Hospital Electronic Health Research Articles

Delirium at the intensive care unit and long-term survival: a retrospective study

BackgroundDelirium is a common complication in patients at the intensive care unit (ICU) and is associated with prolonged ICU-stay and hospitalization and with increased morbidity. The impact of ICU-delirium on long-term survival is not clearly understood.MethodsThis retrospective single center observational study was conducted at the Institute of Intensive Care Medicine at the University Hospital Zurich, Switzerland. All adult ICU-survivors over a four-year period were screened for eligibility. ICU-delirium was defined based on the Intensive Care Delirium Screening Checklist (ICDSC), together with the coded diagnosis F05 in the International Classification of Diseases (ICD-2019). ICU-survivors who developed delirium during their ICU stay (group D) were compared with ICU-survivors who did not (group ND). Survival was evaluated according to data from hospital electronic health records up to four years from ICU-discharge. The survival analysis was reported using Kaplan-Meier curves and absolute risk differences (ARD). A multivariable logistic regression model was fitted with long-term survival at four years after ICU-discharge as outcome of interest, including several clinical conditions and interventions associated with long-term survival for ICU patients. For subgroup analysis, ICU-survivors were grouped based on age at the time of admission (45–54, 55–64, ≥ 65 years), and on relevant clinical conditions.ResultsA total of 9’604 patients fulfilled the inclusion criteria, of them 22.6% (n = 2’171) developed ICU-delirium. Overall, patients in the group D had a significantly lower probability of survival than patients in the group ND (p < 0.0001, ARD = 11.8%). In the multivariable analysis, ICU-delirium was confirmed as independently associated with long-term survival. After grouping for age categories, patients between 55 and 64 years of age in the group D were less likely to survive than patients in the group ND at every time point analyzed, up to four years after ICU discharge (p < 0.001, ARD = 7.3%). This difference was even more significant in the comparison between patients over 65 years (p < 0.0001, ARD 11.1%). No significant difference was observed in the other age groups.ConclusionsIn the study population, ICU-delirium was independently associated with a reduced long-term survival. Patients who developed ICU-delirium had a reduced survival up to four years after ICU discharge and this association was particularly evident in patients above 55 years of age.

Prevalence and incidence of multiple sclerosis in the Netherlands

BackgroundThe rising prevalence estimates of multiple sclerosis (MS) globally underscore the imperative for up-to-date epidemiological data specific to the Netherlands. MethodsData from two Dutch healthcare databases (Nivel Primary Care Database and Vektis Database) comprising both general practices and hospital electronic health records in 2019 were combined to assess age- and sex-standardized MS prevalence and incidence estimates. Differences in prevalence estimates based on latitude and between primary versus secondary care records were examined. ResultsThe age and sex-standardized prevalence estimate was 210.4 per 100,000 inhabitants. Incidence estimate of MS was 7.1 per 100,000 person-years within the year 2019. One-third of the MS cohort from primary care records was not documented in the hospital database over two consecutive years. Prevalence estimates were higher in northern areas versus southern areas (respectively 240.6 versus 208.3 per 100,000 inhabitants). ConclusionAligning results from different global regions, prevalence estimates of MS in the Netherlands have increased significantly over the last decennia, while incidence estimates are stable. The discrepancy between MS records within the primary and secondary care setting could indicate that a substantial proportion of Dutch patients with MS are not under specialized neurological care.

Evaluation of Pharmacist Involvement in Emergency Response.

Purpose: Emergency response teams are designed to promptly deliver care to hospitalized patients experiencing acute decompensation events. Pharmacists are an integrated part of emergency response teams and their presence at emergency response events has been shown to improve adherence to institutional and advanced cardiac life support (ACLS) guidelines. This study assesses the impact of pharmacist involvement at emergency responses and time clinical pharmacists dedicate to emergency response. Methods: A single-center, retrospective chart review assessed inpatient and ambulatory emergency responses for patients 18 and older from August 2021 through January 2022. Emergency response event-specific information was assessed using intervention documentations in the hospital electronic health record (EHR). The amount of time dedicated to emergency response by pharmacists was then converted to full-time equivalents (FTE). Results: Of the 296 emergency response documentations assessed, 242 responses were included in analysis. The primary outcome of time pharmacists dedicate to emergency responses over a six-month period was found to be 9480minutes (158hours). The average amount of time spent at each response was 40.7minutes (SD 27.4minutes), ranging from 5-210minutes. Conclusion: The total time spent by clinical pharmacists at emergency responses within a six-month period was equivalent to approximately 26% of an FTE. Due to inability of pharmacists to document all emergency responses, this may be under-represented. More than 70% of emergency responses required 6-10 medications be prepared by pharmacists. Pharmacists made interventions 47% of the time, indicating that pharmacists play an integral role as members of emergency response teams.

