstrated their suboptimal performance, evident from the incomplete and inaccurate data unfit for practical use. There is a consensus that nonfinancial incentives can enhance health care providers' commitment toward achieving the desired health care quality. However, there is limited evidence regarding the effectiveness of nonfinancial incentives in improving the data quality of institutional birth services in Ethiopia. OBJECTIVE: This study aimed to evaluate the effect of performance-based nonfinancial incentives on the completeness and consistency of data in the individual medical records of women who availed institutional birth services in northwest Ethiopia. METHODS: We used a quasi-experimental design with a comparator group in the pre-post period, using a sample of 1969 women's medical records. The study was conducted in the "Wegera" and "Tach-armacheho" districts, which served as the intervention and comparator districts, respectively. The intervention comprised a multicomponent nonfinancial incentive, including smartphones, flash disks, power banks, certificates, and scholarships. Personal records of women who gave birth within 6 months before (April to September 2020) and after (February to July 2021) the intervention were included. Three distinct women's birth records were examined: the integrated card, integrated individual folder, and delivery register. The completeness of the data was determined by examining the presence of data elements, whereas the consistency check involved evaluating the agreement of data elements among women's birth records. The average treatment effect on the treated (ATET), with 95% CIs, was computed using a difference-in-differences model. RESULTS: In the intervention district, data completeness in women's personal records was nearly 4 times higher (ATET 3.8, 95% CI 2.2-5.5; P=.02), and consistency was approximately 12 times more likely (ATET 11.6, 95% CI 4.18-19; P=.03) than in the comparator district. CONCLUSIONS: This study indicates that performance-based nonfinancial incentives enhance data quality in the personal records of institutional births. Health care planners can adapt these incentives to improve the data quality of comparable medical records, particularly pregnancy-related data within health care facilities. Future research is needed to assess the effectiveness of nonfinancial incentives across diverse contexts to support successful scale-up.

The effect of electronic medical records on medication errors, workload, and medical information availability among qualified nurses in Israel- a cross sectional study.

BACKGROUND: Errors in medication administration by qualified nursing staff in hospitals are a significant risk factor for patient safety. In recent decades, electronic medical records (EMR) systems have been implemented in hospitals, and it has been claimed that they contribute to reducing such errors. However, systematic research on the subject in Israel is scarce. This study examines the position of the qualified nursing staff regarding the impact of electronic medical records systems on factors related to patient safety, including errors in medication administration, workload, and availability of medical information. METHODS: This cross-sectional study examines three main variables: Medication errors, workload, and medical information availability, comparing two periods- before and after EMR implementation based on self-reports. A final sample of 591 Israeli nurses was recruited using online private social media groups to complete an online structured questionnaire. The questionnaires included items assessing workload (using the Expanding Nursing Stress Scale), medical information availability (the Carrington-Gephart Unintended Consequences of Electronic Health Record Questionnaire), and medical errors (the Medical Error Checklists). Items were assessed twice, once for the period before the introduction of electronic records and once after. In addition, participants answered open-ended questions that were qualitatively analyzed. RESULTS: Nurses perceive the EMR as reducing the extent of errors in drug administration (mean difference = -0.92 ± 0.90SD, p < 0.001), as well as the workload (mean difference = -0.83 ± 1.03SD, p < 0.001) by ∼ 30% on average, each. Concurrently, the systems are perceived to require a longer documentation time at the expense of patients' treatment time, and they may impair the availability of medical information by about 10% on average. CONCLUSION: The results point to nurses' perceived importance of EMR systems in reducing medication errors and relieving the workload. Despite the overall positive attitudes toward EMR systems, nurses also report that they reduce information availability compared to the previous pen-and-paper approach. A need arises to improve the systems in terms of planning and adaptation to the field and provide appropriate technical and educational support to nurses using them.

Impact of COVID-19 pandemic on depression incidence and healthcare service use among patients with depression: an interrupted time-series analysis from a 9-year population-based study.

