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Most adults aged ≥65 years live with multiple chronic conditions (MCC), and nearly one in four have recognized or unrecognized Alzheimer's disease and related dementias (ADRD), including an estimated 7.2 million Americans. Together, MCC and ADRD increase treatment complexity, medication burden, and the risk of adverse outcomes. Among patients who meet clinical criteria for mild cognitive impairment (MCI) or ADRD but lack a formal diagnosis, MCC burden remains unclear. This study examined the association between MCC burden and undiagnosed MCI and ADRD in a diverse cohort of older adults in primary care. We conducted a cross-sectional analysis of 324 adults aged ≥65 from primary care clinics in Indiana and South Florida (2021-2023), as part of a larger ADRD detection study. Patients without documented MCI or ADRD completed standardized cognitive assessments. Cognitive status (normal, MCI, ADRD) was determined by interdisciplinary consensus. Chronic conditions and medications were extracted from electronic health records. Multinomial logistic regression was used to examine the association between MCC profiles and cognitive status. Among 324 older adults, 51.9% were determined to have MCI and 8% ADRD. Patients with MCI and ADRD had more chronic conditions (mean = 5-6) and medications (mean = 4-5) than those with normal cognition ( < 0.001). Anticholinergic use was more common in the MCI (23.8%) and ADRD (23.1%) groups than in those with normal cognition (10.8%). In adjusted models, MCI and ADRD were associated with higher odds of having more chronic conditions. Cerebrovascular disease was associated with both MCI and ADRD; diabetes, sleep apnea, and insomnia with MCI; and ischemic heart disease and insomnia with ADRD. Older adults with unrecognized MCI and ADRD experience substantial MCC and medication burden. These findings highlight the need for targeted primary care interventions that integrate cognitive screening, support MCC management, optimize self-management capacity, and promote safer prescribing.

Background Choosing whether to pursue a trial of labor after cesarean (TOLAC) or scheduled repeat cesarean delivery (SRCD) requires prenatal assessment of risks and benefits. Providers and patients play a central role in this process. However, the influence of provider-associated characteristics on delivery methods remains unclear. We hypothesized that different provider practice groups have different obstetric outcomes in patients with one prior cesarean delivery (CD). Methods This was a retrospective cohort study of deliveries between April 29, 2015 – April 29, 2020. Subjects were divided into three cohorts: SRCD, successful VBAC, and unsuccessful VBAC (patients who chose TOLAC but had a CD). Disparities were reviewed between five different obstetric provider practice groups, determined from a breakdown of different providers delivering at the study site during the study period. Proportional differences were examined using Chi-squared tests and logistic regression models. Results 1,439 deliveries were included in the study. There were significant proportional disparities between patients in the different groups. Specifically, patients from Group D were significantly more likely to undergo successful VBAC, while patients seeing a provider from Group A were more likely to deliver by SRCD. In our multivariate analysis of successful versus unsuccessful VBAC, patients from Group D had greater odds ratios of successful VBAC compared to Group A. Patients delivered by Group E had a significantly lower odds ratio of successful VBAC. Conclusion This study suggests an association between provider practice groups and delivery outcomes among patients with one prior CD. These data contribute to a growing body of literature around patient choice in pregnancy and the interplay of patients and providers. These findings help to guide future investigations to improve outcomes among patients with a history of CD.

The Indiana Network for Patient Care (INPC) is a local health information infrastructure (LHII) that includes information from the five major hospital systems (fifteen separate hospitals), the county and state public health departments, and Indiana Medicaid and RxHub and that carries 660 million separate results. It provides cross-institutional access to physicians in emergency rooms and hospitals based on patient-physician proximity or on hospital credentialing. The network includes and delivers laboratory, radiology, dictation, and other documents to a majority of Indianapolis office practices. The INPC began operation seven years ago and is one of the first and best examples of an LHII. [PUBLICATION ABSTRACT]

Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate the practical utility of different data modalities and cohort segmentation strategies when designing models for emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications. Segmentation compares a cohort of insomnia patients to a cohort of general non-insomnia patients under varying age and disease severity criteria. Transfer testing between the two cohorts is introduced to demonstrate that an insomnia-specific model is not necessary when predicting future ED visits, but may have merit when predicting IH visits especially for patients with an insomnia diagnosis. The results also indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. Based on these findings, the proposed evaluation methodologies are recommended to ascertain the utility of disease-specific models in addition to the traditional intra-cohort testing.

Background Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identification of chronic cough patients by other mechanisms. We developed a deep clustering algorithm with auto-encoder embedding (DCAE) to identify clusters of chronic cough patients based on data from a large cohort of 264,146 patients from the Electronic Medical Records (EMR) system. We constructed features using the diagnosis within the EMR, then built a clustering-oriented loss function directly on embedded features of the deep autoencoder to jointly perform feature refinement and cluster assignment. Lastly, we performed statistical analysis on the identified clusters to characterize the chronic cough patients compared to the non-chronic cough patients. Results The experimental results show that the DCAE model generated three chronic cough clusters and one non-chronic cough patient cluster. We found various diagnoses, medications, and lab tests highly associated with chronic cough patients by comparing the chronic cough cluster with the non-chronic cough cluster. Comparison of chronic cough clusters demonstrated that certain combinations of medications and diagnoses characterize some chronic cough clusters. Conclusions To the best of our knowledge, this study is the first to test the potential of unsupervised deep learning methods for chronic cough investigation, which also shows a great advantage over existing algorithms for patient data clustering.

Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients.These data span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, and temporal information. Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support. Objective: A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes. Design: A comprehensive literature search was conducted using Google Scholar to identify high impact fusion architectures over multi-modal routine care EHR data during the period 2018 to 2023. The guidelines from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review were followed. The findings were derived from the selected studies using a thematic and comparative analysis. Results: The scoping review revealed the lack of standard definition for EHR data elements as they are transformed into input modalities. These definitions ignore one or more key characteristics of the data including source, encoding scheme, and concept level. Moreover, in order to adapt to emergent generative learning techniques, the classification of fusion architectures should distinguish fusion from learning and take into consideration that learning can concurrently happen in all three layers of new fusion architectures (i.e., encoding, representation, and decision). These aspects constitute the first step towards a streamlined approach to the design of multi-modal fusion architectures for routine care EHR data. In addition, current pretrained encoding models are inconsistent in their handling of temporal and semantic information thereby hindering their re-use for different applications and clinical settings. Conclusions: Current routine care EHR fusion architectures mostly follow a design-by-example methodology. Guidelines are needed for the design of efficient multi-modal models for a broad range of healthcare applications. In addition to promoting re-use, these guidelines need to outline best practices for combining multiple modalities while leveraging transfer learning and co-learning as well as semantic and temporal encoding.

Background Non-adherence to psychotropic medications is common in schizophrenia and bipolar disorders (BDs) leading to adverse outcomes. We examined patterns of antipsychotic use in schizophrenia and BD and their impact on subsequent acute care utilization. Methods We used electronic health record (EHR) data of 577 individuals with schizophrenia, 795 with BD, and 618 using antipsychotics without a diagnosis of either illness at two large health systems. We structured three antipsychotics exposure variables: the proportion of days covered (PDC) to measure adherence; medication switch as a new antipsychotic prescription that was different than the initial antipsychotic; and medication stoppage as the lack of an antipsychotic order or fill data in the EHR after the date when the previous supply would have been depleted. Outcome measures included the frequency of inpatient and emergency department (ED) visits up to 12 months after treatment initiation. Results Approximately half of the study population were adherent to their antipsychotic medication (a PDC ≥ 0.80): 53.6% of those with schizophrenia, 52.4% of those with BD, and 50.3% of those without either diagnosis. Among schizophrenia patients, 22.5% switched medications and 15.1% stopped therapy. Switching and stopping occurred in 15.8% and 15.1% of BD patients and 7.4% and 20.1% of those without either diagnosis, respectively. Across the three cohorts, non-adherence, switching, and stopping therapy were all associated with increased acute care utilization, even after adjusting for baseline demographics, health insurance, past acute care utilization, and comorbidity. Conclusion Non-continuous antipsychotic use is common and associated with high acute care utilization.

Adverse drug events (ADEs) account for a significant mortality, morbidity, and cost burden. Pharmacogenetic testing has the potential to reduce ADEs and inefficacy. The objective of this INGENIOUS trial (NCT02297126) analysis was to determine whether conducting and reporting pharmacogenetic panel testing impacts ADE frequency. The trial was a pragmatic, randomized controlled clinical trial, adapted as a propensity matched analysis in individuals (N = 2612) receiving a new prescription for one or more of 26 pharmacogenetic-actionable drugs across a community safety-net and academic health system. The intervention was a pharmacogenetic testing panel for 26 drugs with dosage and selection recommendations returned to the health record. The primary outcome was occurrence of ADEs within 1 year, according to modified Common Terminology Criteria for Adverse Events (CTCAE). In the propensity-matched analysis, 16.1% of individuals experienced any ADE within 1-year. Serious ADEs (CTCAE level ≥ 3) occurred in 3.2% of individuals. When combining all 26 drugs, no significant difference was observed between the pharmacogenetic testing and control arms for any ADE (Odds ratio 0.96, 95% CI: 0.78–1.18), serious ADEs (OR: 0.91, 95% CI: 0.58–1.40), or mortality (OR: 0.60, 95% CI: 0.28–1.21). However, sub-group analyses revealed a reduction in serious ADEs and death in individuals who underwent pharmacogenotyping for aripiprazole and serotonin or serotonin-norepinephrine reuptake inhibitors (OR 0.34, 95% CI: 0.12–0.85). In conclusion, no change in overall ADEs was observed after pharmacogenetic testing. However, limitations incurred during INGENIOUS likely affected the results. Future studies may consider preemptive, rather than reactive, pharmacogenetic panel testing.

Research on genomic medicine integration has focused on applications at the individual level, with less attention paid to implementation within clinical settings. Therefore, we conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify system-level factors that played a role in implementation of genomic medicine within Implementing GeNomics In PracTicE (IGNITE) Network projects. Up to four study personnel, including principal investigators and study coordinators from each of six IGNITE projects, were interviewed using a semistructured interview guide that asked interviewees to describe study site(s), progress at each site, and factors facilitating or impeding project implementation. Interviews were coded following CFIR inner-setting constructs. Key barriers included (1) limitations in integrating genomic data and clinical decision support tools into electronic health records, (2) physician reluctance toward genomic research participation and clinical implementation due to a limited evidence base, (3) inadequate reimbursement for genomic medicine, (4) communication among and between investigators and clinicians, and (5) lack of clinical and leadership engagement. Implementation of genomic medicine is hindered by several system-level barriers to both research and practice. Addressing these barriers may serve as important facilitators for studying and implementing genomics in practice.

A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions. The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation. Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression. IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.