Serious infections and tuberculosis adversely impact outcomes of juvenile onset systemic lupus erythematosus in India.

Infections are a major cause of morbidity and mortality in juvenile systemic lupus erythematosus (SLE). We assessed the incidence and risk factors for major infections in juvenile SLE. A retrospective review of 225 patients of juvenile SLE (ACR 1997 criteria) with age <18years visiting the rheumatology clinic at a single centre between 2000 to 2020 was done from case records and the hospital electronic health records. Serious infection was defined as the need for hospitalization, or infection resulting in disability or death. Cox regression was used to determine factors associated with a serious infection and the effect of serious infection on overall survival. We reviewed 225 children (197 girls, mean age 13.89 ± 3.42years) with a cumulative follow up of 1153.45 person-years. Eighty serious infections occurred in 63 (28% of the cohort) children at a rate of 69.35 serious infections per 1000 person-years. A second serious infection occurred in 12 children and 5 of them developed three infections.Among the cases with known etiology (78.75% of cases), bacterial infections were most common (N = 33) including S. Aureus (11), E. Coli (7), K. Pneumoniae (3), E. Fecalis (3), S. Pneumoniae (2), Acinetobacter spp. (2), Citrobacter (2), Salmonella (2) and P. Aeruginosa (1). Twenty six (32.5%) opportunistic infections occurred: Mycobacterium tuberculosis (18), Cytomegalovirus (3), disseminated Herpes zoster (4) and invasive candidiasis (1) with 15 (83.3%) of the tuberculosis cases being extrapulmonary. On multivariate analysis, fever (HR 8.51, 1.17-61.44), gastrointestinal involvement (HR 4.73, 1.13-19.94), current steroid dose (HR 1.36,1.14-1.62), average cumulative steroid dose per year (HR 1.004, 1.002-1.005) and cyclophosphamide (HR 2.22, 1.11-4.46) were associated with serious infection.Hospitalization rates were significantly higher in those with any serious infection (Rate-ratio 2.79, 1.81-3.77) as was damage accrual (SLICC damage index 1.04 vs 0.22). Serious infection-free survival at 1 year and 5 years was 84% (79.1-89.2) and 72% (65.4-79.2). There were 19 deaths with infection attributable mortality in 10 (52.6%). Serious infection predisposed to higher overall mortality with recurrent infections conferring a hazard ratio of 36.02 (8.07-160.62). Serious infections are a major cause of mortality and damage in SLE. Constitutional symptoms, gastrointestinal involvement, current and cumulative steroid dose and cyclophosphamide predict serious infections. TB prophylaxis in patients with SLE should be considered in endemic areas, especially when using high-dose steroid therapy.

Clinical Efficacy of Ezetimibe Combined with Rosuvastatin in the Treatment of Patients with Primary Hypercholesterolemia Inadequately Controlled by Statin Therapy

Aims/Background Primary hypercholesterolemia (PHC) is a major risk factor for atherosclerotic cardiovascular disease (ASCVD). Although the fact that statins effectively lower low-density lipoprotein cholesterol (LDL-C) levels, some patients fail to achieve target LDL-C levels and continue to have a high risk of cardiovascular disease. This study aims to evaluate the clinical efficacy and safety of ezetimibe combined with rosuvastatin in patients with PHC. Methods This study retrospectively examined 101 patients with PHC who received statins at the cardiology department of Jilin Province FAW General Hospital, between 2021 and 2024. Patients were divided into the observation (ezetimibe combined with rosuvastatin, n = 45) and control (rosuvastatin, n = 66) groups in accordance with their treatment regimens. Data were sourced from the hospital's electronic health records system, and statistical analysis was performed by using SPSS 25.0 software (IBM Corporation, Armonk, NY, USA). Results Baseline characteristics were similar between the two groups. After 12 weeks of treatment, the reduction in LDL-C levels in the observation group (–0.373 [–0.427, –0.348]) was greater than that in the control group (–0.240 [–0.318, –0.222], p &lt; 0.001). The percentage changes in total cholesterol (TC), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) levels were significantly better in the observation group (TC: –0.230 [–0.302, –0.144], TG: –0.292 [–0.333, –0.237], and HDL-C: 0.081 [0.067, 0.111]) than in the control group (TC: –0.127 [–0.158, –0.119], TG: –0.082 [–0.101, –0.067], and HDL-C: 0.000 [–0.163, 0.133] with p &lt; 0.001, p &lt; 0.001, and p = 0.011, respectively). Regarding drug safety, the incidence of adverse events was comparable between the two groups (11.10% vs. 12.10%, p = 0.871). Conclusion The combination of ezetimibe and rosuvastatin demonstrates superior lipid-lowering efficacy and good safety in patients with PHC inadequately controlled by statin therapy, providing an effective alternative treatment option. Further large-scale, multicenter randomized controlled trials are warranted to confirm its long-term efficacy and safety.