BACKGROUND: Most studies on the impact of the COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of the pandemic is less well-studied. In this population-based cohort study, we examined the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression. METHODS: Using the territory-wide electronic medical records in Hong Kong, we identified all patients aged ≥ 10 years with new diagnoses of depression from 2014 to 2022. We performed an interrupted time-series (ITS) analysis to examine changes in incidence of medically attended depression before and during the pandemic. We then divided all patients into nine cohorts based on year of depression incidence and studied their initial and ongoing service use patterns until the end of 2022. We applied generalized linear modeling to compare the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among prevalent patients with depression. RESULTS: We found an immediate increase in depression incidence (RR = 1.21, 95% CI: 1.10-1.33, p < 0.001) in the population after the pandemic began with non-significant slope change, suggesting a sustained effect until the end of 2022. Subgroup analysis showed that the increases in incidence were significant among adults and the older population, but not adolescents. Depression patients newly diagnosed during the pandemic used 11% fewer resources than the pre-pandemic patients in the first diagnosis year. Pre-existing depression patients also had an immediate decrease of 16% in overall all-cause service use since the pandemic, with a positive slope change indicating a gradual rebound over a 3-year period. CONCLUSIONS: During the pandemic, service provision for depression was suboptimal in the face of increased demand generated by the increasing depression incidence during the COVID-19 pandemic. Our findings indicate the need to improve mental health resource planning preparedness for future public health crises.

The true complexities of "standard" family practice visits unmasked: an observational cross-sectional study in Regina.

BACKGROUND: Many patients present to their family medicine clinic with more than one health concern, placing an increased demand on family physicians. Research into the average number of concerns per regular family medicine visit is limited. Recognition of the frequency that family physicians address more than one concern per visit and adapting practices accordingly is important for improving patient care. OBJECTIVE: To examine whether family physicians routinely address multiple different patient concerns during a single visit and if this is influenced by patient demographics. METHODS: This study was conducted at a multi-physician family medicine clinic in Regina, Saskatchewan, Canada. Five physicians contributed their 500 most recent charts, extending retrospectively from 1 June 2023, from in-person visits by patients over 18 years of age and billed as regular appointments without billed procedures. Each chart was reviewed for the number of concerns addressed in the visit. RESULTS: Fifty percent of visits addressed more than 1 concern (range = 1-8). A generalized linear mixed model using Poisson distribution showed certain physicians (incident rate ratio [IRR]: 1.192, 95% CI: 1.087-1.307, P < 0.001) and adults older than 65 years compared to adults less than 40 years (IRR 1.151, 95% CI: 1.069-1.239, P < 0.001) were more likely to present with multiple concerns, but patient sex was not a significant predictor. CONCLUSIONS: Family physicians routinely address more than one concern per visit. Standard visit length and billing practices should be adapted to reflect this complexity.

Validation of GPT-4 for clinical event classification: A comparative analysis with ICD codes and human reviewers.

BACKGROUND AND AIM: Effective clinical event classification is essential for clinical research and quality improvement. The validation of artificial intelligence (AI) models like Generative Pre-trained Transformer 4 (GPT-4) for this task and comparison with conventional methods remains unexplored. METHODS: We evaluated the performance of the GPT-4 model for classifying gastrointestinal (GI) bleeding episodes from 200 medical discharge summaries and compared the results with human review and an International Classification of Diseases (ICD) code-based system. The analysis included accuracy, sensitivity, and specificity evaluation, using ground truth determined by physician reviewers. RESULTS: GPT-4 exhibited an accuracy of 94.4% in identifying GI bleeding occurrences, outperforming ICD codes (accuracy 63.5%, P < 0.001). GPT-4's accuracy was either slightly lower or statistically similar to individual human reviewers (Reviewer 1: 98.5%, P < 0.001; Reviewer 2: 90.8%, P = 0.170). For location classification, GPT-4 achieved accuracies of 81.7% and 83.5% for confirmed and probable GI bleeding locations, respectively, with figures that were either slightly lower or comparable with those of human reviewers. GPT-4 was highly efficient, analyzing the dataset in 12.7 min at a cost of 21.2 USD, whereas human reviewers required 8-9 h each. CONCLUSION: Our study indicates GPT-4 offers a reliable, cost-efficient, and faster alternative to current clinical event classification methods, outperforming the conventional ICD coding system and performing comparably to individual expert human reviewers. Its implementation could facilitate more accurate and granular clinical research and quality audits. Future research should explore scalability, prompt and model tuning, and ethical implications of high-performance AI models in clinical data processing.

Adhesive restorations in primary dentition: A retrospective analysis of survival rate and associated factors.

