Medical Care最新文献

Importance: Anesthesia care is delivered by Certified Registered Nurse Anesthetists (CRNAs) working independently, physician anesthesiologists working alone, and anesthesia care teams with CRNAs supervised by physician anesthesiologists. The impact of these models and CRNA supervision on patient satisfaction remains unclear.

Objective: To identify associations between anesthesia care team credentials, CRNA supervision, and patient satisfaction with care access, provider ratings, and overall satisfaction.

Design: We linked survey data on patient satisfaction with administrative data from the Veterans Health Administration (VHA) to gather veteran demographics, staffing, and clinical features of each surgical case.

Participants: Our sample included 45,757 veterans who responded to the Survey of Healthcare Experiences of Patients following an invasive surgical procedure performed in a VHA operating room between 2016 and 2023.

Main measures: Satisfaction was assessed using 4 outpatient survey items: overall VHA satisfaction, provider ratings, whether the provider listened carefully, and whether the provider showed respect. Inpatient measures included hospital ratings, doctors' courtesy and respect, doctors' attentiveness, willingness to recommend the VHA, and preference for VHA over free care elsewhere.

Results: Anesthesia care models and supervision ratios were not significantly associated with veterans' overall satisfaction, provider or hospital ratings, or likelihood of recommending the VHA. Small positive effects of CRNA involvement were observed on provider attentiveness and respect. Satisfaction was high across all provider types, and findings were robust to exclusion of COVID-19 data and lower-complexity cases.

Conclusions: Veterans' overall satisfaction with anesthesia care reflects a consistently high standard across models and credentials, with subtle benefits from CRNA involvement in patient-provider communication.

This study analyzed COVID-19 disease severity distributions among different age, racial, and ethnic groups for pre-Omicron and Omicron variant periods from May 2020 to October 2022. Disease severity categories were defined by ICD-10-CM diagnostic codes recorded in the electronic health record in the 7 days preceding and 13 days following the SARS-CoV-2 positive laboratory record (index date) and were grouped into 4 mutually exclusive categories: severe complications, high, moderate, and low disease severity. Low severity was defined as the absence of codes for any of the other categories. Among 1,613,706 included patients, there was a lower prevalence of disease severity during the Omicron variant period across all race and ethnicity groups (P<0.001) compared with the pre-Omicron variant period; however, the Omicron period had a higher prevalence of severe complications (P<0.05). Relative to White patients with high disease severity, Black patients and patients of other races had 37.1% and 52.4% (Pt<0.0001) greater risk of having high disease severity, respectively, in the pre-Omicron period, but high disease severity was similar across racial groups during the Omicron period. During pre-Omicron, mean monthly relative differences among Hispanic patients with high disease severity and severe complications compared with non-Hispanic patients were -5.17% and -39.4%, respectively, which shifted to 24.4% and 44.1% in the Omicron period (Pt<0.0001). These findings provide valuable insight into patterns of COVID-19 disease severity, especially for marginalized populations, and highlight the need for targeted public health strategies as variant-specific trends evolve over time.

Background: Scientific advances and cost efficiencies in genetics and genomics are expanding clinical application for prevention, diagnosis, and treatment.

Objective: PCORnet®, a research network that includes participation from 78 health systems nationally and is linked to more than 47 million unique patients with at least one encounter annually, can help (1) understand the ability of genetics/genomics to predict health outcomes, (2) identify diseases impacted by genetic/genomic factors, (3) evaluate pharmacogenomics' role in medication optimization, (4) evaluate emerging gene therapies, and (5) compare clinical genetic or genomic strategies within learning health systems to improve outcomes, while (6) facilitating patient and other partner engagement across these areas.

Main arguments: The breadth of data accessible via PCORnet represents a unique opportunity to study relationships among genetic markers and clinical and exposome-based disease risk factors, particularly as more genomic data become available. The network's experience developing computable phenotypes for identifying specific diseases can be leveraged to evaluate the role of genetics/genomics in health. The PCORnet infrastructure can be used to identify patients with particular conditions for predictive modeling or comparative clinical effectiveness research using electronic health record data. The network can also recruit patients for observational cohorts or pragmatic clinical trials on pharmacogenomics or the return of genetic results, evaluation of emerging gene therapies, or embedded research into learning health systems to compare clinical genetics/genomics implementation approaches in health care. The partner engagement focus of the PCORnet® Network Partners can enrich research and improve health care delivery and outcomes. The rise of clinical genetics and genomics will profoundly impact health care in the next decade, and the PCORnet® Network Partners are primed to make a leading contribution in this area.