The association between the mental health disorders, substance abuse, and tobacco use with head & neck cancer stage at diagnosis.

Mental health disorders, substance abuse, and tobacco use are prevalent in the US population. However, the association between these conditions and head and neck cancer (HNC) stage is poorly understood. This research aims to uncover the relationship between pre-existing mental health disorders, substance abuse, and tobacco use and HNC stage at diagnosis in patients receiving care in an integrated, public safety-net healthcare system. This study was a secondary data analysis of linked hospital tumor registries and electronic health record (EHR) data. The study's primary independent variables were the comorbidities of mental health disorders, substance abuse, and tobacco use. The dependent variable was HNC stage at diagnosis, operationalized as early stage (i.e., stages I, II, and III) and advanced stage (stage IV, IVA, IVB, or IVC). The analysis included multivariable logistic regression adjusted for covariates of demographic variables, tumor anato RESULTS: The study population consisted of 357 patients with median age of 59years, and was primarily male (77%), diverse (Black or African American 41%; Hispanic 22%), and from neighborhoods with low income (median average annual household income $39,785). Patients with a history of mental health disorders with or without tobacco use had significantly lower odds of advanced stage HNC at diagnosis (adjusted OR = 0.35, 95% Confidence Interval [CI]: 0.17-0.72.) while patients with a history of substance abuse with or without tobacco use had significantly higher odds of advanced stage HNC at diagnosis (adjusted OR 1.41, 95% CI: 1.01-1.98) than patients with no history of mental health disorders, substance abuse, or tobacco use. The relationship between HNC stage at diagnosis and the comorbidities of mental health disorders, substance abuse, or tobacco differs depending on the type and co-occurrence of these comorbidities. These findings demonstrate the need for innovative care delivery models and education initiatives tailored to meet the needs of patients with mental health disorders, substance abuse, and tobacco use that facilitate early detection of HNC.

Using Existing Clinical Data to Measure Older Adult Inpatients' Frailty at Admission and Discharge: Hospital Patient Register Study.

Frailty is a widespread geriatric syndrome among older adults, including hospitalized older inpatients. Some countries use electronic frailty measurement tools to identify frailty at the primary care level, but this method has rarely been investigated during hospitalization in acute care hospitals. An electronic frailty measurement instrument based on population-based hospital electronic health records could effectively detect frailty, frailty-related problems, and complications as well be a clinical alert. Identifying frailty among older adults using existing patient health data would greatly aid the management and support of frailty identification and could provide a valuable public health instrument without additional costs. We aim to explore a data-driven frailty measurement instrument for older adult inpatients using data routinely collected at hospital admission and discharge. A retrospective electronic patient register study included inpatients aged ≥65 years admitted to and discharged from a public hospital between 2015 and 2017. A dataset of 53,690 hospitalizations was used to customize this data-driven frailty measurement instrument inspired by the Edmonton Frailty Scale developed by Rolfson et al. A 2-step hierarchical cluster procedure was applied to compute e-Frail-CH (Switzerland) scores at hospital admission and discharge. Prevalence, central tendency, comparative, and validation statistics were computed. Mean patient age at admission was 78.4 (SD 7.9) years, with more women admitted (28,018/53,690, 52.18%) than men (25,672/53,690, 47.81%). Our 2-step hierarchical clustering approach computed 46,743 inputs of hospital admissions and 47,361 for discharges. Clustering solutions scored from 0.5 to 0.8 on a scale from 0 to 1. Patients considered frail comprised 42.02% (n=19,643) of admissions and 48.23% (n=22,845) of discharges. Within e-Frail-CH's 0-12 range, a score ≥6 indicated frailty. We found a statistically significant mean e-Frail-CH score change between hospital admission (5.3, SD 2.6) and discharge (5.75, SD 2.7; P<.001). Sensitivity and specificity cut point values were 0.82 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.85. Comparing the e-Frail-CH instrument to the existing Functional Independence Measure (FIM) instrument, FIM scores indicating severe dependence equated to e-Frail-CH scores of ≥9, with a sensitivity and specificity of 0.97 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.92. There was a strong negative association between e-Frail-CH scores at hospital discharge and FIM scores (rs=-0.844; P<.001). An electronic frailty measurement instrument was constructed and validated using patient data routinely collected during hospitalization, especially at admission and discharge. The mean e-Frail-CH score was higher at discharge than at admission. The routine calculation of e-Frail-CH scores during hospitalization could provide very useful clinical alerts on the health trajectories of older adults and help select interventions for preventing or mitigating frailty.