BACKGROUND: Several clinical and individual factors may play a role in the survival rate of dental restorations, such as characteristics related to the child's age and oral hygiene, and factors associated with the tooth, such as the type of material and number of surfaces to be restored. AIM: To analyse the survival rate of adhesive restorations on primary teeth and factors associated with restoration survival. DESIGN: The study included dental records of children aged 3-12 years having received adhesive restorations on primary teeth at a Brazilian dental school between 2009 and 2019. A Kaplan-Meier survival curve was used to plot survival rates using the log-rank test. A multivariate Cox regression model was run to identify individual and dental factors associated with restoration failure. RESULTS: The sample comprised 269 restored teeth in 111 children. Survival curves were similar for all materials (p = .20) and types of isolation (p = .05). The annual failure rate was 3.60% for glass ionomer cement, 1.23% for resin-modified glass ionomer cement and 0.40% for composite resin. The following variables were associated with more failures: Class II restoration compared with Class I (HR = 1.96; 95%CI: 1.28-2.99, p < .001), proportion of decayed teeth (HR = 11.89; 95%CI: 2.80-50.57, p < .001) and child's age (HR = 1.17; 95%CI: 1.06-1.29, p < .001). CONCLUSION: The different materials and types of isolation had similar survival rates. Children with more decayed teeth have an increased risk of restoration failure.

Missed opportunities in heart failure diagnosis and management: study of an urban UK population.

AIMS: This study aimed to examine the diagnostic pathways and outcomes of patients with heart failure (HF), stratified by left ventricular ejection fraction (EF), and to highlight deficiencies in real-world HF diagnosis and management. METHODS AND RESULTS: We conducted a retrospective cohort study in Salford, United Kingdom, utilizing linked primary and secondary care data for HF patients diagnosed between January 2010 and November 2019. We evaluated characteristics, diagnostic patterns, healthcare resource utilization, and outcomes. Patients were categorized according to baseline (the latest measure prior to or within 90 days post-diagnosis) as having HF with reduced EF (HFrEF), mildly reduced EF (HFmrEF), or preserved EF (HFpEF). The data encompassed a 2 year period before diagnosis and up to 5 years post-diagnosis. A total of 3227 patients were diagnosed with HF between January 2010 and November 2019. The mean follow-up time was 2.6 [±1.9 standard deviation (SD)] years. The mean age at diagnosis was 74.8 (±12.7 SD) years, and 1469 (45.5%) were female. HFpEF was the largest cohort (46.6%, npEF = 1505), HFmrEF constituted 16.1% (nmrEF = 520), and HFrEF 18.5% (nrEF = 596) of the population, while 18.8% (nu = 606) of patients remained unassigned due to insufficient evidence to support categorization. At baseline, measurement of natriuretic peptide (NP; brain NP and N-terminal pro-B-type NP) and echocardiographic report data were available for 592 (18.3%) and 2621 (81.2%) patients, respectively. A total of 2099 (65.0%) of the HF cohort had access to a cardiology-led outpatient clinic prior to the HF diagnosis, and 602 (18.7%) attended cardiac rehabilitation post-diagnosis. The 5 year crude survival rate was 37.8% [95% confidence interval (CI) (35.2-40.7%)], 42.3% [95% CI (38.0-47.2%)], and 45.5% [95% CI (41.0-50.4%)] for HFpEF, HFrEF, and HFmrEF, respectively. CONCLUSIONS: Low survival rates were observed across all HF groups, along with suboptimal rates of NP testing and specialist assessments. These findings suggest missed opportunities for timely and accurate HF diagnosis, a pivotal first step in improving outcomes for HF patients. Addressing these gaps in diagnosis and management is urgently needed.

A Novel Electronic Record System for Documentation and Efficient Workflow for Community Health Workers: Development and Usability Study.