Health plans and public payers (ie, Medicare and Medicaid) maintain records of millions of transactions between patients and health care providers from claims submitted by hospitals, clinics, pharmacies, and other care settings. A major use of claims data in research is to supplement the information contained in patients' medical records. PCORnet is a large, distributed "network of networks" funded by the Patient-Centered Outcomes Research Institute (PCORI) to improve the nation's capacity to efficiently conduct definitive health research. Seventy-eight partner health systems nationwide map clinical data from their electronic health records to the PCORnet® Common Data Model (CDM) so that the data may be efficiently used for research purposes. The ability to link data from electronic health records in the PCORnet infrastructure with complementary data from other sources, such as health insurance claims, further enhances the capacity for comparative clinical effectiveness research (CER). This commentary showcases the health plan partnerships of 3 PCORnet® Clinical Research Networks (CRNs)-REACHnet, PaTH, and OneFlorida+-that enhance the capacity for CER involving linked clinical and claims data. We describe the transferable regulatory and technical infrastructures in place to efficiently link data for research purposes. To demonstrate these partnerships and data linkage in action, we also discuss research use cases pertaining to weight-related outcomes and diabetes that align with payers' interests in chronic disease management.

The maternal mortality rate in the United States is higher than peer countries throughout the world. There is a critical need to implement and evaluate the effectiveness of interventions to address factors that contribute to maternal mortality and morbidity (MMM). Legislation passed by the US Congress in 2019 reauthorized funding for the Patient-Centered Outcomes Research Institute (PCORI) and identified maternal morbidity and mortality as a research priority. PCORnet® is a large, distributed "network of networks" funded by PCORI to improve the nation's capacity to efficiently conduct definitive health research. PCORnet® Network Partners convened a workgroup of experts in topics related to MMM-including patient stakeholders-and developed an exploratory query to identify and characterize the cohort of patients with pregnancy-related health events served by health systems participating in PCORnet. This article presents query results for 1.1 million pregnancies resulting in delivery or interruption that occurred between July 28, 2021, and July 28, 2023 among patients receiving care at 72 sites participating in PCORnet. Three percent of patients experienced severe maternal morbidity, and 357 cases of mortality were recorded. The results also include occurrence of mental and physical comorbidities in the prenatal, peripartum, and postpartum periods. These data are intended to support use of the PCORnet research infrastructure to produce evidence that matters to patients, caregivers, and the broader public health and health care communities. We also discuss ways to enhance the PCORnet infrastructure to accelerate maternal health research, including work that is currently underway to augment data pertinent to studying MMM.

Objective: This project sought to (1) identify critical gaps in knowledge of intellectual/developmental disabilities (IDD) clinical care and accelerate research by identifying a set of high-priority patient-centered comparative clinical effectiveness research (CER) questions that may be answered using PCORnet and (2) provide recommendations to advance CER for people with IDD (PwIDD).

Background: National-scale research is needed to better identify PwIDD, determine appropriate interventions, and evaluate care quality throughout individuals' life course to improve health outcomes and address health inequities.

Methods: PCORnet® Network Partners convened Workgroup members who: (1) provided input on research gaps based on their research, clinical work, and/or lived experiences, (2) conducted a literature scan, (3) examined the current capabilities through a data query of PCORnet data resources, (4) surveyed PCORnet® partner sites to describe current infrastructure, (5) identified gaps in knowledge, (6) prioritized unanswered patient-centered CER questions, and (7) characterized infrastructure needs to address CER questions.

Results: Sites participating in PCORnet® collectively serve many individuals across the range of IDD conditions, including more than 300,000 individuals with diagnosed autism. There is high utilization of the emergency department (19%-35%) and inpatient setting (8%-31%) across IDD conditions. We identified 3 broad evidence gaps and generated CER questions to address them.

Conclusions: Our findings provide insight into the current gaps in knowledge of IDD clinical care, the use of the PCORnet infrastructure to improve cohort ascertainment for IDD CER, and opportunities to enhance the PCORnet® Common Data Model (CDM) to standardize additional patient-centered and IDD-focused data elements for future CER.

Background: The PCORnet® infrastructure was funded by PCORI in 2014 to streamline clinical trials, increase patient-centered research, and generate knowledge that leads to improved health care and outcomes. In this paper, we summarize the significant achievements of the infrastructure over the last decade as well as recent accomplishments. We also provide an update on the expanded patient population who receive care at sites participating in PCORnet® Clinical Research Networks (CRNs) and share priorities for the future.

Methods: The electronic health records of 71 health systems participating in PCORnet® CRNs as of July 2024 were queried, and data were analyzed to describe 10 common health conditions, stratified by demographic characteristics of age, sex, race, ethnicity, and an index of social deprivation.

Results: Out of over 100M total patients with activity in the last 10 years, health systems participating in PCORnet® CRNs had over 47 million unique patients with at least one encounter in 2023. The most common chronic conditions among these patients were hypertension (18%), anxiety disorders (10%), type 2 diabetes (8%), and asthma (5%). Over 20% of patients receiving care at a site participating in PCORnet were in the top 50% of metrics for area deprivation. The PCORnet infrastructure supported 51 PCORnet® studies, all of which met established guidelines for use of the PCORnet® Common Data Model (CDM), patient-engagement, and commitment to return of results.

Discussion: PCORnet® CRNs represent a diverse and expanding patient population and often include data on the socioeconomic status of the communities. Through continued efforts to engage communities and patients and national-scale research, the PCORnet® infrastructure can help improve care and outcomes for patients affected by common and rare conditions.