Risk for Suicide Attempts Assessed Using the Patient Health Questionnaire–9 Modified for Teens

Suicide is a leading cause of death in US youths. To assess whether screening with supplemental items 10 to 13 on the Patient Health Questionnaire-9 modified for teens (PHQ-9M) improves prediction of youth suicide attempts beyond the information provided by the first 9 items alone (the PHQ-9). This retrospective cohort study used a retrospective cohort of adolescents aged 12 to 17 years who were screened for depression in outpatient facilities within a pediatric health care system between January 1, 2016, and December 31, 2022, with up to 1 year of follow-up to assess the occurrence of suicidal behavior. Follow-up was completed on December 31, 2023. Screening with the PHQ-9M. This study developed and compared prediction using 3 Cox proportional hazards regression models (CR-9, CR-13, and CR-3) of subsequent suicide attempts, determined by the hospital's electronic health records up to 1 year following the last PHQ-9M screening. The CR-9 model used the PHQ-9 and the CR-13 model used all 13 items of PHQ-9M. The CR-3 model used the 3 most impactful variables selected from the 13 PHQ-9M items and PHQ-9 total score. All models were evaluated across 4 prediction horizons (30, 90, 180, and 365 days) following PHQ-9M screenings. Evaluation metrics were the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC). Of 130 028 outpatients (65 520 [50.4%] male) with 272 402 PHQ-9M screenings, 549 (0.4%) had subsequent suicide attempts within 1 year following the PHQ-9M screening. The AUROC of the CR-9 model in the 365-day horizon was 0.77 (95% CI, 0.75-0.79); of the CR-13 model, 0.80 (95% CI, 0.78-0.82); and of the CR-3 model, 0.79 (95% CI, 0.76-0.81); the AUPRC of the CR-9 model was 0.02 (95% CI, 0.02-0.03); of the CR-13 model, 0.03 (95% CI, 0.02-0.03); and of the CR-3 model, 0.02 (95% CI, 0.02-0.03). The 3 most impactful items using adjusted hazard ratios were supplemental item 13 (lifetime suicide attempts; 3.06 [95% CI, 2.47-3.80]), supplemental item 10 (depressed mood severity in the past year; 2.99 [95% CI, 2.32-3.86]), and supplemental item 12 (serious suicidal ideation in the past month; 1.63 [95% CI, 1.25-2.12]). All of the models achieved higher AUROCs as prediction horizons shortened. In this cohort study of adolescent PHQ-9M screenings, the supplemental items on PHQ-9M screening improved prediction of youth suicide attempts compared with screening using the PHQ-9 across all prediction horizons, suggesting that PHQ-9M screening should be considered during outpatient visits to improve prediction of suicide attempts.

Novel linkage of the Society of Thoracic Surgeons database and hospital electronic health records.

Society of Thoracic Surgeons Congenital Heart Surgery Database is the largest congenital heart surgery database worldwide but does not provide information beyond primary episode of care. Linkage to hospital electronic health records would capture complications and comorbidities along with long-term outcomes for patients with CHD surgeries. The current study explores linkage success between Society of Thoracic Surgeons Congenital Heart Surgery Database and electronic health record data in North Carolina and Georgia. The Society of Thoracic Surgeons Congenital Heart Surgery Database was linked to hospital electronic health records from four North Carolina congenital heart surgery using indirect identifiers like date of birth, sex, admission, and discharge dates, from 2008 to 2013. Indirect linkage was performed at the admissions level and compared to two other linkages using a "direct identifier," medical record number: (1) linkage between Society of Thoracic Surgeons Congenital Heart Surgery Database and electronic health records from a subset of patients from one North Carolina institution and (2) linkage between Society of Thoracic Surgeons data from two Georgia facilities and Georgia's CHD repository, which also uses direct identifiers for linkage. Indirect identifiers successfully linked 79% (3692/4685) of Society of Thoracic Surgeons Congenital Heart Surgery Database admissions across four North Carolina hospitals. Direct linkage techniques successfully matched Society of Thoracic Surgeons Congenital Heart Surgery Database to 90.2% of electronic health records from the North Carolina subsample. Linkage between Society of Thoracic Surgeons and Georgia's CHD repository was 99.5% (7,544/7,585). Linkage methodology was successfully demonstrated between surgical data and hospital-based electronic health records in North Carolina and Georgia, uniting granular procedural details with clinical, developmental, and economic data. Indirect identifiers linked most patients, consistent with similar linkages in adult populations. Future directions include applying these linkage techniques with other data sources and exploring long-term outcomes in linked populations.