BACKGROUND: The COVID-19 pandemic added to the decades of evidence that public health institutions are routinely stretched beyond their capacity. Community health workers (CHWs) can be a crucial extension of public health resources to address health inequities, but systems to document CHW efforts are often fragmented and prone to unneeded redundancy, errors, and inefficiency. OBJECTIVE: We sought to develop a more efficient data collection system for recording the wide range of community-based efforts performed by CHWs. METHODS: The Communities Organizing to Promote Equity (COPE) project is an initiative to address health disparities across Kansas, in part, through the deployment of CHWs. Our team iteratively designed and refined the features of a novel data collection system for CHWs. Pilot tests with CHWs occurred over several months to ensure that the functionality supported their daily use. Following implementation of the database, procedures were set to sustain the collection of feedback from CHWs, community partners, and organizations with similar systems to continually modify the database to meet the needs of users. A continuous quality improvement process was conducted monthly to evaluate CHW performance; feedback was exchanged at team and individual levels regarding the continuous quality improvement results and opportunities for improvement. Further, a 15-item feedback survey was distributed to all 33 COPE CHWs and supervisors for assessing the feasibility of database features, accessibility, and overall satisfaction. RESULTS: At launch, the database had 60 active users in 20 counties. Documented client interactions begin with needs assessments (modified versions of the Arizona Self-sufficiency Matrix and PRAPARE [Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences]) and continue with the longitudinal tracking of progress toward goals. A user-specific automated alerts-based dashboard displays clients needing follow-up and upcoming events. The database contains over 55,000 documented encounters across more than 5079 clients. Available resources from over 2500 community organizations have been documented. Survey data indicated that 84% (27/32) of the respondents considered the overall navigation of the database as very easy. The majority of the respondents indicated they were overall very satisfied (14/32, 44%) or satisfied (15/32, 48%) with the database. Open-ended responses indicated the database features, documentation of community organizations and visual confirmation of consent form and data storage on a Health Insurance Portability and Accountability Act-compliant record system, improved client engagement, enrollment processes, and identification of resources. CONCLUSIONS: Our database extends beyond conventional electronic medical records and provides flexibility for ever-changing needs. The COPE database provides real-world data on CHW accomplishments, thereby improving the uniformity of data collection to enhance monitoring and evaluation. This database can serve as a model for community-based documentation systems and be adapted for use in other community settings.

Depression risk in chronic tonsillitis patients underwent tonsillectomy: a global federated health network analysis.

Background: Tonsillectomy is a common surgery in the US, with possible postoperative complications. While small studies indicate postoperative depressive symptoms may occur, large-scale evidence is lacking on the tonsillectomy-depression link. Methods: We conducted a retrospective cohort study using the TriNetX US collaborative network, offering de-identified electronic health data from 59 collaborative healthcare organizations (HCOs) in the United States. In this study, people being diagnosed of chronic tonsillitis between January 2005 and December 2017 were enrolled. Patients deceased, with previous record of cancers or psychiatric events before index date were excluded. 14,874 chronic tonsillitis patients undergoing tonsillectomy were propensity score matched 1:1 to controls for age, sex, and race. New-onset depression risks were evaluated over 5 years post-tonsillectomy and stratified by age and sex. Confounders were adjusted for including demographics, medications, comorbidities and socioeconomic statuses. Results: After matching, the difference of key baseline characteristics including age, sex, comedications status and obesity status was insignificant between tonsillectomy and non-tonsillectomy groups. Tonsillectomy had a 1.29 times higher 5-year depression risk versus matched controls (95% CI, 1.19-1.40), with elevated risks seen at 1 year (HR=1.51; 95% CI, 1.28-1.79) and 3 years (HR=1.30; 95% CI, 1.18-1.43). By stratifications, risks were increased for both males (HR=1.30; 95% CI, 1.08-1.57) and females (HR=1.30; 95% CI, 1.18-1.42), and significantly higher in ages 18-64 years (HR=1.37; 1.26-1.49), but no significance observed for those 65 years and older. After performing sensitivity analyses and applying washout periods of 6, 12, and 36 months, the outcome remained consistent with unadjusted results. Conclusion: This real-world analysis found tonsillectomy was associated with a 30% higher 5-year depression risk versus matched non-tonsillectomy patients with chronic tonsillitis. Further mechanistic research is needed to clarify the pathophysiologic association between depression and tonsillectomy. Depression is not commonly mentioned in the current post-tonsillectomy care realm; however, the outcome of our study emphasized the possibility of these suffering condition after operation. Attention to psychological impacts following tonsillectomy is warranted to support patient well-being, leading to better management of post-tonsillectomy individuals.

Hidradenitis suppurativa as a potential risk factor of periodontitis: a multi-center, propensity-score-matched cohort study.