Development of a minimum data set for a blockchain-based personal health records (PHRs), for patient/physician interaction in family medicine.

The primary requirement for a capable patient health record (PHR) that can interact appropriately with the family medicine system and collect and share qualified data is a minimum data set (MDS) aligned with family medicine's functions and goals. The aim of this research was to determine the MDS for a blockchain-based PHR system that can effectively interact with family medicine providers and collect and share qualified data. This MDS is intended to be applicable to all members of the community covered by family medicine. This applied study was conducted in 2022 in a cross-sectional and descriptive approach in three phases. In the first phase, a content analysis related to the research objectives was conducted in scientific databases, search engines, and websites of the centers and scientific research organizations with publications and policy-making in this field. Consequently, 11 studies were selected for use in the second and third phases. In the second phase, to determine information needs, a researcher-developed questionnaire including 17 classes was given to 50 people under the cover of the family medicine plan in Shiraz city. By choosing one of the two options "Yes" or "No" by them, the necessary data classes were determined. In the third phase, the second researcher-developed questionnaire was designed and administered to 100 family physicians in Shiraz city. This questionnaire included the data elements corresponding to the data classes approved in the previous phase. The family physicians were asked to rate the importance of each data element using a Likert scale with five options, ranging from "very unimportant" to "very important." The necessary data elements were determined based on these scores. In the first questionnaire, 16 of the 17 data classes received approval from individuals covered by the family medicine plan. Consequently, a questionnaire comprising 16 classes and 105 data elements was administered to the family physicians. Ultimately, the MDS was determined to include 16 classes and 72 data elements. Determining essential data elements, especially for patient/physician interaction in family medicine, should be such that they can be managed by the person while being comprehensive and providing sufficient help to the physician during the treatment process. This MDS can be used to interact with and refer PHRs to other physicians and specialists, as well as help interoperability between the PHR and other health systems, such as hospital information systems (HIS) and electronic health records (EHRs).

Excess health care use is significantly and persistently reduced following diagnosis of late-onset epilepsy.

The incidence of late-onset epilepsy (LOE) is rising, and these patients may use an excess of health care resources. This study aimed to measure pre-/post-diagnostic health care use (HCU) for patients with LOE compared to controls. This was an observational open cohort study covering years 1998-2019 using UK population-based linked primary care (Clinical Practice Research Datalink [CPRD]) and hospital (HES) electronic health records. The participants included patients with incident LOE enrolled in CPRD and 1:10 age-, sex-, and general practice-matched controls. The exposure was incident LOE (diagnosed at age ≥65) using a 5-year washout. The main outcome was all HCU (primary care [PC], accident and emergency [A&E], admitted patient and outpatient care) using inverse proportional weighting to PC use and HCU by setting. An interrupted time-series analysis was used to examine pre-/post-diagnostic HCU between patients with LOE and controls over 4 years either side of diagnosis/matching date. An adjusted mixed-effects negative binomial regression was used for post-diagnosis HCU interactions. Of 2 569 874 people ≥65 years of age, 1048 (4%) developed incident LOE. Mean weighted total HCU increased by 32 visits per patient-year (95% confidence interval [95% CI]: 13-50, p = .003) until LOE diagnosis, and then dropped by a mean of 60 visits per patient-year (95% CI: -81 to -40). There was an acute rise and fall over the 1-2 years immediately pre-/post-diagnosis. Incident HCU remained higher for LOE compared to controls post-diagnosis (adjusted incidence rate ratio: 1.72; 95% CI: 1.65-1.70; p < .001), including A&E, outpatient, and admitted care. Health care use demonstrates an acute on chronic rise over the 4 years before diagnosis of LOE. To what extent the partial reversal of the acute pre-diagnosis rise, and the mediators of the accelerated increase compared to controls are attributed to epilepsy, comorbid and bidirectional disease states, or a combination of both warrants further exploration.

Conference Report: Leveraging the Global Malnutrition Composite Score for Quality Improvement, Health Equity, and Better Patient Outcomes

Effective January 2024, the United States Centers for Medicare &amp; Medicaid Services (CMS) for the first time has included a nutrition-focused quality measure in the Hospital Inpatient Prospective Payment System (IPPS) Inpatient Quality Reporting (IQR) Program: the Global Malnutrition Composite Score (GMCS). The GMCS is an electronic clinical quality measure (eCQM) that hospitals can choose to report voluntarily to meet the requirements for IPPS IQR payments. The GMCS can help improve hospital malnutrition care and help advance hospital health equity goals. This conference report summarizes a panel discussion at the Academy of Nutrition and Dietetics Food and Nutrition Conference &amp; Expo in October 2023. During the panel, four health and nutrition experts described strategies for integrating the GMCS into hospital electronic health records (EHRs). They also outlined how GMCS data can be used to facilitate successful patient transitions to post-acute care through addressing food insecurity and other reasons for malnutrition. Such strategies can serve to help avoid preventable complications and readmissions, improve patient outcomes and health, and meet quality and health equity objectives, while helping reduce overall healthcare costs.

Improving Electronic Health Record (EHR) data linkages for identifying Aged Care Residents through publicly available data

IntroductionAccurate identification of Aged Care residents accessing hospital services is challenging due to multiple hospital Electronic Health Record (EHR) systems and entry points resulting in inconsistent addresses and coding. Improving accuracy and completeness of addresses is crucial for cohort identification and data linkage. ObjectiveDevelop methods to validate cross-sectoral data linkages to improve identification of aged care residents within EHR systems. ApproachAmong individuals aged ≥65 years (or ≥50 identifying as Aboriginal or Torres Strait Islander) between January 2013 to December 2023 within our Data Platform, we first used data items mandated by the Department of Health to identify aged care residency. Data quality was enhanced using a residential address index derived from Australian Geocoded National Address File (G-NAF). We evaluated sensitivity, specificity, and Positive Predictive Values (PPVs) of recorded addresses against the Australian Institute of Health and Welfare (AIHW) aged care services list. ResultsAmong 409,677 addresses extracted, 55.4% (raw) had sufficient data for validation. This increased to 89.9% (enhanced) with G-NAF. Among 79,028 admitted and 88,876 emergency patients, 22.7% and 18.4% respectively were aged care residents. We compared performance of raw vs enhanced comparisons were: sensitivity (admission: 34.1% vs 73.8%), (emergency: 42.6% vs 66.2%); specificity (admission: 96.2% vs 90.9%), emergency (96.8% vs 95.2%); Positive Predictive Value (admission: 61.3% vs 58.8%), (emergency: 64.2% vs 65.3%). Conclusions and ImplicationsOur approach significantly increased sensitivity without sacrificing specificity, enhancing Aged Care resident identification in EHR data. It also improved linkage with external datasets and Statistical Area Level 1 (SA1) mapping.

Perceptions of hospital electronic health record (EHR) training, support, and patient safety by staff position and tenure

BackgroundHospitals rely on their electronic health record (EHR) systems to assist with the provision of safe, high quality, and efficient health care. However, EHR systems have been found to disrupt clinical workflows and may lead to unintended consequences associated with patient safety and health care professionals’ perceptions of and burden with EHR usability and interoperability. This study sought to explore the differences in staff perceptions of the usability and safety of their hospital EHR system by staff position and tenure.MethodsWe used data from the AHRQ Surveys on Patient Safety Culture® (SOPS®) Hospital Survey Version 1.0 Database and the SOPS Health Information Technology Patient Safety Supplemental Items (“Health IT item set”) collected from 44 hospitals and 8,880 staff in 2017. We used regression modeling to examine perceptions of EHR system training, EHR support & communication, EHR-related workflow, satisfaction with the EHR system, and the frequency of EHR-related patient safety and quality issues by staff position and tenure, while controlling for hospital ownership type and bed-size.ResultsIn comparison to RNs, pharmacists had significantly lower (unfavorable) scores for EHR system training (regression coefficient = -0.07; p = 0.047), and physicians, hospital management, and the IT staff were significantly more likely to report high frequency of inaccurate EHR information (ORs = 2.03, 1.34, 1.72, respectively). Compared to staff with 11 or more years of hospital tenure, new staff (less than 1 year at the hospital) had significantly lower scores for EHR system training, but higher scores for EHR support & communication (p < 0.0001). Dissatisfaction of the EHR system was highest among physicians and among staff with 11 or more years tenure at the hospital.ConclusionsThere were significant differences in the Health IT item set’s results across staff positions and hospital tenure. Hospitals can implement the SOPS Health IT Patient Safety Supplemental Items as a valuable tool for identifying incongruity in the perceptions of EHR usability and satisfaction across staff groups to inform targeted investment in EHR system training and support.