Background: Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease associated with systemic symptoms. Periodontitis, a prevalent dental disease, shares immune-mediated inflammatory characteristics with HS. This cohort study aims to evaluate the association between HS and periodontitis. Methods: Using the TriNetX research network, a global-federated database of electronic health records, we conducted a retrospective cohort study. People being diagnosed of HS were identified and propensity score matching was performed to identify proper control group, via balancing critical covariates Within the follow-up time of 1 year, 3 year and 5 years, hazard ratios were calculated to assess the risk of periodontitis in HS patients compared to controls. Results: Within the 53,968 HS patients and the same number of matched controls, the HS patients exhibited a significantly increased risk of developing periodontitis compared to controls after 3 years of follow-up (HR: 1.64, 95% CI: 1.11, 2.44) and 5 years of follow-up (HR: 1.64, 95% CI: 1.21, 2.24) of follow-up. Sensitivity analyses supported these findings under various matching models and washout periods. While comparing with patients with psoriasis, the association between HS and periodontitis remained significant (HR: 1.73, 95% CI: 1.23, 2.44). Conclusion: The observed increased risk suggests the need for heightened awareness and potential interdisciplinary care for individuals with HS to address periodontal health.

The Influence of COVID-19 on Patient Mobilization and Injury Attributes in the ICU: A Retrospective Analysis of a Level II Trauma Center.

The objectives of this study were to determine the effect of COVID-19 on physical therapy (PT) mobilization of trauma patients and to determine if mobilization affected patient course in the ICU. This retrospective study included patients who were admitted to the ICU of a level II trauma center. The patients were divided into two groups, i.e., those admitted before (n = 378) and after (n = 499) 1 April 2020 when Georgia's COVID-19 shelter-in-place order was mandated. The two groups were contrasted on nominal and ratio variables using Chi-square and Student's t-tests. A secondary analysis focused specifically on the after-COVID patients examined the extent to which mobilization (n = 328) or lack of mobilization (n = 171) influenced ICU outcomes (e.g., mortality, readmission). The two groups were contrasted on nominal and ratio variables using Chi-square and Student's t-tests. The after-COVID patients had higher injury severity as a greater proportion was classified as severely injured (i.e., >15 on Injury Severity Score) compared to the before-COVID patients. After-COVID patients also had a greater cumulative number of comorbidities and experienced greater complications in the ICU. Despite this, there was no difference between patients in receiving a PT consultation or days to mobilization. Within the after-COVID cohort, those who were mobilized were older, had greater Glasgow Coma Scale scores, had longer total hospital days, and had a lesser mortality rate, and a higher proportion were female. Despite shifting patient injury attributes post-COVID-19, a communicable disease, mobilization care remained consistent and effective.

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study.

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519.

Overcoming Electronic Medical Records Adoption Challenges in Saudi Arabia.

In the digital era, the seamless integration of electronic medical records (EMR) stands as a pivotal milestone in transforming healthcare delivery, with Saudi Arabia at the forefront of this revolution in the Middle East. This literature review comprehensively explores the challenges and opportunities associated with adopting EMR in the Kingdom of Saudi Arabia (KSA) in alignment with the nation's Vision 2030 healthcare objectives. The review synthesizes research from various scholarly sources, utilizing databases such as PubMed, Scopus, Google Scholar, and regional databases, and focuses on literature published between 2010 and 2023. Our methodology included a strategic combination of keywords and a stringent selection criterion to ensure a focus on relevant EMR adoption studies within KSA. The review addresses key aspects of EMR adoption, including technical challenges, financial constraints, human factors, cultural and organizational barriers, privacy and security concerns, and policy and regulatory challenges. It also explores the integration of EMR with other digital health initiatives like telehealth, personal health records, and community pharmacy services. The findings reveal a complex interplay of factors influencing EMR adoption, highlighting the need for comprehensive strategies that address technical, financial, cultural, and policy-related barriers. The review concludes that while significant challenges exist, strategic approaches and solutions tailored to the specific context of Saudi Arabia can effectively facilitate EMR integration, thereby enhancing healthcare quality and efficiency in line with the nation's Vision 2030 goals.

Physicians' and Patients' Expectations From Digital Agents for Consultations: Interview Study Among Physicians and Patients.