ADVANCE: a biomedical informatics approach to investigate acute kidney injury in infants.

Acute kidney injury (AKI) occurs in up to half of infants admitted to the neonatal intensive care unit (NICU) and is associated with increased risks of death and more days of mechanical ventilation, hospitalization, and vasopressor drug support. Our objective was to build a granular relational database to study the impact that AKI has on infants admitted to Level-IV NICUs. A relational database was created by linking data from the Children's Hospitals Neonatal Database with AKI-focused data from electronic health records from 9 centers. The current cohort consists of 24,870 infants with a median (IQR) gestational age of birth of 37 weeks (32 weeks, 39 weeks), and a median birth weight of 2.720 kg (1.750 kg, 3.310 kg). There was a male predominance with 14,214 (57%) males. In all, 2434 (9.8%) of the mothers were of Hispanic ethnicity. The maternal race breakdown of the cohort was as follows: 741 (3.0%) Asian, 5911 (24%) Black, and 14,945 (60%) White. Overall mortality was 5.8%. The ADVANCE relational database is an innovative research tool to rigorously study the epidemiology of AKI in a large national cohort of infants admitted to Level-IV NICUs involved in the Children's Hospital Neonatal Consortium. We used a biomedical informatics approach to build a relational database to study acute kidney injury in infants. We highlight our methodology linking Children's Hospital Neonatal Consortium and electronic health record data from nine neonatal intensive care units. The ADVANCE relational database is a granular and innovative research tool to study risk factors and in-hospital outcomes of acute kidney injury and mortality in a vulnerable patient population.

Retrospective analysis of hospital electronic health records reveals unseen cases of acute hepatitis with unknown aetiology in adults in Oxfordshire

BackgroundAn outbreak of acute severe hepatitis of unknown aetiology (AS-Hep-UA) in children during 2022 was subsequently linked to infections with adenovirus-associated virus 2 and other ‘helper viruses’, including human adenovirus. It is possible that evidence of such an outbreak could be identified at a population level based on routine data captured by electronic health records (EHR).MethodsWe used anonymised EHR to collate retrospective data for all emergency presentations to Oxford University Hospitals NHS Foundation Trust in the UK, between 2016–2022, for all ages from 18 months and older. We investigated clinical characteristics and temporal distribution of presentations of acute hepatitis and of adenovirus infections based on laboratory data and clinical coding. We relaxed the stringent case definition adopted during the AS-Hep-UA to identify all cases of acute hepatitis with unknown aetiology (termed AHUA). We compared events within the outbreak period (defined as 1st Oct 2021—31 Aug 2022) to the rest of our study period.ResultsOver the study period, there were 903,433 acute presentations overall, of which 391 (0.04%) were classified as AHUA. AHUA episodes had significantly higher critical care admission rates (p < 0.0001, OR = 41.7, 95% CI:26.3–65.0) and longer inpatient admissions (p < 0.0001) compared with the rest of the patient population. During the outbreak period, significantly more adults (≥ 16 years) were diagnosed with AHUA (p < 0.0001, OR = 3.01, 95% CI: 2.20–4.12), and there were significantly more human adenovirus (HadV) infections in children (p < 0.001, OR = 1.78, 95% CI:1.27–2.47). There were also more HAdV tests performed during the outbreak (p < 0.0001, OR = 1.27, 95% CI:1.17–1.37). Among 3,707 individuals who were tested for HAdV, 179 (4.8%) were positive. However, there was no evidence of more acute hepatitis or increased severity of illness in HadV-positive compared to negative cases.ConclusionsOur results highlight an increase in AHUA in adults coinciding with the period of the outbreak in children, but not linked to documented HAdV infection. Tracking changes in routinely collected clinical data through EHR could be used to support outbreak surveillance.

Enhancing team communication to improve implementation of a supervised walking program for hospitalized veterans: Evidence from a multi-site trial in the Veterans Health Administration.

The timely translation of evidence-based programs into real-world clinical settings is a persistent challenge due to complexities related to organizational context and team function, particularly in inpatient settings. Strategies are needed to promote quality improvement efforts and implementation of new clinical programs. This study examines the role of CONNECT, a complexity science-based implementation intervention to promote team readiness, for enhancing implementation of the 'Assisted Early Mobility for Hospitalized Older Veterans' program (STRIDE), an inpatient, supervised walking program. We conducted a stepped-wedge cluster randomized trial using a convergent mixed-methods design. Within each randomly assigned stepped-wedge sequence, Veterans Affairs Medical Centers (VAMCs) were randomized to receive standardized implementation support only or additional training via the CONNECT intervention. Data for the study were obtained from hospital administrative and electronic health records, surveys, and semi-structured interviews with clinicians before and after implementation of STRIDE. Eight U.S. VAMCs. Three hundred fifty-three survey participants before STRIDE implementation and 294 surveys after STRIDE implementation. Ninety-two interview participants. CONNECT, a complexity-science-based intervention to improve team function. The implementation outcomes included STRIDE reach and fidelity. Secondary outcomes included validated measures of team function (i.e., team communication, coordination, role clarity). At four VAMCs randomized to CONNECT, reach was higher (mean 12.4% vs. 3.8%), and fidelity was similar to four non-CONNECT VAMCs. VAMC STRIDE delivery teams receiving CONNECT reported improvements in team function domains, similar to non-CONNECT VAMCs. Qualitative findings highlight CONNECT's impact and the influence of team characteristics and contextual factors, including team cohesion, leadership support, and role clarity, on reach and fidelity. CONNECT may promote greater reach of STRIDE, but improvement in team function among CONNECT VAMCs was similar to improvement among non-CONNECT VAMCs. Qualitative findings suggest that CONNECT may improve team function and implementation outcomes but may not be sufficient to overcome structural barriers related to implementation capacity.

Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study.

Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare. In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN. We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase. Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.

Inequalities in children’s tooth decay requiring dental extraction under general anaesthetic: a longitudinal study using linked electronic health records

BackgroundDental extraction under general anaesthetic (DGA) is the most severe and irreversible dental treatment for childhood tooth decay. We investigated inequalities in DGA in an ethnically diverse, disadvantaged school-age population...

Natural Language Processing (NLP) Accurately Identifies LTCF Exposure from Clinical Notes: A Proof-of-Principle Study

Background: Residence or recent stay in a long-term care facility (LTCF) is one of the most important risk factors for multidrug-resistant organism (MDRO) carriage and infection, making reliable identification of LTCF-exposed inpatients a critical priority for infection control day-to-day practice and research. However, because most hospital electronic health records (EHRs) do not include a dedicated field for documenting LTCF exposure, absent manual review of patient charts, identifying LTCF-exposed inpatients is challenging. We aimed to develop an automated, natural language processing (NLP)-based classifier for identifying LTCF exposure from clinical notes. Methods: We randomly sampled 1020 adult admissions from 2016-2021 across the 12-hospital University of Maryland Medical System and manually reviewed each admission’s history &amp; physical (H&amp;P) note for mention of LTCF exposure (Figure 1). After H&amp;P pre-processing, we calculated feature representations for documents based on term frequencies and visually explored between-group (LTCF-exposed vs. LTCF-unexposed) feature differences. To predict LTCF status from the H&amp;P notes, we trained and tuned a LASSO regression-based classifier on 70% of the data with 3-fold cross-validation and 1:1 up-sampling to address class imbalance. The final classifier was evaluated on the 30% held-out sample (not up-sampled), with calculation of the C-statistic (area-under-the-curve, AUC) with bootstrapped 95% confidence intervals, and construction of receiver-operating-characteristic and variable importance plots (R Version 4.3.2). Results: 7% (n=76 cases) of H&amp;P notes documented LTCF exposure. In our visual analysis, the H&amp;P words and phrases that were over-represented among LTCF patients had high face validity (Figure 2). The final LASSO-regression-based classifier achieved a C-statistic of 0.89 (95% CI: 0.80–0.98) on the held-out data for identifying LTCF exposure from the H&amp;P notes (Figure 3). The most important model predictors (i.e., words) for distinguishing LTCF-exposed from LTCF-unexposed patients are reflected in Figure 4. The most important predictor-words of LTCF-exposure were “rehab,” “place,” “status,” “egd,” and “dementia.” Conclusion: In this multi-center study, even a simple NLP classifier demonstrated very strong discrimination for identifying LTCF exposure status from H&amp;P notes, which could substantially reduce the manual review time required to identify LTCF-exposed inpatients. If automated in the electronic health record, it could also inform real-time MDRO screening decisions. Future research is planned to build more sophisticated classifiers using machine learning best practices, to build classifiers for additional MDRO risk factors, and to externally validate NLP classifiers on notes from an external healthcare system.