BACKGROUND: Physicians are currently overwhelmed by administrative tasks and spend very little time in consultations with patients, which hampers health literacy, shared decision-making, and treatment adherence. OBJECTIVE: This study aims to examine whether digital agents constructed using fast-evolving generative artificial intelligence, such as ChatGPT, have the potential to improve consultations, adherence to treatment, and health literacy. We interviewed patients and physicians to obtain their opinions about 3 digital agents-a silent digital expert, a communicative digital expert, and a digital companion (DC). METHODS: We conducted in-depth interviews with 25 patients and 22 physicians from a purposeful sample, with the patients having a wide age range and coming from different educational backgrounds and the physicians having different medical specialties. Transcripts of the interviews were deductively coded using MAXQDA (VERBI Software GmbH) and then summarized according to code and interview before being clustered for interpretation. RESULTS: Statements from patients and physicians were categorized according to three consultation phases: (1) silent and communicative digital experts that are part of the consultation, (2) digital experts that hand over to a DC, and (3) DCs that support patients in the period between consultations. Overall, patients and physicians were open to these forms of digital support but had reservations about all 3 agents. CONCLUSIONS: Ultimately, we derived 9 requirements for designing digital agents to support consultations, treatment adherence, and health literacy based on the literature and our qualitative findings.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

The Effect of an Electronic Medical Record-Based Clinical Decision Support System on Adherence to Clinical Protocols in Inflammatory Bowel Disease Care: Interrupted Time Series Study.

Background: Clinical decision support systems (CDSSs) embedded in electronic medical records (EMRs), also called electronic health records, have the potential to improve the adoption of clinical guidelines. The University of Alberta Inflammatory Bowel Disease (IBD) Group developed a CDSS for patients with IBD who might be experiencing disease flare and deployed it within a clinical information system in 2 continuous time periods. Objective: This study aims to evaluate the impact of the IBD CDSS on the adherence of health care providers (ie, physicians and nurses) to institutionally agreed clinical management protocols. Methods: A 2-period interrupted time series (ITS) design, comparing adherence to a clinical flare management protocol during outpatient visits before and after the CDSS implementation, was used. Each interruption was initiated with user training and a memo with instructions for use. A group of 7 physicians, 1 nurse practitioner, and 4 nurses were invited to use the CDSS. In total, 31,726 flare encounters were extracted from the clinical information system database, and 9217 of them were manually screened for inclusion. Each data point in the ITS analysis corresponded to 1 month of individual patient encounters, with a total of 18 months of data (9 before and 9 after interruption) for each period. The study was designed in accordance with the Statement on Reporting of Evaluation Studies in Health Informatics (STARE-HI) guidelines for health informatics evaluations. Results: Following manual screening, 623 flare encounters were confirmed and designated for ITS analysis. The CDSS was activated in 198 of 623 encounters, most commonly in cases where the primary visit reason was a suspected IBD flare. In Implementation Period 1, before-and-after analysis demonstrates an increase in documentation of clinical scores from 3.5% to 24.1% (P<.001), with a statistically significant level change in ITS analysis (P=.03). In Implementation Period 2, the before-and-after analysis showed further increases in the ordering of acute disease flare lab tests (47.6% to 65.8%; P<.001), including the biomarker fecal calprotectin (27.9% to 37.3%; P=.03) and stool culture testing (54.6% to 66.9%; P=.005); the latter is a test used to distinguish a flare from an infectious disease. There were no significant slope or level changes in ITS analyses in Implementation Period 2. The overall provider adoption rate was moderate at approximately 25%, with greater adoption by nurse providers (used in 30.5% of flare encounters) compared to physicians (used in 6.7% of flare encounters). Conclusions: This is one of the first studies to investigate the implementation of a CDSS for IBD, designed with a leading EMR software (Epic Systems), providing initial evidence of an improvement over routine care. Several areas for future research were identified, notably the effect of CDSSs on outcomes and how to design a CDSS with greater utility for physicians. CDSSs for IBD should also be evaluated on a larger scale; this can be facilitated by regional and national centralized EMR systems.

Real world performance of the 21st Century Cures Act population-level application programming interface.

OBJECTIVE: To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. MATERIALS AND METHODS: We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. RESULTS: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. DISCUSSION: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. CONCLUSION: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.